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(8.9,0,8.8,0,8.3,0,7.5,0,7.2,0,7.4,0,8.8,0,9.3,0,9.3,0,8.7,0,8.2,0,8.3,0,8.5,0,8.6,0,8.5,0,8.2,0,8.1,0,7.9,0,8.6,0,8.7,0,8.7,0,8.5,0,8.4,0,8.5,0,8.7,0,8.7,0,8.6,0,8.5,0,8.3,0,8,0,8.2,0,8.1,0,8.1,0,8,0,7.9,0,7.9,0,8,0,8,0,7.9,0,8,0,7.7,0,7.2,0,7.5,0,7.3,0,7,0,7,0,7,0,7.2,0,7.3,1,7.1,1,6.8,1,6.4,1,6.1,1,6.5,1,7.7,1,7.9,1,7.5,1,6.9,1,6.6,1,6.9,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> 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
Y X
1 8.9 0
2 8.8 0
3 8.3 0
4 7.5 0
5 7.2 0
6 7.4 0
7 8.8 0
8 9.3 0
9 9.3 0
10 8.7 0
11 8.2 0
12 8.3 0
13 8.5 0
14 8.6 0
15 8.5 0
16 8.2 0
17 8.1 0
18 7.9 0
19 8.6 0
20 8.7 0
21 8.7 0
22 8.5 0
23 8.4 0
24 8.5 0
25 8.7 0
26 8.7 0
27 8.6 0
28 8.5 0
29 8.3 0
30 8.0 0
31 8.2 0
32 8.1 0
33 8.1 0
34 8.0 0
35 7.9 0
36 7.9 0
37 8.0 0
38 8.0 0
39 7.9 0
40 8.0 0
41 7.7 0
42 7.2 0
43 7.5 0
44 7.3 0
45 7.0 0
46 7.0 0
47 7.0 0
48 7.2 0
49 7.3 1
50 7.1 1
51 6.8 1
52 6.4 1
53 6.1 1
54 6.5 1
55 7.7 1
56 7.9 1
57 7.5 1
58 6.9 1
59 6.6 1
60 6.9 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
8.140 -1.165
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.13958 -0.27344 0.01042 0.46042 1.16042
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.13958 0.08386 97.065 < 2e-16 ***
X -1.16458 0.18751 -6.211 6.11e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.581 on 58 degrees of freedom
Multiple R-squared: 0.3994, Adjusted R-squared: 0.3891
F-statistic: 38.57 on 1 and 58 DF, p-value: 6.108e-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,] 0.9209000 0.15819994 0.07909997
[2,] 0.9150295 0.16994092 0.08497046
[3,] 0.9167650 0.16646996 0.08323498
[4,] 0.9675199 0.06496029 0.03248014
[5,] 0.9855098 0.02898031 0.01449016
[6,] 0.9786862 0.04262769 0.02131385
[7,] 0.9641397 0.07172068 0.03586034
[8,] 0.9423329 0.11533416 0.05766708
[9,] 0.9164473 0.16710532 0.08355266
[10,] 0.8902355 0.21952893 0.10976446
[11,] 0.8535081 0.29298381 0.14649190
[12,] 0.8047630 0.39047393 0.19523697
[13,] 0.7524262 0.49514762 0.24757381
[14,] 0.7139647 0.57207068 0.28603534
[15,] 0.6745976 0.65080481 0.32540241
[16,] 0.6558149 0.68837021 0.34418510
[17,] 0.6420907 0.71581862 0.35790931
[18,] 0.5974252 0.80514966 0.40257483
[19,] 0.5435038 0.91299235 0.45649617
[20,] 0.5056533 0.98869338 0.49434669
[21,] 0.5197726 0.96045475 0.48022737
[22,] 0.5488968 0.90220649 0.45110325
[23,] 0.5664548 0.86709047 0.43354523
[24,] 0.5747077 0.85058462 0.42529231
[25,] 0.5571929 0.88561419 0.44280710
[26,] 0.5272423 0.94551545 0.47275773
[27,] 0.5080259 0.98394817 0.49197409
[28,] 0.4858783 0.97175656 0.51412172
[29,] 0.4694924 0.93898481 0.53050760
[30,] 0.4515210 0.90304203 0.54847899
[31,] 0.4335011 0.86700227 0.56649886
[32,] 0.4172356 0.83447127 0.58276436
[33,] 0.4145968 0.82919355 0.58540322
[34,] 0.4255087 0.85101750 0.57449125
[35,] 0.4374172 0.87483436 0.56258282
[36,] 0.4960936 0.99218711 0.50390645
[37,] 0.5224088 0.95518245 0.47759122
[38,] 0.5629309 0.87413817 0.43706909
[39,] 0.5672776 0.86544487 0.43272244
[40,] 0.5683805 0.86323894 0.43161947
[41,] 0.5886767 0.82264658 0.41132329
[42,] 0.5878143 0.82437149 0.41218575
[43,] 0.5726214 0.85475720 0.42737860
[44,] 0.5179009 0.96419818 0.48209909
[45,] 0.4419587 0.88391741 0.55804130
[46,] 0.3453999 0.69079978 0.65460011
[47,] 0.2561499 0.51229971 0.74385015
[48,] 0.2382460 0.47649202 0.76175399
[49,] 0.3797712 0.75954239 0.62022880
[50,] 0.4070712 0.81414241 0.59292880
[51,] 0.3651029 0.73020587 0.63489706
> postscript(file="/var/www/html/rcomp/tmp/12wth1259936168.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21zdu1259936168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nnlk1259936168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4aiaj1259936168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5tme51259936168.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 = 60
