R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(96.8,92.9,114.1,107.7,110.3,103.5,103.9,91.1,101.6,79.8,94.6,71.9,95.9,82.9,104.7,90.1,102.8,100.7,98.1,90.7,113.9,108.8,80.9,44.1,95.7,93.6,113.2,107.4,105.9,96.5,108.8,93.6,102.3,76.5,99,76.7,100.7,84,115.5,103.3,100.7,88.5,109.9,99,114.6,105.9,85.4,44.7,100.5,94,114.8,107.1,116.5,104.8,112.9,102.5,102,77.7,106,85.2,105.3,91.3,118.8,106.5,106.1,92.4,109.3,97.5,117.2,107,92.5,51.1,104.2,98.6,112.5,102.2,122.4,114.3,113.3,99.4,100,72.5,110.7,92.3,112.8,99.4,109.8,85.9,117.3,109.4,109.1,97.6,115.9,104.7,96,56.9,99.8,86.7,116.8,108.5,115.7,103.4,99.4,86.2,94.3,71,91,75.9,93.2,87.1,103.1,102,94.1,88.5,91.8,87.8,102.7,100.8,82.6,50.6),dim=c(2,60),dimnames=list(c('Totind','Bouw'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Totind','Bouw'),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
Totind Bouw M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 96.8 92.9 1 0 0 0 0 0 0 0 0 0 0
2 114.1 107.7 0 1 0 0 0 0 0 0 0 0 0
3 110.3 103.5 0 0 1 0 0 0 0 0 0 0 0
4 103.9 91.1 0 0 0 1 0 0 0 0 0 0 0
5 101.6 79.8 0 0 0 0 1 0 0 0 0 0 0
6 94.6 71.9 0 0 0 0 0 1 0 0 0 0 0
7 95.9 82.9 0 0 0 0 0 0 1 0 0 0 0
8 104.7 90.1 0 0 0 0 0 0 0 1 0 0 0
9 102.8 100.7 0 0 0 0 0 0 0 0 1 0 0
10 98.1 90.7 0 0 0 0 0 0 0 0 0 1 0
11 113.9 108.8 0 0 0 0 0 0 0 0 0 0 1
12 80.9 44.1 0 0 0 0 0 0 0 0 0 0 0
13 95.7 93.6 1 0 0 0 0 0 0 0 0 0 0
14 113.2 107.4 0 1 0 0 0 0 0 0 0 0 0
15 105.9 96.5 0 0 1 0 0 0 0 0 0 0 0
16 108.8 93.6 0 0 0 1 0 0 0 0 0 0 0
17 102.3 76.5 0 0 0 0 1 0 0 0 0 0 0
18 99.0 76.7 0 0 0 0 0 1 0 0 0 0 0
19 100.7 84.0 0 0 0 0 0 0 1 0 0 0 0
20 115.5 103.3 0 0 0 0 0 0 0 1 0 0 0
21 100.7 88.5 0 0 0 0 0 0 0 0 1 0 0
22 109.9 99.0 0 0 0 0 0 0 0 0 0 1 0
23 114.6 105.9 0 0 0 0 0 0 0 0 0 0 1
24 85.4 44.7 0 0 0 0 0 0 0 0 0 0 0
25 100.5 94.0 1 0 0 0 0 0 0 0 0 0 0
26 114.8 107.1 0 1 0 0 0 0 0 0 0 0 0
27 116.5 104.8 0 0 1 0 0 0 0 0 0 0 0
28 112.9 102.5 0 0 0 1 0 0 0 0 0 0 0
29 102.0 77.7 0 0 0 0 1 0 0 0 0 0 0
30 106.0 85.2 0 0 0 0 0 1 0 0 0 0 0
31 105.3 91.3 0 0 0 0 0 0 1 0 0 0 0
32 118.8 106.5 0 0 0 0 0 0 0 1 0 0 0
33 106.1 92.4 0 0 0 0 0 0 0 0 1 0 0
34 109.3 97.5 0 0 0 0 0 0 0 0 0 1 0
35 117.2 107.0 0 0 0 0 0 0 0 0 0 0 1
36 92.5 51.1 0 0 0 0 0 0 0 0 0 0 0
37 104.2 98.6 1 0 0 0 0 0 0 0 0 0 0
38 112.5 102.2 0 1 0 0 0 0 0 0 0 0 0
39 122.4 114.3 0 0 1 0 0 0 0 0 0 0 0
40 113.3 99.4 0 0 0 1 0 0 0 0 0 0 0
41 100.0 72.5 0 0 0 0 1 0 0 0 0 0 0
42 110.7 92.3 0 0 0 0 0 1 0 0 0 0 0
43 112.8 99.4 0 0 0 0 0 0 1 0 0 0 0
44 109.8 85.9 0 0 0 0 0 0 0 1 0 0 0
45 117.3 109.4 0 0 0 0 0 0 0 0 1 0 0
46 109.1 97.6 0 0 0 0 0 0 0 0 0 1 0
47 115.9 104.7 0 0 0 0 0 0 0 0 0 0 1
48 96.0 56.9 0 0 0 0 0 0 0 0 0 0 0
49 99.8 86.7 1 0 0 0 0 0 0 0 0 0 0
50 116.8 108.5 0 1 0 0 0 0 0 0 0 0 0
51 115.7 103.4 0 0 1 0 0 0 0 0 0 0 0
52 99.4 86.2 0 0 0 1 0 0 0 0 0 0 0
53 94.3 71.0 0 0 0 0 1 0 0 0 0 0 0
54 91.0 75.9 0 0 0 0 0 1 0 0 0 0 0
55 93.2 87.1 0 0 0 0 0 0 1 0 0 0 0
56 103.1 102.0 0 0 0 0 0 0 0 1 0 0 0
57 94.1 88.5 0 0 0 0 0 0 0 0 1 0 0
58 91.8 87.8 0 0 0 0 0 0 0 0 0 1 0
59 102.7 100.8 0 0 0 0 0 0 0 0 0 0 1
60 82.6 50.6 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) Bouw M1 M2 M3 M4
46.4686 0.8288 -24.2841 -20.5273 -18.9233 -17.1845
M5 M6 M7 M8 M9 M10
-9.0066 -12.8480 -18.6064 -16.9511 -21.7552 -21.1713
