R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(25,0,23.6,0,22.3,0,21.8,0,20.8,0,19.7,0,18.3,0,17.4,0,17,0,18.1,0,23.9,0,25.6,0,25.3,0,23.6,0,21.9,0,21.4,0,20.6,0,20.5,0,20.2,0,20.6,0,19.7,0,19.3,0,22.8,0,23.5,0,23.8,0,22.6,0,22,0,21.7,0,20.7,0,20.2,0,19.1,0,19.5,0,18.7,0,18.6,0,22.2,0,23.2,0,23.5,1,21.3,1,20,1,18.7,1,18.9,1,18.3,1,18.4,1,19.9,1,19.2,1,18.5,1,20.9,1,20.5,1,19.4,1,18.1,1,17,1,17,1,17.3,1,16.7,1,15.5,1,15.3,1,13.7,1,14.1,1,17.3,1,18.1,1,18.1,1),dim=c(2,61),dimnames=list(c('Werklozen','Samenwerking'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Werklozen','Samenwerking'),1:61))
> 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
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
Werklozen Samenwerking M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 25.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 23.6 0 0 1 0 0 0 0 0 0 0 0 0 2
3 22.3 0 0 0 1 0 0 0 0 0 0 0 0 3
4 21.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 20.8 0 0 0 0 0 1 0 0 0 0 0 0 5
6 19.7 0 0 0 0 0 0 1 0 0 0 0 0 6
7 18.3 0 0 0 0 0 0 0 1 0 0 0 0 7
8 17.4 0 0 0 0 0 0 0 0 1 0 0 0 8
9 17.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 18.1 0 0 0 0 0 0 0 0 0 0 1 0 10
11 23.9 0 0 0 0 0 0 0 0 0 0 0 1 11
12 25.6 0 0 0 0 0 0 0 0 0 0 0 0 12
13 25.3 0 1 0 0 0 0 0 0 0 0 0 0 13
14 23.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 21.9 0 0 0 1 0 0 0 0 0 0 0 0 15
16 21.4 0 0 0 0 1 0 0 0 0 0 0 0 16
17 20.6 0 0 0 0 0 1 0 0 0 0 0 0 17
18 20.5 0 0 0 0 0 0 1 0 0 0 0 0 18
19 20.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 20.6 0 0 0 0 0 0 0 0 1 0 0 0 20
21 19.7 0 0 0 0 0 0 0 0 0 1 0 0 21
22 19.3 0 0 0 0 0 0 0 0 0 0 1 0 22
23 22.8 0 0 0 0 0 0 0 0 0 0 0 1 23
24 23.5 0 0 0 0 0 0 0 0 0 0 0 0 24
25 23.8 0 1 0 0 0 0 0 0 0 0 0 0 25
26 22.6 0 0 1 0 0 0 0 0 0 0 0 0 26
27 22.0 0 0 0 1 0 0 0 0 0 0 0 0 27
28 21.7 0 0 0 0 1 0 0 0 0 0 0 0 28
29 20.7 0 0 0 0 0 1 0 0 0 0 0 0 29
30 20.2 0 0 0 0 0 0 1 0 0 0 0 0 30
31 19.1 0 0 0 0 0 0 0 1 0 0 0 0 31
32 19.5 0 0 0 0 0 0 0 0 1 0 0 0 32
33 18.7 0 0 0 0 0 0 0 0 0 1 0 0 33
34 18.6 0 0 0 0 0 0 0 0 0 0 1 0 34
35 22.2 0 0 0 0 0 0 0 0 0 0 0 1 35
36 23.2 0 0 0 0 0 0 0 0 0 0 0 0 36
37 23.5 1 1 0 0 0 0 0 0 0 0 0 0 37
38 21.3 1 0 1 0 0 0 0 0 0 0 0 0 38
39 20.0 1 0 0 1 0 0 0 0 0 0 0 0 39
40 18.7 1 0 0 0 1 0 0 0 0 0 0 0 40
41 18.9 1 0 0 0 0 1 0 0 0 0 0 0 41
42 18.3 1 0 0 0 0 0 1 0 0 0 0 0 42
43 18.4 1 0 0 0 0 0 0 1 0 0 0 0 43
44 19.9 1 0 0 0 0 0 0 0 1 0 0 0 44
45 19.2 1 0 0 0 0 0 0 0 0 1 0 0 45
46 18.5 1 0 0 0 0 0 0 0 0 0 1 0 46
47 20.9 1 0 0 0 0 0 0 0 0 0 0 1 47
48 20.5 1 0 0 0 0 0 0 0 0 0 0 0 48
49 19.4 1 1 0 0 0 0 0 0 0 0 0 0 49
50 18.1 1 0 1 0 0 0 0 0 0 0 0 0 50
51 17.0 1 0 0 1 0 0 0 0 0 0 0 0 51
52 17.0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 17.3 1 0 0 0 0 1 0 0 0 0 0 0 53
54 16.7 1 0 0 0 0 0 1 0 0 0 0 0 54
55 15.5 1 0 0 0 0 0 0 1 0 0 0 0 55
56 15.3 1 0 0 0 0 0 0 0 1 0 0 0 56
57 13.7 1 0 0 0 0 0 0 0 0 1 0 0 57
58 14.1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 17.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 18.1 1 0 0 0 0 0 0 0 0 0 0 0 60
61 18.1 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Samenwerking M1 M2 M3
24.97647 -1.15609 0.12810 -0.98834 -2.12351
M4 M5 M6 M7 M8
-2.57868 -2.97384 -3.48901 -4.20417 -3.89934
M9 M10 M11 t
