R version 2.8.0 (2008-10-20)
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(91.2,0,99.2,0,108.2,0,101.5,0,106.9,0,104.4,0,77.9,0,60,0,99.5,0,95,0,105.6,0,102.5,0,93.3,0,97.3,0,127,0,111.7,0,96.4,0,133,0,72.2,0,95.8,0,124.1,0,127.6,0,110.7,0,104.6,0,112.7,0,115.3,0,139.4,0,119,0,97.4,0,154,0,81.5,0,88.8,0,127.7,1,105.1,1,114.9,1,106.4,1,104.5,1,121.6,1,141.4,1,99,1,126.7,1,134.1,1,81.3,1,88.6,1,132.7,1,132.9,1,134.4,1,103.7,1,119.7,1,115,1,132.9,1,108.5,1,113.9,1,142,1,97.7,1,92.2,1,128.8,1,134.9,1,128.2,1,114.8,1),dim=c(2,60),dimnames=list(c('transportmiddelen','conjunctuur'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('transportmiddelen','conjunctuur'),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
transportmiddelen conjunctuur
1 91.2 0
2 99.2 0
3 108.2 0
4 101.5 0
5 106.9 0
6 104.4 0
7 77.9 0
8 60.0 0
9 99.5 0
10 95.0 0
11 105.6 0
12 102.5 0
13 93.3 0
14 97.3 0
15 127.0 0
16 111.7 0
17 96.4 0
18 133.0 0
19 72.2 0
20 95.8 0
21 124.1 0
22 127.6 0
23 110.7 0
24 104.6 0
25 112.7 0
26 115.3 0
27 139.4 0
28 119.0 0
29 97.4 0
30 154.0 0
31 81.5 0
32 88.8 0
33 127.7 1
34 105.1 1
35 114.9 1
36 106.4 1
37 104.5 1
38 121.6 1
39 141.4 1
40 99.0 1
41 126.7 1
42 134.1 1
43 81.3 1
44 88.6 1
45 132.7 1
46 132.9 1
47 134.4 1
48 103.7 1
49 119.7 1
50 115.0 1
51 132.9 1
52 108.5 1
53 113.9 1
54 142.0 1
55 97.7 1
56 92.2 1
57 128.8 1
58 134.9 1
59 128.2 1
60 114.8 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) conjunctuur
104.80 12.47
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-44.803 -11.029 -1.337 12.196 49.197
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 104.803 3.206 32.689 <2e-16 ***
conjunctuur 12.468 4.693 2.657 0.0102 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 18.14 on 58 degrees of freedom
Multiple R-squared: 0.1085, Adjusted R-squared: 0.09311
F-statistic: 7.058 on 1 and 58 DF, p-value: 0.01018
> 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,] 0.08646819 0.17293637 0.9135318
[2,] 0.03053157 0.06106314 0.9694684
[3,] 0.14549592 0.29099183 0.8545041
[4,] 0.59327901 0.81344198 0.4067210
[5,] 0.48419417 0.96838834 0.5158058
[6,] 0.37753088 0.75506176 0.6224691
[7,] 0.31020121 0.62040242 0.6897988
[8,] 0.23538416 0.47076832 0.7646158
[9,] 0.17310170 0.34620340 0.8268983
[10,] 0.12097555 0.24195109 0.8790245
[11,] 0.24317174 0.48634348 0.7568283
[12,] 0.20791782 0.41583563 0.7920822
[13,] 0.15702590 0.31405180 0.8429741
[14,] 0.30644720 0.61289439 0.6935528
[15,] 0.47316430 0.94632859 0.5268357
[16,] 0.41794656 0.83589312 0.5820534
[17,] 0.45126999 0.90253999 0.5487300
[18,] 0.50517445 0.98965110 0.4948256
[19,] 0.43876354 0.87752708 0.5612365
[20,] 0.36907888 0.73815777 0.6309211
[21,] 0.31268899 0.62537798 0.6873110
[22,] 0.26684584 0.53369169 0.7331542
[23,] 0.42634169 0.85268337 0.5736583
[24,] 0.39118699 0.78237399 0.6088130
[25,] 0.33301619 0.66603238 0.6669838
[26,] 0.82865569 0.34268863 0.1713443
[27,] 0.81679604 0.36640793 0.1832040
[28,] 0.77925817 0.44148366 0.2207418
[29,] 0.73081173 0.53837654 0.2691883
[30,] 0.70108677 0.59782647 0.2989132
[31,] 0.63101740 0.73796519 0.3689826
[32,] 0.57839571 0.84320858 0.4216043
[33,] 0.53254528 0.93490945 0.4674547
[34,] 0.46133487 0.92266975 0.5386651
[35,] 0.51889825 0.96220350 0.4811017
[36,] 0.51526969 0.96946062 0.4847303
[37,] 0.45395946 0.90791893 0.5460405
[38,] 0.43764015 0.87528031 0.5623598
[39,] 0.67572016 0.64855968 0.3242798
[40,] 0.81058441 0.37883117 0.1894156
[41,] 0.78597510 0.42804980 0.2140249
[42,] 0.76132974 0.47734052 0.2386703
[43,] 0.75035020 0.49929960 0.2496498
[44,] 0.72305105 0.55389790 0.2769490
[45,] 0.62750300 0.74499400 0.3724970
