R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
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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
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> x <- array(list(9.3,14.2,17.3,23,16.3,18.4,14.2,9.1,5.9,7.2,6.8,8,14.3,14.6,17.5,17.2,17.2,14.1,10.4,6.8,4.1,6.5,6.1,6.3,9.3,16.4,16.1,18,17.6,14,10.5,6.9,2.8,0.7,3.6,6.7,12.5,14.4,16.5,18.7,19.4,15.8,11.3,9.7,2.9,0.1,2.5,6.7,10.3,11.2,17.4,20.5,17,14.2,10.6,6.1),dim=c(1,56),dimnames=list(c('GT'),1:56))
> y <- array(NA,dim=c(1,56),dimnames=list(c('GT'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
> 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
GT M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.3 1 0 0 0 0 0 0 0 0 0 0 1
2 14.2 0 1 0 0 0 0 0 0 0 0 0 2
3 17.3 0 0 1 0 0 0 0 0 0 0 0 3
4 23.0 0 0 0 1 0 0 0 0 0 0 0 4
5 16.3 0 0 0 0 1 0 0 0 0 0 0 5
6 18.4 0 0 0 0 0 1 0 0 0 0 0 6
7 14.2 0 0 0 0 0 0 1 0 0 0 0 7
8 9.1 0 0 0 0 0 0 0 1 0 0 0 8
9 5.9 0 0 0 0 0 0 0 0 1 0 0 9
10 7.2 0 0 0 0 0 0 0 0 0 1 0 10
11 6.8 0 0 0 0 0 0 0 0 0 0 1 11
12 8.0 0 0 0 0 0 0 0 0 0 0 0 12
13 14.3 1 0 0 0 0 0 0 0 0 0 0 13
14 14.6 0 1 0 0 0 0 0 0 0 0 0 14
15 17.5 0 0 1 0 0 0 0 0 0 0 0 15
16 17.2 0 0 0 1 0 0 0 0 0 0 0 16
17 17.2 0 0 0 0 1 0 0 0 0 0 0 17
18 14.1 0 0 0 0 0 1 0 0 0 0 0 18
19 10.4 0 0 0 0 0 0 1 0 0 0 0 19
20 6.8 0 0 0 0 0 0 0 1 0 0 0 20
21 4.1 0 0 0 0 0 0 0 0 1 0 0 21
22 6.5 0 0 0 0 0 0 0 0 0 1 0 22
23 6.1 0 0 0 0 0 0 0 0 0 0 1 23
24 6.3 0 0 0 0 0 0 0 0 0 0 0 24
25 9.3 1 0 0 0 0 0 0 0 0 0 0 25
26 16.4 0 1 0 0 0 0 0 0 0 0 0 26
27 16.1 0 0 1 0 0 0 0 0 0 0 0 27
28 18.0 0 0 0 1 0 0 0 0 0 0 0 28
29 17.6 0 0 0 0 1 0 0 0 0 0 0 29
30 14.0 0 0 0 0 0 1 0 0 0 0 0 30
31 10.5 0 0 0 0 0 0 1 0 0 0 0 31
32 6.9 0 0 0 0 0 0 0 1 0 0 0 32
33 2.8 0 0 0 0 0 0 0 0 1 0 0 33
34 0.7 0 0 0 0 0 0 0 0 0 1 0 34
35 3.6 0 0 0 0 0 0 0 0 0 0 1 35
36 6.7 0 0 0 0 0 0 0 0 0 0 0 36
37 12.5 1 0 0 0 0 0 0 0 0 0 0 37
38 14.4 0 1 0 0 0 0 0 0 0 0 0 38
39 16.5 0 0 1 0 0 0 0 0 0 0 0 39
40 18.7 0 0 0 1 0 0 0 0 0 0 0 40
41 19.4 0 0 0 0 1 0 0 0 0 0 0 41
42 15.8 0 0 0 0 0 1 0 0 0 0 0 42
43 11.3 0 0 0 0 0 0 1 0 0 0 0 43
44 9.7 0 0 0 0 0 0 0 1 0 0 0 44
45 2.9 0 0 0 0 0 0 0 0 1 0 0 45
46 0.1 0 0 0 0 0 0 0 0 0 1 0 46
47 2.5 0 0 0 0 0 0 0 0 0 0 1 47
48 6.7 0 0 0 0 0 0 0 0 0 0 0 48
49 10.3 1 0 0 0 0 0 0 0 0 0 0 49
50 11.2 0 1 0 0 0 0 0 0 0 0 0 50
51 17.4 0 0 1 0 0 0 0 0 0 0 0 51
52 20.5 0 0 0 1 0 0 0 0 0 0 0 52
53 17.0 0 0 0 0 1 0 0 0 0 0 0 53
54 14.2 0 0 0 0 0 1 0 0 0 0 0 54
55 10.6 0 0 0 0 0 0 1 0 0 0 0 55
56 6.1 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
8.19875 4.00271 7.06517 9.90763 12.47008 10.53254
M6 M7 M8 M9 M10 M11
8.37500 4.51746 0.87992 -3.12737 -3.38492 -2.21746
t
-0.04246
