From fca864c9c6dd5646c0f8e83df66f80d169f12853 Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Tue, 19 May 2020 22:34:53 +0530 Subject: [PATCH 01/13] clean up imports --- DESCRIPTION | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index dbd41ac..f9b4dad 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -39,6 +39,4 @@ VignetteBuilder: knitr Encoding: UTF-8 LazyData: true RoxygenNote: 7.1.0 -LinkingTo: Rcpp -Remotes: - tidyverse/dplyr +LinkingTo: Rcpp \ No newline at end of file From ff85a2f858abd41a11aba74158aeba6d222bb53b Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Wed, 27 May 2020 14:29:31 +0530 Subject: [PATCH 02/13] remove scales --- DESCRIPTION | 1 - R/blr-bivariate-analysis.R | 15 ++++++++++----- R/blr-gains-table.R | 4 ++-- R/blr-roc-curve.R | 5 +++-- 4 files changed, 15 insertions(+), 10 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f9b4dad..19359da 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -19,7 +19,6 @@ Imports: gridExtra, lest, Rcpp, - scales, stats, utils Suggests: diff --git a/R/blr-bivariate-analysis.R b/R/blr-bivariate-analysis.R index c5bd04a..fba8cc8 100644 --- a/R/blr-bivariate-analysis.R +++ b/R/blr-bivariate-analysis.R @@ -331,13 +331,18 @@ plot.blr_segment_dist <- function(x, title = NA, xaxis_title = "Levels", geom_col(aes(y = `n%`), fill = bar_color) + geom_line(aes(y = `1s%`, group = 1), color = line_color) + xlab(xaxis_title) + ggtitle(plot_title) + ylab(yaxis_title) + - scale_y_continuous(labels = scales::percent, - sec.axis = sec_axis(~. / sec_axis_scale, name = sec_yaxis_title, - labels = scales::percent)) + scale_y_continuous( + breaks = seq(0, 1, by = 0.1), + labels = paste0(seq(0, 1, by = 0.1) * 100, '%'), + sec.axis = sec_axis( + trans = ~., + breaks = seq(0, 1, by = 0.1), + labels = paste0(seq(0, 1, by = 0.1) * 100, '%'), + name = sec_yaxis_title)) if (print_plot) { print(p) - } + } invisible(p) } @@ -347,6 +352,6 @@ secondary_axis_scale_comp <- function(x) { d <- x$dist_table d$sec <- d$`n%` / d$`1s%` - min(d$sec) + max(d$sec) } diff --git a/R/blr-gains-table.R b/R/blr-gains-table.R index de3563c..94605e6 100644 --- a/R/blr-gains-table.R +++ b/R/blr-gains-table.R @@ -100,8 +100,8 @@ plot.blr_gains_table <- function(x, title = "Lift Chart", xaxis_title = "% Popul geom_line(aes(x = cum_total_per, y = cum_1s_per), color = lift_curve_col) + geom_line(aes(x = cum_total_per, y = cum_total_y), color = diag_line_col) + ggtitle(title) + xlab(xaxis_title) + ylab(yaxis_title) + - scale_x_continuous(labels = scales::percent) + - scale_y_continuous(labels = scales::percent) + + scale_x_continuous(labels = c('0%', '25%', '50%', '75%', '100%')) + + scale_y_continuous(labels = c('0%', '25%', '50%', '75%', '100%')) + theme(plot.title = element_text(hjust = plot_title_justify)) if (print_plot) { diff --git a/R/blr-roc-curve.R b/R/blr-roc-curve.R index c9092a6..fcee57a 100644 --- a/R/blr-roc-curve.R +++ b/R/blr-roc-curve.R @@ -53,8 +53,9 @@ blr_roc_curve <- function(gains_table, title = "ROC Curve", ggplot(plot_data, aes(x = `1 - specificity`, y = sensitivity_per)) + geom_point(shape = point_shape, fill = point_fill, color = point_color) + geom_line(color = roc_curve_col) + ggtitle(title) + - scale_x_continuous(labels = scales::percent) + xlab(xaxis_title) + - scale_y_continuous(labels = scales::percent) + ylab(yaxis_title) + + scale_x_continuous(labels = c('0%', '25%', '50%', '75%', '100%')) + + scale_y_continuous(labels = c('0%', '25%', '50%', '75%', '100%')) + + xlab(xaxis_title) + ylab(yaxis_title) + theme(plot.title = element_text(hjust = plot_title_justify)) + geom_line(aes(x = `1 - specificity`, y = `1 - specificity`), color = diag_line_col) From be70a2321a60c2442861e8da421cd1d3de60057e Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Wed, 27 May 2020 22:51:32 +0530 Subject: [PATCH 03/13] add confusion matrix --- DESCRIPTION | 4 +- NAMESPACE | 4 +- R/blr-model-validation.R | 92 ++++++++++++++++++++++++-- man/blr_confusion_matrix.Rd | 11 ++- tests/testthat/test-model-validation.R | 53 +++++++++++++++ 5 files changed, 150 insertions(+), 14 deletions(-) create mode 100644 tests/testthat/test-model-validation.R diff --git a/DESCRIPTION b/DESCRIPTION index 19359da..c0a840b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -12,9 +12,7 @@ Depends: R(>= 3.3) Imports: car, - caret, data.table, - e1071, ggplot2, gridExtra, lest, @@ -38,4 +36,4 @@ VignetteBuilder: knitr Encoding: UTF-8 LazyData: true RoxygenNote: 7.1.0 -LinkingTo: Rcpp \ No newline at end of file +LinkingTo: Rcpp diff --git a/NAMESPACE b/NAMESPACE index 831d41a..ceb836b 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -2,6 +2,7 @@ S3method(blr_bivariate_analysis,default) S3method(blr_coll_diag,default) +S3method(blr_confusion_matrix,default) S3method(blr_gains_table,default) S3method(blr_model_fit_stats,default) S3method(blr_multi_model_fit_stats,default) @@ -30,6 +31,7 @@ S3method(plot,blr_step_p_forward) S3method(plot,blr_woe_iv) S3method(print,blr_bivariate_analysis) S3method(print,blr_coll_diag) +S3method(print,blr_confusion_matrix) S3method(print,blr_gains_table) S3method(print,blr_model_fit_stats) S3method(print,blr_multi_model_fit_stats) @@ -119,14 +121,12 @@ export(blr_woe_iv) export(blr_woe_iv_stats) importFrom(Rcpp,sourceCpp) importFrom(car,Anova) -importFrom(caret,confusionMatrix) importFrom(data.table,":=") importFrom(data.table,.N) importFrom(data.table,data.table) importFrom(data.table,rbindlist) importFrom(data.table,setDF) importFrom(data.table,setorder) -importFrom(e1071,classAgreement) importFrom(ggplot2,aes) importFrom(ggplot2,annotate) importFrom(ggplot2,element_blank) diff --git a/R/blr-model-validation.R b/R/blr-model-validation.R index 36644f2..0447c35 100644 --- a/R/blr-model-validation.R +++ b/R/blr-model-validation.R @@ -1,10 +1,12 @@ #' Confusion matrix #' -#' Wrapper for \code{confMatrix} from the caret package. +#' Confusion matrix and statistics. #' #' @param model An object of class \code{glm}. #' @param data A \code{tibble} or a \code{data.frame}. #' @param cutoff Cutoff for classification. +#' @param x An object of class \code{blr_confusion_matrix}. +#' @param ... Other arguments. #' #' @return Confusion matix. #' @@ -14,20 +16,22 @@ #' #' blr_confusion_matrix(model, cutoff = 0.4) #' -#' @importFrom caret confusionMatrix -#' @importFrom e1071 classAgreement -#' #' @family model validation techniques #' #' @export #' -blr_confusion_matrix <- function(model, cutoff = 0.5, data = NULL) { +blr_confusion_matrix <- function(model, cutoff = 0.5, data = NULL, ...) UseMethod("blr_confusion_matrix") + +#' @rdname blr_confusion_matrix +#' @export +#' +blr_confusion_matrix.default <- function(model, cutoff = 0.5, data = NULL, ...) { blr_check_model(model) blr_check_values(cutoff, 0, 1) namu <- formula(model)[[2]] - + if (is.null(data)) { data <- model$model response <- data[[1]] @@ -38,7 +42,81 @@ blr_confusion_matrix <- function(model, cutoff = 0.5, data = NULL) { p_data <- predict(model, newdata = data, type = "response") c_data <- as.factor(as.numeric(p_data > cutoff)) + out <- table(Prediction = c_data, Reference = response) + + a <- out[4] + b <- out[2] + c <- out[3] + d <- out[1] + + accuracy <- (a + d) / (a + b + c + d) + no_inf_rate <- table(response)[[1]] / sum(table(response)) + sensitivity <- a / (a + c) + specificity <- d / (b + d) + prevalence <- (a + c) / (a + b + c + d) + detect_rate <- a / (a + b + c + d) + detect_prev <- (a + b) / (a + b + c + d) + bal_accuracy <- (sensitivity + specificity) / 2 + precision <- a / (a + b) + recall <- a / (a + c) + kappa <- blr_kappa(out) + mcnemar_p <- mcnemar.test(out)$p.value + + ppv <- (sensitivity * prevalence) / ((sensitivity * prevalence) + + ((1 - specificity) * (1 - prevalence))) + + npv <- specificity * (1 - prevalence) / (((1 - sensitivity) * prevalence) + + (specificity * (1 - prevalence))) - confusionMatrix(data = c_data, reference = response, positive = '1') + result <- list( + accuracy = accuracy, + balanced_accuracy = bal_accuracy, + conf_matrix = out, + detection_prevalence = detect_prev, + detection_rate = detect_rate, + mcnemar_kappa = kappa, + mcnemar_test_p_val = mcnemar_p, + negative_predicted_value = npv, + no_information_rate = no_inf_rate, + positive_predicted_value = ppv, + precision = precision, + prevalence = prevalence, + recall = recall, + sensitivity = sensitivity, + specificity = specificity) + + class(result) <- "blr_confusion_matrix" + return(result) + +} + +#' @export +#' +print.blr_confusion_matrix <- function(x, ...) { + + cat('Confusion Matrix and Statistics', '\n\n') + print(x$conf_matrix) + cat('\n\n') + cat(' Accuracy :', format(round(x$accuracy, 4), nsmall = 4), '\n') + cat(' No Information Rate :', format(round(x$no_information_rate, 4), nsmall = 4), '\n\n') + cat(' Kappa :', format(round(x$mcnemar_kappa, 4), nsmall = 4), '\n\n') + cat("McNemars's Test P-Value :", format(round(x$mcnemar_test_p_val, 4), nsmall = 4), '\n\n') + cat(' Sensitivity :', format(round(x$sensitivity, 4), nsmall = 4), '\n') + cat(' Specificity :', format(round(x$specificity, 4), nsmall = 4), '\n') + cat(' Pos Pred Value :', format(round(x$positive_predicted_value, 4), nsmall = 4), '\n') + cat(' Neg Pred Value :', format(round(x$negative_predicted_value, 4), nsmall = 4), '\n') + cat(' Prevalence :', format(round(x$prevalence, 4), nsmall = 4), '\n') + cat(' Detection Rate :', format(round(x$detection_rate, 4), nsmall = 4), '\n') + cat(' Detection Prevalence :', format(round(x$detection_prevalence, 4), nsmall = 4), '\n') + cat(' Balanced Accuracy :', format(round(x$balanced_accuracy, 4), nsmall = 4), '\n') + cat(' Precision :', format(round(x$precision, 4), nsmall = 4), '\n') + cat(' Recall :', format(round(x$recall, 4), nsmall = 4), '\n\n') + cat(" 'Positive' Class : 1") + +} +blr_kappa <- function(out) { + agreement <- sum(diag(out)) / sum(out) + expected <- sum(rowSums(out) * colSums(out)) / (sum(out) ^ 2) + (agreement - expected) / (1 - expected) } diff --git a/man/blr_confusion_matrix.Rd b/man/blr_confusion_matrix.Rd index af74aa2..c22f954 100644 --- a/man/blr_confusion_matrix.Rd +++ b/man/blr_confusion_matrix.Rd @@ -2,9 +2,12 @@ % Please edit documentation in R/blr-model-validation.R \name{blr_confusion_matrix} \alias{blr_confusion_matrix} +\alias{blr_confusion_matrix.default} \title{Confusion matrix} \usage{ -blr_confusion_matrix(model, cutoff = 0.5, data = NULL) +blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...) + +\method{blr_confusion_matrix}{default}(model, cutoff = 0.5, data = NULL, ...) } \arguments{ \item{model}{An object of class \code{glm}.} @@ -12,12 +15,16 @@ blr_confusion_matrix(model, cutoff = 0.5, data = NULL) \item{cutoff}{Cutoff for classification.} \item{data}{A \code{tibble} or a \code{data.frame}.} + +\item{...}{Other arguments.} + +\item{x}{An object of class \code{blr_confusion_matrix}.} } \value{ Confusion matix. } \description{ -Wrapper for \code{confMatrix} from the caret package. +Confusion matrix and statistics. } \examples{ model <- glm(honcomp ~ female + read + science, data = hsb2, diff --git a/tests/testthat/test-model-validation.R b/tests/testthat/test-model-validation.R new file mode 100644 index 0000000..ba49e60 --- /dev/null +++ b/tests/testthat/test-model-validation.R @@ -0,0 +1,53 @@ +context('test-blr-confusion-matrix') + +model <- glm( + honcomp ~ female + read + science, data = hsb2, + family = binomial(link = "logit") +) + +test_that("prints the correct output", { + + k <- blr_confusion_matrix(model, cutoff = 0.4) + + x <- cat("Confusion Matrix and Statistics + + Reference +Prediction 0 1 + 0 125 16 + 1 22 37 + + + Accuracy : 0.8100 + No Information Rate : 0.7350 + + Kappa : 0.5293 + +McNemars's Test P-Value : 0.4173 + + Sensitivity : 0.6981 + Specificity : 0.8503 + Pos Pred Value : 0.6271 + Neg Pred Value : 0.8865 + Prevalence : 0.2650 + Detection Rate : 0.1850 + Detection Prevalence : 0.2950 + Balanced Accuracy : 0.7742 + Precision : 0.6271 + Recall : 0.6981 + + 'Positive' Class : 1") + + expect_output(print(k), x) +}) + + +test_that('throws an error when the model is not of the class glm', { + model <- lm(read ~ female + science, data = hsb2) + error <- cat("Hmmm.. Looks like you have specified an incorrect model. The below steps might be helpful:\n\n* Check if you have used the glm() function to build the model.\n* If you have never used it before, you can learn more by typing ?glm or help(glm) in the Console.\n\n Please specify the model in the below format:\n\n glm( honcomp ~ female + read + science , data = hsb2 , family = binomial(link = 'logit'))\n\n Happy modeling :)") + expect_error(blr_confusion_matrix(model), error) +}) + +test_that('cutoff value is between 0 and 1', { + error <- cat('cutoff can take on values between 0 and 1 only. You have used 1.4 , please specify a value between 0 and 1 only.') + expect_error(blr_confusion_matrix(model, 1.4), error) +}) From 7dd9423cd07124396fed654d36080b535c91e416 Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Thu, 28 May 2020 13:18:57 +0530 Subject: [PATCH 04/13] fix build errors --- R/blr-gains-table.R | 4 +- R/blr-model-validation.R | 3 +- man/blr_confusion_matrix.Rd | 2 - .../segment-distribution-plot.svg | 72 ++++++++++--------- 4 files changed, 40 insertions(+), 41 deletions(-) diff --git a/R/blr-gains-table.R b/R/blr-gains-table.R index 94605e6..9bf0323 100644 --- a/R/blr-gains-table.R +++ b/R/blr-gains-table.R @@ -168,8 +168,8 @@ blr_ks_chart <- function(gains_table, title = "KS Chart", yaxis_title = " ", annotate("text", x = annotate_x, y = annotate_y, label = paste0("KS: ", ks_stat, "%")) + ggtitle(title) + xlab(xaxis_title) + ylab(yaxis_title) + - scale_x_continuous(labels = scales::percent) + - scale_y_continuous(labels = scales::percent) + + scale_x_continuous(labels = c('0%', '25%', '50%', '75%', '100%')) + + scale_y_continuous(labels = c('0%', '25%', '50%', '75%', '100%')) + theme(plot.title = element_text(hjust = 0.5), legend.title = element_blank()) diff --git a/R/blr-model-validation.R b/R/blr-model-validation.R index 0447c35..ed9db7c 100644 --- a/R/blr-model-validation.R +++ b/R/blr-model-validation.R @@ -5,7 +5,6 @@ #' @param model An object of class \code{glm}. #' @param data A \code{tibble} or a \code{data.frame}. #' @param cutoff Cutoff for classification. -#' @param x An object of class \code{blr_confusion_matrix}. #' @param ... Other arguments. #' #' @return Confusion matix. @@ -60,7 +59,7 @@ blr_confusion_matrix.default <- function(model, cutoff = 0.5, data = NULL, ...) precision <- a / (a + b) recall <- a / (a + c) kappa <- blr_kappa(out) - mcnemar_p <- mcnemar.test(out)$p.value + mcnemar_p <- stats::mcnemar.test(out)$p.value ppv <- (sensitivity * prevalence) / ((sensitivity * prevalence) + ((1 - specificity) * (1 - prevalence))) diff --git a/man/blr_confusion_matrix.Rd b/man/blr_confusion_matrix.Rd index c22f954..be493bb 100644 --- a/man/blr_confusion_matrix.Rd +++ b/man/blr_confusion_matrix.Rd @@ -17,8 +17,6 @@ blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...) \item{data}{A \code{tibble} or a \code{data.frame}.} \item{...}{Other arguments.} - -\item{x}{An object of class \code{blr_confusion_matrix}.