ROCtest: Display ROC curve and related AUC statistic, or...
In LOGIT: Functions, Data and Code for Binary and Binomial Data
Description
Usage
Arguments
Format
Details
Value
Note
Author(s)
References
See Also
Examples
Description
Provides two options following the glm() function with binomial family.
1: Senstivity-specificity plot with optimal cut point statistic
2: ROC plot with Area Under Curve (AUC) statistic
Usage
1
Arguments
model
model name
fold
number of k-folds
type
type of plot
Format
- x
-
The function has three arguments: modelname, folds, type of plot
Details
ROCtest is a post-estimation function for logistic regression, following the use
of glm(). Options to display a sensitivity-specificity plot or ROC curve are
available.
Value
plot
Note
ROCtest() must be loaded into memory in order to be effectve. As a function in LOGIT,
it is immediately available to a user.
Author(s)
Rafael de Souza, ELTE, Hungary,
Joseph M. Hilbe, Arizona State University.
References
Hilbe, Joseph M. (2016), Practical Guide to Logistic Regression, Chapman & Hall/CRC.
Hilbe, Joseph M. (2009), Logistic Regression Models, Chapman & Hall/CRC.
See Also
Examples
1
2
3
4
5
6
7
8
9
10
library(MASS)
library(LOGIT)
data(R84)
R84$cage <- R84$age - mean(R84$age)
R84$cdoc <- R84$docvis - mean(R84$docvis)
mylogit <- glm(outwork ~ cdoc + female + kids + cage + factor(edlevel),
family=binomial, data=R84)
summary(mylogit)
ROCtest(mylogit, fold=10, type="Sensitivity")
ROCtest(mylogit, fold=10, type="ROC")
Example output
Call:
glm(formula = outwork ~ cdoc + female + kids + cage + factor(edlevel),
family = binomial, data = R84)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3615 -0.6807 -0.4320 0.8541 2.8220
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.022806 0.085790 -23.579 < 2e-16 ***
cdoc 0.024480 0.006255 3.914 9.08e-05 ***
female 2.268467 0.084039 26.993 < 2e-16 ***
kids 0.373053 0.090586 4.118 3.82e-05 ***
cage 0.054355 0.004207 12.921 < 2e-16 ***
factor(edlevel)2 0.009293 0.173172 0.054 0.957201
factor(edlevel)3 0.478016 0.153786 3.108 0.001882 **
factor(edlevel)4 -0.830282 0.214336 -3.874 0.000107 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 5091.1 on 3873 degrees of freedom
Residual deviance: 3889.9 on 3866 degrees of freedom
AIC: 3905.9
Number of Fisher Scoring iterations: 4
$plot
$Observed
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Levels: 0 1
$Predicted
[1] 0 1 1 1 0 1 0 1 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0
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[260] 0 1 0 0 1 0 0 0 1 0 1 1 0 0 1 1 1 1 1 0 1 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 1
[297] 1 0 0 1 1 1 1 0 1 1 0 1 0 1 0 1 0 1 0 1 1 1 0 1 0 1 0 0 0 1 0 1 1 0 1 0 1
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[1074] 1 1 0 1 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 1 0 1 0 0 0
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[1148] 1 1 1 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 1 0 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 1 0
[1185] 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 1 0 1 0 1 1 0 0 1 1 0 1 1 1 1 0 0
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[1592] 0 0 1 1 0 0 1 1 1 0 0 0 0 1 1 0 1 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1
[1629] 0 1 1 0 1 1 0 1 0 1 1 1 1 0 1 0 1 1 0 0 1 1 0 1 0 0 0 1 0 0 0 0 1 0 0 0 1
[1666] 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 1 1 1 1 1 1
[1703] 0 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 0 1 1 0 0 1 0 0 1 0 1 0 1 1 1 0 1 1 0 0 0
[1740] 0 0 1 0 0 0 0 1 1 0 0 1 1 1 0 1 1 1 0 1 0 0 0 0 0 1 0 1 0 0 1 1 1 0 1 0 1
[1777] 0 1 1 1 0 0 0 1 0 0 1 0 1 0 0 0 1 1 0 0 1 1 1 0 1 1 0 1 1 1 0 1 0 1 0 0 0
[1814] 1 0 1 1 1 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 0 1 0 0 1 1 0 0 1 0 0 1 0 1 0 0 1
[1851] 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 0 0 0 1 0 0 1 0 1 1 1 0 1 0 1 1 0 1 1 0 0
[1888] 1 1 0 1 1 0 1 1 1 1 1 1 0 1 0 0 1 0 0 1 1 1 1 0 0 0 1 1 1 0 1 1 1 0 0 1 0
[1925] 1 0 0 1 0 1 1 1 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 1 0 1 0 0 1 1 0 0 1 0 1
[1962] 1 0 1 1 0 1 1 1 1 1 1 0 1 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 1 0 1 1 1 1 1 0 0
[1999] 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 0 0 1 0 0
[2036] 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0 1 0 1 0 0 1 0 1 0 0 0 1 1 0 1 1 0 0 1 1 1
[2073] 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 0 0 1 0 0
