R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(0,6.3,0,6.2,0,6.1,0,6.3,0,6.5,0,6.6,0,6.5,0,6.2,0,6.2,0,5.9,0,6.1,0,6.1,0,6.1,0,6.1,0,6.1,0,6.4,0,6.7,0,6.9,0,7,0,7,0,6.8,0,6.4,0,5.9,0,5.5,0,5.5,0,5.6,0,5.8,0,5.9,0,6.1,0,6.1,0,6,0,6,0,5.9,0,5.5,0,5.6,0,5.4,0,5.2,0,5.2,0,5.2,0,5.5,1,5.8,1,5.8,1,5.5,1,5.3,1,5.1,1,5.2,1,5.8,1,5.8,1,5.5,1,5,1,4.9,1,5.3,1,6.1,1,6.5,1,6.8,1,6.6,1,6.4,1,6.4),dim=c(2,58),dimnames=list(c('X','Y'),1:58))
> y <- array(NA,dim=c(2,58),dimnames=list(c('X','Y'),1:58))
> 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 = '2'
> #'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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 6.3 0 1 0 0 0 0 0 0 0 0 0 0 1
2 6.2 0 0 1 0 0 0 0 0 0 0 0 0 2
3 6.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 6.3 0 0 0 0 1 0 0 0 0 0 0 0 4
5 6.5 0 0 0 0 0 1 0 0 0 0 0 0 5
6 6.6 0 0 0 0 0 0 1 0 0 0 0 0 6
7 6.5 0 0 0 0 0 0 0 1 0 0 0 0 7
8 6.2 0 0 0 0 0 0 0 0 1 0 0 0 8
9 6.2 0 0 0 0 0 0 0 0 0 1 0 0 9
10 5.9 0 0 0 0 0 0 0 0 0 0 1 0 10
11 6.1 0 0 0 0 0 0 0 0 0 0 0 1 11
12 6.1 0 0 0 0 0 0 0 0 0 0 0 0 12
13 6.1 0 1 0 0 0 0 0 0 0 0 0 0 13
14 6.1 0 0 1 0 0 0 0 0 0 0 0 0 14
15 6.1 0 0 0 1 0 0 0 0 0 0 0 0 15
16 6.4 0 0 0 0 1 0 0 0 0 0 0 0 16
17 6.7 0 0 0 0 0 1 0 0 0 0 0 0 17
18 6.9 0 0 0 0 0 0 1 0 0 0 0 0 18
19 7.0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 7.0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 6.8 0 0 0 0 0 0 0 0 0 1 0 0 21
22 6.4 0 0 0 0 0 0 0 0 0 0 1 0 22
23 5.9 0 0 0 0 0 0 0 0 0 0 0 1 23
24 5.5 0 0 0 0 0 0 0 0 0 0 0 0 24
25 5.5 0 1 0 0 0 0 0 0 0 0 0 0 25
26 5.6 0 0 1 0 0 0 0 0 0 0 0 0 26
27 5.8 0 0 0 1 0 0 0 0 0 0 0 0 27
28 5.9 0 0 0 0 1 0 0 0 0 0 0 0 28
29 6.1 0 0 0 0 0 1 0 0 0 0 0 0 29
30 6.1 0 0 0 0 0 0 1 0 0 0 0 0 30
31 6.0 0 0 0 0 0 0 0 1 0 0 0 0 31
32 6.0 0 0 0 0 0 0 0 0 1 0 0 0 32
33 5.9 0 0 0 0 0 0 0 0 0 1 0 0 33
34 5.5 0 0 0 0 0 0 0 0 0 0 1 0 34
35 5.6 0 0 0 0 0 0 0 0 0 0 0 1 35
36 5.4 0 0 0 0 0 0 0 0 0 0 0 0 36
37 5.2 0 1 0 0 0 0 0 0 0 0 0 0 37
38 5.2 0 0 1 0 0 0 0 0 0 0 0 0 38
39 5.2 0 0 0 1 0 0 0 0 0 0 0 0 39
40 5.5 0 0 0 0 1 0 0 0 0 0 0 0 40
41 5.8 1 0 0 0 0 1 0 0 0 0 0 0 41
42 5.8 1 0 0 0 0 0 1 0 0 0 0 0 42
43 5.5 1 0 0 0 0 0 0 1 0 0 0 0 43
44 5.3 1 0 0 0 0 0 0 0 1 0 0 0 44
45 5.1 1 0 0 0 0 0 0 0 0 1 0 0 45
46 5.2 1 0 0 0 0 0 0 0 0 0 1 0 46
47 5.8 1 0 0 0 0 0 0 0 0 0 0 1 47
48 5.8 1 0 0 0 0 0 0 0 0 0 0 0 48
49 5.5 1 1 0 0 0 0 0 0 0 0 0 0 49
50 5.0 1 0 1 0 0 0 0 0 0 0 0 0 50
51 4.9 1 0 0 1 0 0 0 0 0 0 0 0 51
52 5.3 1 0 0 0 1 0 0 0 0 0 0 0 52
53 6.1 1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.5 1 0 0 0 0 0 1 0 0 0 0 0 54
55 6.8 1 0 0 0 0 0 0 1 0 0 0 0 55
56 6.6 1 0 0 0 0 0 0 0 1 0 0 0 56
57 6.4 1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.4 1 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
6.16058 0.04808 -0.05636 -0.14061 -0.12486 0.15090
M5 M6 M7 M8 M9 M10
0.51704 0.67279 0.66854 0.54429 0.42005 0.23580
M11 t
0.13425 -0.01575
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.81981 -0.25567 -0.06567 0.27053 0.86923
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.16058 0.25502 24.157 < 2e-16 ***
