R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> 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),dim=c(2,60),dimnames=list(c('Werklozen','Samenwerking'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Werklozen','Samenwerking'),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 = '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
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Samenwerking M1 M2 M3
24.75556 -1.31389 0.59361 -0.90944 -2.05250
M4 M5 M6 M7 M8
-2.51556 -2.91861 -3.44167 -4.16472 -3.86778
M9 M10 M11 t
-4.69083 -4.57389 -0.81694 -0.05694
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.0322 -0.6718 0.1911 0.6913 3.0117
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.75556 0.80891 30.604 < 2e-16 ***
Samenwerking -1.31389 0.72642 -1.809 0.077033 .
M1 0.59361 0.90171 0.658 0.513614
M2 -0.90944 0.89657 -1.014 0.315718
M3 -2.05250 0.89190 -2.301 0.025958 *
M4 -2.51556 0.88770 -2.834 0.006809 **
M5 -2.91861 0.88398 -3.302 0.001864 **
M6 -3.44167 0.88074 -3.908 0.000304 ***
M7 -4.16472 0.87799 -4.743 2.07e-05 ***
M8 -3.86778 0.87573 -4.417 6.04e-05 ***
M9 -4.69083 0.87397 -5.367 2.54e-06 ***
M10 -4.57389 0.87271 -5.241 3.90e-06 ***
M11 -0.81694 0.87195 -0.937 0.353697
t -0.05694 0.02097 -2.716 0.009289 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.378 on 46 degrees of freedom
Multiple R-squared: 0.7952, Adjusted R-squared: 0.7374
F-statistic: 13.74 on 13 and 46 DF, p-value: 9.53e-12
> 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,] 0.01903243 0.03806487 0.98096757
[2,] 0.03464305 0.06928611 0.96535695
[3,] 0.17973678 0.35947357 0.82026322
[4,] 0.58851007 0.82297986 0.41148993
[5,] 0.69353235 0.61293531 0.30646765
[6,] 0.69699356 0.60601289 0.30300644
[7,] 0.82549114 0.34901773 0.17450886
[8,] 0.95683502 0.08632996 0.04316498
[9,] 0.96327948 0.07344104 0.03672052
[10,] 0.95031140 0.09937721 0.04968860
[11,] 0.92607061 0.14785878 0.07392939
[12,] 0.91104075 0.17791851 0.08895925
[13,] 0.86220212 0.27559577 0.13779788
[14,] 0.79786160 0.40427680 0.20213840
[15,] 0.72868679 0.54262642 0.27131321
[16,] 0.67752413 0.64495174 0.32247587
[17,] 0.60787539 0.78424923 0.39212461
[18,] 0.55607370 0.88785259 0.44392630
[19,] 0.50837504 0.98324992 0.49162496
[20,] 0.44558463 0.89116927 0.55441537
[21,] 0.37108128 0.74216255 0.62891872
[22,] 0.27541338 0.55082676 0.72458662
[23,] 0.18901819 0.37803639 0.81098181
[24,] 0.19276086 0.38552172 0.80723914
[25,] 0.22066670 0.44133339 0.77933330
[26,] 0.34952415 0.69904830 0.65047585
[27,] 0.30729898 0.61459796 0.69270102
> postscript(file="/var/www/html/freestat/rcomp/tmp/1762y1229460240.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2enxs1229460240.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3hrsm1229460240.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/42rwa1229460240.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/59vo81229460240.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
-0.29222222 -0.13222222 -0.23222222 -0.21222222 -0.75222222 -1.27222222
7 8 9 10 11 12
-1.89222222 -3.03222222 -2.55222222 -1.51222222 0.58777778 1.52777778
13 14 15 16 17 18
0.69111111 0.55111111 0.05111111 0.07111111 -0.26888889 0.21111111
