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(20366,0,22782,0,19169,0,13807,0,29743,0,25591,0,29096,0,26482,0,22405,0,27044,0,17970,0,18730,0,19684,0,19785,0,18479,0,10698,0,31956,0,29506,0,34506,0,27165,0,26736,0,23691,0,18157,0,17328,0,18205,0,20995,0,17382,0,9367,0,31124,0,26551,0,30651,0,25859,0,25100,0,25778,0,20418,0,18688,0,20424,0,24776,0,19814,0,12738,0,31566,0,30111,0,30019,0,31934,1,25826,1,26835,1,20205,1,17789,1,20520,1,22518,1,15572,1,11509,1,25447,1,24090,1,27786,1,26195,1,20516,1,22759,1,19028,1,16971,1,20036,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> 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 20366 0
2 22782 0
3 19169 0
4 13807 0
5 29743 0
6 25591 0
7 29096 0
8 26482 0
9 22405 0
10 27044 0
11 17970 0
12 18730 0
13 19684 0
14 19785 0
15 18479 0
16 10698 0
17 31956 0
18 29506 0
19 34506 0
20 27165 0
21 26736 0
22 23691 0
23 18157 0
24 17328 0
25 18205 0
26 20995 0
27 17382 0
28 9367 0
29 31124 0
30 26551 0
31 30651 0
32 25859 0
33 25100 0
34 25778 0
35 20418 0
36 18688 0
37 20424 0
38 24776 0
39 19814 0
40 12738 0
41 31566 0
42 30111 0
43 30019 0
44 31934 1
45 25826 1
46 26835 1
47 20205 1
48 17789 1
49 20520 1
50 22518 1
51 15572 1
52 11509 1
53 25447 1
54 24090 1
55 27786 1
56 26195 1
57 20516 1
58 22759 1
59 19028 1
60 16971 1
61 20036 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
23034 -1059
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-13666.5 -4185.2 -251.5 4010.5 11472.5
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23033.5 876.4 26.283 <2e-16 ***
X -1059.3 1613.3 -0.657 0.514
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5747 on 59 degrees of freedom
Multiple R-squared: 0.007255, Adjusted R-squared: -0.009572
F-statistic: 0.4311 on 1 and 59 DF, p-value: 0.514
> 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,] 0.7333863 0.5332275 0.26661373
[2,] 0.6432218 0.7135563 0.35677817
[3,] 0.6585951 0.6828098 0.34140492
[4,] 0.5696106 0.8607788 0.43038941
[5,] 0.4487656 0.8975313 0.55123437
[6,] 0.3798314 0.7596628 0.62016860
[7,] 0.3672502 0.7345004 0.63274979
[8,] 0.3233369 0.6466738 0.67666309
[9,] 0.2609772 0.5219545 0.73902277
[10,] 0.2041893 0.4083786 0.79581072
[11,] 0.1725788 0.3451576 0.82742120
[12,] 0.4157099 0.8314198 0.58429008
[13,] 0.5739002 0.8521995 0.42609975
[14,] 0.6038802 0.7922396 0.39611982
[15,] 0.7955325 0.4089350 0.20446752
[16,] 0.7628774 0.4742452 0.23712259
[17,] 0.7216777 0.5566447 0.27832233
[18,] 0.6522455 0.6955090 0.34775452
[19,] 0.6290858 0.7418283 0.37091416
[20,] 0.6231819 0.7536362 0.37681812
[21,] 0.5979435 0.8041130 0.40205651
[22,] 0.5315083 0.9369835 0.46849173
[23,] 0.5274341 0.9451317 0.47256586
[24,] 0.8347846 0.3304309 0.16521543
[25,] 0.8673359 0.2653281 0.13266407
[26,] 0.8363261 0.3273477 0.16367387
[27,] 0.8605793 0.2788413 0.13942066
[28,] 0.8234853 0.3530294 0.17651468
[29,] 0.7753334 0.4493332 0.22466662
[30,] 0.7266281 0.5467438 0.27337189
[31,] 0.6747948 0.6504105 0.32520525
[32,] 0.6520334 0.6959332 0.34796662
[33,] 0.6055303 0.7889393 0.39446966
[34,] 0.5314786 0.9370429 0.46852144
[35,] 0.5066633 0.9866733 0.49333665
[36,] 0.8617790 0.2764420 0.13822098
[37,] 0.8467160 0.3065679 0.15328396
[38,] 0.8143856 0.3712287 0.18561437
[39,] 0.7741870 0.4516260 0.22581302
[40,] 0.8765258 0.2469483 0.12347417
[41,] 0.8634937 0.2730127 0.13650634
[42,] 0.8683134 0.2633731 0.13168656
[43,] 0.8253821 0.3492359 0.17461793
[44,] 0.7947928 0.4104144 0.20520718
[45,] 0.7191419 0.5617162 0.28085809
[46,] 0.6299553 0.7400893 0.37004467
[47,] 0.6370608 0.7258784 0.36293922
[48,] 0.8915347 0.2169305 0.10846527
[49,] 0.8482817 0.3034365 0.15171826
[50,] 0.7644185 0.4711631 0.23558153
[51,] 0.8356669 0.3286662 0.16433309