X 0.04808 0.21014 0.229 0.82010
M1 -0.05636 0.29155 -0.193 0.84760
M2 -0.14061 0.29120 -0.483 0.63159
M3 -0.12486 0.29097 -0.429 0.66994
M4 0.15090 0.29085 0.519 0.60649
M5 0.51704 0.29302 1.764 0.08459 .
M6 0.67279 0.29247 2.300 0.02623 *
M7 0.66854 0.29203 2.289 0.02692 *
M8 0.54429 0.29170 1.866 0.06873 .
M9 0.42005 0.29149 1.441 0.15665
M10 0.23580 0.29139 0.809 0.42273
M11 0.13425 0.30655 0.438 0.66358
t -0.01575 0.00576 -2.735 0.00896 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4335 on 44 degrees of freedom
Multiple R-squared: 0.4835, Adjusted R-squared: 0.3309
F-statistic: 3.168 on 13 and 44 DF, p-value: 0.002088
> 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,] 0.02249528 0.04499055 0.9775047
[2,] 0.01403002 0.02806004 0.9859700
[3,] 0.01908674 0.03817348 0.9809133
[4,] 0.05857915 0.11715831 0.9414208
[5,] 0.06455415 0.12910830 0.9354459
[6,] 0.05703530 0.11407060 0.9429647
[7,] 0.05603418 0.11206835 0.9439658
[8,] 0.11048855 0.22097710 0.8895114
[9,] 0.22809944 0.45619887 0.7719006
[10,] 0.30383063 0.60766126 0.6961694
[11,] 0.50396109 0.99207783 0.4960389
[12,] 0.82583436 0.34833128 0.1741656
[13,] 0.82370609 0.35258782 0.1762939
[14,] 0.80557337 0.38885326 0.1944266
[15,] 0.78144426 0.43711148 0.2185557
[16,] 0.77382970 0.45234059 0.2261703
[17,] 0.84207207 0.31585586 0.1579279
[18,] 0.79982743 0.40034514 0.2001726
[19,] 0.72174501 0.55650997 0.2782550
[20,] 0.70873990 0.58252021 0.2912601
[21,] 0.72334884 0.55330232 0.2766512
[22,] 0.61215551 0.77568897 0.3878445
[23,] 0.48549643 0.97099287 0.5145036
[24,] 0.34017514 0.68035027 0.6598249
[25,] 0.68352198 0.63295605 0.3164780
> postscript(file="/var/www/html/rcomp/tmp/1wt431258663303.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/247hh1258663303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zc481258663303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4tmjj1258663303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57z0c1258663303.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 = 58
Frequency = 1
1 2 3 4 5 6
0.21153846 0.21153846 0.11153846 0.05153846 -0.09884615 -0.13884615
7 8 9 10 11 12
-0.21884615 -0.37884615 -0.23884615 -0.33884615 -0.02153846 0.12846154
13 14 15 16 17 18
0.20057692 0.30057692 0.30057692 0.34057692 0.29019231 0.35019231
19 20 21 22 23 24
0.47019231 0.61019231 0.55019231 0.35019231 -0.03250000 -0.28250000
25 26 27 28 29 30
-0.21038462 -0.01038462 0.18961538 0.02961538 -0.12076923 -0.26076923
31 32 33 34 35 36
-0.34076923 -0.20076923 -0.16076923 -0.36076923 -0.14346154 -0.19346154
37 38 39 40 41 42
-0.32134615 -0.22134615 -0.22134615 -0.18134615 -0.27980769 -0.41980769
43 44 45 46 47 48
-0.69980769 -0.75980769 -0.81980769 -0.51980769 0.19750000 0.34750000
49 50 51 52 53 54
0.11961538 -0.28038462 -0.38038462 -0.24038462 0.20923077 0.46923077
55 56 57 58
0.78923077 0.72923077 0.66923077 0.86923077
> postscript(file="/var/www/html/rcomp/tmp/6t62s1258663303.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 0.21153846 NA
1 0.21153846 0.21153846
2 0.11153846 0.21153846
