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(22.680,1,22.052,1,21.467,1,21.383,1,21.777,1,21.928,1,21.814,1,22.937,1,23.595,1,20.830,1,19.650,1,19.195,1,19.644,0,18.483,0,18.079,0,19.178,0,18.391,0,18.441,0,18.584,0,20.108,0,20.148,0,19.394,0,17.745,0,17.696,0,17.032,0,16.438,0,15.683,0,15.594,0,15.713,0,15.937,0,16.171,0,15.928,0,16.348,0,15.579,0,15.305,0,15.648,0,14.954,0,15.137,0,15.839,0,16.050,0,15.168,0,17.064,0,16.005,0,14.886,0,14.931,0,14.544,0,13.812,0),dim=c(2,47),dimnames=list(c('gk','cr'),1:47))
> y <- array(NA,dim=c(2,47),dimnames=list(c('gk','cr'),1:47))
> 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 = '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
gk cr M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 22.680 1 1 0 0 0 0 0 0 0 0 0 0 1
2 22.052 1 0 1 0 0 0 0 0 0 0 0 0 2
3 21.467 1 0 0 1 0 0 0 0 0 0 0 0 3
4 21.383 1 0 0 0 1 0 0 0 0 0 0 0 4
5 21.777 1 0 0 0 0 1 0 0 0 0 0 0 5
6 21.928 1 0 0 0 0 0 1 0 0 0 0 0 6
7 21.814 1 0 0 0 0 0 0 1 0 0 0 0 7
8 22.937 1 0 0 0 0 0 0 0 1 0 0 0 8
9 23.595 1 0 0 0 0 0 0 0 0 1 0 0 9
10 20.830 1 0 0 0 0 0 0 0 0 0 1 0 10
11 19.650 1 0 0 0 0 0 0 0 0 0 0 1 11
12 19.195 1 0 0 0 0 0 0 0 0 0 0 0 12
13 19.644 0 1 0 0 0 0 0 0 0 0 0 0 13
14 18.483 0 0 1 0 0 0 0 0 0 0 0 0 14
15 18.079 0 0 0 1 0 0 0 0 0 0 0 0 15
16 19.178 0 0 0 0 1 0 0 0 0 0 0 0 16
17 18.391 0 0 0 0 0 1 0 0 0 0 0 0 17
18 18.441 0 0 0 0 0 0 1 0 0 0 0 0 18
19 18.584 0 0 0 0 0 0 0 1 0 0 0 0 19
20 20.108 0 0 0 0 0 0 0 0 1 0 0 0 20
21 20.148 0 0 0 0 0 0 0 0 0 1 0 0 21
22 19.394 0 0 0 0 0 0 0 0 0 0 1 0 22
23 17.745 0 0 0 0 0 0 0 0 0 0 0 1 23
24 17.696 0 0 0 0 0 0 0 0 0 0 0 0 24
25 17.032 0 1 0 0 0 0 0 0 0 0 0 0 25
26 16.438 0 0 1 0 0 0 0 0 0 0 0 0 26
27 15.683 0 0 0 1 0 0 0 0 0 0 0 0 27
28 15.594 0 0 0 0 1 0 0 0 0 0 0 0 28
29 15.713 0 0 0 0 0 1 0 0 0 0 0 0 29
30 15.937 0 0 0 0 0 0 1 0 0 0 0 0 30
31 16.171 0 0 0 0 0 0 0 1 0 0 0 0 31
32 15.928 0 0 0 0 0 0 0 0 1 0 0 0 32
33 16.348 0 0 0 0 0 0 0 0 0 1 0 0 33
34 15.579 0 0 0 0 0 0 0 0 0 0 1 0 34
35 15.305 0 0 0 0 0 0 0 0 0 0 0 1 35
36 15.648 0 0 0 0 0 0 0 0 0 0 0 0 36
37 14.954 0 1 0 0 0 0 0 0 0 0 0 0 37
38 15.137 0 0 1 0 0 0 0 0 0 0 0 0 38
39 15.839 0 0 0 1 0 0 0 0 0 0 0 0 39
40 16.050 0 0 0 0 1 0 0 0 0 0 0 0 40
41 15.168 0 0 0 0 0 1 0 0 0 0 0 0 41
42 17.064 0 0 0 0 0 0 1 0 0 0 0 0 42
43 16.005 0 0 0 0 0 0 0 1 0 0 0 0 43
44 14.886 0 0 0 0 0 0 0 0 1 0 0 0 44
45 14.931 0 0 0 0 0 0 0 0 0 1 0 0 45
46 14.544 0 0 0 0 0 0 0 0 0 0 1 0 46
47 13.812 0 0 0 0 0 0 0 0 0 0 0 1 47
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) cr M1 M2 M3 M4
20.756198 1.293847 0.406804 0.009907 -0.097489 0.339864
M5 M6 M7 M8 M9 M10
0.203967 0.937321 0.891424 1.365777 1.809631 0.793984
M11 t
-0.011663 -0.153103
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.29467 -0.52180 -0.05575 0.50346 1.80082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 20.756198 0.713346 29.097 < 2e-16 ***
cr 1.293847 0.467581 2.767 0.0092 **
M1 0.406804 0.681237 0.597 0.5545
M2 0.009907 0.678980 0.015 0.9884
M3 -0.097489 0.677067 -0.144 0.8864
M4 0.339864 0.675499 0.503 0.6182
M5 0.203967 0.674281 0.302 0.7642
M6 0.937321 0.673413 1.392 0.1733
M7 0.891424 0.672897 1.325 0.1944
M8 1.365777 0.672734 2.030 0.0505 .
