R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(149,134,123,116,117,111,105,102,95,93,124,130,124,115,106,105,105,101,95,93,84,87,116,120,117,109,105,107,109,109,108,107,99,103,131,137,135,124,118,121,121,118,113,107,100,102,130,136,133,120),dim=c(1,50),dimnames=list(c('-25'),1:50))
> y <- array(NA,dim=c(1,50),dimnames=list(c('-25'),1:50))
> 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 = 'Include Monthly 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
-25 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 149 1 0 0 0 0 0 0 0 0 0 0
2 134 0 1 0 0 0 0 0 0 0 0 0
3 123 0 0 1 0 0 0 0 0 0 0 0
4 116 0 0 0 1 0 0 0 0 0 0 0
5 117 0 0 0 0 1 0 0 0 0 0 0
6 111 0 0 0 0 0 1 0 0 0 0 0
7 105 0 0 0 0 0 0 1 0 0 0 0
8 102 0 0 0 0 0 0 0 1 0 0 0
9 95 0 0 0 0 0 0 0 0 1 0 0
10 93 0 0 0 0 0 0 0 0 0 1 0
11 124 0 0 0 0 0 0 0 0 0 0 1
12 130 0 0 0 0 0 0 0 0 0 0 0
13 124 1 0 0 0 0 0 0 0 0 0 0
14 115 0 1 0 0 0 0 0 0 0 0 0
15 106 0 0 1 0 0 0 0 0 0 0 0
16 105 0 0 0 1 0 0 0 0 0 0 0
17 105 0 0 0 0 1 0 0 0 0 0 0
18 101 0 0 0 0 0 1 0 0 0 0 0
19 95 0 0 0 0 0 0 1 0 0 0 0
20 93 0 0 0 0 0 0 0 1 0 0 0
21 84 0 0 0 0 0 0 0 0 1 0 0
22 87 0 0 0 0 0 0 0 0 0 1 0
23 116 0 0 0 0 0 0 0 0 0 0 1
24 120 0 0 0 0 0 0 0 0 0 0 0
25 117 1 0 0 0 0 0 0 0 0 0 0
26 109 0 1 0 0 0 0 0 0 0 0 0
27 105 0 0 1 0 0 0 0 0 0 0 0
28 107 0 0 0 1 0 0 0 0 0 0 0
29 109 0 0 0 0 1 0 0 0 0 0 0
30 109 0 0 0 0 0 1 0 0 0 0 0
31 108 0 0 0 0 0 0 1 0 0 0 0
32 107 0 0 0 0 0 0 0 1 0 0 0
33 99 0 0 0 0 0 0 0 0 1 0 0
34 103 0 0 0 0 0 0 0 0 0 1 0
35 131 0 0 0 0 0 0 0 0 0 0 1
36 137 0 0 0 0 0 0 0 0 0 0 0
37 135 1 0 0 0 0 0 0 0 0 0 0
38 124 0 1 0 0 0 0 0 0 0 0 0
39 118 0 0 1 0 0 0 0 0 0 0 0
40 121 0 0 0 1 0 0 0 0 0 0 0
41 121 0 0 0 0 1 0 0 0 0 0 0
42 118 0 0 0 0 0 1 0 0 0 0 0
43 113 0 0 0 0 0 0 1 0 0 0 0
44 107 0 0 0 0 0 0 0 1 0 0 0
45 100 0 0 0 0 0 0 0 0 1 0 0
46 102 0 0 0 0 0 0 0 0 0 1 0
47 130 0 0 0 0 0 0 0 0 0 0 1
48 136 0 0 0 0 0 0 0 0 0 0 0
49 133 1 0 0 0 0 0 0 0 0 0 0
50 120 0 1 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
130.75 0.85 -10.35 -17.75 -18.50 -17.75
M6 M7 M8 M9 M10 M11
-21.00 -25.50 -28.50 -36.25 -34.50 -5.50
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-14.600 -7.187 0.875 5.187 17.400
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 130.750 4.155 31.465 < 2e-16 ***
M1 0.850 5.575 0.152 0.879627
M2 -10.350 5.575 -1.856 0.071151 .
