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(8.6,0,8.5,0,8.3,0,7.8,0,7.8,0,8,0,8.6,0,8.9,0,8.9,0,8.6,0,8.3,0,8.3,0,8.3,0,8.4,0,8.5,0,8.4,0,8.6,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.5,0,8.6,0,8.4,0,8.1,0,8,0,8,0,8,0,8,0,7.9,0,7.8,0,7.8,0,7.9,0,8.1,0,8,0,7.6,0,7.3,0,7,0,6.8,0,7,0,7.1,0,7.2,0,7.1,1,6.9,1,6.7,1,6.7,1,6.6,1,6.9,1,7.3,1,7.5,1,7.3,1,7.1,1,6.9,1,7.1,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 = '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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.6 0 1 0 0 0 0 0 0 0 0 0 0
2 8.5 0 0 1 0 0 0 0 0 0 0 0 0
3 8.3 0 0 0 1 0 0 0 0 0 0 0 0
4 7.8 0 0 0 0 1 0 0 0 0 0 0 0
5 7.8 0 0 0 0 0 1 0 0 0 0 0 0
6 8.0 0 0 0 0 0 0 1 0 0 0 0 0
7 8.6 0 0 0 0 0 0 0 1 0 0 0 0
8 8.9 0 0 0 0 0 0 0 0 1 0 0 0
9 8.9 0 0 0 0 0 0 0 0 0 1 0 0
10 8.6 0 0 0 0 0 0 0 0 0 0 1 0
11 8.3 0 0 0 0 0 0 0 0 0 0 0 1
12 8.3 0 0 0 0 0 0 0 0 0 0 0 0
13 8.3 0 1 0 0 0 0 0 0 0 0 0 0
14 8.4 0 0 1 0 0 0 0 0 0 0 0 0
15 8.5 0 0 0 1 0 0 0 0 0 0 0 0
16 8.4 0 0 0 0 1 0 0 0 0 0 0 0
17 8.6 0 0 0 0 0 1 0 0 0 0 0 0
18 8.5 0 0 0 0 0 0 1 0 0 0 0 0
19 8.5 0 0 0 0 0 0 0 1 0 0 0 0
20 8.5 0 0 0 0 0 0 0 0 1 0 0 0
21 8.5 0 0 0 0 0 0 0 0 0 1 0 0
22 8.5 0 0 0 0 0 0 0 0 0 0 1 0
23 8.5 0 0 0 0 0 0 0 0 0 0 0 1
24 8.5 0 0 0 0 0 0 0 0 0 0 0 0
25 8.5 0 1 0 0 0 0 0 0 0 0 0 0
26 8.5 0 0 1 0 0 0 0 0 0 0 0 0
27 8.5 0 0 0 1 0 0 0 0 0 0 0 0
28 8.5 0 0 0 0 1 0 0 0 0 0 0 0
29 8.6 0 0 0 0 0 1 0 0 0 0 0 0
30 8.4 0 0 0 0 0 0 1 0 0 0 0 0
31 8.1 0 0 0 0 0 0 0 1 0 0 0 0
32 8.0 0 0 0 0 0 0 0 0 1 0 0 0
33 8.0 0 0 0 0 0 0 0 0 0 1 0 0
34 8.0 0 0 0 0 0 0 0 0 0 0 1 0
35 8.0 0 0 0 0 0 0 0 0 0 0 0 1
36 7.9 0 0 0 0 0 0 0 0 0 0 0 0
37 7.8 0 1 0 0 0 0 0 0 0 0 0 0
38 7.8 0 0 1 0 0 0 0 0 0 0 0 0
39 7.9 0 0 0 1 0 0 0 0 0 0 0 0
40 8.1 0 0 0 0 1 0 0 0 0 0 0 0
41 8.0 0 0 0 0 0 1 0 0 0 0 0 0
42 7.6 0 0 0 0 0 0 1 0 0 0 0 0
43 7.3 0 0 0 0 0 0 0 1 0 0 0 0
44 7.0 0 0 0 0 0 0 0 0 1 0 0 0
45 6.8 0 0 0 0 0 0 0 0 0 1 0 0
46 7.0 0 0 0 0 0 0 0 0 0 0 1 0
47 7.1 0 0 0 0 0 0 0 0 0 0 0 1
48 7.2 0 0 0 0 0 0 0 0 0 0 0 0
49 7.1 1 1 0 0 0 0 0 0 0 0 0 0
50 6.9 1 0 1 0 0 0 0 0 0 0 0 0
51 6.7 1 0 0 1 0 0 0 0 0 0 0 0
52 6.7 1 0 0 0 1 0 0 0 0 0 0 0
53 6.6 1 0 0 0 0 1 0 0 0 0 0 0
54 6.9 1 0 0 0 0 0 1 0 0 0 0 0
55 7.3 1 0 0 0 0 0 0 1 0 0 0 0
56 7.5 1 0 0 0 0 0 0 0 1 0 0 0
57 7.3 1 0 0 0 0 0 0 0 0 1 0 0
58 7.1 1 0 0 0 0 0 0 0 0 0 1 0
59 6.9 1 0 0 0 0 0 0 0 0 0 0 1
60 7.1 1 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
8.027 -1.135 0.260 0.220 0.180 0.100
M5 M6 M7 M8 M9 M10
0.120 0.080 0.160 0.180 0.100 0.040
M11
-0.040
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.3271 -0.2317 0.1229 0.3129 0.7729
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.0271 0.2344 34.240 < 2e-16 ***
X -1.1354 0.1674 -6.780 1.76e-08 ***
M1 0.2600 0.3281 0.792 0.432
M2 0.2200 0.3281 0.670 0.506
M3 0.1800 0.3281 0.549 0.586
M4 0.1000 0.3281 0.305 0.762
M5 0.1200 0.3281 0.366 0.716
M6 0.0800 0.3281 0.244 0.808
M7 0.1600 0.3281 0.488 0.628
M8 0.1800 0.3281 0.549 0.586
M9 0.1000 0.3281 0.305 0.762
M10 0.0400 0.3281 0.122 0.903
