R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(6.3,1000.00,3.00,2.1,2547000.00,4.00,9.1,10550.00,4.00,15.8,0.023,1.00,5.2,160000.00,4.00,10.9,3300.00,1.00,8.3,52160.00,1.00,11.0,0.425,4.00,3.2,465000.00,5.00,6.3,0.075,1.00,6.6,0.785,2.00,9.5,0.200,2.00,3.3,27660.00,5.00,11.0,0.120,2.00,4.7,85000.00,1.00,10.4,0.101,3.00,7.4,1040.00,4.00,2.1,521000.00,5.00,17.9,0.010,1.00,6.1,62000.00,1.00,11.9,.023,3.00,13.8,1700.00,1.00,14.3,3500.00,1.00,15.2,0.480,2.00,10.0,10000.00,4.00,11.9,1620.00,2.00,6.5,192000.00,4.00,7.5,2500.00,5.00,10.6,0.280,3.00,7.4,4235.00,1.00,8.4,6800.00,2.00,5.7,0.750,2.00,4.9,3600.00,3.00,3.2,55500.00,5.00,11.0,0.900,2.00,4.9,2000.00,3.00,13.2,0.104,2.00,9.7,4190.00,4.00,12.8,3500.00,1.00),dim=c(3,39),dimnames=list(c('SWS','Wb','D'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('SWS','Wb','D'),1:39))
> 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
SWS Wb D
1 6.3 1.000e+03 3
2 2.1 2.547e+06 4
3 9.1 1.055e+04 4
4 15.8 2.300e-02 1
5 5.2 1.600e+05 4
6 10.9 3.300e+03 1
7 8.3 5.216e+04 1
8 11.0 4.250e-01 4
9 3.2 4.650e+05 5
10 6.3 7.500e-02 1
11 6.6 7.850e-01 2
12 9.5 2.000e-01 2
13 3.3 2.766e+04 5
14 11.0 1.200e-01 2
15 4.7 8.500e+04 1
16 10.4 1.010e-01 3
17 7.4 1.040e+03 4
18 2.1 5.210e+05 5
19 17.9 1.000e-02 1
20 6.1 6.200e+04 1
21 11.9 2.300e-02 3
22 13.8 1.700e+03 1
23 14.3 3.500e+03 1
24 15.2 4.800e-01 2
25 10.0 1.000e+04 4
26 11.9 1.620e+03 2
27 6.5 1.920e+05 4
28 7.5 2.500e+03 5
29 10.6 2.800e-01 3
30 7.4 4.235e+03 1
31 8.4 6.800e+03 2
32 5.7 7.500e-01 2
33 4.9 3.600e+03 3
34 3.2 5.550e+04 5
35 11.0 9.000e-01 2
36 4.9 2.000e+03 3
37 13.2 1.040e-01 2
38 9.7 4.190e+03 4
39 12.8 3.500e+03 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Wb D
1.250e+01 -2.559e-06 -1.313e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.2691 -2.5772 0.1558 2.2519 6.7134
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.250e+01 1.129e+00 11.068 3.86e-13 ***
Wb -2.559e-06 1.317e-06 -1.943 0.05991 .
