R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(5125,0,5366,0,5078,0,2775,0,2952,0,2784,0,2350,0,2413,0,2203,0,705,0,765,0,800,0,1161,0,1223,0,1188,0,1178,0,1225,0,1100,0,1087,0,1104,0,1046,0,571,0,591,0,536,0,347,0,390,0,339,0,76,0,68,0,68,0,4044,1,4976,1,2208,1,2721,1,1837,1,2255,1,549,1,669,1,959,1,1158,1,894,1,1074,1,841,1,1107,1,459,1,564,1,284,1,332,1,59,1,71,1),dim=c(2,50),dimnames=list(c('aantalrokers','rookverbod'),1:50))
> y <- array(NA,dim=c(2,50),dimnames=list(c('aantalrokers','rookverbod'),1:50))
> 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'
> #'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
aantalrokers rookverbod
1 5125 0
2 5366 0
3 5078 0
4 2775 0
5 2952 0
6 2784 0
7 2350 0
8 2413 0
9 2203 0
10 705 0
11 765 0
12 800 0
13 1161 0
14 1223 0
15 1188 0
16 1178 0
17 1225 0
18 1100 0
19 1087 0
20 1104 0
21 1046 0
22 571 0
23 591 0
24 536 0
25 347 0
26 390 0
27 339 0
28 76 0
29 68 0
30 68 0
31 4044 1
32 4976 1
33 2208 1
34 2721 1
35 1837 1
36 2255 1
37 549 1
38 669 1
39 959 1
40 1158 1
41 894 1
42 1074 1
43 841 1
44 1107 1
45 459 1
46 564 1
47 284 1
48 332 1
49 59 1
50 71 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) rookverbod
1553.8 -200.7
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1485.8 -945.6 -451.8 759.5 3812.2
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1553.8 259.0 6.00 2.51e-07 ***
rookverbod -200.7 409.5 -0.49 0.626
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1418 on 48 degrees of freedom
Multiple R-squared: 0.004983, Adjusted R-squared: -0.01575
F-statistic: 0.2404 on 1 and 48 DF, p-value: 0.6262
> 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,] 0.8773203 2.453594e-01 1.226797e-01
[2,] 0.8955468 2.089063e-01 1.044532e-01
[3,] 0.9227477 1.545047e-01 7.725235e-02
[4,] 0.9317697 1.364606e-01 6.823031e-02
[5,] 0.9413929 1.172141e-01 5.860707e-02
[6,] 0.9827213 3.455742e-02 1.727871e-02
[7,] 0.9907387 1.852265e-02 9.261325e-03
[8,] 0.9929067 1.418663e-02 7.093316e-03
[9,] 0.9918733 1.625336e-02 8.126680e-03
[10,] 0.9898323 2.033544e-02 1.016772e-02
[11,] 0.9869490 2.610202e-02 1.305101e-02
[12,] 0.9829207 3.415854e-02 1.707927e-02
[13,] 0.9773227 4.535465e-02 2.267732e-02
[14,] 0.9703614 5.927728e-02 2.963864e-02
[15,] 0.9613462 7.730763e-02 3.865381e-02
[16,] 0.9499331 1.001339e-01 5.006693e-02
[17,] 0.9361564 1.276873e-01 6.384363e-02
[18,] 0.9233264 1.533472e-01 7.667361e-02
[19,] 0.9062021 1.875958e-01 9.379791e-02
[20,] 0.8854278 2.291444e-01 1.145722e-01
[21,] 0.8633256 2.733488e-01 1.366744e-01
[22,] 0.8345384 3.309232e-01 1.654616e-01
[23,] 0.8008500 3.983000e-01 1.991500e-01
[24,] 0.7683569 4.632861e-01 2.316431e-01
[25,] 0.7290063 5.419875e-01 2.709937e-01
[26,] 0.6823514 6.352972e-01 3.176486e-01
[27,] 0.8001017 3.997967e-01 1.998983e-01
[28,] 0.9930672 1.386552e-02 6.932759e-03
[29,] 0.9954887 9.022559e-03 4.511279e-03
[30,] 0.9993327 1.334546e-03 6.672732e-04
[31,] 0.9995852 8.295131e-04 4.147566e-04
[32,] 0.9999878 2.449692e-05 1.224846e-05
[33,] 0.9999677 6.462819e-05 3.231409e-05
[34,] 0.9999000 2.000216e-04 1.000108e-04
