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(23,14,8,6,19,12,39,23,22,13,55,40,94,69,50,35,98,74,182,127,117,77,188,137,347,258,188,148,310,257,498,430,298,248,404,336,646,590,404,349,491,455,802,731,535,465,642,578,786,783,642,593,753,675,875,830,811,703,879,818,985,909,1015,912,1034,951,1022,1011,1269,1150,1243,1149,1064,1034,1442,1368,1355,1299,1120,1126,1555,1506,1437,1411,1270,1213,1610,1599,1412,1433,1285,1297,1537,1560,1339,1388,1271,1261,1386,1486,1211,1299,1289,1237,1159,1274,1089,1142,1197,1280,992,1061,940,1012,1086,1133,803,923,806,858,998,1085,664,725,687,760,842,874,509,581,528,605,742,817,381,443,456,479,623,669,271,322,346,414,514,570,199,236,242,281,423,460,137,165,168,199,264,357,87,109,123,150,158,213,49,66,67,89,105,107,27,35,35,47,59,93,10,17,17,28,46,45,6,6,12,14,16,25,4,5,8,7,22,18,1,2,4,6,3,14,1,1,2,4,5,3,0,1,1,1),dim=c(6,35),dimnames=list(c('VerstrB','ExactB','VerstrV','ExactV','VerstrW','ExactW
'),1:35))
> y <- array(NA,dim=c(6,35),dimnames=list(c('VerstrB','ExactB','VerstrV','ExactV','VerstrW','ExactW
'),1:35))
> 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
VerstrB ExactB VerstrV ExactV VerstrW ExactW\r t
1 23 14 8 6 19 12 1
2 39 23 22 13 55 40 2
3 94 69 50 35 98 74 3
4 182 127 117 77 188 137 4
5 347 258 188 148 310 257 5
6 498 430 298 248 404 336 6
7 646 590 404 349 491 455 7
8 802 731 535 465 642 578 8
9 786 783 642 593 753 675 9
10 875 830 811 703 879 818 10
11 985 909 1015 912 1034 951 11
12 1022 1011 1269 1150 1243 1149 12
13 1064 1034 1442 1368 1355 1299 13
14 1120 1126 1555 1506 1437 1411 14
15 1270 1213 1610 1599 1412 1433 15
16 1285 1297 1537 1560 1339 1388 16
17 1271 1261 1386 1486 1211 1299 17
18 1289 1237 1159 1274 1089 1142 18
19 1197 1280 992 1061 940 1012 19
20 1086 1133 803 923 806 858 20
21 998 1085 664 725 687 760 21
22 842 874 509 581 528 605 22
23 742 817 381 443 456 479 23
24 623 669 271 322 346 414 24
25 514 570 199 236 242 281 25
26 423 460 137 165 168 199 26
27 264 357 87 109 123 150 27
28 158 213 49 66 67 89 28
29 105 107 27 35 35 47 29
30 59 93 10 17 17 28 30
31 46 45 6 6 12 14 31
32 16 25 4 5 8 7 32
33 22 18 1 2 4 6 33
34 3 14 1 1 2 4 34
35 5 3 0 1 1 1 35
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ExactB VerstrV ExactV VerstrW `ExactW\r`
29.4333 0.7792 -0.5458 0.2891 0.5995 -0.1107
t
-1.2516
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-61.266 -24.267 3.757 18.711 56.026
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.4333 20.6375 1.426 0.1649
ExactB 0.7792 0.1088 7.162 8.56e-08 ***
VerstrV -0.5458 0.3766 -1.449 0.1584
ExactV 0.2891 0.2657 1.088 0.2857
VerstrW 0.5995 0.3239 1.851 0.0748 .
`ExactW\r` -0.1107 0.4975 -0.223 0.8255
t -1.2516 0.7565 -1.654 0.1092
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 32.78 on 28 degrees of freedom
Multiple R-squared: 0.9959, Adjusted R-squared: 0.995
F-statistic: 1138 on 6 and 28 DF, p-value: < 2.2e-16
> 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,] 0.00102398 0.002047960 0.998976020
[2,] 0.65943246 0.681135076 0.340567538
[3,] 0.62819885 0.743602306 0.371801153
[4,] 0.70761686 0.584766278 0.292383139
[5,] 0.75279983 0.494400348 0.247200174
[6,] 0.75232231 0.495355374 0.247677687
[7,] 0.82492900 0.350142007 0.175071004
[8,] 0.78892474 0.422150516 0.211075258
[9,] 0.82273788 0.354524248 0.177262124
[10,] 0.94599714 0.108005729 0.054002864
[11,] 0.91685266 0.166294688 0.083147344
[12,] 0.93943808 0.121123844 0.060561922
[13,] 0.99548494 0.009030118 0.004515059
[14,] 0.98449297 0.031014062 0.015507031
[15,] 0.97489930 0.050201407 0.025100704
[16,] 0.96618994 0.067620121 0.033810061
> postscript(file="/var/wessaorg/rcomp/tmp/1bbcf1322059991.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2d5fu1322059991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jawk1322059991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/477l61322059991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5m70l1322059991.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 = 35
Frequency = 1
1 2 3 4 5 6 7
-23.520816 -26.146607 -18.831083 2.672522 25.217945 26.964693 41.213866
8 9 10 11 12 13 14
49.652133 -40.027927 14.333630 36.746855 -38.032682 -31.846650 -61.266086
15 16 17 18 19 20 21
42.746515 3.756856 24.919428 56.026325 -22.860329 -18.039763 -25.514477
22 23 24 25 26 27 28
19.345063 -35.736786 -4.467862 -1.883413 16.054425 -50.981590 -25.012905
29 30 31 32 33 34 35
17.324755 -11.901612 16.197097 3.853473 18.076744 4.711893 16.256371
