Nothing
tests/testthat/test-backward-elimination.R
In blorr: Tools for Developing Binary Logistic Regression Models
context("test-backward-elimination.R")
test_that("output from backward variable elimination is as expected", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
actual <- blr_step_aic_backward(model)$predictors
expected <- c("prog", "socst")
expect_equivalent(actual, expected)
})
test_that("output from backward variable p elimination is as expected", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
actual <- blr_step_p_backward(model)$removed
expected <- c("prog", "socst")
expect_equivalent(actual, expected)
})
test_that("print output from backward variable elimination is as expected", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
x <- cat("Backward Elimination Method
---------------------------
Candidate Terms:
1 . female
2 . read
3 . science
4 . math
5 . prog
6 . socst
Variables Removed:
✖ prog
✖ socst
Backward Elimination Summary
--------------------------------------------
Variable AIC BIC Deviance
--------------------------------------------
Full Model 162.424 188.811 146.424
prog 158.953 178.743 146.953
socst 157.286 173.777 147.286
--------------------------------------------")
expect_output(print(blr_step_aic_backward(model)), x)
})
test_that("print output from backward variable p elimination is as expected", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
x <- cat("Backward Elimination Method
---------------------------
Candidate Terms:
1 . female1
2 . read
3 . science
4 . math
5 . prog2
6 . prog3
7 . socst
We are eliminating variables based on p value...
Variables Removed:
✖ prog3
✖ prog2
✖ socst
No more variables satisfy the condition of p value = 0.3
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 honcomp 200 199 195 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.5773 2.1568 -6.7589 0.0000
female1 1 1.3622 0.4605 2.9580 0.0031
read 1 0.0631 0.0281 2.2455 0.0247
science 1 0.0569 0.0326 1.7429 0.0814
math 1 0.1113 0.0338 3.2992 0.0010
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8835 Somers' D 0.7669
% Discordant 0.1165 Gamma 0.7669
% Tied 0.0000 Tau-a 0.3003
Pairs 7791 c 0.8835
---------------------------------------------------------------
Elimination Summary
----------------------------------------------------
Variable
Step Removed AIC BIC Deviance
----------------------------------------------------
1 prog3 160.7102 183.7984 146.7102
2 prog2 158.9527 178.7427 146.9527
3 socst 157.2856 173.7772 147.2856
----------------------------------------------------")
expect_output(print(blr_step_p_backward(model)), x)
})
test_that("output from backward variable elimination is as expected when details == TRUE", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
x <- cat("Backward Elimination Method
---------------------------
Candidate Terms:
1 . female
2 . read
3 . science
4 . math
5 . prog
6 . socst
Step 0: AIC = 162.4241
honcomp ~ female + read + science + math + prog + socst
-------------------------------------------------
Variable DF AIC BIC Deviance
-------------------------------------------------
prog 1 158.953 178.743 146.953
socst 1 160.745 183.834 146.745
read 1 163.394 186.482 149.394
science 1 163.815 186.903 149.815
female 1 169.570 192.658 155.570
math 1 170.017 193.105 156.017
-------------------------------------------------
✖ prog
Step 1 : AIC = 158.9527
honcomp ~ female + read + science + math + socst
-------------------------------------------------
Variable DF AIC BIC Deviance
-------------------------------------------------
socst 1 157.286 173.777 147.286
science 1 159.941 176.432 149.941
read 1 160.307 176.798 150.307
female 1 166.114 182.606 156.114
math 1 168.669 185.161 158.669
-------------------------------------------------
✖ socst
Step 2 : AIC = 157.2856
honcomp ~ female + read + science + math
-------------------------------------------------
Variable DF AIC BIC Deviance
-------------------------------------------------
science 1 158.420 171.613 150.420
read 1 160.658 173.851 152.658
female 1 164.988 178.181 156.988
math 1 168.236 181.430 160.236
-------------------------------------------------
No more variables to be removed.