M11
-21.0024
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.960 -1.821 0.540 2.161 9.084
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 46.46856 4.78929 9.703 8.41e-13 ***
Bouw 0.82885 0.09028 9.181 4.69e-12 ***
M1 -24.28412 4.63836 -5.235 3.78e-06 ***
M2 -20.52727 5.70425 -3.599 0.000767 ***
M3 -18.92326 5.53512 -3.419 0.001309 **
M4 -17.18450 4.74629 -3.621 0.000718 ***
M5 -9.00665 3.38840 -2.658 0.010707 *
M6 -12.84800 3.70883 -3.464 0.001145 **
M7 -18.60637 4.31910 -4.308 8.34e-05 ***
M8 -16.95105 4.98049 -3.403 0.001369 **
M9 -21.75516 4.85043 -4.485 4.68e-05 ***
M10 -21.17135 4.74319 -4.464 5.02e-05 ***
M11 -21.00238 5.61140 -3.743 0.000495 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.861 on 47 degrees of freedom
Multiple R-squared: 0.8675, Adjusted R-squared: 0.8337
F-statistic: 25.64 on 12 and 47 DF, p-value: < 2.2e-16
> 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,] 5.054000e-02 1.010800e-01 0.9494600
[2,] 3.479770e-02 6.959540e-02 0.9652023
[3,] 1.303499e-02 2.606999e-02 0.9869650
[4,] 1.655242e-02 3.310483e-02 0.9834476
[5,] 5.984594e-03 1.196919e-02 0.9940154
[6,] 1.734448e-02 3.468896e-02 0.9826555
[7,] 3.401371e-02 6.802742e-02 0.9659863
[8,] 2.029151e-02 4.058303e-02 0.9797085
[9,] 1.583291e-02 3.166582e-02 0.9841671
[10,] 1.285099e-02 2.570198e-02 0.9871490
[11,] 6.572852e-03 1.314570e-02 0.9934271
[12,] 7.173855e-03 1.434771e-02 0.9928261
[13,] 3.494481e-03 6.988961e-03 0.9965055
[14,] 1.536392e-03 3.072784e-03 0.9984636
[15,] 7.666563e-04 1.533313e-03 0.9992333
[16,] 3.671860e-04 7.343720e-04 0.9996328
[17,] 1.493940e-04 2.987881e-04 0.9998506
[18,] 3.656393e-04 7.312785e-04 0.9996344
[19,] 2.547887e-04 5.095775e-04 0.9997452
[20,] 1.666751e-04 3.333503e-04 0.9998333
[21,] 1.565433e-04 3.130867e-04 0.9998435
[22,] 1.075581e-04 2.151162e-04 0.9998924
[23,] 5.113612e-05 1.022722e-04 0.9999489
[24,] 2.104395e-05 4.208790e-05 0.9999790
[25,] 7.318151e-06 1.463630e-05 0.9999927
[26,] 3.279883e-06 6.559766e-06 0.9999967
[27,] 1.043012e-06 2.086024e-06 0.9999990
[28,] 7.507274e-07 1.501455e-06 0.9999992
[29,] 4.206855e-02 8.413710e-02 0.9579314
> postscript(file="/var/www/html/rcomp/tmp/1oje71258727878.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bp9f1258727878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3q0yt1258727878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4l01h1258727878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5n8i81258727878.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
-2.38449931 -1.10831066 -3.03115120 -0.89218315 -2.00404984 1.38521480
7 8 9 10 11 12
-0.67375325 0.50321204 -5.37847424 -2.37379759 -1.74493197 -2.12079346
13 14 15 16 17 18
-4.06469347 -1.75965602 -1.62920960 1.93569485 1.43115120 1.80674056
19 20 21 22 23 24
3.21451307 0.36240789 2.63348112 2.54675738 1.35872955 1.88189726
25 26 27 28 29 30
0.40376701 0.08899862 2.09134536 -1.34105947 0.13653264 1.76152576
31 32 33 34 35 36
1.76391683 1.01009173 4.80097080 3.19003058 3.04699587 3.67726494
37 38 39 40 41 42
0.29106253 1.85035774 0.11728176 1.62837181 2.44654640 0.57669929
43 44 45 46 47 48
2.55024156 9.08437700 1.91054121 2.90714570 3.65334811 2.36994191
49 50 51 52 53 54
5.75436325 0.92861030 2.45173368 -1.33082404 -2.01018040 -5.53018040
