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(8.9,1.4,8.8,1.2,8.3,1,7.5,1.7,7.2,2.4,7.4,2,8.8,2.1,9.3,2,9.3,1.8,8.7,2.7,8.2,2.3,8.3,1.9,8.5,2,8.6,2.3,8.5,2.8,8.2,2.4,8.1,2.3,7.9,2.7,8.6,2.7,8.7,2.9,8.7,3,8.5,2.2,8.4,2.3,8.5,2.8,8.7,2.8,8.7,2.8,8.6,2.2,8.5,2.6,8.3,2.8,8,2.5,8.2,2.4,8.1,2.3,8.1,1.9,8,1.7,7.9,2,7.9,2.1,8,1.7,8,1.8,7.9,1.8,8,1.8,7.7,1.3,7.2,1.3,7.5,1.3,7.3,1.2,7,1.4,7,2.2,7,2.9,7.2,3.1,7.3,3.5,7.1,3.6,6.8,4.4,6.4,4.1,6.1,5.1,6.5,5.8,7.7,5.9,7.9,5.4,7.5,5.5,6.9,4.8,6.6,3.2,6.9,2.7),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 8.9 1.4 1 0 0 0 0 0 0 0 0 0 0
2 8.8 1.2 0 1 0 0 0 0 0 0 0 0 0
3 8.3 1.0 0 0 1 0 0 0 0 0 0 0 0
4 7.5 1.7 0 0 0 1 0 0 0 0 0 0 0
5 7.2 2.4 0 0 0 0 1 0 0 0 0 0 0
6 7.4 2.0 0 0 0 0 0 1 0 0 0 0 0
7 8.8 2.1 0 0 0 0 0 0 1 0 0 0 0
8 9.3 2.0 0 0 0 0 0 0 0 1 0 0 0
9 9.3 1.8 0 0 0 0 0 0 0 0 1 0 0
10 8.7 2.7 0 0 0 0 0 0 0 0 0 1 0
11 8.2 2.3 0 0 0 0 0 0 0 0 0 0 1
12 8.3 1.9 0 0 0 0 0 0 0 0 0 0 0
13 8.5 2.0 1 0 0 0 0 0 0 0 0 0 0
14 8.6 2.3 0 1 0 0 0 0 0 0 0 0 0
15 8.5 2.8 0 0 1 0 0 0 0 0 0 0 0
16 8.2 2.4 0 0 0 1 0 0 0 0 0 0 0
17 8.1 2.3 0 0 0 0 1 0 0 0 0 0 0
18 7.9 2.7 0 0 0 0 0 1 0 0 0 0 0
19 8.6 2.7 0 0 0 0 0 0 1 0 0 0 0
20 8.7 2.9 0 0 0 0 0 0 0 1 0 0 0
21 8.7 3.0 0 0 0 0 0 0 0 0 1 0 0
22 8.5 2.2 0 0 0 0 0 0 0 0 0 1 0
23 8.4 2.3 0 0 0 0 0 0 0 0 0 0 1
24 8.5 2.8 0 0 0 0 0 0 0 0 0 0 0
25 8.7 2.8 1 0 0 0 0 0 0 0 0 0 0
26 8.7 2.8 0 1 0 0 0 0 0 0 0 0 0
27 8.6 2.2 0 0 1 0 0 0 0 0 0 0 0
28 8.5 2.6 0 0 0 1 0 0 0 0 0 0 0
29 8.3 2.8 0 0 0 0 1 0 0 0 0 0 0
30 8.0 2.5 0 0 0 0 0 1 0 0 0 0 0
31 8.2 2.4 0 0 0 0 0 0 1 0 0 0 0
32 8.1 2.3 0 0 0 0 0 0 0 1 0 0 0
33 8.1 1.9 0 0 0 0 0 0 0 0 1 0 0
34 8.0 1.7 0 0 0 0 0 0 0 0 0 1 0
35 7.9 2.0 0 0 0 0 0 0 0 0 0 0 1
36 7.9 2.1 0 0 0 0 0 0 0 0 0 0 0
37 8.0 1.7 1 0 0 0 0 0 0 0 0 0 0
38 8.0 1.8 0 1 0 0 0 0 0 0 0 0 0
39 7.9 1.8 0 0 1 0 0 0 0 0 0 0 0
40 8.0 1.8 0 0 0 1 0 0 0 0 0 0 0
41 7.7 1.3 0 0 0 0 1 0 0 0 0 0 0
42 7.2 1.3 0 0 0 0 0 1 0 0 0 0 0
43 7.5 1.3 0 0 0 0 0 0 1 0 0 0 0
44 7.3 1.2 0 0 0 0 0 0 0 1 0 0 0
45 7.0 1.4 0 0 0 0 0 0 0 0 1 0 0
46 7.0 2.2 0 0 0 0 0 0 0 0 0 1 0
47 7.0 2.9 0 0 0 0 0 0 0 0 0 0 1
48 7.2 3.1 0 0 0 0 0 0 0 0 0 0 0
49 7.3 3.5 1 0 0 0 0 0 0 0 0 0 0
50 7.1 3.6 0 1 0 0 0 0 0 0 0 0 0
51 6.8 4.4 0 0 1 0 0 0 0 0 0 0 0
52 6.4 4.1 0 0 0 1 0 0 0 0 0 0 0
53 6.1 5.1 0 0 0 0 1 0 0 0 0 0 0
54 6.5 5.8 0 0 0 0 0 1 0 0 0 0 0
55 7.7 5.9 0 0 0 0 0 0 1 0 0 0 0
56 7.9 5.4 0 0 0 0 0 0 0 1 0 0 0
57 7.5 5.5 0 0 0 0 0 0 0 0 1 0 0
58 6.9 4.8 0 0 0 0 0 0 0 0 0 1 0
59 6.6 3.2 0 0 0 0 0 0 0 0 0 0 1
60 6.9 2.7 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
8.4235 -0.2633 0.4568 0.4326 0.2389 -0.0400
M5 M6 M7 M8 M9 M10
-0.2115 -0.2705 0.4948 0.5632 0.4127 0.1127
M11
-0.1347
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.4676 -0.4136 0.1012 0.4965 0.9378
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.42354 0.36092 23.339 < 2e-16 ***
X -0.26331 0.07699 -3.420 0.00130 **
M1 0.45681 0.43080 1.060 0.29440
M2 0.43260 0.43063 1.005 0.32024
M3 0.23894 0.43045 0.555 0.58147
M4 -0.04000 0.43040 -0.093 0.92635
M5 -0.21154 0.43087 -0.491 0.62574
M6 -0.27047 0.43120 -0.627 0.53352
M7 0.49479 0.43130 1.147 0.25710
M8 0.56319 0.43080 1.307 0.19746
M9 0.41266 0.43068 0.958 0.34288