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.85900 -0.91975 -0.01988 1.12412 2.81075
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.19875 0.98154 8.353 1.51e-10 ***
M1 4.00271 1.18011 3.392 0.001500 **
M2 7.06517 1.17930 5.991 3.77e-07 ***
M3 9.90763 1.17866 8.406 1.27e-10 ***
M4 12.47008 1.17821 10.584 1.50e-13 ***
M5 10.53254 1.17793 8.942 2.30e-11 ***
M6 8.37500 1.17784 7.110 8.91e-09 ***
M7 4.51746 1.17793 3.835 0.000405 ***
M8 0.87992 1.17821 0.747 0.459232
M9 -3.12737 1.24233 -2.517 0.015631 *
M10 -3.38492 1.24190 -2.726 0.009247 **
M11 -2.21746 1.24164 -1.786 0.081167 .
t -0.04246 0.01463 -2.902 0.005830 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.756 on 43 degrees of freedom
Multiple R-squared: 0.9233, Adjusted R-squared: 0.9019
F-statistic: 43.16 on 12 and 43 DF, p-value: < 2.2e-16
> 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,] 0.9801999 0.03960016 0.01980008
[2,] 0.9584087 0.08318267 0.04159134
[3,] 0.9658583 0.06828343 0.03414171
[4,] 0.9574271 0.08514583 0.04257292
[5,] 0.9338990 0.13220205 0.06610102
[6,] 0.8892330 0.22153409 0.11076705
[7,] 0.9539857 0.09202868 0.04601434
[8,] 0.9520830 0.09583400 0.04791700
[9,] 0.9208024 0.15839515 0.07919758
[10,] 0.9134645 0.17307105 0.08653553
[11,] 0.9730927 0.05381451 0.02690725
[12,] 0.9562416 0.08751678 0.04375839
[13,] 0.9496343 0.10073148 0.05036574
[14,] 0.9375452 0.12490965 0.06245482
[15,] 0.9259746 0.14805080 0.07402540
[16,] 0.9016540 0.19669194 0.09834597
[17,] 0.9152458 0.16950845 0.08475423
[18,] 0.8806480 0.23870406 0.11935203
[19,] 0.8929985 0.21400294 0.10700147
[20,] 0.8277530 0.34449405 0.17224703
[21,] 0.7681115 0.46377704 0.23188852
[22,] 0.7263645 0.54727106 0.27363553
[23,] 0.6911621 0.61767570 0.30883785
[24,] 0.6438928 0.71221440 0.35610720
[25,] 0.8684745 0.26305105 0.13152553
> postscript(file="/var/www/rcomp/tmp/183w31292938849.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/283w31292938849.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/31cvn1292938849.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/41cvn1292938849.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/51cvn1292938849.ps",horizontal=F,onefile=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 = 56
Frequency = 1
1 2 3 4 5 6 7 8
-2.85900 -0.97900 -0.67900 2.50100 -2.21900 2.08100 1.78100 0.36100
9 10 11 12 13 14 15 16
1.21075 2.81075 1.28575 0.31075 2.65050 -0.06950 0.03050 -2.78950
17 18 19 20 21 22 23 24
-0.80950 -1.70950 -1.50950 -1.42950 -0.07975 2.62025 1.09525 -0.87975
25 26 27 28 29 30 31 32
-1.84000 2.24000 -0.86000 -1.48000 0.10000 -1.30000 -0.90000 -0.82000
33 34 35 36 37 38 39 40
-0.87025 -2.67025 -0.89525 0.02975 1.86950 0.74950 0.04950 -0.27050
41 42 43 44 45 46 47 48
2.40950 1.00950 0.40950 2.48950 -0.26075 -2.76075 -1.48575 0.53925
