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(1.4,0.0,1.6,0.0,1.7,0.0,2.0,0.0,2.0,0.0,2.1,0.0,2.5,0.0,2.5,0.0,2.6,0.0,2.7,0.0,3.7,0.0,4.0,0.0,5.0,0.0,5.1,0.0,5.1,0.0,5.0,0.0,5.1,0.0,4.7,0.0,4.5,0.0,4.5,0.0,4.6,0.0,4.6,0.0,4.6,0.0,4.6,0.0,5.3,0.0,5.4,0.0,5.3,0.0,5.2,0.0,5.0,0.0,4.2,0.0,4.3,0.0,4.3,0.0,4.3,0.0,4.0,0.0,4.0,0.0,4.1,0.0,4.4,0.0,3.6,0.0,3.7,0.0,3.8,0.0,3.3,0.0,3.3,0.0,3.3,0.0,3.5,0.0,3.3,0.0,3.3,0.0,3.4,0.0,3.4,0.0,5.2,0.0,5.3,0.0,4.8,1.0,5.0,1.0,4.6,1.0,4.6,1.0,3.5,1.0,3.5,1.0),dim=c(2,56),dimnames=list(c('IndGez','InvlMex'),1:56))
> y <- array(NA,dim=c(2,56),dimnames=list(c('IndGez','InvlMex'),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 = 'No 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
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
IndGez InvlMex M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.4 0 1 0 0 0 0 0 0 0 0 0 0
2 1.6 0 0 1 0 0 0 0 0 0 0 0 0
3 1.7 0 0 0 1 0 0 0 0 0 0 0 0
4 2.0 0 0 0 0 1 0 0 0 0 0 0 0
5 2.0 0 0 0 0 0 1 0 0 0 0 0 0
6 2.1 0 0 0 0 0 0 1 0 0 0 0 0
7 2.5 0 0 0 0 0 0 0 1 0 0 0 0
8 2.5 0 0 0 0 0 0 0 0 1 0 0 0
9 2.6 0 0 0 0 0 0 0 0 0 1 0 0
10 2.7 0 0 0 0 0 0 0 0 0 0 1 0
11 3.7 0 0 0 0 0 0 0 0 0 0 0 1
12 4.0 0 0 0 0 0 0 0 0 0 0 0 0
13 5.0 0 1 0 0 0 0 0 0 0 0 0 0
14 5.1 0 0 1 0 0 0 0 0 0 0 0 0
15 5.1 0 0 0 1 0 0 0 0 0 0 0 0
16 5.0 0 0 0 0 1 0 0 0 0 0 0 0
17 5.1 0 0 0 0 0 1 0 0 0 0 0 0
18 4.7 0 0 0 0 0 0 1 0 0 0 0 0
19 4.5 0 0 0 0 0 0 0 1 0 0 0 0
20 4.5 0 0 0 0 0 0 0 0 1 0 0 0
21 4.6 0 0 0 0 0 0 0 0 0 1 0 0
22 4.6 0 0 0 0 0 0 0 0 0 0 1 0
23 4.6 0 0 0 0 0 0 0 0 0 0 0 1
24 4.6 0 0 0 0 0 0 0 0 0 0 0 0
25 5.3 0 1 0 0 0 0 0 0 0 0 0 0
26 5.4 0 0 1 0 0 0 0 0 0 0 0 0
27 5.3 0 0 0 1 0 0 0 0 0 0 0 0
28 5.2 0 0 0 0 1 0 0 0 0 0 0 0
29 5.0 0 0 0 0 0 1 0 0 0 0 0 0
30 4.2 0 0 0 0 0 0 1 0 0 0 0 0
31 4.3 0 0 0 0 0 0 0 1 0 0 0 0
32 4.3 0 0 0 0 0 0 0 0 1 0 0 0
33 4.3 0 0 0 0 0 0 0 0 0 1 0 0
34 4.0 0 0 0 0 0 0 0 0 0 0 1 0
35 4.0 0 0 0 0 0 0 0 0 0 0 0 1
36 4.1 0 0 0 0 0 0 0 0 0 0 0 0
37 4.4 0 1 0 0 0 0 0 0 0 0 0 0
38 3.6 0 0 1 0 0 0 0 0 0 0 0 0
39 3.7 0 0 0 1 0 0 0 0 0 0 0 0
40 3.8 0 0 0 0 1 0 0 0 0 0 0 0
41 3.3 0 0 0 0 0 1 0 0 0 0 0 0
42 3.3 0 0 0 0 0 0 1 0 0 0 0 0
43 3.3 0 0 0 0 0 0 0 1 0 0 0 0
44 3.5 0 0 0 0 0 0 0 0 1 0 0 0
45 3.3 0 0 0 0 0 0 0 0 0 1 0 0
46 3.3 0 0 0 0 0 0 0 0 0 0 1 0
47 3.4 0 0 0 0 0 0 0 0 0 0 0 1
48 3.4 0 0 0 0 0 0 0 0 0 0 0 0
49 5.2 0 1 0 0 0 0 0 0 0 0 0 0
50 5.3 0 0 1 0 0 0 0 0 0 0 0 0
51 4.8 1 0 0 1 0 0 0 0 0 0 0 0
52 5.0 1 0 0 0 1 0 0 0 0 0 0 0
53 4.6 1 0 0 0 0 1 0 0 0 0 0 0
54 4.6 1 0 0 0 0 0 1 0 0 0 0 0
55 3.5 1 0 0 0 0 0 0 1 0 0 0 0
56 3.5 1 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) InvlMex M1 M2 M3 M4
4.02500 0.54583 0.23500 0.17500 -0.01417 0.06583
M5 M6 M7 M8 M9 M10
-0.13417 -0.35417 -0.51417 -0.47417 -0.32500 -0.37500
M11
-0.10000
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.8600 -0.5652 0.1517 0.9023 1.2892
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.02500 0.58580 6.871 1.98e-08 ***
InvlMex 0.54583 0.53476 1.021 0.313