} } \value{ Confusion matix. diff --git a/tests/figs/test-blr-plots-r/segment-distribution-plot.svg b/tests/figs/test-blr-plots-r/segment-distribution-plot.svg index 026f2fd..65cb6d6 100644 --- a/tests/figs/test-blr-plots-r/segment-distribution-plot.svg +++ b/tests/figs/test-blr-plots-r/segment-distribution-plot.svg @@ -14,51 +14,53 @@ - - + + - - - - - - + + + + + + -0.0% -10.0% -20.0% -30.0% -40.0% -50.0% - - - - - - - - - - - -0.0% -5.0% -10.0% -15.0% -20.0% - +0% +10% +20% +30% +40% +50% + + + + + + + + + + + + +0% +10% +20% +30% +40% +50% + - -1 + +1 2 -3 +3 Levels Sample Distribution 1s Distribution -pred +pred From 9f16b336ad6197eee0185dcd97b8230614219562 Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Thu, 28 May 2020 13:20:12 +0530 Subject: [PATCH 05/13] car needs R >= 3.5 --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index c0a840b..63dbf5a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -9,7 +9,7 @@ Description: Tools designed to make it easier for beginner and intermediate user model fit statistics, variable selection procedures, model validation techniques and a 'shiny' app for interactive model building. Depends: - R(>= 3.3) + R(>= 3.5) Imports: car, data.table, From 9f8678e799f298047087f0e303e9e1a703abf264 Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Thu, 28 May 2020 13:37:24 +0530 Subject: [PATCH 06/13] remove commented out code --- R/blr-stepwise-backward-regression.R | 4 --- R/blr-stepwise-forward-regression.R | 9 ----- R/blr-stepwise-regression.R | 53 ---------------------------- 3 files changed, 66 deletions(-) diff --git a/R/blr-stepwise-backward-regression.R b/R/blr-stepwise-backward-regression.R index 7d13e96..16b86a6 100644 --- a/R/blr-stepwise-backward-regression.R +++ b/R/blr-stepwise-backward-regression.R @@ -99,10 +99,6 @@ blr_step_p_backward.default <- function(model, prem = 0.3, details = FALSE, ...) m <- glm(paste(response, "~", paste(preds, collapse = " + ")), l, family = binomial(link = 'logit')) m_sum <- Anova(m, test.statistic = "Wald") pvals <- m_sum$`Pr(>Chisq)` - # m_sum <- summary(m) - # pvals <- unname(m_sum$coefficients[, 4])[-1] - # m <- ols_regress(paste(response, "~", paste(preds, collapse = " + ")), l) - # pvals <- m$pvalues[-1] maxp <- which(pvals == max(pvals)) suppressWarnings( diff --git a/R/blr-stepwise-forward-regression.R b/R/blr-stepwise-forward-regression.R index 69ba098..d50a50d 100644 --- a/R/blr-stepwise-forward-regression.R +++ b/R/blr-stepwise-forward-regression.R @@ -160,18 +160,9 @@ blr_step_p_forward.default <- function(model, penter = 0.3, details = FALSE, ... m_sum <- Anova(m, test.statistic = "Wald") pvals[i] <- m_sum$`Pr(>Chisq)`[ppos] tvals[i] <- m_sum$Chisq[ppos] - # m_sum <- summary(m) - # pvals[i] <- unname(m_sum$coefficients[, 4])[ppos] - # tvals[i] <- unname(m_sum$coefficients[, 3])[ppos] - # m <- blr_regress(paste(response, "~", - # paste(predictors, collapse = " + ")), l) - # pvals[i] <- m$pval[ppos] - # tvals[i] <- m$zval[ppos] } minp <- which(pvals == min(pvals)) - # tvals <- abs(tvals) - # maxt <- which(tvals == max(tvals)) if (pvals[minp] <= penter) { diff --git a/R/blr-stepwise-regression.R b/R/blr-stepwise-regression.R index 22b7186..365f7d8 100644 --- a/R/blr-stepwise-regression.R +++ b/R/blr-stepwise-regression.R @@ -114,17 +114,9 @@ blr_step_p_both.default <- function(model, pent = 0.1, prem = 0.3, details = FAL m_sum <- Anova(m, test.statistic = "Wald") pvals[i] <- m_sum$`Pr(>Chisq)`[ppos] tvals[i] <- m_sum$Chisq[ppos] - # m_sum <- summary(m) - # pvals[i] <- unname(m_sum$coefficients[, 4])[ppos] - # tvals[i] <- unname(m_sum$coefficients[, 3])[ppos] - # m <- ols_regress(paste(response, "~", paste(predictors, collapse = " + ")), l) - # pvals[i] <- m$pvalues[ppos] - # tvals[i] <- m$tvalues[ppos] } minp <- which(pvals == min(pvals)) - # tvals <- abs(tvals) - # maxt <- which(tvals == max(tvals)) preds <- all_pred[minp] lpreds <- length(preds) fr <- glm(paste(response, "~", paste(preds, collapse = " + ")), l, family = binomial(link = 'logit')) @@ -132,18 +124,6 @@ blr_step_p_both.default <- function(model, pent = 0.1, prem = 0.3, details = FAL aic <- mfs$m_aic bic <- mfs$m_bic dev <- mfs$m_deviance - # fr <- ols_regress(paste(response, "~", - # paste(preds, collapse = " + ")), l) - # rsq <- fr$rsq - # adjrsq <- fr$adjr - # cp <- ols_mallows_cp(fr$model, model) - # aic <- ols_aic(fr$model) - # sbc <- ols_sbc(fr$model) - # sbic <- ols_sbic(fr$model, model) - # rmse <- sqrt(fr$ems) - # betas <- append(betas, fr$betas) - # lbetas <- append(lbetas, length(fr$betas)) - # pvalues <- append(pvalues, fr$pvalues) if (details) { cat("\n") @@ -186,18 +166,9 @@ blr_step_p_both.default <- function(model, pent = 0.1, prem = 0.3, details = FAL m_sum <- Anova(m, test.statistic = "Wald") pvals[i] <- m_sum$`Pr(>Chisq)`[ppos] tvals[i] <- m_sum$Chisq[ppos] - # m_sum <- summary(m) - # pvals[i] <- unname(m_sum$coefficients[, 4])[ppos] - # tvals[i] <- unname(m_sum$coefficients[, 3])[ppos] - # m <- ols_regress(paste(response, "~", - # paste(predictors, collapse = " + ")), l) - # pvals[i] <- m$pvalues[ppos] - # tvals[i] <- m$tvalues[ppos] } minp <- which(pvals == min(pvals)) - # tvals <- abs(tvals) - # maxt <- which(tvals == max(tvals)) if (pvals[minp] <= pent) { @@ -211,18 +182,6 @@ blr_step_p_both.default <- function(model, pent = 0.1, prem = 0.3, details = FAL aic <- c(aic, mfs$m_aic) bic <- c(bic, mfs$m_bic) dev <- c(dev, mfs$m_deviance) - # fr <- ols_regress(paste(response, "~", - # paste(preds, collapse = " + ")), l) - # rsq <- c(rsq, fr$rsq) - # adjrsq <- c(adjrsq, fr$adjr) - # aic <- c(aic, ols_aic(fr$model)) - # sbc <- c(sbc, ols_sbc(fr$model)) - # sbic <- c(sbic, ols_sbic(fr$model, model)) - # cp <- c(cp, ols_mallows_cp(fr$model, model)) - # rmse <- c(rmse, sqrt(fr$ems)) - # betas <- append(betas, fr$betas) - # lbetas <- append(lbetas, length(fr$betas)) - # pvalues <- append(pvalues, fr$pvalues) if (details == TRUE) { cat("\n") @@ -255,8 +214,6 @@ blr_step_p_both.default <- function(model, pent = 0.1, prem = 0.3, details = FAL family = binomial(link = 'logit')) m_sum <- Anova(m2, test.statistic = "Wald") pvals_r <- m_sum$`Pr(>Chisq)` - # tvals_r <- m_sum$Chisq[ppos] - # tvals_r <- abs(unname(m_sum$coefficients[, 3])[-1]) maxp <- which(pvals_r == max(pvals_r)) if (pvals_r[maxp] > prem) { @@ -271,16 +228,6 @@ blr_step_p_both.default <- function(model, pent = 0.1, prem = 0.3, details = FAL aic <- c(aic, mfs$m_aic) bic <- c(bic, mfs$m_bic) dev <- c(dev, mfs$m_deviance) - # rsq <- c(rsq, fr$rsq) - # adjrsq <- c(adjrsq, fr$adjr) - # aic <- c(aic, ols_aic(fr$model)) - # sbc <- c(sbc, ols_sbc(fr$model)) - # sbic <- c(sbic, ols_sbic(fr$model, model)) - # cp <- c(cp, ols_mallows_cp(fr$model, model)) - # rmse <- c(rmse, sqrt(fr$ems)) - # betas <- append(betas, fr$betas) - # lbetas <- append(lbetas, length(fr$betas)) - # pvalues <- append(pvalues, fr$pvalues) if (details) { cat("\n") From 14f75654dd8011ea013c2b1f6d6b66bb280865cf Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Thu, 28 May 2020 13:37:42 +0530 Subject: [PATCH 07/13] update version --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 63dbf5a..0d1a3ec 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: blorr Type: Package Title: Tools for Developing Binary Logistic Regression Models -Version: 0.2.2.9000 +Version: 0.3.0 Authors@R: person("Aravind", "Hebbali", email = "hebbali.aravind@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9220-9669")) Description: Tools designed to make it easier for beginner and intermediate users to build and validate From 23552054f956819a2ce3086ef2cd87a147cdb4a8 Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Thu, 28 May 2020 14:38:25 +0530 Subject: [PATCH 08/13] review badges --- README.Rmd | 6 +-- README.md | 93 ++++++++++++++----------------------- tools/README-kschart-1.png | Bin 8760 -> 8759 bytes 3 files changed, 35 insertions(+), 64 deletions(-) diff --git a/README.Rmd b/README.Rmd index 4b02c1d..bb7be58 100644 --- a/README.Rmd +++ b/README.Rmd @@ -17,8 +17,7 @@ options(tibble.width = Inf) > Tools for building binary logistic regression models -[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/blorr)](https://cran.r-project.org/package=blorr) [![cran checks](https://cranchecks.info/badges/summary/blorr)](https://cran.r-project.org/web/checks/check_results_blorr.html) -[![Travis-CI Build Status](https://travis-ci.org/rsquaredacademy/blorr.svg?branch=master)](https://travis-ci.org/rsquaredacademy/blorr) [![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/rsquaredacademy/blorr?branch=master&svg=true)](https://ci.appveyor.com/project/rsquaredacademy/blorr) [![](https://cranlogs.r-pkg.org/badges/grand-total/blorr)](https://cran.r-project.org/package=blorr) [![Coverage status](https://codecov.io/gh/rsquaredacademy/blorr/branch/master/graph/badge.svg)](https://codecov.io/github/rsquaredacademy/blorr?branch=master) ![](https://img.shields.io/badge/lifecycle-maturing-blue.svg) +[![cran checks](https://cranchecks.info/badges/summary/blorr)](https://cran.r-project.org/web/checks/check_results_blorr.html) [![Travis-CI Build Status](https://travis-ci.org/rsquaredacademy/blorr.svg?branch=master)](https://travis-ci.org/rsquaredacademy/blorr) [![](https://cranlogs.r-pkg.org/badges/grand-total/blorr)](https://cran.r-project.org/package=blorr) [![Coverage status](https://codecov.io/gh/rsquaredacademy/blorr/branch/master/graph/badge.svg)](https://codecov.io/github/rsquaredacademy/blorr?branch=master) ![](https://img.shields.io/badge/lifecycle-maturing-blue.svg) [![status](https://tinyverse.netlify.com/badge/blorr)](https://CRAN.R-project.org/package=blorr) ## Overview @@ -137,6 +136,3 @@ If you encounter a bug, please file a minimal reproducible example using [reprex](https://reprex.tidyverse.org/index.html) on github. For questions and clarifications, use [StackOverflow](https://stackoverflow.com/). -## Code of Conduct - -Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms. diff --git a/README.md b/README.md index f429d16..ddd4909 100644 --- a/README.md +++ b/README.md @@ -5,17 +5,15 @@ > Tools for building binary logistic regression models -[![CRAN\_Status\_Badge](https://www.r-pkg.org/badges/version/blorr)](https://cran.r-project.org/package=blorr) [![cran checks](https://cranchecks.info/badges/summary/blorr)](https://cran.r-project.org/web/checks/check_results_blorr.html) [![Travis-CI Build Status](https://travis-ci.org/rsquaredacademy/blorr.svg?branch=master)](https://travis-ci.org/rsquaredacademy/blorr) -[![AppVeyor Build -Status](https://ci.appveyor.com/api/projects/status/github/rsquaredacademy/blorr?branch=master&svg=true)](https://ci.appveyor.com/project/rsquaredacademy/blorr) [![](https://cranlogs.r-pkg.org/badges/grand-total/blorr)](https://cran.r-project.org/package=blorr) [![Coverage status](https://codecov.io/gh/rsquaredacademy/blorr/branch/master/graph/badge.svg)](https://codecov.io/github/rsquaredacademy/blorr?branch=master) ![](https://img.shields.io/badge/lifecycle-maturing-blue.svg) +[![status](https://tinyverse.netlify.com/badge/blorr)](https://CRAN.R-project.org/package=blorr) ## Overview @@ -71,7 +69,7 @@ blr_bivariate_analysis(hsb2, honcomp, female, prog, race, schtyp) blr_woe_iv(hsb2, prog, honcomp) #> Weight of Evidence #> ------------------------------------------------------------------------- -#> levels 0s_count 1s_count 0s_dist 1s_dist woe iv +#> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 1 38 7 0.26 0.13 0.67 0.08 #> 2 65 40 0.44 0.75 -0.53 0.17 @@ -98,10 +96,6 @@ model <- glm(honcomp ~ female + read + science, data = hsb2, ``` r blr_regress(model) -#> - Creating model overview. -#> - Creating response profile. -#> - Extracting maximum likelihood estimates. -#> - Estimating concordant and discordant pairs. #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence @@ -156,32 +150,33 @@ blr_model_fit_stats(model) ``` r blr_confusion_matrix(model) -#> Confusion Matrix and Statistics +#> Confusion Matrix and Statistics #> #> Reference #> Prediction 0 1 #> 0 135 26 #> 1 12 27 -#> -#> Accuracy : 0.81 -#> 95% CI : (0.7487, 0.8619) -#> No Information Rate : 0.735 -#> P-Value [Acc > NIR] : 0.008453 -#> -#> Kappa : 0.4673 -#> Mcnemar's Test P-Value : 0.034955 -#> -#> Sensitivity : 0.5094 -#> Specificity : 0.9184 -#> Pos Pred Value : 0.6923 -#> Neg Pred Value : 0.8385 -#> Prevalence : 0.2650 -#> Detection Rate : 0.1350 -#> Detection Prevalence : 0.1950 -#> Balanced Accuracy : 0.7139 -#> -#> 'Positive' Class : 1 #> +#> +#> Accuracy : 0.8100 +#> No Information Rate : 0.7350 +#> +#> Kappa : 0.4673 +#> +#> McNemars's Test P-Value : 0.0350 +#> +#> Sensitivity : 0.5094 +#> Specificity : 0.9184 +#> Pos Pred Value : 0.6923 +#> Neg Pred Value : 0.8385 +#> Prevalence : 0.2650 +#> Detection Rate : 0.1350 +#> Detection Prevalence : 0.1950 +#> Balanced Accuracy : 0.7139 +#> Precision : 0.6923 +#> Recall : 0.5094 +#> +#> 'Positive' Class : 1 ``` ### Hosmer Lemeshow Test @@ -217,31 +212,17 @@ blr_test_hosmer_lemeshow(model) ``` r blr_gains_table(model) -#> # A tibble: 10 x 12 -#> decile total `1` `0` ks tp tn fp fn sensitivity -#> -#> 1 1 20 14 6 22.3 14 141 6 39 26.4 -#> 2 2 20 13 7 42.1 27 134 13 26 50.9 -#> 3 3 20 10 10 54.2 37 124 23 16 69.8 -#> 4 4 20 7 13 58.5 44 111 36 9 83.0 -#> 5 5 20 3 17 52.6 47 94 53 6 88.7 -#> 6 6 20 3 17 46.7 50 77 70 3 94.3 -#> 7 7 20 1 19 35.7 51 58 89 2 96.2 -#> 8 8 20 2 18 27.2 53 40 107 0 100 -#> 9 9 20 0 20 13.6 53 20 127 0 100 -#> 10 10 20 0 20 0 53 0 147 0 100 -#> specificity accuracy -#> -#> 1 95.9 77.5 -#> 2 91.2 80.5 -#> 3 84.4 80.5 -#> 4 75.5 77.5 -#> 5 63.9 70.5 -#> 6 52.4 63.5 -#> 7 39.5 54.5 -#> 8 27.2 46.5 -#> 9 13.6 36.5 -#> 10 0 26.5 +#> decile total 1 0 ks tp tn fp fn sensitivity specificity accuracy +#> 1 1 20 14 6 22.33346 14 141 6 39 26.41509 95.91837 77.5 +#> 2 2 20 13 7 42.09986 27 134 13 26 50.94340 91.15646 80.5 +#> 3 3 20 10 10 54.16506 37 124 23 16 69.81132 84.35374 80.5 +#> 4 4 20 7 13 58.52907 44 111 36 9 83.01887 75.51020 77.5 +#> 5 5 20 3 17 52.62482 47 94 53 6 88.67925 63.94558 70.5 +#> 6 6 20 3 17 46.72058 50 77 70 3 94.33962 52.38095 63.5 +#> 7 7 20 1 19 35.68220 51 58 89 2 96.22642 39.45578 54.5 +#> 8 8 20 2 18 27.21088 53 40 107 0 100.00000 27.21088 46.5 +#> 9 9 20 0 20 13.60544 53 20 127 0 100.00000 13.60544 36.5 +#> 10 10 20 0 20 0.00000 53 0 147 0 100.00000 0.00000 26.5 ``` ### Lift Chart @@ -288,9 +269,3 @@ If you encounter a bug, please file a minimal reproducible example using [reprex](https://reprex.tidyverse.org/index.html) on github. 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From 59c86fc48866bb2a6c09f87a6b581f49417dab13 Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Thu, 28 May 2020 17:40:01 +0530 Subject: [PATCH 11/13] add code of conduct --- README.Rmd | 3 +++ README.md | 6 ++++++ 2 files changed, 9 insertions(+) diff --git a/README.Rmd b/README.Rmd index bb7be58..a3a042a 100644 --- a/README.Rmd +++ b/README.Rmd @@ -136,3 +136,6 @@ If you encounter a bug, please file a minimal reproducible example using [reprex](https://reprex.tidyverse.org/index.html) on github. For questions and clarifications, use [StackOverflow](https://stackoverflow.com/). +## Code of Conduct + +Please note that this project is released with a [Contributor Code of Conduct](CONDUCT.md). By participating in this project you agree to abide by its terms. diff --git a/README.md b/README.md index ddd4909..87ba4f9 100644 --- a/README.md +++ b/README.md @@ -269,3 +269,9 @@ If you encounter a bug, please file a minimal reproducible example using [reprex](https://reprex.tidyverse.org/index.html) on github. For questions and clarifications, use [StackOverflow](https://stackoverflow.com/). + +## Code of Conduct + +Please note that this project is released with a [Contributor Code of +Conduct](CONDUCT.md). By participating in this project you agree to +abide by its terms. 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docs/bootstrap-toc.css create mode 100644 docs/bootstrap-toc.js diff --git a/docs/404.html b/docs/404.html index ba47c9e..c08b807 100644 --- a/docs/404.html +++ b/docs/404.html @@ -8,23 +8,29 @@ Page not found (404) • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,12 @@ + + @@ -49,7 +57,7 @@ - +
@@ -100,7 +108,6 @@ -
+ + @@ -138,7 +151,7 @@