M3 -17.750 5.877 -3.020 0.004496 **
M4 -18.500 5.877 -3.148 0.003194 **
M5 -17.750 5.877 -3.020 0.004496 **
M6 -21.000 5.877 -3.573 0.000978 ***
M7 -25.500 5.877 -4.339 0.000102 ***
M8 -28.500 5.877 -4.850 2.12e-05 ***
M9 -36.250 5.877 -6.168 3.34e-07 ***
M10 -34.500 5.877 -5.871 8.56e-07 ***
M11 -5.500 5.877 -0.936 0.355230
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.311 on 38 degrees of freedom
Multiple R-squared: 0.73, Adjusted R-squared: 0.6518
F-statistic: 9.339 on 11 and 38 DF, p-value: 8.222e-08
> 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,] 0.9335805 1.328390e-01 6.641951e-02
[2,] 0.9002037 1.995926e-01 9.979631e-02
[3,] 0.8708137 2.583725e-01 1.291863e-01
[4,] 0.8399520 3.200959e-01 1.600480e-01
[5,] 0.8285777 3.428446e-01 1.714223e-01
[6,] 0.8112806 3.774387e-01 1.887194e-01
[7,] 0.8232589 3.534821e-01 1.767411e-01
[8,] 0.8266794 3.466412e-01 1.733206e-01
[9,] 0.8391288 3.217424e-01 1.608712e-01
[10,] 0.8916145 2.167710e-01 1.083855e-01
[11,] 0.9733948 5.321032e-02 2.660516e-02
[12,] 0.9892754 2.144922e-02 1.072461e-02
[13,] 0.9939547 1.209069e-02 6.045346e-03
[14,] 0.9982953 3.409490e-03 1.704745e-03
[15,] 0.9997367 5.266825e-04 2.633412e-04
[16,] 0.9999745 5.102517e-05 2.551259e-05
[17,] 0.9999884 2.317566e-05 1.158783e-05
[18,] 0.9999231 1.537364e-04 7.686821e-05
[19,] 0.9995658 8.683382e-04 4.341691e-04
[20,] 0.9977533 4.493373e-03 2.246687e-03
[21,] 0.9882442 2.351162e-02 1.175581e-02
> postscript(file="/var/www/html/rcomp/tmp/1oei91291128284.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2gnzu1291128284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gnzu1291128284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gnzu1291128284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gnzu1291128284.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 = 50
Frequency = 1
1 2 3 4 5 6 7 8 9 10 11
17.40 13.60 10.00 3.75 4.00 1.25 -0.25 -0.25 0.50 -3.25 -1.25
12 13 14 15 16 17 18 19 20 21 22
-0.75 -7.60 -5.40 -7.00 -7.25 -8.00 -8.75 -10.25 -9.25 -10.50 -9.25
23 24 25 26 27 28 29 30 31 32 33
-9.25 -10.75 -14.60 -11.40 -8.00 -5.25 -4.00 -0.75 2.75 4.75 4.50
34 35 36 37 38 39 40 41 42 43 44
6.75 5.75 6.25 3.40 3.60 5.00 8.75 8.00 8.25 7.75 4.75
45 46 47 48 49 50
5.50 5.75 4.75 5.25 1.40 -0.40
> postscript(file="/var/www/html/rcomp/tmp/69wyx1291128284.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 = 50
Frequency = 1
lag(myerror, k = 1) myerror
0 17.40 NA
1 13.60 17.40
2 10.00 13.60
3 3.75 10.00
4 4.00 3.75
5 1.25 4.00
6 -0.25 1.25
7 -0.25 -0.25
8 0.50 -0.25
9 -3.25 0.50
10 -1.25 -3.25
11 -0.75 -1.25
12 -7.60 -0.75
13 -5.40 -7.60
14 -7.00 -5.40
15 -7.25 -7.00
16 -8.00 -7.25
17 -8.75 -8.00
18 -10.25 -8.75
19 -9.25 -10.25
20 -10.50 -9.25
21 -9.25 -10.50
22 -9.25 -9.25
23 -10.75 -9.25
24 -14.60 -10.75
25 -11.40 -14.60
26 -8.00 -11.40
27 -5.25 -8.00
28 -4.00 -5.25
29 -0.75 -4.00
30 2.75 -0.75
31 4.75 2.75
32 4.50 4.75
33 6.75 4.50
34 5.75 6.75
35 6.25 5.75
36 3.40 6.25
37 3.60 3.40
38 5.00 3.60
39 8.75 5.00
40 8.00 8.75
41 8.25 8.00
42 7.75 8.25
43 4.75 7.75
44 5.50 4.75
45 5.75 5.50
46 4.75 5.75
47 5.25 4.75
48 1.40 5.25
49 -0.40 1.40
50 NA -0.40
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 13.60 17.40