M1 0.23500 0.78593 0.299 0.766
M2 0.17500 0.78593 0.223 0.825
M3 -0.01417 0.79317 -0.018 0.986
M4 0.06583 0.79317 0.083 0.934
M5 -0.13417 0.79317 -0.169 0.866
M6 -0.35417 0.79317 -0.447 0.657
M7 -0.51417 0.79317 -0.648 0.520
M8 -0.47417 0.79317 -0.598 0.553
M9 -0.32500 0.82844 -0.392 0.697
M10 -0.37500 0.82844 -0.453 0.653
M11 -0.10000 0.82844 -0.121 0.904
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.172 on 43 degrees of freedom
Multiple R-squared: 0.06975, Adjusted R-squared: -0.1899
F-statistic: 0.2687 on 12 and 43 DF, p-value: 0.9913
> 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,] 0.9999951 9.854947e-06 4.927473e-06
[2,] 0.9999967 6.552196e-06 3.276098e-06
[3,] 0.9999960 8.060854e-06 4.030427e-06
[4,] 0.9999936 1.277802e-05 6.389012e-06
[5,] 0.9999898 2.049230e-05 1.024615e-05
[6,] 0.9999832 3.356646e-05 1.678323e-05
[7,] 0.9999751 4.977183e-05 2.488592e-05
[8,] 0.9999509 9.822718e-05 4.911359e-05
[9,] 0.9998994 2.012484e-04 1.006242e-04
[10,] 0.9998408 3.183450e-04 1.591725e-04
[11,] 0.9998062 3.876995e-04 1.938498e-04
[12,] 0.9998058 3.883509e-04 1.941754e-04
[13,] 0.9997411 5.177943e-04 2.588972e-04
[14,] 0.9997598 4.803598e-04 2.401799e-04
[15,] 0.9994413 1.117322e-03 5.586611e-04
[16,] 0.9994171 1.165862e-03 5.829309e-04
[17,] 0.9994219 1.156261e-03 5.781305e-04
[18,] 0.9990786 1.842709e-03 9.213546e-04
[19,] 0.9979707 4.058541e-03 2.029270e-03
[20,] 0.9952530 9.493995e-03 4.746997e-03
[21,] 0.9899846 2.003080e-02 1.001540e-02
[22,] 0.9810580 3.788410e-02 1.894205e-02
[23,] 0.9944717 1.105668e-02 5.528340e-03
[24,] 0.9809233 3.815350e-02 1.907675e-02
[25,] 0.9453264 1.093473e-01 5.467365e-02
> postscript(file="/var/www/html/rcomp/tmp/1milq1258731719.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2sr221258731719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ypob1258731719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ckd91258731719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5i7og1258731719.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 = 56
Frequency = 1
1 2 3 4 5 6
-2.86000000 -2.60000000 -2.31083333 -2.09083333 -1.89083333 -1.57083333
7 8 9 10 11 12
-1.01083333 -1.05083333 -1.10000000 -0.95000000 -0.22500000 -0.02500000
13 14 15 16 17 18
0.74000000 0.90000000 1.08916667 0.90916667 1.20916667 1.02916667
19 20 21 22 23 24
0.98916667 0.94916667 0.90000000 0.95000000 0.67500000 0.57500000
25 26 27 28 29 30
1.04000000 1.20000000 1.28916667 1.10916667 1.10916667 0.52916667
31 32 33 34 35 36
0.78916667 0.74916667 0.60000000 0.35000000 0.07500000 0.07500000
37 38 39 40 41 42
0.14000000 -0.60000000 -0.31083333 -0.29083333 -0.59083333 -0.37083333
43 44 45 46 47 48
-0.21083333 -0.05083333 -0.40000000 -0.35000000 -0.52500000 -0.62500000
49 50 51 52 53 54
0.94000000 1.10000000 0.24333333 0.36333333 0.16333333 0.38333333
55 56
-0.55666667 -0.59666667
> postscript(file="/var/www/html/rcomp/tmp/6cyal1258731719.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.86000000 NA