-4.71450 -4.58967 -0.82483 -0.06483
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.1585 -0.8703 0.1796 0.6917 3.0117
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.97647 0.80874 30.883 < 2e-16 ***
Samenwerking -1.15609 0.73044 -1.583 0.120190
M1 0.12810 0.86440 0.148 0.882819
M2 -0.98834 0.90889 -1.087 0.282399
M3 -2.12351 0.90442 -2.348 0.023139 *
M4 -2.57868 0.90039 -2.864 0.006235 **
M5 -2.97384 0.89683 -3.316 0.001767 **
M6 -3.48901 0.89373 -3.904 0.000301 ***
M7 -4.20417 0.89109 -4.718 2.17e-05 ***
M8 -3.89934 0.88893 -4.387 6.46e-05 ***
M9 -4.71450 0.88725 -5.314 2.89e-06 ***
M10 -4.58967 0.88605 -5.180 4.57e-06 ***
M11 -0.82483 0.88532 -0.932 0.356265
t -0.06483 0.02067 -3.137 0.002945 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.399 on 47 degrees of freedom
Multiple R-squared: 0.7862, Adjusted R-squared: 0.727
F-statistic: 13.29 on 13 and 47 DF, p-value: 1.245e-11
> 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,] 0.01996159 0.03992318 0.98003841
[2,] 0.03683471 0.07366943 0.96316529
[3,] 0.19076838 0.38153677 0.80923162
[4,] 0.60995365 0.78009270 0.39004635
[5,] 0.71225105 0.57549790 0.28774895
[6,] 0.70708240 0.58583519 0.29291760
[7,] 0.82035196 0.35929609 0.17964804
[8,] 0.94406471 0.11187058 0.05593529
[9,] 0.95758776 0.08482448 0.04241224
[10,] 0.94361988 0.11276025 0.05638012
[11,] 0.91688276 0.16623449 0.08311724
[12,] 0.89902716 0.20194568 0.10097284
[13,] 0.84689229 0.30621543 0.15310771
[14,] 0.77974321 0.44051358 0.22025679
[15,] 0.70845077 0.58309846 0.29154923
[16,] 0.65503165 0.68993671 0.34496835
[17,] 0.58415617 0.83168765 0.41584383
[18,] 0.53027319 0.93945362 0.46972681
[19,] 0.48539570 0.97079140 0.51460430
[20,] 0.42807888 0.85615775 0.57192112
[21,] 0.32849157 0.65698313 0.67150843
[22,] 0.24398705 0.48797409 0.75601295
[23,] 0.16727737 0.33455474 0.83272263
[24,] 0.15464140 0.30928280 0.84535860
[25,] 0.14472372 0.28944744 0.85527628
[26,] 0.15872453 0.31744906 0.84127547
[27,] 0.10981754 0.21963507 0.89018246
[28,] 0.11351222 0.22702445 0.88648778
> postscript(file="/var/www/html/rcomp/tmp/1tq641229441970.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/225dw1229441970.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3hdum1229441970.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4l0q31229441970.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wg0z1229441970.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 = 61
Frequency = 1
1 2 3 4 5 6
-0.03974359 -0.25846154 -0.35846154 -0.33846154 -0.87846154 -1.39846154
7 8 9 10 11 12
-2.01846154 -3.15846154 -2.67846154 -1.63846154 0.46153846 1.40153846
13 14 15 16 17 18
1.03826923 0.51955128 0.01955128 0.03955128 -0.30044872 0.17955128
19 20 21 22 23 24
0.65955128 0.81955128 0.79955128 0.33955128 0.13955128 0.07955128
25 26 27 28 29 30
0.31628205 0.29756410 0.89756410 1.11756410 0.57756410 0.65756410
31 32 33 34 35 36
0.33756410 0.49756410 0.57756410 0.41756410 0.31756410 0.55756410
37 38 39 40 41 42
1.95038462 0.93166667 0.83166667 0.05166667 0.71166667 0.69166667
43 44 45 46 47 48
1.57166667 2.83166667 3.01166667 2.25166667 0.95166667 -0.20833333
49 50 51 52 53 54
-1.37160256 -1.49032051 -1.39032051 -0.87032051 -0.11032051 -0.13032051
55 56 57 58 59 60
