R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
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(89,0,0,0,113,0,0,1,96,1,1,0,81,1,1,0,142,0,1,0,97,1,0,0,110,0,0,1,101,0,0,1,135,0,0,0,82,0,1,0,103,0,0,1,79,1,0,0,98,1,0,0,104,1,0,1,108,0,0,1,122,1,1,0,87,1,0,0,111,1,1,0,93,0,0,0,109,0,0,1,117,0,0,0,99,1,0,1,115,1,0,0,145,0,1,0,113,1,1,0,94,0,1,0,119,1,0,0,92,0,0,1,95,0,0,1,104,1,0,0,115,1,0,1,128,0,0,0,131,0,1,0,110,0,0,1,94,1,0,0,97,1,1,0,99,0,1,0,120,1,0,0,128,0,1,0,118,1,0,1),dim=c(4,40),dimnames=list(c('IQ','geslacht','gewest1','gewest2'),1:40))
> y <- array(NA,dim=c(4,40),dimnames=list(c('IQ','geslacht','gewest1','gewest2'),1:40))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'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: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
IQ geslacht gewest1 gewest2
1 89 0 0 0
2 113 0 0 1
3 96 1 1 0
4 81 1 1 0
5 142 0 1 0
6 97 1 0 0
7 110 0 0 1
8 101 0 0 1
9 135 0 0 0
10 82 0 1 0
11 103 0 0 1
12 79 1 0 0
13 98 1 0 0
14 104 1 0 1
15 108 0 0 1
16 122 1 1 0
17 87 1 0 0
18 111 1 1 0
19 93 0 0 0
20 109 0 0 1
21 117 0 0 0
22 99 1 0 1
23 115 1 0 0
24 145 0 1 0
25 113 1 1 0
26 94 0 1 0
27 119 1 0 0
28 92 0 0 1
29 95 0 0 1
30 104 1 0 0
31 115 1 0 1
32 128 0 0 0
33 131 0 1 0
34 110 0 0 1
35 94 1 0 0
36 97 1 1 0
37 99 0 1 0
38 120 1 0 0
39 128 0 1 0
40 118 1 0 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) geslacht gewest1 gewest2
110.100 -7.378 4.151 -1.907
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-32.251 -11.453 1.042 13.858 30.749
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 110.100 5.532 19.903 <2e-16 ***
geslacht -7.378 5.344 -1.381 0.176
gewest1 4.151 6.323 0.657 0.516
gewest2 -1.907 6.500 -0.293 0.771
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 16.22 on 36 degrees of freedom
Multiple R-squared: 0.07268, Adjusted R-squared: -0.004599
F-statistic: 0.9405 on 3 and 36 DF, p-value: 0.4312
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.7874891 0.4250218 0.21251090
[2,] 0.6792865 0.6414271 0.32071354
[3,] 0.7580101 0.4839799 0.24198995
[4,] 0.9483939 0.1032122 0.05160611
[5,] 0.9105307 0.1789385 0.08946927
[6,] 0.9160613 0.1678773 0.08393865
[7,] 0.8794823 0.2410353 0.12051766
[8,] 0.8460346 0.3079308 0.15396541
[9,] 0.7741988 0.4516024 0.22580118
[10,] 0.8061184 0.3877633 0.19388165
[11,] 0.7993657 0.4012687 0.20063434
[12,] 0.7377455 0.5245090 0.26225450
[13,] 0.7508627 0.4982746 0.24913729
[14,] 0.6635112 0.6729776 0.33648879
[15,] 0.5923485 0.8153030 0.40765152
[16,] 0.4945889 0.9891778 0.50541109
[17,] 0.4560026 0.9120052 0.54399742
[18,] 0.6981691 0.6036618 0.30183089
[19,] 0.6085213 0.7829575 0.39147874
[20,] 0.6573719 0.6852561 0.34262807
[21,] 0.6096714 0.7806572 0.39032862
[22,] 0.6222572 0.7554857 0.37774284
[23,] 0.7006361 0.5987277 0.29936387
[24,] 0.5870177 0.8259647 0.41298234
[25,] 0.4832227 0.9664454 0.51677731
[26,] 0.3807809 0.7615617 0.61921913
[27,] 0.3671387 0.7342773 0.63286133
> postscript(file="/var/fisher/rcomp/tmp/1efv51355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/20e321355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3eoff1355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4pg2j1355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5s7551355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 40
Frequency = 1
1 2 3 4 5 6 7
-21.100179 4.806752 -10.873354 -25.873354 27.748590 -5.722123 1.806752
8 9 10 11 12 13 14
-7.193248 24.899821 -32.251410 -5.193248 -23.722123 -4.722123 3.184808
15 16 17 18 19 20 21
-0.193248 15.126646 -15.722123 4.126646 -17.100179 0.806752 6.899821
22 23 24 25 26 27 28
-1.815192 12.277877 30.748590 6.126646 -20.251410 16.277877 -16.193248
29 30 31 32 33 34 35
-13.193248 1.277877 14.184808 17.899821 16.748590 1.806752 -8.722123
36 37 38 39 40
-9.873354 -15.251410 17.277877 13.748590 17.184808
> postscript(file="/var/fisher/rcomp/tmp/6bttx1355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 40
Frequency = 1
lag(myerror, k = 1) myerror
0 -21.100179 NA
1 4.806752 -21.100179
2 -10.873354 4.806752
3 -25.873354 -10.873354
4 27.748590 -25.873354
5 -5.722123 27.748590
6 1.806752 -5.722123
7 -7.193248 1.806752
8 24.899821 -7.193248
9 -32.251410 24.899821
10 -5.193248 -32.251410
11 -23.722123 -5.193248
12 -4.722123 -23.722123
13 3.184808 -4.722123
14 -0.193248 3.184808
