R version 2.7.0 (2008-04-22)
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.
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(101,0,104,0,99,0,105,0,107,0,111,0,117,0,119,0,127,0,128,0,135,0,132,0,136,0,143,0,142,0,153,0,145,0,138,0,148,0,152,0,169,0,169,0,161,0,174,0,179,0,191,0,190,0,182,0,175,0,181,0,197,0,194,0,197,0,216,0,221,0,218,0,230,0,227,0,204,0,197,0,199,0,208,0,191,0,202,0,211,0,224,1,224,1,231,1,244,1,235,1,250,1,266,1,288,1,283,1,295,1,312,1,334,1,348,1,383,1,407,1),dim=c(2,60),dimnames=list(c('IGrSt','D'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('IGrSt','D'),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
IGrSt D
1 101 0
2 104 0
3 99 0
4 105 0
5 107 0
6 111 0
7 117 0
8 119 0
9 127 0
10 128 0
11 135 0
12 132 0
13 136 0
14 143 0
15 142 0
16 153 0
17 145 0
18 138 0
19 148 0
20 152 0
21 169 0
22 169 0
23 161 0
24 174 0
25 179 0
26 191 0
27 190 0
28 182 0
29 175 0
30 181 0
31 197 0
32 194 0
33 197 0
34 216 0
35 221 0
36 218 0
37 230 0
38 227 0
39 204 0
40 197 0
41 199 0
42 208 0
43 191 0
44 202 0
45 211 0
46 224 1
47 224 1
48 231 1
49 244 1
50 235 1
51 250 1
52 266 1
53 288 1
54 283 1
55 295 1
56 312 1
57 334 1
58 348 1
59 383 1
60 407 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) D
165.0 123.3
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-66.000 -37.250 1.867 32.000 118.733
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 165.000 6.609 24.965 < 2e-16 ***
D 123.267 13.219 9.325 3.87e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 44.34 on 58 degrees of freedom
Multiple R-squared: 0.5999, Adjusted R-squared: 0.593
F-statistic: 86.96 on 1 and 58 DF, p-value: 3.87e-13
> 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,] 0.0009570972 0.0019141944 0.9990429
[2,] 0.0003673877 0.0007347754 0.9996326
[3,] 0.0003625048 0.0007250095 0.9996375
[4,] 0.0002394847 0.0004789694 0.9997605
[5,] 0.0003822141 0.0007644282 0.9996178
[6,] 0.0003583607 0.0007167214 0.9996416
[7,] 0.0005327899 0.0010655798 0.9994672
[8,] 0.0004156452 0.0008312904 0.9995844
[9,] 0.0003922971 0.0007845941 0.9996077
[10,] 0.0005458795 0.0010917589 0.9994541
[11,] 0.0005609993 0.0011219985 0.9994390
[12,] 0.0010469494 0.0020938987 0.9989531
[13,] 0.0009595829 0.0019191658 0.9990404
[14,] 0.0006829849 0.0013659698 0.9993170
[15,] 0.0006849719 0.0013699439 0.9993150
[16,] 0.0007772092 0.0015544185 0.9992228
[17,] 0.0019235605 0.0038471209 0.9980764
[18,] 0.0033483110 0.0066966220 0.9966517
[19,] 0.0036873239 0.0073746478 0.9963127
[20,] 0.0057875936 0.0115751871 0.9942124
[21,] 0.0090906535 0.0181813069 0.9909093
[22,] 0.0176903710 0.0353807420 0.9823096
[23,] 0.0259651106 0.0519302212 0.9740349
[24,] 0.0281315103 0.0562630205 0.9718685
[25,] 0.0265540457 0.0531080914 0.9734460
[26,] 0.0263589850 0.0527179700 0.9736410
[27,] 0.0325615442 0.0651230884 0.9674385
[28,] 0.0349649923 0.0699299846 0.9650350
[29,] 0.0370678067 0.0741356135 0.9629322
[30,] 0.0515705942 0.1031411884 0.9484294
[31,] 0.0692126479 0.1384252957 0.9307874
[32,] 0.0789017224 0.1578034448 0.9210983
[33,] 0.1022388764 0.2044777528 0.8977611
[34,] 0.1162150902 0.2324301804 0.8837849
[35,] 0.0972870277 0.1945740554 0.9027130
[36,] 0.0758103777 0.1516207554 0.9241896
[37,] 0.0580673270 0.1161346539 0.9419327
[38,] 0.0461155404 0.0922310808 0.9538845
[39,] 0.0322406975 0.0644813949 0.9677593
[40,] 0.0229263639 0.0458527278 0.9770736
[41,] 0.0167528015 0.0335056029 0.9832472
[42,] 0.0177963351 0.0355926703 0.9822037
[43,] 0.0217527524 0.0435055048 0.9782472
[44,] 0.0272795720 0.0545591439 0.9727204
[45,] 0.0310575621 0.0621151241 0.9689424
[46,] 0.0543309580 0.1086619161 0.9456690
[47,] 0.0860184470 0.1720368940 0.9139816
[48,] 0.1193373093 0.2386746185 0.8806627
[49,] 0.1258488129 0.2516976259 0.8741512
[50,] 0.1771716784 0.3543433567 0.8228283