Variables Removed:
✖ prog
✖ socst
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 honcomp 200 199 195 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.5773 2.1568 -6.7589 0.0000
female1 1 1.3622 0.4605 2.9580 0.0031
read 1 0.0631 0.0281 2.2455 0.0247
science 1 0.0569 0.0326 1.7429 0.0814
math 1 0.1113 0.0338 3.2992 0.0010
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8835 Somers' D 0.7669
% Discordant 0.1165 Gamma 0.7669
% Tied 0.0000 Tau-a 0.3003
Pairs 7791 c 0.8835
---------------------------------------------------------------
Backward Elimination Summary
--------------------------------------------
Variable AIC BIC Deviance
--------------------------------------------
Full Model 162.424 188.811 146.424
prog 158.953 178.743 146.953
socst 157.286 173.777 147.286
--------------------------------------------")
expect_output(print(blr_step_aic_backward(model, details = TRUE)), x)
})
test_that("output from backward variable elimination p is as expected when details == TRUE", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
x <- cat("Backward Elimination Method
---------------------------
Candidate Terms:
1 . female1
2 . read
3 . science
4 . math
5 . prog2
6 . prog3
7 . socst
We are eliminating variables based on p value...
✖ prog3
Backward Elimination: Step 1
Variable prog3 Removed
✔ 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 honcomp 200 199 193 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.7222 2.2246 -6.6180 0.0000
female1 1 1.3547 0.4659 2.9075 0.0036
read 1 0.0541 0.0311 1.7420 0.0815
science 1 0.0605 0.0341 1.7729 0.0762
math 1 0.1033 0.0357 2.8968 0.0038
prog2 1 0.2382 0.4839 0.4922 0.6226
socst 1 0.0135 0.0273 0.4953 0.6204
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8845 Somers' D 0.7690
% Discordant 0.1155 Gamma 0.7690
% Tied 0.0000 Tau-a 0.3011
Pairs 7791 c 0.8845
---------------------------------------------------------------
✖ prog2
Backward Elimination: Step 2
Variable prog2 Removed
✔ 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 honcomp 200 199 194 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.8131 2.2101 -6.7024 0.0000
female1 1 1.3351 0.4634 2.8813 0.0040
read 1 0.0558 0.0309 1.8050 0.0711
science 1 0.0561 0.0329 1.7036 0.0885
math 1 0.1086 0.0342 3.1761 0.0015
socst 1 0.0155 0.0270 0.5738 0.5661
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8822 Somers' D 0.7643
% Discordant 0.1178 Gamma 0.7643
% Tied 0.0000 Tau-a 0.2992
Pairs 7791 c 0.8822
---------------------------------------------------------------
✖ socst
Backward Elimination: Step 3
Variable socst Removed
✔ 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 honcomp 200 199 195 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.5773 2.1568 -6.7589 0.0000
female1 1 1.3622 0.4605 2.9580 0.0031
read 1 0.0631 0.0281 2.2455 0.0247
science 1 0.0569 0.0326 1.7429 0.0814
math 1 0.1113 0.0338 3.2992 0.0010
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8835 Somers' D 0.7669
% Discordant 0.1165 Gamma 0.7669
% Tied 0.0000 Tau-a 0.3003
Pairs 7791 c 0.8835
---------------------------------------------------------------
No more variables satisfy the condition of p value = 0.3
Variables Removed:
✖ prog3
✖ prog2
✖ socst
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 honcomp 200 199 195 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.5773 2.1568 -6.7589 0.0000
female1 1 1.3622 0.4605 2.9580 0.0031
read 1 0.0631 0.0281 2.2455 0.0247
science 1 0.0569 0.0326 1.7429 0.0814
math 1 0.1113 0.0338 3.2992 0.0010
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8835 Somers' D 0.7669
% Discordant 0.1165 Gamma 0.7669
% Tied 0.0000 Tau-a 0.3003
Pairs 7791 c 0.8835
---------------------------------------------------------------
Elimination Summary
----------------------------------------------------
Variable
Step Removed AIC BIC Deviance
----------------------------------------------------
1 prog3 160.7102 183.7984 146.7102
2 prog2 158.9527 178.7427 146.9527
3 socst 157.2856 173.7772 147.2856
----------------------------------------------------")
expect_output(print(blr_step_p_backward(model, details = TRUE)), x)
})
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context("test-backward-elimination.R")
test_that("output from backward variable elimination is as expected", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
actual <- blr_step_aic_backward(model)$predictors
expected <- c("prog", "socst")
expect_equivalent(actual, expected)
})
test_that("output from backward variable p elimination is as expected", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
actual <- blr_step_p_backward(model)$removed
expected <- c("prog", "socst")
expect_equivalent(actual, expected)
})
test_that("print output from backward variable elimination is as expected", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
x <- cat("Backward Elimination Method
---------------------------
Candidate Terms:
1 . female
2 . read
3 . science
4 . math
5 . prog
6 . socst
Variables Removed:
✖ prog
✖ socst
Backward Elimination Summary
--------------------------------------------
Variable AIC BIC Deviance
--------------------------------------------
Full Model 162.424 188.811 146.424
prog 158.953 178.743 146.953
socst 157.286 173.777 147.286
--------------------------------------------")
expect_output(print(blr_step_aic_backward(model)), x)
})
test_that("print output from backward variable p elimination is as expected", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
x <- cat("Backward Elimination Method
---------------------------
Candidate Terms:
1 . female1
2 . read
3 . science
4 . math
5 . prog2
6 . prog3
7 . socst
We are eliminating variables based on p value...
