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.
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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
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> x <- array(list(0,467,0,460,0,448,0,443,0,436,0,431,0,484,0,510,1,513,1,503,1,471,1,471,1,476,1,475,1,470,1,461,1,455,1,456,1,517,1,525,1,523,1,519,1,509,1,512,1,519,1,517,1,510,1,509,1,501,1,507,1,569,1,580,1,578,1,565,1,547,1,555),dim=c(2,36),dimnames=list(c('Dummy','Werkloosheid'),1:36))
> y <- array(NA,dim=c(2,36),dimnames=list(c('Dummy','Werkloosheid'),1:36))
> 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 = 'Include Monthly Dummies'
> par1 = '2'
> #'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
Werkloosheid Dummy M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 467 0 1 0 0 0 0 0 0 0 0 0 0 1
2 460 0 0 1 0 0 0 0 0 0 0 0 0 2
3 448 0 0 0 1 0 0 0 0 0 0 0 0 3
4 443 0 0 0 0 1 0 0 0 0 0 0 0 4
5 436 0 0 0 0 0 1 0 0 0 0 0 0 5
6 431 0 0 0 0 0 0 1 0 0 0 0 0 6
7 484 0 0 0 0 0 0 0 1 0 0 0 0 7
8 510 0 0 0 0 0 0 0 0 1 0 0 0 8
9 513 1 0 0 0 0 0 0 0 0 1 0 0 9
10 503 1 0 0 0 0 0 0 0 0 0 1 0 10
11 471 1 0 0 0 0 0 0 0 0 0 0 1 11
12 471 1 0 0 0 0 0 0 0 0 0 0 0 12
13 476 1 1 0 0 0 0 0 0 0 0 0 0 13
14 475 1 0 1 0 0 0 0 0 0 0 0 0 14
15 470 1 0 0 1 0 0 0 0 0 0 0 0 15
16 461 1 0 0 0 1 0 0 0 0 0 0 0 16
17 455 1 0 0 0 0 1 0 0 0 0 0 0 17
18 456 1 0 0 0 0 0 1 0 0 0 0 0 18
19 517 1 0 0 0 0 0 0 1 0 0 0 0 19
20 525 1 0 0 0 0 0 0 0 1 0 0 0 20
21 523 1 0 0 0 0 0 0 0 0 1 0 0 21
22 519 1 0 0 0 0 0 0 0 0 0 1 0 22
23 509 1 0 0 0 0 0 0 0 0 0 0 1 23
24 512 1 0 0 0 0 0 0 0 0 0 0 0 24
25 519 1 1 0 0 0 0 0 0 0 0 0 0 25
26 517 1 0 1 0 0 0 0 0 0 0 0 0 26
27 510 1 0 0 1 0 0 0 0 0 0 0 0 27
28 509 1 0 0 0 1 0 0 0 0 0 0 0 28
29 501 1 0 0 0 0 1 0 0 0 0 0 0 29
30 507 1 0 0 0 0 0 1 0 0 0 0 0 30
31 569 1 0 0 0 0 0 0 1 0 0 0 0 31
32 580 1 0 0 0 0 0 0 0 1 0 0 0 32
33 578 1 0 0 0 0 0 0 0 0 1 0 0 33
34 565 1 0 0 0 0 0 0 0 0 0 1 0 34
35 547 1 0 0 0 0 0 0 0 0 0 0 1 35
36 555 1 0 0 0 0 0 0 0 0 0 0 0 36
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Dummy M1 M2 M3 M4
449.7917 -16.3750 5.5312 -1.1042 -12.4063 -20.7083
M5 M6 M7 M8 M9 M10
-31.0104 -33.6458 21.7187 33.4167 35.2396 22.9375
M11 t
-0.3646 3.3021
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-15.0000 -3.5417 -0.3542 2.7396 14.6250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 449.7917 5.9246 75.920 < 2e-16 ***
Dummy -16.3750 4.9295 -3.322 0.003098 **
M1 5.5312 6.7445 0.820 0.420949
M2 -1.1042 6.7194 -0.164 0.870976
M3 -12.4063 6.6999 -1.852 0.077536 .