Page not found (404)

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/CONDUCT.html b/docs/CONDUCT.html index 20e1d14..842faf9 100644 --- a/docs/CONDUCT.html +++ b/docs/CONDUCT.html @@ -8,23 +8,29 @@ Contributor Code of Conduct • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,12 @@ + + @@ -49,7 +57,7 @@ - +
@@ -100,7 +108,6 @@ -
+ + @@ -146,7 +159,7 @@

Contributor Code of Conduct

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index fbb64bd..465b5cd 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -8,23 +8,29 @@ License • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,12 @@ + + @@ -49,7 +57,7 @@ - +
@@ -100,7 +108,6 @@ -
+ + @@ -140,7 +153,7 @@

License

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/LICENSE.html b/docs/LICENSE.html index dfa82fa..b06bcd1 100644 --- a/docs/LICENSE.html +++ b/docs/LICENSE.html @@ -8,23 +8,29 @@ MIT License • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,12 @@ + + @@ -49,7 +57,7 @@ - +
@@ -100,7 +108,6 @@ -
+ + @@ -144,7 +157,7 @@

MIT License

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/articles/index.html b/docs/articles/index.html index 30400a9..5cba232 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -8,23 +8,29 @@ Articles • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,12 @@ + + @@ -49,7 +57,7 @@ - +
@@ -100,7 +108,6 @@ -
@@ -142,7 +150,7 @@

All vignettes

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/articles/introduction.html b/docs/articles/introduction.html index 6fe7f8e..8aa9dbb 100644 --- a/docs/articles/introduction.html +++ b/docs/articles/introduction.html @@ -6,19 +6,19 @@ A Short Introduction to the blorr Package • blorr - - - - + + + + + - - + - +

Libraries

The following libraries are used in the examples in the vignette:

-
library(blorr)
-library(magrittr)
+
library(blorr)
+library(magrittr)
@@ -127,128 +127,128 @@

Bivariate Analysis

Let us begin with careful bivariate analysis of each possible variable and the outcome variable. We will use information value and likelihood ratio chi square test for selecting the initial set of predictors for our model. The bivariate analysis is currently avialable for categorical predictors only.

- +
blr_bivariate_analysis(bank_marketing, y, job, marital, education, default,
+  housing, loan, contact, poutcome)
+#>                           Bivariate Analysis                           
+#> ----------------------------------------------------------------------
+#> Variable     Information Value    LR Chi Square    LR DF    LR p-value 
+#> ----------------------------------------------------------------------
+#>    job             0.16              75.2690        11        0.0000   
+#>  marital           0.05              21.6821         2        0.0000   
+#> education          0.05              25.0466         3        0.0000   
+#>  default           0.02              6.0405          1        0.0140   
+#>  housing           0.16              72.2813         1        0.0000   
+#>   loan             0.06              26.6615         1        0.0000   
+#>  contact           0.31             124.3834         2        0.0000   
+#> poutcome           0.53             270.6450         3        0.0000   
+#> ----------------------------------------------------------------------

Weight of Evidence & Information Value

Weight of evidence (WoE) is used to assess the relative risk of di¤erent attributes for a characteristic and as a means to transform characteristics into variables. It is also a very useful tool for binning. The WoE for any group with average odds is zero. A negative WoE indicates that the proportion of defaults is higher for that attribute than the overall proportion and indicates higher risk.

The information value is used to rank order variables in terms of their predictive power. A high information value indicates a high ability to discriminate. Values for the information value will always be positive and may be above 3 when assessing highly predictive characteristics. Characteristics with information values less than 0:10 are typically viewed as weak, while values over 0.30 are sought after.

- +
blr_woe_iv(bank_marketing, job, y)
+#>                                Weight of Evidence                                
+#> --------------------------------------------------------------------------------
+#>    levels        count_0s    count_1s    dist_0s    dist_1s        woe      iv   
+#> --------------------------------------------------------------------------------
+#>  management        809         130          0.20       0.25      -0.22     0.01  
+#>  technician        682          79          0.17       0.15       0.11     0.00  
+#> entrepreneur       139          12          0.03       0.02       0.40     0.00  
+#>  blue-collar       937          73          0.23       0.14       0.51     0.05  
+#>    unknown          29          2           0.01       0.00       0.61     0.00  
+#>    retired         152          47          0.04       0.09      -0.87     0.05  
+#>    admin.          433          61          0.11       0.12      -0.09     0.00  
+#>   services         392          39          0.10       0.08       0.26     0.01  
+#> self-employed      132          22          0.03       0.04      -0.26     0.00  
+#>  unemployed        126          15          0.03       0.03       0.08     0.00  
+#>   housemaid        110          12          0.03       0.02       0.17     0.00  
+#>    student          63          25          0.02       0.05      -1.13     0.04  
+#> --------------------------------------------------------------------------------
+#> 
+#>       Information Value       
+#> -----------------------------
+#> Variable    Information Value 
+#> -----------------------------
+#>   job            0.1594       
+#> -----------------------------

Plot

-
k <- blr_woe_iv(bank_marketing, job, y)
-plot(k)
+
k <- blr_woe_iv(bank_marketing, job, y)
+plot(k)

Multiple Variables

We can generate the weight of evidence and information value for multiple variables using blr_woe_iv_stats().

-
blr_woe_iv_stats(bank_marketing, y, job, marital, education)
-#> Variable: job
-#> 
-#>                                Weight of Evidence                                
-#> --------------------------------------------------------------------------------
-#>    levels        0s_count    1s_count    0s_dist    1s_dist        woe      iv   
-#> --------------------------------------------------------------------------------
-#>  management        809         130          0.20       0.25      -0.22     0.01  
-#>  technician        682          79          0.17       0.15       0.11     0.00  
-#> entrepreneur       139          12          0.03       0.02       0.40     0.00  
-#>  blue-collar       937          73          0.23       0.14       0.51     0.05  
-#>    unknown          29          2           0.01       0.00       0.61     0.00  
-#>    retired         152          47          0.04       0.09      -0.87     0.05  
-#>    admin.          433          61          0.11       0.12      -0.09     0.00  
-#>   services         392          39          0.10       0.08       0.26     0.01  
-#> self-employed      132          22          0.03       0.04      -0.26     0.00  
-#>  unemployed        126          15          0.03       0.03       0.08     0.00  
-#>   housemaid        110          12          0.03       0.02       0.17     0.00  
-#>    student          63          25          0.02       0.05      -1.13     0.04  
-#> --------------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#>   job            0.1594       
-#> -----------------------------
-#> 
-#> 
-#> Variable: marital
-#> 
-#>                             Weight of Evidence                              
-#> ---------------------------------------------------------------------------
-#>  levels     0s_count    1s_count    0s_dist    1s_dist        woe      iv   
-#> ---------------------------------------------------------------------------
-#> married       2467        273          0.62       0.53       0.15     0.01  
-#>  single       1079        191          0.27       0.37      -0.32     0.03  
-#> divorced      458          53          0.11       0.10       0.11     0.00  
-#> ---------------------------------------------------------------------------
-#> 
-#>       Information Value       
-#> -----------------------------
-#> Variable    Information Value 
-#> -----------------------------
-#> marital          0.0464       
-#> -----------------------------
-#> 
-#> 
-#> Variable: education
-#> 
-#>                              Weight of Evidence                              
-#> ----------------------------------------------------------------------------
-#>  levels      0s_count    1s_count    0s_dist    1s_dist        woe      iv   
-#> ----------------------------------------------------------------------------
-#> tertiary       1104        195          0.28       0.38      -0.31     0.03  
-#> secondary      2121        231          0.53       0.45       0.17     0.01  
-#>  unknown       154          25          0.04       0.05      -0.23     0.00  
-#>  primary       625          66          0.16       0.13       0.20     0.01  
-#> ----------------------------------------------------------------------------
-#> 
-#>       Information Value        
-#> ------------------------------
-#> Variable     Information Value 
-#> ------------------------------
-#> education         0.0539       
-#> ------------------------------
+
blr_woe_iv_stats(bank_marketing, y, job, marital, education)
+#> Variable: job
+#> 
+#>                                Weight of Evidence                                
+#> --------------------------------------------------------------------------------
+#>    levels        count_0s    count_1s    dist_0s    dist_1s        woe      iv   
+#> --------------------------------------------------------------------------------
+#>  management        809         130          0.20       0.25      -0.22     0.01  
+#>  technician        682          79          0.17       0.15       0.11     0.00  
+#> entrepreneur       139          12          0.03       0.02       0.40     0.00  
+#>  blue-collar       937          73          0.23       0.14       0.51     0.05  
+#>    unknown          29          2           0.01       0.00       0.61     0.00  
+#>    retired         152          47          0.04       0.09      -0.87     0.05  
+#>    admin.          433          61          0.11       0.12      -0.09     0.00  
+#>   services         392          39          0.10       0.08       0.26     0.01  
+#> self-employed      132          22          0.03       0.04      -0.26     0.00  
+#>  unemployed        126          15          0.03       0.03       0.08     0.00  
+#>   housemaid        110          12          0.03       0.02       0.17     0.00  
+#>    student          63          25          0.02       0.05      -1.13     0.04  
+#> --------------------------------------------------------------------------------
+#> 
+#>       Information Value       
+#> -----------------------------
+#> Variable    Information Value 
+#> -----------------------------
+#>   job            0.1594       
+#> -----------------------------
+#> 
+#> 
+#> Variable: marital
+#> 
+#>                             Weight of Evidence                              
+#> ---------------------------------------------------------------------------
+#>  levels     count_0s    count_1s    dist_0s    dist_1s        woe      iv   
+#> ---------------------------------------------------------------------------
+#> married       2467        273          0.62       0.53       0.15     0.01  
+#>  single       1079        191          0.27       0.37      -0.32     0.03  
+#> divorced      458          53          0.11       0.10       0.11     0.00  
+#> ---------------------------------------------------------------------------
+#> 
+#>       Information Value       
+#> -----------------------------
+#> Variable    Information Value 
+#> -----------------------------
+#> marital          0.0464       
+#> -----------------------------
+#> 
+#> 
+#> Variable: education
+#> 
+#>                              Weight of Evidence                              
+#> ----------------------------------------------------------------------------
+#>  levels      count_0s    count_1s    dist_0s    dist_1s        woe      iv   
+#> ----------------------------------------------------------------------------
+#> tertiary       1104        195          0.28       0.38      -0.31     0.03  
+#> secondary      2121        231          0.53       0.45       0.17     0.01  
+#>  unknown       154          25          0.04       0.05      -0.23     0.00  
+#>  primary       625          66          0.16       0.13       0.20     0.01  
+#> ----------------------------------------------------------------------------
+#> 
+#>       Information Value        
+#> ------------------------------
+#> Variable     Information Value 
+#> ------------------------------
+#> education         0.0539       
+#> ------------------------------

blr_woe_iv() and blr_woe_iv_stats() are currently avialable for categorical predictors only.

@@ -260,109 +260,109 @@

Model

-
model <- glm(y ~ ., data = bank_marketing, family = binomial(link = 'logit'))
+
model <- glm(y ~ ., data = bank_marketing, family = binomial(link = 'logit'))

Selection Summary

- +
blr_step_aic_both(model)
+#> Stepwise Selection Method 
+#> -------------------------
+#> 
+#> Candidate Terms: 
+#> 
+#> 1 . age 
+#> 2 . job 
+#> 3 . marital 
+#> 4 . education 
+#> 5 . default 
+#> 6 . balance 
+#> 7 . housing 
+#> 8 . loan 
+#> 9 . contact 
+#> 10 . day 
+#> 11 . month 
+#> 12 . duration 
+#> 13 . campaign 
+#> 14 . pdays 
+#> 15 . previous 
+#> 16 . poutcome 
+#> 
+#> 
+#> Variables Entered/Removed: 
+#> 
+#> - duration added 
+#> - poutcome added 
+#> - month added 
+#> - contact added 
+#> - housing added 
+#> - loan added 
+#> - campaign added 
+#> - marital added 
+#> - education added 
+#> - age added 
+#> 
+#> No more variables to be added or removed.
+#> 
+#> 
+#>                      Stepwise Summary                      
+#> ---------------------------------------------------------
+#> Variable      Method       AIC         BIC       Deviance 
+#> ---------------------------------------------------------
+#> duration     addition    2674.384    2687.217    2670.384 
+#> poutcome     addition    2396.014    2428.097    2386.014 
+#> month        addition    2274.109    2376.773    2242.109 
+#> contact      addition    2207.884    2323.381    2171.884 
+#> housing      addition    2184.550    2306.463    2146.550 
+#> loan         addition    2171.972    2300.302    2131.972 
+#> campaign     addition    2164.164    2298.910    2122.164 
+#> marital      addition    2158.524    2306.103    2112.524 
+#> education    addition    2155.837    2322.666    2103.837 
+#> age          addition    2154.272    2327.517    2100.272 
+#> ---------------------------------------------------------

Plot

- +
model %>%
+  blr_step_aic_both() %>%
+  plot()
+#> Stepwise Selection Method 
+#> -------------------------
+#> 
+#> Candidate Terms: 
+#> 
+#> 1 . age 
+#> 2 . job 
+#> 3 . marital 
+#> 4 . education 
+#> 5 . default 
+#> 6 . balance 
+#> 7 . housing 
+#> 8 . loan 
+#> 9 . contact 
+#> 10 . day 
+#> 11 . month 
+#> 12 . duration 
+#> 13 . campaign 
+#> 14 . pdays 
+#> 15 . previous 
+#> 16 . poutcome 
+#> 
+#> 
+#> Variables Entered/Removed: 
+#> 
+#> - duration added 
+#> - poutcome added 
+#> - month added 
+#> - contact added 
+#> - housing added 
+#> - loan added 
+#> - campaign added 
+#> - marital added 
+#> - education added 
+#> - age added 
+#> 
+#> No more variables to be added or removed.

@@ -374,9 +374,9 @@

Model

We can use bivariate analysis and stepwise selection procedure to shortlist predictors and build the model using the glm(). The predictors used in the below model are for illustration purposes and not necessarily shortlisted from the bivariate analysis and variable selection procedures.

-
model <- glm(y ~  age + duration + previous + housing + default +
-             loan + poutcome + job + marital, data = bank_marketing, 
-             family = binomial(link = 'logit'))
+
model <- glm(y ~  age + duration + previous + housing + default +
+             loan + poutcome + job + marital, data = bank_marketing,
+             family = binomial(link = 'logit'))

Use blr_regress() to generate comprehensive regression output. It accepts either of the following

Using Formula

Let us use the model formula and the data set to generate the above results.

