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(705,4,370,74,67,535,315,6166,53,54,65,4,684,68,62,765,17,449,80,73,70,8,643,72,68,71,56,1551,74,68,605,15,616,61,60,515,503,36660,53,50,78,26,403,82,74,76,26,346,79,73,575,44,2471,58,57,61,24,7427,63,59,645,23,2992,65,64,785,38,233,82,75,79,18,609,82,76,61,96,7615,63,59,70,90,370,73,67,70,49,1066,73,67,72,66,600,76,68,645,21,4873,66,63,545,592,3485,56,53,565,73,2364,57,56,645,14,1016,67,62,645,88,1062,67,62,73,39,480,77,69,72,6,559,75,69,69,32,259,74,64,64,11,1340,67,61,785,26,275,82,75,53,23,12550,54,52,75,32,965,78,72,525,NA,25229,55,50,685,11,4883,71,66,70,5,1189,72,68,705,3,226,75,66,76,3,611,79,73,755,13,404,79,72,745,56,576,78,71,65,29,3096,67,63,54,NA,23193,56,52),dim=c(5,40),dimnames=list(c('Yt','X1t','X2t','X3t','X4t'),1:40))
> y <- array(NA,dim=c(5,40),dimnames=list(c('Yt','X1t','X2t','X3t','X4t'),1:40))
> 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
Yt X1t X2t X3t X4t
1 705 4 370 74 67
2 535 315 6166 53 54
3 65 4 684 68 62
4 765 17 449 80 73
5 70 8 643 72 68
6 71 56 1551 74 68
7 605 15 616 61 60
8 515 503 36660 53 50
9 78 26 403 82 74
10 76 26 346 79 73
11 575 44 2471 58 57
12 61 24 7427 63 59
13 645 23 2992 65 64
14 785 38 233 82 75
15 79 18 609 82 76
16 61 96 7615 63 59
17 70 90 370 73 67
18 70 49 1066 73 67
19 72 66 600 76 68
20 645 21 4873 66 63
21 545 592 3485 56 53
22 565 73 2364 57 56
23 645 14 1016 67 62
24 645 88 1062 67 62
25 73 39 480 77 69
26 72 6 559 75 69
27 69 32 259 74 64
28 64 11 1340 67 61
29 785 26 275 82 75
30 53 23 12550 54 52
31 75 32 965 78 72
32 525 NA 25229 55 50
33 685 11 4883 71 66
34 70 5 1189 72 68
35 705 3 226 75 66
36 76 3 611 79 73
37 755 13 404 79 72
38 745 56 576 78 71
39 65 29 3096 67 63
40 54 NA 23193 56 52
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1t X2t X3t X4t
-91.908028 0.529691 -0.003203 -29.452079 38.120188
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-318.7 -266.5 -156.6 298.6 489.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -91.908028 750.240579 -0.123 0.903
X1t 0.529691 0.533294 0.993 0.328
X2t -0.003203 0.011209 -0.286 0.777
X3t -29.452079 28.448859 -1.035 0.308
X4t 38.120188 37.246024 1.023 0.314
Residual standard error: 312.2 on 33 degrees of freedom
(2 observations deleted due to missingness)
Multiple R-squared: 0.05943, Adjusted R-squared: -0.05458
F-statistic: 0.5213 on 4 and 33 DF, p-value: 0.7207
> 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,] 0.8532955 0.2934090 0.1467045
[2,] 0.8043078 0.3913845 0.1956922
[3,] 0.7463122 0.5073756 0.2536878
[4,] 0.6345721 0.7308557 0.3654279
[5,] 0.5890608 0.8218784 0.4109392
[6,] 0.5047570 0.9904860 0.4952430
[7,] 0.5681178 0.8637643 0.4318822
[8,] 0.5552267 0.8895466 0.4447733
[9,] 0.4994367 0.9988735 0.5005633
[10,] 0.4545861 0.9091723 0.5454139
[11,] 0.4010157 0.8020314 0.5989843
[12,] 0.3342413 0.6684826 0.6657587
[13,] 0.3025338 0.6050675 0.6974662
[14,] 0.2652087 0.5304175 0.7347913
[15,] 0.2024536 0.4049072 0.7975464
[16,] 0.2869812 0.5739624 0.7130188
[17,] 0.3636282 0.7272563 0.6363718
[18,] 0.3594817 0.7189634 0.6405183
[19,] 0.2968583 0.5937166 0.7031417
[20,] 0.5051300 0.9897400 0.4948700
[21,] 0.3878147 0.7756293 0.6121853
[22,] 0.3456994 0.6913988 0.6543006
[23,] 0.3318921 0.6637843 0.6681079
[24,] 0.3903664 0.7807328 0.6096336
[25,] 0.2617063 0.5234125 0.7382937
> postscript(file="/var/www/html/rcomp/tmp/1n2in1290510042.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$resid)
Warning message:
In x[, 1] - mysum$resid :
longer object length is not a multiple of shorter object length
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2n2in1290510042.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3fb081290510042.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fb081290510042.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5fb081290510042.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 38
Frequency = 1
1 2 3 4 5 6 7
421.37560 -17.72613 -203.73025 422.73398 -311.89315 -274.50602 200.30117
8 9 10 11 12 13 14
112.83943 -248.39660 -300.81521 186.88567 -229.62730 209.00110 413.58243
15 16 17 18 19 20 21
-318.73966 -267.16296 -288.62995 -264.68344 -222.94466 283.65726 -36.56101
22 23 24 25 26 27 28
169.85002 342.58409 303.53425 -216.69543 -258.86675 -115.45071 -197.66894
29 30 31 33 34 35 36
420.07325 -218.91696 -294.34271 361.88603 -308.55533 489.01635 -287.78356
37 38 39
423.37673 399.81899 -276.81961
> postscript(file="/var/www/html/rcomp/tmp/6q2ha1290510042.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 = 38
Frequency = 1
lag(myerror, k = 1) myerror
0 421.37560 NA