> postscript(file="/var/wessaorg/rcomp/tmp/6cyo71322059991.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 = 35
Frequency = 1
lag(myerror, k = 1) myerror
0 -23.520816 NA
1 -26.146607 -23.520816
2 -18.831083 -26.146607
3 2.672522 -18.831083
4 25.217945 2.672522
5 26.964693 25.217945
6 41.213866 26.964693
7 49.652133 41.213866
8 -40.027927 49.652133
9 14.333630 -40.027927
10 36.746855 14.333630
11 -38.032682 36.746855
12 -31.846650 -38.032682
13 -61.266086 -31.846650
14 42.746515 -61.266086
15 3.756856 42.746515
16 24.919428 3.756856
17 56.026325 24.919428
18 -22.860329 56.026325
19 -18.039763 -22.860329
20 -25.514477 -18.039763
21 19.345063 -25.514477
22 -35.736786 19.345063
23 -4.467862 -35.736786
24 -1.883413 -4.467862
25 16.054425 -1.883413
26 -50.981590 16.054425
27 -25.012905 -50.981590
28 17.324755 -25.012905
29 -11.901612 17.324755
30 16.197097 -11.901612
31 3.853473 16.197097
32 18.076744 3.853473
33 4.711893 18.076744
34 16.256371 4.711893
35 NA 16.256371
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -26.146607 -23.520816
[2,] -18.831083 -26.146607
[3,] 2.672522 -18.831083
[4,] 25.217945 2.672522
[5,] 26.964693 25.217945
[6,] 41.213866 26.964693
[7,] 49.652133 41.213866
[8,] -40.027927 49.652133
[9,] 14.333630 -40.027927
[10,] 36.746855 14.333630
[11,] -38.032682 36.746855
[12,] -31.846650 -38.032682
[13,] -61.266086 -31.846650
[14,] 42.746515 -61.266086
[15,] 3.756856 42.746515
[16,] 24.919428 3.756856
[17,] 56.026325 24.919428
[18,] -22.860329 56.026325
[19,] -18.039763 -22.860329
[20,] -25.514477 -18.039763
[21,] 19.345063 -25.514477
[22,] -35.736786 19.345063
[23,] -4.467862 -35.736786
[24,] -1.883413 -4.467862
[25,] 16.054425 -1.883413
[26,] -50.981590 16.054425
[27,] -25.012905 -50.981590
[28,] 17.324755 -25.012905
[29,] -11.901612 17.324755
[30,] 16.197097 -11.901612
[31,] 3.853473 16.197097
[32,] 18.076744 3.853473
[33,] 4.711893 18.076744
[34,] 16.256371 4.711893
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -26.146607 -23.520816
2 -18.831083 -26.146607
3 2.672522 -18.831083
4 25.217945 2.672522
5 26.964693 25.217945
6 41.213866 26.964693
7 49.652133 41.213866
8 -40.027927 49.652133
9 14.333630 -40.027927
10 36.746855 14.333630
11 -38.032682 36.746855
12 -31.846650 -38.032682
13 -61.266086 -31.846650
14 42.746515 -61.266086
15 3.756856 42.746515
16 24.919428 3.756856
17 56.026325 24.919428
18 -22.860329 56.026325
19 -18.039763 -22.860329
20 -25.514477 -18.039763
21 19.345063 -25.514477
22 -35.736786 19.345063
23 -4.467862 -35.736786
24 -1.883413 -4.467862
25 16.054425 -1.883413
26 -50.981590 16.054425
27 -25.012905 -50.981590
28 17.324755 -25.012905
29 -11.901612 17.324755
30 16.197097 -11.901612
31 3.853473 16.197097
32 18.076744 3.853473
33 4.711893 18.076744
34 16.256371 4.711893
> 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/7vxql1322059991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8l6iw1322059991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9hvcv1322059991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/100rqj1322059991.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112m491322059991.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/12tqgg1322059991.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/13xh1a1322059991.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/14fqhb1322059992.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/15n8w91322059992.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/16ee6w1322059992.tab")
+ }
>
> try(system("convert tmp/1bbcf1322059991.ps tmp/1bbcf1322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d5fu1322059991.ps tmp/2d5fu1322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jawk1322059991.ps tmp/3jawk1322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/477l61322059991.ps tmp/477l61322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m70l1322059991.ps tmp/5m70l1322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cyo71322059991.ps tmp/6cyo71322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vxql1322059991.ps tmp/7vxql1322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l6iw1322059991.ps tmp/8l6iw1322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/9hvcv1322059991.ps tmp/9hvcv1322059991.png",intern=TRUE))
character(0)
> try(system("convert tmp/100rqj1322059991.ps tmp/100rqj1322059991.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.033 0.558 3.966