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(1225,40786,0,31.00,1214,40787,0,34.40,1205,40788,0,35.60,1196,40789,0,32.80,1209,40790,1,23.30,1192,40791,0,17.00,1196,40792,1,20.00,1174,40793,1,16.70,1183,40794,0,17.80,1210,40795,0,21.20,1210,40796,0,23.90,1218,40797,0,28.80,1219,40798,0,25.60,1215,40799,0,29.40,1206,40800,0,22.80,1202,40801,0,16.10,1195,40802,0,16.10,1203,40803,0,20.00,1194,40804,0,20.60,1170,40805,1,18.30,1189,40806,1,21.60,1199,40807,0,22.80,1196,40808,0,22.80,1189,40809,0,17.20,1185,40811,0,22.20,1192,40812,0,20.60,1188,40813,0,18.30,1176,40814,0,16.70,1177,40816,0,13.90,1166,40817,0,10.00,1176,40818,0,16.10,1181,40819,0,20.60,1176,40820,0,19.40,1177,40821,0,25.60),dim=c(4,34),dimnames=list(c('TimIN','Date','Precip','Temp'),1:34))
> y <- array(NA,dim=c(4,34),dimnames=list(c('TimIN','Date','Precip','Temp'),1:34))
> 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
> 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
TimIN Date Precip Temp
1 1225 40786 0 31.0
2 1214 40787 0 34.4
3 1205 40788 0 35.6
4 1196 40789 0 32.8
5 1209 40790 1 23.3
6 1192 40791 0 17.0
7 1196 40792 1 20.0
8 1174 40793 1 16.7
9 1183 40794 0 17.8
10 1210 40795 0 21.2
11 1210 40796 0 23.9
12 1218 40797 0 28.8
13 1219 40798 0 25.6
14 1215 40799 0 29.4
15 1206 40800 0 22.8
16 1202 40801 0 16.1
17 1195 40802 0 16.1
18 1203 40803 0 20.0
19 1194 40804 0 20.6
20 1170 40805 1 18.3
21 1189 40806 1 21.6
22 1199 40807 0 22.8
23 1196 40808 0 22.8
24 1189 40809 0 17.2
25 1185 40811 0 22.2
26 1192 40812 0 20.6
27 1188 40813 0 18.3
28 1176 40814 0 16.7
29 1177 40816 0 13.9
30 1166 40817 0 10.0
31 1176 40818 0 16.1
32 1181 40819 0 20.6
33 1176 40820 0 19.4
34 1177 40821 0 25.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Date Precip Temp
34121.5084 -0.8074 -11.3253 0.9116
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-21.232 -6.354 2.680 6.931 15.598
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34121.5084 8192.9048 4.165 0.000242 ***
Date -0.8074 0.2007 -4.024 0.000358 ***
Precip -11.3253 4.9835 -2.273 0.030383 *
Temp 0.9116 0.3527 2.584 0.014865 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.526 on 30 degrees of freedom
Multiple R-squared: 0.658, Adjusted R-squared: 0.6238
F-statistic: 19.24 on 3 and 30 DF, p-value: 3.774e-07
> 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,] 0.009391749 1.878350e-02 9.906083e-01
[2,] 0.444038318 8.880766e-01 5.559617e-01
[3,] 0.980298482 3.940304e-02 1.970152e-02
[4,] 0.999960918 7.816414e-05 3.908207e-05
[5,] 0.999963925 7.214902e-05 3.607451e-05
[6,] 0.999911032 1.779355e-04 8.896775e-05
[7,] 0.999879057 2.418858e-04 1.209429e-04
[8,] 0.999657640 6.847203e-04 3.423601e-04
[9,] 0.999088201 1.823599e-03 9.117995e-04
[10,] 0.998207420 3.585159e-03 1.792580e-03
[11,] 0.995940697 8.118605e-03 4.059303e-03
[12,] 0.992609632 1.478074e-02 7.390368e-03
[13,] 0.991094896 1.781021e-02 8.905104e-03
[14,] 0.999533062 9.338763e-04 4.669382e-04
[15,] 0.998472219 3.055563e-03 1.527781e-03
[16,] 0.995750965 8.498069e-03 4.249035e-03
[17,] 0.988525614 2.294877e-02 1.147439e-02