[52,] 0.9164214 0.1671573 0.08357863
> postscript(file="/var/www/html/rcomp/tmp/1bjpy1258728556.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2s2n71258728556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36d401258728556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/436kt1258728556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xwqq1258728556.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 = 61
Frequency = 1
1 2 3 4 5 6
-2667.5349 -251.5349 -3864.5349 -9226.5349 6709.4651 2557.4651
7 8 9 10 11 12
6062.4651 3448.4651 -628.5349 4010.4651 -5063.5349 -4303.5349
13 14 15 16 17 18
-3349.5349 -3248.5349 -4554.5349 -12335.5349 8922.4651 6472.4651
19 20 21 22 23 24
11472.4651 4131.4651 3702.4651 657.4651 -4876.5349 -5705.5349
25 26 27 28 29 30
-4828.5349 -2038.5349 -5651.5349 -13666.5349 8090.4651 3517.4651
31 32 33 34 35 36
7617.4651 2825.4651 2066.4651 2744.4651 -2615.5349 -4345.5349
37 38 39 40 41 42
-2609.5349 1742.4651 -3219.5349 -10295.5349 8532.4651 7077.4651
43 44 45 46 47 48
6985.4651 9959.7778 3851.7778 4860.7778 -1769.2222 -4185.2222
49 50 51 52 53 54
-1454.2222 543.7778 -6402.2222 -10465.2222 3472.7778 2115.7778
55 56 57 58 59 60
5811.7778 4220.7778 -1458.2222 784.7778 -2946.2222 -5003.2222
61
-1938.2222
> postscript(file="/var/www/html/rcomp/tmp/6xu2e1258728556.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -2667.5349 NA
1 -251.5349 -2667.5349
2 -3864.5349 -251.5349
3 -9226.5349 -3864.5349
4 6709.4651 -9226.5349
5 2557.4651 6709.4651
6 6062.4651 2557.4651
7 3448.4651 6062.4651
8 -628.5349 3448.4651
9 4010.4651 -628.5349
10 -5063.5349 4010.4651
11 -4303.5349 -5063.5349
12 -3349.5349 -4303.5349
13 -3248.5349 -3349.5349
14 -4554.5349 -3248.5349
15 -12335.5349 -4554.5349
16 8922.4651 -12335.5349
17 6472.4651 8922.4651
18 11472.4651 6472.4651
19 4131.4651 11472.4651
20 3702.4651 4131.4651
21 657.4651 3702.4651
22 -4876.5349 657.4651
23 -5705.5349 -4876.5349
24 -4828.5349 -5705.5349
25 -2038.5349 -4828.5349
26 -5651.5349 -2038.5349
27 -13666.5349 -5651.5349
28 8090.4651 -13666.5349
29 3517.4651 8090.4651
30 7617.4651 3517.4651
31 2825.4651 7617.4651
32 2066.4651 2825.4651
33 2744.4651 2066.4651
34 -2615.5349 2744.4651
35 -4345.5349 -2615.5349
36 -2609.5349 -4345.5349
37 1742.4651 -2609.5349
38 -3219.5349 1742.4651
39 -10295.5349 -3219.5349
40 8532.4651 -10295.5349
41 7077.4651 8532.4651
42 6985.4651 7077.4651
43 9959.7778 6985.4651
44 3851.7778 9959.7778
45 4860.7778 3851.7778
46 -1769.2222 4860.7778
47 -4185.2222 -1769.2222
48 -1454.2222 -4185.2222
49 543.7778 -1454.2222
50 -6402.2222 543.7778
51 -10465.2222 -6402.2222
52 3472.7778 -10465.2222
53 2115.7778 3472.7778
54 5811.7778 2115.7778
55 4220.7778 5811.7778
56 -1458.2222 4220.7778
57 784.7778 -1458.2222
58 -2946.2222 784.7778
59 -5003.2222 -2946.2222
60 -1938.2222 -5003.2222
61 NA -1938.2222
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -251.5349 -2667.5349
[2,] -3864.5349 -251.5349
[3,] -9226.5349 -3864.5349
[4,] 6709.4651 -9226.5349
[5,] 2557.4651 6709.4651
[6,] 6062.4651 2557.4651
[7,] 3448.4651 6062.4651
[8,] -628.5349 3448.4651
[9,] 4010.4651 -628.5349
[10,] -5063.5349 4010.4651
[11,] -4303.5349 -5063.5349
[12,] -3349.5349 -4303.5349
[13,] -3248.5349 -3349.5349
[14,] -4554.5349 -3248.5349
[15,] -12335.5349 -4554.5349
[16,] 8922.4651 -12335.5349
[17,] 6472.4651 8922.4651
[18,] 11472.4651 6472.4651
[19,] 4131.4651 11472.4651
[20,] 3702.4651 4131.4651
[21,] 657.4651 3702.4651
[22,] -4876.5349 657.4651
[23,] -5705.5349 -4876.5349
[24,] -4828.5349 -5705.5349
[25,] -2038.5349 -4828.5349
[26,] -5651.5349 -2038.5349
[27,] -13666.5349 -5651.5349
[28,] 8090.4651 -13666.5349
[29,] 3517.4651 8090.4651
[30,] 7617.4651 3517.4651
[31,] 2825.4651 7617.4651