3 0.05153846 0.11153846
4 -0.09884615 0.05153846
5 -0.13884615 -0.09884615
6 -0.21884615 -0.13884615
7 -0.37884615 -0.21884615
8 -0.23884615 -0.37884615
9 -0.33884615 -0.23884615
10 -0.02153846 -0.33884615
11 0.12846154 -0.02153846
12 0.20057692 0.12846154
13 0.30057692 0.20057692
14 0.30057692 0.30057692
15 0.34057692 0.30057692
16 0.29019231 0.34057692
17 0.35019231 0.29019231
18 0.47019231 0.35019231
19 0.61019231 0.47019231
20 0.55019231 0.61019231
21 0.35019231 0.55019231
22 -0.03250000 0.35019231
23 -0.28250000 -0.03250000
24 -0.21038462 -0.28250000
25 -0.01038462 -0.21038462
26 0.18961538 -0.01038462
27 0.02961538 0.18961538
28 -0.12076923 0.02961538
29 -0.26076923 -0.12076923
30 -0.34076923 -0.26076923
31 -0.20076923 -0.34076923
32 -0.16076923 -0.20076923
33 -0.36076923 -0.16076923
34 -0.14346154 -0.36076923
35 -0.19346154 -0.14346154
36 -0.32134615 -0.19346154
37 -0.22134615 -0.32134615
38 -0.22134615 -0.22134615
39 -0.18134615 -0.22134615
40 -0.27980769 -0.18134615
41 -0.41980769 -0.27980769
42 -0.69980769 -0.41980769
43 -0.75980769 -0.69980769
44 -0.81980769 -0.75980769
45 -0.51980769 -0.81980769
46 0.19750000 -0.51980769
47 0.34750000 0.19750000
48 0.11961538 0.34750000
49 -0.28038462 0.11961538
50 -0.38038462 -0.28038462
51 -0.24038462 -0.38038462
52 0.20923077 -0.24038462
53 0.46923077 0.20923077
54 0.78923077 0.46923077
55 0.72923077 0.78923077
56 0.66923077 0.72923077
57 0.86923077 0.66923077
58 NA 0.86923077
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.21153846 0.21153846
[2,] 0.11153846 0.21153846
[3,] 0.05153846 0.11153846
[4,] -0.09884615 0.05153846
[5,] -0.13884615 -0.09884615
[6,] -0.21884615 -0.13884615
[7,] -0.37884615 -0.21884615
[8,] -0.23884615 -0.37884615
[9,] -0.33884615 -0.23884615
[10,] -0.02153846 -0.33884615
[11,] 0.12846154 -0.02153846
[12,] 0.20057692 0.12846154
[13,] 0.30057692 0.20057692
[14,] 0.30057692 0.30057692
[15,] 0.34057692 0.30057692
[16,] 0.29019231 0.34057692
[17,] 0.35019231 0.29019231
[18,] 0.47019231 0.35019231
[19,] 0.61019231 0.47019231
[20,] 0.55019231 0.61019231
[21,] 0.35019231 0.55019231
[22,] -0.03250000 0.35019231
[23,] -0.28250000 -0.03250000
[24,] -0.21038462 -0.28250000
[25,] -0.01038462 -0.21038462
[26,] 0.18961538 -0.01038462
[27,] 0.02961538 0.18961538
[28,] -0.12076923 0.02961538
[29,] -0.26076923 -0.12076923
[30,] -0.34076923 -0.26076923
[31,] -0.20076923 -0.34076923
[32,] -0.16076923 -0.20076923
[33,] -0.36076923 -0.16076923
[34,] -0.14346154 -0.36076923
[35,] -0.19346154 -0.14346154
[36,] -0.32134615 -0.19346154
[37,] -0.22134615 -0.32134615
[38,] -0.22134615 -0.22134615
[39,] -0.18134615 -0.22134615
[40,] -0.27980769 -0.18134615
[41,] -0.41980769 -0.27980769
[42,] -0.69980769 -0.41980769
[43,] -0.75980769 -0.69980769
[44,] -0.81980769 -0.75980769
[45,] -0.51980769 -0.81980769
[46,] 0.19750000 -0.51980769
[47,] 0.34750000 0.19750000
[48,] 0.11961538 0.34750000
[49,] -0.28038462 0.11961538
[50,] -0.38038462 -0.28038462
[51,] -0.24038462 -0.38038462
[52,] 0.20923077 -0.24038462
[53,] 0.46923077 0.20923077
[54,] 0.78923077 0.46923077
