cran: prep for release

rsquaredacademy · Feb 12, 2024 · 4aebc28 · 4aebc28
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -17,20 +17,21 @@ Imports:
     gridExtra,
     nortest,
     stats,
-    utils
+    utils,
+    xplorerr
 Suggests:
     covr,
     descriptr,
     knitr,
     rmarkdown,
     testthat,
-    vdiffr,
-    xplorerr
+    vdiffr
 License: MIT + file LICENSE
 URL: https://olsrr.rsquaredacademy.com/, https://github.com/rsquaredacademy/olsrr
 BugReports: https://github.com/rsquaredacademy/olsrr/issues
 Encoding: UTF-8
 LazyData: true
 VignetteBuilder: knitr
-RoxygenNote: 7.1.1
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.2.3
 Config/testthat/edition: 3
diff --git a/NEWS.md b/NEWS.md
@@ -1,10 +1,22 @@
-# olsrr 0.5.3.9000
+# olsrr 0.6.0
+
+This is a minor release for bug fixes and other enhancements.
 
 ## New Features
 
-- Force variables in/out in variable selection procedures
-- Hierarchical selection
-- Variable selection using r-squared and adjusted r-squared
+- hierarchical selection can be enables when using `p` values as variable selection metric
+
+## Enhancements
+
+- force variables to be included or excluded from the model at all stages of variable selection
+- Variable selection methods allow use of the following metrics:
+    - p value
+    - akaike information criterion (aic)
+    - schwarz bayesian criterion (sbc)
+    - sawa bayesian criterion (sbic)
+    - r-square
+    - adjusted r-square
+- Choose threshold for determining influential observations in `ols_plot_dffits()`
 
 ## Bug Fixes
 

diff --git a/README.Rmd b/README.Rmd
@@ -16,9 +16,8 @@ knitr::opts_chunk$set(
 
 <!-- badges: start -->
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/olsrr)](https://cran.r-project.org/package=olsrr)
-[![cran checks](https://badges.cranchecks.info/summary/olsrr.svg)](https://cran.r-project.org/web/checks/check_results_olsrr.html)
 [![R build status](https://github.com/rsquaredacademy/olsrr/workflows/R-CMD-check/badge.svg)](https://github.com/rsquaredacademy/olsrr/actions)
-[![Coverage status](https://codecov.io/gh/rsquaredacademy/olsrr/branch/master/graph/badge.svg)](https://app.codecov.io/github/rsquaredacademy/olsrr?branch=master) [![status](https://tinyverse.netlify.com/badge/olsrr)](https://CRAN.R-project.org/package=olsrr) [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html) [![](https://cranlogs.r-pkg.org/badges/grand-total/olsrr)](https://cran.r-project.org/package=olsrr) 
+[![Coverage status](https://codecov.io/gh/rsquaredacademy/olsrr/branch/master/graph/badge.svg)](https://app.codecov.io/github/rsquaredacademy/olsrr?branch=master) 
 <!-- badges: end -->
 
 ## Overview
@@ -41,8 +40,8 @@ The olsrr package provides following tools for building OLS regression models us
 install.packages("olsrr")
 
 # Install development version from GitHub
-# install.packages("devtools")
-devtools::install_github("rsquaredacademy/olsrr")
+# install.packages("pak")
+pak::pak("rsquaredacademy/olsrr")
 ```
 
 ## Articles
@@ -56,8 +55,6 @@ devtools::install_github("rsquaredacademy/olsrr")
 
 ## Usage
 
-olsrr uses consistent prefix `ols_` for easy tab completion.
-
 ```{r, echo=FALSE, message=FALSE}
 library(olsrr)
 library(dplyr)
@@ -67,58 +64,14 @@ library(nortest)
 library(goftest)
 ```
 
-olsrr is built with the aim of helping those users who are new to the R language. If you know how to 
-write a `formula` or build models using `lm`, you will find olsrr very useful. Most of the functions
-use an object of class `lm` as input. So you just need to build a model using `lm` and then pass it onto 
-the functions in olsrr. Below is a quick demo:
+olsrr uses consistent prefix `ols_` for easy tab completion. If you know how to write a `formula` or build models using `lm`, you will find olsrr very useful. Most of the functions use an object of class `lm` as input. So you just need to build a model using `lm` and then pass it onto the functions in olsrr. Below is
+a quick demo:
 
 #### Regression
 
