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.
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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(6.30,0.00,3,2.10,3406028945.00,4,9.10,102325246.00,4,15.80,-1638272164.00,1,5.20,2204119983.00,4,10.90,0.51851394,1,8.30,1717337583.00,1,11.00,-0.37161107,4,3.20,2667452953.00,5,6.30,-1124938737.00,1,6.60,-0.105130343,2,9.50,-0.698970004,2,3.30,1441852176.00,5,11.00,-0.920818754,2,4.70,1929418926.00,1,10.40,-0.995678626,3,7.40,0.017033339,4,2.10,2716837723.00,5,17.90,-2.00,1,6.10,1792391689.00,1,11.90,-1638272164.00,3,13.80,0.230448921,1,14.30,0.544068044,1,15.20,-0.318758763,2,10.00,1.00,4,11.90,0.209515015,2,6.50,2283301229.00,4,7.50,0.397940009,5,10.60,-0.552841969,3,7.40,0.626853415,1,8.40,0.832508913,2,5.70,-0.124938737,2,4.90,0.556302501,3,3.20,1744292983.00,5,11.00,-0.045757491,2,4.90,0.301029996,3,13.20,-0.982966661,2,9.70,0.622214023,4,12.80,0.544068044,1),dim=c(3,39),dimnames=list(c('Sws','Wb','danger'),1:39))
> y <- array(NA,dim=c(3,39),dimnames=list(c('Sws','Wb','danger'),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 danger
1 6.3 0.000000e+00 3
2 2.1 3.406029e+09 4
3 9.1 1.023252e+08 4
4 15.8 -1.638272e+09 1
5 5.2 2.204120e+09 4
6 10.9 5.185139e-01 1
7 8.3 1.717338e+09 1
8 11.0 -3.716111e-01 4
9 3.2 2.667453e+09 5
10 6.3 -1.124939e+09 1
11 6.6 -1.051303e-01 2
12 9.5 -6.989700e-01 2
13 3.3 1.441852e+09 5
14 11.0 -9.208188e-01 2
15 4.7 1.929419e+09 1
16 10.4 -9.956786e-01 3
17 7.4 1.703334e-02 4
18 2.1 2.716838e+09 5
19 17.9 -2.000000e+00 1
20 6.1 1.792392e+09 1
21 11.9 -1.638272e+09 3
22 13.8 2.304489e-01 1
23 14.3 5.440680e-01 1
24 15.2 -3.187588e-01 2
25 10.0 1.000000e+00 4
26 11.9 2.095150e-01 2
27 6.5 2.283301e+09 4
28 7.5 3.979400e-01 5
29 10.6 -5.528420e-01 3
30 7.4 6.268534e-01 1
31 8.4 8.325089e-01 2
32 5.7 -1.249387e-01 2
33 4.9 5.563025e-01 3
34 3.2 1.744293e+09 5
35 11.0 -4.575749e-02 2
36 4.9 3.010300e-01 3
37 13.2 -9.829667e-01 2
38 9.7 6.222140e-01 4
39 12.8 5.440680e-01 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Wb danger
1.197e+01 -1.790e-09 -9.159e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7683 -1.6248 0.3188 1.7190 6.8451
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.197e+01 9.962e-01 12.017 3.70e-14 ***
Wb -1.790e-09 4.419e-10 -4.051 0.00026 ***
danger -9.159e-01 3.564e-01 -2.570 0.01445 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.839 on 36 degrees of freedom
Multiple R-squared: 0.515, Adjusted R-squared: 0.4881
F-statistic: 19.11 on 2 and 36 DF, p-value: 2.204e-06
> 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,] 0.30088468 0.6017694 0.6991153
[2,] 0.15657232 0.3131446 0.8434277
[3,] 0.14178272 0.2835654 0.8582173
[4,] 0.07185016 0.1437003 0.9281498
[5,] 0.53149454 0.9370109 0.4685055
[6,] 0.53264108 0.9347178 0.4673589
[7,] 0.42272369 0.8454474 0.5772763
[8,] 0.36249369 0.7249874 0.6375063
[9,] 0.29498947 0.5899789 0.7050105
[10,] 0.26417827 0.5283565 0.7358217
[11,] 0.20456202 0.4091240 0.7954380
[12,] 0.14570635 0.2914127 0.8542936
[13,] 0.09722962 0.1944592 0.9027704
