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(1.4,0.0,1.6,0.0,1.7,0.0,2.0,0.0,2.0,0.0,2.1,0.0,2.5,0.0,2.5,0.0,2.6,0.0,2.7,0.0,3.7,0.0,4.0,0.0,5.0,0.0,5.1,0.0,5.1,0.0,5.0,0.0,5.1,0.0,4.7,0.0,4.5,0.0,4.5,0.0,4.6,0.0,4.6,0.0,4.6,0.0,4.6,0.0,5.3,0.0,5.4,0.0,5.3,0.0,5.2,0.0,5.0,0.0,4.2,0.0,4.3,0.0,4.3,0.0,4.3,0.0,4.0,0.0,4.0,0.0,4.1,0.0,4.4,0.0,3.6,0.0,3.7,0.0,3.8,0.0,3.3,0.0,3.3,0.0,3.3,0.0,3.5,0.0,3.3,1.0,3.3,1.0,3.4,1.0,3.4,1.0,5.2,1.0,5.3,1.0,4.8,1.0,5.0,1.0,4.6,1.0,4.6,1.0,3.5,1.0,3.5,1.0),dim=c(2,56),dimnames=list(c('IndGez','InvlCrisis'),1:56))
> y <- array(NA,dim=c(2,56),dimnames=list(c('IndGez','InvlCrisis'),1:56))
> 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
IndGez InvlCrisis
1 1.4 0
2 1.6 0
3 1.7 0
4 2.0 0
5 2.0 0
6 2.1 0
7 2.5 0
8 2.5 0
9 2.6 0
10 2.7 0
11 3.7 0
12 4.0 0
13 5.0 0
14 5.1 0
15 5.1 0
16 5.0 0
17 5.1 0
18 4.7 0
19 4.5 0
20 4.5 0
21 4.6 0
22 4.6 0
23 4.6 0
24 4.6 0
25 5.3 0
26 5.4 0
27 5.3 0
28 5.2 0
29 5.0 0
30 4.2 0
31 4.3 0
32 4.3 0
33 4.3 0
34 4.0 0
35 4.0 0
36 4.1 0
37 4.4 0
38 3.6 0
39 3.7 0
40 3.8 0
41 3.3 0
42 3.3 0
43 3.3 0
44 3.5 0
45 3.3 1
46 3.3 1
47 3.4 1
48 3.4 1
49 5.2 1
50 5.3 1
51 4.8 1
52 5.0 1
53 4.6 1
54 4.6 1
55 3.5 1
56 3.5 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) InvlCrisis
3.8750 0.2833
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.4750 -0.6833 0.2750 0.7500 1.5250
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.8750 0.1624 23.856 <2e-16 ***
InvlCrisis 0.2833 0.3509 0.807 0.423
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.077 on 54 degrees of freedom
Multiple R-squared: 0.01193, Adjusted R-squared: -0.006368
F-statistic: 0.652 on 1 and 54 DF, p-value: 0.4230
> 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,] 0.05270944 0.1054188797 9.472906e-01
[2,] 0.03379427 0.0675885347 9.662057e-01
[3,] 0.06512743 0.1302548591 9.348726e-01
[4,] 0.07967260 0.1593452093 9.203274e-01
[5,] 0.10465825 0.2093164927 8.953418e-01
[6,] 0.14714264 0.2942852723 8.528574e-01
[7,] 0.53710565 0.9257886949 4.628943e-01
[8,] 0.81450553 0.3709889445 1.854945e-01
[9,] 0.98409340 0.0318131956 1.590660e-02
[10,] 0.99792755 0.0041449078 2.072454e-03
[11,] 0.99951899 0.0009620187 4.810094e-04
[12,] 0.99979317 0.0004136556 2.068278e-04
[13,] 0.99990494 0.0001901247 9.506237e-05
[14,] 0.99989706 0.0002058746 1.029373e-04
[15,] 0.99985030 0.0002993962 1.496981e-04
[16,] 0.99977398 0.0004520473 2.260236e-04
[17,] 0.99967761 0.0006447879 3.223939e-04
[18,] 0.99952846 0.0009430885 4.715443e-04
[19,] 0.99929953 0.0014009491 7.004745e-04
[20,] 0.99895124 0.0020975134 1.048757e-03
[21,] 0.99935007 0.0012998533 6.499267e-04
[22,] 0.99969247 0.0006150588 3.075294e-04
[23,] 0.99984511 0.0003097872 1.548936e-04
[24,] 0.99991960 0.0001608035 8.040176e-05
[25,] 0.99994620 0.0001076041 5.380205e-05
[26,] 0.99988805 0.0002239008 1.119504e-04
[27,] 0.99979413 0.0004117434 2.058717e-04
[28,] 0.99963880 0.0007223943 3.611972e-04
[29,] 0.99939904 0.0012019275 6.009637e-04
[30,] 0.99880559 0.0023888199 1.194410e-03
[31,] 0.99772604 0.0045479132 2.273957e-03
[32,] 0.99611803 0.0077639309 3.881965e-03
[33,] 0.99552303 0.0089539344 4.476967e-03
[34,] 0.99148247 0.0170350579 8.517529e-03
[35,] 0.98473112 0.0305377521 1.526888e-02
[36,] 0.97503951 0.0499209856 2.496049e-02
[37,] 0.95780385 0.0843922944 4.219615e-02
[38,] 0.93123838 0.1375232382 6.876162e-02
[39,] 0.89256787 0.2148642537 1.074321e-01
[40,] 0.83559413 0.3288117461 1.644059e-01
[41,] 0.81471232 0.3705753665 1.852877e-01
[42,] 0.80520220 0.3895955950 1.947978e-01