M11 -0.0400 0.3281 -0.122 0.903
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5188 on 47 degrees of freedom
Multiple R-squared: 0.5031, Adjusted R-squared: 0.3763
F-statistic: 3.966 on 12 and 47 DF, p-value: 0.0003135
> 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,] 0.152018032 0.304036063 0.8479820
[2,] 0.230788219 0.461576438 0.7692118
[3,] 0.181243437 0.362486874 0.8187566
[4,] 0.105369833 0.210739667 0.8946302
[5,] 0.077117483 0.154234966 0.9228825
[6,] 0.060600170 0.121200339 0.9393998
[7,] 0.038354925 0.076709850 0.9616451
[8,] 0.026777583 0.053555165 0.9732224
[9,] 0.018677232 0.037354464 0.9813228
[10,] 0.010663208 0.021326417 0.9893368
[11,] 0.006528823 0.013057646 0.9934712
[12,] 0.004442003 0.008884005 0.9955580
[13,] 0.004920577 0.009841154 0.9950794
[14,] 0.007282367 0.014564734 0.9927176
[15,] 0.006518900 0.013037800 0.9934811
[16,] 0.007090901 0.014181803 0.9929091
[17,] 0.014011642 0.028023285 0.9859884
[18,] 0.027479591 0.054959183 0.9725204
[19,] 0.037298175 0.074596350 0.9627018
[20,] 0.046247748 0.092495495 0.9537523
[21,] 0.048381129 0.096762259 0.9516189
[22,] 0.050921848 0.101843696 0.9490782
[23,] 0.056907254 0.113814508 0.9430927
[24,] 0.078757478 0.157514955 0.9212425
[25,] 0.168044458 0.336088916 0.8319555
[26,] 0.634515866 0.730968268 0.3654841
[27,] 0.877698865 0.244602269 0.1223011
[28,] 0.843466168 0.313067664 0.1565338
[29,] 0.869982636 0.260034729 0.1300174
> postscript(file="/var/www/html/rcomp/tmp/1wcdu1258716014.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2v88g1258716014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32bq91258716014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4zk3o1258716014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5vkrm1258716014.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
0.31291667 0.25291667 0.09291667 -0.32708333 -0.34708333 -0.10708333
7 8 9 10 11 12
0.41291667 0.69291667 0.77291667 0.53291667 0.31291667 0.27291667
13 14 15 16 17 18
0.01291667 0.15291667 0.29291667 0.27291667 0.45291667 0.39291667
19 20 21 22 23 24
0.31291667 0.29291667 0.37291667 0.43291667 0.51291667 0.47291667
25 26 27 28 29 30
0.21291667 0.25291667 0.29291667 0.37291667 0.45291667 0.29291667
31 32 33 34 35 36
-0.08708333 -0.20708333 -0.12708333 -0.06708333 0.01291667 -0.12708333
37 38 39 40 41 42
-0.48708333 -0.44708333 -0.30708333 -0.02708333 -0.14708333 -0.50708333
43 44 45 46 47 48
-0.88708333 -1.20708333 -1.32708333 -1.06708333 -0.88708333 -0.82708333
49 50 51 52 53 54
-0.05166667 -0.21166667 -0.37166667 -0.29166667 -0.41166667 -0.07166667
55 56 57 58 59 60
0.24833333 0.42833333 0.30833333 0.16833333 0.04833333 0.20833333
> postscript(file="/var/www/html/rcomp/tmp/609fc1258716014.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 0.31291667 NA
1 0.25291667 0.31291667
2 0.09291667 0.25291667
3 -0.32708333 0.09291667
4 -0.34708333 -0.32708333
5 -0.10708333 -0.34708333
6 0.41291667 -0.10708333
7 0.69291667 0.41291667
8 0.77291667 0.69291667
9 0.53291667 0.77291667
