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(8.9,11.1,8.9,10.9,8.6,10,8.3,9.2,8.3,9.2,8.3,9.5,8.4,9.6,8.5,9.5,8.4,9.1,8.6,8.9,8.5,9,8.5,10.1,8.4,10.3,8.5,10.2,8.5,9.6,8.5,9.2,8.5,9.3,8.5,9.4,8.5,9.4,8.5,9.2,8.5,9,8.6,9,8.4,9,8.1,9.8,8.0,10,8.0,9.8,8.0,9.3,8.0,9,7.9,9,7.8,9.1,7.8,9.1,7.9,9.1,8.1,9.2,8.0,8.8,7.6,8.3,7.3,8.4,7.0,8.1,6.8,7.7,7.0,7.9,7.1,7.9,7.2,8,7.1,7.9,6.9,7.6,6.7,7.1,6.7,6.8,6.6,6.5,6.9,6.9,7.3,8.2,7.5,8.7,7.3,8.3,7.1,7.9,6.9,7.5,7.1,7.8,7.5,8.3,7.7,8.4,7.8,8.2,7.8,7.7,7.7,7.2,7.8,7.3,7.8,8.1,7.9,8.5),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.9 11.1 1 0 0 0 0 0 0 0 0 0 0 1
2 8.9 10.9 0 1 0 0 0 0 0 0 0 0 0 2
3 8.6 10.0 0 0 1 0 0 0 0 0 0 0 0 3
4 8.3 9.2 0 0 0 1 0 0 0 0 0 0 0 4
5 8.3 9.2 0 0 0 0 1 0 0 0 0 0 0 5
6 8.3 9.5 0 0 0 0 0 1 0 0 0 0 0 6
7 8.4 9.6 0 0 0 0 0 0 1 0 0 0 0 7
8 8.5 9.5 0 0 0 0 0 0 0 1 0 0 0 8
9 8.4 9.1 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 8.9 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 9.0 0 0 0 0 0 0 0 0 0 0 1 11
12 8.5 10.1 0 0 0 0 0 0 0 0 0 0 0 12
13 8.4 10.3 1 0 0 0 0 0 0 0 0 0 0 13
14 8.5 10.2 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 9.6 0 0 1 0 0 0 0 0 0 0 0 15
16 8.5 9.2 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 9.3 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 9.4 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 9.4 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 9.2 0 0 0 0 0 0 0 1 0 0 0 20
21 8.5 9.0 0 0 0 0 0 0 0 0 1 0 0 21
22 8.6 9.0 0 0 0 0 0 0 0 0 0 1 0 22
23 8.4 9.0 0 0 0 0 0 0 0 0 0 0 1 23
24 8.1 9.8 0 0 0 0 0 0 0 0 0 0 0 24
25 8.0 10.0 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 9.8 0 1 0 0 0 0 0 0 0 0 0 26
27 8.0 9.3 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 9.0 0 0 0 1 0 0 0 0 0 0 0 28
29 7.9 9.0 0 0 0 0 1 0 0 0 0 0 0 29
30 7.8 9.1 0 0 0 0 0 1 0 0 0 0 0 30
31 7.8 9.1 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 9.1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.1 9.2 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 8.8 0 0 0 0 0 0 0 0 0 1 0 34
35 7.6 8.3 0 0 0 0 0 0 0 0 0 0 1 35
36 7.3 8.4 0 0 0 0 0 0 0 0 0 0 0 36
37 7.0 8.1 1 0 0 0 0 0 0 0 0 0 0 37
38 6.8 7.7 0 1 0 0 0 0 0 0 0 0 0 38
39 7.0 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.1 7.9 0 0 0 1 0 0 0 0 0 0 0 40
41 7.2 8.0 0 0 0 0 1 0 0 0 0 0 0 41
42 7.1 7.9 0 0 0 0 0 1 0 0 0 0 0 42
43 6.9 7.6 0 0 0 0 0 0 1 0 0 0 0 43
44 6.7 7.1 0 0 0 0 0 0 0 1 0 0 0 44
45 6.7 6.8 0 0 0 0 0 0 0 0 1 0 0 45
46 6.6 6.5 0 0 0 0 0 0 0 0 0 1 0 46
47 6.9 6.9 0 0 0 0 0 0 0 0 0 0 1 47
48 7.3 8.2 0 0 0 0 0 0 0 0 0 0 0 48
49 7.5 8.7 1 0 0 0 0 0 0 0 0 0 0 49
50 7.3 8.3 0 1 0 0 0 0 0 0 0 0 0 50
51 7.1 7.9 0 0 1 0 0 0 0 0 0 0 0 51
52 6.9 7.5 0 0 0 1 0 0 0 0 0 0 0 52
53 7.1 7.8 0 0 0 0 1 0 0 0 0 0 0 53
54 7.5 8.3 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 8.4 0 0 0 0 0 0 1 0 0 0 0 55
56 7.8 8.2 0 0 0 0 0 0 0 1 0 0 0 56
57 7.8 7.7 0 0 0 0 0 0 0 0 1 0 0 57
58 7.7 7.2 0 0 0 0 0 0 0 0 0 1 0 58
59 7.8 7.3 0 0 0 0 0 0 0 0 0 0 1 59
60 7.8 8.1 0 0 0 0 0 0 0 0 0 0 0 60
61 7.9 8.5 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) X M1 M2 M3 M4
1.347108 0.710519 -0.210594 -0.194876 0.054556 0.241357
M5 M6 M7 M8 M9 M10
0.207109 0.116019 0.147033 0.305941 0.507479 0.703228
M11 t
0.625822 0.003196
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.41157 -0.13942 -0.02678 0.14171 0.52910
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.347108 0.735547 1.831 0.073378 .
