R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-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(70.5,4.0,370,53.5,315.0,6166,65.0,4.0,684,76.5,1.7,449,70.0,8.0,643,71.0,5.6,1551,60.5,15.0,616,51.5,503.0,36660,78.0,2.6,403,76.0,2.6,346,57.5,44.0,2471,61.0,24.0,7427,64.5,23.0,2992,78.5,3.8,233,79.0,1.8,609,61.0,96.0,7615,70.0,90.0,370,70.0,4.9,1066,72.0,6.6,600,64.5,21.0,4873,54.5,592.0,3485,56.5,73.0,2364,64.5,14.0,1016,64.5,8.8,1062,73.0,3.9,480,72.0,6.0,559,69.0,3.2,259,64.0,11.0,1340,78.5,2.6,275,53.0,23.0,12550,75.0,3.2,965,68.5,11.0,4883,70.0,5.0,1189,70.5,3.0,226,76.0,3.0,611,75.5,1.3,404,74.5,5.6,576,65.0,29.0,3096),dim=c(3,38),dimnames=list(c('le','ppt','ppp'),1:38))
> y <- array(NA,dim=c(3,38),dimnames=list(c('le','ppt','ppp'),1:38))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
> 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
le ppt ppp t
1 70.5 4.0 370 1
2 53.5 315.0 6166 2
3 65.0 4.0 684 3
4 76.5 1.7 449 4
5 70.0 8.0 643 5
6 71.0 5.6 1551 6
7 60.5 15.0 616 7
8 51.5 503.0 36660 8
9 78.0 2.6 403 9
10 76.0 2.6 346 10
11 57.5 44.0 2471 11
12 61.0 24.0 7427 12
13 64.5 23.0 2992 13
14 78.5 3.8 233 14
15 79.0 1.8 609 15
16 61.0 96.0 7615 16
17 70.0 90.0 370 17
18 70.0 4.9 1066 18
19 72.0 6.6 600 19
20 64.5 21.0 4873 20
21 54.5 592.0 3485 21
22 56.5 73.0 2364 22
23 64.5 14.0 1016 23
24 64.5 8.8 1062 24
25 73.0 3.9 480 25
26 72.0 6.0 559 26
27 69.0 3.2 259 27
28 64.0 11.0 1340 28
29 78.5 2.6 275 29
30 53.0 23.0 12550 30
31 75.0 3.2 965 31
32 68.5 11.0 4883 32
33 70.0 5.0 1189 33
34 70.5 3.0 226 34
35 76.0 3.0 611 35
36 75.5 1.3 404 36
37 74.5 5.6 576 37
38 65.0 29.0 3096 38
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ppt ppp t
69.4869389 -0.0229480 -0.0004293 0.0373536
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.6925 -4.1382 0.2885 4.6338 9.2555
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 69.4869389 2.1776959 31.908 <2e-16 ***
ppt -0.0229480 0.0098560 -2.328 0.0260 *
ppp -0.0004293 0.0002049 -2.095 0.0436 *
t 0.0373536 0.0917426 0.407 0.6864
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.076 on 34 degrees of freedom
Multiple R-squared: 0.4428, Adjusted R-squared: 0.3936
F-statistic: 9.005 on 3 and 34 DF, p-value: 0.0001576
> 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,] 0.5743439 0.851312212 0.425656106
[2,] 0.6911250 0.617750064 0.308875032
[3,] 0.8349698 0.330060301 0.165030150
[4,] 0.7813341 0.437331863 0.218665931
[5,] 0.9523189 0.095362278 0.047681139
[6,] 0.9502122 0.099575553 0.049787776
[7,] 0.9195221 0.160955714 0.080477857
[8,] 0.9647747 0.070450629 0.035225315
[9,] 0.9856340 0.028731935 0.014365968
[10,] 0.9847336 0.030532724 0.015266362
[11,] 0.9756994 0.048601147 0.024300574
[12,] 0.9609142 0.078171695 0.039085848
[13,] 0.9502940 0.099411936 0.049705968
[14,] 0.9487147 0.102570606 0.051285303
[15,] 0.9872083 0.025583358 0.012791679
[16,] 0.9953071 0.009385765 0.004692882
[17,] 0.9899223 0.020155481 0.010077740
[18,] 0.9855092 0.028981694 0.014490847
[19,] 0.9757076 0.048584861 0.024292430
[20,] 0.9593349 0.081330276 0.040665138
[21,] 0.9309094 0.138181297 0.069090648
[22,] 0.9714724 0.057055200 0.028527600
[23,] 0.9677079 0.064584253 0.032292126
[24,] 0.9546139 0.090772261 0.045386131
[25,] 0.9805436 0.038912721 0.019456361
> postscript(file="/var/wessaorg/rcomp/tmp/1k3iq1322134383.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2d12p1322134383.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3u3du1322134383.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hm5l1322134383.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/514qz1322134383.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 = 38
Frequency = 1
1 2 3 4 5 6
1.22632636 -6.18619310 -4.21359279 7.09539664 0.78589227 2.08323293
7 8 9 10 11 12
-8.63976896 8.99381663 8.40953559 6.34771403 -10.32741069 -5.19630408
13 14 15 16 17 18
-3.66038107 8.67733049 9.25548318 -3.61276014 2.10219760 0.41073366
19 20 21 22 23 24
2.21235573 -3.16031151 -0.69016166 -11.11873926 -5.08867069 -5.22560799
25 26 27 28 29 30
2.87476280 1.91951166 -1.31087493 -5.70520221 8.10751715 -11.69251042
31 32 33 34 35 36
4.84276919 0.16625715 -0.09447706 -0.09110570 5.53680638 4.87158395
37 38
4.00673978 -3.91189093
> postscript(file="/var/wessaorg/rcomp/tmp/6vmcu1322134383.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 = 38
Frequency = 1
lag(myerror, k = 1) myerror
0 1.22632636 NA
1 -6.18619310 1.22632636
2 -4.21359279 -6.18619310
3 7.09539664 -4.21359279
4 0.78589227 7.09539664
5 2.08323293 0.78589227
6 -8.63976896 2.08323293
7 8.99381663 -8.63976896
8 8.40953559 8.99381663