49 50 51 52 53 54 55 56
0.17900 -1.94100 1.45900 2.03900 0.51900 -0.08100 0.21900 -0.60100
> postscript(file="/var/www/rcomp/tmp/6ulcq1292938849.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.85900 NA
1 -0.97900 -2.85900
2 -0.67900 -0.97900
3 2.50100 -0.67900
4 -2.21900 2.50100
5 2.08100 -2.21900
6 1.78100 2.08100
7 0.36100 1.78100
8 1.21075 0.36100
9 2.81075 1.21075
10 1.28575 2.81075
11 0.31075 1.28575
12 2.65050 0.31075
13 -0.06950 2.65050
14 0.03050 -0.06950
15 -2.78950 0.03050
16 -0.80950 -2.78950
17 -1.70950 -0.80950
18 -1.50950 -1.70950
19 -1.42950 -1.50950
20 -0.07975 -1.42950
21 2.62025 -0.07975
22 1.09525 2.62025
23 -0.87975 1.09525
24 -1.84000 -0.87975
25 2.24000 -1.84000
26 -0.86000 2.24000
27 -1.48000 -0.86000
28 0.10000 -1.48000
29 -1.30000 0.10000
30 -0.90000 -1.30000
31 -0.82000 -0.90000
32 -0.87025 -0.82000
33 -2.67025 -0.87025
34 -0.89525 -2.67025
35 0.02975 -0.89525
36 1.86950 0.02975
37 0.74950 1.86950
38 0.04950 0.74950
39 -0.27050 0.04950
40 2.40950 -0.27050
41 1.00950 2.40950
42 0.40950 1.00950
43 2.48950 0.40950
44 -0.26075 2.48950
45 -2.76075 -0.26075
46 -1.48575 -2.76075
47 0.53925 -1.48575
48 0.17900 0.53925
49 -1.94100 0.17900
50 1.45900 -1.94100
51 2.03900 1.45900
52 0.51900 2.03900
53 -0.08100 0.51900
54 0.21900 -0.08100
55 -0.60100 0.21900
56 NA -0.60100
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.97900 -2.85900
[2,] -0.67900 -0.97900
[3,] 2.50100 -0.67900
[4,] -2.21900 2.50100
[5,] 2.08100 -2.21900
[6,] 1.78100 2.08100
[7,] 0.36100 1.78100
[8,] 1.21075 0.36100
[9,] 2.81075 1.21075
[10,] 1.28575 2.81075
[11,] 0.31075 1.28575
[12,] 2.65050 0.31075
[13,] -0.06950 2.65050
[14,] 0.03050 -0.06950
[15,] -2.78950 0.03050
[16,] -0.80950 -2.78950
[17,] -1.70950 -0.80950
[18,] -1.50950 -1.70950
[19,] -1.42950 -1.50950
[20,] -0.07975 -1.42950
[21,] 2.62025 -0.07975
[22,] 1.09525 2.62025
[23,] -0.87975 1.09525
[24,] -1.84000 -0.87975
[25,] 2.24000 -1.84000
[26,] -0.86000 2.24000
[27,] -1.48000 -0.86000
[28,] 0.10000 -1.48000
[29,] -1.30000 0.10000
[30,] -0.90000 -1.30000
[31,] -0.82000 -0.90000
[32,] -0.87025 -0.82000
[33,] -2.67025 -0.87025
[34,] -0.89525 -2.67025
[35,] 0.02975 -0.89525
[36,] 1.86950 0.02975
[37,] 0.74950 1.86950
[38,] 0.04950 0.74950
[39,] -0.27050 0.04950
[40,] 2.40950 -0.27050
[41,] 1.00950 2.40950
[42,] 0.40950 1.00950
[43,] 2.48950 0.40950
[44,] -0.26075 2.48950
[45,] -2.76075 -0.26075
[46,] -1.48575 -2.76075
[47,] 0.53925 -1.48575
[48,] 0.17900 0.53925
[49,] -1.94100 0.17900
[50,] 1.45900 -1.94100
[51,] 2.03900 1.45900
[52,] 0.51900 2.03900
[53,] -0.08100 0.51900
[54,] 0.21900 -0.08100
[55,] -0.60100 0.21900
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.97900 -2.85900
2 -0.67900 -0.97900
