ols_plot_dffits()
+ols_test_outlier() does not find any outliers, it returns largest positive residual instead of largest absolute residual (#177)ols_step_all_possible() with Model created from dynamic function leads to "Error in eval(model$call$data) . . . not found" (#176)ols_coll_diag(model)
-
-ols_vif_tol(model)
-
-ols_eigen_cindex(model)ols_coll_diag(model)
+
+ols_vif_tol(model)
+
+ols_eigen_cindex(model)# model
-model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
-
-# vif and tolerance
-ols_vif_tol(model)
+ # model
+model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
+
+# vif and tolerance
+ols_vif_tol(model)
#> Variables Tolerance VIF
#> 1 disp 0.1218116 8.209402
#> 2 hp 0.3454979 2.894373
#> 3 wt 0.1962092 5.096601
#> 4 drat 0.4386836 2.279547
-
-# eigenvalues and condition indices
-ols_eigen_cindex(model)
+
+# eigenvalues and condition indices
+ols_eigen_cindex(model)
#> Eigenvalue Condition Index intercept disp hp wt
#> 1 4.692806914 1.000000 0.0002323252 0.001106455 0.002566185 0.0007172086
#> 2 0.240308641 4.419078 0.0036813894 0.034132904 0.031334562 0.0009394254
@@ -161,9 +161,9 @@ Examples#> 3 0.0026259361
#> 4 0.0568226912
#> 5 0.9253487552
-
-# collinearity diagnostics
-ols_coll_diag(model)
+
+# collinearity diagnostics
+ols_coll_diag(model)
#> Tolerance and Variance Inflation Factor
#> ---------------------------------------
#> Variables Tolerance VIF
@@ -187,7 +187,7 @@ Examples#> 3 0.0026259361
#> 4 0.0568226912
#> 5 0.9253487552
-
+
ols_plot_dffits(model, size_adj_threshold = TRUE, print_plot = TRUE)ols_plot_dffits(model, size_adj_threshold = TRUE, print_plot = TRUE)examine how much all of the fitted values change when the ith observation is deleted.
An observation is deemed influential if the absolute value of its DFFITS value is greater than: $$2\sqrt((p + 1) / (n - p -1))$$
-A size-adjusted cutoff recommended by Belsley, Kuh, and Welsch is -$$2\sqrt(p / n)$$ and is used by default in **olsrr**.
+A size-adjusted cutoff recommended by Belsley, Kuh, and Welsch is +$$2\sqrt(p / n)$$ and is used by default in olsrr.
where n is the number of observations and p is the number of predictors including intercept.
ols_plot_resid_lev(model, threshold = NULL, print_plot = TRUE)ols_plot_resid_lev(model, threshold = NULL, print_plot = TRUE)[ols_plot_resid_stud_fit()], [ols_plot_resid_lev()]
ols_plot_resid_stud_fit(), ols_plot_resid_lev()
ols_plot_resid_stand(model, threshold = NULL, print_plot = TRUE)ols_plot_resid_stand(model, threshold = NULL, print_plot = TRUE)
ols_plot_resid_stud(model, threshold = NULL, print_plot = TRUE)ols_plot_resid_stud(model, threshold = NULL, print_plot = TRUE)
model <- lm(mpg ~ disp, data = mtcars)
-ols_pure_error_anova(model)
+ model <- lm(mpg ~ disp, data = mtcars)
+ols_pure_error_anova(model)
#> Lack of Fit F Test
#> -----------------
#> Response : mpg
@@ -191,7 +191,7 @@ Examples#> Lack of fit 25 304.2787 12.17115 4.724824 0.04563623
#> Pure Error 5 12.88 2.576
#> ----------------------------------------------------------------------
-
+
ols_regress(object, ...)
-
-# S3 method for lm
-ols_regress(object, ...)ols_regress(object, ...)
+
+# S3 method for lm
+ols_regress(object, ...)https://www.ssc.wisc.edu/~hemken/Stataworkshops/stdBeta/Getting
+https://www.ssc.wisc.edu/~hemken/Stataworkshops/stdBeta/Getting%20Standardized%20Coefficients%20Right.pdf
ols_regress(mpg ~ disp + hp + wt, data = mtcars)
+ ols_regress(mpg ~ disp + hp + wt, data = mtcars)
#> Model Summary
#> ---------------------------------------------------------------
#> R 0.909 RMSE 2.468
@@ -243,9 +243,9 @@ Examples#> hp -0.031 0.011 -0.354 -2.724 0.011 -0.055 -0.008
#> wt -3.801 1.066 -0.617 -3.565 0.001 -5.985 -1.617
#> ----------------------------------------------------------------------------------------
-
-# if model includes interaction terms set iterm to TRUE
-ols_regress(mpg ~ disp * wt, data = mtcars, iterm = TRUE)
+
+# if model includes interaction terms set iterm to TRUE
+ols_regress(mpg ~ disp * wt, data = mtcars, iterm = TRUE)
#> Model Summary
#> ---------------------------------------------------------------
#> R 0.922 RMSE 2.296
@@ -279,7 +279,7 @@ Examples#> wt -6.496 1.313 -0.616 -4.946 0.000 -9.186 -3.805
#> disp:wt 0.012 0.003 0.278 3.596 0.001 0.005 0.018
#> ----------------------------------------------------------------------------------------
-
+
ols_test_bartlett(data, ...)
-
-# S3 method for default
-ols_test_bartlett(data, ..., group_var = NULL)ols_test_bartlett(data, ...)
+
+# S3 method for default
+ols_test_bartlett(data, ..., group_var = NULL)# using grouping variable
-library(descriptr)
+ # using grouping variable
+if (require("descriptr")) {
+ library(descriptr)
+ ols_test_bartlett(mtcarz, 'mpg', group_var = 'cyl')
+}
+#> Loading required package: descriptr
+#>
+#> Attaching package: ‘descriptr’
+#> The following object is masked _by_ ‘.GlobalEnv’:
#>
-#> Attaching package: 'descriptr'
-#> The following object is masked from 'package:olsrr':
+#> hsb
+#> The following object is masked from ‘package:olsrr’:
#>
#> hsb
-ols_test_bartlett(mtcarz, 'mpg', group_var = 'cyl')
#>
#> Bartlett's Test of Homogenity of Variances
#> ------------------------------------------------
@@ -144,9 +150,9 @@ Examples#> DF = 2
#> Chi2 = 8.39345
#> Prob > Chi2 = 0.01504477
-
-# using variables
-ols_test_bartlett(hsb, 'read', 'write')
+
+# using variables
+ols_test_bartlett(hsb, 'read', 'write')
#>
#> Bartlett's Test of Homogenity of Variances
#> ------------------------------------------------
@@ -162,7 +168,7 @@ Examples#> DF = 1
#> Chi2 = 1.222871
#> Prob > Chi2 = 0.2687979
-
+
Maintainer: Aravind Hebbali hebbali.aravind@gmail.com
+