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(102,0,0,0,120,0,1,0,98,1,1,0,99,0,0,0,98,1,0,1,105,1,1,0,100,1,0,1,97,0,1,0,89,1,0,0,111,0,0,0,122,1,1,0,123,0,0,1,126,1,1,0,107,1,0,0,94,0,1,0,100,0,0,1,108,0,0,0,109,1,1,0,115,0,0,1,95,1,0,1,89,0,0,0,116,1,1,0,120,1,0,1,114,1,0,1,110,0,1,0,125,1,1,0,97,1,0,0,102,0,1,0,100,1,0,1,101,0,0,1,116,0,1,0,126,1,0,1,99,1,0,0,94,0,0,1,104,1,0,1,122,0,0,0,130,1,1,0,104,0,1,0,95,0,0,0,112,1,1,0),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
1 102 0 0 0
2 120 0 1 0
3 98 1 1 0
4 99 0 0 0
5 98 1 0 1
6 105 1 1 0
7 100 1 0 1
8 97 0 1 0
9 89 1 0 0
10 111 0 0 0
11 122 1 1 0
12 123 0 0 1
13 126 1 1 0
14 107 1 0 0
15 94 0 1 0
16 100 0 0 1
17 108 0 0 0
18 109 1 1 0
19 115 0 0 1
20 95 1 0 1
21 89 0 0 0
22 116 1 1 0
23 120 1 0 1
24 114 1 0 1
25 110 0 1 0
26 125 1 1 0
27 97 1 0 0
28 102 0 1 0
29 100 1 0 1
30 101 0 0 1
31 116 0 1 0
32 126 1 0 1
33 99 1 0 0
34 94 0 0 1
35 104 1 0 1
36 122 0 0 0
37 130 1 1 0
38 104 0 1 0
39 95 0 0 0
40 112 1 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Geslacht Gewest1 Gewest2
100.668 2.662 9.459 4.617
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-16.128 -7.947 -2.729 9.337 21.331
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.668 3.513 28.657 <2e-16 ***
Geslacht 2.662 3.511 0.758 0.4533
Gewest1 9.459 4.308 2.196 0.0347 *
Gewest2 4.617 4.534 1.018 0.3153
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 10.85 on 36 degrees of freedom
Multiple R-squared: 0.1454, Adjusted R-squared: 0.07418
F-statistic: 2.042 on 3 and 36 DF, p-value: 0.1254
> 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,] 0.03152028 0.06304056 0.9684797
[2,] 0.33878141 0.67756281 0.6612186
[3,] 0.24400416 0.48800832 0.7559958
[4,] 0.24891917 0.49783833 0.7510808
[5,] 0.47685078 0.95370156 0.5231492
[6,] 0.57134597 0.85730806 0.4286540
[7,] 0.71649929 0.56700142 0.2835007
[8,] 0.67191281 0.65617438 0.3280872
[9,] 0.78066689 0.43866622 0.2193331
[10,] 0.71766779 0.56466442 0.2823322
[11,] 0.66118905 0.67762190 0.3388110
[12,] 0.58504016 0.82991968 0.4149598
[13,] 0.57413855 0.85172290 0.4258615
[14,] 0.60347812 0.79304376 0.3965219
[15,] 0.60706966 0.78586068 0.3929303
[16,] 0.52926563 0.94146874 0.4707344
[17,] 0.54226340 0.91547320 0.4577366
[18,] 0.46495869 0.92991737 0.5350413
[19,] 0.36179906 0.72359812 0.6382009
[20,] 0.34411788 0.68823577 0.6558821
[21,] 0.30866784 0.61733567 0.6913322
[22,] 0.26256055 0.52512110 0.7374394
[23,] 0.21885863 0.43771725 0.7811414
[24,] 0.14303680 0.28607359 0.8569632
[25,] 0.09247394 0.18494788 0.9075261
[26,] 0.17972204 0.35944408 0.8202780
[27,] 0.20438781 0.40877562 0.7956122
> postscript(file="/var/fisher/rcomp/tmp/1y1bm1356127333.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/275gb1356127333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3y67k1356127333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/49xo71356127333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5drmw1356127333.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
1.3315046 9.8721712 -14.7894665 -1.6684954 -9.9467837 -7.7894665
7 8 9 10 11 12
-7.9467837 -13.1278288 -14.3301330 10.3315046 9.2105335 17.7148539
13 14 15 16 17 18
13.2105335 3.6698670 -16.1278288 -5.2851461 7.3315046 -3.7894665
19 20 21 22 23 24
9.7148539 -12.9467837 -11.6684954 3.2105335 12.0532163 6.0532163
25 26 27 28 29 30
-0.1278288 12.2105335 -6.3301330 -8.1278288 -7.9467837 -4.2851461
31 32 33 34 35 36
5.8721712 18.0532163 -4.3301330 -11.2851461 -3.9467837 21.3315046
37 38 39 40
17.2105335 -6.1278288 -5.6684954 -0.7894665
> postscript(file="/var/fisher/rcomp/tmp/6q8ka1356127333.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 1.3315046 NA
1 9.8721712 1.3315046
2 -14.7894665 9.8721712
3 -1.6684954 -14.7894665
4 -9.9467837 -1.6684954
5 -7.7894665 -9.9467837
6 -7.9467837 -7.7894665
7 -13.1278288 -7.9467837
8 -14.3301330 -13.1278288
9 10.3315046 -14.3301330
10 9.2105335 10.3315046
11 17.7148539 9.2105335
12 13.2105335 17.7148539
13 3.6698670 13.2105335
14 -16.1278288 3.6698670
15 -5.2851461 -16.1278288
16 7.3315046 -5.2851461
17 -3.7894665 7.3315046
18 9.7148539 -3.7894665
19 -12.9467837 9.7148539
20 -11.6684954 -12.9467837
21 3.2105335 -11.6684954
22 12.0532163 3.2105335
