rsquaredacademy / blorr Goto Github PK

Similar to this

Add a plot for visualizing capture rate by decile

Add a shiny app for users to explore the package interactively.

Add a function to print WOE & IV for multiple variables.

Use symbols such as tick and cross only if the function is used in an interactive session.

Add decile wise lift chart

Add an interactive tutorial using the learnr package.

Add a function blr_stepwise() for step wise selection of predictors. It should include the following arguments:

• model: a binary logistic regression model
• include: predictors to be included in the model at the beginning of the step wise process
• enter: significance level at which the predictor will enter the model
• retain: significance level at which the predictor will be retained in the model

Add a function blr_step_forward() for forward selection of predictors. It should include the following arguments:

• model: a binary logistic regression model
• include: the predictors to be included in the forward selection
• enter: significance level for entering the model
• stop: number of predictors to be added to the model before stopping forward selection
• details: if TRUE, model summary will be printed to the console after each step. Default value is FALSE

Add function blr_influence_diag() to create a panel of plots where the following are plotted against the observation id:

• pearson residual
• deviance residual
• leverage
• ci displacement c
• ci displacement cbar
• delta deviance
• delta chisquare

Currently, the end user can view the plot only for AIC in the following:

• blr_step_aic_backward()
• blr_step_aic_forward()
• blr_step_aic_both()

The plots must be generated for BIC and deviance as well and the user must be able to choose the plot to be displayed.

Use patchwork to combine plots instead of gridExtra.

Use the standard template for README:

• Overview
• Installation
• Shiny App
• Usage
• Articles
• Features
• Getting Help
• Code of Conduct

Add a function blr_collinearity_diagnostics() (similar to ) to detect multicollinearity.

Integrate the logistic regression report template from reportr.

Add blr_fit_diag() to create a panel of plots where the following are plotted against the fitted values:

• delta deviance
• delta chi square
• leverage
• ci displacement c

Generate automated report for logistic regression. Check the report package.

Prepare for release:

• Check that description is informative
• Check licensing of included files
• devtools::check_win_devel()
• rhub::check_for_cran()

Perform release:

• Bump version (in DESCRIPTION and NEWS)
• devtools::check_win_devel() (again!)
• devtools::submit_cran()
• pkgdown::build_site()
• Approve email

Wait for CRAN...

• Tag release
• Bump dev version
• Write blog post
• Tweet
• Add link to blog post in pkgdown news menu

Template from r-lib/usethis#338

Move shiny app to xplorerr package.

Use rlang equivalents for errors, warnings and messages.

Import the shiny app from the xplorerr package.

• CI Displacements C vs leverage
• Diff Chi Square vs leverage
• Diff Deviance vs leverage
• fitted values vs leverage

Similar to this.

blr_model_fit_stats() should compare model fit statistics for different models.

• CI Displacements C vs fitted values
• Diff Chi Square vs fitted values
• Diff Deviance vs fitted values

The following variable selection procedures should return the final model as an object of class glm():

• blr_step_aic_forward()
• blr_step_aic_backward()
• blr_step_aic_both()
• blr_step_p_forward()
• blr_step_p_backward()
• blr_step_p_both()

#45

Prepare for release:

• devtools::check_win_devel()
• rhub::check_for_cran()
• Polish NEWS

Perform release:

• Bump version (in DESCRIPTION and NEWS)
• devtools::check_win_devel() (again!)
• devtools::submit_cran()
• pkgdown::build_site()
• Approve email

Wait for CRAN...

• Tag release
• Bump dev version
• Publish blog post
• Share on Twitter

Template from r-lib/usethis#338

Add the following plots for residual diagnostics:

• pearson standardised residual vs fitted values
• pearson standardised residual vs case number
• deviance residual vs fitted values
• deviance residual vs case number
• leverage vs fitted values
• leverage vs case number
• CI Displacements C vs case number
• CI Displacements CBar vs case number
• Diff Chi Square vs case number
• Diff Deviance vs case number
• DfBetas vs case number

Hi,
I failed to install the package from CRAN/Github. Here are the few errors I found:

1. Error in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]) : there is no package called 'rio'
   Installed rio and tried again

2. Error in loadNamespace(i, c(lib.loc, .libPaths()), versionCheck = vI[[i]]) : there is no package called 'carData'
   Installed carData and tried again

3. Error : .onLoad failed in loadNamespace() for 'checkmate', details:
   call: NULL
   error: 'import' is not an exported object from 'namespace:backports'
   ERROR: lazy loading failed for package 'blorr'

What shall I do now?

sessionInfo() # For Reference
R version 3.3.3 (2017-03-06)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)

locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C
[5] LC_TIME=English_United States.1252

attached base packages:
[1] stats graphics grDevices utils datasets methods base

loaded via a namespace (and not attached):
[1] httr_1.3.1 R6_2.2.2 tools_3.3.3 withr_2.1.1 curl_3.1 memoise_1.0.0
[7] knitr_1.19 git2r_0.18.0 digest_0.6.12 devtools_1.12.0

Add a function blr_step_backward() for backward elimination. It should include the following arguments:

• model: a binary logistic regression model
• include: predictors to be included at the beginning of backward elimination process
• retain: significance level at which the predictors will be retained in the model
• stop: number of predictors to be eliminated

We are contacting you because you are the maintainer of blorr, which imports ggplot2 and uses vdiffr to manage visual test cases. The upcoming release of ggplot2 includes several improvements to plot rendering, including the ability to specify lineend and linejoin in geom_rect() and geom_tile(), and improved rendering of text. These improvements will result in subtle changes to your vdiffr dopplegangers when the new version is released.