10 0.31291667 0.53291667
11 0.27291667 0.31291667
12 0.01291667 0.27291667
13 0.15291667 0.01291667
14 0.29291667 0.15291667
15 0.27291667 0.29291667
16 0.45291667 0.27291667
17 0.39291667 0.45291667
18 0.31291667 0.39291667
19 0.29291667 0.31291667
20 0.37291667 0.29291667
21 0.43291667 0.37291667
22 0.51291667 0.43291667
23 0.47291667 0.51291667
24 0.21291667 0.47291667
25 0.25291667 0.21291667
26 0.29291667 0.25291667
27 0.37291667 0.29291667
28 0.45291667 0.37291667
29 0.29291667 0.45291667
30 -0.08708333 0.29291667
31 -0.20708333 -0.08708333
32 -0.12708333 -0.20708333
33 -0.06708333 -0.12708333
34 0.01291667 -0.06708333
35 -0.12708333 0.01291667
36 -0.48708333 -0.12708333
37 -0.44708333 -0.48708333
38 -0.30708333 -0.44708333
39 -0.02708333 -0.30708333
40 -0.14708333 -0.02708333
41 -0.50708333 -0.14708333
42 -0.88708333 -0.50708333
43 -1.20708333 -0.88708333
44 -1.32708333 -1.20708333
45 -1.06708333 -1.32708333
46 -0.88708333 -1.06708333
47 -0.82708333 -0.88708333
48 -0.05166667 -0.82708333
49 -0.21166667 -0.05166667
50 -0.37166667 -0.21166667
51 -0.29166667 -0.37166667
52 -0.41166667 -0.29166667
53 -0.07166667 -0.41166667
54 0.24833333 -0.07166667
55 0.42833333 0.24833333
56 0.30833333 0.42833333
57 0.16833333 0.30833333
58 0.04833333 0.16833333
59 0.20833333 0.04833333
60 NA 0.20833333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.25291667 0.31291667
[2,] 0.09291667 0.25291667
[3,] -0.32708333 0.09291667
[4,] -0.34708333 -0.32708333
[5,] -0.10708333 -0.34708333
[6,] 0.41291667 -0.10708333
[7,] 0.69291667 0.41291667
[8,] 0.77291667 0.69291667
[9,] 0.53291667 0.77291667
[10,] 0.31291667 0.53291667
[11,] 0.27291667 0.31291667
[12,] 0.01291667 0.27291667
[13,] 0.15291667 0.01291667
[14,] 0.29291667 0.15291667
[15,] 0.27291667 0.29291667
[16,] 0.45291667 0.27291667
[17,] 0.39291667 0.45291667
[18,] 0.31291667 0.39291667
[19,] 0.29291667 0.31291667
[20,] 0.37291667 0.29291667
[21,] 0.43291667 0.37291667
[22,] 0.51291667 0.43291667
[23,] 0.47291667 0.51291667
[24,] 0.21291667 0.47291667
[25,] 0.25291667 0.21291667
[26,] 0.29291667 0.25291667
[27,] 0.37291667 0.29291667
[28,] 0.45291667 0.37291667
[29,] 0.29291667 0.45291667
[30,] -0.08708333 0.29291667
[31,] -0.20708333 -0.08708333
[32,] -0.12708333 -0.20708333
[33,] -0.06708333 -0.12708333
[34,] 0.01291667 -0.06708333
[35,] -0.12708333 0.01291667
[36,] -0.48708333 -0.12708333
[37,] -0.44708333 -0.48708333
[38,] -0.30708333 -0.44708333
[39,] -0.02708333 -0.30708333
[40,] -0.14708333 -0.02708333
[41,] -0.50708333 -0.14708333
[42,] -0.88708333 -0.50708333
[43,] -1.20708333 -0.88708333
[44,] -1.32708333 -1.20708333
[45,] -1.06708333 -1.32708333
[46,] -0.88708333 -1.06708333
[47,] -0.82708333 -0.88708333
[48,] -0.05166667 -0.82708333
[49,] -0.21166667 -0.05166667
[50,] -0.37166667 -0.21166667
[51,] -0.29166667 -0.37166667
[52,] -0.41166667 -0.29166667
[53,] -0.07166667 -0.41166667
[54,] 0.24833333 -0.07166667
[55,] 0.42833333 0.24833333
[56,] 0.30833333 0.42833333
[57,] 0.16833333 0.30833333
[58,] 0.04833333 0.16833333
