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*Unverified author*

R Software Module

rwasp_logisticregression.wasp

Title produced by software

Bias-Reduced Logistic Regression

Date of computation

Sun, 25 Mar 2018 20:08:23 +0200

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Mar/25/t152200135767j514wzcqp6kvn.htm/, Retrieved Mon, 29 Apr 2024 13:48:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315015, Retrieved Mon, 29 Apr 2024 13:48:21 +0000

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148

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-       [Bias-Reduced Logistic Regression] [] [2018-03-25 18:08:23] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Department,Age,Gender,Monthly Income,Months At Company,JobSatisfaction,Satisfaction with Manager,Satisfaction with Company,Attrition
1,22,2,23972,6,3,1,1,0
2,38,1,20520,10,3,1,1,1
1,26,1,8360,0,2,1,1,1
2,35,1,11636,8,3,1,1,1
1,35,2,13872,2,2,1,1,0
1,0,2,12272,7,1,1,1,0
2,34,2,10680,1,4,1,1,0
1,20,2,10772,1,4,3,1,0
1,27,1,38104,9,4,1,2,1
3,14,1,20948,7,2,1,1,1
1,34,1,9704,5,4,1,1,1
2,34,1,16772,9,3,1,1,1
1,20,2,11644,5,2,1,1,0
1,39,2,10644,2,4,1,5,0
1,24,1,8112,4,2,1,1,0
3,35,1,39920,10,3,1,1,1
1,28,2,13192,6,4,4,1,0
3,0,2,11740,1,4,1,1,1
1,31,1,61708,25,4,1,1,0
1,0,1,15776,3,4,1,1,1
3,35,2,16044,4,2,1,1,0
3,34,2,13628,5,2,1,1,1
1,15,1,47976,12,3,1,1,1
2,28,2,4928,0,3,1,1,1
1,32,1,11840,4,1,3,1,0
1,38,1,76376,14,4,1,5,1
1,0,2,15676,10,3,1,1,0
2,21,2,27300,9,4,3,2,0
1,0,1,40992,22,3,1,1,1
1,0,2,75788,2,3,1,1,0
2,40,2,9984,1,3,1,1,0
2,0,1,25860,4,2,1,1,1
1,0,1,8824,10,4,1,1,1
3,26,2,8344,1,4,1,1,0
2,28,2,9172,2,2,1,1,0
2,42,2,10580,5,3,1,1,0
1,0,2,10732,3,3,1,1,1
1,21,2,8056,2,4,1,1,0
1,23,1,13676,1,3,2,1,0
1,26,1,21504,5,3,1,1,1
1,40,1,7804,1,1,1,1,0
3,27,1,9364,1,4,1,1,0
1,0,2,9172,1,1,1,1,0
3,33,1,34904,9,3,1,2,1
1,19,1,16044,12,3,1,1,1
1,0,2,78180,22,4,1,1,0
1,0,2,18272,9,2,1,1,0
1,0,1,12088,1,3,1,1,1
1,0,2,23088,9,2,2,1,0
1,18,1,9076,1,1,1,1,0
1,27,2,21524,1,2,4,1,0
1,21,2,13764,2,4,1,1,0
2,49,1,21816,4,3,1,1,1
3,29,1,39536,4,1,1,1,1
2,24,2,16628,2,4,1,1,0
2,0,2,53832,15,3,1,1,1
3,21,1,36276,9,3,1,1,1
1,28,2,16056,2,4,1,1,0
3,25,1,23660,7,2,1,2,1
1,31,2,23972,7,2,5,2,0