-
blr_regress(y ~  age + duration + previous + housing + default +
-             loan + poutcome + job + marital, data = bank_marketing)
-#> - Creating model overview. 
-#> - Creating response profile. 
-#> - Extracting maximum likelihood estimates. 
-#> - Estimating concordant and discordant pairs.
-#>                              Model Overview                              
-#> ------------------------------------------------------------------------
-#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
-#> ------------------------------------------------------------------------
-#>   data         y        4521      4520           4498           TRUE     
-#> ------------------------------------------------------------------------
-#> 
-#>                     Response Summary                     
-#> --------------------------------------------------------
-#> Outcome        Frequency        Outcome        Frequency 
-#> --------------------------------------------------------
-#>    0             4004              1              517    
-#> --------------------------------------------------------
-#> 
-#>                      Maximum Likelihood Estimates                       
-#> -----------------------------------------------------------------------
-#>    Parameter        DF    Estimate    Std. Error    z value     Pr(>|z|) 
-#> -----------------------------------------------------------------------
-#>   (Intercept)       1     -5.1347        0.3728    -13.7729      0.0000 
-#>       age           1      0.0096        0.0067      1.4299      0.1528 
-#>     duration        1      0.0042         2e-04     20.7853      0.0000 
-#>     previous        1     -0.0357        0.0392     -0.9089      0.3634 
-#>    housingno        1      0.7894        0.1232      6.4098      0.0000 
-#>    defaultyes       1     -0.8691        0.6919     -1.2562      0.2091 
-#>      loanno         1      0.6598        0.1945      3.3925       7e-04 
-#> poutcomefailure     1      0.6085        0.2012      3.0248      0.0025 
-#>  poutcomeother      1      1.1354        0.2700      4.2057      0.0000 
-#> poutcomesuccess     1      3.2481        0.2462     13.1913      0.0000 
-#>  jobtechnician      1     -0.2713        0.1806     -1.5019      0.1331 
-#> jobentrepreneur     1     -0.7041        0.3809     -1.8486      0.0645 
-#>  jobblue-collar     1     -0.6132        0.1867     -3.2851      0.0010 
-#>    jobunknown       1     -0.9932        0.8226     -1.2073      0.2273 
-#>    jobretired       1      0.3197        0.2729      1.1713      0.2415 
-#>    jobadmin.        1      0.1120        0.2001      0.5599      0.5755 
-#>   jobservices       1     -0.1750        0.2265     -0.7728      0.4397 
-#> jobself-employed    1     -0.1408        0.3009     -0.4680      0.6398 
-#>  jobunemployed      1     -0.6581        0.3432     -1.9174      0.0552 
-#>   jobhousemaid      1     -0.7456        0.3932     -1.8963      0.0579 
-#>    jobstudent       1      0.1927        0.3433      0.5613      0.5746 
-#>  maritalsingle      1      0.5451        0.1387      3.9299       1e-04 
-#> maritaldivorced     1     -0.1989        0.1986     -1.0012      0.3167 
-#> -----------------------------------------------------------------------
-#> 
-#>  Association of Predicted Probabilities and Observed Responses  
-#> ---------------------------------------------------------------
-#> % Concordant          0.8886          Somers' D        0.7773   
-#> % Discordant          0.1114          Gamma            0.7773   
-#> % Tied                0.0000          Tau-a            0.1575   
-#> Pairs                2070068          c                0.8886   
-#> ---------------------------------------------------------------
+
blr_regress(y ~  age + duration + previous + housing + default +
+             loan + poutcome + job + marital, data = bank_marketing)
+#>                              Model Overview                              
+#> ------------------------------------------------------------------------
+#> Data Set    Resp Var    Obs.    Df. Model    Df. Residual    Convergence 
+#> ------------------------------------------------------------------------
+#>   data         y        4521      4520           4498           TRUE     
+#> ------------------------------------------------------------------------
+#> 
+#>                     Response Summary                     
+#> --------------------------------------------------------
+#> Outcome        Frequency        Outcome        Frequency 
+#> --------------------------------------------------------
+#>    0             4004              1              517    
+#> --------------------------------------------------------
+#> 
+#>                      Maximum Likelihood Estimates                       
+#> -----------------------------------------------------------------------
+#>    Parameter        DF    Estimate    Std. Error    z value     Pr(>|z|) 
+#> -----------------------------------------------------------------------
+#>   (Intercept)       1     -5.1347        0.3728    -13.7729      0.0000 
+#>       age           1      0.0096        0.0067      1.4299      0.1528 
+#>     duration        1      0.0042         2e-04     20.7853      0.0000 
+#>     previous        1     -0.0357        0.0392     -0.9089      0.3634 
+#>    housingno        1      0.7894        0.1232      6.4098      0.0000 
+#>    defaultyes       1     -0.8691        0.6919     -1.2562      0.2091 
+#>      loanno         1      0.6598        0.1945      3.3925       7e-04 
+#> poutcomefailure     1      0.6085        0.2012      3.0248      0.0025 
+#>  poutcomeother      1      1.1354        0.2700      4.2057      0.0000 
+#> poutcomesuccess     1      3.2481        0.2462     13.1913      0.0000 
+#>  jobtechnician      1     -0.2713        0.1806     -1.5019      0.1331 
+#> jobentrepreneur     1     -0.7041        0.3809     -1.8486      0.0645 
+#>  jobblue-collar     1     -0.6132        0.1867     -3.2851      0.0010 
+#>    jobunknown       1     -0.9932        0.8226     -1.2073      0.2273 
+#>    jobretired       1      0.3197        0.2729      1.1713      0.2415 
+#>    jobadmin.        1      0.1120        0.2001      0.5599      0.5755 
+#>   jobservices       1     -0.1750        0.2265     -0.7728      0.4397 
+#> jobself-employed    1     -0.1408        0.3009     -0.4680      0.6398 
+#>  jobunemployed      1     -0.6581        0.3432     -1.9174      0.0552 
+#>   jobhousemaid      1     -0.7456        0.3932     -1.8963      0.0579 
+#>    jobstudent       1      0.1927        0.3433      0.5613      0.5746 
+#>  maritalsingle      1      0.5451        0.1387      3.9299       1e-04 
+#> maritaldivorced     1     -0.1989        0.1986     -1.0012      0.3167 
+#> -----------------------------------------------------------------------
+#> 
+#>  Association of Predicted Probabilities and Observed Responses  
+#> ---------------------------------------------------------------
+#> % Concordant          0.8886          Somers' D        0.7773   
+#> % Discordant          0.1114          Gamma            0.7773   
+#> % Tied                0.0000          Tau-a            0.1575   
+#> Pairs                2070068          c                0.8886   
+#> ---------------------------------------------------------------
@@ -514,18 +506,18 @@

Single Model

- +
blr_model_fit_stats(model)
+#>                                Model Fit Statistics                                
+#> ----------------------------------------------------------------------------------
+#> Log-Lik Intercept Only:     -1607.330    Log-Lik Full Model:             -1123.340 
+#> Deviance(4498):              2246.679    LR(22):                           967.980 
+#>                                          Prob > LR:                          0.000 
+#> MCFadden's R2                   0.301    McFadden's Adj R2:                  0.287 
+#> ML (Cox-Snell) R2:              0.193    Cragg-Uhler(Nagelkerke) R2:         0.379 
+#> McKelvey & Zavoina's R2:        0.388    Efron's R2:                         0.278 
+#> Count R2:                       0.904    Adj Count R2:                       0.157 
+#> BIC:                         2440.259    AIC:                             2292.679 
+#> ----------------------------------------------------------------------------------
@@ -535,33 +527,34 @@

Confusion Matrix

In the event of deciding a cut-off point on the probability scores of a logistic regression model, a confusion matrix is created corresponding to a particular cut-off. The observations with probability scores above the cut-off score are predicted to be events and those below the cut-off score, as non-events. The confusion matrix, a 2X2 table, then calculates the number of correctly classified and miss-classified observations.

- +
blr_confusion_matrix(model, cutoff = 0.5)
+#> Confusion Matrix and Statistics 
+#> 
+#>           Reference
+#> Prediction    0    1
+#>          0 3920  352
+#>          1   84  165
+#> 
+#> 
+#>                 Accuracy : 0.9036 
+#>      No Information Rate : 0.8856 
+#> 
+#>                    Kappa : 0.3851 
+#> 
+#> McNemars's Test P-Value  : 0.0000 
+#> 
+#>              Sensitivity : 0.3191 
+#>              Specificity : 0.9790 
+#>           Pos Pred Value : 0.6627 
+#>           Neg Pred Value : 0.9176 
+#>               Prevalence : 0.1144 
+#>           Detection Rate : 0.0365 
+#>     Detection Prevalence : 0.0551 
+#>        Balanced Accuracy : 0.6491 
+#>                Precision : 0.6627 
+#>                   Recall : 0.3191 
+#> 
+#>         'Positive' Class : 1

The validity of a cut-off is measured using sensitivity, specificity and accuracy.

Gains Table & Lift Chart

A lift curve is a graphical representation of the % of cumulative events captured at a specific cut-off. The cut-off can be a particular decile or a percentile. Similar, to rank ordering procedure, the data is in descending order of the scores and is then grouped into deciles/percentiles. The cumulative number of observations and events are then computed for each decile/percentile. The lift curve is the created using the cumulative % population as the x-axis and the cumulative percentage of events as the y-axis.

- +
blr_gains_table(model)
+#>    decile total   1   0       ks  tp   tn   fp  fn sensitivity specificity
+#> 1       1   452 256 196 44.62134 256 3808  196 261    49.51644    95.10490
+#> 2       2   452 128 324 61.28765 384 3484  520 133    74.27466    87.01299
+#> 3       3   452  62 390 63.53965 446 3094  910  71    86.26692    77.27273
+#> 4       4   452  31 421 59.02130 477 2673 1331  40    92.26306    66.75824
+#> 5       5   452  10 442 49.91657 487 2231 1773  30    94.19729    55.71928
+#> 6       6   452  14 438 41.68544 501 1793 2211  16    96.90522    44.78022
+#> 7       7   452   7 445 31.92552 508 1348 2656   9    98.25919    33.66633
+#> 8       8   452   4 448 21.51040 512  900 3104   5    99.03288    22.47752
+#> 9       9   452   3 449 10.87689 515  451 3553   2    99.61315    11.26374
+#> 10     10   453   2 451  0.00000 517    0 4004   0   100.00000     0.00000
+#>    accuracy
+#> 1  89.89162
+#> 2  85.55629
+#> 3  78.30126
+#> 4  69.67485
+#> 5  60.11944
+#> 6  50.74099
+#> 7  41.05286
+#> 8  31.23203
+#> 9  21.36695
+#> 10 11.43552

Lift Chart

- +
model %>%
+    blr_gains_table() %>%
+    plot()

@@ -635,43 +636,43 @@

ROC Curve

ROC curve is a graphical representation of the validity of cut-offs for a logistic regression model. The ROC curve is plotted using the sensitivity and specificity for all possible cut-offs, i.e., all the probability scores. The graph is plotted using sensitivity on the y-axis and 1-specificity on the x-axis. Any point on the ROC curve represents a sensitivity X (1-specificity) measure corresponding to a cut-off. The area under the ROC curve is used as a validation measure for the model – the bigger the area the better is the model.

- +
model %>%
+    blr_gains_table() %>%
+  blr_roc_curve()

KS Chart

The KS Statistic is again a measure of model efficiency, and it is created using the lift curve. The lift curve is created to plot % events. If we also plot % non-events on the same scale, with % population at x-axis, we would get another curve. The maximum distance between the lift curve for events and that for non-events is termed as KS. For a good model, KS should be big (>=0.3) and should occur as close to the event rate as possible.

- +
model %>%
+    blr_gains_table() %>%
+  blr_ks_chart()

Decile Lift Chart

The decile lift chart displays the lift over the global mean event rate for each decile. For a model with good discriminatory power, the top deciles should have an event/conversion rate greater than the global mean.

- +

Capture Rate by Decile

If the model has good discriminatory power, the top deciles should have a higher event/conversion rate compared to the bottom deciles.

- +

Lorenz Curve

The Lorenz curve is a simple graphic device which illustrates the degree of inequality in the distribution of thevariable concerned. It is a visual representation of inequality used to measure the discriminatory power of the predictive model.

- +

@@ -682,40 +683,29 @@

Influence Diagnostics

- +

Leverage Diagnostics

- +

Fitted Values Diagnostics

- +

- @@ -726,7 +716,7 @@

-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

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@@ -100,7 +108,6 @@ -
-

Site built with pkgdown 1.4.1.

+

Site built with pkgdown 1.5.1.

diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css new file mode 100644 index 0000000..5a85941 --- /dev/null +++ b/docs/bootstrap-toc.css @@ -0,0 +1,60 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ + +/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ + +/* All levels of nav */ +nav[data-toggle='toc'] .nav > li > a { + display: block; + padding: 4px 20px; + font-size: 13px; + font-weight: 500; + color: #767676; +} +nav[data-toggle='toc'] .nav > li > a:hover, +nav[data-toggle='toc'] .nav > li > a:focus { + padding-left: 19px; + color: #563d7c; + text-decoration: none; + background-color: transparent; + border-left: 1px solid #563d7c; +} +nav[data-toggle='toc'] .nav > .active > a, +nav[data-toggle='toc'] .nav > .active:hover > a, +nav[data-toggle='toc'] .nav > .active:focus > a { + padding-left: 18px; + font-weight: bold; + color: #563d7c; + background-color: transparent; + border-left: 2px solid #563d7c; +} + +/* Nav: second level (shown on .active) */ +nav[data-toggle='toc'] .nav .nav { + display: none; /* Hide by default, but at >768px, show it */ + padding-bottom: 10px; +} +nav[data-toggle='toc'] .nav .nav > li > a { + padding-top: 1px; + padding-bottom: 1px; + padding-left: 30px; + font-size: 12px; + font-weight: normal; +} +nav[data-toggle='toc'] .nav .nav > li > a:hover, +nav[data-toggle='toc'] .nav .nav > li > a:focus { + padding-left: 29px; +} +nav[data-toggle='toc'] .nav .nav > .active > a, +nav[data-toggle='toc'] .nav .nav > .active:hover > a, +nav[data-toggle='toc'] .nav .nav > .active:focus > a { + padding-left: 28px; + font-weight: 500; +} + +/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ +nav[data-toggle='toc'] .nav > .active > ul { + display: block; +} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js new file mode 100644 index 0000000..1cdd573 --- /dev/null +++ b/docs/bootstrap-toc.js @@ -0,0 +1,159 @@ +/*! + * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) + * Copyright 2015 Aidan Feldman + * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ +(function() { + 'use strict'; + + window.Toc = { + helpers: { + // return all matching elements in the set, or their descendants + findOrFilter: function($el, selector) { + // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ + // http://stackoverflow.com/a/12731439/358804 + var $descendants = $el.find(selector); + return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); + }, + + generateUniqueIdBase: function(el) { + var text = $(el).text(); + var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); + return anchor || el.tagName.toLowerCase(); + }, + + generateUniqueId: function(el) { + var anchorBase = this.generateUniqueIdBase(el); + for (var i = 0; ; i++) { + var anchor = anchorBase; + if (i > 0) { + // add suffix + anchor += '-' + i; + } + // check if ID already exists + if (!document.getElementById(anchor)) { + return anchor; + } + } + }, + + generateAnchor: function(el) { + if (el.id) { + return el.id; + } else { + var anchor = this.generateUniqueId(el); + el.id = anchor; + return anchor; + } + }, + + createNavList: function() { + return $(''); + }, + + createChildNavList: function($parent) { + var $childList = this.createNavList(); + $parent.append($childList); + return $childList; + }, + + generateNavEl: function(anchor, text) { + var $a = $(''); + $a.attr('href', '#' + anchor); + $a.text(text); + var $li = $('
  • '); + $li.append($a); + return $li; + }, + + generateNavItem: function(headingEl) { + var anchor = this.generateAnchor(headingEl); + var $heading = $(headingEl); + var text = $heading.data('toc-text') || $heading.text(); + return this.generateNavEl(anchor, text); + }, + + // Find the first heading level (`

    `, then `

    `, etc.) that has more than one element. Defaults to 1 (for `

    `). + getTopLevel: function($scope) { + for (var i = 1; i <= 6; i++) { + var $headings = this.findOrFilter($scope, 'h' + i); + if ($headings.length > 1) { + return i; + } + } + + return 1; + }, + + // returns the elements for the top level, and the next below it + getHeadings: function($scope, topLevel) { + var topSelector = 'h' + topLevel; + + var secondaryLevel = topLevel + 1; + var secondarySelector = 'h' + secondaryLevel; + + return this.findOrFilter($scope, topSelector + ',' + secondarySelector); + }, + + getNavLevel: function(el) { + return parseInt(el.tagName.charAt(1), 10); + }, + + populateNav: function($topContext, topLevel, $headings) { + var $context = $topContext; + var $prevNav; + + var helpers = this; + $headings.each(function(i, el) { + var $newNav = helpers.generateNavItem(el); + var navLevel = helpers.getNavLevel(el); + + // determine the proper $context + if (navLevel === topLevel) { + // use top level + $context = $topContext; + } else if ($prevNav && $context === $topContext) { + // create a new level of the tree and switch to it + $context = helpers.createChildNavList($prevNav); + } // else use the current $context + + $context.append($newNav); + + $prevNav = $newNav; + }); + }, + + parseOps: function(arg) { + var opts; + if (arg.jquery) { + opts = { + $nav: arg + }; + } else { + opts = arg; + } + opts.$scope = opts.$scope || $(document.body); + return opts; + } + }, + + // accepts a jQuery object, or an options object + init: function(opts) { + opts = this.helpers.parseOps(opts); + + // ensure that the data attribute is in place for styling + opts.$nav.attr('data-toggle', 'toc'); + + var $topContext = this.helpers.createChildNavList(opts.$nav); + var topLevel = this.helpers.getTopLevel(opts.$scope); + var $headings = this.helpers.getHeadings(opts.$scope, topLevel); + this.helpers.populateNav($topContext, topLevel, $headings); + } + }; + + $(function() { + $('nav[data-toggle="toc"]').each(function(i, el) { + var $nav = $(el); + Toc.init($nav); + }); + }); +})(); diff --git a/docs/docsearch.json b/docs/docsearch.json index 2032754..9514a4e 100644 --- a/docs/docsearch.json +++ b/docs/docsearch.json @@ -74,7 +74,7 @@ "selector": ".contents h2, .contents h3", "default_value": "Context" }, - "text": ".contents p, .contents li, .tempate-article .contents .pre" + "text": ".contents p, .contents li" } }, "selectors_exclude": [ diff --git a/docs/index.html b/docs/index.html index d6c60f2..dd596b2 100644 --- a/docs/index.html +++ b/docs/index.html @@ -31,17 +31,18 @@ - - - - + + + + + - +
    - diff --git a/docs/reference/blorr.html b/docs/reference/blorr.html index 9f90e45..0317398 100644 --- a/docs/reference/blorr.html +++ b/docs/reference/blorr.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0
    diff --git a/docs/reference/blr_bivariate_analysis.html b/docs/reference/blr_bivariate_analysis.html index 0d1d913..19d9d0a 100644 --- a/docs/reference/blr_bivariate_analysis.html +++ b/docs/reference/blr_bivariate_analysis.html @@ -40,8 +40,8 @@ - @@ -74,7 +74,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -137,8 +137,8 @@

    Bivariate analysis

    -

    Information value and likelihood ratio chi square test for initial - variable/predictor selection. Currently avialable for categorical +

    Information value and likelihood ratio chi square test for initial + variable/predictor selection. Currently avialable for categorical predictors only.

    diff --git a/docs/reference/blr_coll_diag.html b/docs/reference/blr_coll_diag.html index f804526..abc7c47 100644 --- a/docs/reference/blr_coll_diag.html +++ b/docs/reference/blr_coll_diag.html @@ -8,23 +8,29 @@ Collinearity diagnostics — blr_coll_diag • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Variance inflation factor, tolerance, eigenvalues and condition indices.