Variables Removed:
✖ prog3
✖ prog2
✖ socst
No more variables satisfy the condition of p value = 0.3
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 honcomp 200 199 195 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.5773 2.1568 -6.7589 0.0000
female1 1 1.3622 0.4605 2.9580 0.0031
read 1 0.0631 0.0281 2.2455 0.0247
science 1 0.0569 0.0326 1.7429 0.0814
math 1 0.1113 0.0338 3.2992 0.0010
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8835 Somers' D 0.7669
% Discordant 0.1165 Gamma 0.7669
% Tied 0.0000 Tau-a 0.3003
Pairs 7791 c 0.8835
---------------------------------------------------------------
Elimination Summary
----------------------------------------------------
Variable
Step Removed AIC BIC Deviance
----------------------------------------------------
1 prog3 160.7102 183.7984 146.7102
2 prog2 158.9527 178.7427 146.9527
3 socst 157.2856 173.7772 147.2856
----------------------------------------------------")
expect_output(print(blr_step_p_backward(model)), x)
})
test_that("output from backward variable elimination is as expected when details == TRUE", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
x <- cat("Backward Elimination Method
---------------------------
Candidate Terms:
1 . female
2 . read
3 . science
4 . math
5 . prog
6 . socst
Step 0: AIC = 162.4241
honcomp ~ female + read + science + math + prog + socst
-------------------------------------------------
Variable DF AIC BIC Deviance
-------------------------------------------------
prog 1 158.953 178.743 146.953
socst 1 160.745 183.834 146.745
read 1 163.394 186.482 149.394
science 1 163.815 186.903 149.815
female 1 169.570 192.658 155.570
math 1 170.017 193.105 156.017
-------------------------------------------------
✖ prog
Step 1 : AIC = 158.9527
honcomp ~ female + read + science + math + socst
-------------------------------------------------
Variable DF AIC BIC Deviance
-------------------------------------------------
socst 1 157.286 173.777 147.286
science 1 159.941 176.432 149.941
read 1 160.307 176.798 150.307
female 1 166.114 182.606 156.114
math 1 168.669 185.161 158.669
-------------------------------------------------
✖ socst
Step 2 : AIC = 157.2856
honcomp ~ female + read + science + math
-------------------------------------------------
Variable DF AIC BIC Deviance
-------------------------------------------------
science 1 158.420 171.613 150.420
read 1 160.658 173.851 152.658
female 1 164.988 178.181 156.988
math 1 168.236 181.430 160.236
-------------------------------------------------
No more variables to be removed.