[2110] 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0 0 1 1 0 0 1 0 0 1 1 0 1 1 1 1 1 1
[2147] 1 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1
[2184] 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 1 0 0 0 1 0 1 1 1 0 0 0 0 0 1 0 1
[2221] 1 1 1 0 0 0 1 1 1 0 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 1 0 1 1 0 1 0 1 1 1 0 0
[2258] 0 1 0 1 1 1 0 0 0 1 0 0 1 0 1 1 1 1 0 1 0 0 1 0 1 1 0 0 1 0 0 1 0 1 0 1 1
[2295] 0 1 0 1 1 1 1 1 1 0 0 0 1 0 1 1 1 0 1 1 0 1 1 0 1 0 1 0 0 1 1 0 0 1 0 0 1
[2332] 1 1 1 0 1 0 0 0 1 1 1 1 0 0 0 1 1 1 1 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 0 0
[2369] 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 1 1 0 1 1 1 1
[2406] 1 1 1 1 0 0 0 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1 1 0 1 0 1 0 0
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[2480] 1 1 1 1 0 0 1 1 1 1 0 1 0 1 0 0 0 1 1 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 1 0
[2517] 0 1 1 0 1 0 0 1 0 1 0 1 0 1 0 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1
[2554] 0 1 0 1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 1 1 0 0 1 1 1 1 0 0 1 1 0 1 0 1 1 0 0
[2591] 0 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 1 1 0 1 0 1 0 0 0 1 0 1
[2628] 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 0 0 1 1 0 0 0 1 0 0
[2665] 1 0 1 1 0 0 0 1 1 0 1 1 0 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 1 0 1 1 1 0 1 0
[2702] 1 1 0 1 0 0 0 1 0 1 1 1 0 1 1 0 0 0 1 0 1 0 1 1 0 1 1 0 1 1 0 1 1 1 1 0 0
[2739] 0 1 1 1 1 1 1 1 1 0 1 0 0 0 1 1 0 1 0 1 1 1 0 1 1 1 1 0 1 0 1 0 0 0 1 0 1
[2776] 1 0 1 0 1 0 0 0 1 1 1 1 0 0 1 1 1 1 1 1 0 1 0 0 1 1 0 0 0 1 1 1 1 1 0 0 1
[2813] 1 0 1 1 1 0 0 1 1 0 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 1 1 0 1 1 1 1 0 1 0 1 1
[2850] 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 1 1 1 1 0 1 0 0 0 0 1 0
[2887] 0 1 0 0 0 0 1 1 0 1 1 1 0 0 1 0 0 0 1 1 0 0 0 1 1 0 1 0 1 1 1 0 0 1 0 0 0
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[2998] 1 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 1 1 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 0 1 0
[3035] 1 0 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 0 0 1 1 1 1 0 0 1 1 0 1 0 0 1 0 1 0 1 0
[3072] 0 1 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 1 0 0 1 1 1 1
[3109] 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 1
[3146] 1 0 1 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0 1 1 1 0 1 0 1 0 1 0 0 0 0 1
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[3220] 0 1 1 0 1 1 1 0 0 1 0 1 1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0
[3257] 0 1 1 1 0 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1
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[3331] 0 0 0 0 0 1 1 0 1 0 0 0 1 1 1 0 1 0 1 1 1 0 0 1 0 1 0 0 1 1 1 1 1 0 1 0 1
[3368] 1 0 0 0 0 1 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 1 1 1 0
[3405] 0 1 1 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 1 0 1 1 0 1 0 0 0 1 1 1 0 1 1 1
[3442] 1 1 0 1 1 0 1 0 0 1 1 0 1 1 1 0 1 1 1 0 0 0 0 1 1 1 0 0 0 1 1 1 0 1 1 1 1
[3479] 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 0
[3516] 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 1 0
[3553] 0 1 0 1 0 1 1 1 1 0 1 0 0 0 1 0 1 1 0 0 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 0 1
[3590] 0 1 0 1 0 0 1 0 0 0 1 1 0 1 1 1 0 0 1 1 0 0 1 1 1 1 1 0 1 0 0 0 0 1 0 0 0
[3627] 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 1 0 1 0 0 1 1 1 0 1 0 0 1 0
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[3849] 1 1 1 1 0 1 1 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 1 0 1 0
Levels: 0 1
$cut
[1] 0.3801702
$plot
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[38] 1 0 0 1 0 1 0 0 0 1 0 1 1 1 1 0 0 0 1 1 0 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0
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[186] 0 0 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 1 1 0 1 1
[223] 1 0 0 0 1 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 1 0 1 0 1 0 1 1 0
[260] 0 1 0 1 1 0 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0
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[334] 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
[371] 0 0 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 1
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[482] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 1 0 1 1 0 1 0 0 0 0 1 0 0 1 0 1 1
[519] 1 1 0 1 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1