    -
    blr_coll_diag(model)
    @@ -140,7 +143,7 @@ 

    Collinearity diagnostics

    blr_vif_tol(model) blr_eigen_cindex(model)
    - +

    Arguments

    @@ -149,7 +152,7 @@

    Arg

    An object of class glm.

    - +

    Value

    blr_coll_diag returns an object of class "blr_coll_diag". @@ -158,7 +161,6 @@

    Value

    vif_t

    tolerance and variance inflation factors

    eig_cindex

    eigen values and condition index

    -

    Details

    Collinearity implies two variables are near perfect linear combinations of @@ -184,12 +186,10 @@

    Details of each of these linear combinations is called an eigenvalue. Collinearity is spotted by finding 2 or more variables that have large proportions of variance (.50 or more) that correspond to large condition indices. A rule of thumb is to label as large those condition indices in the range of 30 or larger.

    -

    References

    Belsley, D. A., Kuh, E., and Welsch, R. E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: John Wiley & Sons.

    -

    Examples

    # model @@ -197,52 +197,48 @@

    Examp family = binomial(link = 'logit')) # vif and tolerance -blr_vif_tol(model)

    #> # A tibble: 3 x 3 -#> Variable Tolerance VIF -#> <chr> <dbl> <dbl> -#> 1 female1 0.982 1.02 -#> 2 read 0.602 1.66 -#> 3 science 0.594 1.68
    +blr_vif_tol(model)
    #> Variable Tolerance VIF +#> 1 female1 0.9824347 1.017879 +#> 2 read 0.6021370 1.660752 +#> 3 science 0.5939847 1.683545
    # eigenvalues and condition indices -blr_eigen_cindex(model)
    #> Eigenvalue Condition Index intercept female1 read science -#> 1 3.57391760 1.000000 0.002062502 0.02386799 0.001675904 0.001561977 -#> 2 0.39409893 3.011408 0.003037479 0.91508462 0.003889972 0.004232601 -#> 3 0.01888407 13.757025 0.961793702 0.04701680 0.291423043 0.090184221 -#> 4 0.01309940 16.517583 0.033106318 0.01403058 0.703011081 0.904021201
    +blr_eigen_cindex(model)
    #> Eigenvalue Condition Index intercept female1 read +#> 1 3.57391760 1.000000 3.500782e-06 2.232611e-05 0.0006577134 +#> 2 0.39409893 3.011408 5.155657e-06 8.559696e-04 0.0015266309 +#> 3 0.01888407 13.757025 1.632498e-03 4.397949e-05 0.1143698337 +#> 4 0.01309940 16.517583 5.619292e-05 1.312420e-05 0.2758987745 +#> science +#> 1 0.0009448623 +#> 2 0.0025603607 +#> 3 0.0545537252 +#> 4 0.5468553525
    # collinearity diagnostics blr_coll_diag(model)
    #> Tolerance and Variance Inflation Factor #> --------------------------------------- -#> # A tibble: 3 x 3 -#> Variable Tolerance VIF -#> <chr> <dbl> <dbl> -#> 1 female1 0.982 1.02 -#> 2 read 0.602 1.66 -#> 3 science 0.594 1.68 +#> Variable Tolerance VIF +#> 1 female1 0.9824347 1.017879 +#> 2 read 0.6021370 1.660752 +#> 3 science 0.5939847 1.683545 #> #> #> Eigenvalue and Condition Index #> ------------------------------ -#> Eigenvalue Condition Index intercept female1 read science -#> 1 3.57391760 1.000000 0.002062502 0.02386799 0.001675904 0.001561977 -#> 2 0.39409893 3.011408 0.003037479 0.91508462 0.003889972 0.004232601 -#> 3 0.01888407 13.757025 0.961793702 0.04701680 0.291423043 0.090184221 -#> 4 0.01309940 16.517583 0.033106318 0.01403058 0.703011081 0.904021201
    -
    +#> Eigenvalue Condition Index intercept female1 read +#> 1 3.57391760 1.000000 3.500782e-06 2.232611e-05 0.0006577134 +#> 2 0.39409893 3.011408 5.155657e-06 8.559696e-04 0.0015266309 +#> 3 0.01888407 13.757025 1.632498e-03 4.397949e-05 0.1143698337 +#> 4 0.01309940 16.517583 5.619292e-05 1.312420e-05 0.2758987745 +#> science +#> 1 0.0009448623 +#> 2 0.0025603607 +#> 3 0.0545537252 +#> 4 0.5468553525
    +
    - @@ -253,7 +249,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_confusion_matrix.html b/docs/reference/blr_confusion_matrix.html index 82ee3b8..eb04f7d 100644 --- a/docs/reference/blr_confusion_matrix.html +++ b/docs/reference/blr_confusion_matrix.html @@ -40,7 +40,7 @@ - + @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -135,10 +135,13 @@

    Confusion matrix

    -

    Wrapper for confMatrix from the caret package.

    +

    Confusion matrix and statistics.

    -
    blr_confusion_matrix(model, cutoff = 0.5, data = NULL)
    +
    blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...)
    +
    +# S3 method for default
    +blr_confusion_matrix(model, cutoff = 0.5, data = NULL, ...)

    Arguments

    @@ -155,6 +158,10 @@

    Arg

    + + + +
    data

    A tibble or a data.frame.

    ...

    Other arguments.

    Value

    @@ -176,33 +183,33 @@

    Examp
    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) -blr_confusion_matrix(model, cutoff = 0.4)
    #> Confusion Matrix and Statistics +blr_confusion_matrix(model, cutoff = 0.4)
    #> Confusion Matrix and Statistics #> #> Reference #> Prediction 0 1 #> 0 125 16 #> 1 22 37 -#> -#> Accuracy : 0.81 -#> 95% CI : (0.7487, 0.8619) -#> No Information Rate : 0.735 -#> P-Value [Acc > NIR] : 0.008453 -#> -#> Kappa : 0.5293 -#> -#> Mcnemar's Test P-Value : 0.417304 -#> -#> Sensitivity : 0.6981 -#> Specificity : 0.8503 -#> Pos Pred Value : 0.6271 -#> Neg Pred Value : 0.8865 -#> Prevalence : 0.2650 -#> Detection Rate : 0.1850 -#> Detection Prevalence : 0.2950 -#> Balanced Accuracy : 0.7742 -#> -#> 'Positive' Class : 1 -#>
    +#> +#> +#> Accuracy : 0.8100 +#> No Information Rate : 0.7350 +#> +#> Kappa : 0.5293 +#> +#> McNemars's Test P-Value : 0.4173 +#> +#> Sensitivity : 0.6981 +#> Specificity : 0.8503 +#> Pos Pred Value : 0.6271 +#> Neg Pred Value : 0.8865 +#> Prevalence : 0.2650 +#> Detection Rate : 0.1850 +#> Detection Prevalence : 0.2950 +#> Balanced Accuracy : 0.7742 +#> Precision : 0.6271 +#> Recall : 0.6981 +#> +#> 'Positive' Class : 1
    @@ -145,7 +145,8 @@

    Event rate by decile

    title = "Capture Rate by Decile", bar_color = "blue", text_size = 3.5, - text_vjust = -0.3 + text_vjust = -0.3, + print_plot = TRUE )

    Arguments

    @@ -179,6 +180,10 @@

    Arg text_vjust

    Vertical justification of the bar labels.

    + + print_plot +

    logical; if TRUE, prints the plot else returns a plot object.

    +

    See also

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    Decile lift chart

    title = "Decile Lift Chart", bar_color = "blue", text_size = 3.5, - text_vjust = -0.3 + text_vjust = -0.3, + print_plot = TRUE )

    Arguments

    @@ -179,6 +180,10 @@

    Arg text_vjust

    Vertical justification of the bar labels.

    + + print_plot +

    logical; if TRUE, prints the plot else returns a plot object.

    +

    See also

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8ace2db..7bc115b 100644 --- a/docs/reference/blr_gains_table.html +++ b/docs/reference/blr_gains_table.html @@ -73,7 +73,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -151,6 +151,7 @@

    Gains table & lift chart

    diag_line_col = "red", lift_curve_col = "blue", plot_title_justify = 0.5, + print_plot = TRUE, ... ) @@ -193,6 +194,10 @@

    Arg plot_title_justify

    Horizontal justification on the plot title.

    + + print_plot +

    logical; if TRUE, prints the plot else returns a plot object.

    + ...

    Other inputs.

    @@ -226,24 +231,21 @@

    Examp
    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) # gains table -blr_gains_table(model)
    #> # A tibble: 10 x 12 -#> decile total `1` `0` ks tp tn fp fn sensitivity -#> <dbl> <int> <int> <int> <dbl> <int> <int> <int> <int> <dbl> -#> 1 1 20 14 6 22.3 14 141 6 39 26.4 -#> 2 2 20 13 7 42.1 27 134 13 26 50.9 -#> 3 3 20 10 10 54.2 37 124 23 16 69.8 -#> 4 4 20 7 13 58.5 44 111 36 9 83.0 -#> 5 5 20 3 17 52.6 47 94 53 6 88.7 -#> 6 6 20 3 17 46.7 50 77 70 3 94.3 -#> 7 7 20 1 19 35.7 51 58 89 2 96.2 -#> 8 8 20 2 18 27.2 53 40 107 0 100 -#> 9 9 20 0 20 13.6 53 20 127 0 100 -#> 10 10 20 0 20 0 53 0 147 0 100 -#> # ... with 2 more variables: specificity <dbl>, accuracy <dbl>
    +blr_gains_table(model)
    #> decile total 1 0 ks tp tn fp fn sensitivity specificity accuracy +#> 1 1 20 14 6 22.33346 14 141 6 39 26.41509 95.91837 77.5 +#> 2 2 20 13 7 42.09986 27 134 13 26 50.94340 91.15646 80.5 +#> 3 3 20 10 10 54.16506 37 124 23 16 69.81132 84.35374 80.5 +#> 4 4 20 7 13 58.52907 44 111 36 9 83.01887 75.51020 77.5 +#> 5 5 20 3 17 52.62482 47 94 53 6 88.67925 63.94558 70.5 +#> 6 6 20 3 17 46.72058 50 77 70 3 94.33962 52.38095 63.5 +#> 7 7 20 1 19 35.68220 51 58 89 2 96.22642 39.45578 54.5 +#> 8 8 20 2 18 27.21088 53 40 107 0 100.00000 27.21088 46.5 +#> 9 9 20 0 20 13.60544 53 20 127 0 100.00000 13.60544 36.5 +#> 10 10 20 0 20 0.00000 53 0 147 0 100.00000 0.00000 26.5
    # lift chart k <- blr_gains_table(model) plot(k)
    -
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a/docs/reference/blr_ks_chart.html b/docs/reference/blr_ks_chart.html index fd14c50..24ece20 100644 --- a/docs/reference/blr_ks_chart.html +++ b/docs/reference/blr_ks_chart.html @@ -76,7 +76,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -151,7 +151,8 @@

    KS chart

    title = "KS Chart", yaxis_title = " ", xaxis_title = "Cumulative Population %", - ks_line_color = "black" + ks_line_color = "black", + print_plot = TRUE )

    Arguments

    @@ -177,6 +178,10 @@

    Arg ks_line_color

    Color of the line indicating maximum KS statistic.

    + + print_plot +

    logical; if TRUE, prints the plot else returns a plot object.

    +

    References

    diff --git a/docs/reference/blr_launch_app.html b/docs/reference/blr_launch_app.html index 627fd53..f9a8687 100644 --- a/docs/reference/blr_launch_app.html +++ b/docs/reference/blr_launch_app.html @@ -8,23 +8,29 @@ Launch shiny app — blr_launch_app • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Launches shiny app for interactive model building.

    -
    blr_launch_app()
    - + +

    Examples

    if (FALSE) { blr_launch_app() }
    - @@ -160,7 +161,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_linktest.html b/docs/reference/blr_linktest.html index b184f2d..26c268d 100644 --- a/docs/reference/blr_linktest.html +++ b/docs/reference/blr_linktest.html @@ -8,23 +8,29 @@ Model specification error — blr_linktest • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Test for model specification error.

    -
    blr_linktest(model)
    - +

    Arguments

    @@ -145,17 +148,15 @@

    Arg

    An object of class glm.

    - +

    Value

    An object of class glm.

    -

    References

    Pregibon, D. 1979. Data analytic methods for generalized linear models. PhD diss., University of Toronto.

    Pregibon, D. 1980. Goodness of link tests for generalized linear models.

    Tukey, J. W. 1949. One degree of freedom for non-additivity.

    -

    Examples

    @@ -189,18 +190,10 @@

    Examp #>

    - @@ -211,7 +204,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

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    Lorenz curve

    xaxis_title = "Cumulative Events %", yaxis_title = "Cumulative Non Events %", diag_line_col = "red", - lorenz_curve_col = "blue" + lorenz_curve_col = "blue", + print_plot = TRUE )

    Arguments

    @@ -181,6 +182,10 @@

    Arg lorenz_curve_col

    Color of the lorenz curve.

    + + print_plot +

    logical; if TRUE, prints the plot else returns a plot object.

    +

    See also

    diff --git a/docs/reference/blr_model_fit_stats.html b/docs/reference/blr_model_fit_stats.html index ee2a2ae..ab6bf4d 100644 --- a/docs/reference/blr_model_fit_stats.html +++ b/docs/reference/blr_model_fit_stats.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/blr_multi_model_fit_stats.html b/docs/reference/blr_multi_model_fit_stats.html index f2af54d..35c5ebd 100644 --- a/docs/reference/blr_multi_model_fit_stats.html +++ b/docs/reference/blr_multi_model_fit_stats.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -179,22 +179,22 @@

    Examp model2 <- glm(honcomp ~ female + read + math, data = hsb2, family = binomial(link = 'logit')) -blr_multi_model_fit_stats(model, model2)
    #> Measures Model 1 Model 2 -#> 1 Log-Lik Intercept Only -115.644 -115.644 -#> 2 Log-Lik Full Model -80.118 -75.210 -#> 3 Deviance 160.236 150.420 -#> 4 LR 71.052 80.869 -#> 5 Prob > LR 0.000 0.000 -#> 6 MCFadden's R2 0.307 0.350 -#> 7 McFadden's Adj R2 0.273 0.315 -#> 8 ML (Cox-Snell) R2 168.236 158.420 -#> 9 Cragg-Uhler(Nagelkerke) R2 0.299 0.333 -#> 10 McKelvey & Zavoina's R2 181.430 171.613 -#> 11 Efron's R2 0.518 0.523 -#> 12 Count R2 0.330 0.379 -#> 13 Adj Count R2 0.436 0.485 -#> 14 AIC 0.810 0.830 -#> 15 BIC 0.283 0.358
    +blr_multi_model_fit_stats(model, model2)
    #> Measures Model 1 Model 2 +#> loglik_null Log-Lik Intercept Only -115.644 -115.644 +#> loglik_model Log-Lik Full Model -80.118 -75.210 +#> m_deviance Deviance 160.236 150.420 +#> lr_ratio LR 71.052 80.869 +#> lr_pval Prob > LR 0.000 0.000 +#> mcfadden MCFadden's R2 0.307 0.350 +#> adj_mcfadden McFadden's Adj R2 0.273 0.315 +#> m_aic ML (Cox-Snell) R2 168.236 158.420 +#> cox_snell Cragg-Uhler(Nagelkerke) R2 0.299 0.333 +#> m_bic McKelvey & Zavoina's R2 181.430 171.613 +#> mckelvey Efron's R2 0.518 0.523 +#> effron Count R2 0.330 0.379 +#> nagelkerke Adj Count R2 0.436 0.485 +#> count_r2 AIC 0.810 0.830 +#> count_adj BIC 0.283 0.358
    @@ -173,11 +173,11 @@

    Examp
    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) -blr_pairs(model)
    #> # A tibble: 1 x 8 -#> pairs concordance discordance tied somers_d gamma tau c -#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 7791 0.856 0.142 0.00141 0.715 0.714 0.279 0.857
    -
    +blr_pairs(model)
    #> pairs concordance discordance tied somers_d gamma tau +#> 1 7791 0.856116 0.1424721 0.001411886 0.714653 0.7136439 0.279397 +#> c +#> 1 0.856822
    +
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a/docs/reference/blr_plot_dfbetas_panel.html +++ b/docs/reference/blr_plot_dfbetas_panel.html @@ -8,23 +8,29 @@ DFBETAs panel — blr_plot_dfbetas_panel • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Panel of plots to detect influential observations using DFBETAs.

    -
    -
    blr_plot_dfbetas_panel(model)
    - +
    blr_plot_dfbetas_panel(model, print_plot = TRUE)
    +

    Arguments

    @@ -144,15 +147,18 @@

    Arg

    + + + +
    model

    An object of class glm.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    - +

    Value

    list; blr_dfbetas_panel returns a list of tibbles (for intercept and each predictor) with the observation number and DFBETA of observations that exceed the threshold for classifying an observation as an outlier/influential observation.

    -

    Details

    DFBETA measures the difference in each parameter estimate with and without @@ -162,14 +168,12 @@

    Details in estimating a given parameter. Belsley, Kuh, and Welsch recommend 2 as a general cutoff value to indicate influential observations and \(2/\sqrt(n)\) as a size-adjusted cutoff.