Because vdiffr test cases do not run on CRAN by default, your CRAN checks will still pass. However, we suggest updating your visual test cases with the new version of ggplot2 as soon as possible to avoid confusion. You can install the development version of ggplot2 using remotes::install_github("tidyverse/ggplot2").

If you have any questions, let me know!

Add a test (similar to ) to detect specification error.

library(blorr)

model <- glm(
  honcomp ~ female + read + science, data = hsb2,
  family = binomial(link = "logit")
)
gtable <- blr_gains_table(model, hsb2)
#> New names:
#> * value -> value...1
#> * value -> value...2
#> Error: Can't subset columns that don't exist.
#> x Column `value` doesn't exist.

^{Created on 2020-04-29 by the reprex package (v0.3.0)}

This then is one failure we see when testing this package against dplyr 1.0.0. This apparently comes from these lines in gains_table_prep():

  response %>%
    as_tibble() %>%
    bind_cols(predict.glm(model, newdata = data, type = "response") %>%
                as_tibble())

because both have value column, so name repair kicks in.

Add blr_leverage_diag() to create a panel of plots where the following are plotted against leverage:

• delta deviance
• delta chi square
• ci displacement c
• fitted values

Return plot objects instead of printing. Use the argument print_plot with the default value TRUE.

Prepare for release:

• devtools::check_win_devel()
• rhub::check_for_cran()
• Polish NEWS

Perform release:

• Bump version (in DESCRIPTION and NEWS)
• devtools::check_win_devel() (again!)
• devtools::submit_cran()
• pkgdown::build_site()
• Approve email

Wait for CRAN...

• Tag release
• Bump dev version

Template from r-lib/usethis#338

Use a line chart instead of bar chart for weight of evidence

Check the odds ratio estimates and confidence intervals when weights are specified in the regression model.

Hi,

I was wondering would it be possible to create a function that does model selection based on p values (like ols_step_both_p from the olsrr package). Specifically p values from ANOVA/Chi Square Test similar to the STEPWISE procedure in SAS (https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_logistic_sect029.htm)

Thank you!
Aaron

Users should be able to specify variables which must be included in the models using the include argument and it should be available in the following procedures:

• blr_step_p_forward()
• blr_step_aic_forward()
• blr_step_p_backward()
• blr_step_aic_backward()
• blr_step_p_both()
• blr_step_aic_both()

Add a function to return the current version of the package on CRAN and GitHub.

Add the following:

• Contributing Guide
• Issue Template

The R code for computing concordance and discordance is extremely slow due to the double for loops. Rewrite the code in c++ to reduce the computation time.

Users should be able to select plot from the following libraries:

• ggplot2 (default)
• plotly
• rbokeh

To arrange plots, use grid_plot() in rbokeh and subplot() in plotly.

blr_bivariate_analysis() should be able to accommodate multiple predictors.

Add a print method for the two way segmentation of the response variable.

== CHECK RESULTS ========================================

• checking examples ... ERROR

  Running examples in ‘blorr-Ex.R’ failed
  The error most likely occurred in:
  
  > ### Name: blr_multi_model_fit_stats
  > ### Title: Multi model fit statistics
  > ### Aliases: blr_multi_model_fit_stats blr_multi_model_fit_stats.default
  >
  > ### ** Examples
  >
  > model <- glm(honcomp ~ female + read + science, data = hsb2,
  + family = binomial(link = 'logit'))
  >
  > model2 <- glm(honcomp ~ female + read + math, data = hsb2,
  + family = binomial(link = 'logit'))
  >
  > blr_multi_model_fit_stats(model, model2)
  Error: Columns 1, 2 must be named.
  Use .name_repair to specify repair.
  Execution halted
  

• checking tests ...

   ERROR
  Running the tests in ‘tests/testthat.R’ failed.
  Last 13 lines of output:
                         Added/
    Step    Variable    Removed        AIC           BIC           C(p)
    ----------------------------------------------------------------------
       1       x6       addition     18869.627     18885.434    18865.6270
       2       x1       addition     18571.376     18595.087    18565.3760
       3       x3       addition     18016.724     18048.338    18008.7240
       4       x2       addition     16642.374     16681.891    16632.3740
       5       x5       addition     16640.883     16688.304    16628.8830
       6       x6       removal      16639.219     16678.736    16629.2190
    ----------------------------------------------------------------------══
  

testthat results
═══════════════════════════════════════════════════════════
OK: 76 SKIPPED: 28 FAILED: 1
1. Error: blr_multi_model_fit_stats prints the correct output
(@test-model-fit-stats.R#154)

  Error: testthat unit tests failed
  Execution halted

Currently, the end user cannot modify the appearance of the plots generated by blorr. The plots are useful for absolute beginners while others would want to generate the plots using their own code. To address this issue, all the functions used in generating data for the plots must be exported. Those users who are well versed with R plotting libraries will then be able to use the above exported functions to prep the data and then use their favorite library to generate the plots. The functions used for preparing data for the following plots must be exported:

• blr_roc_curve
• blr_lorenz_curve
• blr_ks_chart
• blr_decile_capture_rate
• blr_decile_lift_chart

Prepare for release:

• devtools::check_win_devel()
• rhub::check_for_cran()
• Polish NEWS

Perform release:

• Bump version (in DESCRIPTION and NEWS)
• devtools::check_win_devel() (again!)
• devtools::submit_cran()
• pkgdown::build_site()
• Approve email

Wait for CRAN...

• Tag release
• Bump dev version

Template from r-lib/usethis#338