Nothing
tests/testthat/test-stepwise-selection.R
In blorr: Tools for Developing Binary Logistic Regression Models
context("test-stepwise-selection.R")
test_that("output from stepwise variable selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
actual <- blr_step_aic_both(model)$predictors
expected <- c("x6", "x1", "x3", "x2", "x6", "x5")
expect_equivalent(actual, expected)
})
test_that("output from stepwise variable p selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
actual <- blr_step_p_both(model)$orders
expected <- c("x1", "x2", "x3", "x6", "x5", "x6")
expect_equivalent(actual, expected)
})
test_that("print output from forward variable selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
x <- cat("Stepwise Selection Method
-------------------------
Candidate Terms:
1 . x1
2 . x2
3 . x3
4 . x4
5 . x5
6 . x6
Variables Entered/Removed:
✔ x6
✔ x1
✔ x3
✔ x2
✖ x6
✔ x5
No more variables to be added or removed.
Stepwise Summary
-----------------------------------------------------------
Variable Method AIC BIC Deviance
-----------------------------------------------------------
x6 addition 18869.627 18885.434 18865.627
x1 addition 18571.376 18595.087 18565.376
x3 addition 18016.724 18048.338 18008.724
x2 addition 16642.374 16681.891 16632.374
x6 removal 16640.653 16672.267 16632.653
x5 addition 16639.219 16678.736 16629.219
-----------------------------------------------------------")
expect_output(print(blr_step_aic_both(model)), x)
})
test_that("print output from forward variable selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
x <- cat("Stepwise Selection Method
---------------------------
Candidate Terms:
1. x1
2. x2
3. x3
4. x4
5. x5
6. x6
We are selecting variables based on p value...
Variables Entered/Removed:
✔ x6
✔ x1
✔ x3
✔ x2
✔ x5
✖ x6
No more variables to be added/removed.
Final Model Output
------------------
✔ 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 20000 19999 19995 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5228 0.0287 -53.1302 0.0000
x1 1 1.0276 0.0212 48.4927 0.0000
x3 1 1.0260 0.0213 48.2799 0.0000
x2 1 0.9807 0.0209 46.9690 0.0000
x5 1 0.0357 0.0192 1.8531 0.0639
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8911 Somers' D 0.7821
% Discordant 0.1089 Gamma 0.7821
% Tied 0.0000 Tau-a 0.3911
Pairs 99998319 c 0.8911
---------------------------------------------------------------
Stepwise Selection Summary
----------------------------------------------------------------------
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
----------------------------------------------------------------------")
expect_output(print(blr_step_p_both(model)), x)
})
test_that("print output from forward variable p selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
x <- cat("Stepwise Selection Method
---------------------------
Candidate Terms:
1. x1
2. x2
3. x3
4. x4
5. x5
6. x6
We are selecting variables based on p value...
Stepwise Selection: Step 1
✔ x6
✔ 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 20000 19999 19998 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.1331 0.0242 -46.8217 0.0000
x6 1 0.7490 0.0107 69.8309 0.0000
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8558 Somers' D 0.7115
% Discordant 0.1442 Gamma 0.7115
% Tied 0.0000 Tau-a 0.3558
Pairs 99998319 c 0.8558
---------------------------------------------------------------
Stepwise Selection: Step 2
✔ x1
✔ 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 20000 19999 19997 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.1836 0.0248 -47.8070 0.0000
x6 1 0.6619 0.0116 56.9586 0.0000
x1 1 0.3595 0.0210 17.0908 0.0000
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8610 Somers' D 0.7220
% Discordant 0.1390 Gamma 0.7220
% Tied 0.0000 Tau-a 0.3610
Pairs 99998319 c 0.8610
---------------------------------------------------------------
Stepwise Selection: Step 3
✔ x3
✔ 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 20000 19999 19996 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.2730 0.0257 -49.4449 0.0000
x6 1 0.4865 0.0135 35.9577 0.0000
x1 1 0.5367 0.0229 23.4089 0.0000
x3 1 0.5331 0.0232 22.9908 0.0000
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8704 Somers' D 0.7407
% Discordant 0.1296 Gamma 0.7407
% Tied 0.0000 Tau-a 0.3704
Pairs 99998319 c 0.8704
---------------------------------------------------------------
Stepwise Selection: Step 4
✔ x2
✔ 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 20000 19999 19995 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5229 0.0287 -53.1387 0.0000
x6 1 -0.0102 0.0193 -0.5290 0.5968
x1 1 1.0381 0.0287 36.1728 0.0000
x3 1 1.0365 0.0290 35.7651 0.0000
x2 1 0.9910 0.0285 34.7112 0.0000
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8910 Somers' D 0.7820
% Discordant 0.1090 Gamma 0.7820
% Tied 0.0000 Tau-a 0.3910
Pairs 99998319 c 0.8910
---------------------------------------------------------------
Stepwise Selection: Step 5
✔ x5