[18,] 0.971382098 5.723580e-02 2.861790e-02
[19,] 0.978592716 4.281457e-02 2.140728e-02
[20,] 0.943921709 1.121566e-01 5.607829e-02
[21,] 0.910213273 1.795735e-01 8.978673e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1ecly1336451168.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/26da21336451168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3brbq1336451168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4vamy1336451168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5crfp1336451168.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 = 34
Frequency = 1
1 2 3 4 5
6.986205e+00 -6.305645e+00 -1.559208e+01 -2.123231e+01 1.256017e+01
6 7 8 9 10
-9.214923e+00 4.183144e+00 -1.400131e+01 -1.652188e+01 8.186269e+00
11 12 13 14 15
6.532505e+00 1.087333e+01 1.559772e+01 8.941251e+00 6.764924e+00
16 17 18 19 20
9.679753e+00 3.487180e+00 8.739554e+00 5.068379e-05 -9.770655e+00
21 22 23 24 25
7.028650e+00 5.416919e+00 3.224347e+00 2.136468e+00 -4.806438e+00
26 27 28 29 30
4.459474e+00 3.363472e+00 -6.370617e+00 -1.203414e+00 -7.840933e+00
31 32 33 34
-2.593974e+00 -8.885314e-01 -3.987241e+00 -7.831437e+00
> postscript(file="/var/wessaorg/rcomp/tmp/6712o1336451168.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 = 34
Frequency = 1
lag(myerror, k = 1) myerror
0 6.986205e+00 NA
1 -6.305645e+00 6.986205e+00
2 -1.559208e+01 -6.305645e+00
3 -2.123231e+01 -1.559208e+01
4 1.256017e+01 -2.123231e+01
5 -9.214923e+00 1.256017e+01
6 4.183144e+00 -9.214923e+00
7 -1.400131e+01 4.183144e+00
8 -1.652188e+01 -1.400131e+01
9 8.186269e+00 -1.652188e+01
10 6.532505e+00 8.186269e+00
11 1.087333e+01 6.532505e+00
12 1.559772e+01 1.087333e+01
13 8.941251e+00 1.559772e+01
14 6.764924e+00 8.941251e+00
15 9.679753e+00 6.764924e+00
16 3.487180e+00 9.679753e+00
17 8.739554e+00 3.487180e+00
18 5.068379e-05 8.739554e+00
19 -9.770655e+00 5.068379e-05
20 7.028650e+00 -9.770655e+00
21 5.416919e+00 7.028650e+00
22 3.224347e+00 5.416919e+00
23 2.136468e+00 3.224347e+00
24 -4.806438e+00 2.136468e+00
25 4.459474e+00 -4.806438e+00
26 3.363472e+00 4.459474e+00
27 -6.370617e+00 3.363472e+00
28 -1.203414e+00 -6.370617e+00
29 -7.840933e+00 -1.203414e+00
30 -2.593974e+00 -7.840933e+00
31 -8.885314e-01 -2.593974e+00
32 -3.987241e+00 -8.885314e-01
33 -7.831437e+00 -3.987241e+00
34 NA -7.831437e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.305645e+00 6.986205e+00
[2,] -1.559208e+01 -6.305645e+00
[3,] -2.123231e+01 -1.559208e+01
[4,] 1.256017e+01 -2.123231e+01
[5,] -9.214923e+00 1.256017e+01
[6,] 4.183144e+00 -9.214923e+00
[7,] -1.400131e+01 4.183144e+00
[8,] -1.652188e+01 -1.400131e+01
[9,] 8.186269e+00 -1.652188e+01
[10,] 6.532505e+00 8.186269e+00
[11,] 1.087333e+01 6.532505e+00
[12,] 1.559772e+01 1.087333e+01
[13,] 8.941251e+00 1.559772e+01
[14,] 6.764924e+00 8.941251e+00
[15,] 9.679753e+00 6.764924e+00
[16,] 3.487180e+00 9.679753e+00
[17,] 8.739554e+00 3.487180e+00
[18,] 5.068379e-05 8.739554e+00
[19,] -9.770655e+00 5.068379e-05
[20,] 7.028650e+00 -9.770655e+00
[21,] 5.416919e+00 7.028650e+00