M9 1.809631 0.672923 2.689 0.0111 *
M10 0.793984 0.673466 1.179 0.2468
M11 -0.011663 0.674360 -0.017 0.9863
t -0.153103 0.015411 -9.934 1.91e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8802 on 33 degrees of freedom
Multiple R-squared: 0.9226, Adjusted R-squared: 0.8922
F-statistic: 30.27 on 13 and 33 DF, p-value: 1.378e-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,] 0.083573888 0.167147776 0.9164261
[2,] 0.031617395 0.063234789 0.9683826
[3,] 0.009173988 0.018347975 0.9908260
[4,] 0.004867030 0.009734059 0.9951330
[5,] 0.003402739 0.006805478 0.9965973
[6,] 0.088153070 0.176306139 0.9118469
[7,] 0.178760044 0.357520088 0.8212400
[8,] 0.282161855 0.564323710 0.7178381
[9,] 0.302922619 0.605845238 0.6970774
[10,] 0.241535351 0.483070703 0.7584646
[11,] 0.169335212 0.338670424 0.8306648
[12,] 0.170557228 0.341114456 0.8294428
[13,] 0.092399483 0.184798966 0.9076005
[14,] 0.526534402 0.946931196 0.4734656
> postscript(file="/var/www/html/rcomp/tmp/1b6ke1258789077.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2sib11258789077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gjag1258789077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qi6m1258789077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pvu41258789077.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 = 47
Frequency = 1
1 2 3 4 5 6
0.37625495 0.29825495 -0.02624505 -0.39449505 0.28850495 -0.14074505
7 8 9 10 11 12
-0.05574505 0.74600495 1.11325495 -0.48299505 -0.70424505 -1.01780446
13 14 15 16 17 18
0.47134158 -0.13965842 -0.28315842 0.53159158 0.03359158 -0.49665842
19 20 21 22 23 24
-0.15465842 1.04809158 0.79734158 1.21209158 0.52184158 0.61428218
25 26 27 28 29 30
-0.30341832 -0.34741832 -0.84191832 -1.21516832 -0.80716832 -1.16341832
31 32 33 34 35 36
-0.73041832 -1.29466832 -1.16541832 -0.76566832 -0.08091832 0.40352228
37 38 39 40 41 42
-0.54417822 0.18882178 1.15132178 1.07807178 0.48507178 1.80082178
43 44 45 46 47
0.94082178 -0.49942822 -0.74517822 0.03657178 0.26332178
> postscript(file="/var/www/html/rcomp/tmp/6shbz1258789077.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 = 47
Frequency = 1
lag(myerror, k = 1) myerror
0 0.37625495 NA
1 0.29825495 0.37625495
2 -0.02624505 0.29825495
3 -0.39449505 -0.02624505
4 0.28850495 -0.39449505
5 -0.14074505 0.28850495
6 -0.05574505 -0.14074505
7 0.74600495 -0.05574505
8 1.11325495 0.74600495
9 -0.48299505 1.11325495
10 -0.70424505 -0.48299505
11 -1.01780446 -0.70424505
12 0.47134158 -1.01780446
13 -0.13965842 0.47134158
14 -0.28315842 -0.13965842
15 0.53159158 -0.28315842
16 0.03359158 0.53159158
17 -0.49665842 0.03359158
18 -0.15465842 -0.49665842
19 1.04809158 -0.15465842
20 0.79734158 1.04809158
21 1.21209158 0.79734158
22 0.52184158 1.21209158
23 0.61428218 0.52184158
24 -0.30341832 0.61428218
25 -0.34741832 -0.30341832
26 -0.84191832 -0.34741832
27 -1.21516832 -0.84191832
28 -0.80716832 -1.21516832
29 -1.16341832 -0.80716832
30 -0.73041832 -1.16341832
31 -1.29466832 -0.73041832
32 -1.16541832 -1.29466832
33 -0.76566832 -1.16541832
34 -0.08091832 -0.76566832
35 0.40352228 -0.08091832
36 -0.54417822 0.40352228
37 0.18882178 -0.54417822
38 1.15132178 0.18882178
39 1.07807178 1.15132178
40 0.48507178 1.07807178
41 1.80082178 0.48507178
42 0.94082178 1.80082178
43 -0.49942822 0.94082178
44 -0.74517822 -0.49942822
45 0.03657178 -0.74517822
46 0.26332178 0.03657178
47 NA 0.26332178
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.29825495 0.37625495
[2,] -0.02624505 0.29825495
[3,] -0.39449505 -0.02624505
[4,] 0.28850495 -0.39449505
[5,] -0.14074505 0.28850495
[6,] -0.05574505 -0.14074505
[7,] 0.74600495 -0.05574505
[8,] 1.11325495 0.74600495
[9,] -0.48299505 1.11325495
[10,] -0.70424505 -0.48299505