    -

    References

    Belsley, David A.; Kuh, Edwin; Welsh, Roy E. (1980). Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. Wiley Series in Probability and Mathematical Statistics. New York: John Wiley & Sons. pp. ISBN 0-471-05856-4.

    -

    Examples

    if (FALSE) { @@ -179,20 +183,10 @@

    Examp blr_plot_dfbetas_panel(model) }

    - @@ -203,7 +197,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

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a/docs/reference/blr_plot_diag_fit.html b/docs/reference/blr_plot_diag_fit.html index 5137866..b05af99 100644 --- a/docs/reference/blr_plot_diag_fit.html +++ b/docs/reference/blr_plot_diag_fit.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -138,7 +138,7 @@

    Fitted values diagnostics plot

    Diagnostic plots for fitted values.

    -
    blr_plot_diag_fit(model)
    +
    blr_plot_diag_fit(model, print_plot = TRUE)

    Arguments

    @@ -147,6 +147,10 @@

    Arg

    + + + +
    model

    An object of class glm.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    Value

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    Influence diagnostics plot

    Reisudal diagnostic plots for detecting influential observations.

    -
    blr_plot_diag_influence(model)
    +
    blr_plot_diag_influence(model, print_plot = TRUE)

    Arguments

    @@ -147,6 +147,10 @@

    Arg

    + + + +
    model

    An object of class glm.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    Value

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zR&KV|7HOAx25dDhKwdzzL{S_wx|kN(D_TAXgy1Ru?6dml(zS*Br$=`s3s^4*o_A=p zqK9HmxE@8vJ99mB%)Iqxn}ur5w)on7QR{n3iklzykJU{s>R&vYqad5{ZnmYWhS!v4 z4&y0QH z%7V2^8_5Gr>lG blorr - 0.2.2.9000 + 0.3.0 @@ -138,7 +138,7 @@

    Leverage diagnostics plot

    Diagnostic plots for leverage.

    -
    blr_plot_diag_leverage(model)
    +
    blr_plot_diag_leverage(model, print_plot = TRUE)

    Arguments

    @@ -147,6 +147,10 @@

    Arg

    + + + +
    model

    An object of class glm.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    Value

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    @@ -103,7 +109,6 @@ -
    -

    Data for generating decile capture rate.

    -
    blr_prep_dcrate_data(gains_table)
    - +

    Arguments

    @@ -145,35 +148,29 @@

    Arg

    An object of clas blr_gains_table

    - +

    Examples

    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) -blr_prep_dcrate_data(gt)
    #> # A tibble: 10 x 4 -#> decile total `1` decile_mean -#> <dbl> <int> <int> <dbl> -#> 1 1 20 14 0.7 -#> 2 2 20 13 0.65 -#> 3 3 20 10 0.5 -#> 4 4 20 7 0.35 -#> 5 5 20 3 0.15 -#> 6 6 20 3 0.15 -#> 7 7 20 1 0.05 -#> 8 8 20 2 0.1 -#> 9 9 20 0 0 -#> 10 10 20 0 0
    -
    +blr_prep_dcrate_data(gt)
    #> decile total 1 decile_mean +#> 1 1 20 14 0.70 +#> 2 2 20 13 0.65 +#> 3 3 20 10 0.50 +#> 4 4 20 7 0.35 +#> 5 5 20 3 0.15 +#> 6 6 20 3 0.15 +#> 7 7 20 1 0.05 +#> 8 8 20 2 0.10 +#> 9 9 20 0 0.00 +#> 10 10 20 0 0.00
    +
    - @@ -184,7 +181,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_prep_kschart_data.html b/docs/reference/blr_prep_kschart_data.html index a450af9..8805d39 100644 --- a/docs/reference/blr_prep_kschart_data.html +++ b/docs/reference/blr_prep_kschart_data.html @@ -8,23 +8,29 @@ KS Chart data — blr_prep_kschart_data • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Data for generating KS chart.

    -
    blr_prep_kschart_data(gains_table)
    @@ -144,7 +147,7 @@ 

    KS Chart data

    blr_prep_kschart_stat(ks_line) blr_prep_ksannotate_x(ks_line)
    - +

    Arguments

    @@ -157,37 +160,31 @@

    Arg

    Overall conversion rate.

    - +

    Examples

    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) -blr_prep_kschart_data(gt)
    #> # A tibble: 11 x 3 -#> cum_total_per cum_1s_per cum_0s_per -#> <dbl> <dbl> <dbl> -#> 1 0 0 0 -#> 2 0.1 0.264 0.0408 -#> 3 0.2 0.509 0.0884 -#> 4 0.3 0.698 0.156 -#> 5 0.4 0.830 0.245 -#> 6 0.5 0.887 0.361 -#> 7 0.6 0.943 0.476 -#> 8 0.7 0.962 0.605 -#> 9 0.8 1 0.728 -#> 10 0.9 1 0.864 -#> 11 1 1 1
    ks_line <- blr_prep_kschart_line(gt) +blr_prep_kschart_data(gt)
    #> cum_total_per cum_1s_per cum_0s_per +#> 1 0.0 0.0000000 0.00000000 +#> 2 0.1 0.2641509 0.04081633 +#> 3 0.2 0.5094340 0.08843537 +#> 4 0.3 0.6981132 0.15646259 +#> 5 0.4 0.8301887 0.24489796 +#> 6 0.5 0.8867925 0.36054422 +#> 7 0.6 0.9433962 0.47619048 +#> 8 0.7 0.9622642 0.60544218 +#> 9 0.8 1.0000000 0.72789116 +#> 10 0.9 1.0000000 0.86394558 +#> 11 1.0 1.0000000 1.00000000
    ks_line <- blr_prep_kschart_line(gt) blr_prep_kschart_stat(ks_line)
    #> [1] 59
    blr_prep_ksannotate_y(ks_line)
    #> [1] 0.5375433
    blr_prep_ksannotate_x(ks_line)
    #> [1] 0.5
    -
    + - @@ -198,7 +195,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_prep_lchart_gmean.html b/docs/reference/blr_prep_lchart_gmean.html index 73bc9f3..3aea4b3 100644 --- a/docs/reference/blr_prep_lchart_gmean.html +++ b/docs/reference/blr_prep_lchart_gmean.html @@ -8,23 +8,29 @@ Lift Chart data — blr_prep_lchart_gmean • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Data for generating lift chart.

    -
    blr_prep_lchart_gmean(gains_table)
     
     blr_prep_lchart_data(gains_table, global_mean)
    - +

    Arguments

    @@ -151,36 +154,30 @@

    Arg

    Overall conversion rate.

    - +

    Examples

    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) globalmean <- blr_prep_lchart_gmean(gt) -blr_prep_lchart_data(gt, globalmean)
    #> # A tibble: 10 x 5 -#> decile total `1` decile_mean d_by_g_mean -#> <dbl> <int> <int> <dbl> <dbl> -#> 1 1 20 14 0.7 2.64 -#> 2 2 20 13 0.65 2.45 -#> 3 3 20 10 0.5 1.89 -#> 4 4 20 7 0.35 1.32 -#> 5 5 20 3 0.15 0.566 -#> 6 6 20 3 0.15 0.566 -#> 7 7 20 1 0.05 0.189 -#> 8 8 20 2 0.1 0.377 -#> 9 9 20 0 0 0 -#> 10 10 20 0 0 0
    -
    +blr_prep_lchart_data(gt, globalmean)
    #> decile total 1 decile_mean d_by_g_mean +#> 1 1 20 14 0.70 2.6415094 +#> 2 2 20 13 0.65 2.4528302 +#> 3 3 20 10 0.50 1.8867925 +#> 4 4 20 7 0.35 1.3207547 +#> 5 5 20 3 0.15 0.5660377 +#> 6 6 20 3 0.15 0.5660377 +#> 7 7 20 1 0.05 0.1886792 +#> 8 8 20 2 0.10 0.3773585 +#> 9 9 20 0 0.00 0.0000000 +#> 10 10 20 0 0.00 0.0000000
    +
    - @@ -191,7 +188,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_prep_lorenz_data.html b/docs/reference/blr_prep_lorenz_data.html index dd90c3b..a6d0230 100644 --- a/docs/reference/blr_prep_lorenz_data.html +++ b/docs/reference/blr_prep_lorenz_data.html @@ -8,23 +8,29 @@ Lorenz curve data — blr_prep_lorenz_data • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Data for generating Lorenz curve.

    -
    blr_prep_lorenz_data(model, data = NULL, test_data = FALSE)
    - +

    Arguments

    @@ -153,37 +156,31 @@

    Arg

    Logical; TRUE if data is test data and FALSE if training data.

    - +

    Examples

    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) data <- model$data -blr_prep_lorenz_data(model, data, FALSE)
    #> # A tibble: 12 x 2 -#> cum_0s_per cum_1s_per -#> <dbl> <dbl> -#> 1 0 0 -#> 2 0.0408 0.264 -#> 3 0.0884 0.509 -#> 4 0.156 0.698 -#> 5 0.245 0.830 -#> 6 0.361 0.887 -#> 7 0.476 0.943 -#> 8 0.605 0.962 -#> 9 0.728 1 -#> 10 0.864 1 -#> 11 1 1 -#> 12 1 1
    -
    +blr_prep_lorenz_data(model, data, FALSE)
    #> cum_0s_per cum_1s_per +#> 1 0.00000000 0.0000000 +#> 2 0.04081633 0.2641509 +#> 3 0.08843537 0.5094340 +#> 4 0.15646259 0.6981132 +#> 5 0.24489796 0.8301887 +#> 6 0.36054422 0.8867925 +#> 7 0.47619048 0.9433962 +#> 8 0.60544218 0.9622642 +#> 9 0.72789116 1.0000000 +#> 10 0.86394558 1.0000000 +#> 11 1.00000000 1.0000000 +#> 12 1.00000000 1.0000000
    +
    - @@ -194,7 +191,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_prep_roc_data.html b/docs/reference/blr_prep_roc_data.html index 584be22..cb451c1 100644 --- a/docs/reference/blr_prep_roc_data.html +++ b/docs/reference/blr_prep_roc_data.html @@ -8,23 +8,29 @@ ROC curve data — blr_prep_roc_data • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Data for generating ROC curve.

    -
    blr_prep_roc_data(gains_table)
    - +

    Arguments

    @@ -145,36 +148,30 @@

    Arg

    An object of clas blr_gains_table

    - +

    Examples

    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) gt <- blr_gains_table(model) -blr_prep_roc_data(gt)
    #> # A tibble: 11 x 4 -#> sensitivity specificity sensitivity_per `1 - specificity` -#> <dbl> <dbl> <dbl> <dbl> -#> 1 NA NA 0 0 -#> 2 26.4 95.9 0.264 0.0408 -#> 3 50.9 91.2 0.509 0.0884 -#> 4 69.8 84.4 0.698 0.156 -#> 5 83.0 75.5 0.830 0.245 -#> 6 88.7 63.9 0.887 0.361 -#> 7 94.3 52.4 0.943 0.476 -#> 8 96.2 39.5 0.962 0.605 -#> 9 100 27.2 1 0.728 -#> 10 100 13.6 1 0.864 -#> 11 100 0 1 1
    -
    +blr_prep_roc_data(gt)
    #> sensitivity specificity sensitivity_per 1 - specificity +#> 1 NA NA 0.0000000 0.00000000 +#> 2 26.41509 95.91837 0.2641509 0.04081633 +#> 3 50.94340 91.15646 0.5094340 0.08843537 +#> 4 69.81132 84.35374 0.6981132 0.15646259 +#> 5 83.01887 75.51020 0.8301887 0.24489796 +#> 6 88.67925 63.94558 0.8867925 0.36054422 +#> 7 94.33962 52.38095 0.9433962 0.47619048 +#> 8 96.22642 39.45578 0.9622642 0.60544218 +#> 9 100.00000 27.21088 1.0000000 0.72789116 +#> 10 100.00000 13.60544 1.0000000 0.86394558 +#> 11 100.00000 0.00000 1.0000000 1.00000000
    +
    - @@ -185,7 +182,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_regress.html b/docs/reference/blr_regress.html index b87c430..72ef20f 100644 --- a/docs/reference/blr_regress.html +++ b/docs/reference/blr_regress.html @@ -8,23 +8,29 @@ Binary logistic regression — blr_regress • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Binary logistic regression.

    -
    blr_regress(object, ...)
     
     # S3 method for glm
     blr_regress(object, odd_conf_limit = FALSE, ...)
    - +

    Arguments

    @@ -158,14 +161,11 @@

    Arg

    If TRUE, odds ratio confidence limts will be displayed.

    - +

    Examples

    # using formula -blr_regress(object = honcomp ~ female + read + science, data = hsb2)
    #> - Creating model overview. -#> - Creating response profile. -#> - Extracting maximum likelihood estimates. -#> - Estimating concordant and discordant pairs.
    #> Model Overview +blr_regress(object = honcomp ~ female + read + science, data = hsb2)
    #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ @@ -201,10 +201,7 @@

    Examp model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) -blr_regress(model)

    #> - Creating model overview. -#> - Creating response profile. -#> - Extracting maximum likelihood estimates. -#> - Estimating concordant and discordant pairs.
    #> Model Overview +blr_regress(model)
    #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ @@ -237,11 +234,7 @@

    Examp #> --------------------------------------------------------------- #>

    # odds ratio estimates -blr_regress(model, odd_conf_limit = TRUE)
    #> - Creating model overview. -#> - Creating response profile. -#> - Extracting maximum likelihood estimates. -#> - Computing odds ratio estimates. -#> - Estimating concordant and discordant pairs.
    #> Model Overview +blr_regress(model, odd_conf_limit = TRUE)
    #> Model Overview #> ------------------------------------------------------------------------ #> Data Set Resp Var Obs. Df. Model Df. Residual Convergence #> ------------------------------------------------------------------------ @@ -284,14 +277,10 @@

    Examp #>

    - @@ -302,7 +291,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_residual_diagnostics.html b/docs/reference/blr_residual_diagnostics.html index f20f2ed..554ce12 100644 --- a/docs/reference/blr_residual_diagnostics.html +++ b/docs/reference/blr_residual_diagnostics.html @@ -8,23 +8,29 @@ Residual diagnostics — blr_residual_diagnostics • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,11 +38,11 @@ - + - + @@ -53,7 +59,7 @@ - +
    @@ -104,7 +110,6 @@ -
    -

    Diagnostics for confidence interval displacement and detecting ill fitted observations.

    -
    blr_residual_diagnostics(model)
    - +

    Arguments

    @@ -147,42 +150,222 @@

    Arg

    An object of class glm.

    - +

    Value

    C, CBAR, DIFDEV and DIFCHISQ.