M4 -20.7083 6.6859 -3.097 0.005258 **
M5 -31.0104 6.6774 -4.644 0.000125 ***
M6 -33.6458 6.6746 -5.041 4.77e-05 ***
M7 21.7187 6.6774 3.253 0.003650 **
M8 33.4167 6.6859 4.998 5.29e-05 ***
M9 35.2396 6.5984 5.341 2.32e-05 ***
M10 22.9375 6.5841 3.484 0.002105 **
M11 -0.3646 6.5756 -0.055 0.956284
t 3.3021 0.1937 17.052 3.63e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.05 on 22 degrees of freedom
Multiple R-squared: 0.9749, Adjusted R-squared: 0.9601
F-statistic: 65.86 on 13 and 22 DF, p-value: 1.599e-14
> 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,] 0.3831803 0.7663606 0.6168197
[2,] 0.3392913 0.6785825 0.6607087
[3,] 0.4384563 0.8769126 0.5615437
> postscript(file="/var/www/html/rcomp/tmp/102us1230055184.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28vuq1230055184.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r70b1230055184.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jw471230055184.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54n4j1230055184.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 = 36
Frequency = 1
1 2 3 4 5
8.375000e+00 4.708333e+00 7.083333e-01 7.083333e-01 7.083333e-01
6 7 8 9 10
-4.958333e+00 -1.062500e+01 3.750000e-01 1.462500e+01 1.362500e+01
11 12 13 14 15
1.625000e+00 -2.041667e+00 -5.875000e+00 -3.541667e+00 -5.416667e-01
16 17 18 19 20
-4.541667e+00 -3.541667e+00 -3.208333e+00 -8.750000e-01 -7.875000e+00
21 22 23 24 25
-1.500000e+01 -1.000000e+01 -5.329071e-15 -6.666667e-01 -2.500000e+00
26 27 28 29 30
-1.166667e+00 -1.666667e-01 3.833333e+00 2.833333e+00 8.166667e+00
31 32 33 34 35
1.150000e+01 7.500000e+00 3.750000e-01 -3.625000e+00 -1.625000e+00
36
2.708333e+00
> postscript(file="/var/www/html/rcomp/tmp/6667u1230055184.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 = 36
Frequency = 1
lag(myerror, k = 1) myerror
0 8.375000e+00 NA
1 4.708333e+00 8.375000e+00
2 7.083333e-01 4.708333e+00
3 7.083333e-01 7.083333e-01
4 7.083333e-01 7.083333e-01
5 -4.958333e+00 7.083333e-01
6 -1.062500e+01 -4.958333e+00
7 3.750000e-01 -1.062500e+01
8 1.462500e+01 3.750000e-01
9 1.362500e+01 1.462500e+01
10 1.625000e+00 1.362500e+01
11 -2.041667e+00 1.625000e+00
12 -5.875000e+00 -2.041667e+00
13 -3.541667e+00 -5.875000e+00
14 -5.416667e-01 -3.541667e+00
15 -4.541667e+00 -5.416667e-01
16 -3.541667e+00 -4.541667e+00
17 -3.208333e+00 -3.541667e+00
18 -8.750000e-01 -3.208333e+00
19 -7.875000e+00 -8.750000e-01
20 -1.500000e+01 -7.875000e+00
21 -1.000000e+01 -1.500000e+01
22 -5.329071e-15 -1.000000e+01
23 -6.666667e-01 -5.329071e-15
24 -2.500000e+00 -6.666667e-01
25 -1.166667e+00 -2.500000e+00
26 -1.666667e-01 -1.166667e+00
27 3.833333e+00 -1.666667e-01
28 2.833333e+00 3.833333e+00
29 8.166667e+00 2.833333e+00
30 1.150000e+01 8.166667e+00
31 7.500000e+00 1.150000e+01
32 3.750000e-01 7.500000e+00
33 -3.625000e+00 3.750000e-01
34 -1.625000e+00 -3.625000e+00
35 2.708333e+00 -1.625000e+00
36 NA 2.708333e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.708333e+00 8.375000e+00
[2,] 7.083333e-01 4.708333e+00
[3,] 7.083333e-01 7.083333e-01
[4,] 7.083333e-01 7.083333e-01
[5,] -4.958333e+00 7.083333e-01
[6,] -1.062500e+01 -4.958333e+00
[7,] 3.750000e-01 -1.062500e+01
[8,] 1.462500e+01 3.750000e-01
[9,] 1.362500e+01 1.462500e+01
[10,] 1.625000e+00 1.362500e+01
[11,] -2.041667e+00 1.625000e+00
[12,] -5.875000e+00 -2.041667e+00