[35,] 0.9997396 5.208511e-04 2.604256e-04
[36,] 0.9995826 8.347246e-04 4.173623e-04
[37,] 0.9988956 2.208811e-03 1.104406e-03
[38,] 0.9983134 3.373214e-03 1.686607e-03
[39,] 0.9959001 8.199870e-03 4.099935e-03
[40,] 0.9987371 2.525885e-03 1.262942e-03
[41,] 0.9935076 1.298476e-02 6.492380e-03
> postscript(file="/var/www/rcomp/tmp/19zt61290706986.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/29zt61290706986.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3krbr1290706986.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4krbr1290706986.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5krbr1290706986.ps",horizontal=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 = 50
Frequency = 1
1 2 3 4 5 6 7 8
3571.20 3812.20 3524.20 1221.20 1398.20 1230.20 796.20 859.20
9 10 11 12 13 14 15 16
649.20 -848.80 -788.80 -753.80 -392.80 -330.80 -365.80 -375.80
17 18 19 20 21 22 23 24
-328.80 -453.80 -466.80 -449.80 -507.80 -982.80 -962.80 -1017.80
25 26 27 28 29 30 31 32
-1206.80 -1163.80 -1214.80 -1477.80 -1485.80 -1485.80 2690.95 3622.95
33 34 35 36 37 38 39 40
854.95 1367.95 483.95 901.95 -804.05 -684.05 -394.05 -195.05
41 42 43 44 45 46 47 48
-459.05 -279.05 -512.05 -246.05 -894.05 -789.05 -1069.05 -1021.05
49 50
-1294.05 -1282.05
> postscript(file="/var/www/rcomp/tmp/6ciau1290706986.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 50
Frequency = 1
lag(myerror, k = 1) myerror
0 3571.20 NA
1 3812.20 3571.20
2 3524.20 3812.20
3 1221.20 3524.20
4 1398.20 1221.20
5 1230.20 1398.20
6 796.20 1230.20
7 859.20 796.20
8 649.20 859.20
9 -848.80 649.20
10 -788.80 -848.80
11 -753.80 -788.80
12 -392.80 -753.80
13 -330.80 -392.80
14 -365.80 -330.80
15 -375.80 -365.80
16 -328.80 -375.80
17 -453.80 -328.80
18 -466.80 -453.80
19 -449.80 -466.80
20 -507.80 -449.80
21 -982.80 -507.80
22 -962.80 -982.80
23 -1017.80 -962.80
24 -1206.80 -1017.80
25 -1163.80 -1206.80
26 -1214.80 -1163.80
27 -1477.80 -1214.80
28 -1485.80 -1477.80
29 -1485.80 -1485.80
30 2690.95 -1485.80
31 3622.95 2690.95
32 854.95 3622.95
33 1367.95 854.95
34 483.95 1367.95
35 901.95 483.95
36 -804.05 901.95
37 -684.05 -804.05
38 -394.05 -684.05
39 -195.05 -394.05
40 -459.05 -195.05
41 -279.05 -459.05
42 -512.05 -279.05
43 -246.05 -512.05
44 -894.05 -246.05
45 -789.05 -894.05
46 -1069.05 -789.05
47 -1021.05 -1069.05
48 -1294.05 -1021.05
49 -1282.05 -1294.05
50 NA -1282.05
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3812.20 3571.20
[2,] 3524.20 3812.20
[3,] 1221.20 3524.20
[4,] 1398.20 1221.20
[5,] 1230.20 1398.20
[6,] 796.20 1230.20
[7,] 859.20 796.20
[8,] 649.20 859.20
[9,] -848.80 649.20
[10,] -788.80 -848.80
[11,] -753.80 -788.80
[12,] -392.80 -753.80
[13,] -330.80 -392.80
[14,] -365.80 -330.80
[15,] -375.80 -365.80
[16,] -328.80 -375.80
[17,] -453.80 -328.80
[18,] -466.80 -453.80
[19,] -449.80 -466.80
[20,] -507.80 -449.80
[21,] -982.80 -507.80
[22,] -962.80 -982.80
[23,] -1017.80 -962.80
[24,] -1206.80 -1017.80
[25,] -1163.80 -1206.80
[26,] -1214.80 -1163.80
[27,] -1477.80 -1214.80
[28,] -1485.80 -1477.80
[29,] -1485.80 -1485.80
[30,] 2690.95 -1485.80
[31,] 3622.95 2690.95