    -

    Examples

    model <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) -blr_residual_diagnostics(model)
    #> # A tibble: 200 x 4 -#> c cbar difdev difchisq -#> <dbl> <dbl> <dbl> <dbl> -#> 1 0.00171 0.00168 0.175 0.0906 -#> 2 0.143 0.139 4.00 5.71 -#> 3 0.000996 0.000981 0.130 0.0666 -#> 4 0.00902 0.00876 0.537 0.301 -#> 5 0.000598 0.000592 0.110 0.0563 -#> 6 0.00288 0.00280 0.209 0.108 -#> 7 0.000900 0.000889 0.149 0.0769 -#> 8 0.00000990 0.00000988 0.00771 0.00386 -#> 9 0.0122 0.0119 0.801 0.481 -#> 10 0.00216 0.00213 0.229 0.120 -#> # ... with 190 more rows
    -
    +blr_residual_diagnostics(model)
    #> c cbar difdev difchisq +#> 1 1.714775e-03 1.682909e-03 0.175200955 0.090559751 +#> 2 1.429214e-01 1.394315e-01 3.998937227 5.710209661 +#> 3 9.959732e-04 9.813000e-04 0.130007342 0.066607468 +#> 4 9.023852e-03 8.761106e-03 0.536722606 0.300894248 +#> 5 5.980764e-04 5.917929e-04 0.110220460 0.056328100 +#> 6 2.876007e-03 2.801553e-03 0.208574176 0.108218735 +#> 7 8.995744e-04 8.891766e-04 0.149175691 0.076927702 +#> 8 9.902361e-06 9.877010e-06 0.007711173 0.003858052 +#> 9 1.219551e-02 1.189404e-02 0.800918692 0.481155824 +#> 10 2.163331e-03 2.125096e-03 0.229423218 0.120235791 +#> 11 4.832076e-03 4.727697e-03 0.401357308 0.218861551 +#> 12 6.191650e-02 6.043716e-02 2.609131129 2.529552930 +#> 13 2.995002e-02 2.852142e-02 0.975083831 0.597941207 +#> 14 1.048892e-01 1.034023e-01 4.369886254 7.294114623 +#> 15 2.555955e-05 2.544456e-05 0.011305342 0.005655812 +#> 16 1.890339e-04 1.876253e-04 0.049929085 0.025180243 +#> 17 3.927621e-04 3.891513e-04 0.083321991 0.042330035 +#> 18 4.470660e-03 4.394080e-03 0.461923138 0.256523888 +#> 19 8.222657e-02 7.856862e-02 2.147893802 1.766130095 +#> 20 1.922340e-03 1.894221e-03 0.245647296 0.129496817 +#> 21 3.360228e-02 3.254494e-02 1.465662476 1.034273716 +#> 22 3.070469e-02 2.919663e-02 0.971586410 0.594457116 +#> 23 1.187856e-03 1.172889e-03 0.179275765 0.093083875 +#> 24 1.024315e-02 1.002336e-02 0.779442376 0.467132920 +#> 25 1.666718e-05 1.661460e-05 0.010522075 0.005266506 +#> 26 9.902361e-06 9.877010e-06 0.007711173 0.003858052 +#> 27 3.868987e-02 3.760510e-02 1.754720088 1.341233680 +#> 28 1.397582e-03 1.376421e-03 0.175507244 0.090907237 +#> 29 5.883201e-05 5.851604e-05 0.021732193 0.010895452 +#> 30 1.538551e-03 1.518165e-03 0.218615492 0.114575242 +#> 31 1.206777e-04 1.199131e-04 0.037621469 0.018926550 +#> 32 1.582197e-01 1.510534e-01 3.149359572 3.334983697 +#> 33 2.790231e-02 2.700378e-02 1.254920166 0.838561702 +#> 34 1.832472e-02 1.690403e-02 0.414238978 0.218036836 +#> 35 4.199876e-03 4.115064e-03 0.375701229 0.203778702 +#> 36 4.902223e-02 4.797035e-02 2.417385169 2.235629715 +#> 37 6.184782e-02 5.972581e-02 2.102207404 1.740757786 +#> 38 2.790231e-02 2.700378e-02 1.254920166 0.838561702 +#> 39 7.221982e-05 7.175699e-05 0.022341692 0.011196792 +#> 40 2.371239e-05 2.362595e-05 0.012944686 0.006481368 +#> 41 1.484749e-04 1.473695e-04 0.039352793 0.019794657 +#> 42 3.430254e-03 3.374692e-03 0.382426519 0.208346373 +#> 43 1.538551e-03 1.518165e-03 0.218615492 0.114575242 +#> 44 1.890339e-04 1.876253e-04 0.049929085 0.025180243 +#> 45 3.282110e-02 3.187881e-02 1.538424442 1.110378773 +#> 46 3.133334e-02 2.980554e-02 0.993508264 0.611278393 +#> 47 5.583226e-04 5.524581e-04 0.103098442 0.052595923 +#> 48 2.821714e-02 2.740703e-02 1.378342821 0.954620784 +#> 49 5.980764e-04 5.917929e-04 0.110220460 0.056328100 +#> 50 1.714775e-03 1.682909e-03 0.175200955 0.090559751 +#> 51 1.007565e-01 9.926312e-02 4.216014793 6.697126795 +#> 52 7.587586e-05 7.546277e-05 0.027607615 0.013860744 +#> 53 2.523756e-04 2.503371e-04 0.061302700 0.030993042 +#> 54 1.005310e-02 9.782114e-03 0.631353787 0.362895817 +#> 55 1.049232e-02 1.010920e-02 0.500933885 0.276855002 +#> 56 3.884597e-06 3.876617e-06 0.003774610 0.001887141 +#> 57 7.089019e-06 7.072193e-06 0.005957749 0.002979759 +#> 58 5.314174e-04 5.251688e-04 0.087936379 0.044663202 +#> 59 1.219551e-02 1.189404e-02 0.800918692 0.481155824 +#> 60 2.250623e-04 2.231714e-04 0.052660204 0.026562307 +#> 61 1.937427e-03 1.907634e-03 0.235987514 0.124050481 +#> 62 2.447935e-01 2.312153e-01 3.612373822 4.168455201 +#> 63 3.099590e-05 3.085479e-05 0.013539849 0.006777256 +#> 64 6.322787e-06 6.309041e-06 0.005801949 0.002902014 +#> 65 1.324601e-04 1.313238e-04 0.030518715 0.015308706 +#> 66 2.523756e-04 2.503371e-04 0.061302700 0.030993042 +#> 67 5.521616e-03 5.408598e-03 0.475398199 0.264241258 +#> 68 1.484749e-04 1.473695e-04 0.039352793 0.019794657 +#> 69 2.843505e-02 2.759905e-02 1.362233451 0.938735201 +#> 70 3.258439e-04 3.226311e-04 0.064728136 0.032721649 +#> 71 5.883201e-05 5.851604e-05 0.021732193 0.010895452 +#> 72 7.100426e-03 6.959611e-03 0.610177412 0.350928915 +#> 73 2.223940e-02 2.127829e-02 0.828148303 0.492362786 +#> 74 1.711199e-01 1.636562e-01 3.349721185 3.752139610 +#> 75 3.430254e-03 3.374692e-03 0.382426519 0.208346373 +#> 76 2.115485e-04 2.098617e-04 0.052173679 0.026319642 +#> 77 1.778370e-01 1.751576e-01 5.295761642 11.625223156 +#> 78 4.554600e-02 4.401450e-02 1.736019169 1.308970340 +#> 79 5.923198e-06 5.910812e-06 0.005651206 0.002826627 +#> 80 1.484749e-04 1.473695e-04 0.039352793 0.019794657 +#> 81 1.005310e-02 9.782114e-03 0.631353787 0.362895817 +#> 82 6.867317e-04 6.747790e-04 0.076771905 0.038768758 +#> 83 6.184782e-02 5.972581e-02 2.102207404 1.740757786 +#> 84 3.683128e-02 3.586186e-02 1.770358544 1.362493836 +#> 85 8.739224e-04 8.603094e-04 0.108422252 0.055230101 +#> 86 7.733278e-02 7.545805e-02 2.935899226 3.112646806 +#> 87 1.199785e-03 1.184777e-03 0.182277117 0.094717594 +#> 88 1.568983e-05 1.564199e-05 0.010248659 0.005129580 +#> 89 1.040428e-04 1.031947e-04 0.025264629 0.012659537 +#> 90 5.648486e-03 5.535976e-03 0.497125122 0.277928055 +#> 91 2.296646e-01 2.269371e-01 6.278388092 19.108468289 +#> 92 3.360228e-02 3.254494e-02 1.465662476 1.034273716 +#> 93 2.507119e-03 2.448154e-03 0.200719009 0.104092427 +#> 94 4.620611e-04 4.574798e-04 0.090692860 0.046140576 +#> 95 1.279493e-03 1.263815e-03 0.197703474 0.103142180 +#> 96 5.656904e-02 5.404523e-02 1.663597494 1.211374960 +#> 97 2.249922e-03 2.200995e-03 0.195221725 0.101213040 +#> 98 5.683130e-03 5.505435e-03 0.330657695 0.176077827 +#> 99 1.101009e-04 1.093947e-04 0.033936548 0.017056493 +#> 100 9.143496e-03 8.924746e-03 0.645164254 0.373044051 +#> 101 2.507119e-03 2.448154e-03 0.200719009 0.104092427 +#> 102 7.065249e-02 6.554781e-02 1.382022263 0.907229749 +#> 103 5.496769e-04 5.417171e-04 0.074013559 0.037409012 +#> 104 3.606158e-02 3.480992e-02 1.437638887 1.002911024 +#> 105 2.946148e-02 2.853544e-02 1.331287766 0.907840313 +#> 106 5.676501e-03 5.522539e-03 0.377058254 0.203613395 +#> 107 3.826369e-02 3.727759e-02 1.842392766 1.446484548 +#> 108 9.579393e-04 9.427774e-04 0.116711853 0.059565092 +#> 109 2.558035e-03 2.523557e-03 0.346142203 0.187228311 +#> 110 6.989810e-03 6.772784e-03 0.402548738 0.218132880 +#> 111 3.907660e-02 3.809320e-02 1.897852151 1.513681841 +#> 112 5.546298e-04 5.483299e-04 0.094862141 0.048273447 +#> 113 5.299547e-02 4.993601e-02 1.315205267 0.864984721 +#> 114 7.541574e-03 7.336359e-03 0.486192864 0.269608174 +#> 115 1.021654e-02 9.796266e-03 0.438780016 0.238142198 +#> 116 2.294176e-04 2.275486e-04 0.055328822 0.027930516 +#> 117 4.649203e-03 4.543908e-03 0.370955406 0.200632248 +#> 118 5.889263e-03 5.700167e-03 0.333349523 0.177527665 +#> 119 1.501761e-02 1.469141e-02 1.052883879 0.676357480 +#> 120 3.333480e-04 3.302306e-04 0.069748542 0.035312784 +#> 121 1.926803e-02 1.877147e-02 1.119684321 0.728390444 +#> 122 3.469276e-03 3.422783e-03 0.458987480 0.255407134 +#> 123 1.706084e-03 1.674517e-03 0.175082379 0.090500084 +#> 124 2.023481e-03 1.997373e-03 0.290115157 0.154804754 +#> 125 3.696036e-01 3.642264e-01 6.950796928 25.035516433 +#> 126 1.838113e-02 1.802134e-02 1.330233919 0.920702469 +#> 127 2.330179e-03 2.293319e-03 0.273338950 0.144976356 +#> 128 1.215048e-03 1.197280e-03 0.158677951 0.081875583 +#> 129 2.763419e-03 2.724278e-03 0.354973148 0.192339395 +#> 130 2.783763e-05 2.772063e-05 0.013175835 0.006595622 +#> 131 1.039078e-02 1.019111e-02 0.864222041 0.530342453 +#> 132 2.214178e-02 2.178123e-02 1.729127087 1.337620342 +#> 133 3.552271e-02 3.367987e-02 1.045504661 0.649215871 +#> 134 1.019456e-03 1.007207e-03 0.162085818 0.083830337 +#> 135 2.219997e-02 2.119220e-02 0.793356119 0.466837321 +#> 136 9.896093e-04 9.720041e-04 0.107415754 0.054637523 +#> 137 1.163020e-02 1.130601e-02 0.695134995 0.405593537 +#> 138 1.217273e-03 1.201953e-03 0.183728947 0.095501951 +#> 139 3.521794e-04 3.489574e-04 0.075228440 0.038142864 +#> 140 3.359504e-03 3.297549e-03 0.332778538 0.178807876 +#> 141 1.033954e-04 1.026746e-04 0.029347552 0.014729117 +#> 142 1.626688e-03 1.603332e-03 0.213484010 0.111669095 +#> 143 9.542028e-04 9.406140e-04 0.128917831 0.066049834 +#> 144 1.418413e-03 1.399681e-03 0.203004768 0.105986475 +#> 145 5.989086e-03 5.904450e-03 0.705683222 0.417816515 +#> 146 2.023481e-03 1.997373e-03 0.290115157 0.154804754 +#> 147 4.364463e-02 4.289970e-02 2.574734358 2.513440758 +#> 148 9.098518e-04 8.993024e-04 0.150363722 0.077561516 +#> 149 1.035674e-03 1.021108e-03 0.141267367 0.072604021 +#> 150 4.072810e-04 4.023678e-04 0.066034991 0.033354067 +#> 151 1.213217e-03 1.196667e-03 0.169461679 0.087721966 +#> 152 6.738631e-02 6.568886e-02 2.660468673 2.607750122 +#> 153 1.783288e-03 1.757971e-03 0.235429953 0.123830352 +#> 154 4.078075e-03 4.012484e-03 0.450196915 0.249471329 +#> 155 1.798284e-02 1.763743e-02 1.327136586 0.918244408 +#> 156 9.586315e-02 9.257885e-02 2.750869718 2.702220617 +#> 157 6.920782e-03 6.818132e-03 0.765144454 0.459685870 +#> 158 1.019456e-03 1.007207e-03 0.162085818 0.083830337 +#> 159 5.468997e-04 5.412187e-04 0.102147214 0.052101738 +#> 160 6.956322e-03 6.783894e-03 0.491955607 0.273683670 +#> 161 6.768584e-02 6.659719e-02 3.367956058 4.140599894 +#> 162 1.068424e-02 1.049175e-02 0.931617433 0.582360803 +#> 163 9.161703e-02 8.684071e-02 2.085772458 1.665733364 +#> 164 1.794676e-02 1.749512e-02 1.079052614 0.695190763 +#> 165 8.183569e-03 8.053310e-03 0.829246770 0.505948870 +#> 166 6.112573e-04 6.042976e-04 0.104062462 0.053074637 +#> 167 2.691798e-02 2.597419e-02 1.143807429 0.740814692 +#> 168 7.248779e-04 7.164235e-04 0.119983373 0.061425852 +#> 169 2.032750e-02 1.999351e-02 1.620327724 1.216833025 +#> 170 2.470070e-02 2.429277e-02 1.843798016 1.470963397 +#> 171 1.626688e-03 1.603332e-03 0.213484010 0.111669095 +#> 172 1.832056e-03 1.808523e-03 0.265475697 0.140796573 +#> 173 1.628309e-04 1.613977e-04 0.036505512 0.018336703 +#> 174 5.031675e-02 4.911828e-02 2.308866824 2.062197192 +#> 175 3.718687e-04 3.677289e-04 0.065376651 0.033032492 +#> 176 1.932064e-02 1.881967e-02 1.116748565 0.725807280 +#> 177 1.580237e-02 1.535157e-02 0.881118321 0.538126313 +#> 178 2.184174e-03 2.141404e-03 0.209906333 0.109356853 +#> 179 1.353245e-02 1.320813e-02 0.895192843 0.551116791 +#> 180 3.295260e-02 3.237871e-02 2.147516973 1.859195000 +#> 181 3.826369e-02 3.727759e-02 1.842392766 1.446484548 +#> 182 5.546298e-04 5.483299e-04 0.094862141 0.048273447 +#> 183 4.059106e-02 3.897641e-02 1.420184895 0.979840258 +#> 184 3.989745e-02 3.924613e-02 2.506277005 2.404079826 +#> 185 6.050690e-02 5.680204e-02 1.391329501 0.927678395 +#> 186 1.042797e-02 1.016529e-02 0.690776927 0.403546839 +#> 187 5.989086e-03 5.904450e-03 0.705683222 0.417816515 +#> 188 1.213217e-03 1.196667e-03 0.169461679 0.087721966 +#> 189 1.666544e-02 1.625457e-02 1.034466544 0.659304844 +#> 190 5.036356e-03 4.962806e-03 0.591189342 0.339831218 +#> 191 5.468997e-04 5.412187e-04 0.102147214 0.052101738 +#> 192 8.813239e-04 8.685670e-04 0.117465633 0.060005835 +#> 193 7.150669e-03 7.028620e-03 0.698578733 0.411798254 +#> 194 1.897529e-04 1.881278e-04 0.043648756 0.021966086 +#> 195 7.498085e-02 7.397022e-02 3.841734514 5.488016022 +#> 196 5.676501e-03 5.522539e-03 0.377058254 0.203613395 +#> 197 2.848621e-04 2.821357e-04 0.058393203 0.029477645 +#> 198 1.419097e-02 1.395780e-02 1.248919982 0.849491713 +#> 199 2.255815e-02 2.211660e-02 1.543189126 1.129927516 +#> 200 2.267186e-02 2.204654e-02 1.204840140 0.799328652
    +
    - @@ -193,7 +376,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

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    ROC curve

    point_shape = 18, point_fill = "blue", point_color = "blue", - plot_title_justify = 0.5 + plot_title_justify = 0.5, + print_plot = TRUE )

    Arguments

    @@ -196,6 +197,10 @@

    Arg plot_title_justify

    Horizontal justification on the plot title.

    + + print_plot +

    logical; if TRUE, prints the plot else returns a plot object.

    +

    References

    diff --git a/docs/reference/blr_rsq_adj_count.html b/docs/reference/blr_rsq_adj_count.html index a01ad79..4535d35 100644 --- a/docs/reference/blr_rsq_adj_count.html +++ b/docs/reference/blr_rsq_adj_count.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/blr_rsq_count.html b/docs/reference/blr_rsq_count.html index c5a0edd..b73ee33 100644 --- a/docs/reference/blr_rsq_count.html +++ b/docs/reference/blr_rsq_count.html @@ -8,23 +8,29 @@ Count R2 — blr_rsq_count • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    Count r-squared.

    -
    blr_rsq_count(model)
    - +

    Arguments

    @@ -145,11 +148,10 @@

    Arg

    An object of class glm.

    - +

    Value

    Count r-squared.

    -

    Examples

    model <- glm(honcomp ~ female + read + science, data = hsb2, @@ -158,16 +160,10 @@

    Examp blr_rsq_count(model)

    #> [1] 0.81
    - @@ -178,7 +174,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_rsq_cox_snell.html b/docs/reference/blr_rsq_cox_snell.html index 75bdc5b..34b31db 100644 --- a/docs/reference/blr_rsq_cox_snell.html +++ b/docs/reference/blr_rsq_cox_snell.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/blr_rsq_effron.html b/docs/reference/blr_rsq_effron.html index 6dce143..32202ba 100644 --- a/docs/reference/blr_rsq_effron.html +++ b/docs/reference/blr_rsq_effron.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/blr_rsq_mcfadden.html b/docs/reference/blr_rsq_mcfadden.html index 4a5758d..7a29298 100644 --- a/docs/reference/blr_rsq_mcfadden.html +++ b/docs/reference/blr_rsq_mcfadden.html @@ -8,23 +8,29 @@ McFadden's R2 — blr_rsq_mcfadden • blorr + - + - - + + + + + + + - - + + - + - - + + @@ -32,10 +38,10 @@ - + - + @@ -52,7 +58,7 @@ - +
    @@ -103,7 +109,6 @@ -
    -

    McFadden's pseudo r-squared for the model.

    -
    blr_rsq_mcfadden(model)
    - +

    Arguments

    @@ -145,15 +148,13 @@

    Arg

    An object of class glm.

    - +

    Value

    McFadden's r-squared.

    -

    References

    https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf

    -

    Examples

    model <- glm(honcomp ~ female + read + science, data = hsb2, @@ -162,18 +163,10 @@

    Examp blr_rsq_mcfadden(model)

    #> [1] 0.3072023
    - @@ -184,7 +177,7 @@

    Contents

    -

    Site built with pkgdown 1.4.1.