D -1.313e+00 3.864e-01 -3.399 0.00167 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.259 on 36 degrees of freedom
Multiple R-squared: 0.361, Adjusted R-squared: 0.3255
F-statistic: 10.17 on 2 and 36 DF, p-value: 0.0003158
> 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,] 0.5002125 0.9995751 0.4997875
[2,] 0.5326711 0.9346578 0.4673289
[3,] 0.5447305 0.9105389 0.4552695
[4,] 0.4553336 0.9106673 0.5446664
[5,] 0.5767278 0.8465443 0.4232722
[6,] 0.5431439 0.9137121 0.4568561
[7,] 0.4299630 0.8599260 0.5700370
[8,] 0.3928663 0.7857326 0.6071337
[9,] 0.3170107 0.6340214 0.6829893
[10,] 0.5037106 0.9925788 0.4962894
[11,] 0.4453736 0.8907473 0.5546264
[12,] 0.3478417 0.6956833 0.6521583
[13,] 0.3094369 0.6188739 0.6905631
[14,] 0.6294546 0.7410908 0.3705454
[15,] 0.7017532 0.5964936 0.2982468
[16,] 0.6938169 0.6123662 0.3061831
[17,] 0.6508575 0.6982850 0.3491425
[18,] 0.6317298 0.7365404 0.3682702
[19,] 0.7717741 0.4564517 0.2282259
[20,] 0.7422603 0.5154795 0.2577397
[21,] 0.6986212 0.6027575 0.3013788
[22,] 0.6719174 0.6561652 0.3280826
[23,] 0.5652036 0.8695928 0.4347964
[24,] 0.4957442 0.9914884 0.5042558
[25,] 0.4831632 0.9663263 0.5168368
[26,] 0.3630197 0.7260395 0.6369803
[27,] 0.4403635 0.8807271 0.5596365
[28,] 0.4804302 0.9608605 0.5195698
> postscript(file="/var/www/html/rcomp/tmp/146ul1292080861.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/246ul1292080861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3fyu61292080861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fyu61292080861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fyu61292080861.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 = 39
Frequency = 1
1 2 3 4 5 6 7
-2.2575442 1.3707980 1.8801477 4.6133891 -1.6374172 -0.2781664 -2.7531361
8 9 10 11 12 13 14
3.7531518 -1.5436833 -4.8866108 -3.2733551 -0.3733566 -2.5628148 1.1266432
15 16 17 18 19 20 21
-6.2691002 1.8398971 0.1558120 -2.5003821 6.7133891 -4.9279560 3.3398969
22 23 24 25 26 27 28
2.6177393 3.1223454 5.3266442 2.7787402 2.0307884 -0.2555307 1.5728020
29 30 31 32 33 34 35
2.0398975 -3.7757738 -1.4559562 -4.1733551 -3.6508909 -2.5915736 1.1266452
36 37 38 39
-3.6549853 3.3266432 2.4638727 1.6223454
> postscript(file="/var/www/html/rcomp/tmp/6q7tr1292080861.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 = 39
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.2575442 NA
1 1.3707980 -2.2575442
2 1.8801477 1.3707980
3 4.6133891 1.8801477
4 -1.6374172 4.6133891
5 -0.2781664 -1.6374172
6 -2.7531361 -0.2781664
7 3.7531518 -2.7531361
8 -1.5436833 3.7531518
9 -4.8866108 -1.5436833
10 -3.2733551 -4.8866108
11 -0.3733566 -3.2733551
12 -2.5628148 -0.3733566
13 1.1266432 -2.5628148
14 -6.2691002 1.1266432
15 1.8398971 -6.2691002
16 0.1558120 1.8398971
17 -2.5003821 0.1558120
18 6.7133891 -2.5003821
19 -4.9279560 6.7133891
20 3.3398969 -4.9279560
21 2.6177393 3.3398969
22 3.1223454 2.6177393
23 5.3266442 3.1223454
24 2.7787402 5.3266442
25 2.0307884 2.7787402
26 -0.2555307 2.0307884
27 1.5728020 -0.2555307
28 2.0398975 1.5728020
29 -3.7757738 2.0398975
30 -1.4559562 -3.7757738
31 -4.1733551 -1.4559562
32 -3.6508909 -4.1733551
33 -2.5915736 -3.6508909
34 1.1266452 -2.5915736
35 -3.6549853 1.1266452
36 3.3266432 -3.6549853
37 2.4638727 3.3266432
38 1.6223454 2.4638727
39 NA 1.6223454
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.3707980 -2.2575442
[2,] 1.8801477 1.3707980
[3,] 4.6133891 1.8801477
[4,] -1.6374172 4.6133891
[5,] -0.2781664 -1.6374172
[6,] -2.7531361 -0.2781664
