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olsrr 0.6.1

This is a patch release for urgent bug fixes.

Bug Fixes

Limit maximum subset order in ols_step_all_possible() (#202)
Check model type (#204)
Mismatch in column names in ols_step_all_possible() (#211)
RMSE is not square root of MSE in ols_regress() (#213)
geom_segment() warning in ols_plot_obs_fit() (#217)
New snapshot added every time tests are run (#218)

olsrr 0.6.0

This is a minor release for bug fixes and other enhancements.

New Features

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

Allow users to specify threshold for detecting outliers (#178)
If ols_test_outlier() does not find any outliers, it returns largest positive residual instead of largest absolute residual (#177)
using ols_step_all_possible() with Model created from dynamic function leads to "Error in eval(model$call$data) . . . not found" (#176)
ols_step_both_p(): Error in if (pvals[minp] <= pent) {: argument is of length zero (#175)
Handle extremely significant variables (#173)
ols_correlations() returns error for models with 2 predictors (#168)
ols_step_both_aic() doesn’t return model (#167)
ols_regress() returned residual standard error instead of RMSE (@jens-daniel-mueller, #165)
Extracting model data (#159)
ols_plot_resid_stud() fails to plot outliers due to y-axis range (#155)
ols_correlations error (#191)
Mallow’s Cp behaves inconsistently depending on model specification (#196)
ols_step_forward_p(…) problem using the funtion ols_step_forward_p (#200)
Output of the command “ols_step_both_aic” doesn’t contain final model (#201)

olsrr 0.5.3

This is a patch release to reduce the number of packages imported and fix other CRAN errors.

New Features

Bonferroni outlier test (#129)

Breaking Changes

The following functions will now require the variable names to be enclosed within quotes

ols_test_bartlett()
ols_plot_resid_regressor()

olsrr 0.5.2

This is a minor release to fix bugs from breaking changes in recipes package and other enhancements.

Enhancements

variable selection procedures now return the final model as an object of class lm (#81)
data preparation functions of selected plots are now exported to enable end users to create customized plots and to use plotting library of their choice (#86)

olsrr 0.5.1

This is a patch release to fix minor bugs and improve error messages.

Enhancements

olsrr now throws better error messages keeping in mind beginner and intermediate R users. It is a work in progress and should get better in future releases.

Bug Fixes

Variable selection procedures based on p values now handle categorical variables in the same way as the procedures based on AIC values.

olsrr 0.5.0

This is a minor release for bug fixes and API changes.

API Changes

We have made some changes to the API to make it more user friendly:

all the variable selection procedures start with ols_step_*
all the test start with ols_test_*
all the plots start with ols_plot_*

Bug Fixes

ols_regress returns error in the presence of interaction terms in the formula (#49)
ols_regress returns error in the presence of interaction terms in the formula (#47)
return current version (#48)

olsrr 0.4.0

Enhancements

use ols_launch_app() to launch a shiny app for building models
save beta coefficients for each independent variable in ols_all_subset() (#41)

Bug Fixes

mismatch in sign of partial and semi partial correlations (#44)
error in diagnostic panel (#45)
standardized betas in the presence of interaction terms (#46)

A big thanks goes to (Dr. Kimberly Henry) for identifying bugs and other valuable feedback that helped improve the package.

olsrr 0.3.0

This is a minor release containing bug fixes.

Bug Fixes

output from reg_compute rounded up to 3 decimal points (#24)
added variable plot fails when model includes categorical variables (#25)
all possible regression fails when model includes categorical predictors (#26)
output from bartlett test rounded to 3 decimal points (#27)
best subsets regression fails when model includes categorical predictors (#28)
output from breusch pagan test rounded to 4 decimal points (#29)
output from collinearity diagnostics rounded to 3 decimal points (#30)
cook’s d bar plot threshold rounded to 3 decimal points (#31)
cook’s d chart threshold rounded to 3 decimal points (#32)
output from f test rounded to 3 decimal points (#33)
output from measures of influence rounded to 4 decimal points (#34)
output from information criteria rounded to 4 decimal points (#35)
studentized residuals vs leverage plot threshold rounded to 3 decimal points (#36)
output from score test rounded to 3 decimal points (#37)
step AIC backward method AIC value rounded to 3 decimal points (#38)
step AIC backward method AIC value rounded to 3 decimal points (#39)
step AIC both direction method AIC value rounded to 3 decimal points (#40)

olsrr 0.2.0

This is a minor release containing bug fixes and minor improvements.

Bug Fixes

inline functions in model formula caused errors in stepwise regression (#2)
added variable plots (ols_avplots) returns error when model formula contains inline functions (#3)
all possible regression (ols_all_subset) returns an error when the model formula contains inline functions or interaction variables (#4)
best subset regression (ols_best_subset) returns an error when the model formula contains inline functions or interaction variables (#5)
studentized residual plot (ols_srsd_plot) returns an error when the model formula contains inline functions (#6)
stepwise backward regression (ols_step_backward) returns an error when the model formula contains inline functions or interaction variables (#7)
stepwise forward regression (ols_step_backward) returns an error when the model formula contains inline functions (#8)
stepAIC backward regression (ols_stepaic_backward) returns an error when the model formula contains inline functions (#9)
stepAIC forward regression (ols_stepaic_forward) returns an error when the model formula contains inline functions (#10)
stepAIC regression (ols_stepaic_both) returns an error when the model formula contains inline functions (#11)
outliers incorrectly plotted in (ols_cooksd_barplot) cook’s d bar plot (#12)
regression (ols_regress) returns an error when the model formula contains inline functions (#21)
output from step AIC backward regression (ols_stepaic_backward) is not properly formatted (#22)
output from step AIC regression (ols_stepaic_both) is not properly formatted (#23)

Enhancements

cook’s d bar plot (ols_cooksd_barplot) returns the threshold value used to classify the observations as outliers (#13)
cook’s d chart (ols_cooksd_chart) returns the threshold value used to classify the observations as outliers (#14)
DFFITs plot (ols_dffits_plot) returns the threshold value used to classify the observations as outliers (#15)
deleted studentized residuals vs fitted values plot (ols_dsrvsp_plot) returns the threshold value used to classify the observations as outliers (#16)
studentized residuals vs leverage plot (ols_rsdlev_plot) returns the threshold value used to detect outliers/high leverage observations (#17)
standarized residuals chart (ols_srsd_chart) returns the threshold value used to classify the observations as outliers (#18)
studentized residuals plot (ols_srsd_plot) returns the threshold value used to classify the observations as outliers (#19)

Documentation

There were errors in the description of the values returned by some functions. The documentation has been thoroughly revised and improved in this release.

olsrr 0.1.0

First release.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.