X 0.710519 0.068971 10.302 1.22e-13 ***
M1 -0.210594 0.152691 -1.379 0.174357
M2 -0.194876 0.158685 -1.228 0.225535
M3 0.054556 0.160565 0.340 0.735540
M4 0.241357 0.165707 1.457 0.151895
M5 0.207109 0.162893 1.271 0.209828
M6 0.116019 0.159755 0.726 0.471298
M7 0.147033 0.159403 0.922 0.361032
M8 0.305941 0.161149 1.898 0.063781 .
M9 0.507479 0.164758 3.080 0.003452 **
M10 0.703228 0.170139 4.133 0.000146 ***
M11 0.625822 0.168503 3.714 0.000541 ***
t 0.003196 0.003538 0.903 0.370931
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2492 on 47 degrees of freedom
Multiple R-squared: 0.8784, Adjusted R-squared: 0.8448
F-statistic: 26.12 on 13 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,] 0.0031360842 0.0062721685 0.99686392
[2,] 0.0023836183 0.0047672367 0.99761638
[3,] 0.0009283376 0.0018566753 0.99907166
[4,] 0.0003573228 0.0007146457 0.99964268
[5,] 0.0001522017 0.0003044033 0.99984780
[6,] 0.0089518149 0.0179036299 0.99104819
[7,] 0.0277916928 0.0555833855 0.97220831
[8,] 0.0687665737 0.1375331474 0.93123343
[9,] 0.1134522381 0.2269044762 0.88654776
[10,] 0.1091719761 0.2183439523 0.89082802
[11,] 0.1414035337 0.2828070674 0.85859647
[12,] 0.3202417746 0.6404835491 0.67975823
[13,] 0.5367186203 0.9265627594 0.46328138
[14,] 0.6295721032 0.7408557935 0.37042790
[15,] 0.6718653869 0.6562692262 0.32813461
[16,] 0.6902764154 0.6194471692 0.30972358
[17,] 0.6614734912 0.6770530175 0.33852651
[18,] 0.6381808786 0.7236382428 0.36181912
[19,] 0.6345463553 0.7309072895 0.36545364
[20,] 0.5408222716 0.9183554568 0.45917773
[21,] 0.4613643682 0.9227287364 0.53863563
[22,] 0.4578855834 0.9157711667 0.54211442
[23,] 0.4267763743 0.8535527486 0.57322363
[24,] 0.4291192098 0.8582384197 0.57088079
[25,] 0.6544152492 0.6911695016 0.34558475
[26,] 0.8761245309 0.2477509381 0.12387547
[27,] 0.9331963810 0.1336072381 0.06680362
[28,] 0.9588435097 0.0823129806 0.04115649
> postscript(file="/var/www/html/rcomp/tmp/1ly7f1258723099.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qmob1258723099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38c9a1258723099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ij6c1258723099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5itok1258723099.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
-0.12646804 -0.00327818 0.08356043 0.16197830 0.19303017 0.06776792
7 8 9 10 11 12
0.06250568 0.07145380 0.05092643 0.19408493 0.09724343 -0.06170181
13 14 15 16 17 18
-0.09640820 0.05572979 0.22941278 0.32362316 0.28362316 0.30046466
19 20 21 22 23 24
0.26625428 0.24625428 0.18362316 0.08467791 -0.04111171 -0.28690134
25 26 27 28 29 30
-0.32160773 -0.19841787 -0.09578674 -0.07262824 -0.14157637 -0.22473487
31 32 33 34 35 36
-0.25894524 -0.32104899 -0.39683573 -0.41157348 -0.38210375 -0.13053026
37 38 39 40 41 42
-0.00997729 0.05531632 -0.13941567 -0.22941278 -0.16941278 -0.11046754
43 44 45 46 47 48
-0.13152230 -0.13836668 -0.12994593 -0.21573555 -0.12573267 -0.02678166
49 50 51 52 53 54
0.02535633 0.09064994 -0.07777081 -0.18356043 -0.16566418 -0.03303017
55 56 57 58 59 60
0.06170758 0.14170758 0.29223208 0.34854620 0.45170470 0.50591507
61
0.52910493
> postscript(file="/var/www/html/rcomp/tmp/60odv1258723099.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 -0.12646804 NA