 ```{r regress}
-ols_regress(mpg ~ disp + hp + wt + qsec, data = mtcars)
-```
-
-#### Stepwise Regression
-
-Build regression model from a set of candidate predictor variables by entering and removing predictors based on 
-p values, in a stepwise manner until there is no variable left to enter or remove any more.
-
-#### Variable Selection
-
-```{r stepwise1}
-# stepwise regression
-model <- lm(y ~ ., data = surgical)
-ols_step_both_p(model)
-```
-
-#### Stepwise AIC Backward Regression
-
-Build regression model from a set of candidate predictor variables by removing predictors based on 
-Akaike Information Criteria, in a stepwise manner until there is no variable left to remove any more.
-
-##### Variable Selection
-
-```{r stepaicb1}
-# stepwise aic backward regression
-model <- lm(y ~ ., data = surgical)
-k <- ols_step_backward_aic(model)
-k
-```
-
-#### Breusch Pagan Test
-
-Breusch Pagan test is used to test for herteroskedasticity (non-constant error variance). It tests whether the variance of the errors from a regression is dependent on the values of the independent variables. It is a $\chi^{2}$ test.
-
-```{r bp1}
-model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
-ols_test_breusch_pagan(model)
-```
-
-#### Collinearity Diagnostics
-
-```{r colldiag}
 model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
-ols_coll_diag(model)
+ols_regress(model)
 ```
 
 ## Getting Help

diff --git a/README.md b/README.md
@@ -6,16 +6,10 @@
 <!-- badges: start -->
 
 [![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/olsrr)](https://cran.r-project.org/package=olsrr)
-[![cran
-checks](https://badges.cranchecks.info/summary/olsrr.svg)](https://cran.r-project.org/web/checks/check_results_olsrr.html)
 [![R build
 status](https://github.com/rsquaredacademy/olsrr/workflows/R-CMD-check/badge.svg)](https://github.com/rsquaredacademy/olsrr/actions)
 [![Coverage
 status](https://codecov.io/gh/rsquaredacademy/olsrr/branch/master/graph/badge.svg)](https://app.codecov.io/github/rsquaredacademy/olsrr?branch=master)
-[![status](https://tinyverse.netlify.com/badge/olsrr)](https://CRAN.R-project.org/package=olsrr)
-[![Lifecycle:
-stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html)
-[![](https://cranlogs.r-pkg.org/badges/grand-total/olsrr)](https://cran.r-project.org/package=olsrr)
 <!-- badges: end -->
 
 ## Overview
@@ -39,8 +33,8 @@ models using R:
 install.packages("olsrr")
 
 # Install development version from GitHub
-# install.packages("devtools")
-devtools::install_github("rsquaredacademy/olsrr")
+# install.packages("pak")
+pak::pak("rsquaredacademy/olsrr")
 ```
 
 ## Articles
@@ -59,19 +53,17 @@ devtools::install_github("rsquaredacademy/olsrr")
 
 ## Usage
 
-olsrr uses consistent prefix `ols_` for easy tab completion.
-
-olsrr is built with the aim of helping those users who are new to the R
-language. If you know how to write a `formula` or build models using
-`lm`, you will find olsrr very useful. Most of the functions use an
-object of class `lm` as input. So you just need to build a model using
-`lm` and then pass it onto the functions in olsrr. Below is a quick
-demo:
+olsrr uses consistent prefix `ols_` for easy tab completion. If you know
+how to write a `formula` or build models using `lm`, you will find olsrr
+very useful. Most of the functions use an object of class `lm` as input.
+So you just need to build a model using `lm` and then pass it onto the
+functions in olsrr. Below is a quick demo:
 
 #### Regression
 
 ``` r
-ols_regress(mpg ~ disp + hp + wt + qsec, data = mtcars)
+model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
+ols_regress(model)
 #>                          Model Summary                          
 #> ---------------------------------------------------------------
 #> R                       0.914       RMSE                 2.409 
@@ -108,195 +100,6 @@ ols_regress(mpg ~ disp + hp + wt + qsec, data = mtcars)
 #> ----------------------------------------------------------------------------------------
 ```
 