[59,] 0.20833333 0.04833333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.25291667 0.31291667
2 0.09291667 0.25291667
3 -0.32708333 0.09291667
4 -0.34708333 -0.32708333
5 -0.10708333 -0.34708333
6 0.41291667 -0.10708333
7 0.69291667 0.41291667
8 0.77291667 0.69291667
9 0.53291667 0.77291667
10 0.31291667 0.53291667
11 0.27291667 0.31291667
12 0.01291667 0.27291667
13 0.15291667 0.01291667
14 0.29291667 0.15291667
15 0.27291667 0.29291667
16 0.45291667 0.27291667
17 0.39291667 0.45291667
18 0.31291667 0.39291667
19 0.29291667 0.31291667
20 0.37291667 0.29291667
21 0.43291667 0.37291667
22 0.51291667 0.43291667
23 0.47291667 0.51291667
24 0.21291667 0.47291667
25 0.25291667 0.21291667
26 0.29291667 0.25291667
27 0.37291667 0.29291667
28 0.45291667 0.37291667
29 0.29291667 0.45291667
30 -0.08708333 0.29291667
31 -0.20708333 -0.08708333
32 -0.12708333 -0.20708333
33 -0.06708333 -0.12708333
34 0.01291667 -0.06708333
35 -0.12708333 0.01291667
36 -0.48708333 -0.12708333
37 -0.44708333 -0.48708333
38 -0.30708333 -0.44708333
39 -0.02708333 -0.30708333
40 -0.14708333 -0.02708333
41 -0.50708333 -0.14708333
42 -0.88708333 -0.50708333
43 -1.20708333 -0.88708333
44 -1.32708333 -1.20708333
45 -1.06708333 -1.32708333
46 -0.88708333 -1.06708333
47 -0.82708333 -0.88708333
48 -0.05166667 -0.82708333
49 -0.21166667 -0.05166667
50 -0.37166667 -0.21166667
51 -0.29166667 -0.37166667
52 -0.41166667 -0.29166667
53 -0.07166667 -0.41166667
54 0.24833333 -0.07166667
55 0.42833333 0.24833333
56 0.30833333 0.42833333
57 0.16833333 0.30833333
58 0.04833333 0.16833333
59 0.20833333 0.04833333
> 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/7mvnm1258716014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8b7zw1258716014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/923yv1258716014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10p9kk1258716014.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11f4ee1258716014.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/12er961258716014.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/132g0u1258716014.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/14ag3f1258716014.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/15zpza1258716014.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/16leeb1258716014.tab")
+ }
>
> system("convert tmp/1wcdu1258716014.ps tmp/1wcdu1258716014.png")
> system("convert tmp/2v88g1258716014.ps tmp/2v88g1258716014.png")
> system("convert tmp/32bq91258716014.ps tmp/32bq91258716014.png")
> system("convert tmp/4zk3o1258716014.ps tmp/4zk3o1258716014.png")
> system("convert tmp/5vkrm1258716014.ps tmp/5vkrm1258716014.png")
> system("convert tmp/609fc1258716014.ps tmp/609fc1258716014.png")
> system("convert tmp/7mvnm1258716014.ps tmp/7mvnm1258716014.png")
> system("convert tmp/8b7zw1258716014.ps tmp/8b7zw1258716014.png")
> system("convert tmp/923yv1258716014.ps tmp/923yv1258716014.png")
> system("convert tmp/10p9kk1258716014.ps tmp/10p9kk1258716014.png")
>
>
> proc.time()
user system elapsed
2.369 1.514 2.801