1 -0.00327818 -0.12646804
2 0.08356043 -0.00327818
3 0.16197830 0.08356043
4 0.19303017 0.16197830
5 0.06776792 0.19303017
6 0.06250568 0.06776792
7 0.07145380 0.06250568
8 0.05092643 0.07145380
9 0.19408493 0.05092643
10 0.09724343 0.19408493
11 -0.06170181 0.09724343
12 -0.09640820 -0.06170181
13 0.05572979 -0.09640820
14 0.22941278 0.05572979
15 0.32362316 0.22941278
16 0.28362316 0.32362316
17 0.30046466 0.28362316
18 0.26625428 0.30046466
19 0.24625428 0.26625428
20 0.18362316 0.24625428
21 0.08467791 0.18362316
22 -0.04111171 0.08467791
23 -0.28690134 -0.04111171
24 -0.32160773 -0.28690134
25 -0.19841787 -0.32160773
26 -0.09578674 -0.19841787
27 -0.07262824 -0.09578674
28 -0.14157637 -0.07262824
29 -0.22473487 -0.14157637
30 -0.25894524 -0.22473487
31 -0.32104899 -0.25894524
32 -0.39683573 -0.32104899
33 -0.41157348 -0.39683573
34 -0.38210375 -0.41157348
35 -0.13053026 -0.38210375
36 -0.00997729 -0.13053026
37 0.05531632 -0.00997729
38 -0.13941567 0.05531632
39 -0.22941278 -0.13941567
40 -0.16941278 -0.22941278
41 -0.11046754 -0.16941278
42 -0.13152230 -0.11046754
43 -0.13836668 -0.13152230
44 -0.12994593 -0.13836668
45 -0.21573555 -0.12994593
46 -0.12573267 -0.21573555
47 -0.02678166 -0.12573267
48 0.02535633 -0.02678166
49 0.09064994 0.02535633
50 -0.07777081 0.09064994
51 -0.18356043 -0.07777081
52 -0.16566418 -0.18356043
53 -0.03303017 -0.16566418
54 0.06170758 -0.03303017
55 0.14170758 0.06170758
56 0.29223208 0.14170758
57 0.34854620 0.29223208
58 0.45170470 0.34854620
59 0.50591507 0.45170470
60 0.52910493 0.50591507
61 NA 0.52910493
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.00327818 -0.12646804
[2,] 0.08356043 -0.00327818
[3,] 0.16197830 0.08356043
[4,] 0.19303017 0.16197830
[5,] 0.06776792 0.19303017
[6,] 0.06250568 0.06776792
[7,] 0.07145380 0.06250568
[8,] 0.05092643 0.07145380
[9,] 0.19408493 0.05092643
[10,] 0.09724343 0.19408493
[11,] -0.06170181 0.09724343
[12,] -0.09640820 -0.06170181
[13,] 0.05572979 -0.09640820
[14,] 0.22941278 0.05572979
[15,] 0.32362316 0.22941278
[16,] 0.28362316 0.32362316
[17,] 0.30046466 0.28362316
[18,] 0.26625428 0.30046466
[19,] 0.24625428 0.26625428
[20,] 0.18362316 0.24625428
[21,] 0.08467791 0.18362316
[22,] -0.04111171 0.08467791
[23,] -0.28690134 -0.04111171
[24,] -0.32160773 -0.28690134
[25,] -0.19841787 -0.32160773
[26,] -0.09578674 -0.19841787
[27,] -0.07262824 -0.09578674
[28,] -0.14157637 -0.07262824
[29,] -0.22473487 -0.14157637
[30,] -0.25894524 -0.22473487
[31,] -0.32104899 -0.25894524
[32,] -0.39683573 -0.32104899
[33,] -0.41157348 -0.39683573
[34,] -0.38210375 -0.41157348
[35,] -0.13053026 -0.38210375
[36,] -0.00997729 -0.13053026
[37,] 0.05531632 -0.00997729
[38,] -0.13941567 0.05531632
[39,] -0.22941278 -0.13941567
[40,] -0.16941278 -0.22941278
[41,] -0.11046754 -0.16941278
[42,] -0.13152230 -0.11046754
[43,] -0.13836668 -0.13152230
[44,] -0.12994593 -0.13836668
[45,] -0.21573555 -0.12994593
[46,] -0.12573267 -0.21573555
[47,] -0.02678166 -0.12573267
[48,] 0.02535633 -0.02678166
[49,] 0.09064994 0.02535633
[50,] -0.07777081 0.09064994
[51,] -0.18356043 -0.07777081
[52,] -0.16566418 -0.18356043
[53,] -0.03303017 -0.16566418