19 20 21 22 23 24
0.69111111 0.85111111 0.83111111 0.37111111 0.17111111 0.11111111
25 26 27 28 29 30
-0.12555556 0.23444444 0.83444444 1.05444444 0.51444444 0.59444444
31 32 33 34 35 36
0.27444444 0.43444444 0.51444444 0.35444444 0.25444444 0.49444444
37 38 39 40 41 42
1.57166667 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
-1.84500000 -1.58500000 -1.48500000 -0.96500000 -0.20500000 -0.22500000
55 56 57 58 59 60
-0.64500000 -1.08500000 -1.80500000 -1.46500000 -1.96500000 -1.92500000
> postscript(file="/var/www/html/freestat/rcomp/tmp/60z7i1229460240.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 -0.29222222 NA
1 -0.13222222 -0.29222222
2 -0.23222222 -0.13222222
3 -0.21222222 -0.23222222
4 -0.75222222 -0.21222222
5 -1.27222222 -0.75222222
6 -1.89222222 -1.27222222
7 -3.03222222 -1.89222222
8 -2.55222222 -3.03222222
9 -1.51222222 -2.55222222
10 0.58777778 -1.51222222
11 1.52777778 0.58777778
12 0.69111111 1.52777778
13 0.55111111 0.69111111
14 0.05111111 0.55111111
15 0.07111111 0.05111111
16 -0.26888889 0.07111111
17 0.21111111 -0.26888889
18 0.69111111 0.21111111
19 0.85111111 0.69111111
20 0.83111111 0.85111111
21 0.37111111 0.83111111
22 0.17111111 0.37111111
23 0.11111111 0.17111111
24 -0.12555556 0.11111111
25 0.23444444 -0.12555556
26 0.83444444 0.23444444
27 1.05444444 0.83444444
28 0.51444444 1.05444444
29 0.59444444 0.51444444
30 0.27444444 0.59444444
31 0.43444444 0.27444444
32 0.51444444 0.43444444
33 0.35444444 0.51444444
34 0.25444444 0.35444444
35 0.49444444 0.25444444
36 1.57166667 0.49444444
37 0.93166667 1.57166667
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 -1.84500000 -0.20833333
49 -1.58500000 -1.84500000
50 -1.48500000 -1.58500000
51 -0.96500000 -1.48500000
52 -0.20500000 -0.96500000
53 -0.22500000 -0.20500000
54 -0.64500000 -0.22500000
55 -1.08500000 -0.64500000
56 -1.80500000 -1.08500000
57 -1.46500000 -1.80500000
58 -1.96500000 -1.46500000
59 -1.92500000 -1.96500000
60 NA -1.92500000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.13222222 -0.29222222
[2,] -0.23222222 -0.13222222
[3,] -0.21222222 -0.23222222
[4,] -0.75222222 -0.21222222
[5,] -1.27222222 -0.75222222
[6,] -1.89222222 -1.27222222
[7,] -3.03222222 -1.89222222
[8,] -2.55222222 -3.03222222
[9,] -1.51222222 -2.55222222
[10,] 0.58777778 -1.51222222
[11,] 1.52777778 0.58777778
[12,] 0.69111111 1.52777778
[13,] 0.55111111 0.69111111
[14,] 0.05111111 0.55111111
[15,] 0.07111111 0.05111111
[16,] -0.26888889 0.07111111
[17,] 0.21111111 -0.26888889
[18,] 0.69111111 0.21111111
[19,] 0.85111111 0.69111111
[20,] 0.83111111 0.85111111
[21,] 0.37111111 0.83111111
[22,] 0.17111111 0.37111111
[23,] 0.11111111 0.17111111
[24,] -0.12555556 0.11111111
[25,] 0.23444444 -0.12555556
[26,] 0.83444444 0.23444444
[27,] 1.05444444 0.83444444
[28,] 0.51444444 1.05444444
[29,] 0.59444444 0.51444444
[30,] 0.27444444 0.59444444
[31,] 0.43444444 0.27444444
[32,] 0.51444444 0.43444444
[33,] 0.35444444 0.51444444
[34,] 0.25444444 0.35444444
[35,] 0.49444444 0.25444444
[36,] 1.57166667 0.49444444
[37,] 0.93166667 1.57166667
[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,] -1.84500000 -0.20833333
[49,] -1.58500000 -1.84500000
[50,] -1.48500000 -1.58500000