[2,] 10.00 13.60
[3,] 3.75 10.00
[4,] 4.00 3.75
[5,] 1.25 4.00
[6,] -0.25 1.25
[7,] -0.25 -0.25
[8,] 0.50 -0.25
[9,] -3.25 0.50
[10,] -1.25 -3.25
[11,] -0.75 -1.25
[12,] -7.60 -0.75
[13,] -5.40 -7.60
[14,] -7.00 -5.40
[15,] -7.25 -7.00
[16,] -8.00 -7.25
[17,] -8.75 -8.00
[18,] -10.25 -8.75
[19,] -9.25 -10.25
[20,] -10.50 -9.25
[21,] -9.25 -10.50
[22,] -9.25 -9.25
[23,] -10.75 -9.25
[24,] -14.60 -10.75
[25,] -11.40 -14.60
[26,] -8.00 -11.40
[27,] -5.25 -8.00
[28,] -4.00 -5.25
[29,] -0.75 -4.00
[30,] 2.75 -0.75
[31,] 4.75 2.75
[32,] 4.50 4.75
[33,] 6.75 4.50
[34,] 5.75 6.75
[35,] 6.25 5.75
[36,] 3.40 6.25
[37,] 3.60 3.40
[38,] 5.00 3.60
[39,] 8.75 5.00
[40,] 8.00 8.75
[41,] 8.25 8.00
[42,] 7.75 8.25
[43,] 4.75 7.75
[44,] 5.50 4.75
[45,] 5.75 5.50
[46,] 4.75 5.75
[47,] 5.25 4.75
[48,] 1.40 5.25
[49,] -0.40 1.40
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 13.60 17.40
2 10.00 13.60
3 3.75 10.00
4 4.00 3.75
5 1.25 4.00
6 -0.25 1.25
7 -0.25 -0.25
8 0.50 -0.25
9 -3.25 0.50
10 -1.25 -3.25
11 -0.75 -1.25
12 -7.60 -0.75
13 -5.40 -7.60
14 -7.00 -5.40
15 -7.25 -7.00
16 -8.00 -7.25
17 -8.75 -8.00
18 -10.25 -8.75
19 -9.25 -10.25
20 -10.50 -9.25
21 -9.25 -10.50
22 -9.25 -9.25
23 -10.75 -9.25
24 -14.60 -10.75
25 -11.40 -14.60
26 -8.00 -11.40
27 -5.25 -8.00
28 -4.00 -5.25
29 -0.75 -4.00
30 2.75 -0.75
31 4.75 2.75
32 4.50 4.75
33 6.75 4.50
34 5.75 6.75
35 6.25 5.75
36 3.40 6.25
37 3.60 3.40
38 5.00 3.60
39 8.75 5.00
40 8.00 8.75
41 8.25 8.00
42 7.75 8.25
43 4.75 7.75
44 5.50 4.75
45 5.75 5.50
46 4.75 5.75
47 5.25 4.75
48 1.40 5.25
49 -0.40 1.40
> 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/72ny01291128284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/82ny01291128284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/92ny01291128284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dxx31291128284.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gxvq1291128284.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/121yce1291128284.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/13xqsn1291128284.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/14iqqb1291128284.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/154q6z1291128284.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/167rnn1291128284.tab")
+ }
>
> try(system("convert tmp/1oei91291128284.ps tmp/1oei91291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gnzu1291128284.ps tmp/2gnzu1291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gnzu1291128284.ps tmp/3gnzu1291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gnzu1291128284.ps tmp/4gnzu1291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gnzu1291128284.ps tmp/5gnzu1291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/69wyx1291128284.ps tmp/69wyx1291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/72ny01291128284.ps tmp/72ny01291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/82ny01291128284.ps tmp/82ny01291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/92ny01291128284.ps tmp/92ny01291128284.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dxx31291128284.ps tmp/10dxx31291128284.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.357 1.600 6.358