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(8.9,1.6,8.8,1.3,8.3,1.1,7.5,1.6,7.2,1.9,7.4,1.6,8.8,1.7,9.3,1.6,9.3,1.4,8.7,2.1,8.2,1.9,8.3,1.7,8.5,1.8,8.6,2,8.5,2.5,8.2,2.1,8.1,2.1,7.9,2.3,8.6,2.4,8.7,2.4,8.7,2.3,8.5,1.7,8.4,2,8.5,2.3,8.7,2,8.7,2,8.6,1.3,8.5,1.7,8.3,1.9,8,1.7,8.2,1.6,8.1,1.7,8.1,1.8,8,1.9,7.9,1.9,7.9,1.9,8,2,8,2.1,7.9,1.9,8,1.9,7.7,1.3,7.2,1.3,7.5,1.4,7.3,1.2,7,1.3,7,1.8,7,2.2,7.2,2.6,7.3,2.8,7.1,3.1,6.8,3.9,6.4,3.7,6.1,4.6,6.5,5.1,7.7,5.2,7.9,4.9,7.5,5.1,6.9,4.8,6.6,3.9,6.9,3.5),dim=c(2,60),dimnames=list(c('TWIB','GI'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('TWIB','GI'),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
TWIB GI
1 8.9 1.6
2 8.8 1.3
3 8.3 1.1
4 7.5 1.6
5 7.2 1.9
6 7.4 1.6
7 8.8 1.7
8 9.3 1.6
9 9.3 1.4
10 8.7 2.1
11 8.2 1.9
12 8.3 1.7
13 8.5 1.8
14 8.6 2.0
15 8.5 2.5
16 8.2 2.1
17 8.1 2.1
18 7.9 2.3
19 8.6 2.4
20 8.7 2.4
21 8.7 2.3
22 8.5 1.7
23 8.4 2.0
24 8.5 2.3
25 8.7 2.0
26 8.7 2.0
27 8.6 1.3
28 8.5 1.7
29 8.3 1.9
30 8.0 1.7
31 8.2 1.6
32 8.1 1.7
33 8.1 1.8
34 8.0 1.9
35 7.9 1.9
36 7.9 1.9
37 8.0 2.0
38 8.0 2.1
39 7.9 1.9
40 8.0 1.9
41 7.7 1.3
42 7.2 1.3
43 7.5 1.4
44 7.3 1.2
45 7.0 1.3
46 7.0 1.8
47 7.0 2.2
48 7.2 2.6
49 7.3 2.8
50 7.1 3.1
51 6.8 3.9
52 6.4 3.7
53 6.1 4.6
54 6.5 5.1
55 7.7 5.2
56 7.9 4.9
57 7.5 5.1
58 6.9 4.8
59 6.6 3.9
60 6.9 3.5
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) GI
8.7432 -0.3626
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.2717250 -0.5395756 0.0002556 0.5843216 1.1370673
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.74316 0.19592 44.626 < 2e-16 ***
GI -0.36264 0.07707 -4.705 1.62e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6378 on 58 degrees of freedom
Multiple R-squared: 0.2763, Adjusted R-squared: 0.2638
F-statistic: 22.14 on 1 and 58 DF, p-value: 1.615e-05
> 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,] 0.6929324 0.6141351 0.30706756
[2,] 0.6384006 0.7231989 0.36159943
[3,] 0.7407400 0.5185199 0.25925996
[4,] 0.8723379 0.2553243 0.12766214
[5,] 0.8964634 0.2070732 0.10353662
[6,] 0.9048003 0.1903993 0.09519967
[7,] 0.8552995 0.2894011 0.14470055
[8,] 0.7940065 0.4119871 0.20599354
[9,] 0.7348194 0.5303613 0.26518063
[10,] 0.6938684 0.6122632 0.30613161
[11,] 0.6550710 0.6898580 0.34492902
[12,] 0.5789034 0.8421933 0.42109665
[13,] 0.5024491 0.9951018 0.49755091
[14,] 0.4328644 0.8657287 0.56713564
[15,] 0.4204229 0.8408458 0.57957709
[16,] 0.4301572 0.8603145 0.56984276
[17,] 0.4370465 0.8740930 0.56295348
[18,] 0.3880350 0.7760700 0.61196502
[19,] 0.3425130 0.6850260 0.65748698
[20,] 0.3264272 0.6528544 0.67357280
[21,] 0.3477947 0.6955893 0.65220533
[22,] 0.3853761 0.7707523 0.61462387
[23,] 0.3784428 0.7568855 0.62155724
[24,] 0.3825265 0.7650530 0.61747348
[25,] 0.3728647 0.7457294 0.62713532
[26,] 0.3494031 0.6988063 0.65059686
[27,] 0.3320397 0.6640793 0.66796034
[28,] 0.3162726 0.6325452 0.68372740
[29,] 0.3088792 0.6177583 0.69112084
[30,] 0.3031533 0.6063067 0.69684665