[51,] -0.96500000 -1.48500000
[52,] -0.20500000 -0.96500000
[53,] -0.22500000 -0.20500000
[54,] -0.64500000 -0.22500000
[55,] -1.08500000 -0.64500000
[56,] -1.80500000 -1.08500000
[57,] -1.46500000 -1.80500000
[58,] -1.96500000 -1.46500000
[59,] -1.92500000 -1.96500000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.13222222 -0.29222222
2 -0.23222222 -0.13222222
3 -0.21222222 -0.23222222
4 -0.75222222 -0.21222222
5 -1.27222222 -0.75222222
6 -1.89222222 -1.27222222
7 -3.03222222 -1.89222222
8 -2.55222222 -3.03222222
9 -1.51222222 -2.55222222
10 0.58777778 -1.51222222
11 1.52777778 0.58777778
12 0.69111111 1.52777778
13 0.55111111 0.69111111
14 0.05111111 0.55111111
15 0.07111111 0.05111111
16 -0.26888889 0.07111111
17 0.21111111 -0.26888889
18 0.69111111 0.21111111
19 0.85111111 0.69111111
20 0.83111111 0.85111111
21 0.37111111 0.83111111
22 0.17111111 0.37111111
23 0.11111111 0.17111111
24 -0.12555556 0.11111111
25 0.23444444 -0.12555556
26 0.83444444 0.23444444
27 1.05444444 0.83444444
28 0.51444444 1.05444444
29 0.59444444 0.51444444
30 0.27444444 0.59444444
31 0.43444444 0.27444444
32 0.51444444 0.43444444
33 0.35444444 0.51444444
34 0.25444444 0.35444444
35 0.49444444 0.25444444
36 1.57166667 0.49444444
37 0.93166667 1.57166667
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 -1.84500000 -0.20833333
49 -1.58500000 -1.84500000
50 -1.48500000 -1.58500000
51 -0.96500000 -1.48500000
52 -0.20500000 -0.96500000
53 -0.22500000 -0.20500000
54 -0.64500000 -0.22500000
55 -1.08500000 -0.64500000
56 -1.80500000 -1.08500000
57 -1.46500000 -1.80500000
58 -1.96500000 -1.46500000
59 -1.92500000 -1.96500000
> 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/freestat/rcomp/tmp/7xgjy1229460240.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8zq8j1229460240.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/97kn51229460240.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10ghv91229460240.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11mmmz1229460240.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/freestat/rcomp/tmp/121xy11229460240.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/freestat/rcomp/tmp/13cnqx1229460240.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/freestat/rcomp/tmp/14wuq11229460240.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/freestat/rcomp/tmp/153p9s1229460240.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/freestat/rcomp/tmp/16bn211229460240.tab")
+ }
>
> system("convert tmp/1762y1229460240.ps tmp/1762y1229460240.png")
> system("convert tmp/2enxs1229460240.ps tmp/2enxs1229460240.png")
> system("convert tmp/3hrsm1229460240.ps tmp/3hrsm1229460240.png")
> system("convert tmp/42rwa1229460240.ps tmp/42rwa1229460240.png")
> system("convert tmp/59vo81229460240.ps tmp/59vo81229460240.png")
> system("convert tmp/60z7i1229460240.ps tmp/60z7i1229460240.png")
> system("convert tmp/7xgjy1229460240.ps tmp/7xgjy1229460240.png")
> system("convert tmp/8zq8j1229460240.ps tmp/8zq8j1229460240.png")
> system("convert tmp/97kn51229460240.ps tmp/97kn51229460240.png")
> system("convert tmp/10ghv91229460240.ps tmp/10ghv91229460240.png")
>
>
> proc.time()
user system elapsed
3.690 2.540 4.117