Nothing
tests/testthat/test-forward-selection.R
In blorr: Tools for Developing Binary Logistic Regression Models
context("test-forward-selection.R")
test_that("output from forward variable selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
actual <- blr_step_aic_forward(model)$predictors
expected <- c("read", "female", "science")
expect_equivalent(actual, expected)
})
test_that("output from forward variable p selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
actual <- blr_step_p_forward(model)$predictors
expected <- c("read", "female", "science")
expect_equivalent(actual, expected)
})
test_that("output from forward variable selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
x <- cat("Forward Selection Method
------------------------
Candidate Terms:
1 . female
2 . read
3 . science
Variables Entered:
✔ read
✔ female
✔ science
Selection Summary
---------------------------------------------------
Step Variable AIC BIC Deviance
---------------------------------------------------
1 read 183.063 189.660 179.063
2 female 176.887 176.887 176.887
3 science 168.236 168.236 168.236
---------------------------------------------------")
expect_output(print(blr_step_aic_forward(model)), x)
})
test_that("output from forward variable p selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
x <- cat("Forward Selection Method
---------------------------
Candidate Terms:
1. female1
2. read
3. science
We are selecting variables based on p value...
Variables Entered:
✔ read
✔ science
✔ female1
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 372 371 368 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -11.5804 1.3980 -8.2836 0.0000
read 1 0.1000 0.0210 4.7651 0.0000
science 1 0.0798 0.0246 3.2390 0.0012
female1 1 0.9324 0.3126 2.9826 0.0029
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8259 Somers' D 0.6607
% Discordant 0.1687 Gamma 0.6572
% Tied 0.0054 Tau-a 0.2265
Pairs 23780 c 0.8286
---------------------------------------------------------------
Selection Summary
----------------------------------------------------
Variable
Step Entered AIC BIC Deviance
----------------------------------------------------
1 read 316.3831 324.2209 312.3831
2 science 310.5294 322.2861 304.5294
3 female1 303.0855 318.7611 295.0855
----------------------------------------------------")
expect_output(print(blr_step_p_forward(model)), x)
})
test_that("output from forward variable selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
x <- cat("Forward Selection Method
---------------------------
Candidate Terms:
1. female1
2. read
3. science
We are selecting variables based on p value...
Forward Selection: Step 1
✔ read
✔ 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 372 371 370 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -8.5704 0.9875 -8.6789 0.0000
read 1 0.1340 0.0172 7.7741 0.0000
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.7771 Somers' D 0.6582
% Discordant 0.1602 Gamma 0.6169
% Tied 0.0627 Tau-a 0.2126
Pairs 23780 c 0.8085
---------------------------------------------------------------
Forward Selection: Step 2
✔ science
✔ 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 372 371 369 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -10.1484 1.2142 -8.3581 0.0000
read 1 0.0990 0.0207 4.7938 0.0000
science 1 0.0643 0.0233 2.7592 0.0058
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8181 Somers' D 0.6510
% Discordant 0.1729 Gamma 0.6452
% Tied 0.0090 Tau-a 0.2223
Pairs 23780 c 0.8226
---------------------------------------------------------------
Forward Selection: Step 3
✔ female1
✔ 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 372 371 368 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -11.5804 1.3980 -8.2836 0.0000
read 1 0.1000 0.0210 4.7651 0.0000
science 1 0.0798 0.0246 3.2390 0.0012
female1 1 0.9324 0.3126 2.9826 0.0029
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8259 Somers' D 0.6607
% Discordant 0.1687 Gamma 0.6572
% Tied 0.0054 Tau-a 0.2265
Pairs 23780 c 0.8286
---------------------------------------------------------------
Variables Entered:
✔ read
✔ science
✔ female1
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 372 371 368 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -11.5804 1.3980 -8.2836 0.0000
read 1 0.1000 0.0210 4.7651 0.0000
science 1 0.0798 0.0246 3.2390 0.0012
female1 1 0.9324 0.3126 2.9826 0.0029
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8259 Somers' D 0.6607
% Discordant 0.1687 Gamma 0.6572
% Tied 0.0054 Tau-a 0.2265
Pairs 23780 c 0.8286
---------------------------------------------------------------
Selection Summary
----------------------------------------------------
Variable
Step Entered AIC BIC Deviance
----------------------------------------------------
1 read 316.3831 324.2209 312.3831
2 science 310.5294 322.2861 304.5294
3 female1 303.0855 318.7611 295.0855
----------------------------------------------------")
expect_output(print(blr_step_aic_forward(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-forward-selection.R")
test_that("output from forward variable selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
actual <- blr_step_aic_forward(model)$predictors
expected <- c("read", "female", "science")
expect_equivalent(actual, expected)
})
test_that("output from forward variable p selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
actual <- blr_step_p_forward(model)$predictors
expected <- c("read", "female", "science")
expect_equivalent(actual, expected)
})
test_that("output from forward variable selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
x <- cat("Forward Selection Method
------------------------
Candidate Terms:
1 . female
2 . read
3 . science
Variables Entered:
✔ read
✔ female
✔ science
Selection Summary
---------------------------------------------------
Step Variable AIC BIC Deviance
---------------------------------------------------
1 read 183.063 189.660 179.063
2 female 176.887 176.887 176.887
3 science 168.236 168.236 168.236
---------------------------------------------------")
expect_output(print(blr_step_aic_forward(model)), x)
})
test_that("output from forward variable p selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
x <- cat("Forward Selection Method
---------------------------
Candidate Terms:
1. female1
2. read
3. science
We are selecting variables based on p value...