1 -17.72613 421.37560
2 -203.73025 -17.72613
3 422.73398 -203.73025
4 -311.89315 422.73398
5 -274.50602 -311.89315
6 200.30117 -274.50602
7 112.83943 200.30117
8 -248.39660 112.83943
9 -300.81521 -248.39660
10 186.88567 -300.81521
11 -229.62730 186.88567
12 209.00110 -229.62730
13 413.58243 209.00110
14 -318.73966 413.58243
15 -267.16296 -318.73966
16 -288.62995 -267.16296
17 -264.68344 -288.62995
18 -222.94466 -264.68344
19 283.65726 -222.94466
20 -36.56101 283.65726
21 169.85002 -36.56101
22 342.58409 169.85002
23 303.53425 342.58409
24 -216.69543 303.53425
25 -258.86675 -216.69543
26 -115.45071 -258.86675
27 -197.66894 -115.45071
28 420.07325 -197.66894
29 -218.91696 420.07325
30 -294.34271 -218.91696
31 361.88603 -294.34271
32 -308.55533 361.88603
33 489.01635 -308.55533
34 -287.78356 489.01635
35 423.37673 -287.78356
36 399.81899 423.37673
37 -276.81961 399.81899
38 NA -276.81961
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -17.72613 421.37560
[2,] -203.73025 -17.72613
[3,] 422.73398 -203.73025
[4,] -311.89315 422.73398
[5,] -274.50602 -311.89315
[6,] 200.30117 -274.50602
[7,] 112.83943 200.30117
[8,] -248.39660 112.83943
[9,] -300.81521 -248.39660
[10,] 186.88567 -300.81521
[11,] -229.62730 186.88567
[12,] 209.00110 -229.62730
[13,] 413.58243 209.00110
[14,] -318.73966 413.58243
[15,] -267.16296 -318.73966
[16,] -288.62995 -267.16296
[17,] -264.68344 -288.62995
[18,] -222.94466 -264.68344
[19,] 283.65726 -222.94466
[20,] -36.56101 283.65726
[21,] 169.85002 -36.56101
[22,] 342.58409 169.85002
[23,] 303.53425 342.58409
[24,] -216.69543 303.53425
[25,] -258.86675 -216.69543
[26,] -115.45071 -258.86675
[27,] -197.66894 -115.45071
[28,] 420.07325 -197.66894
[29,] -218.91696 420.07325
[30,] -294.34271 -218.91696
[31,] 361.88603 -294.34271
[32,] -308.55533 361.88603
[33,] 489.01635 -308.55533
[34,] -287.78356 489.01635
[35,] 423.37673 -287.78356
[36,] 399.81899 423.37673
[37,] -276.81961 399.81899
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -17.72613 421.37560
2 -203.73025 -17.72613
3 422.73398 -203.73025
4 -311.89315 422.73398
5 -274.50602 -311.89315
6 200.30117 -274.50602
7 112.83943 200.30117
8 -248.39660 112.83943
9 -300.81521 -248.39660
10 186.88567 -300.81521
11 -229.62730 186.88567
12 209.00110 -229.62730
13 413.58243 209.00110
14 -318.73966 413.58243
15 -267.16296 -318.73966
16 -288.62995 -267.16296
17 -264.68344 -288.62995
18 -222.94466 -264.68344
19 283.65726 -222.94466
20 -36.56101 283.65726
21 169.85002 -36.56101
22 342.58409 169.85002
23 303.53425 342.58409
24 -216.69543 303.53425
25 -258.86675 -216.69543
26 -115.45071 -258.86675
27 -197.66894 -115.45071
28 420.07325 -197.66894
29 -218.91696 420.07325
30 -294.34271 -218.91696
31 361.88603 -294.34271
32 -308.55533 361.88603
33 489.01635 -308.55533
34 -287.78356 489.01635
35 423.37673 -287.78356
36 399.81899 423.37673
37 -276.81961 399.81899
> 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/7jbgw1290510042.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/html/rcomp/tmp/8jbgw1290510042.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/html/rcomp/tmp/9jbgw1290510042.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/html/rcomp/tmp/10b3xg1290510042.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/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11flwm1290510042.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12i4ds1290510042.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13zfch1290510043.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14s6bj1290510043.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/15d6a71290510043.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/16z78d1290510043.tab")
+ }
>
> try(system("convert tmp/1n2in1290510042.ps tmp/1n2in1290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n2in1290510042.ps tmp/2n2in1290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/3fb081290510042.ps tmp/3fb081290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fb081290510042.ps tmp/4fb081290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/5fb081290510042.ps tmp/5fb081290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q2ha1290510042.ps tmp/6q2ha1290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jbgw1290510042.ps tmp/7jbgw1290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jbgw1290510042.ps tmp/8jbgw1290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jbgw1290510042.ps tmp/9jbgw1290510042.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b3xg1290510042.ps tmp/10b3xg1290510042.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.259 1.580 6.500