-0.55032051 -0.99032051 -1.71032051 -1.37032051 -1.87032051 -1.83032051
61
-1.89358974
> postscript(file="/var/www/html/rcomp/tmp/6314n1229441970.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.03974359 NA
1 -0.25846154 -0.03974359
2 -0.35846154 -0.25846154
3 -0.33846154 -0.35846154
4 -0.87846154 -0.33846154
5 -1.39846154 -0.87846154
6 -2.01846154 -1.39846154
7 -3.15846154 -2.01846154
8 -2.67846154 -3.15846154
9 -1.63846154 -2.67846154
10 0.46153846 -1.63846154
11 1.40153846 0.46153846
12 1.03826923 1.40153846
13 0.51955128 1.03826923
14 0.01955128 0.51955128
15 0.03955128 0.01955128
16 -0.30044872 0.03955128
17 0.17955128 -0.30044872
18 0.65955128 0.17955128
19 0.81955128 0.65955128
20 0.79955128 0.81955128
21 0.33955128 0.79955128
22 0.13955128 0.33955128
23 0.07955128 0.13955128
24 0.31628205 0.07955128
25 0.29756410 0.31628205
26 0.89756410 0.29756410
27 1.11756410 0.89756410
28 0.57756410 1.11756410
29 0.65756410 0.57756410
30 0.33756410 0.65756410
31 0.49756410 0.33756410
32 0.57756410 0.49756410
33 0.41756410 0.57756410
34 0.31756410 0.41756410
35 0.55756410 0.31756410
36 1.95038462 0.55756410
37 0.93166667 1.95038462
38 0.83166667 0.93166667
39 0.05166667 0.83166667
40 0.71166667 0.05166667
41 0.69166667 0.71166667
42 1.57166667 0.69166667
43 2.83166667 1.57166667
44 3.01166667 2.83166667
45 2.25166667 3.01166667
46 0.95166667 2.25166667
47 -0.20833333 0.95166667
48 -1.37160256 -0.20833333
49 -1.49032051 -1.37160256
50 -1.39032051 -1.49032051
51 -0.87032051 -1.39032051
52 -0.11032051 -0.87032051
53 -0.13032051 -0.11032051
54 -0.55032051 -0.13032051
55 -0.99032051 -0.55032051
56 -1.71032051 -0.99032051
57 -1.37032051 -1.71032051
58 -1.87032051 -1.37032051
59 -1.83032051 -1.87032051
60 -1.89358974 -1.83032051
61 NA -1.89358974
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.25846154 -0.03974359
[2,] -0.35846154 -0.25846154
[3,] -0.33846154 -0.35846154
[4,] -0.87846154 -0.33846154
[5,] -1.39846154 -0.87846154
[6,] -2.01846154 -1.39846154
[7,] -3.15846154 -2.01846154
[8,] -2.67846154 -3.15846154
[9,] -1.63846154 -2.67846154
[10,] 0.46153846 -1.63846154
[11,] 1.40153846 0.46153846
[12,] 1.03826923 1.40153846
[13,] 0.51955128 1.03826923
[14,] 0.01955128 0.51955128
[15,] 0.03955128 0.01955128
[16,] -0.30044872 0.03955128
[17,] 0.17955128 -0.30044872
[18,] 0.65955128 0.17955128
[19,] 0.81955128 0.65955128
[20,] 0.79955128 0.81955128
[21,] 0.33955128 0.79955128
[22,] 0.13955128 0.33955128
[23,] 0.07955128 0.13955128
[24,] 0.31628205 0.07955128
[25,] 0.29756410 0.31628205
[26,] 0.89756410 0.29756410
[27,] 1.11756410 0.89756410
[28,] 0.57756410 1.11756410
[29,] 0.65756410 0.57756410
[30,] 0.33756410 0.65756410
[31,] 0.49756410 0.33756410
[32,] 0.57756410 0.49756410
[33,] 0.41756410 0.57756410
[34,] 0.31756410 0.41756410
[35,] 0.55756410 0.31756410
[36,] 1.95038462 0.55756410
[37,] 0.93166667 1.95038462
[38,] 0.83166667 0.93166667
[39,] 0.05166667 0.83166667
[40,] 0.71166667 0.05166667
[41,] 0.69166667 0.71166667
[42,] 1.57166667 0.69166667
[43,] 2.83166667 1.57166667
[44,] 3.01166667 2.83166667
[45,] 2.25166667 3.01166667
[46,] 0.95166667 2.25166667
[47,] -0.20833333 0.95166667
[48,] -1.37160256 -0.20833333
[49,] -1.49032051 -1.37160256
[50,] -1.39032051 -1.49032051
[51,] -0.87032051 -1.39032051
[52,] -0.11032051 -0.87032051