[14,] 0.50027528 0.9994494 0.4997247
[15,] 0.44881385 0.8976277 0.5511861
[16,] 0.35380517 0.7076103 0.6461948
[17,] 0.33338389 0.6667678 0.6666161
[18,] 0.34204157 0.6840831 0.6579584
[19,] 0.54529386 0.9094123 0.4547061
[20,] 0.48609412 0.9721882 0.5139059
[21,] 0.44119199 0.8823840 0.5588080
[22,] 0.39113247 0.7822649 0.6088675
[23,] 0.29035016 0.5807003 0.7096498
[24,] 0.24402159 0.4880432 0.7559784
[25,] 0.26334421 0.5266884 0.7366558
[26,] 0.18334054 0.3666811 0.8166595
[27,] 0.27683126 0.5536625 0.7231687
[28,] 0.35476332 0.7095266 0.6452367
> postscript(file="/var/www/html/rcomp/tmp/16e2y1292360056.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26e2y1292360056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3z5jj1292360056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4z5jj1292360056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z5jj1292360056.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
-2.9230932 -0.1110547 0.9759615 1.8128948 0.8377632 -0.1549182 0.3187804
8 9 10 11 12 13 14
2.6928193 0.5829512 -6.7683387 -3.5390057 -0.6390057 -1.5106346 0.8609943
15 16 17 18 19 20 21
-2.9016355 1.1769068 -0.9071807 -0.4286597 6.8450818 -1.7468874 -0.2552802
22 23 24 25 26 27 28
2.7450818 3.2450818 5.0609943 1.6928193 1.7609943 2.2794822 0.1087318
29 30 31 32 33 34 35
1.3769068 -3.6549182 -1.7390057 -4.4390057 -4.3230932 -1.0693247 0.8609943
36 37 38 39
-4.3230932 3.0609943 1.3928193 1.7450818
> postscript(file="/var/www/html/rcomp/tmp/6axjm1292360056.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 -2.9230932 NA
1 -0.1110547 -2.9230932
2 0.9759615 -0.1110547
3 1.8128948 0.9759615
4 0.8377632 1.8128948
5 -0.1549182 0.8377632
6 0.3187804 -0.1549182
7 2.6928193 0.3187804
8 0.5829512 2.6928193
9 -6.7683387 0.5829512
10 -3.5390057 -6.7683387
11 -0.6390057 -3.5390057
12 -1.5106346 -0.6390057
13 0.8609943 -1.5106346
14 -2.9016355 0.8609943
15 1.1769068 -2.9016355
16 -0.9071807 1.1769068
17 -0.4286597 -0.9071807
18 6.8450818 -0.4286597
19 -1.7468874 6.8450818
20 -0.2552802 -1.7468874
21 2.7450818 -0.2552802
22 3.2450818 2.7450818
23 5.0609943 3.2450818
24 1.6928193 5.0609943
25 1.7609943 1.6928193
26 2.2794822 1.7609943
27 0.1087318 2.2794822
28 1.3769068 0.1087318
29 -3.6549182 1.3769068
30 -1.7390057 -3.6549182
31 -4.4390057 -1.7390057
32 -4.3230932 -4.4390057
33 -1.0693247 -4.3230932
34 0.8609943 -1.0693247
35 -4.3230932 0.8609943
36 3.0609943 -4.3230932
37 1.3928193 3.0609943
38 1.7450818 1.3928193
39 NA 1.7450818
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1110547 -2.9230932
[2,] 0.9759615 -0.1110547
[3,] 1.8128948 0.9759615
[4,] 0.8377632 1.8128948
[5,] -0.1549182 0.8377632
[6,] 0.3187804 -0.1549182
[7,] 2.6928193 0.3187804
[8,] 0.5829512 2.6928193
[9,] -6.7683387 0.5829512
[10,] -3.5390057 -6.7683387
[11,] -0.6390057 -3.5390057
[12,] -1.5106346 -0.6390057
[13,] 0.8609943 -1.5106346
[14,] -2.9016355 0.8609943
[15,] 1.1769068 -2.9016355
[16,] -0.9071807 1.1769068
[17,] -0.4286597 -0.9071807
[18,] 6.8450818 -0.4286597
[19,] -1.7468874 6.8450818
[20,] -0.2552802 -1.7468874