[31,] 0.2974580 0.5949160 0.70254199
[32,] 0.2945970 0.5891940 0.70540298
[33,] 0.3091473 0.6182946 0.69085268
[34,] 0.3410018 0.6820037 0.65899815
[35,] 0.3705868 0.7411736 0.62941321
[36,] 0.4512693 0.9025386 0.54873069
[37,] 0.4913482 0.9826964 0.50865178
[38,] 0.5273679 0.9452643 0.47263214
[39,] 0.5370318 0.9259363 0.46296817
[40,] 0.5311991 0.9376017 0.46880086
[41,] 0.5284272 0.9431457 0.47157284
[42,] 0.5355005 0.9289990 0.46449950
[43,] 0.5428967 0.9142066 0.45710330
[44,] 0.5385409 0.9229183 0.46145913
[45,] 0.5673191 0.8653618 0.43268089
[46,] 0.5920980 0.8158040 0.40790201
[47,] 0.5070682 0.9858636 0.49293179
[48,] 0.4304673 0.8609346 0.56953272
[49,] 0.5986611 0.8026779 0.40133894
[50,] 0.8180661 0.3638678 0.18193389
[51,] 0.6974740 0.6050519 0.30252595
> postscript(file="/var/www/html/rcomp/tmp/1gc3l1258756549.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28pnw1258756549.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wffv1258756549.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4drdj1258756549.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5per81258756549.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
0.737067253 0.528275050 -0.044253086 -0.662932747 -0.854140543 -0.762932747
7 8 9 10 11 12
0.673331321 1.137067253 1.064539118 0.718387593 0.145859457 0.173331321
13 14 15 16 17 18
0.409595389 0.582123525 0.663443865 0.218387593 0.118387593 -0.009084271
19 20 21 22 23 24
0.727179797 0.827179797 0.790915729 0.373331321 0.382123525 0.590915729
25 26 27 28 29 30
0.682123525 0.682123525 0.328275050 0.373331321 0.245859457 -0.126668679
31 32 33 34 35 36
0.037067253 -0.026668679 0.009595389 -0.054140543 -0.154140543 -0.154140543
37 38 39 40 41 42
-0.017876475 0.018387593 -0.154140543 -0.054140543 -0.571724950 -1.071724950
43 44 45 46 47 48
-0.735460882 -1.007989018 -1.271724950 -1.090404611 -0.945348339 -0.600292067
49 50 51 52 53 54
-0.427763932 -0.518971728 -0.528859185 -1.001387320 -0.975010709 -0.393690370
55 56 57 58 59 60
0.842573698 0.933781495 0.606309630 -0.102482573 -0.728859185 -0.573915456
> postscript(file="/var/www/html/rcomp/tmp/6yxjy1258756549.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 0.737067253 NA
1 0.528275050 0.737067253
2 -0.044253086 0.528275050
3 -0.662932747 -0.044253086
4 -0.854140543 -0.662932747
5 -0.762932747 -0.854140543
6 0.673331321 -0.762932747
7 1.137067253 0.673331321
8 1.064539118 1.137067253
9 0.718387593 1.064539118
10 0.145859457 0.718387593
11 0.173331321 0.145859457
12 0.409595389 0.173331321
13 0.582123525 0.409595389
14 0.663443865 0.582123525
15 0.218387593 0.663443865
16 0.118387593 0.218387593
17 -0.009084271 0.118387593
18 0.727179797 -0.009084271
19 0.827179797 0.727179797
20 0.790915729 0.827179797
21 0.373331321 0.790915729
22 0.382123525 0.373331321
23 0.590915729 0.382123525
24 0.682123525 0.590915729
25 0.682123525 0.682123525
26 0.328275050 0.682123525
27 0.373331321 0.328275050
28 0.245859457 0.373331321