1 -2.60000000 -2.86000000
2 -2.31083333 -2.60000000
3 -2.09083333 -2.31083333
4 -1.89083333 -2.09083333
5 -1.57083333 -1.89083333
6 -1.01083333 -1.57083333
7 -1.05083333 -1.01083333
8 -1.10000000 -1.05083333
9 -0.95000000 -1.10000000
10 -0.22500000 -0.95000000
11 -0.02500000 -0.22500000
12 0.74000000 -0.02500000
13 0.90000000 0.74000000
14 1.08916667 0.90000000
15 0.90916667 1.08916667
16 1.20916667 0.90916667
17 1.02916667 1.20916667
18 0.98916667 1.02916667
19 0.94916667 0.98916667
20 0.90000000 0.94916667
21 0.95000000 0.90000000
22 0.67500000 0.95000000
23 0.57500000 0.67500000
24 1.04000000 0.57500000
25 1.20000000 1.04000000
26 1.28916667 1.20000000
27 1.10916667 1.28916667
28 1.10916667 1.10916667
29 0.52916667 1.10916667
30 0.78916667 0.52916667
31 0.74916667 0.78916667
32 0.60000000 0.74916667
33 0.35000000 0.60000000
34 0.07500000 0.35000000
35 0.07500000 0.07500000
36 0.14000000 0.07500000
37 -0.60000000 0.14000000
38 -0.31083333 -0.60000000
39 -0.29083333 -0.31083333
40 -0.59083333 -0.29083333
41 -0.37083333 -0.59083333
42 -0.21083333 -0.37083333
43 -0.05083333 -0.21083333
44 -0.40000000 -0.05083333
45 -0.35000000 -0.40000000
46 -0.52500000 -0.35000000
47 -0.62500000 -0.52500000
48 0.94000000 -0.62500000
49 1.10000000 0.94000000
50 0.24333333 1.10000000
51 0.36333333 0.24333333
52 0.16333333 0.36333333
53 0.38333333 0.16333333
54 -0.55666667 0.38333333
55 -0.59666667 -0.55666667
56 NA -0.59666667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.60000000 -2.86000000
[2,] -2.31083333 -2.60000000
[3,] -2.09083333 -2.31083333
[4,] -1.89083333 -2.09083333
[5,] -1.57083333 -1.89083333
[6,] -1.01083333 -1.57083333
[7,] -1.05083333 -1.01083333
[8,] -1.10000000 -1.05083333
[9,] -0.95000000 -1.10000000
[10,] -0.22500000 -0.95000000
[11,] -0.02500000 -0.22500000
[12,] 0.74000000 -0.02500000
[13,] 0.90000000 0.74000000
[14,] 1.08916667 0.90000000
[15,] 0.90916667 1.08916667
[16,] 1.20916667 0.90916667
[17,] 1.02916667 1.20916667
[18,] 0.98916667 1.02916667
[19,] 0.94916667 0.98916667
[20,] 0.90000000 0.94916667
[21,] 0.95000000 0.90000000
[22,] 0.67500000 0.95000000
[23,] 0.57500000 0.67500000
[24,] 1.04000000 0.57500000
[25,] 1.20000000 1.04000000
[26,] 1.28916667 1.20000000
[27,] 1.10916667 1.28916667
[28,] 1.10916667 1.10916667
[29,] 0.52916667 1.10916667
[30,] 0.78916667 0.52916667
[31,] 0.74916667 0.78916667
[32,] 0.60000000 0.74916667
[33,] 0.35000000 0.60000000
[34,] 0.07500000 0.35000000
[35,] 0.07500000 0.07500000
[36,] 0.14000000 0.07500000
[37,] -0.60000000 0.14000000
[38,] -0.31083333 -0.60000000
[39,] -0.29083333 -0.31083333
[40,] -0.59083333 -0.29083333
[41,] -0.37083333 -0.59083333
[42,] -0.21083333 -0.37083333
[43,] -0.05083333 -0.21083333
[44,] -0.40000000 -0.05083333
[45,] -0.35000000 -0.40000000
[46,] -0.52500000 -0.35000000
[47,] -0.62500000 -0.52500000
[48,] 0.94000000 -0.62500000
[49,] 1.10000000 0.94000000
[50,] 0.24333333 1.10000000
[51,] 0.36333333 0.24333333
[52,] 0.16333333 0.36333333