[32,] 854.95 3622.95
[33,] 1367.95 854.95
[34,] 483.95 1367.95
[35,] 901.95 483.95
[36,] -804.05 901.95
[37,] -684.05 -804.05
[38,] -394.05 -684.05
[39,] -195.05 -394.05
[40,] -459.05 -195.05
[41,] -279.05 -459.05
[42,] -512.05 -279.05
[43,] -246.05 -512.05
[44,] -894.05 -246.05
[45,] -789.05 -894.05
[46,] -1069.05 -789.05
[47,] -1021.05 -1069.05
[48,] -1294.05 -1021.05
[49,] -1282.05 -1294.05
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3812.20 3571.20
2 3524.20 3812.20
3 1221.20 3524.20
4 1398.20 1221.20
5 1230.20 1398.20
6 796.20 1230.20
7 859.20 796.20
8 649.20 859.20
9 -848.80 649.20
10 -788.80 -848.80
11 -753.80 -788.80
12 -392.80 -753.80
13 -330.80 -392.80
14 -365.80 -330.80
15 -375.80 -365.80
16 -328.80 -375.80
17 -453.80 -328.80
18 -466.80 -453.80
19 -449.80 -466.80
20 -507.80 -449.80
21 -982.80 -507.80
22 -962.80 -982.80
23 -1017.80 -962.80
24 -1206.80 -1017.80
25 -1163.80 -1206.80
26 -1214.80 -1163.80
27 -1477.80 -1214.80
28 -1485.80 -1477.80
29 -1485.80 -1485.80
30 2690.95 -1485.80
31 3622.95 2690.95
32 854.95 3622.95
33 1367.95 854.95
34 483.95 1367.95
35 901.95 483.95
36 -804.05 901.95
37 -684.05 -804.05
38 -394.05 -684.05
39 -195.05 -394.05
40 -459.05 -195.05
41 -279.05 -459.05
42 -512.05 -279.05
43 -246.05 -512.05
44 -894.05 -246.05
45 -789.05 -894.05
46 -1069.05 -789.05
47 -1021.05 -1069.05
48 -1294.05 -1021.05
49 -1282.05 -1294.05
> 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/www/rcomp/tmp/75r9x1290706986.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/85r9x1290706986.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/95r9x1290706986.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10yiqi1290706986.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/1111po1290706986.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/www/rcomp/tmp/125j5u1290706986.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/www/rcomp/tmp/13mun01290706987.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/www/rcomp/tmp/14x4431290706987.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/www/rcomp/tmp/15i4lr1290706987.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/www/rcomp/tmp/16ew0i1290706987.tab")
+ }
>
> try(system("convert tmp/19zt61290706986.ps tmp/19zt61290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/29zt61290706986.ps tmp/29zt61290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/3krbr1290706986.ps tmp/3krbr1290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/4krbr1290706986.ps tmp/4krbr1290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/5krbr1290706986.ps tmp/5krbr1290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ciau1290706986.ps tmp/6ciau1290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/75r9x1290706986.ps tmp/75r9x1290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/85r9x1290706986.ps tmp/85r9x1290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/95r9x1290706986.ps tmp/95r9x1290706986.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yiqi1290706986.ps tmp/10yiqi1290706986.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.520 1.120 4.582