-#### Stepwise Regression
-
-Build regression model from a set of candidate predictor variables by
-entering and removing predictors based on p values, in a stepwise manner
-until there is no variable left to enter or remove any more.
-
-#### Variable Selection
-
-``` r
-# stepwise regression
-model <- lm(y ~ ., data = surgical)
-ols_step_both_p(model)
-#> 
-#> 
-#>                                 Stepwise Summary                                
-#> ------------------------------------------------------------------------------
-#> Step    Variable             AIC        SBC       SBIC        R2       Adj. R2 
-#> ------------------------------------------------------------------------------
-#>  0      Base Model         802.606    806.584    646.794    0.00000    0.00000 
-#>  1      liver_test (+)     771.875    777.842    616.009    0.45454    0.44405 
-#>  2      alc_heavy (+)      761.439    769.395    605.506    0.56674    0.54975 
-#>  3      enzyme_test (+)    750.509    760.454    595.297    0.65900    0.63854 
-#>  4      pindex (+)         735.715    747.649    582.943    0.75015    0.72975 
-#>  5      bcs (+)            730.620    744.543    579.638    0.78091    0.75808 
-#> ------------------------------------------------------------------------------
-#> 
-#> Final Model Output 
-#> ------------------
-#> 
-#>                            Model Summary                            
-#> -------------------------------------------------------------------
-#> R                         0.884       RMSE                 184.276 
-#> R-Squared                 0.781       MSE                38202.426 
-#> Adj. R-Squared            0.758       Coef. Var             27.839 
-#> Pred R-Squared            0.700       AIC                  730.620 
-#> MAE                     137.656       SBC                  744.543 
-#> -------------------------------------------------------------------
-#>  RMSE: Root Mean Square Error 
-#>  MSE: Mean Square Error 
-#>  MAE: Mean Absolute Error 
-#>  AIC: Akaike Information Criteria 
-#>  SBC: Schwarz Bayesian Criteria 
-#> 
-#>                                  ANOVA                                  
-#> -----------------------------------------------------------------------
-#>                    Sum of                                              
-#>                   Squares        DF    Mean Square      F         Sig. 
-#> -----------------------------------------------------------------------
-#> Regression    6535804.090         5    1307160.818    34.217    0.0000 
-#> Residual      1833716.447        48      38202.426                     
-#> Total         8369520.537        53                                    
-#> -----------------------------------------------------------------------
-#> 
-#>                                       Parameter Estimates                                        
-#> ------------------------------------------------------------------------------------------------
-#>       model         Beta    Std. Error    Std. Beta      t        Sig         lower       upper 
-#> ------------------------------------------------------------------------------------------------
-#> (Intercept)    -1178.330       208.682                 -5.647    0.000    -1597.914    -758.746 
-#>  liver_test       58.064        40.144        0.156     1.446    0.155      -22.652     138.779 
-#>   alc_heavy      317.848        71.634        0.314     4.437    0.000      173.818     461.878 
-#> enzyme_test        9.748         1.656        0.521     5.887    0.000        6.419      13.077 
-#>      pindex        8.924         1.808        0.380     4.935    0.000        5.288      12.559 
-#>         bcs       59.864        23.060        0.241     2.596    0.012       13.498     106.230 
-#> ------------------------------------------------------------------------------------------------
-```
-
-#### Stepwise AIC Backward Regression
-
-Build regression model from a set of candidate predictor variables by
-removing predictors based on Akaike Information Criteria, in a stepwise
-manner until there is no variable left to remove any more.
-
-##### Variable Selection
-
-``` r
-# stepwise aic backward regression
-model <- lm(y ~ ., data = surgical)
-k <- ols_step_backward_aic(model)
-k
-#> 
-#> 
-#>                              Stepwise Summary                              
-#> -------------------------------------------------------------------------
-#> Step    Variable        AIC        SBC       SBIC        R2       Adj. R2 
-#> -------------------------------------------------------------------------
-#>  0      Full Model    736.390    756.280    586.665    0.78184    0.74305 
-#>  1      alc_mod       734.407    752.308    583.884    0.78177    0.74856 
-#>  2      gender        732.494    748.406    581.290    0.78142    0.75351 
