R version 3.0.1 (2013-05-16) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- array(list(0
+ ,-11.15
+ ,11.67
+ ,2.14
+ ,1.31
+ ,-1.45
+ ,-1.45
+ ,-1.67
+ ,-1.67
+ ,-3.57
+ ,-1.67
+ ,-4.81
+ ,-5.54
+ ,6.50
+ ,3.40
+ ,0
+ ,3.61
+ ,13.67
+ ,2.14
+ ,-0.33
+ ,-1.45
+ ,-1.45
+ ,-2.04
+ ,-2.04
+ ,-2.14
+ ,-1.30
+ ,-5.19
+ ,-5.54
+ ,1.33
+ ,3.40
+ ,1
+ ,18.36
+ ,13.00
+ ,3.93
+ ,0.98
+ ,-1.64
+ ,-1.64
+ ,-2.04
+ ,-2.04
+ ,-2.14
+ ,0.00
+ ,0.00
+ ,-6.00
+ ,4.67
+ ,3.96
+ ,1
+ ,19.34
+ ,6.83
+ ,3.93
+ ,0.82
+ ,-1.45
+ ,-1.45
+ ,-1.85
+ ,-1.85
+ ,3.39
+ ,0.74
+ ,-1.11
+ ,-2.31
+ ,6.00
+ ,3.77
+ ,1
+ ,-8.81
+ ,2.76
+ ,0.73
+ ,-5.18
+ ,1.43
+ ,1.43
+ ,-4.75
+ ,-4.75
+ ,4.29
+ ,-2.04
+ ,3.73
+ ,1.31
+ ,-4.91
+ ,-2.83
+ ,1
+ ,-4.41
+ ,1.72
+ ,0.73
+ ,-5.89
+ ,5.00
+ ,5.00
+ ,1.36
+ ,1.36
+ ,5.18
+ ,-2.04
+ ,2.16
+ ,-0.16
+ ,-4.91
+ ,-2.83
+ ,0
+ ,-4.07
+ ,6.03
+ ,0.73
+ ,-7.50
+ ,3.21
+ ,3.21
+ ,-1.69
+ ,-1.69
+ ,6.07
+ ,-2.04
+ ,1.18
+ ,-0.98
+ ,-2.98
+ ,-2.83
+ ,1
+ ,0.68
+ ,7.41
+ ,11.45
+ ,-4.46
+ ,3.57
+ ,3.57
+ ,-2.71
+ ,-2.71
+ ,6.25
+ ,-0.56
+ ,2.94
+ ,4.59
+ ,-2.46
+ ,-5.83
+ ,1
+ ,11.86
+ ,4.41
+ ,5.93
+ ,0.00
+ ,6.17
+ ,6.17
+ ,-2.42
+ ,-2.42
+ ,1.61
+ ,-1.48
+ ,-0.52
+ ,0.57
+ ,9.50
+ ,2.30
+ ,1
+ ,11.69
+ ,6.61
+ ,2.41
+ ,0.00
+ ,6.17
+ ,6.17
+ ,-3.06
+ ,-3.06
+ ,0.18
+ ,-2.22
+ ,-0.52
+ ,0.75
+ ,5.17
+ ,0.16
+ ,1
+ ,13.05
+ ,5.76
+ ,6.67
+ ,-0.68
+ ,4.50
+ ,4.50
+ ,-4.03
+ ,-4.03
+ ,1.43
+ ,-2.22
+ ,-1.03
+ ,1.70
+ ,5.33
+ ,-1.48
+ ,1
+ ,13.39
+ ,4.24
+ ,7.78
+ ,4.24
+ ,8.50
+ ,8.50
+ ,-5.16
+ ,-5.16
+ ,1.07
+ ,-2.22
+ ,-2.76
+ ,1.70
+ ,5.33
+ ,-0.98
+ ,0
+ ,9.83
+ ,-1.43
+ ,4.07
+ ,-1.13
+ ,-6.10
+ ,-6.10
+ ,6.15
+ ,6.15
+ ,-1.61
+ ,1.70
+ ,-3.75
+ ,-22.26
+ ,-1.17
+ ,-4.26
+ ,0
+ ,7.12
+ ,2.14
+ ,7.29
+ ,-1.13
+ ,-3.05
+ ,-3.05
+ ,-0.96
+ ,-0.96
+ ,-3.21
+ ,-0.38
+ ,-3.75
+ ,-15.28
+ ,-1.17
+ ,-4.26
+ ,0
+ ,9.83
+ ,3.93
+ ,7.97
+ ,1.13
+ ,-5.08
+ ,-5.08
+ ,-6.35
+ ,-6.35
+ ,-3.21
+ ,-0.75
+ ,-1.96
+ ,-15.09
+ ,-1.17
+ ,-3.15
+ ,0