    +

    Site built with pkgdown 1.5.1.

    diff --git a/docs/reference/blr_rsq_mcfadden_adj.html b/docs/reference/blr_rsq_mcfadden_adj.html index a40055a..44bd742 100644 --- a/docs/reference/blr_rsq_mcfadden_adj.html +++ b/docs/reference/blr_rsq_mcfadden_adj.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/blr_rsq_mckelvey_zavoina.html b/docs/reference/blr_rsq_mckelvey_zavoina.html index 8b127a9..a97c3dc 100644 --- a/docs/reference/blr_rsq_mckelvey_zavoina.html +++ b/docs/reference/blr_rsq_mckelvey_zavoina.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/blr_rsq_nagelkerke.html b/docs/reference/blr_rsq_nagelkerke.html index 2d30b32..3037a78 100644 --- a/docs/reference/blr_rsq_nagelkerke.html +++ b/docs/reference/blr_rsq_nagelkerke.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/blr_segment.html b/docs/reference/blr_segment.html index 90876a7..2247b3d 100644 --- a/docs/reference/blr_segment.html +++ b/docs/reference/blr_segment.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -175,7 +175,7 @@

    See a

    Examples

    blr_segment(hsb2, honcomp, prog)
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z+UMOa=i3M`d;H&j2WL1-U}<|(naP$ufKj`eh0s%ve+gn30y=I*nm zMqYM0PJ?yK9;&X;NSJlQ>Zl}6&UzL9>i-d2&dT9Ar(vy;@B~~Y=aK+bs@f3Agw;Oy zrFY;(oW|MIHy6!tNSp-qtCg;)kwL8691~qQ13>KYZrs5W2B{2eXN_`r&|9X=8f0mr*C2Ln-;w@2uZUW9gB?-KO3js zBUpAeX1R14)tA^X=@=SK1aMmm(L8au%D0`T^yjY6pF`#x7Jt_Nn$qArLz6=bIIDaV z^9de?d5*SuTb3(sx`xD^Lk?oIqbOayYyA;iU_<;r`x@9G{2trAY9^rrjo^wK8JVh* Ly5c+eTMzyR&964y diff --git a/docs/reference/blr_segment_dist.html b/docs/reference/blr_segment_dist.html index 04ba519..aa05789 100644 --- a/docs/reference/blr_segment_dist.html +++ b/docs/reference/blr_segment_dist.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0
    @@ -149,6 +149,7 @@

    Response distribution

    sec_yaxis_title = "1s Distribution", bar_color = "blue", line_color = "red", + print_plot = TRUE, ... )
    @@ -195,6 +196,10 @@

    Arg line_color

    Line color.

    + + print_plot +

    logical; if TRUE, prints the plot else returns a plot object.

    + ...

    Other inputs.

    @@ -217,7 +222,7 @@

    Examp
    k <- blr_segment_dist(hsb2, honcomp, prog) k
    #> Event Segmentation #> ------------------------------------- -#> prog n 1s n% 1s% +#> pred n 1s n% 1s% #> ------------------------------------- #> 1 45 7 0.22 0.04 #> 2 105 40 0.52 0.20 diff --git a/docs/reference/blr_segment_twoway.html b/docs/reference/blr_segment_twoway.html index fdf38f8..41007d6 100644 --- a/docs/reference/blr_segment_twoway.html +++ b/docs/reference/blr_segment_twoway.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0
    @@ -177,9 +177,9 @@

    See a blr_woe_iv()

    Examples

    -
    blr_segment_twoway(hsb2, honcomp, prog, female)
    #> female +
    blr_segment_twoway(hsb2, honcomp, prog, female)
    #> prog #> ----------------------- -#> prog 0 1 +#> honcomp 0 1 #> ----------------------- #> 1 0.010 0.025 #> ----------------------- diff --git a/docs/reference/blr_step_aic_backward.html b/docs/reference/blr_step_aic_backward.html index 8231551..7813b81 100644 --- a/docs/reference/blr_step_aic_backward.html +++ b/docs/reference/blr_step_aic_backward.html @@ -74,7 +74,7 @@ blorr - 0.2.2.9000 + 0.3.0
    diff --git a/docs/reference/blr_step_aic_both.html b/docs/reference/blr_step_aic_both.html index 1fc5734..a77d59b 100644 --- a/docs/reference/blr_step_aic_both.html +++ b/docs/reference/blr_step_aic_both.html @@ -74,7 +74,7 @@ blorr - 0.2.2.9000 + 0.3.0
    diff --git a/docs/reference/blr_step_aic_forward.html b/docs/reference/blr_step_aic_forward.html index 9dff73e..b607517 100644 --- a/docs/reference/blr_step_aic_forward.html +++ b/docs/reference/blr_step_aic_forward.html @@ -74,7 +74,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/blr_step_p_backward.html b/docs/reference/blr_step_p_backward.html index f9c9cf1..4434577 100644 --- a/docs/reference/blr_step_p_backward.html +++ b/docs/reference/blr_step_p_backward.html @@ -74,7 +74,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -148,7 +148,7 @@

    Stepwise backward regression

    blr_step_p_backward(model, prem = 0.3, details = FALSE, ...) # S3 method for blr_step_p_backward -plot(x, model = NA, ...)
    +plot(x, model = NA, print_plot = TRUE, ...)

    Arguments

    @@ -176,6 +176,10 @@

    Arg

    + + + +
    x

    An object of class blr_step_p_backward.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    Value

    diff --git a/docs/reference/blr_step_p_both.html b/docs/reference/blr_step_p_both.html index f885748..a0a76ce 100644 --- a/docs/reference/blr_step_p_both.html +++ b/docs/reference/blr_step_p_both.html @@ -74,7 +74,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -148,7 +148,7 @@

    Stepwise regression

    blr_step_p_both(model, pent = 0.1, prem = 0.3, details = FALSE, ...) # S3 method for blr_step_p_both -plot(x, model = NA, ...) +plot(x, model = NA, print_plot = TRUE, ...)

    Arguments

    @@ -181,6 +181,10 @@

    Arg

    + + + +
    x

    An object of class blr_step_p_both.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    Value

    diff --git a/docs/reference/blr_step_p_forward.html b/docs/reference/blr_step_p_forward.html index d19d347..65c3dae 100644 --- a/docs/reference/blr_step_p_forward.html +++ b/docs/reference/blr_step_p_forward.html @@ -74,7 +74,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -148,7 +148,7 @@

    Stepwise forward regression

    blr_step_p_forward(model, penter = 0.3, details = FALSE, ...) # S3 method for blr_step_p_forward -plot(x, model = NA, ...) +plot(x, model = NA, print_plot = TRUE, ...)

    Arguments

    @@ -176,6 +176,10 @@

    Arg

    + + + +
    x

    An object of class blr_step_p_forward.

    print_plot

    logical; if TRUE, prints the plot else returns a plot object.

    Value

    diff --git a/docs/reference/blr_test_hosmer_lemeshow.html b/docs/reference/blr_test_hosmer_lemeshow.html index bebd365..9ee47c5 100644 --- a/docs/reference/blr_test_hosmer_lemeshow.html +++ b/docs/reference/blr_test_hosmer_lemeshow.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -155,7 +155,7 @@

    Arg

    References

    -

    Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). +

    Hosmer, D.W., Jr., & Lemeshow, S. (2000), Applied logistic regression(2nd ed.). New York: John Wiley & Sons.

    See also

    diff --git a/docs/reference/blr_test_lr.html b/docs/reference/blr_test_lr.html index bacc9ef..b9500b6 100644 --- a/docs/reference/blr_test_lr.html +++ b/docs/reference/blr_test_lr.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 @@ -162,8 +162,7 @@

    Value

    Two tibbles with model information and test results.

    See also

    -

    lrtest

    -

    Other model fit statistics: +

    Other model fit statistics: blr_model_fit_stats(), blr_multi_model_fit_stats(), blr_pairs(), @@ -180,24 +179,14 @@

    Examp model_1 <- glm(honcomp ~ female + read + science, data = hsb2, family = binomial(link = 'logit')) -blr_test_lr(model_1)

    #> Likelihood Ratio Test -#> ------------------------------ -#> Chi-Square DF Pr > ChiSq -#> ------------------------------ -#> 71.0525 3 0.0000 -#> ------------------------------
    +blr_test_lr(model_1) + # compare full model with nested model # nested model model_2 <- glm(honcomp ~ female + read, data = hsb2, family = binomial(link = 'logit')) -blr_test_lr(model_1, model_2)
    #> Likelihood Ratio Test -#> ------------------------------ -#> Chi-Square DF Pr > ChiSq -#> ------------------------------ -#> 10.6511 3 0.0138 -#> ------------------------------
    -
    +blr_test_lr(model_1, model_2)
    @@ -136,7 +136,7 @@

    WoE & IV

    -

    Weight of evidence and information value. Currently avialable for categorical +

    Weight of evidence and information value. Currently avialable for categorical predictors only.

    @@ -150,6 +150,7 @@

    WoE & IV

    yaxis_title = "WoE", bar_color = "blue", line_color = "red", + print_plot = TRUE, ... ) @@ -200,6 +201,10 @@

    Arg line_color

    Color of the horizontal line.

    + + print_plot +

    logical; if TRUE, prints the plot else returns a plot object.

    +

    Value

    @@ -207,7 +212,7 @@

    Value

    A tibble.

    References

    -

    Siddiqi N (2006): Credit Risk Scorecards: developing and implementing intelligent +

    Siddiqi N (2006): Credit Risk Scorecards: developing and implementing intelligent credit scoring. New Jersey, Wiley.

    See also

    @@ -223,7 +228,7 @@

    Examp k <- blr_woe_iv(hsb2, female, honcomp) k
    #> Weight of Evidence #> ------------------------------------------------------------------------- -#> levels 0s_count 1s_count 0s_dist 1s_dist woe iv +#> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 0 73 18 0.50 0.34 0.38 0.06 #> 1 74 35 0.50 0.66 -0.27 0.04 diff --git a/docs/reference/blr_woe_iv_stats.html b/docs/reference/blr_woe_iv_stats.html index fd61ce4..397b8ce 100644 --- a/docs/reference/blr_woe_iv_stats.html +++ b/docs/reference/blr_woe_iv_stats.html @@ -40,7 +40,7 @@ - @@ -73,7 +73,7 @@ blorr - 0.2.2.9000 + 0.3.0
    @@ -136,7 +136,7 @@

    Multi variable WOE & IV

    -

    Prints weight of evidence and information value for multiple variables. +

    Prints weight of evidence and information value for multiple variables. Currently avialable for categorical predictors only.

    @@ -169,11 +169,11 @@

    See a blr_woe_iv()

    Examples

    -
    blr_woe_iv_stats(hsb2, honcomp, prog, race, female, schtyp)
    #> Variable: prog +
    blr_woe_iv_stats(hsb2, honcomp, prog, race, female, schtyp)
    #> Variable: prog #> #> Weight of Evidence #> ------------------------------------------------------------------------- -#> levels 0s_count 1s_count 0s_dist 1s_dist woe iv +#> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 1 38 7 0.26 0.13 0.67 0.08 #> 2 65 40 0.44 0.75 -0.53 0.17 @@ -188,11 +188,11 @@

    Examp #> ----------------------------- #> #> -#> Variable: race +#> Variable: race #> #> Weight of Evidence #> ------------------------------------------------------------------------- -#> levels 0s_count 1s_count 0s_dist 1s_dist woe iv +#> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 1 22 2 0.15 0.04 1.38 0.15 #> 2 6 5 0.04 0.09 -0.84 0.04 @@ -208,11 +208,11 @@

    Examp #> ----------------------------- #> #> -#> Variable: female +#> Variable: female #> #> Weight of Evidence #> ------------------------------------------------------------------------- -#> levels 0s_count 1s_count 0s_dist 1s_dist woe iv +#> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------- #> 0 73 18 0.50 0.34 0.38 0.06 #> 1 74 35 0.50 0.66 -0.27 0.04 @@ -226,11 +226,11 @@

    Examp #> ----------------------------- #> #> -#> Variable: schtyp +#> Variable: schtyp #> #> Weight of Evidence #> ------------------------------------------------------------------------ -#> levels 0s_count 1s_count 0s_dist 1s_dist woe iv +#> levels count_0s count_1s dist_0s dist_1s woe iv #> ------------------------------------------------------------------------ #> 1 123 45 0.84 0.85 -0.01 0.00 #> 2 24 8 0.16 0.15 0.08 0.00 @@ -244,7 +244,7 @@

    Examp #> ----------------------------- #> #>

    -
    +
    diff --git a/docs/reference/index.html b/docs/reference/index.html index 88724e3..b5ca1a9 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -71,7 +71,7 @@ blorr - 0.2.2.9000 + 0.3.0 diff --git a/docs/reference/stepwise.html b/docs/reference/stepwise.html index 4d7b448..036dcd6 100644 --- a/docs/reference/stepwise.html +++ b/docs/reference/stepwise.html @@ -72,7 +72,7 @@ blorr - 0.2.2.9000 + 0.3.0 From 2e275127ae3f3c41e9e327446e4604ae017e0ac7 Mon Sep 17 00:00:00 2001 From: rsquaredin Date: Thu, 28 May 2020 17:40:56 +0530 Subject: [PATCH 13/13] src i386 --- src-i386/RcppExports.cpp | 29 +++++++++++++++++++++++++ src-i386/RcppExports.o | Bin 0 -> 78111 bytes src-i386/blr-pairs-cpp.cpp | 43 +++++++++++++++++++++++++++++++++++++ src-i386/blr-pairs-cpp.o | Bin 0 -> 108448 bytes 4 files changed, 72 insertions(+) create mode 100644 src-i386/RcppExports.cpp create mode 100644 src-i386/RcppExports.o create mode 100644 src-i386/blr-pairs-cpp.cpp create mode 100644 src-i386/blr-pairs-cpp.o diff --git a/src-i386/RcppExports.cpp b/src-i386/RcppExports.cpp new file mode 100644 index 0000000..e6f369e --- /dev/null +++ b/src-i386/RcppExports.cpp @@ -0,0 +1,29 @@ +// Generated by using Rcpp::compileAttributes() -> do not edit by hand +// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +#include + +using namespace Rcpp; + +// blr_pairs_cpp +DataFrame blr_pairs_cpp(NumericVector x, NumericVector y); +RcppExport SEXP _blorr_blr_pairs_cpp(SEXP xSEXP, SEXP ySEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< NumericVector >::type x(xSEXP); + Rcpp::traits::input_parameter< NumericVector >::type y(ySEXP); + rcpp_result_gen = Rcpp::wrap(blr_pairs_cpp(x, y)); + return rcpp_result_gen; +END_RCPP +} + +static const R_CallMethodDef CallEntries[] = { + {"_blorr_blr_pairs_cpp", (DL_FUNC) &_blorr_blr_pairs_cpp, 2}, + {NULL, NULL, 0} +}; + +RcppExport void R_init_blorr(DllInfo *dll) { + R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); + R_useDynamicSymbols(dll, FALSE); +} diff --git a/src-i386/RcppExports.o b/src-i386/RcppExports.o new file mode 100644 index 0000000000000000000000000000000000000000..0457e7ada0d10dd066c64eda52767b58cc5486ff GIT binary patch literal 78111 zcmeFa34D~***5-6W+n*;Oi)p*+o(|y6|$26Y9d4?FkmF86seOC21pG_noL-#wb3NX z7=x%(X|+n#y3`Hph88u70-)X$|Nr}*U!Iw3 z?sJ{{zR$irGns$EBL-bZ++`TiG(tXOOf0%AMo?ZonTGNHfrb$}n+1QwThOSi9y7lt zR-;HlW*D~}VHgjd$3h?8N{z~%G4onlOf;j^YyQNz&1mWwTVR!E;o~#VXTE}2mDOYO z$C*X&*f3tT@k+CDO2>}NweW`E(+=?dDg7fi1TQzI&`cz|@7weVySe$rW**u7#>QiP z!J7=; z=aQ$>TU284kJ3Be&F;8-OFbaFi@?k50KlcwJI>mW$k`ZpN0@jby~Q~e|0umTxY;c( zu+)39dmDI@P5MN7i;FEiLN$2n!J8*}R9}SMl3a(~k)si4x`e)xLPvUo;9X<4TT*P% zC)x*i8zc|amhI-|K8t+L%_}lDBud8`@a~iP66vsZM3jyjz?*-vb){YZegxD|H$qR@Y1^ga3#`PSZK}{%IEjN z%a*)EJ{RSfI}WmYfLVagoX3 ziy?U5#1nS&a*EC6L3Y0}N5p5=munelh?j|qHuz!yT)KSoaxDXJHNy4=Zip~0{KCb}pV#(9(C@(+HoV;rA)_`|~!@qn> 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< n2; j++) { + + pairs_count++; + + if (x[i] > y[j]) { + concordant++; + } else if (x[i] == y[j]) { + ties++; + } else { + discordant++; + } + + } + } + + DataFrame df = DataFrame::create(Named("pairs") = pairs_count, + Named("concordant") = concordant, + Named("discordant") = discordant, + Named("ties") = ties); + + return df; + +} + diff --git a/src-i386/blr-pairs-cpp.o b/src-i386/blr-pairs-cpp.o new file mode 100644 index 0000000000000000000000000000000000000000..9f4ba3de7fc3f0c0d7fc191d251ab72244cd3108 GIT binary patch literal 108448 zcmeFa33yaR);E5W^i9HMCoCEq#dZvcijX~Nf|{`2mX2l=6%{22L2y_yq!Dyng6Sx2 z(+s1yjG{8GxZ{q9PLS0-;)aTf>!ghf?zr;*opb6|-`gD>X1?!x-}m{y^z+>A->o`T z=Tz0HQ>T`Dd&E9IBrmBB(wqyBWq$2O37slM-+vetbr4-QX#c zLX&JF{N~`g>u|%EvRMMt@Ka^fwhlg{u~9sT)*&VCaLR8D@^gNCIkj~| zip!lrkk~MuO`@yHDX1D!Ug)Chj=MI{%~JA_7))1KQ0gQkzT1-WW4?vO6;3+h`$-a= z`bZ3QB>}#7~-1)x;v8j78e#f zQxo6)lJb*!E_IcI_zp^<R*}ND0d$_HkN_X8SLMZi36XE& zdtOq0%(vX{l8^WMUtGrZ>@t|{lxE%h96g`~)x}Hx9 zrLMoif+AT1Oik%&a_$~z9-p>Hw 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