Frequency = 1
1 2 3 4 5 6
0.76041667 0.66041667 0.16041667 -0.63958333 -0.93958333 -0.73958333
7 8 9 10 11 12
0.66041667 1.16041667 1.16041667 0.56041667 0.06041667 0.16041667
13 14 15 16 17 18
0.36041667 0.46041667 0.36041667 0.06041667 -0.03958333 -0.23958333
19 20 21 22 23 24
0.46041667 0.56041667 0.56041667 0.36041667 0.26041667 0.36041667
25 26 27 28 29 30
0.56041667 0.56041667 0.46041667 0.36041667 0.16041667 -0.13958333
31 32 33 34 35 36
0.06041667 -0.03958333 -0.03958333 -0.13958333 -0.23958333 -0.23958333
37 38 39 40 41 42
-0.13958333 -0.13958333 -0.23958333 -0.13958333 -0.43958333 -0.93958333
43 44 45 46 47 48
-0.63958333 -0.83958333 -1.13958333 -1.13958333 -1.13958333 -0.93958333
49 50 51 52 53 54
0.32500000 0.12500000 -0.17500000 -0.57500000 -0.87500000 -0.47500000
55 56 57 58 59 60
0.72500000 0.92500000 0.52500000 -0.07500000 -0.37500000 -0.07500000
> postscript(file="/var/www/html/rcomp/tmp/60nx31259936168.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.76041667 NA
1 0.66041667 0.76041667
2 0.16041667 0.66041667
3 -0.63958333 0.16041667
4 -0.93958333 -0.63958333
5 -0.73958333 -0.93958333
6 0.66041667 -0.73958333
7 1.16041667 0.66041667
8 1.16041667 1.16041667
9 0.56041667 1.16041667
10 0.06041667 0.56041667
11 0.16041667 0.06041667
12 0.36041667 0.16041667
13 0.46041667 0.36041667
14 0.36041667 0.46041667
15 0.06041667 0.36041667
16 -0.03958333 0.06041667
17 -0.23958333 -0.03958333
18 0.46041667 -0.23958333
19 0.56041667 0.46041667
20 0.56041667 0.56041667
21 0.36041667 0.56041667
22 0.26041667 0.36041667
23 0.36041667 0.26041667
24 0.56041667 0.36041667
25 0.56041667 0.56041667
26 0.46041667 0.56041667
27 0.36041667 0.46041667
28 0.16041667 0.36041667
29 -0.13958333 0.16041667
30 0.06041667 -0.13958333
31 -0.03958333 0.06041667
32 -0.03958333 -0.03958333
33 -0.13958333 -0.03958333
34 -0.23958333 -0.13958333
35 -0.23958333 -0.23958333
36 -0.13958333 -0.23958333
37 -0.13958333 -0.13958333
38 -0.23958333 -0.13958333
39 -0.13958333 -0.23958333
40 -0.43958333 -0.13958333
41 -0.93958333 -0.43958333
42 -0.63958333 -0.93958333
43 -0.83958333 -0.63958333
44 -1.13958333 -0.83958333
45 -1.13958333 -1.13958333
46 -1.13958333 -1.13958333
47 -0.93958333 -1.13958333
48 0.32500000 -0.93958333
49 0.12500000 0.32500000
50 -0.17500000 0.12500000
51 -0.57500000 -0.17500000
52 -0.87500000 -0.57500000
53 -0.47500000 -0.87500000
54 0.72500000 -0.47500000
55 0.92500000 0.72500000
56 0.52500000 0.92500000
57 -0.07500000 0.52500000
58 -0.37500000 -0.07500000
59 -0.07500000 -0.37500000
60 NA -0.07500000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.66041667 0.76041667
[2,] 0.16041667 0.66041667
[3,] -0.63958333 0.16041667
[4,] -0.93958333 -0.63958333
[5,] -0.73958333 -0.93958333
[6,] 0.66041667 -0.73958333
[7,] 1.16041667 0.66041667
[8,] 1.16041667 1.16041667
[9,] 0.56041667 1.16041667
[10,] 0.06041667 0.56041667
[11,] 0.16041667 0.06041667
[12,] 0.36041667 0.16041667
[13,] 0.46041667 0.36041667
[14,] 0.36041667 0.46041667
[15,] 0.06041667 0.36041667
[16,] -0.03958333 0.06041667
[17,] -0.23958333 -0.03958333
[18,] 0.46041667 -0.23958333
[19,] 0.56041667 0.46041667
[20,] 0.56041667 0.56041667
[21,] 0.36041667 0.56041667
[22,] 0.26041667 0.36041667
[23,] 0.36041667 0.26041667
[24,] 0.56041667 0.36041667
[25,] 0.56041667 0.56041667
[26,] 0.46041667 0.56041667
[27,] 0.36041667 0.46041667
[28,] 0.16041667 0.36041667
[29,] -0.13958333 0.16041667
[30,] 0.06041667 -0.13958333
[31,] -0.03958333 0.06041667
[32,] -0.03958333 -0.03958333
[33,] -0.13958333 -0.03958333
[34,] -0.23958333 -0.13958333
[35,] -0.23958333 -0.23958333