[43,] 0.79825812 0.4034837507 2.017419e-01
[44,] 0.82016790 0.3596642040 1.798321e-01
[45,] 0.78579342 0.4284131638 2.142066e-01
[46,] 0.78069937 0.4386012501 2.193006e-01
[47,] 0.67061241 0.6587751847 3.293876e-01
> postscript(file="/var/www/html/rcomp/tmp/1vzkp1258725189.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2mgir1258725189.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a8xc1258725189.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4supt1258725189.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z9br1258725189.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 = 56
Frequency = 1
1 2 3 4 5 6 7
-2.4750000 -2.2750000 -2.1750000 -1.8750000 -1.8750000 -1.7750000 -1.3750000
8 9 10 11 12 13 14
-1.3750000 -1.2750000 -1.1750000 -0.1750000 0.1250000 1.1250000 1.2250000
15 16 17 18 19 20 21
1.2250000 1.1250000 1.2250000 0.8250000 0.6250000 0.6250000 0.7250000
22 23 24 25 26 27 28
0.7250000 0.7250000 0.7250000 1.4250000 1.5250000 1.4250000 1.3250000
29 30 31 32 33 34 35
1.1250000 0.3250000 0.4250000 0.4250000 0.4250000 0.1250000 0.1250000
36 37 38 39 40 41 42
0.2250000 0.5250000 -0.2750000 -0.1750000 -0.0750000 -0.5750000 -0.5750000
43 44 45 46 47 48 49
-0.5750000 -0.3750000 -0.8583333 -0.8583333 -0.7583333 -0.7583333 1.0416667
50 51 52 53 54 55 56
1.1416667 0.6416667 0.8416667 0.4416667 0.4416667 -0.6583333 -0.6583333
> postscript(file="/var/www/html/rcomp/tmp/69ems1258725189.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.4750000 NA
1 -2.2750000 -2.4750000
2 -2.1750000 -2.2750000
3 -1.8750000 -2.1750000
4 -1.8750000 -1.8750000
5 -1.7750000 -1.8750000
6 -1.3750000 -1.7750000
7 -1.3750000 -1.3750000
8 -1.2750000 -1.3750000
9 -1.1750000 -1.2750000
10 -0.1750000 -1.1750000
11 0.1250000 -0.1750000
12 1.1250000 0.1250000
13 1.2250000 1.1250000
14 1.2250000 1.2250000
15 1.1250000 1.2250000
16 1.2250000 1.1250000
17 0.8250000 1.2250000
18 0.6250000 0.8250000
19 0.6250000 0.6250000
20 0.7250000 0.6250000
21 0.7250000 0.7250000
22 0.7250000 0.7250000
23 0.7250000 0.7250000
24 1.4250000 0.7250000
25 1.5250000 1.4250000
26 1.4250000 1.5250000
27 1.3250000 1.4250000
28 1.1250000 1.3250000
29 0.3250000 1.1250000
30 0.4250000 0.3250000
31 0.4250000 0.4250000
32 0.4250000 0.4250000
33 0.1250000 0.4250000
34 0.1250000 0.1250000
35 0.2250000 0.1250000
36 0.5250000 0.2250000
37 -0.2750000 0.5250000
38 -0.1750000 -0.2750000
39 -0.0750000 -0.1750000
40 -0.5750000 -0.0750000
41 -0.5750000 -0.5750000
42 -0.5750000 -0.5750000
43 -0.3750000 -0.5750000
44 -0.8583333 -0.3750000
45 -0.8583333 -0.8583333
46 -0.7583333 -0.8583333
47 -0.7583333 -0.7583333
48 1.0416667 -0.7583333
49 1.1416667 1.0416667
50 0.6416667 1.1416667
51 0.8416667 0.6416667
52 0.4416667 0.8416667
53 0.4416667 0.4416667
54 -0.6583333 0.4416667
55 -0.6583333 -0.6583333
56 NA -0.6583333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.2750000 -2.4750000
[2,] -2.1750000 -2.2750000
[3,] -1.8750000 -2.1750000
[4,] -1.8750000 -1.8750000
[5,] -1.7750000 -1.8750000
[6,] -1.3750000 -1.7750000
[7,] -1.3750000 -1.3750000
[8,] -1.2750000 -1.3750000
[9,] -1.1750000 -1.2750000
[10,] -0.1750000 -1.1750000
[11,] 0.1250000 -0.1750000
[12,] 1.1250000 0.1250000
[13,] 1.2250000 1.1250000
[14,] 1.2250000 1.2250000
[15,] 1.1250000 1.2250000
[16,] 1.2250000 1.1250000
[17,] 0.8250000 1.2250000
[18,] 0.6250000 0.8250000
[19,] 0.6250000 0.6250000
[20,] 0.7250000 0.6250000
[21,] 0.7250000 0.7250000
[22,] 0.7250000 0.7250000
[23,] 0.7250000 0.7250000
[24,] 1.4250000 0.7250000
[25,] 1.5250000 1.4250000
[26,] 1.4250000 1.5250000
[27,] 1.3250000 1.4250000