55 56 57 58 59 60
-6.85491821 -10.96008867 -3.96651888 -6.27013607 -6.31414157 -5.80831066
> postscript(file="/var/www/html/rcomp/tmp/6mzxj1258727878.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 -2.38449931 NA
1 -1.10831066 -2.38449931
2 -3.03115120 -1.10831066
3 -0.89218315 -3.03115120
4 -2.00404984 -0.89218315
5 1.38521480 -2.00404984
6 -0.67375325 1.38521480
7 0.50321204 -0.67375325
8 -5.37847424 0.50321204
9 -2.37379759 -5.37847424
10 -1.74493197 -2.37379759
11 -2.12079346 -1.74493197
12 -4.06469347 -2.12079346
13 -1.75965602 -4.06469347
14 -1.62920960 -1.75965602
15 1.93569485 -1.62920960
16 1.43115120 1.93569485
17 1.80674056 1.43115120
18 3.21451307 1.80674056
19 0.36240789 3.21451307
20 2.63348112 0.36240789
21 2.54675738 2.63348112
22 1.35872955 2.54675738
23 1.88189726 1.35872955
24 0.40376701 1.88189726
25 0.08899862 0.40376701
26 2.09134536 0.08899862
27 -1.34105947 2.09134536
28 0.13653264 -1.34105947
29 1.76152576 0.13653264
30 1.76391683 1.76152576
31 1.01009173 1.76391683
32 4.80097080 1.01009173
33 3.19003058 4.80097080
34 3.04699587 3.19003058
35 3.67726494 3.04699587
36 0.29106253 3.67726494
37 1.85035774 0.29106253
38 0.11728176 1.85035774
39 1.62837181 0.11728176
40 2.44654640 1.62837181
41 0.57669929 2.44654640
42 2.55024156 0.57669929
43 9.08437700 2.55024156
44 1.91054121 9.08437700
45 2.90714570 1.91054121
46 3.65334811 2.90714570
47 2.36994191 3.65334811
48 5.75436325 2.36994191
49 0.92861030 5.75436325
50 2.45173368 0.92861030
51 -1.33082404 2.45173368
52 -2.01018040 -1.33082404
53 -5.53018040 -2.01018040
54 -6.85491821 -5.53018040
55 -10.96008867 -6.85491821
56 -3.96651888 -10.96008867
57 -6.27013607 -3.96651888
58 -6.31414157 -6.27013607
59 -5.80831066 -6.31414157
60 NA -5.80831066
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.10831066 -2.38449931
[2,] -3.03115120 -1.10831066
[3,] -0.89218315 -3.03115120
[4,] -2.00404984 -0.89218315
[5,] 1.38521480 -2.00404984
[6,] -0.67375325 1.38521480
[7,] 0.50321204 -0.67375325
[8,] -5.37847424 0.50321204
[9,] -2.37379759 -5.37847424
[10,] -1.74493197 -2.37379759
[11,] -2.12079346 -1.74493197
[12,] -4.06469347 -2.12079346
[13,] -1.75965602 -4.06469347
[14,] -1.62920960 -1.75965602
[15,] 1.93569485 -1.62920960
[16,] 1.43115120 1.93569485
[17,] 1.80674056 1.43115120
[18,] 3.21451307 1.80674056
[19,] 0.36240789 3.21451307
[20,] 2.63348112 0.36240789
[21,] 2.54675738 2.63348112
[22,] 1.35872955 2.54675738
[23,] 1.88189726 1.35872955
[24,] 0.40376701 1.88189726
[25,] 0.08899862 0.40376701
[26,] 2.09134536 0.08899862
[27,] -1.34105947 2.09134536
[28,] 0.13653264 -1.34105947
[29,] 1.76152576 0.13653264
[30,] 1.76391683 1.76152576
[31,] 1.01009173 1.76391683
[32,] 4.80097080 1.01009173
[33,] 3.19003058 4.80097080
[34,] 3.04699587 3.19003058
[35,] 3.67726494 3.04699587
[36,] 0.29106253 3.67726494
[37,] 1.85035774 0.29106253
[38,] 0.11728176 1.85035774
[39,] 1.62837181 0.11728176
[40,] 2.44654640 1.62837181
[41,] 0.57669929 2.44654640
[42,] 2.55024156 0.57669929
[43,] 9.08437700 2.55024156
[44,] 1.91054121 9.08437700
[45,] 2.90714570 1.91054121
[46,] 3.65334811 2.90714570
[47,] 2.36994191 3.65334811
[48,] 5.75436325 2.36994191
[49,] 0.92861030 5.75436325
[50,] 2.45173368 0.92861030
[51,] -1.33082404 2.45173368