✔ 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 20000 19999 19994 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5228 0.0287 -53.1294 0.0000
x6 1 -0.0112 0.0193 -0.5790 0.5626
x1 1 1.0388 0.0287 36.1852 0.0000
x3 1 1.0374 0.0290 35.7813 0.0000
x2 1 0.9919 0.0286 34.7282 0.0000
x5 1 0.0360 0.0193 1.8680 0.0618
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8911 Somers' D 0.7821
% Discordant 0.1089 Gamma 0.7821
% Tied 0.0000 Tau-a 0.3911
Pairs 99998319 c 0.8911
---------------------------------------------------------------
Stepwise Selection: Step 6
✖ x6
✔ 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 20000 19999 19995 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5228 0.0287 -53.1302 0.0000
x1 1 1.0276 0.0212 48.4927 0.0000
x3 1 1.0260 0.0213 48.2799 0.0000
x2 1 0.9807 0.0209 46.9690 0.0000
x5 1 0.0357 0.0192 1.8531 0.0639
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8911 Somers' D 0.7821
% Discordant 0.1089 Gamma 0.7821
% Tied 0.0000 Tau-a 0.3911
Pairs 99998319 c 0.8911
---------------------------------------------------------------
No more variables to be added/removed.
Final Model Output
------------------
✔ 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 20000 19999 19995 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5228 0.0287 -53.1302 0.0000
x1 1 1.0276 0.0212 48.4927 0.0000
x3 1 1.0260 0.0213 48.2799 0.0000
x2 1 0.9807 0.0209 46.9690 0.0000
x5 1 0.0357 0.0192 1.8531 0.0639
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8911 Somers' D 0.7821
% Discordant 0.1089 Gamma 0.7821
% Tied 0.0000 Tau-a 0.3911
Pairs 99998319 c 0.8911
---------------------------------------------------------------
Stepwise Selection Summary
----------------------------------------------------------------------
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
----------------------------------------------------------------------")
expect_output(print(blr_step_p_both(model, details = TRUE)), x)
})
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blorr documentation built on July 2, 2020, 2:15 a.m.
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context("test-stepwise-selection.R")
test_that("output from stepwise variable selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
actual <- blr_step_aic_both(model)$predictors
expected <- c("x6", "x1", "x3", "x2", "x6", "x5")
expect_equivalent(actual, expected)
})
test_that("output from stepwise variable p selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
actual <- blr_step_p_both(model)$orders
expected <- c("x1", "x2", "x3", "x6", "x5", "x6")
expect_equivalent(actual, expected)
})
test_that("print output from forward variable selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
x <- cat("Stepwise Selection Method
-------------------------
Candidate Terms:
1 . x1
2 . x2
3 . x3
4 . x4
5 . x5
6 . x6
Variables Entered/Removed:
✔ x6
✔ x1
✔ x3
✔ x2
✖ x6
✔ x5
No more variables to be added or removed.
Stepwise Summary
-----------------------------------------------------------
Variable Method AIC BIC Deviance
-----------------------------------------------------------
x6 addition 18869.627 18885.434 18865.627
x1 addition 18571.376 18595.087 18565.376
x3 addition 18016.724 18048.338 18008.724
x2 addition 16642.374 16681.891 16632.374
x6 removal 16640.653 16672.267 16632.653
x5 addition 16639.219 16678.736 16629.219
-----------------------------------------------------------")
expect_output(print(blr_step_aic_both(model)), x)
})
test_that("print output from forward variable selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
x <- cat("Stepwise Selection Method
---------------------------
Candidate Terms:
1. x1
2. x2
3. x3
4. x4
5. x5
6. x6
We are selecting variables based on p value...
Variables Entered/Removed:
✔ x6
✔ x1
✔ x3
✔ x2
✔ x5
✖ x6
No more variables to be added/removed.
Final Model Output
------------------
✔ 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 20000 19999 19995 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5228 0.0287 -53.1302 0.0000
x1 1 1.0276 0.0212 48.4927 0.0000
x3 1 1.0260 0.0213 48.2799 0.0000
x2 1 0.9807 0.0209 46.9690 0.0000
x5 1 0.0357 0.0192 1.8531 0.0639
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8911 Somers' D 0.7821
% Discordant 0.1089 Gamma 0.7821
% Tied 0.0000 Tau-a 0.3911
Pairs 99998319 c 0.8911
---------------------------------------------------------------
Stepwise Selection Summary
----------------------------------------------------------------------
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
----------------------------------------------------------------------")
expect_output(print(blr_step_p_both(model)), x)
})
test_that("print output from forward variable p selection is as expected", {
model <- glm(y ~ ., data = stepwise, family = binomial(link = 'logit'))
x <- cat("Stepwise Selection Method
---------------------------
Candidate Terms:
1. x1
2. x2
3. x3
4. x4
5. x5
6. x6
We are selecting variables based on p value...