[55,] 0.72923077 0.78923077
[56,] 0.66923077 0.72923077
[57,] 0.86923077 0.66923077
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.21153846 0.21153846
2 0.11153846 0.21153846
3 0.05153846 0.11153846
4 -0.09884615 0.05153846
5 -0.13884615 -0.09884615
6 -0.21884615 -0.13884615
7 -0.37884615 -0.21884615
8 -0.23884615 -0.37884615
9 -0.33884615 -0.23884615
10 -0.02153846 -0.33884615
11 0.12846154 -0.02153846
12 0.20057692 0.12846154
13 0.30057692 0.20057692
14 0.30057692 0.30057692
15 0.34057692 0.30057692
16 0.29019231 0.34057692
17 0.35019231 0.29019231
18 0.47019231 0.35019231
19 0.61019231 0.47019231
20 0.55019231 0.61019231
21 0.35019231 0.55019231
22 -0.03250000 0.35019231
23 -0.28250000 -0.03250000
24 -0.21038462 -0.28250000
25 -0.01038462 -0.21038462
26 0.18961538 -0.01038462
27 0.02961538 0.18961538
28 -0.12076923 0.02961538
29 -0.26076923 -0.12076923
30 -0.34076923 -0.26076923
31 -0.20076923 -0.34076923
32 -0.16076923 -0.20076923
33 -0.36076923 -0.16076923
34 -0.14346154 -0.36076923
35 -0.19346154 -0.14346154
36 -0.32134615 -0.19346154
37 -0.22134615 -0.32134615
38 -0.22134615 -0.22134615
39 -0.18134615 -0.22134615
40 -0.27980769 -0.18134615
41 -0.41980769 -0.27980769
42 -0.69980769 -0.41980769
43 -0.75980769 -0.69980769
44 -0.81980769 -0.75980769
45 -0.51980769 -0.81980769
46 0.19750000 -0.51980769
47 0.34750000 0.19750000
48 0.11961538 0.34750000
49 -0.28038462 0.11961538
50 -0.38038462 -0.28038462
51 -0.24038462 -0.38038462
52 0.20923077 -0.24038462
53 0.46923077 0.20923077
54 0.78923077 0.46923077
55 0.72923077 0.78923077
56 0.66923077 0.72923077
57 0.86923077 0.66923077
> 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/72qy01258663303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ixcc1258663303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/98kjr1258663303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10p5lr1258663303.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11phel1258663303.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/12ho6e1258663303.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/13v1og1258663303.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/14x0v21258663303.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/15ssc21258663303.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/16nzlw1258663303.tab")
+ }
>
> system("convert tmp/1wt431258663303.ps tmp/1wt431258663303.png")
> system("convert tmp/247hh1258663303.ps tmp/247hh1258663303.png")
> system("convert tmp/3zc481258663303.ps tmp/3zc481258663303.png")
> system("convert tmp/4tmjj1258663303.ps tmp/4tmjj1258663303.png")
> system("convert tmp/57z0c1258663303.ps tmp/57z0c1258663303.png")
> system("convert tmp/6t62s1258663303.ps tmp/6t62s1258663303.png")
> system("convert tmp/72qy01258663303.ps tmp/72qy01258663303.png")
> system("convert tmp/8ixcc1258663303.ps tmp/8ixcc1258663303.png")
> system("convert tmp/98kjr1258663303.ps tmp/98kjr1258663303.png")
> system("convert tmp/10p5lr1258663303.ps tmp/10p5lr1258663303.png")
>
>
> proc.time()
user system elapsed
2.324 1.581 2.838