R version 2.8.0 (2008-10-20)
Copyright (C) 2008 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.
Natural language support but running in an English locale
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(6.3,1.0,2.1,2547.0,9.1,10.55,15.8,0.023,5.2,160.0,10.9,3.3,8.3,52.16,11.0,0.425,3.2,465.0,6.3,0.075,6.6,0.785,9.5,0.2,3.3,27.66,11.0,0.12,4.7,85.0,10.4,0.101,7.4,1.04,2.1,521.0,17.9,0.01,6.1,62.0,11.9,0.023,13.8,1.7,14.3,3.5,15.2,0.48,10.0,10.0,11.9,1.62,6.5,192.0,7.5,2.5,10.6,0.28,7.4,4.235,8.4,6.8,5.7,0.75,4.9,3.6,3.2,55.5,11.0,0.9,4.9,2.0,13.2,0.104,9.7,4.19,12.8,3.5),dim=c(2,39),dimnames=list(c('SWS','Wb'),1:39))
> y <- array(NA,dim=c(2,39),dimnames=list(c('SWS','Wb'),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
1 6.3 1.000
2 2.1 2547.000
3 9.1 10.550
4 15.8 0.023
5 5.2 160.000
6 10.9 3.300
7 8.3 52.160
8 11.0 0.425
9 3.2 465.000
10 6.3 0.075
11 6.6 0.785
12 9.5 0.200
13 3.3 27.660
14 11.0 0.120
15 4.7 85.000
16 10.4 0.101
17 7.4 1.040
18 2.1 521.000
19 17.9 0.010
20 6.1 62.000
21 11.9 0.023
22 13.8 1.700
23 14.3 3.500
24 15.2 0.480
25 10.0 10.000
26 11.9 1.620
27 6.5 192.000
28 7.5 2.500
29 10.6 0.280
30 7.4 4.235
31 8.4 6.800
32 5.7 0.750
33 4.9 3.600
34 3.2 55.500
35 11.0 0.900
36 4.9 2.000
37 13.2 0.104
38 9.7 4.190
39 12.8 3.500
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Wb
9.128522 -0.003761
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.72450 -2.82650 0.01115 2.21253 8.77152
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.128522 0.611883 14.919 <2e-16 ***
Wb -0.003761 0.001439 -2.614 0.0129 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.695 on 37 degrees of freedom
Multiple R-squared: 0.1559, Adjusted R-squared: 0.1331
F-statistic: 6.834 on 1 and 37 DF, p-value: 0.01286
> 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,] 0.8494821 0.3010358 0.1505179
[2,] 0.7520760 0.4958479 0.2479240
[3,] 0.6314876 0.7370248 0.3685124
[4,] 0.5183705 0.9632590 0.4816295
[5,] 0.5690351 0.8619299 0.4309649
[6,] 0.5184270 0.9631460 0.4815730
[7,] 0.4512034 0.9024068 0.5487966
[8,] 0.3480722 0.6961443 0.6519278
[9,] 0.4817064 0.9634129 0.5182936
[10,] 0.4212313 0.8424626 0.5787687
[11,] 0.4185178 0.8370357 0.5814822
[12,] 0.3452805 0.6905609 0.6547195
[13,] 0.2789304 0.5578609 0.7210696
[14,] 0.3071266 0.6142531 0.6928734
[15,] 0.7362050 0.5275899 0.2637950
[16,] 0.6816202 0.6367596 0.3183798
[17,] 0.6346032 0.7307935 0.3653968
[18,] 0.6705800 0.6588400 0.3294200
[19,] 0.7383006 0.5233987 0.2616994
[20,] 0.8595857 0.2808286 0.1404143
[21,] 0.8007255 0.3985491 0.1992745
[22,] 0.7802233 0.4395535 0.2197767
[23,] 0.8262896 0.3474208 0.1737104
[24,] 0.7586120 0.4827761 0.2413880
[25,] 0.6756767 0.6486467 0.3243233
[26,] 0.5754603 0.8490794 0.4245397
[27,] 0.4478697 0.8957393 0.5521303
[28,] 0.4362867 0.8725734 0.5637133
[29,] 0.5295511 0.9408978 0.4704489
[30,] 0.3949454 0.7898908 0.6050546
> postscript(file="/var/www/html/freestat/rcomp/tmp/1u0501292275693.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2u0501292275693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3u0501292275693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/44r431292275693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/54r431292275693.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
-2.82476145 2.55019306 0.01115403 6.67156426 -3.32679689 1.78388835
7 8 9 10 11 12
-0.63235977 1.87307610 -4.17975796 -2.82824018 -2.52557002 0.37222992
13 14 15 16 17 18
-5.72449897 1.87192906 -4.10885565 1.27185760 -1.72461102 -5.06915409
19 20 21 22 23 24
8.77151537 -2.79535367 2.77156426 4.67787110 5.18464051 6.07328294
25 26 27 28 29 30
0.90908560 2.77757023 -1.90645182 -1.61912028 1.47253078 -1.71259532
31 32 33 34 35 36
-0.70294891 -3.42570165 -4.21498342 -5.71979876 1.87486247 -4.22100067
37 38 39
4.07186889 0.58723545 3.68464051
> postscript(file="/var/www/html/freestat/rcomp/tmp/64r431292275693.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 -2.82476145 NA
1 2.55019306 -2.82476145
2 0.01115403 2.55019306
3 6.67156426 0.01115403
4 -3.32679689 6.67156426
5 1.78388835 -3.32679689
6 -0.63235977 1.78388835
7 1.87307610 -0.63235977
8 -4.17975796 1.87307610
9 -2.82824018 -4.17975796
10 -2.52557002 -2.82824018
11 0.37222992 -2.52557002
12 -5.72449897 0.37222992
13 1.87192906 -5.72449897
14 -4.10885565 1.87192906
15 1.27185760 -4.10885565
16 -1.72461102 1.27185760
17 -5.06915409 -1.72461102
18 8.77151537 -5.06915409
19 -2.79535367 8.77151537