Stepwise Selection: Step 1
✔ x6
✔ 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 20000 19999 19998 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.1331 0.0242 -46.8217 0.0000
x6 1 0.7490 0.0107 69.8309 0.0000
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8558 Somers' D 0.7115
% Discordant 0.1442 Gamma 0.7115
% Tied 0.0000 Tau-a 0.3558
Pairs 99998319 c 0.8558
---------------------------------------------------------------
Stepwise Selection: Step 2
✔ x1
✔ 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 20000 19999 19997 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.1836 0.0248 -47.8070 0.0000
x6 1 0.6619 0.0116 56.9586 0.0000
x1 1 0.3595 0.0210 17.0908 0.0000
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8610 Somers' D 0.7220
% Discordant 0.1390 Gamma 0.7220
% Tied 0.0000 Tau-a 0.3610
Pairs 99998319 c 0.8610
---------------------------------------------------------------
Stepwise Selection: Step 3
✔ x3
✔ 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 20000 19999 19996 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.2730 0.0257 -49.4449 0.0000
x6 1 0.4865 0.0135 35.9577 0.0000
x1 1 0.5367 0.0229 23.4089 0.0000
x3 1 0.5331 0.0232 22.9908 0.0000
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8704 Somers' D 0.7407
% Discordant 0.1296 Gamma 0.7407
% Tied 0.0000 Tau-a 0.3704
Pairs 99998319 c 0.8704
---------------------------------------------------------------
Stepwise Selection: Step 4
✔ x2
✔ 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 20000 19999 19995 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5229 0.0287 -53.1387 0.0000
x6 1 -0.0102 0.0193 -0.5290 0.5968
x1 1 1.0381 0.0287 36.1728 0.0000
x3 1 1.0365 0.0290 35.7651 0.0000
x2 1 0.9910 0.0285 34.7112 0.0000
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8910 Somers' D 0.7820
% Discordant 0.1090 Gamma 0.7820
% Tied 0.0000 Tau-a 0.3910
Pairs 99998319 c 0.8910
---------------------------------------------------------------
Stepwise Selection: Step 5
✔ x5
✔ 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 20000 19999 19994 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5228 0.0287 -53.1294 0.0000
x6 1 -0.0112 0.0193 -0.5790 0.5626
x1 1 1.0388 0.0287 36.1852 0.0000
x3 1 1.0374 0.0290 35.7813 0.0000
x2 1 0.9919 0.0286 34.7282 0.0000
x5 1 0.0360 0.0193 1.8680 0.0618
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8911 Somers' D 0.7821
% Discordant 0.1089 Gamma 0.7821
% Tied 0.0000 Tau-a 0.3911
Pairs 99998319 c 0.8911
---------------------------------------------------------------
Stepwise Selection: Step 6
✖ x6
✔ 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 20000 19999 19995 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5228 0.0287 -53.1302 0.0000
x1 1 1.0276 0.0212 48.4927 0.0000
x3 1 1.0260 0.0213 48.2799 0.0000
x2 1 0.9807 0.0209 46.9690 0.0000
x5 1 0.0357 0.0192 1.8531 0.0639
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8911 Somers' D 0.7821
% Discordant 0.1089 Gamma 0.7821
% Tied 0.0000 Tau-a 0.3911
Pairs 99998319 c 0.8911
---------------------------------------------------------------
No more variables to be added/removed.
Final Model Output
------------------
✔ 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 20000 19999 19995 TRUE
-------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 10041 1 9959
--------------------------------------------------------
Maximum Likelihood Estimates
------------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
------------------------------------------------------------------
(Intercept) 1 -1.5228 0.0287 -53.1302 0.0000
x1 1 1.0276 0.0212 48.4927 0.0000
x3 1 1.0260 0.0213 48.2799 0.0000
x2 1 0.9807 0.0209 46.9690 0.0000
x5 1 0.0357 0.0192 1.8531 0.0639
------------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8911 Somers' D 0.7821
% Discordant 0.1089 Gamma 0.7821
% Tied 0.0000 Tau-a 0.3911
Pairs 99998319 c 0.8911
---------------------------------------------------------------
Stepwise Selection Summary
----------------------------------------------------------------------
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
----------------------------------------------------------------------")
expect_output(print(blr_step_p_both(model, details = TRUE)), x)
})
Try the blorr package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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