1,22,2,24648,9,1,1,1,0
2,38,1,9624,10,3,1,1,1
2,45,2,74960,27,2,1,1,0
1,24,2,30548,21,3,3,2,0
2,0,2,40384,17,2,1,1,0
1,0,2,59024,5,2,1,1,1
3,29,1,25996,6,1,1,1,1
1,22,2,38896,1,3,1,1,0
1,27,1,8776,3,2,4,2,1
1,26,2,13552,1,3,2,1,0
3,32,2,21892,4,2,1,1,0
1,26,1,10812,5,2,5,2,0
3,21,2,10004,1,2,1,1,0
1,26,2,24880,10,3,1,1,0
1,32,2,12152,1,1,1,1,1
1,25,2,17696,11,3,1,1,0
1,0,2,17248,15,3,1,1,0
1,0,2,52980,0,3,1,1,0
3,39,2,54656,5,4,1,2,1
1,30,1,20084,4,3,1,1,1
1,22,2,20504,10,4,1,1,0
1,29,2,11436,6,4,1,1,1
1,0,1,40956,1,1,1,1,1
2,28,2,21316,13,4,1,1,0
3,27,1,17300,5,4,1,1,1
1,33,1,29040,6,1,3,1,1
1,26,2,9288,0,3,1,3,0
1,0,2,8300,4,1,1,1,0
2,23,1,16608,11,2,3,2,1
1,24,2,38476,9,3,1,1,0
1,29,2,54012,22,4,1,1,0
1,20,2,21764,10,3,1,1,0
2,46,2,20836,11,4,1,1,0
1,26,2,42692,10,4,1,2,0
1,29,2,20040,11,4,1,1,0
1,0,2,54196,4,4,1,1,0
2,39,2,19996,3,2,1,1,0
2,23,2,16884,5,4,1,1,1
3,34,1,55488,37,1,1,1,1
3,34,2,8168,3,4,1,1,0
1,28,1,8292,3,2,1,1,0
1,0,2,11824,1,3,1,1,0
2,21,2,11704,1,4,1,1,0
1,33,2,19236,16,3,1,1,0
1,37,2,20652,1,2,2,1,0
1,28,2,75376,3,4,1,1,0
1,21,1,72688,8,3,1,1,1
1,0,2,22976,6,3,1,1,1
1,38,2,11556,2,4,1,1,0
1,0,1,11484,0,4,1,1,1
2,47,2,29936,13,4,1,1,0
1,26,1,24296,9,2,1,1,0
1,22,2,69312,5,3,1,1,0
1,20,1,11096,5,4,1,1,0
1,37,1,18020,1,3,1,1,0
1,21,2,29712,5,1,1,1,0
1,24,2,46524,11,4,1,1,0
3,29,2,38952,9,4,1,1,0
2,24,2,11340,1,1,1,1,0
1,32,1,67836,25,3,4,2,0
3,21,2,10452,10,1,2,1,0
1,0,2,24584,7,1,1,1,0
3,33,2,19852,5,4,1,1,0
3,33,1,78148,20,3,1,1,1
2,29,2,24688,7,3,1,1,0
1,28,2,9472,5,2,1,1,1
1,0,2,41248,40,4,1,1,0
1,24,2,6700,0,3,1,1,1
1,0,1,10092,2,4,1,1,1
1,45,2,26268,15,4,1,1,0
1,33,2,18956,3,1,1,1,0
1,20,2,36832,2,4,1,1,0
1,47,1,18236,2,2,1,1,0
3,29,1,32756,9,1,1,1,1
3,25,2,11768,8,3,1,1,0
3,23,2,19764,3,4,1,1,0
2,23,1,42600,4,2,1,2,1
2,37,2,23608,15,4,1,1,0
1,23,2,34556,2,3,1,1,0
2,24,2,25388,11,3,1,1,0
1,0,1,16800,5,4,1,2,0
1,22,1,13808,6,3,1,1,1
1,24,1,17268,3,3,1,1,1
1,27,2,10528,5,2,1,1,0
3,18,2,18672,8,4,1,1,0
3,19,2,12816,3,2,1,1,0
1,27,2,10880,5,4,1,1,1
1,25,1,68724,7,4,2,2,0
3,37,2,8952,5,1,1,2,0
3,42,2,5932,1,4,1,1,0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315015&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