15 15.126646 -0.193248
16 -15.722123 15.126646
17 4.126646 -15.722123
18 -17.100179 4.126646
19 0.806752 -17.100179
20 6.899821 0.806752
21 -1.815192 6.899821
22 12.277877 -1.815192
23 30.748590 12.277877
24 6.126646 30.748590
25 -20.251410 6.126646
26 16.277877 -20.251410
27 -16.193248 16.277877
28 -13.193248 -16.193248
29 1.277877 -13.193248
30 14.184808 1.277877
31 17.899821 14.184808
32 16.748590 17.899821
33 1.806752 16.748590
34 -8.722123 1.806752
35 -9.873354 -8.722123
36 -15.251410 -9.873354
37 17.277877 -15.251410
38 13.748590 17.277877
39 17.184808 13.748590
40 NA 17.184808
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.806752 -21.100179
[2,] -10.873354 4.806752
[3,] -25.873354 -10.873354
[4,] 27.748590 -25.873354
[5,] -5.722123 27.748590
[6,] 1.806752 -5.722123
[7,] -7.193248 1.806752
[8,] 24.899821 -7.193248
[9,] -32.251410 24.899821
[10,] -5.193248 -32.251410
[11,] -23.722123 -5.193248
[12,] -4.722123 -23.722123
[13,] 3.184808 -4.722123
[14,] -0.193248 3.184808
[15,] 15.126646 -0.193248
[16,] -15.722123 15.126646
[17,] 4.126646 -15.722123
[18,] -17.100179 4.126646
[19,] 0.806752 -17.100179
[20,] 6.899821 0.806752
[21,] -1.815192 6.899821
[22,] 12.277877 -1.815192
[23,] 30.748590 12.277877
[24,] 6.126646 30.748590
[25,] -20.251410 6.126646
[26,] 16.277877 -20.251410
[27,] -16.193248 16.277877
[28,] -13.193248 -16.193248
[29,] 1.277877 -13.193248
[30,] 14.184808 1.277877
[31,] 17.899821 14.184808
[32,] 16.748590 17.899821
[33,] 1.806752 16.748590
[34,] -8.722123 1.806752
[35,] -9.873354 -8.722123
[36,] -15.251410 -9.873354
[37,] 17.277877 -15.251410
[38,] 13.748590 17.277877
[39,] 17.184808 13.748590
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.806752 -21.100179
2 -10.873354 4.806752
3 -25.873354 -10.873354
4 27.748590 -25.873354
5 -5.722123 27.748590
6 1.806752 -5.722123
7 -7.193248 1.806752
8 24.899821 -7.193248
9 -32.251410 24.899821
10 -5.193248 -32.251410
11 -23.722123 -5.193248
12 -4.722123 -23.722123
13 3.184808 -4.722123
14 -0.193248 3.184808
15 15.126646 -0.193248
16 -15.722123 15.126646
17 4.126646 -15.722123
18 -17.100179 4.126646
19 0.806752 -17.100179
20 6.899821 0.806752
21 -1.815192 6.899821
22 12.277877 -1.815192
23 30.748590 12.277877
24 6.126646 30.748590
25 -20.251410 6.126646
26 16.277877 -20.251410
27 -16.193248 16.277877
28 -13.193248 -16.193248
29 1.277877 -13.193248
30 14.184808 1.277877
31 17.899821 14.184808
32 16.748590 17.899821
33 1.806752 16.748590
34 -8.722123 1.806752
35 -9.873354 -8.722123
36 -15.251410 -9.873354
37 17.277877 -15.251410
38 13.748590 17.277877
39 17.184808 13.748590
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7wpjt1355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8789s1355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9p0gf1355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/1057ec1355173801.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11jyvt1355173801.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, 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.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12ogfp1355173801.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13axio1355173801.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14xmiq1355173802.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15bcct1355173802.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16duc51355173802.tab")
+ }
>
> try(system("convert tmp/1efv51355173801.ps tmp/1efv51355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/20e321355173801.ps tmp/20e321355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/3eoff1355173801.ps tmp/3eoff1355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pg2j1355173801.ps tmp/4pg2j1355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/5s7551355173801.ps tmp/5s7551355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/6bttx1355173801.ps tmp/6bttx1355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wpjt1355173801.ps tmp/7wpjt1355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/8789s1355173801.ps tmp/8789s1355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/9p0gf1355173801.ps tmp/9p0gf1355173801.png",intern=TRUE))
character(0)
> try(system("convert tmp/1057ec1355173801.ps tmp/1057ec1355173801.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.205 1.884 9.090