Variables Removed:
✖ prog
✖ socst
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 honcomp 200 199 195 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.5773 2.1568 -6.7589 0.0000
female1 1 1.3622 0.4605 2.9580 0.0031
read 1 0.0631 0.0281 2.2455 0.0247
science 1 0.0569 0.0326 1.7429 0.0814
math 1 0.1113 0.0338 3.2992 0.0010
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8835 Somers' D 0.7669
% Discordant 0.1165 Gamma 0.7669
% Tied 0.0000 Tau-a 0.3003
Pairs 7791 c 0.8835
---------------------------------------------------------------
Backward Elimination Summary
--------------------------------------------
Variable AIC BIC Deviance
--------------------------------------------
Full Model 162.424 188.811 146.424
prog 158.953 178.743 146.953
socst 157.286 173.777 147.286
--------------------------------------------")
expect_output(print(blr_step_aic_backward(model, details = TRUE)), x)
})
test_that("output from backward variable elimination p is as expected when details == TRUE", {
model <- glm(
honcomp ~ female + read + science + math + prog + socst,
data = hsb2, family = binomial(link = "logit")
)
x <- cat("Backward Elimination Method
---------------------------
Candidate Terms:
1 . female1
2 . read
3 . science
4 . math
5 . prog2
6 . prog3
7 . socst
We are eliminating variables based on p value...
✖ prog3
Backward Elimination: Step 1
Variable prog3 Removed
✔ 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 honcomp 200 199 193 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.7222 2.2246 -6.6180 0.0000
female1 1 1.3547 0.4659 2.9075 0.0036
read 1 0.0541 0.0311 1.7420 0.0815
science 1 0.0605 0.0341 1.7729 0.0762
math 1 0.1033 0.0357 2.8968 0.0038
prog2 1 0.2382 0.4839 0.4922 0.6226
socst 1 0.0135 0.0273 0.4953 0.6204
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8845 Somers' D 0.7690
% Discordant 0.1155 Gamma 0.7690
% Tied 0.0000 Tau-a 0.3011
Pairs 7791 c 0.8845
---------------------------------------------------------------
✖ prog2
Backward Elimination: Step 2
Variable prog2 Removed
✔ 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 honcomp 200 199 194 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.8131 2.2101 -6.7024 0.0000
female1 1 1.3351 0.4634 2.8813 0.0040
read 1 0.0558 0.0309 1.8050 0.0711
science 1 0.0561 0.0329 1.7036 0.0885
math 1 0.1086 0.0342 3.1761 0.0015
socst 1 0.0155 0.0270 0.5738 0.5661
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8822 Somers' D 0.7643
% Discordant 0.1178 Gamma 0.7643
% Tied 0.0000 Tau-a 0.2992
Pairs 7791 c 0.8822
---------------------------------------------------------------
✖ socst
Backward Elimination: Step 3
Variable socst Removed
✔ 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 honcomp 200 199 195 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.5773 2.1568 -6.7589 0.0000
female1 1 1.3622 0.4605 2.9580 0.0031
read 1 0.0631 0.0281 2.2455 0.0247
science 1 0.0569 0.0326 1.7429 0.0814
math 1 0.1113 0.0338 3.2992 0.0010
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8835 Somers' D 0.7669
% Discordant 0.1165 Gamma 0.7669
% Tied 0.0000 Tau-a 0.3003
Pairs 7791 c 0.8835
---------------------------------------------------------------
No more variables satisfy the condition of p value = 0.3
Variables Removed:
✖ prog3
✖ prog2
✖ socst
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 honcomp 200 199 195 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 147 1 53
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -14.5773 2.1568 -6.7589 0.0000
female1 1 1.3622 0.4605 2.9580 0.0031
read 1 0.0631 0.0281 2.2455 0.0247
science 1 0.0569 0.0326 1.7429 0.0814
math 1 0.1113 0.0338 3.2992 0.0010
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8835 Somers' D 0.7669
% Discordant 0.1165 Gamma 0.7669
% Tied 0.0000 Tau-a 0.3003
Pairs 7791 c 0.8835
---------------------------------------------------------------
Elimination Summary
----------------------------------------------------
Variable
Step Removed AIC BIC Deviance
----------------------------------------------------
1 prog3 160.7102 183.7984 146.7102
2 prog2 158.9527 178.7427 146.9527
3 socst 157.2856 173.7772 147.2856
----------------------------------------------------")
expect_output(print(blr_step_p_backward(model, details = TRUE)), x)
})
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