[21,] 2.7450818 -0.2552802
[22,] 3.2450818 2.7450818
[23,] 5.0609943 3.2450818
[24,] 1.6928193 5.0609943
[25,] 1.7609943 1.6928193
[26,] 2.2794822 1.7609943
[27,] 0.1087318 2.2794822
[28,] 1.3769068 0.1087318
[29,] -3.6549182 1.3769068
[30,] -1.7390057 -3.6549182
[31,] -4.4390057 -1.7390057
[32,] -4.3230932 -4.4390057
[33,] -1.0693247 -4.3230932
[34,] 0.8609943 -1.0693247
[35,] -4.3230932 0.8609943
[36,] 3.0609943 -4.3230932
[37,] 1.3928193 3.0609943
[38,] 1.7450818 1.3928193
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1110547 -2.9230932
2 0.9759615 -0.1110547
3 1.8128948 0.9759615
4 0.8377632 1.8128948
5 -0.1549182 0.8377632
6 0.3187804 -0.1549182
7 2.6928193 0.3187804
8 0.5829512 2.6928193
9 -6.7683387 0.5829512
10 -3.5390057 -6.7683387
11 -0.6390057 -3.5390057
12 -1.5106346 -0.6390057
13 0.8609943 -1.5106346
14 -2.9016355 0.8609943
15 1.1769068 -2.9016355
16 -0.9071807 1.1769068
17 -0.4286597 -0.9071807
18 6.8450818 -0.4286597
19 -1.7468874 6.8450818
20 -0.2552802 -1.7468874
21 2.7450818 -0.2552802
22 3.2450818 2.7450818
23 5.0609943 3.2450818
24 1.6928193 5.0609943
25 1.7609943 1.6928193
26 2.2794822 1.7609943
27 0.1087318 2.2794822
28 1.3769068 0.1087318
29 -3.6549182 1.3769068
30 -1.7390057 -3.6549182
31 -4.4390057 -1.7390057
32 -4.3230932 -4.4390057
33 -1.0693247 -4.3230932
34 0.8609943 -1.0693247
35 -4.3230932 0.8609943
36 3.0609943 -4.3230932
37 1.3928193 3.0609943
38 1.7450818 1.3928193
> 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/73oi71292360056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83oi71292360056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93oi71292360056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vxhs1292360056.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11zgyg1292360056.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/12kyw41292360056.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/13y8uu1292360056.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/141rb01292360056.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/15n9r61292360056.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/16qapu1292360056.tab")
+ }
>
> try(system("convert tmp/16e2y1292360056.ps tmp/16e2y1292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/26e2y1292360056.ps tmp/26e2y1292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z5jj1292360056.ps tmp/3z5jj1292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z5jj1292360056.ps tmp/4z5jj1292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/5z5jj1292360056.ps tmp/5z5jj1292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/6axjm1292360056.ps tmp/6axjm1292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/73oi71292360056.ps tmp/73oi71292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/83oi71292360056.ps tmp/83oi71292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/93oi71292360056.ps tmp/93oi71292360056.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vxhs1292360056.ps tmp/10vxhs1292360056.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.325 1.621 5.862