[7,] 3.7531518 -2.7531361
[8,] -1.5436833 3.7531518
[9,] -4.8866108 -1.5436833
[10,] -3.2733551 -4.8866108
[11,] -0.3733566 -3.2733551
[12,] -2.5628148 -0.3733566
[13,] 1.1266432 -2.5628148
[14,] -6.2691002 1.1266432
[15,] 1.8398971 -6.2691002
[16,] 0.1558120 1.8398971
[17,] -2.5003821 0.1558120
[18,] 6.7133891 -2.5003821
[19,] -4.9279560 6.7133891
[20,] 3.3398969 -4.9279560
[21,] 2.6177393 3.3398969
[22,] 3.1223454 2.6177393
[23,] 5.3266442 3.1223454
[24,] 2.7787402 5.3266442
[25,] 2.0307884 2.7787402
[26,] -0.2555307 2.0307884
[27,] 1.5728020 -0.2555307
[28,] 2.0398975 1.5728020
[29,] -3.7757738 2.0398975
[30,] -1.4559562 -3.7757738
[31,] -4.1733551 -1.4559562
[32,] -3.6508909 -4.1733551
[33,] -2.5915736 -3.6508909
[34,] 1.1266452 -2.5915736
[35,] -3.6549853 1.1266452
[36,] 3.3266432 -3.6549853
[37,] 2.4638727 3.3266432
[38,] 1.6223454 2.4638727
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.3707980 -2.2575442
2 1.8801477 1.3707980
3 4.6133891 1.8801477
4 -1.6374172 4.6133891
5 -0.2781664 -1.6374172
6 -2.7531361 -0.2781664
7 3.7531518 -2.7531361
8 -1.5436833 3.7531518
9 -4.8866108 -1.5436833
10 -3.2733551 -4.8866108
11 -0.3733566 -3.2733551
12 -2.5628148 -0.3733566
13 1.1266432 -2.5628148
14 -6.2691002 1.1266432
15 1.8398971 -6.2691002
16 0.1558120 1.8398971
17 -2.5003821 0.1558120
18 6.7133891 -2.5003821
19 -4.9279560 6.7133891
20 3.3398969 -4.9279560
21 2.6177393 3.3398969
22 3.1223454 2.6177393
23 5.3266442 3.1223454
24 2.7787402 5.3266442
25 2.0307884 2.7787402
26 -0.2555307 2.0307884
27 1.5728020 -0.2555307
28 2.0398975 1.5728020
29 -3.7757738 2.0398975
30 -1.4559562 -3.7757738
31 -4.1733551 -1.4559562
32 -3.6508909 -4.1733551
33 -2.5915736 -3.6508909
34 1.1266452 -2.5915736
35 -3.6549853 1.1266452
36 3.3266432 -3.6549853
37 2.4638727 3.3266432
38 1.6223454 2.4638727
> 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/html/rcomp/tmp/70gau1292080861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80gau1292080861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/90gau1292080861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bprx1292080861.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11x8ql1292080861.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/html/rcomp/tmp/120qo91292080861.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/html/rcomp/tmp/13psll1292080861.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/html/rcomp/tmp/140jln1292080861.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/html/rcomp/tmp/153jjt1292080861.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/html/rcomp/tmp/16hbhk1292080861.tab")
+ }
>
> try(system("convert tmp/146ul1292080861.ps tmp/146ul1292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/246ul1292080861.ps tmp/246ul1292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fyu61292080861.ps tmp/3fyu61292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fyu61292080861.ps tmp/4fyu61292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fyu61292080861.ps tmp/5fyu61292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q7tr1292080861.ps tmp/6q7tr1292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/70gau1292080861.ps tmp/70gau1292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/80gau1292080861.ps tmp/80gau1292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/90gau1292080861.ps tmp/90gau1292080861.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bprx1292080861.ps tmp/10bprx1292080861.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.313 1.641 6.339