[46,] 0.52393303 0.95213394 0.4760670
[47,] 0.47828865 0.95657731 0.5217113
[48,] 0.38929471 0.77858941 0.6107053
[49,] 0.27929795 0.55859590 0.7207021
[50,] 0.33542321 0.67084643 0.6645768
[51,] 0.33645220 0.67290440 0.6635478
> postscript(file="/var/www/html/rcomp/tmp/1bfpt1229093270.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2l7m61229093270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nkpd1229093270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gyus1229093270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/561wc1229093270.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 7
-13.603125 -5.603125 3.396875 -3.303125 2.096875 -0.403125 -26.903125
8 9 10 11 12 13 14
-44.803125 -5.303125 -9.803125 0.796875 -2.303125 -11.503125 -7.503125
15 16 17 18 19 20 21
22.196875 6.896875 -8.403125 28.196875 -32.603125 -9.003125 19.296875
22 23 24 25 26 27 28
22.796875 5.896875 -0.203125 7.896875 10.496875 34.596875 14.196875
29 30 31 32 33 34 35
-7.403125 49.196875 -23.303125 -16.003125 10.428571 -12.171429 -2.371429
36 37 38 39 40 41 42
-10.871429 -12.771429 4.328571 24.128571 -18.271429 9.428571 16.828571
43 44 45 46 47 48 49
-35.971429 -28.671429 15.428571 15.628571 17.128571 -13.571429 2.428571
50 51 52 53 54 55 56
-2.271429 15.628571 -8.771429 -3.371429 24.728571 -19.571429 -25.071429
57 58 59 60
11.528571 17.628571 10.928571 -2.471429
> postscript(file="/var/www/html/rcomp/tmp/60hdw1229093270.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 -13.603125 NA
1 -5.603125 -13.603125
2 3.396875 -5.603125
3 -3.303125 3.396875
4 2.096875 -3.303125
5 -0.403125 2.096875
6 -26.903125 -0.403125
7 -44.803125 -26.903125
8 -5.303125 -44.803125
9 -9.803125 -5.303125
10 0.796875 -9.803125
11 -2.303125 0.796875
12 -11.503125 -2.303125
13 -7.503125 -11.503125
14 22.196875 -7.503125
15 6.896875 22.196875
16 -8.403125 6.896875
17 28.196875 -8.403125
18 -32.603125 28.196875
19 -9.003125 -32.603125
20 19.296875 -9.003125
21 22.796875 19.296875
22 5.896875 22.796875
23 -0.203125 5.896875
24 7.896875 -0.203125
25 10.496875 7.896875
26 34.596875 10.496875
27 14.196875 34.596875
28 -7.403125 14.196875
29 49.196875 -7.403125
30 -23.303125 49.196875
31 -16.003125 -23.303125
32 10.428571 -16.003125
33 -12.171429 10.428571
34 -2.371429 -12.171429
35 -10.871429 -2.371429
36 -12.771429 -10.871429
37 4.328571 -12.771429
38 24.128571 4.328571
39 -18.271429 24.128571
40 9.428571 -18.271429
41 16.828571 9.428571
42 -35.971429 16.828571
43 -28.671429 -35.971429
44 15.428571 -28.671429
45 15.628571 15.428571
46 17.128571 15.628571
47 -13.571429 17.128571
48 2.428571 -13.571429
49 -2.271429 2.428571
50 15.628571 -2.271429
51 -8.771429 15.628571
52 -3.371429 -8.771429
53 24.728571 -3.371429
54 -19.571429 24.728571
55 -25.071429 -19.571429
56 11.528571 -25.071429
57 17.628571 11.528571
58 10.928571 17.628571
59 -2.471429 10.928571
60 NA -2.471429
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.603125 -13.603125
[2,] 3.396875 -5.603125
[3,] -3.303125 3.396875
[4,] 2.096875 -3.303125
[5,] -0.403125 2.096875
[6,] -26.903125 -0.403125
[7,] -44.803125 -26.903125
[8,] -5.303125 -44.803125
[9,] -9.803125 -5.303125
[10,] 0.796875 -9.803125
[11,] -2.303125 0.796875
[12,] -11.503125 -2.303125
[13,] -7.503125 -11.503125
[14,] 22.196875 -7.503125
[15,] 6.896875 22.196875
[16,] -8.403125 6.896875
[17,] 28.196875 -8.403125
[18,] -32.603125 28.196875
[19,] -9.003125 -32.603125
[20,] 19.296875 -9.003125
[21,] 22.796875 19.296875
[22,] 5.896875 22.796875
[23,] -0.203125 5.896875
[24,] 7.896875 -0.203125
[25,] 10.496875 7.896875
[26,] 34.596875 10.496875
[27,] 14.196875 34.596875
[28,] -7.403125 14.196875
[29,] 49.196875 -7.403125