3 2.50100 -0.67900
4 -2.21900 2.50100
5 2.08100 -2.21900
6 1.78100 2.08100
7 0.36100 1.78100
8 1.21075 0.36100
9 2.81075 1.21075
10 1.28575 2.81075
11 0.31075 1.28575
12 2.65050 0.31075
13 -0.06950 2.65050
14 0.03050 -0.06950
15 -2.78950 0.03050
16 -0.80950 -2.78950
17 -1.70950 -0.80950
18 -1.50950 -1.70950
19 -1.42950 -1.50950
20 -0.07975 -1.42950
21 2.62025 -0.07975
22 1.09525 2.62025
23 -0.87975 1.09525
24 -1.84000 -0.87975
25 2.24000 -1.84000
26 -0.86000 2.24000
27 -1.48000 -0.86000
28 0.10000 -1.48000
29 -1.30000 0.10000
30 -0.90000 -1.30000
31 -0.82000 -0.90000
32 -0.87025 -0.82000
33 -2.67025 -0.87025
34 -0.89525 -2.67025
35 0.02975 -0.89525
36 1.86950 0.02975
37 0.74950 1.86950
38 0.04950 0.74950
39 -0.27050 0.04950
40 2.40950 -0.27050
41 1.00950 2.40950
42 0.40950 1.00950
43 2.48950 0.40950
44 -0.26075 2.48950
45 -2.76075 -0.26075
46 -1.48575 -2.76075
47 0.53925 -1.48575
48 0.17900 0.53925
49 -1.94100 0.17900
50 1.45900 -1.94100
51 2.03900 1.45900
52 0.51900 2.03900
53 -0.08100 0.51900
54 0.21900 -0.08100
55 -0.60100 0.21900
> 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/rcomp/tmp/7mvcb1292938849.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8mvcb1292938849.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9mvcb1292938849.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10f4bw1292938849.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/1104921292938849.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/rcomp/tmp/12m5qq1292938849.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/rcomp/tmp/13ixoh1292938849.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/rcomp/tmp/143fmm1292938849.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/rcomp/tmp/15py3b1292938849.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/rcomp/tmp/16sg1y1292938849.tab")
+ }
>
> try(system("convert tmp/183w31292938849.ps tmp/183w31292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/283w31292938849.ps tmp/283w31292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/31cvn1292938849.ps tmp/31cvn1292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/41cvn1292938849.ps tmp/41cvn1292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/51cvn1292938849.ps tmp/51cvn1292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ulcq1292938849.ps tmp/6ulcq1292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mvcb1292938849.ps tmp/7mvcb1292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mvcb1292938849.ps tmp/8mvcb1292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/9mvcb1292938849.ps tmp/9mvcb1292938849.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f4bw1292938849.ps tmp/10f4bw1292938849.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.150 1.580 4.738