[36,] -0.13958333 -0.23958333
[37,] -0.13958333 -0.13958333
[38,] -0.23958333 -0.13958333
[39,] -0.13958333 -0.23958333
[40,] -0.43958333 -0.13958333
[41,] -0.93958333 -0.43958333
[42,] -0.63958333 -0.93958333
[43,] -0.83958333 -0.63958333
[44,] -1.13958333 -0.83958333
[45,] -1.13958333 -1.13958333
[46,] -1.13958333 -1.13958333
[47,] -0.93958333 -1.13958333
[48,] 0.32500000 -0.93958333
[49,] 0.12500000 0.32500000
[50,] -0.17500000 0.12500000
[51,] -0.57500000 -0.17500000
[52,] -0.87500000 -0.57500000
[53,] -0.47500000 -0.87500000
[54,] 0.72500000 -0.47500000
[55,] 0.92500000 0.72500000
[56,] 0.52500000 0.92500000
[57,] -0.07500000 0.52500000
[58,] -0.37500000 -0.07500000
[59,] -0.07500000 -0.37500000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.66041667 0.76041667
2 0.16041667 0.66041667
3 -0.63958333 0.16041667
4 -0.93958333 -0.63958333
5 -0.73958333 -0.93958333
6 0.66041667 -0.73958333
7 1.16041667 0.66041667
8 1.16041667 1.16041667
9 0.56041667 1.16041667
10 0.06041667 0.56041667
11 0.16041667 0.06041667
12 0.36041667 0.16041667
13 0.46041667 0.36041667
14 0.36041667 0.46041667
15 0.06041667 0.36041667
16 -0.03958333 0.06041667
17 -0.23958333 -0.03958333
18 0.46041667 -0.23958333
19 0.56041667 0.46041667
20 0.56041667 0.56041667
21 0.36041667 0.56041667
22 0.26041667 0.36041667
23 0.36041667 0.26041667
24 0.56041667 0.36041667
25 0.56041667 0.56041667
26 0.46041667 0.56041667
27 0.36041667 0.46041667
28 0.16041667 0.36041667
29 -0.13958333 0.16041667
30 0.06041667 -0.13958333
31 -0.03958333 0.06041667
32 -0.03958333 -0.03958333
33 -0.13958333 -0.03958333
34 -0.23958333 -0.13958333
35 -0.23958333 -0.23958333
36 -0.13958333 -0.23958333
37 -0.13958333 -0.13958333
38 -0.23958333 -0.13958333
39 -0.13958333 -0.23958333
40 -0.43958333 -0.13958333
41 -0.93958333 -0.43958333
42 -0.63958333 -0.93958333
43 -0.83958333 -0.63958333
44 -1.13958333 -0.83958333
45 -1.13958333 -1.13958333
46 -1.13958333 -1.13958333
47 -0.93958333 -1.13958333
48 0.32500000 -0.93958333
49 0.12500000 0.32500000
50 -0.17500000 0.12500000
51 -0.57500000 -0.17500000
52 -0.87500000 -0.57500000
53 -0.47500000 -0.87500000
54 0.72500000 -0.47500000
55 0.92500000 0.72500000
56 0.52500000 0.92500000
57 -0.07500000 0.52500000
58 -0.37500000 -0.07500000
59 -0.07500000 -0.37500000
> 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/7vuvp1259936168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8pw0z1259936168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ykld1259936168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dt9o1259936168.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1178zq1259936168.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/12tso11259936168.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/13bpd51259936168.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/14ki1i1259936168.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/150h4n1259936168.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/16op4r1259936168.tab")
+ }
>
> system("convert tmp/12wth1259936168.ps tmp/12wth1259936168.png")
> system("convert tmp/21zdu1259936168.ps tmp/21zdu1259936168.png")
> system("convert tmp/3nnlk1259936168.ps tmp/3nnlk1259936168.png")
> system("convert tmp/4aiaj1259936168.ps tmp/4aiaj1259936168.png")
> system("convert tmp/5tme51259936168.ps tmp/5tme51259936168.png")
> system("convert tmp/60nx31259936168.ps tmp/60nx31259936168.png")
> system("convert tmp/7vuvp1259936168.ps tmp/7vuvp1259936168.png")
> system("convert tmp/8pw0z1259936168.ps tmp/8pw0z1259936168.png")
> system("convert tmp/9ykld1259936168.ps tmp/9ykld1259936168.png")
> system("convert tmp/10dt9o1259936168.ps tmp/10dt9o1259936168.png")
>
>
> proc.time()
user system elapsed
2.506 1.591 8.009