[30,] -23.303125 49.196875
[31,] -16.003125 -23.303125
[32,] 10.428571 -16.003125
[33,] -12.171429 10.428571
[34,] -2.371429 -12.171429
[35,] -10.871429 -2.371429
[36,] -12.771429 -10.871429
[37,] 4.328571 -12.771429
[38,] 24.128571 4.328571
[39,] -18.271429 24.128571
[40,] 9.428571 -18.271429
[41,] 16.828571 9.428571
[42,] -35.971429 16.828571
[43,] -28.671429 -35.971429
[44,] 15.428571 -28.671429
[45,] 15.628571 15.428571
[46,] 17.128571 15.628571
[47,] -13.571429 17.128571
[48,] 2.428571 -13.571429
[49,] -2.271429 2.428571
[50,] 15.628571 -2.271429
[51,] -8.771429 15.628571
[52,] -3.371429 -8.771429
[53,] 24.728571 -3.371429
[54,] -19.571429 24.728571
[55,] -25.071429 -19.571429
[56,] 11.528571 -25.071429
[57,] 17.628571 11.528571
[58,] 10.928571 17.628571
[59,] -2.471429 10.928571
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.603125 -13.603125
2 3.396875 -5.603125
3 -3.303125 3.396875
4 2.096875 -3.303125
5 -0.403125 2.096875
6 -26.903125 -0.403125
7 -44.803125 -26.903125
8 -5.303125 -44.803125
9 -9.803125 -5.303125
10 0.796875 -9.803125
11 -2.303125 0.796875
12 -11.503125 -2.303125
13 -7.503125 -11.503125
14 22.196875 -7.503125
15 6.896875 22.196875
16 -8.403125 6.896875
17 28.196875 -8.403125
18 -32.603125 28.196875
19 -9.003125 -32.603125
20 19.296875 -9.003125
21 22.796875 19.296875
22 5.896875 22.796875
23 -0.203125 5.896875
24 7.896875 -0.203125
25 10.496875 7.896875
26 34.596875 10.496875
27 14.196875 34.596875
28 -7.403125 14.196875
29 49.196875 -7.403125
30 -23.303125 49.196875
31 -16.003125 -23.303125
32 10.428571 -16.003125
33 -12.171429 10.428571
34 -2.371429 -12.171429
35 -10.871429 -2.371429
36 -12.771429 -10.871429
37 4.328571 -12.771429
38 24.128571 4.328571
39 -18.271429 24.128571
40 9.428571 -18.271429
41 16.828571 9.428571
42 -35.971429 16.828571
43 -28.671429 -35.971429
44 15.428571 -28.671429
45 15.628571 15.428571
46 17.128571 15.628571
47 -13.571429 17.128571
48 2.428571 -13.571429
49 -2.271429 2.428571
50 15.628571 -2.271429
51 -8.771429 15.628571
52 -3.371429 -8.771429
53 24.728571 -3.371429
54 -19.571429 24.728571
55 -25.071429 -19.571429
56 11.528571 -25.071429
57 17.628571 11.528571
58 10.928571 17.628571
59 -2.471429 10.928571
> 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/7cbl21229093270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8tj3s1229093270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/925691229093270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106tot1229093270.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118ekp1229093270.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/12524i1229093270.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/13nfmn1229093270.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/14jsuy1229093270.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/15mw4q1229093270.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/16w9wq1229093270.tab")
+ }
>
> system("convert tmp/1bfpt1229093270.ps tmp/1bfpt1229093270.png")
> system("convert tmp/2l7m61229093270.ps tmp/2l7m61229093270.png")
> system("convert tmp/3nkpd1229093270.ps tmp/3nkpd1229093270.png")
> system("convert tmp/4gyus1229093270.ps tmp/4gyus1229093270.png")
> system("convert tmp/561wc1229093270.ps tmp/561wc1229093270.png")
> system("convert tmp/60hdw1229093270.ps tmp/60hdw1229093270.png")
> system("convert tmp/7cbl21229093270.ps tmp/7cbl21229093270.png")
> system("convert tmp/8tj3s1229093270.ps tmp/8tj3s1229093270.png")
> system("convert tmp/925691229093270.ps tmp/925691229093270.png")
> system("convert tmp/106tot1229093270.ps tmp/106tot1229093270.png")
>
>
> proc.time()
user system elapsed
2.476 1.584 5.746