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[593] 0 1 1 0 0 0 1 1 0 0 0 1 1 1 1 0 1 1 1 1 1 0 0 1 1 0 0 1 1 1 0 1 0 1 0 0 0
[630] 1 1 0 0 0 0 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 0 1 1 1 0 0 1 1 0 0 1 0 0 1 0 0
[667] 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1 1
[704] 0 1 0 0 0 0 1 1 0 1 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 0 0 0 1 0 0 0
[741] 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0
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[852] 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0
[889] 1 1 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 1 0 1 1 1 1
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[1037] 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1
[1074] 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 1 0 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 0 0 0 0
[1111] 0 1 0 0 0 0 1 0 1 0 1 0 1 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 1 1 1 0 0 0 0 0 0
[1148] 0 1 1 0 0 0 1 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 1
[1185] 0 0 1 1 0 0 0 0 0 0 1 1 0 0 1 0 1 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 1
[1222] 1 1 1 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 1 0 1 0 0 1 1 0 0 1 0 0 1
[1259] 0 1 0 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 1
[1296] 1 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 1 0 1 1 0 0 1 1 0 1 1 0
[1333] 0 0 0 1 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 1 1 1 1 0 1 0 0 0 1 1
[1370] 0 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 0 0 0 1 1 0 0 1 1 1 0 1 0 0 0 0 0 0 1
[1407] 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 1 0
[1444] 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 0
[1481] 0 1 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 0 0 1 0 0 0
[1518] 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0
[1555] 1 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1
[1592] 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 1 0 1 1 0 0 0 1 0
[1629] 0 1 1 0 0 0 0 1 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 1 1 0 0 1 1
[1666] 1 0 1 1 0 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 1 0 1
[1703] 0 1 0 1 0 0 0 1 0 1 0 1 1 0 1 1 0 1 0 0 1 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 1
[1740] 1 0 1 1 1 1 1 0 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 1 1 0 1 1 0 1 1 0 1 0
[1777] 1 0 0 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 1 0
[1814] 0 1 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0
[1851] 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 1 0 0
[1888] 0 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1
[1925] 0 0 0 1 1 1 0 1 0 0 1 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 1 1 0
[1962] 0 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 1 0 1 1 0 1 1 0 1 1 0 0 0 1 1 1
[1999] 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 1 1 1 0 1 1 0 1 0 1 1 0 0
[2036] 0 0 0 1 1 1 1 0 1 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 1 0 0 1 0 1 1 1 0 0 0 1 0
[2073] 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 1 0 1 0
[2110] 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 1 1 1 1 0 1 0 1 1 1 1 0
[2147] 1 0 1 1 0 1 0 0 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0
[2184] 1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 0
[2221] 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 1 1 0 0 0 1 1 0
[2258] 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 1 0 0 0 1 0 1 1 0 0 0 1
[2295] 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 0
[2332] 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0
[2369] 1 0 1 1 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0
[2406] 0 1 1 0 0 1 0 0 1 1 0 1 1 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1
[2443] 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0
[2480] 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 1 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0
[2517] 0 1 0 0 1 0 0 1 0 1 1 0 0 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 1 0 1
[2554] 1 1 1 0 0 1 1 1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0
[2591] 0 0 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 1 1 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 1
[2628] 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0
[2665] 0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1
[2702] 0 0 0 1 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 0 0 0 1 1 1 0 1