[32,] 2066.4651 2825.4651
[33,] 2744.4651 2066.4651
[34,] -2615.5349 2744.4651
[35,] -4345.5349 -2615.5349
[36,] -2609.5349 -4345.5349
[37,] 1742.4651 -2609.5349
[38,] -3219.5349 1742.4651
[39,] -10295.5349 -3219.5349
[40,] 8532.4651 -10295.5349
[41,] 7077.4651 8532.4651
[42,] 6985.4651 7077.4651
[43,] 9959.7778 6985.4651
[44,] 3851.7778 9959.7778
[45,] 4860.7778 3851.7778
[46,] -1769.2222 4860.7778
[47,] -4185.2222 -1769.2222
[48,] -1454.2222 -4185.2222
[49,] 543.7778 -1454.2222
[50,] -6402.2222 543.7778
[51,] -10465.2222 -6402.2222
[52,] 3472.7778 -10465.2222
[53,] 2115.7778 3472.7778
[54,] 5811.7778 2115.7778
[55,] 4220.7778 5811.7778
[56,] -1458.2222 4220.7778
[57,] 784.7778 -1458.2222
[58,] -2946.2222 784.7778
[59,] -5003.2222 -2946.2222
[60,] -1938.2222 -5003.2222
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -251.5349 -2667.5349
2 -3864.5349 -251.5349
3 -9226.5349 -3864.5349
4 6709.4651 -9226.5349
5 2557.4651 6709.4651
6 6062.4651 2557.4651
7 3448.4651 6062.4651
8 -628.5349 3448.4651
9 4010.4651 -628.5349
10 -5063.5349 4010.4651
11 -4303.5349 -5063.5349
12 -3349.5349 -4303.5349
13 -3248.5349 -3349.5349
14 -4554.5349 -3248.5349
15 -12335.5349 -4554.5349
16 8922.4651 -12335.5349
17 6472.4651 8922.4651
18 11472.4651 6472.4651
19 4131.4651 11472.4651
20 3702.4651 4131.4651
21 657.4651 3702.4651
22 -4876.5349 657.4651
23 -5705.5349 -4876.5349
24 -4828.5349 -5705.5349
25 -2038.5349 -4828.5349
26 -5651.5349 -2038.5349
27 -13666.5349 -5651.5349
28 8090.4651 -13666.5349
29 3517.4651 8090.4651
30 7617.4651 3517.4651
31 2825.4651 7617.4651
32 2066.4651 2825.4651
33 2744.4651 2066.4651
34 -2615.5349 2744.4651
35 -4345.5349 -2615.5349
36 -2609.5349 -4345.5349
37 1742.4651 -2609.5349
38 -3219.5349 1742.4651
39 -10295.5349 -3219.5349
40 8532.4651 -10295.5349
41 7077.4651 8532.4651
42 6985.4651 7077.4651
43 9959.7778 6985.4651
44 3851.7778 9959.7778
45 4860.7778 3851.7778
46 -1769.2222 4860.7778
47 -4185.2222 -1769.2222
48 -1454.2222 -4185.2222
49 543.7778 -1454.2222
50 -6402.2222 543.7778
51 -10465.2222 -6402.2222
52 3472.7778 -10465.2222
53 2115.7778 3472.7778
54 5811.7778 2115.7778
55 4220.7778 5811.7778
56 -1458.2222 4220.7778
57 784.7778 -1458.2222
58 -2946.2222 784.7778
59 -5003.2222 -2946.2222
60 -1938.2222 -5003.2222
> 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/7z8x21258728556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gq8z1258728556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/97qkj1258728556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101oiz1258728556.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/118vr31258728556.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/1256631258728556.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/13bs9s1258728556.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/1468um1258728556.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/15ya2s1258728556.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/162wn61258728556.tab")
+ }
>
> system("convert tmp/1bjpy1258728556.ps tmp/1bjpy1258728556.png")
> system("convert tmp/2s2n71258728556.ps tmp/2s2n71258728556.png")
> system("convert tmp/36d401258728556.ps tmp/36d401258728556.png")
> system("convert tmp/436kt1258728556.ps tmp/436kt1258728556.png")
> system("convert tmp/5xwqq1258728556.ps tmp/5xwqq1258728556.png")
> system("convert tmp/6xu2e1258728556.ps tmp/6xu2e1258728556.png")
> system("convert tmp/7z8x21258728556.ps tmp/7z8x21258728556.png")
> system("convert tmp/8gq8z1258728556.ps tmp/8gq8z1258728556.png")
> system("convert tmp/97qkj1258728556.ps tmp/97qkj1258728556.png")
> system("convert tmp/101oiz1258728556.ps tmp/101oiz1258728556.png")
>
>
> proc.time()
user system elapsed
2.464 1.563 2.850