[54,] 0.06170758 -0.03303017
[55,] 0.14170758 0.06170758
[56,] 0.29223208 0.14170758
[57,] 0.34854620 0.29223208
[58,] 0.45170470 0.34854620
[59,] 0.50591507 0.45170470
[60,] 0.52910493 0.50591507
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.00327818 -0.12646804
2 0.08356043 -0.00327818
3 0.16197830 0.08356043
4 0.19303017 0.16197830
5 0.06776792 0.19303017
6 0.06250568 0.06776792
7 0.07145380 0.06250568
8 0.05092643 0.07145380
9 0.19408493 0.05092643
10 0.09724343 0.19408493
11 -0.06170181 0.09724343
12 -0.09640820 -0.06170181
13 0.05572979 -0.09640820
14 0.22941278 0.05572979
15 0.32362316 0.22941278
16 0.28362316 0.32362316
17 0.30046466 0.28362316
18 0.26625428 0.30046466
19 0.24625428 0.26625428
20 0.18362316 0.24625428
21 0.08467791 0.18362316
22 -0.04111171 0.08467791
23 -0.28690134 -0.04111171
24 -0.32160773 -0.28690134
25 -0.19841787 -0.32160773
26 -0.09578674 -0.19841787
27 -0.07262824 -0.09578674
28 -0.14157637 -0.07262824
29 -0.22473487 -0.14157637
30 -0.25894524 -0.22473487
31 -0.32104899 -0.25894524
32 -0.39683573 -0.32104899
33 -0.41157348 -0.39683573
34 -0.38210375 -0.41157348
35 -0.13053026 -0.38210375
36 -0.00997729 -0.13053026
37 0.05531632 -0.00997729
38 -0.13941567 0.05531632
39 -0.22941278 -0.13941567
40 -0.16941278 -0.22941278
41 -0.11046754 -0.16941278
42 -0.13152230 -0.11046754
43 -0.13836668 -0.13152230
44 -0.12994593 -0.13836668
45 -0.21573555 -0.12994593
46 -0.12573267 -0.21573555
47 -0.02678166 -0.12573267
48 0.02535633 -0.02678166
49 0.09064994 0.02535633
50 -0.07777081 0.09064994
51 -0.18356043 -0.07777081
52 -0.16566418 -0.18356043
53 -0.03303017 -0.16566418
54 0.06170758 -0.03303017
55 0.14170758 0.06170758
56 0.29223208 0.14170758
57 0.34854620 0.29223208
58 0.45170470 0.34854620
59 0.50591507 0.45170470
60 0.52910493 0.50591507
> 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/7p1et1258723099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8bg051258723099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9lgu91258723099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ttbc1258723099.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qzxf1258723099.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/12oeua1258723099.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/13lrxk1258723100.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/14jr3c1258723100.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/15am741258723100.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/16vpke1258723100.tab")
+ }
>
> system("convert tmp/1ly7f1258723099.ps tmp/1ly7f1258723099.png")
> system("convert tmp/2qmob1258723099.ps tmp/2qmob1258723099.png")
> system("convert tmp/38c9a1258723099.ps tmp/38c9a1258723099.png")
> system("convert tmp/4ij6c1258723099.ps tmp/4ij6c1258723099.png")
> system("convert tmp/5itok1258723099.ps tmp/5itok1258723099.png")
> system("convert tmp/60odv1258723099.ps tmp/60odv1258723099.png")
> system("convert tmp/7p1et1258723099.ps tmp/7p1et1258723099.png")
> system("convert tmp/8bg051258723099.ps tmp/8bg051258723099.png")
> system("convert tmp/9lgu91258723099.ps tmp/9lgu91258723099.png")
> system("convert tmp/10ttbc1258723099.ps tmp/10ttbc1258723099.png")
>
>
> proc.time()
user system elapsed
2.468 1.588 5.703