[28,] 1.1250000 1.3250000
[29,] 0.3250000 1.1250000
[30,] 0.4250000 0.3250000
[31,] 0.4250000 0.4250000
[32,] 0.4250000 0.4250000
[33,] 0.1250000 0.4250000
[34,] 0.1250000 0.1250000
[35,] 0.2250000 0.1250000
[36,] 0.5250000 0.2250000
[37,] -0.2750000 0.5250000
[38,] -0.1750000 -0.2750000
[39,] -0.0750000 -0.1750000
[40,] -0.5750000 -0.0750000
[41,] -0.5750000 -0.5750000
[42,] -0.5750000 -0.5750000
[43,] -0.3750000 -0.5750000
[44,] -0.8583333 -0.3750000
[45,] -0.8583333 -0.8583333
[46,] -0.7583333 -0.8583333
[47,] -0.7583333 -0.7583333
[48,] 1.0416667 -0.7583333
[49,] 1.1416667 1.0416667
[50,] 0.6416667 1.1416667
[51,] 0.8416667 0.6416667
[52,] 0.4416667 0.8416667
[53,] 0.4416667 0.4416667
[54,] -0.6583333 0.4416667
[55,] -0.6583333 -0.6583333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.2750000 -2.4750000
2 -2.1750000 -2.2750000
3 -1.8750000 -2.1750000
4 -1.8750000 -1.8750000
5 -1.7750000 -1.8750000
6 -1.3750000 -1.7750000
7 -1.3750000 -1.3750000
8 -1.2750000 -1.3750000
9 -1.1750000 -1.2750000
10 -0.1750000 -1.1750000
11 0.1250000 -0.1750000
12 1.1250000 0.1250000
13 1.2250000 1.1250000
14 1.2250000 1.2250000
15 1.1250000 1.2250000
16 1.2250000 1.1250000
17 0.8250000 1.2250000
18 0.6250000 0.8250000
19 0.6250000 0.6250000
20 0.7250000 0.6250000
21 0.7250000 0.7250000
22 0.7250000 0.7250000
23 0.7250000 0.7250000
24 1.4250000 0.7250000
25 1.5250000 1.4250000
26 1.4250000 1.5250000
27 1.3250000 1.4250000
28 1.1250000 1.3250000
29 0.3250000 1.1250000
30 0.4250000 0.3250000
31 0.4250000 0.4250000
32 0.4250000 0.4250000
33 0.1250000 0.4250000
34 0.1250000 0.1250000
35 0.2250000 0.1250000
36 0.5250000 0.2250000
37 -0.2750000 0.5250000
38 -0.1750000 -0.2750000
39 -0.0750000 -0.1750000
40 -0.5750000 -0.0750000
41 -0.5750000 -0.5750000
42 -0.5750000 -0.5750000
43 -0.3750000 -0.5750000
44 -0.8583333 -0.3750000
45 -0.8583333 -0.8583333
46 -0.7583333 -0.8583333
47 -0.7583333 -0.7583333
48 1.0416667 -0.7583333
49 1.1416667 1.0416667
50 0.6416667 1.1416667
51 0.8416667 0.6416667
52 0.4416667 0.8416667
53 0.4416667 0.4416667
54 -0.6583333 0.4416667
55 -0.6583333 -0.6583333
> 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/7iauf1258725189.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wl561258725189.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9jfq51258725189.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104m291258725189.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vcss1258725190.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/12eqzd1258725190.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/1304rz1258725190.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/14myk01258725190.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/15ft051258725190.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/16w81r1258725190.tab")
+ }
>
> system("convert tmp/1vzkp1258725189.ps tmp/1vzkp1258725189.png")
> system("convert tmp/2mgir1258725189.ps tmp/2mgir1258725189.png")
> system("convert tmp/3a8xc1258725189.ps tmp/3a8xc1258725189.png")
> system("convert tmp/4supt1258725189.ps tmp/4supt1258725189.png")
> system("convert tmp/5z9br1258725189.ps tmp/5z9br1258725189.png")
> system("convert tmp/69ems1258725189.ps tmp/69ems1258725189.png")
> system("convert tmp/7iauf1258725189.ps tmp/7iauf1258725189.png")
> system("convert tmp/8wl561258725189.ps tmp/8wl561258725189.png")
> system("convert tmp/9jfq51258725189.ps tmp/9jfq51258725189.png")
> system("convert tmp/104m291258725189.ps tmp/104m291258725189.png")
>
>
> proc.time()
user system elapsed
2.438 1.583 5.110