+ ,11.36
+ ,3.93
+ ,4.07
+ ,3.06
+ ,-2.37
+ ,-2.37
+ ,-4.42
+ ,-4.42
+ ,-2.32
+ ,-1.32
+ ,-4.11
+ ,-5.85
+ ,-1.17
+ ,-1.48
+ ,0
+ ,5.42
+ ,-11.19
+ ,2.20
+ ,-2.78
+ ,-1.75
+ ,-1.75
+ ,0.18
+ ,0.18
+ ,13.75
+ ,-10.20
+ ,0.57
+ ,-2.60
+ ,0.51
+ ,-5.65
+ ,1
+ ,5.76
+ ,-12.37
+ ,2.71
+ ,-2.04
+ ,-2.81
+ ,-2.81
+ ,0.09
+ ,0.09
+ ,14.29
+ ,-7.84
+ ,1.89
+ ,-4.00
+ ,0.17
+ ,-5.65
+ ,0
+ ,7.63
+ ,-12.37
+ ,0.51
+ ,-0.56
+ ,-1.40
+ ,-1.40
+ ,-0.42
+ ,-0.42
+ ,18.39
+ ,-7.84
+ ,2.45
+ ,0.20
+ ,2.20
+ ,-2.74
+ ,0
+ ,6.44
+ ,-10.68
+ ,-0.17
+ ,-5.00
+ ,-0.70
+ ,-0.70
+ ,0.09
+ ,0.09
+ ,16.96
+ ,-0.98
+ ,3.96
+ ,-4.00
+ ,3.05
+ ,-2.42
+ ,1
+ ,-6.95
+ ,2.78
+ ,4.81
+ ,1.30
+ ,4.63
+ ,4.63
+ ,-6.96
+ ,-6.96
+ ,15.89
+ ,-4.31
+ ,-1.13
+ ,2.50
+ ,-2.54
+ ,-8.27
+ ,0
+ ,-4.92
+ ,2.78
+ ,5.37
+ ,1.48
+ ,5.56
+ ,5.56
+ ,-5.29
+ ,-5.29
+ ,15.18
+ ,-4.31
+ ,-1.13
+ ,-3.14
+ ,-1.69
+ ,-7.88
+ ,1
+ ,2.71
+ ,0.56
+ ,5.00
+ ,2.22
+ ,5.56
+ ,5.56
+ ,-5.29
+ ,-5.29
+ ,9.64
+ ,-6.08
+ ,-0.94
+ ,-1.18
+ ,-2.88
+ ,-5.96
+ ,0
+ ,-0.17
+ ,-0.19
+ ,5.37
+ ,2.78
+ ,6.48
+ ,6.48
+ ,-3.33
+ ,-3.33
+ ,10.71
+ ,-3.92
+ ,-0.38
+ ,-0.20
+ ,-3.56
+ ,-5.96)
+ ,dim=c(15
+ ,24)
+ ,dimnames=list(c('Y'
+ ,'X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5'
+ ,'X6'
+ ,'X7'
+ ,'X8'
+ ,'X9'
+ ,'X10'
+ ,'X11'
+ ,'X12'
+ ,'X13'
+ ,'X14')
+ ,1:24))
>  y <- array(NA,dim=c(15,24),dimnames=list(c('Y','X1','X2','X3','X4','X5','X6','X7','X8','X9','X10','X11','X12','X13','X14'),1:24))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: 
> library(brglm)
Loading required package: profileModel
> roc.plot <- function (sd, sdc, newplot = TRUE, ...)
+ {
+ sall <- sort(c(sd, sdc))
+ sens <- 0
+ specc <- 0
+ for (i in length(sall):1) {
+ sens <- c(sens, mean(sd >= sall[i], na.rm = T))
+ specc <- c(specc, mean(sdc >= sall[i], na.rm = T))
+ }
+ if (newplot) {
+ plot(specc, sens, xlim = c(0, 1), ylim = c(0, 1), type = 'l',
+ xlab = '1-specificity', ylab = 'sensitivity', main = 'ROC plot', ...)