29 -0.126668679 0.245859457
30 0.037067253 -0.126668679
31 -0.026668679 0.037067253
32 0.009595389 -0.026668679
33 -0.054140543 0.009595389
34 -0.154140543 -0.054140543
35 -0.154140543 -0.154140543
36 -0.017876475 -0.154140543
37 0.018387593 -0.017876475
38 -0.154140543 0.018387593
39 -0.054140543 -0.154140543
40 -0.571724950 -0.054140543
41 -1.071724950 -0.571724950
42 -0.735460882 -1.071724950
43 -1.007989018 -0.735460882
44 -1.271724950 -1.007989018
45 -1.090404611 -1.271724950
46 -0.945348339 -1.090404611
47 -0.600292067 -0.945348339
48 -0.427763932 -0.600292067
49 -0.518971728 -0.427763932
50 -0.528859185 -0.518971728
51 -1.001387320 -0.528859185
52 -0.975010709 -1.001387320
53 -0.393690370 -0.975010709
54 0.842573698 -0.393690370
55 0.933781495 0.842573698
56 0.606309630 0.933781495
57 -0.102482573 0.606309630
58 -0.728859185 -0.102482573
59 -0.573915456 -0.728859185
60 NA -0.573915456
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.528275050 0.737067253
[2,] -0.044253086 0.528275050
[3,] -0.662932747 -0.044253086
[4,] -0.854140543 -0.662932747
[5,] -0.762932747 -0.854140543
[6,] 0.673331321 -0.762932747
[7,] 1.137067253 0.673331321
[8,] 1.064539118 1.137067253
[9,] 0.718387593 1.064539118
[10,] 0.145859457 0.718387593
[11,] 0.173331321 0.145859457
[12,] 0.409595389 0.173331321
[13,] 0.582123525 0.409595389
[14,] 0.663443865 0.582123525
[15,] 0.218387593 0.663443865
[16,] 0.118387593 0.218387593
[17,] -0.009084271 0.118387593
[18,] 0.727179797 -0.009084271
[19,] 0.827179797 0.727179797
[20,] 0.790915729 0.827179797
[21,] 0.373331321 0.790915729
[22,] 0.382123525 0.373331321
[23,] 0.590915729 0.382123525
[24,] 0.682123525 0.590915729
[25,] 0.682123525 0.682123525
[26,] 0.328275050 0.682123525
[27,] 0.373331321 0.328275050
[28,] 0.245859457 0.373331321
[29,] -0.126668679 0.245859457
[30,] 0.037067253 -0.126668679
[31,] -0.026668679 0.037067253
[32,] 0.009595389 -0.026668679
[33,] -0.054140543 0.009595389
[34,] -0.154140543 -0.054140543
[35,] -0.154140543 -0.154140543
[36,] -0.017876475 -0.154140543
[37,] 0.018387593 -0.017876475
[38,] -0.154140543 0.018387593
[39,] -0.054140543 -0.154140543
[40,] -0.571724950 -0.054140543
[41,] -1.071724950 -0.571724950
[42,] -0.735460882 -1.071724950
[43,] -1.007989018 -0.735460882
[44,] -1.271724950 -1.007989018
[45,] -1.090404611 -1.271724950
[46,] -0.945348339 -1.090404611
[47,] -0.600292067 -0.945348339
[48,] -0.427763932 -0.600292067
[49,] -0.518971728 -0.427763932
[50,] -0.528859185 -0.518971728
[51,] -1.001387320 -0.528859185
[52,] -0.975010709 -1.001387320
[53,] -0.393690370 -0.975010709
[54,] 0.842573698 -0.393690370
[55,] 0.933781495 0.842573698
[56,] 0.606309630 0.933781495
[57,] -0.102482573 0.606309630
[58,] -0.728859185 -0.102482573
[59,] -0.573915456 -0.728859185
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.528275050 0.737067253
2 -0.044253086 0.528275050
3 -0.662932747 -0.044253086
4 -0.854140543 -0.662932747
5 -0.762932747 -0.854140543