23 6.0532163 12.0532163
24 -0.1278288 6.0532163
25 12.2105335 -0.1278288
26 -6.3301330 12.2105335
27 -8.1278288 -6.3301330
28 -7.9467837 -8.1278288
29 -4.2851461 -7.9467837
30 5.8721712 -4.2851461
31 18.0532163 5.8721712
32 -4.3301330 18.0532163
33 -11.2851461 -4.3301330
34 -3.9467837 -11.2851461
35 21.3315046 -3.9467837
36 17.2105335 21.3315046
37 -6.1278288 17.2105335
38 -5.6684954 -6.1278288
39 -0.7894665 -5.6684954
40 NA -0.7894665
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 9.8721712 1.3315046
[2,] -14.7894665 9.8721712
[3,] -1.6684954 -14.7894665
[4,] -9.9467837 -1.6684954
[5,] -7.7894665 -9.9467837
[6,] -7.9467837 -7.7894665
[7,] -13.1278288 -7.9467837
[8,] -14.3301330 -13.1278288
[9,] 10.3315046 -14.3301330
[10,] 9.2105335 10.3315046
[11,] 17.7148539 9.2105335
[12,] 13.2105335 17.7148539
[13,] 3.6698670 13.2105335
[14,] -16.1278288 3.6698670
[15,] -5.2851461 -16.1278288
[16,] 7.3315046 -5.2851461
[17,] -3.7894665 7.3315046
[18,] 9.7148539 -3.7894665
[19,] -12.9467837 9.7148539
[20,] -11.6684954 -12.9467837
[21,] 3.2105335 -11.6684954
[22,] 12.0532163 3.2105335
[23,] 6.0532163 12.0532163
[24,] -0.1278288 6.0532163
[25,] 12.2105335 -0.1278288
[26,] -6.3301330 12.2105335
[27,] -8.1278288 -6.3301330
[28,] -7.9467837 -8.1278288
[29,] -4.2851461 -7.9467837
[30,] 5.8721712 -4.2851461
[31,] 18.0532163 5.8721712
[32,] -4.3301330 18.0532163
[33,] -11.2851461 -4.3301330
[34,] -3.9467837 -11.2851461
[35,] 21.3315046 -3.9467837
[36,] 17.2105335 21.3315046
[37,] -6.1278288 17.2105335
[38,] -5.6684954 -6.1278288
[39,] -0.7894665 -5.6684954
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 9.8721712 1.3315046
2 -14.7894665 9.8721712
3 -1.6684954 -14.7894665
4 -9.9467837 -1.6684954
5 -7.7894665 -9.9467837
6 -7.9467837 -7.7894665
7 -13.1278288 -7.9467837
8 -14.3301330 -13.1278288
9 10.3315046 -14.3301330
10 9.2105335 10.3315046
11 17.7148539 9.2105335
12 13.2105335 17.7148539
13 3.6698670 13.2105335
14 -16.1278288 3.6698670
15 -5.2851461 -16.1278288
16 7.3315046 -5.2851461
17 -3.7894665 7.3315046
18 9.7148539 -3.7894665
19 -12.9467837 9.7148539
20 -11.6684954 -12.9467837
21 3.2105335 -11.6684954
22 12.0532163 3.2105335
23 6.0532163 12.0532163
24 -0.1278288 6.0532163
25 12.2105335 -0.1278288
26 -6.3301330 12.2105335
27 -8.1278288 -6.3301330
28 -7.9467837 -8.1278288
29 -4.2851461 -7.9467837
30 5.8721712 -4.2851461
31 18.0532163 5.8721712
32 -4.3301330 18.0532163
33 -11.2851461 -4.3301330
34 -3.9467837 -11.2851461
35 21.3315046 -3.9467837
36 17.2105335 21.3315046
37 -6.1278288 17.2105335
38 -5.6684954 -6.1278288
39 -0.7894665 -5.6684954
> 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/7lhy21356127333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8vdm31356127333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/90t4y1356127333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/101b841356127333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1102k31356127333.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/12s0ik1356127333.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/13zwbh1356127333.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/14qyjx1356127333.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/15sqkw1356127333.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/16zkrn1356127333.tab")
+ }
>
> try(system("convert tmp/1y1bm1356127333.ps tmp/1y1bm1356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/275gb1356127333.ps tmp/275gb1356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y67k1356127333.ps tmp/3y67k1356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/49xo71356127333.ps tmp/49xo71356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/5drmw1356127333.ps tmp/5drmw1356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q8ka1356127333.ps tmp/6q8ka1356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/7lhy21356127333.ps tmp/7lhy21356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vdm31356127333.ps tmp/8vdm31356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/90t4y1356127333.ps tmp/90t4y1356127333.png",intern=TRUE))
character(0)
> try(system("convert tmp/101b841356127333.ps tmp/101b841356127333.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.708 1.723 7.437