[51,] 0.2510266536 0.5020533073 0.7489733
> postscript(file="/var/www/html/rcomp/tmp/1y25d1227523134.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25gps1227523134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/303ny1227523134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4c6dn1227523134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5hb8m1227523134.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
-64.0000000 -61.0000000 -66.0000000 -60.0000000 -58.0000000 -54.0000000
7 8 9 10 11 12
-48.0000000 -46.0000000 -38.0000000 -37.0000000 -30.0000000 -33.0000000
13 14 15 16 17 18
-29.0000000 -22.0000000 -23.0000000 -12.0000000 -20.0000000 -27.0000000
19 20 21 22 23 24
-17.0000000 -13.0000000 4.0000000 4.0000000 -4.0000000 9.0000000
25 26 27 28 29 30
14.0000000 26.0000000 25.0000000 17.0000000 10.0000000 16.0000000
31 32 33 34 35 36
32.0000000 29.0000000 32.0000000 51.0000000 56.0000000 53.0000000
37 38 39 40 41 42
65.0000000 62.0000000 39.0000000 32.0000000 34.0000000 43.0000000
43 44 45 46 47 48
26.0000000 37.0000000 46.0000000 -64.2666667 -64.2666667 -57.2666667
49 50 51 52 53 54
-44.2666667 -53.2666667 -38.2666667 -22.2666667 -0.2666667 -5.2666667
55 56 57 58 59 60
6.7333333 23.7333333 45.7333333 59.7333333 94.7333333 118.7333333
> postscript(file="/var/www/html/rcomp/tmp/62bxw1227523134.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 -64.0000000 NA
1 -61.0000000 -64.0000000
2 -66.0000000 -61.0000000
3 -60.0000000 -66.0000000
4 -58.0000000 -60.0000000
5 -54.0000000 -58.0000000
6 -48.0000000 -54.0000000
7 -46.0000000 -48.0000000
8 -38.0000000 -46.0000000
9 -37.0000000 -38.0000000
10 -30.0000000 -37.0000000
11 -33.0000000 -30.0000000
12 -29.0000000 -33.0000000
13 -22.0000000 -29.0000000
14 -23.0000000 -22.0000000
15 -12.0000000 -23.0000000
16 -20.0000000 -12.0000000
17 -27.0000000 -20.0000000
18 -17.0000000 -27.0000000
19 -13.0000000 -17.0000000
20 4.0000000 -13.0000000
21 4.0000000 4.0000000
22 -4.0000000 4.0000000
23 9.0000000 -4.0000000
24 14.0000000 9.0000000
25 26.0000000 14.0000000
26 25.0000000 26.0000000
27 17.0000000 25.0000000
28 10.0000000 17.0000000
29 16.0000000 10.0000000
30 32.0000000 16.0000000
31 29.0000000 32.0000000
32 32.0000000 29.0000000
33 51.0000000 32.0000000
34 56.0000000 51.0000000
35 53.0000000 56.0000000
36 65.0000000 53.0000000
37 62.0000000 65.0000000
38 39.0000000 62.0000000
39 32.0000000 39.0000000
40 34.0000000 32.0000000
41 43.0000000 34.0000000
42 26.0000000 43.0000000
43 37.0000000 26.0000000
44 46.0000000 37.0000000
45 -64.2666667 46.0000000
46 -64.2666667 -64.2666667
47 -57.2666667 -64.2666667
48 -44.2666667 -57.2666667
49 -53.2666667 -44.2666667
50 -38.2666667 -53.2666667
51 -22.2666667 -38.2666667
52 -0.2666667 -22.2666667
53 -5.2666667 -0.2666667
54 6.7333333 -5.2666667
55 23.7333333 6.7333333
56 45.7333333 23.7333333
57 59.7333333 45.7333333
58 94.7333333 59.7333333
59 118.7333333 94.7333333
60 NA 118.7333333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -61.0000000 -64.0000000
[2,] -66.0000000 -61.0000000
[3,] -60.0000000 -66.0000000
[4,] -58.0000000 -60.0000000
[5,] -54.0000000 -58.0000000
[6,] -48.0000000 -54.0000000
[7,] -46.0000000 -48.0000000
[8,] -38.0000000 -46.0000000
[9,] -37.0000000 -38.0000000
[10,] -30.0000000 -37.0000000
[11,] -33.0000000 -30.0000000
[12,] -29.0000000 -33.0000000
[13,] -22.0000000 -29.0000000
[14,] -23.0000000 -22.0000000
[15,] -12.0000000 -23.0000000
[16,] -20.0000000 -12.0000000
[17,] -27.0000000 -20.0000000
[18,] -17.0000000 -27.0000000
[19,] -13.0000000 -17.0000000
[20,] 4.0000000 -13.0000000
[21,] 4.0000000 4.0000000
[22,] -4.0000000 4.0000000
[23,] 9.0000000 -4.0000000
[24,] 14.0000000 9.0000000
[25,] 26.0000000 14.0000000
[26,] 25.0000000 26.0000000
[27,] 17.0000000 25.0000000
[28,] 10.0000000 17.0000000
[29,] 16.0000000 10.0000000
[30,] 32.0000000 16.0000000