Variables Entered:
✔ read
✔ science
✔ female1
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 372 371 368 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -11.5804 1.3980 -8.2836 0.0000
read 1 0.1000 0.0210 4.7651 0.0000
science 1 0.0798 0.0246 3.2390 0.0012
female1 1 0.9324 0.3126 2.9826 0.0029
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8259 Somers' D 0.6607
% Discordant 0.1687 Gamma 0.6572
% Tied 0.0054 Tau-a 0.2265
Pairs 23780 c 0.8286
---------------------------------------------------------------
Selection Summary
----------------------------------------------------
Variable
Step Entered AIC BIC Deviance
----------------------------------------------------
1 read 316.3831 324.2209 312.3831
2 science 310.5294 322.2861 304.5294
3 female1 303.0855 318.7611 295.0855
----------------------------------------------------")
expect_output(print(blr_step_p_forward(model)), x)
})
test_that("output from forward variable selection is as expected", {
model <- glm(
honcomp ~ female + read + science, data = hsb2,
family = binomial(link = "logit")
)
x <- cat("Forward Selection Method
---------------------------
Candidate Terms:
1. female1
2. read
3. science
We are selecting variables based on p value...
Forward Selection: Step 1
✔ read
✔ 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 372 371 370 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -8.5704 0.9875 -8.6789 0.0000
read 1 0.1340 0.0172 7.7741 0.0000
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.7771 Somers' D 0.6582
% Discordant 0.1602 Gamma 0.6169
% Tied 0.0627 Tau-a 0.2126
Pairs 23780 c 0.8085
---------------------------------------------------------------
Forward Selection: Step 2
✔ science
✔ 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 372 371 369 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -10.1484 1.2142 -8.3581 0.0000
read 1 0.0990 0.0207 4.7938 0.0000
science 1 0.0643 0.0233 2.7592 0.0058
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8181 Somers' D 0.6510
% Discordant 0.1729 Gamma 0.6452
% Tied 0.0090 Tau-a 0.2223
Pairs 23780 c 0.8226
---------------------------------------------------------------
Forward Selection: Step 3
✔ female1
✔ 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 372 371 368 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -11.5804 1.3980 -8.2836 0.0000
read 1 0.1000 0.0210 4.7651 0.0000
science 1 0.0798 0.0246 3.2390 0.0012
female1 1 0.9324 0.3126 2.9826 0.0029
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8259 Somers' D 0.6607
% Discordant 0.1687 Gamma 0.6572
% Tied 0.0054 Tau-a 0.2265
Pairs 23780 c 0.8286
---------------------------------------------------------------
Variables Entered:
✔ read
✔ science
✔ female1
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 372 371 368 TRUE
------------------------------------------------------------------------
Response Summary
--------------------------------------------------------
Outcome Frequency Outcome Frequency
--------------------------------------------------------
0 290 1 82
--------------------------------------------------------
Maximum Likelihood Estimates
-----------------------------------------------------------------
Parameter DF Estimate Std. Error z value Pr(>|z|)
-----------------------------------------------------------------
(Intercept) 1 -11.5804 1.3980 -8.2836 0.0000
read 1 0.1000 0.0210 4.7651 0.0000
science 1 0.0798 0.0246 3.2390 0.0012
female1 1 0.9324 0.3126 2.9826 0.0029
-----------------------------------------------------------------
Association of Predicted Probabilities and Observed Responses
---------------------------------------------------------------
% Concordant 0.8259 Somers' D 0.6607
% Discordant 0.1687 Gamma 0.6572
% Tied 0.0054 Tau-a 0.2265
Pairs 23780 c 0.8286
---------------------------------------------------------------
Selection Summary
----------------------------------------------------
Variable
Step Entered AIC BIC Deviance
----------------------------------------------------
1 read 316.3831 324.2209 312.3831
2 science 310.5294 322.2861 304.5294
3 female1 303.0855 318.7611 295.0855
----------------------------------------------------")
expect_output(print(blr_step_aic_forward(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
Browse R Packages
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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