[2739] 0 0 0 1 1 0 1 0 1 1 0 0 1 0 1 0 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 1 0
[2776] 1 0 0 1 1 1 0 1 0 0 0 1 0 1 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 0
[2813] 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0
[2850] 0 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0
[2887] 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0
[2924] 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1
[2961] 1 0 0 0 0 0 1 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 1 0
[2998] 0 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0 0 1
[3035] 0 0 0 1 0 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 1 0 0 1 0 1 1 0 1 0
[3072] 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 0 0
[3109] 0 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1
[3146] 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0
[3183] 1 0 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1
[3220] 0 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 1 0 0 1 1 1 0 1 1 0 1 1 0 0 0 0 0 1 0 0 1
[3257] 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
[3294] 0 1 1 1 1 0 0 1 0 0 0 1 1 1 1 0 0 0 0 1 0 1 1 0 1 0 0 1 1 0 1 1 1 1 0 1 1
[3331] 0 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0
[3368] 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0 1 1 0 0 0
[3405] 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0
[3442] 0 1 1 0 1 1 0 0 0 0 1 1 1 1 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0
[3479] 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 1 0 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 1 1
[3516] 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0
[3553] 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1
[3590] 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1 1 0 0 1 1 1 0 0 0 1 0 0 0 1 1 0 1 0 0 1 0
[3627] 0 0 1 1 1 1 0 0 0 0 1 1 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1
[3664] 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 1 0 1 0 0 1 1 1 1 1 1 1 1 0 0
[3701] 0 0 1 0 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 0 0 1 0 1 0 1 0 0 0 0
[3738] 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
[3775] 0 0 0 0 0 1 1 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0
[3812] 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 0 1 1 0 1 1
[3849] 0 0 1 0 1 0 0 1 1 1 0 0 1 0 1 0 1 0 1 0 0 0 1 1 1 1
Levels: 0 1
$Predicted
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[38] 1 0 1 1 0 1 1 0 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0
[75] 1 0 0 1 1 1 0 0 0 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 1 1 0 0 1 1 1 0 1 0 0 1 0
[112] 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 0 0 0 1 0
[149] 1 0 1 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0 1 1 0 1 0 1 0 1 1 0 1 1 0 1 0 1 0
[186] 0 0 0 1 1 0 0 1 1 0 1 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 1 1 0 1 0 0 0 1 1 1 1
[223] 1 0 1 1 1 0 1 1 1 1 1 0 1 0 1 0 0 1 1 0 1 1 0 1 0 0 0 0 1 0 1 1 1 1 0 1 0
[260] 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 0 1 0
[297] 1 1 1 1 0 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 1
[334] 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 0 0 1 0 1 0 0 0 1
[371] 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 0 0 1 1 1
[408] 0 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 0 1 0 0 0 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1 1
[445] 1 0 0 1 0 1 0 0 1 0 1 0 1 0 1 1 0 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0 0 1 0 0 0
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[519] 1 1 0 0 0 1 0 1 0 1 0 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 1 1 1 1 1 0 1 1
[556] 0 0 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 1 0 1 0 0 1
[593] 0 1 1 1 0 0 1 1 0 0 0 0 1 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 1 1 0 1
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[667] 1 0 1 1 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 1 0 1 1 1
[704] 1 1 0 0 0 0 1 1 0 1 1 1 0 1 1 0 1 0 0 1 0 0 1 0 1 1 0 0 1 1 1 1 1 1 0 0 0
[741] 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 1
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[815] 1 0 1 1 1 1 0 1 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 1 1 1 1 1 0 1 1 0 0 1
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[889] 1 1 0 1 0 1 1 0 1 0 1 0 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 1 1 1 1 1 1
[926] 0 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 1 1 0 0 0 0 0 0 0