[53,] -0.13032051 -0.11032051
[54,] -0.55032051 -0.13032051
[55,] -0.99032051 -0.55032051
[56,] -1.71032051 -0.99032051
[57,] -1.37032051 -1.71032051
[58,] -1.87032051 -1.37032051
[59,] -1.83032051 -1.87032051
[60,] -1.89358974 -1.83032051
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.25846154 -0.03974359
2 -0.35846154 -0.25846154
3 -0.33846154 -0.35846154
4 -0.87846154 -0.33846154
5 -1.39846154 -0.87846154
6 -2.01846154 -1.39846154
7 -3.15846154 -2.01846154
8 -2.67846154 -3.15846154
9 -1.63846154 -2.67846154
10 0.46153846 -1.63846154
11 1.40153846 0.46153846
12 1.03826923 1.40153846
13 0.51955128 1.03826923
14 0.01955128 0.51955128
15 0.03955128 0.01955128
16 -0.30044872 0.03955128
17 0.17955128 -0.30044872
18 0.65955128 0.17955128
19 0.81955128 0.65955128
20 0.79955128 0.81955128
21 0.33955128 0.79955128
22 0.13955128 0.33955128
23 0.07955128 0.13955128
24 0.31628205 0.07955128
25 0.29756410 0.31628205
26 0.89756410 0.29756410
27 1.11756410 0.89756410
28 0.57756410 1.11756410
29 0.65756410 0.57756410
30 0.33756410 0.65756410
31 0.49756410 0.33756410
32 0.57756410 0.49756410
33 0.41756410 0.57756410
34 0.31756410 0.41756410
35 0.55756410 0.31756410
36 1.95038462 0.55756410
37 0.93166667 1.95038462
38 0.83166667 0.93166667
39 0.05166667 0.83166667
40 0.71166667 0.05166667
41 0.69166667 0.71166667
42 1.57166667 0.69166667
43 2.83166667 1.57166667
44 3.01166667 2.83166667
45 2.25166667 3.01166667
46 0.95166667 2.25166667
47 -0.20833333 0.95166667
48 -1.37160256 -0.20833333
49 -1.49032051 -1.37160256
50 -1.39032051 -1.49032051
51 -0.87032051 -1.39032051
52 -0.11032051 -0.87032051
53 -0.13032051 -0.11032051
54 -0.55032051 -0.13032051
55 -0.99032051 -0.55032051
56 -1.71032051 -0.99032051
57 -1.37032051 -1.71032051
58 -1.87032051 -1.37032051
59 -1.83032051 -1.87032051
60 -1.89358974 -1.83032051
> 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/7y6ip1229441970.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/82jrg1229441970.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vrlf1229441970.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1073g91229441970.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ctx91229441970.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/12ighb1229441970.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/13gam81229441970.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/14je9y1229441970.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/15zusd1229441971.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/16i4js1229441971.tab")
+ }
>
> system("convert tmp/1tq641229441970.ps tmp/1tq641229441970.png")
> system("convert tmp/225dw1229441970.ps tmp/225dw1229441970.png")
> system("convert tmp/3hdum1229441970.ps tmp/3hdum1229441970.png")
> system("convert tmp/4l0q31229441970.ps tmp/4l0q31229441970.png")
> system("convert tmp/5wg0z1229441970.ps tmp/5wg0z1229441970.png")
> system("convert tmp/6314n1229441970.ps tmp/6314n1229441970.png")
> system("convert tmp/7y6ip1229441970.ps tmp/7y6ip1229441970.png")
> system("convert tmp/82jrg1229441970.ps tmp/82jrg1229441970.png")
> system("convert tmp/9vrlf1229441970.ps tmp/9vrlf1229441970.png")
> system("convert tmp/1073g91229441970.ps tmp/1073g91229441970.png")
>
>
> proc.time()
user system elapsed
2.457 1.585 2.972