0 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=315015&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315015&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 seconds
R Server	Big Analytics Cloud Computing Center

Parameters (Session):

Parameters (R input):

R code (references can be found in the software module):

library(brglm)
roc.plot <- function (sd, sdc, newplot = TRUE, ...)
{
sall <- sort(c(sd, sdc))
sens <- 0
specc <- 0
for (i in length(sall):1) {
sens <- c(sens, mean(sd >= sall[i], na.rm = T))
specc <- c(specc, mean(sdc >= sall[i], na.rm = T))
}
if (newplot) {
plot(specc, sens, xlim = c(0, 1), ylim = c(0, 1), type = 'l',
xlab = '1-specificity', ylab = 'sensitivity', main = 'ROC plot', ...)
abline(0, 1)
}
else lines(specc, sens, ...)
npoints <- length(sens)
area <- sum(0.5 * (sens[-1] + sens[-npoints]) * (specc[-1] -
specc[-npoints]))
lift <- (sens - specc)[-1]
cutoff <- sall[lift == max(lift)][1]
sensopt <- sens[-1][lift == max(lift)][1]
specopt <- 1 - specc[-1][lift == max(lift)][1]
list(area = area, cutoff = cutoff, sensopt = sensopt, specopt = specopt)
}
roc.analysis <- function (object, newdata = NULL, newplot = TRUE, ...)
{
if (is.null(newdata)) {
sd <- object$fitted[object$y == 1]
sdc <- object$fitted[object$y == 0]
}
else {
sd <- predict(object, newdata, type = 'response')[newdata$y ==
1]
sdc <- predict(object, newdata, type = 'response')[newdata$y ==
0]
}
roc.plot(sd, sdc, newplot, ...)
}
hosmerlem <- function (y, yhat, g = 10)
{
cutyhat <- cut(yhat, breaks = quantile(yhat, probs = seq(0,
1, 1/g)), include.lowest = T)
obs <- xtabs(cbind(1 - y, y) ~ cutyhat)
expect <- xtabs(cbind(1 - yhat, yhat) ~ cutyhat)
chisq <- sum((obs - expect)^2/expect)
P <- 1 - pchisq(chisq, g - 2)
c('X^2' = chisq, Df = g - 2, 'P(>Chi)' = P)
}
x <- as.data.frame(t(y))
r <- brglm(x)
summary(r)
rc <- summary(r)$coeff
try(hm <- hosmerlem(y[1,],r$fitted.values),silent=T)
try(hm,silent=T)
bitmap(file='test0.png')
ra <- roc.analysis(r)
dev.off()
te <- array(0,dim=c(2,99))
for (i in 1:99) {
threshold <- i / 100
numcorr1 <- 0
numfaul1 <- 0
numcorr0 <- 0
numfaul0 <- 0
for (j in 1:length(r$fitted.values)) {
if (y[1,j] > 0.99) {
if (r$fitted.values[j] >= threshold) numcorr1 = numcorr1 + 1 else numfaul1 = numfaul1 + 1
} else {
if (r$fitted.values[j] < threshold) numcorr0 = numcorr0 + 1 else numfaul0 = numfaul0 + 1
}
}
te[1,i] <- numfaul1 / (numfaul1 + numcorr1)
te[2,i] <- numfaul0 / (numfaul0 + numcorr0)
}
bitmap(file='test1.png')
op <- par(mfrow=c(2,2))
plot((1:99)/100,te[1,],xlab='Threshold',ylab='Type I error', main='1 - Specificity')
plot((1:99)/100,te[2,],xlab='Threshold',ylab='Type II error', main='1 - Sensitivity')
plot(te[1,],te[2,],xlab='Type I error',ylab='Type II error', main='(1-Sens.) vs (1-Spec.)')
plot((1:99)/100,te[1,]+te[2,],xlab='Threshold',ylab='Sum of Type I & II error', main='(1-Sens.) + (1-Spec.)')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Coefficients of Bias-Reduced Logistic Regression',5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.E.',header=TRUE)
a<-table.element(a,'t-stat',header=TRUE)
a<-table.element(a,'2-sided p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(rc[,1])) {
a<-table.row.start(a)
a<-table.element(a,labels(rc)[[1]][i],header=TRUE)
a<-table.element(a,rc[i,1])
a<-table.element(a,rc[i,2])
a<-table.element(a,rc[i,3])
a<-table.element(a,2*(1-pt(abs(rc[i,3]),r$df.residual)))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of Bias-Reduced Logistic Regression',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Deviance',1,TRUE)
a<-table.element(a,r$deviance)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Penalized deviance',1,TRUE)
a<-table.element(a,r$penalized.deviance)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Residual Degrees of Freedom',1,TRUE)
a<-table.element(a,r$df.residual)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'ROC Area',1,TRUE)
a<-table.element(a,ra$area)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Hosmer–Lemeshow test',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Chi-square',1,TRUE)
phm <- array('NA',dim=3)
for (i in 1:3) { try(phm[i] <- hm[i],silent=T) }
a<-table.element(a,phm[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degrees of Freedom',1,TRUE)
a<-table.element(a,phm[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'P(>Chi)',1,TRUE)
a<-table.element(a,phm[3])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Fit of Logistic Regression',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Index',1,TRUE)
a<-table.element(a,'Actual',1,TRUE)
a<-table.element(a,'Fitted',1,TRUE)
a<-table.element(a,'Error',1,TRUE)
a<-table.row.end(a)
for (i in 1:length(r$fitted.values)) {
a<-table.row.start(a)
a<-table.element(a,i,1,TRUE)
a<-table.element(a,y[1,i])
a<-table.element(a,r$fitted.values[i])
a<-table.element(a,y[1,i]-r$fitted.values[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Type I & II errors for various threshold values',3,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Threshold',1,TRUE)
a<-table.element(a,'Type I',1,TRUE)
a<-table.element(a,'Type II',1,TRUE)
a<-table.row.end(a)
for (i in 1:99) {
a<-table.row.start(a)
a<-table.element(a,i/100,1,TRUE)
a<-table.element(a,te[1,i])
a<-table.element(a,te[2,i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable3.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code