[22,] 3.224347e+00 5.416919e+00
[23,] 2.136468e+00 3.224347e+00
[24,] -4.806438e+00 2.136468e+00
[25,] 4.459474e+00 -4.806438e+00
[26,] 3.363472e+00 4.459474e+00
[27,] -6.370617e+00 3.363472e+00
[28,] -1.203414e+00 -6.370617e+00
[29,] -7.840933e+00 -1.203414e+00
[30,] -2.593974e+00 -7.840933e+00
[31,] -8.885314e-01 -2.593974e+00
[32,] -3.987241e+00 -8.885314e-01
[33,] -7.831437e+00 -3.987241e+00
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.305645e+00 6.986205e+00
2 -1.559208e+01 -6.305645e+00
3 -2.123231e+01 -1.559208e+01
4 1.256017e+01 -2.123231e+01
5 -9.214923e+00 1.256017e+01
6 4.183144e+00 -9.214923e+00
7 -1.400131e+01 4.183144e+00
8 -1.652188e+01 -1.400131e+01
9 8.186269e+00 -1.652188e+01
10 6.532505e+00 8.186269e+00
11 1.087333e+01 6.532505e+00
12 1.559772e+01 1.087333e+01
13 8.941251e+00 1.559772e+01
14 6.764924e+00 8.941251e+00
15 9.679753e+00 6.764924e+00
16 3.487180e+00 9.679753e+00
17 8.739554e+00 3.487180e+00
18 5.068379e-05 8.739554e+00
19 -9.770655e+00 5.068379e-05
20 7.028650e+00 -9.770655e+00
21 5.416919e+00 7.028650e+00
22 3.224347e+00 5.416919e+00
23 2.136468e+00 3.224347e+00
24 -4.806438e+00 2.136468e+00
25 4.459474e+00 -4.806438e+00
26 3.363472e+00 4.459474e+00
27 -6.370617e+00 3.363472e+00
28 -1.203414e+00 -6.370617e+00
29 -7.840933e+00 -1.203414e+00
30 -2.593974e+00 -7.840933e+00
31 -8.885314e-01 -2.593974e+00
32 -3.987241e+00 -8.885314e-01
33 -7.831437e+00 -3.987241e+00
> 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/79nep1336451168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/85g2j1336451168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9snhz1336451168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/101r2d1336451168.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11je3e1336451168.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/12ppc11336451168.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/13hsqo1336451168.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/14ttd31336451168.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/15grtl1336451168.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/168v501336451168.tab")
+ }
>
> try(system("convert tmp/1ecly1336451168.ps tmp/1ecly1336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/26da21336451168.ps tmp/26da21336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/3brbq1336451168.ps tmp/3brbq1336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vamy1336451168.ps tmp/4vamy1336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/5crfp1336451168.ps tmp/5crfp1336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/6712o1336451168.ps tmp/6712o1336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/79nep1336451168.ps tmp/79nep1336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/85g2j1336451168.ps tmp/85g2j1336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/9snhz1336451168.ps tmp/9snhz1336451168.png",intern=TRUE))
character(0)
> try(system("convert tmp/101r2d1336451168.ps tmp/101r2d1336451168.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.110 0.757 3.881