20 2.77156426 -2.79535367
21 4.67787110 2.77156426
22 5.18464051 4.67787110
23 6.07328294 5.18464051
24 0.90908560 6.07328294
25 2.77757023 0.90908560
26 -1.90645182 2.77757023
27 -1.61912028 -1.90645182
28 1.47253078 -1.61912028
29 -1.71259532 1.47253078
30 -0.70294891 -1.71259532
31 -3.42570165 -0.70294891
32 -4.21498342 -3.42570165
33 -5.71979876 -4.21498342
34 1.87486247 -5.71979876
35 -4.22100067 1.87486247
36 4.07186889 -4.22100067
37 0.58723545 4.07186889
38 3.68464051 0.58723545
39 NA 3.68464051
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.55019306 -2.82476145
[2,] 0.01115403 2.55019306
[3,] 6.67156426 0.01115403
[4,] -3.32679689 6.67156426
[5,] 1.78388835 -3.32679689
[6,] -0.63235977 1.78388835
[7,] 1.87307610 -0.63235977
[8,] -4.17975796 1.87307610
[9,] -2.82824018 -4.17975796
[10,] -2.52557002 -2.82824018
[11,] 0.37222992 -2.52557002
[12,] -5.72449897 0.37222992
[13,] 1.87192906 -5.72449897
[14,] -4.10885565 1.87192906
[15,] 1.27185760 -4.10885565
[16,] -1.72461102 1.27185760
[17,] -5.06915409 -1.72461102
[18,] 8.77151537 -5.06915409
[19,] -2.79535367 8.77151537
[20,] 2.77156426 -2.79535367
[21,] 4.67787110 2.77156426
[22,] 5.18464051 4.67787110
[23,] 6.07328294 5.18464051
[24,] 0.90908560 6.07328294
[25,] 2.77757023 0.90908560
[26,] -1.90645182 2.77757023
[27,] -1.61912028 -1.90645182
[28,] 1.47253078 -1.61912028
[29,] -1.71259532 1.47253078
[30,] -0.70294891 -1.71259532
[31,] -3.42570165 -0.70294891
[32,] -4.21498342 -3.42570165
[33,] -5.71979876 -4.21498342
[34,] 1.87486247 -5.71979876
[35,] -4.22100067 1.87486247
[36,] 4.07186889 -4.22100067
[37,] 0.58723545 4.07186889
[38,] 3.68464051 0.58723545
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.55019306 -2.82476145
2 0.01115403 2.55019306
3 6.67156426 0.01115403
4 -3.32679689 6.67156426
5 1.78388835 -3.32679689
6 -0.63235977 1.78388835
7 1.87307610 -0.63235977
8 -4.17975796 1.87307610
9 -2.82824018 -4.17975796
10 -2.52557002 -2.82824018
11 0.37222992 -2.52557002
12 -5.72449897 0.37222992
13 1.87192906 -5.72449897
14 -4.10885565 1.87192906
15 1.27185760 -4.10885565
16 -1.72461102 1.27185760
17 -5.06915409 -1.72461102
18 8.77151537 -5.06915409
19 -2.79535367 8.77151537
20 2.77156426 -2.79535367
21 4.67787110 2.77156426
22 5.18464051 4.67787110
23 6.07328294 5.18464051
24 0.90908560 6.07328294
25 2.77757023 0.90908560
26 -1.90645182 2.77757023
27 -1.61912028 -1.90645182
28 1.47253078 -1.61912028
29 -1.71259532 1.47253078
30 -0.70294891 -1.71259532
31 -3.42570165 -0.70294891
32 -4.21498342 -3.42570165
33 -5.71979876 -4.21498342
34 1.87486247 -5.71979876
35 -4.22100067 1.87486247
36 4.07186889 -4.22100067
37 0.58723545 4.07186889
38 3.68464051 0.58723545
> 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/freestat/rcomp/tmp/7filo1292275693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/88a291292275693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/98a291292275693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/108a291292275693.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11ts1f1292275693.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/freestat/rcomp/tmp/12etz31292275693.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/freestat/rcomp/tmp/13luxw1292275693.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/freestat/rcomp/tmp/14e3wh1292275693.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/freestat/rcomp/tmp/15z3un1292275693.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/freestat/rcomp/tmp/16vvaw1292275693.tab")
+ }
>
> try(system("convert tmp/1u0501292275693.ps tmp/1u0501292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/2u0501292275693.ps tmp/2u0501292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u0501292275693.ps tmp/3u0501292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/44r431292275693.ps tmp/44r431292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/54r431292275693.ps tmp/54r431292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/64r431292275693.ps tmp/64r431292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/7filo1292275693.ps tmp/7filo1292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/88a291292275693.ps tmp/88a291292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/98a291292275693.ps tmp/98a291292275693.png",intern=TRUE))
character(0)
> try(system("convert tmp/108a291292275693.ps tmp/108a291292275693.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.508 2.372 3.857