+ abline(0, 1)
+ }
+ else lines(specc, sens, ...)
+ npoints <- length(sens)
+ area <- sum(0.5 * (sens[-1] + sens[-npoints]) * (specc[-1] -
+ specc[-npoints]))
+ lift <- (sens - specc)[-1]
+ cutoff <- sall[lift == max(lift)][1]
+ sensopt <- sens[-1][lift == max(lift)][1]
+ specopt <- 1 - specc[-1][lift == max(lift)][1]
+ list(area = area, cutoff = cutoff, sensopt = sensopt, specopt = specopt)
+ }
> roc.analysis <- function (object, newdata = NULL, newplot = TRUE, ...)
+ {
+ if (is.null(newdata)) {
+ sd <- object$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)

Call:
brglm(formula = x)


Coefficients: (2 not defined because of singularities)
              Estimate Std. Error z value Pr(>|z|)
(Intercept)  1.7551958  2.7725285   0.633    0.527
X1           0.0622621  0.1029934   0.605    0.545
X2           0.1562897  0.2330700   0.671    0.502
X3          -0.2944653  0.4875756  -0.604    0.546
X4           0.2878921  0.3924378   0.734    0.463
X5          -0.0529684  0.2496399  -0.212    0.832
X6                  NA         NA      NA       NA
X7           0.1668991  0.3198759   0.522    0.602
X8                  NA         NA      NA       NA
X9          -0.2326086  0.2252439  -1.033    0.302
X10         -0.0006833  0.3209320  -0.002    0.998
X11          0.5857739  0.5779303   1.014    0.311
X12          0.1912144  0.2366271   0.808    0.419
X13          0.2783537  0.3535424   0.787    0.431
X14         -0.6431646  0.7419592  -0.867    0.386

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 17.246  on 23  degrees of freedom
Residual deviance: 18.316  on 11  degrees of freedom
Penalized deviance: -23.45013 
AIC:  44.316 

> rc <- summary(r)$coeff
> try(hm <- hosmerlem(y[1,],r$fitted.values),silent=T)
> try(hm,silent=T)
      X^2        Df   P(>Chi) 
7.0179696 8.0000000 0.5346951 
> postscript(file="/var/fisher/rcomp/tmp/13yjw1377767515.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> ra <- roc.analysis(r)
> dev.off()
null device 
          1 
> 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$fitted.values[j] < threshold) numcorr0 = numcorr0 + 1 else numfaul0 = numfaul0 + 1
+ }
+ }
+ te[1,i] <- numfaul1 / (numfaul1 + numcorr1)
+ te[2,i] <- numfaul0 / (numfaul0 + numcorr0)
+ }
> postscript(file="/var/fisher/rcomp/tmp/28mzm1377767515.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,2))
> plot((1:99)/100,te[1,],xlab='Threshold',ylab='Type I error', main='1 - Specificity')
> plot((1:99)/100,te[2,],xlab='Threshold',ylab='Type II error', main='1 - Sensitivity')
> plot(te[1,],te[2,],xlab='Type I error',ylab='Type II error', main='(1-Sens.) vs (1-Spec.)')
> plot((1:99)/100,te[1,]+te[2,],xlab='Threshold',ylab='Sum of Type I & II error', main='(1-Sens.) + (1-Spec.)')
> par(op)
> dev.off()
null device 
          1 
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Coefficients of Bias-Reduced Logistic Regression',5,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.E.',header=TRUE)
> a<-table.element(a,'t-stat',header=TRUE)
> a<-table.element(a,'2-sided p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:length(rc[,1])) {
+ a<-table.row.start(a)
+ a<-table.element(a,labels(rc)[[1]][i],header=TRUE)
+ a<-table.element(a,rc[i,1])
+ a<-table.element(a,rc[i,2])
+ a<-table.element(a,rc[i,3])
+ a<-table.element(a,2*(1-pt(abs(rc[i,3]),r$df.residual)))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/3wzsf1377767515.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="/var/fisher/rcomp/tmp/43gec1377767516.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="/var/fisher/rcomp/tmp/52kdg1377767516.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="/var/fisher/rcomp/tmp/6f44f1377767516.tab") 
> 
> try(system("convert tmp/13yjw1377767515.ps tmp/13yjw1377767515.png",intern=TRUE))
character(0)
> try(system("convert tmp/28mzm1377767515.ps tmp/28mzm1377767515.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.587   0.454   2.021