[53,] 0.38333333 0.16333333
[54,] -0.55666667 0.38333333
[55,] -0.59666667 -0.55666667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.60000000 -2.86000000
2 -2.31083333 -2.60000000
3 -2.09083333 -2.31083333
4 -1.89083333 -2.09083333
5 -1.57083333 -1.89083333
6 -1.01083333 -1.57083333
7 -1.05083333 -1.01083333
8 -1.10000000 -1.05083333
9 -0.95000000 -1.10000000
10 -0.22500000 -0.95000000
11 -0.02500000 -0.22500000
12 0.74000000 -0.02500000
13 0.90000000 0.74000000
14 1.08916667 0.90000000
15 0.90916667 1.08916667
16 1.20916667 0.90916667
17 1.02916667 1.20916667
18 0.98916667 1.02916667
19 0.94916667 0.98916667
20 0.90000000 0.94916667
21 0.95000000 0.90000000
22 0.67500000 0.95000000
23 0.57500000 0.67500000
24 1.04000000 0.57500000
25 1.20000000 1.04000000
26 1.28916667 1.20000000
27 1.10916667 1.28916667
28 1.10916667 1.10916667
29 0.52916667 1.10916667
30 0.78916667 0.52916667
31 0.74916667 0.78916667
32 0.60000000 0.74916667
33 0.35000000 0.60000000
34 0.07500000 0.35000000
35 0.07500000 0.07500000
36 0.14000000 0.07500000
37 -0.60000000 0.14000000
38 -0.31083333 -0.60000000
39 -0.29083333 -0.31083333
40 -0.59083333 -0.29083333
41 -0.37083333 -0.59083333
42 -0.21083333 -0.37083333
43 -0.05083333 -0.21083333
44 -0.40000000 -0.05083333
45 -0.35000000 -0.40000000
46 -0.52500000 -0.35000000
47 -0.62500000 -0.52500000
48 0.94000000 -0.62500000
49 1.10000000 0.94000000
50 0.24333333 1.10000000
51 0.36333333 0.24333333
52 0.16333333 0.36333333
53 0.38333333 0.16333333
54 -0.55666667 0.38333333
55 -0.59666667 -0.55666667
> 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/7hlch1258731719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/849371258731719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yskp1258731719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qaco1258731719.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117lsx1258731719.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/124p2x1258731719.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/13m5w51258731719.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/147am21258731719.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/15j6c81258731719.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/16yjed1258731719.tab")
+ }
>
> system("convert tmp/1milq1258731719.ps tmp/1milq1258731719.png")
> system("convert tmp/2sr221258731719.ps tmp/2sr221258731719.png")
> system("convert tmp/3ypob1258731719.ps tmp/3ypob1258731719.png")
> system("convert tmp/4ckd91258731719.ps tmp/4ckd91258731719.png")
> system("convert tmp/5i7og1258731719.ps tmp/5i7og1258731719.png")
> system("convert tmp/6cyal1258731719.ps tmp/6cyal1258731719.png")
> system("convert tmp/7hlch1258731719.ps tmp/7hlch1258731719.png")
> system("convert tmp/849371258731719.ps tmp/849371258731719.png")
> system("convert tmp/9yskp1258731719.ps tmp/9yskp1258731719.png")
> system("convert tmp/10qaco1258731719.ps tmp/10qaco1258731719.png")
>
>
> proc.time()
user system elapsed
2.323 1.595 2.784