[52,] -2.01018040 -1.33082404
[53,] -5.53018040 -2.01018040
[54,] -6.85491821 -5.53018040
[55,] -10.96008867 -6.85491821
[56,] -3.96651888 -10.96008867
[57,] -6.27013607 -3.96651888
[58,] -6.31414157 -6.27013607
[59,] -5.80831066 -6.31414157
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.10831066 -2.38449931
2 -3.03115120 -1.10831066
3 -0.89218315 -3.03115120
4 -2.00404984 -0.89218315
5 1.38521480 -2.00404984
6 -0.67375325 1.38521480
7 0.50321204 -0.67375325
8 -5.37847424 0.50321204
9 -2.37379759 -5.37847424
10 -1.74493197 -2.37379759
11 -2.12079346 -1.74493197
12 -4.06469347 -2.12079346
13 -1.75965602 -4.06469347
14 -1.62920960 -1.75965602
15 1.93569485 -1.62920960
16 1.43115120 1.93569485
17 1.80674056 1.43115120
18 3.21451307 1.80674056
19 0.36240789 3.21451307
20 2.63348112 0.36240789
21 2.54675738 2.63348112
22 1.35872955 2.54675738
23 1.88189726 1.35872955
24 0.40376701 1.88189726
25 0.08899862 0.40376701
26 2.09134536 0.08899862
27 -1.34105947 2.09134536
28 0.13653264 -1.34105947
29 1.76152576 0.13653264
30 1.76391683 1.76152576
31 1.01009173 1.76391683
32 4.80097080 1.01009173
33 3.19003058 4.80097080
34 3.04699587 3.19003058
35 3.67726494 3.04699587
36 0.29106253 3.67726494
37 1.85035774 0.29106253
38 0.11728176 1.85035774
39 1.62837181 0.11728176
40 2.44654640 1.62837181
41 0.57669929 2.44654640
42 2.55024156 0.57669929
43 9.08437700 2.55024156
44 1.91054121 9.08437700
45 2.90714570 1.91054121
46 3.65334811 2.90714570
47 2.36994191 3.65334811
48 5.75436325 2.36994191
49 0.92861030 5.75436325
50 2.45173368 0.92861030
51 -1.33082404 2.45173368
52 -2.01018040 -1.33082404
53 -5.53018040 -2.01018040
54 -6.85491821 -5.53018040
55 -10.96008867 -6.85491821
56 -3.96651888 -10.96008867
57 -6.27013607 -3.96651888
58 -6.31414157 -6.27013607
59 -5.80831066 -6.31414157
> 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/7dmna1258727878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86nwy1258727878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tt8h1258727878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wnq01258727878.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i6gh1258727878.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/12tyq41258727878.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/13zlw51258727878.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/1429wa1258727878.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/15kqla1258727878.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/163drf1258727878.tab")
+ }
>
> system("convert tmp/1oje71258727878.ps tmp/1oje71258727878.png")
> system("convert tmp/2bp9f1258727878.ps tmp/2bp9f1258727878.png")
> system("convert tmp/3q0yt1258727878.ps tmp/3q0yt1258727878.png")
> system("convert tmp/4l01h1258727878.ps tmp/4l01h1258727878.png")
> system("convert tmp/5n8i81258727878.ps tmp/5n8i81258727878.png")
> system("convert tmp/6mzxj1258727878.ps tmp/6mzxj1258727878.png")
> system("convert tmp/7dmna1258727878.ps tmp/7dmna1258727878.png")
> system("convert tmp/86nwy1258727878.ps tmp/86nwy1258727878.png")
> system("convert tmp/9tt8h1258727878.ps tmp/9tt8h1258727878.png")
> system("convert tmp/10wnq01258727878.ps tmp/10wnq01258727878.png")
>
>
> proc.time()
user system elapsed
2.522 1.661 4.683