[13,] -3.541667e+00 -5.875000e+00
[14,] -5.416667e-01 -3.541667e+00
[15,] -4.541667e+00 -5.416667e-01
[16,] -3.541667e+00 -4.541667e+00
[17,] -3.208333e+00 -3.541667e+00
[18,] -8.750000e-01 -3.208333e+00
[19,] -7.875000e+00 -8.750000e-01
[20,] -1.500000e+01 -7.875000e+00
[21,] -1.000000e+01 -1.500000e+01
[22,] -5.329071e-15 -1.000000e+01
[23,] -6.666667e-01 -5.329071e-15
[24,] -2.500000e+00 -6.666667e-01
[25,] -1.166667e+00 -2.500000e+00
[26,] -1.666667e-01 -1.166667e+00
[27,] 3.833333e+00 -1.666667e-01
[28,] 2.833333e+00 3.833333e+00
[29,] 8.166667e+00 2.833333e+00
[30,] 1.150000e+01 8.166667e+00
[31,] 7.500000e+00 1.150000e+01
[32,] 3.750000e-01 7.500000e+00
[33,] -3.625000e+00 3.750000e-01
[34,] -1.625000e+00 -3.625000e+00
[35,] 2.708333e+00 -1.625000e+00
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.708333e+00 8.375000e+00
2 7.083333e-01 4.708333e+00
3 7.083333e-01 7.083333e-01
4 7.083333e-01 7.083333e-01
5 -4.958333e+00 7.083333e-01
6 -1.062500e+01 -4.958333e+00
7 3.750000e-01 -1.062500e+01
8 1.462500e+01 3.750000e-01
9 1.362500e+01 1.462500e+01
10 1.625000e+00 1.362500e+01
11 -2.041667e+00 1.625000e+00
12 -5.875000e+00 -2.041667e+00
13 -3.541667e+00 -5.875000e+00
14 -5.416667e-01 -3.541667e+00
15 -4.541667e+00 -5.416667e-01
16 -3.541667e+00 -4.541667e+00
17 -3.208333e+00 -3.541667e+00
18 -8.750000e-01 -3.208333e+00
19 -7.875000e+00 -8.750000e-01
20 -1.500000e+01 -7.875000e+00
21 -1.000000e+01 -1.500000e+01
22 -5.329071e-15 -1.000000e+01
23 -6.666667e-01 -5.329071e-15
24 -2.500000e+00 -6.666667e-01
25 -1.166667e+00 -2.500000e+00
26 -1.666667e-01 -1.166667e+00
27 3.833333e+00 -1.666667e-01
28 2.833333e+00 3.833333e+00
29 8.166667e+00 2.833333e+00
30 1.150000e+01 8.166667e+00
31 7.500000e+00 1.150000e+01
32 3.750000e-01 7.500000e+00
33 -3.625000e+00 3.750000e-01
34 -1.625000e+00 -3.625000e+00
35 2.708333e+00 -1.625000e+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/www/html/rcomp/tmp/7sqkf1230055184.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85ldb1230055184.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9f5by1230055184.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ik361230055184.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jw9w1230055184.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/12opwg1230055184.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/13dzr81230055184.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/141eip1230055184.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/15m27e1230055184.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/165nxq1230055184.tab")
+ }
>
> system("convert tmp/102us1230055184.ps tmp/102us1230055184.png")
> system("convert tmp/28vuq1230055184.ps tmp/28vuq1230055184.png")
> system("convert tmp/3r70b1230055184.ps tmp/3r70b1230055184.png")
> system("convert tmp/4jw471230055184.ps tmp/4jw471230055184.png")
> system("convert tmp/54n4j1230055184.ps tmp/54n4j1230055184.png")
> system("convert tmp/6667u1230055184.ps tmp/6667u1230055184.png")
> system("convert tmp/7sqkf1230055184.ps tmp/7sqkf1230055184.png")
> system("convert tmp/85ldb1230055184.ps tmp/85ldb1230055184.png")
> system("convert tmp/9f5by1230055184.ps tmp/9f5by1230055184.png")
> system("convert tmp/10ik361230055184.ps tmp/10ik361230055184.png")
>
>
> proc.time()
user system elapsed
2.132 1.533 2.809