6 0.673331321 -0.762932747
7 1.137067253 0.673331321
8 1.064539118 1.137067253
9 0.718387593 1.064539118
10 0.145859457 0.718387593
11 0.173331321 0.145859457
12 0.409595389 0.173331321
13 0.582123525 0.409595389
14 0.663443865 0.582123525
15 0.218387593 0.663443865
16 0.118387593 0.218387593
17 -0.009084271 0.118387593
18 0.727179797 -0.009084271
19 0.827179797 0.727179797
20 0.790915729 0.827179797
21 0.373331321 0.790915729
22 0.382123525 0.373331321
23 0.590915729 0.382123525
24 0.682123525 0.590915729
25 0.682123525 0.682123525
26 0.328275050 0.682123525
27 0.373331321 0.328275050
28 0.245859457 0.373331321
29 -0.126668679 0.245859457
30 0.037067253 -0.126668679
31 -0.026668679 0.037067253
32 0.009595389 -0.026668679
33 -0.054140543 0.009595389
34 -0.154140543 -0.054140543
35 -0.154140543 -0.154140543
36 -0.017876475 -0.154140543
37 0.018387593 -0.017876475
38 -0.154140543 0.018387593
39 -0.054140543 -0.154140543
40 -0.571724950 -0.054140543
41 -1.071724950 -0.571724950
42 -0.735460882 -1.071724950
43 -1.007989018 -0.735460882
44 -1.271724950 -1.007989018
45 -1.090404611 -1.271724950
46 -0.945348339 -1.090404611
47 -0.600292067 -0.945348339
48 -0.427763932 -0.600292067
49 -0.518971728 -0.427763932
50 -0.528859185 -0.518971728
51 -1.001387320 -0.528859185
52 -0.975010709 -1.001387320
53 -0.393690370 -0.975010709
54 0.842573698 -0.393690370
55 0.933781495 0.842573698
56 0.606309630 0.933781495
57 -0.102482573 0.606309630
58 -0.728859185 -0.102482573
59 -0.573915456 -0.728859185
> 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/7mnye1258756549.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hsl51258756549.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rtrj1258756549.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10u9kk1258756549.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11yi9g1258756549.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/12ictd1258756549.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/13p6an1258756549.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/14ifjz1258756549.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/154b9y1258756550.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/16a51g1258756550.tab")
+ }
>
> system("convert tmp/1gc3l1258756549.ps tmp/1gc3l1258756549.png")
> system("convert tmp/28pnw1258756549.ps tmp/28pnw1258756549.png")
> system("convert tmp/3wffv1258756549.ps tmp/3wffv1258756549.png")
> system("convert tmp/4drdj1258756549.ps tmp/4drdj1258756549.png")
> system("convert tmp/5per81258756549.ps tmp/5per81258756549.png")
> system("convert tmp/6yxjy1258756549.ps tmp/6yxjy1258756549.png")
> system("convert tmp/7mnye1258756549.ps tmp/7mnye1258756549.png")
> system("convert tmp/8hsl51258756549.ps tmp/8hsl51258756549.png")
> system("convert tmp/9rtrj1258756549.ps tmp/9rtrj1258756549.png")
> system("convert tmp/10u9kk1258756549.ps tmp/10u9kk1258756549.png")
>
>
> proc.time()
user system elapsed
2.430 1.568 2.903