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[1037] 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 1 0 1 1 1 0 0 1 1 1 0 1 1 0 0 0 0 1
[1074] 1 1 0 1 1 1 1 0 1 1 0 0 1 1 0 1 1 0 1 0 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 0 1
[1111] 0 1 0 1 1 0 1 1 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 1
[1148] 0 1 1 0 0 1 0 1 1 1 0 0 0 0 1 0 1 0 1 0 0 1 0 1 0 0 0 1 1 0 0 0 0 0 1 0 1
[1185] 0 0 0 1 0 1 1 0 1 0 0 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 0 1 0 1 1 0 1
[1222] 1 1 1 1 0 1 1 1 0 0 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 1 1 1 0 1 1 0 0 1 1 0 1
[1259] 1 1 0 0 1 1 1 0 0 0 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 0 0 0 0 1 1 1 0 0 1 1
[1296] 1 0 1 0 0 1 0 1 0 0 1 1 0 0 1 1 0 0 1 0 0 1 0 1 1 1 0 1 1 1 0 0 1 1 1 0 0
[1333] 0 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 1 1
[1370] 0 0 1 0 0 0 0 1 1 1 1 0 0 1 0 1 0 1 0 0 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 0 1
[1407] 1 1 0 1 1 0 0 0 1 0 1 1 0 0 0 1 1 0 1 1 0 1 0 1 0 0 1 1 1 0 0 1 1 0 0 1 0
[1444] 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 0 0 1 1
[1481] 0 0 0 1 1 1 0 1 1 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 1 1 1 0 0 1 0 0 0
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[2776] 0 0 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 1 0 0 1 0
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[3812] 1 1 0 1 1 1 0 0 1 0 0 1 0 1 0 1 1 0 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 0 1 1
[3849] 1 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1
Levels: 0 1
$cut
[1] 0.3868537
LOGIT documentation built on May 29, 2017, 10:26 a.m.
R Package Documentation
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Description Usage Arguments Format Details Value Note Author(s) References See Also Examples
Description
Provides two options following the glm() function with binomial family. 1: Senstivity-specificity plot with optimal cut point statistic 2: ROC plot with Area Under Curve (AUC) statistic
Usage
1 |
Arguments
model |
model name |
fold |
number of k-folds |
type |
type of plot |
Format
- x
-
The function has three arguments: modelname, folds, type of plot
Details
ROCtest is a post-estimation function for logistic regression, following the use of glm(). Options to display a sensitivity-specificity plot or ROC curve are available.
Value
plot
Note
ROCtest() must be loaded into memory in order to be effectve. As a function in LOGIT, it is immediately available to a user.
Author(s)
Rafael de Souza, ELTE, Hungary, Joseph M. Hilbe, Arizona State University.
References
Hilbe, Joseph M. (2016), Practical Guide to Logistic Regression, Chapman & Hall/CRC. Hilbe, Joseph M. (2009), Logistic Regression Models, Chapman & Hall/CRC.
See Also
Examples
1 2 3 4 5 6 7 8 9 10 | library(MASS)
library(LOGIT)
data(R84)
R84$cage <- R84$age - mean(R84$age)
R84$cdoc <- R84$docvis - mean(R84$docvis)
mylogit <- glm(outwork ~ cdoc + female + kids + cage + factor(edlevel),
family=binomial, data=R84)
summary(mylogit)
ROCtest(mylogit, fold=10, type="Sensitivity")
ROCtest(mylogit, fold=10, type="ROC")
|
Example output
Call:
glm(formula = outwork ~ cdoc + female + kids + cage + factor(edlevel),
family = binomial, data = R84)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3615 -0.6807 -0.4320 0.8541 2.8220
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.022806 0.085790 -23.579 < 2e-16 ***
cdoc 0.024480 0.006255 3.914 9.08e-05 ***
female 2.268467 0.084039 26.993 < 2e-16 ***
kids 0.373053 0.090586 4.118 3.82e-05 ***
cage 0.054355 0.004207 12.921 < 2e-16 ***
factor(edlevel)2 0.009293 0.173172 0.054 0.957201
factor(edlevel)3 0.478016 0.153786 3.108 0.001882 **
factor(edlevel)4 -0.830282 0.214336 -3.874 0.000107 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 5091.1 on 3873 degrees of freedom
Residual deviance: 3889.9 on 3866 degrees of freedom
AIC: 3905.9
Number of Fisher Scoring iterations: 4
$plot
$Observed
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[3775] 1 0 0 1 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 1 0
[3812] 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 1 1 0 1 1 0
[3849] 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 1 1 0
Levels: 0 1
$Predicted
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Levels: 0 1
$cut
[1] 0.3801702
$plot
$Observed
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Levels: 0 1
$cut
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