[11,] -1.01780446 -0.70424505
[12,] 0.47134158 -1.01780446
[13,] -0.13965842 0.47134158
[14,] -0.28315842 -0.13965842
[15,] 0.53159158 -0.28315842
[16,] 0.03359158 0.53159158
[17,] -0.49665842 0.03359158
[18,] -0.15465842 -0.49665842
[19,] 1.04809158 -0.15465842
[20,] 0.79734158 1.04809158
[21,] 1.21209158 0.79734158
[22,] 0.52184158 1.21209158
[23,] 0.61428218 0.52184158
[24,] -0.30341832 0.61428218
[25,] -0.34741832 -0.30341832
[26,] -0.84191832 -0.34741832
[27,] -1.21516832 -0.84191832
[28,] -0.80716832 -1.21516832
[29,] -1.16341832 -0.80716832
[30,] -0.73041832 -1.16341832
[31,] -1.29466832 -0.73041832
[32,] -1.16541832 -1.29466832
[33,] -0.76566832 -1.16541832
[34,] -0.08091832 -0.76566832
[35,] 0.40352228 -0.08091832
[36,] -0.54417822 0.40352228
[37,] 0.18882178 -0.54417822
[38,] 1.15132178 0.18882178
[39,] 1.07807178 1.15132178
[40,] 0.48507178 1.07807178
[41,] 1.80082178 0.48507178
[42,] 0.94082178 1.80082178
[43,] -0.49942822 0.94082178
[44,] -0.74517822 -0.49942822
[45,] 0.03657178 -0.74517822
[46,] 0.26332178 0.03657178
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.29825495 0.37625495
2 -0.02624505 0.29825495
3 -0.39449505 -0.02624505
4 0.28850495 -0.39449505
5 -0.14074505 0.28850495
6 -0.05574505 -0.14074505
7 0.74600495 -0.05574505
8 1.11325495 0.74600495
9 -0.48299505 1.11325495
10 -0.70424505 -0.48299505
11 -1.01780446 -0.70424505
12 0.47134158 -1.01780446
13 -0.13965842 0.47134158
14 -0.28315842 -0.13965842
15 0.53159158 -0.28315842
16 0.03359158 0.53159158
17 -0.49665842 0.03359158
18 -0.15465842 -0.49665842
19 1.04809158 -0.15465842
20 0.79734158 1.04809158
21 1.21209158 0.79734158
22 0.52184158 1.21209158
23 0.61428218 0.52184158
24 -0.30341832 0.61428218
25 -0.34741832 -0.30341832
26 -0.84191832 -0.34741832
27 -1.21516832 -0.84191832
28 -0.80716832 -1.21516832
29 -1.16341832 -0.80716832
30 -0.73041832 -1.16341832
31 -1.29466832 -0.73041832
32 -1.16541832 -1.29466832
33 -0.76566832 -1.16541832
34 -0.08091832 -0.76566832
35 0.40352228 -0.08091832
36 -0.54417822 0.40352228
37 0.18882178 -0.54417822
38 1.15132178 0.18882178
39 1.07807178 1.15132178
40 0.48507178 1.07807178
41 1.80082178 0.48507178
42 0.94082178 1.80082178
43 -0.49942822 0.94082178
44 -0.74517822 -0.49942822
45 0.03657178 -0.74517822
46 0.26332178 0.03657178
> 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/7aot01258789077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88il51258789077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9o8j71258789077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/100fgn1258789077.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u3es1258789077.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/128d3s1258789077.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/132uoh1258789078.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/14jnpp1258789078.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/15u1b11258789078.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/16hf151258789078.tab")
+ }
>
> system("convert tmp/1b6ke1258789077.ps tmp/1b6ke1258789077.png")
> system("convert tmp/2sib11258789077.ps tmp/2sib11258789077.png")
> system("convert tmp/3gjag1258789077.ps tmp/3gjag1258789077.png")
> system("convert tmp/4qi6m1258789077.ps tmp/4qi6m1258789077.png")
> system("convert tmp/5pvu41258789077.ps tmp/5pvu41258789077.png")
> system("convert tmp/6shbz1258789077.ps tmp/6shbz1258789077.png")
> system("convert tmp/7aot01258789077.ps tmp/7aot01258789077.png")
> system("convert tmp/88il51258789077.ps tmp/88il51258789077.png")
> system("convert tmp/9o8j71258789077.ps tmp/9o8j71258789077.png")
> system("convert tmp/100fgn1258789077.ps tmp/100fgn1258789077.png")
>
>
> proc.time()
user system elapsed
2.241 1.525 3.258