9 6.34771403 8.40953559
10 -10.32741069 6.34771403
11 -5.19630408 -10.32741069
12 -3.66038107 -5.19630408
13 8.67733049 -3.66038107
14 9.25548318 8.67733049
15 -3.61276014 9.25548318
16 2.10219760 -3.61276014
17 0.41073366 2.10219760
18 2.21235573 0.41073366
19 -3.16031151 2.21235573
20 -0.69016166 -3.16031151
21 -11.11873926 -0.69016166
22 -5.08867069 -11.11873926
23 -5.22560799 -5.08867069
24 2.87476280 -5.22560799
25 1.91951166 2.87476280
26 -1.31087493 1.91951166
27 -5.70520221 -1.31087493
28 8.10751715 -5.70520221
29 -11.69251042 8.10751715
30 4.84276919 -11.69251042
31 0.16625715 4.84276919
32 -0.09447706 0.16625715
33 -0.09110570 -0.09447706
34 5.53680638 -0.09110570
35 4.87158395 5.53680638
36 4.00673978 4.87158395
37 -3.91189093 4.00673978
38 NA -3.91189093
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.18619310 1.22632636
[2,] -4.21359279 -6.18619310
[3,] 7.09539664 -4.21359279
[4,] 0.78589227 7.09539664
[5,] 2.08323293 0.78589227
[6,] -8.63976896 2.08323293
[7,] 8.99381663 -8.63976896
[8,] 8.40953559 8.99381663
[9,] 6.34771403 8.40953559
[10,] -10.32741069 6.34771403
[11,] -5.19630408 -10.32741069
[12,] -3.66038107 -5.19630408
[13,] 8.67733049 -3.66038107
[14,] 9.25548318 8.67733049
[15,] -3.61276014 9.25548318
[16,] 2.10219760 -3.61276014
[17,] 0.41073366 2.10219760
[18,] 2.21235573 0.41073366
[19,] -3.16031151 2.21235573
[20,] -0.69016166 -3.16031151
[21,] -11.11873926 -0.69016166
[22,] -5.08867069 -11.11873926
[23,] -5.22560799 -5.08867069
[24,] 2.87476280 -5.22560799
[25,] 1.91951166 2.87476280
[26,] -1.31087493 1.91951166
[27,] -5.70520221 -1.31087493
[28,] 8.10751715 -5.70520221
[29,] -11.69251042 8.10751715
[30,] 4.84276919 -11.69251042
[31,] 0.16625715 4.84276919
[32,] -0.09447706 0.16625715
[33,] -0.09110570 -0.09447706
[34,] 5.53680638 -0.09110570
[35,] 4.87158395 5.53680638
[36,] 4.00673978 4.87158395
[37,] -3.91189093 4.00673978
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.18619310 1.22632636
2 -4.21359279 -6.18619310
3 7.09539664 -4.21359279
4 0.78589227 7.09539664
5 2.08323293 0.78589227
6 -8.63976896 2.08323293
7 8.99381663 -8.63976896
8 8.40953559 8.99381663
9 6.34771403 8.40953559
10 -10.32741069 6.34771403
11 -5.19630408 -10.32741069
12 -3.66038107 -5.19630408
13 8.67733049 -3.66038107
14 9.25548318 8.67733049
15 -3.61276014 9.25548318
16 2.10219760 -3.61276014
17 0.41073366 2.10219760
18 2.21235573 0.41073366
19 -3.16031151 2.21235573
20 -0.69016166 -3.16031151
21 -11.11873926 -0.69016166
22 -5.08867069 -11.11873926
23 -5.22560799 -5.08867069
24 2.87476280 -5.22560799
25 1.91951166 2.87476280
26 -1.31087493 1.91951166
27 -5.70520221 -1.31087493
28 8.10751715 -5.70520221
29 -11.69251042 8.10751715
30 4.84276919 -11.69251042
31 0.16625715 4.84276919
32 -0.09447706 0.16625715
33 -0.09110570 -0.09447706
34 5.53680638 -0.09110570
35 4.87158395 5.53680638
36 4.00673978 4.87158395
37 -3.91189093 4.00673978
> 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/wessaorg/rcomp/tmp/7l7de1322134383.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8xhsi1322134383.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ka8x1322134383.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10bbn61322134383.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11f00w1322134383.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/wessaorg/rcomp/tmp/12n6io1322134383.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/wessaorg/rcomp/tmp/13a8n51322134383.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/wessaorg/rcomp/tmp/14v00g1322134383.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/wessaorg/rcomp/tmp/158dv51322134383.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/wessaorg/rcomp/tmp/16vnnf1322134383.tab")
+ }
>
> try(system("convert tmp/1k3iq1322134383.ps tmp/1k3iq1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d12p1322134383.ps tmp/2d12p1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u3du1322134383.ps tmp/3u3du1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hm5l1322134383.ps tmp/4hm5l1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/514qz1322134383.ps tmp/514qz1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vmcu1322134383.ps tmp/6vmcu1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/7l7de1322134383.ps tmp/7l7de1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xhsi1322134383.ps tmp/8xhsi1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ka8x1322134383.ps tmp/9ka8x1322134383.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bbn61322134383.ps tmp/10bbn61322134383.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.931 0.570 3.551