[31,] 29.0000000 32.0000000
[32,] 32.0000000 29.0000000
[33,] 51.0000000 32.0000000
[34,] 56.0000000 51.0000000
[35,] 53.0000000 56.0000000
[36,] 65.0000000 53.0000000
[37,] 62.0000000 65.0000000
[38,] 39.0000000 62.0000000
[39,] 32.0000000 39.0000000
[40,] 34.0000000 32.0000000
[41,] 43.0000000 34.0000000
[42,] 26.0000000 43.0000000
[43,] 37.0000000 26.0000000
[44,] 46.0000000 37.0000000
[45,] -64.2666667 46.0000000
[46,] -64.2666667 -64.2666667
[47,] -57.2666667 -64.2666667
[48,] -44.2666667 -57.2666667
[49,] -53.2666667 -44.2666667
[50,] -38.2666667 -53.2666667
[51,] -22.2666667 -38.2666667
[52,] -0.2666667 -22.2666667
[53,] -5.2666667 -0.2666667
[54,] 6.7333333 -5.2666667
[55,] 23.7333333 6.7333333
[56,] 45.7333333 23.7333333
[57,] 59.7333333 45.7333333
[58,] 94.7333333 59.7333333
[59,] 118.7333333 94.7333333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -61.0000000 -64.0000000
2 -66.0000000 -61.0000000
3 -60.0000000 -66.0000000
4 -58.0000000 -60.0000000
5 -54.0000000 -58.0000000
6 -48.0000000 -54.0000000
7 -46.0000000 -48.0000000
8 -38.0000000 -46.0000000
9 -37.0000000 -38.0000000
10 -30.0000000 -37.0000000
11 -33.0000000 -30.0000000
12 -29.0000000 -33.0000000
13 -22.0000000 -29.0000000
14 -23.0000000 -22.0000000
15 -12.0000000 -23.0000000
16 -20.0000000 -12.0000000
17 -27.0000000 -20.0000000
18 -17.0000000 -27.0000000
19 -13.0000000 -17.0000000
20 4.0000000 -13.0000000
21 4.0000000 4.0000000
22 -4.0000000 4.0000000
23 9.0000000 -4.0000000
24 14.0000000 9.0000000
25 26.0000000 14.0000000
26 25.0000000 26.0000000
27 17.0000000 25.0000000
28 10.0000000 17.0000000
29 16.0000000 10.0000000
30 32.0000000 16.0000000
31 29.0000000 32.0000000
32 32.0000000 29.0000000
33 51.0000000 32.0000000
34 56.0000000 51.0000000
35 53.0000000 56.0000000
36 65.0000000 53.0000000
37 62.0000000 65.0000000
38 39.0000000 62.0000000
39 32.0000000 39.0000000
40 34.0000000 32.0000000
41 43.0000000 34.0000000
42 26.0000000 43.0000000
43 37.0000000 26.0000000
44 46.0000000 37.0000000
45 -64.2666667 46.0000000
46 -64.2666667 -64.2666667
47 -57.2666667 -64.2666667
48 -44.2666667 -57.2666667
49 -53.2666667 -44.2666667
50 -38.2666667 -53.2666667
51 -22.2666667 -38.2666667
52 -0.2666667 -22.2666667
53 -5.2666667 -0.2666667
54 6.7333333 -5.2666667
55 23.7333333 6.7333333
56 45.7333333 23.7333333
57 59.7333333 45.7333333
58 94.7333333 59.7333333
59 118.7333333 94.7333333
> 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/7gdpg1227523134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87yp01227523134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/985er1227523134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ij9g1227523134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114y321227523134.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/12u1671227523134.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/1389sx1227523134.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/143sjj1227523134.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/15tnvy1227523134.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/167yye1227523134.tab")
+ }
>
> system("convert tmp/1y25d1227523134.ps tmp/1y25d1227523134.png")
> system("convert tmp/25gps1227523134.ps tmp/25gps1227523134.png")
> system("convert tmp/303ny1227523134.ps tmp/303ny1227523134.png")
> system("convert tmp/4c6dn1227523134.ps tmp/4c6dn1227523134.png")
> system("convert tmp/5hb8m1227523134.ps tmp/5hb8m1227523134.png")
> system("convert tmp/62bxw1227523134.ps tmp/62bxw1227523134.png")
> system("convert tmp/7gdpg1227523134.ps tmp/7gdpg1227523134.png")
> system("convert tmp/87yp01227523134.ps tmp/87yp01227523134.png")
> system("convert tmp/985er1227523134.ps tmp/985er1227523134.png")
> system("convert tmp/10ij9g1227523134.ps tmp/10ij9g1227523134.png")
>
>
> proc.time()
user system elapsed
4.993 2.702 5.376