-#>  3      age           730.620    744.543    578.844    0.78091    0.75808 
-#> -------------------------------------------------------------------------
-#> 
-#> Final Model Output 
-#> ------------------
-#> 
-#>                            Model Summary                            
-#> -------------------------------------------------------------------
-#> R                         0.884       RMSE                 184.276 
-#> R-Squared                 0.781       MSE                38202.426 
-#> Adj. R-Squared            0.758       Coef. Var             27.839 
-#> Pred R-Squared            0.700       AIC                  730.620 
-#> MAE                     137.656       SBC                  744.543 
-#> -------------------------------------------------------------------
-#>  RMSE: Root Mean Square Error 
-#>  MSE: Mean Square Error 
-#>  MAE: Mean Absolute Error 
-#>  AIC: Akaike Information Criteria 
-#>  SBC: Schwarz Bayesian Criteria 
-#> 
-#>                                  ANOVA                                  
-#> -----------------------------------------------------------------------
-#>                    Sum of                                              
-#>                   Squares        DF    Mean Square      F         Sig. 
-#> -----------------------------------------------------------------------
-#> Regression    6535804.090         5    1307160.818    34.217    0.0000 
-#> Residual      1833716.447        48      38202.426                     
-#> Total         8369520.537        53                                    
-#> -----------------------------------------------------------------------
-#> 
-#>                                       Parameter Estimates                                        
-#> ------------------------------------------------------------------------------------------------
-#>       model         Beta    Std. Error    Std. Beta      t        Sig         lower       upper 
-#> ------------------------------------------------------------------------------------------------
-#> (Intercept)    -1178.330       208.682                 -5.647    0.000    -1597.914    -758.746 
-#>         bcs       59.864        23.060        0.241     2.596    0.012       13.498     106.230 
-#>      pindex        8.924         1.808        0.380     4.935    0.000        5.288      12.559 
-#> enzyme_test        9.748         1.656        0.521     5.887    0.000        6.419      13.077 
-#>  liver_test       58.064        40.144        0.156     1.446    0.155      -22.652     138.779 
-#>   alc_heavy      317.848        71.634        0.314     4.437    0.000      173.818     461.878 
-#> ------------------------------------------------------------------------------------------------
-```
-
-#### Breusch Pagan Test
-
-Breusch Pagan test is used to test for herteroskedasticity (non-constant
-error variance). It tests whether the variance of the errors from a
-regression is dependent on the values of the independent variables. It
-is a $\chi^{2}$ test.
-
-``` r
-model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
-ols_test_breusch_pagan(model)
-#> 
-#>  Breusch Pagan Test for Heteroskedasticity
-#>  -----------------------------------------
-#>  Ho: the variance is constant            
-#>  Ha: the variance is not constant        
-#> 
-#>              Data               
-#>  -------------------------------
-#>  Response : mpg 
-#>  Variables: fitted values of mpg 
-#> 
-#>        Test Summary         
-#>  ---------------------------
-#>  DF            =    1 
-#>  Chi2          =    1.429672 
-#>  Prob > Chi2   =    0.231818
-```
-
-#### Collinearity Diagnostics
-
-``` r
-model <- lm(mpg ~ disp + hp + wt + qsec, data = mtcars)
-ols_coll_diag(model)
-#> Tolerance and Variance Inflation Factor
-#> ---------------------------------------
-#>   Variables Tolerance      VIF
-#> 1      disp 0.1252279 7.985439
-#> 2        hp 0.1935450 5.166758
-#> 3        wt 0.1445726 6.916942
-#> 4      qsec 0.3191708 3.133119
-#> 
-#> 
-#> Eigenvalue and Condition Index
-#> ------------------------------
-#>    Eigenvalue Condition Index   intercept        disp          hp           wt
-#> 1 4.721487187        1.000000 0.000123237 0.001132468 0.001413094 0.0005253393
-#> 2 0.216562203        4.669260 0.002617424 0.036811051 0.027751289 0.0002096014
-#> 3 0.050416837        9.677242 0.001656551 0.120881424 0.392366164 0.0377028008
-#> 4 0.010104757       21.616057 0.025805998 0.777260487 0.059594623 0.7017528428
-#> 5 0.001429017       57.480524 0.969796790 0.063914571 0.518874831 0.2598094157
-#>           qsec
-#> 1 0.0001277169
-#> 2 0.0046789491
-#> 3 0.0001952599
-#> 4 0.0024577686
-#> 5 0.9925403056
-```
-
 ## Getting Help
 
 If you encounter a bug, please file a minimal reproducible example using