M10 0.11266 0.43068 0.262 0.79478
M11 -0.13473 0.43041 -0.313 0.75564
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6805 on 47 degrees of freedom
Multiple R-squared: 0.3323, Adjusted R-squared: 0.1618
F-statistic: 1.949 on 12 and 47 DF, p-value: 0.05206
> 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,] 0.119686201 0.239372402 0.88031380
[2,] 0.161740877 0.323481755 0.83825912
[3,] 0.099753113 0.199506225 0.90024689
[4,] 0.053236710 0.106473419 0.94676329
[5,] 0.045570612 0.091141224 0.95442939
[6,] 0.037947708 0.075895417 0.96205229
[7,] 0.023613242 0.047226484 0.97638676
[8,] 0.015370007 0.030740014 0.98462999
[9,] 0.011165769 0.022331538 0.98883423
[10,] 0.006982389 0.013964779 0.99301761
[11,] 0.004799764 0.009599528 0.99520024
[12,] 0.003607192 0.007214384 0.99639281
[13,] 0.008089446 0.016178892 0.99191055
[14,] 0.019189701 0.038379402 0.98081030
[15,] 0.020183498 0.040366996 0.97981650
[16,] 0.017383448 0.034766896 0.98261655
[17,] 0.028514408 0.057028816 0.97148559
[18,] 0.042527962 0.085055923 0.95747204
[19,] 0.045190300 0.090380600 0.95480970
[20,] 0.053565876 0.107131752 0.94643412
[21,] 0.055898158 0.111796315 0.94410184
[22,] 0.052753018 0.105506037 0.94724698
[23,] 0.056213094 0.112426187 0.94378691
[24,] 0.062780719 0.125561438 0.93721928
[25,] 0.162069036 0.324138073 0.83793096
[26,] 0.643226370 0.713547261 0.35677363
[27,] 0.906006447 0.187987106 0.09399355
[28,] 0.861306352 0.277387296 0.13869365
[29,] 0.795227593 0.409544815 0.20477241
> postscript(file="/var/www/html/rcomp/tmp/1aqgj1258482869.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/26pz71258482869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ayoy1258482869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/49dfk1258482869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52hko1258482869.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
0.38828709 0.25982646 -0.09916658 -0.43591430 -0.38005785 -0.22644671
7 8 9 10 11 12
0.43461810 0.83988430 0.93775468 0.87473380 0.51680557 0.37674772
13 14 15 16 17 18
0.14627316 0.34946759 0.57479165 0.44840278 0.49361114 0.45787038
19 20 21 22 23 24
0.39260418 0.47686342 0.65372684 0.54307873 0.71680557 0.81372684
25 26 27 28 29 30
0.55692127 0.58112266 0.51680557 0.80106481 0.82526620 0.50520835
31 32 33 34 35 36
-0.08638886 -0.28112266 -0.23591430 -0.08857633 0.13781253 0.02940975
37 38 39 40 41 42
-0.43271987 -0.38218747 -0.28851848 0.09041671 -0.16969899 -0.61076380
43 44 45 46 47 48
-1.07603000 -1.37076380 -1.46756937 -0.95692127 -0.52520835 -0.40728013
49 50 51 52 53 54
-0.65876165 -0.80822924 -0.70391215 -0.90397000 -0.76912051 -0.12586823
55 56 57 58 59 60
0.33519658 0.33513873 0.11200215 -0.37231494 -0.84621532 -0.81260418
> postscript(file="/var/www/html/rcomp/tmp/62qnj1258482869.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 0.38828709 NA
1 0.25982646 0.38828709
2 -0.09916658 0.25982646
3 -0.43591430 -0.09916658
4 -0.38005785 -0.43591430
5 -0.22644671 -0.38005785
6 0.43461810 -0.22644671
7 0.83988430 0.43461810
8 0.93775468 0.83988430
9 0.87473380 0.93775468
10 0.51680557 0.87473380
11 0.37674772 0.51680557
12 0.14627316 0.37674772
13 0.34946759 0.14627316
14 0.57479165 0.34946759
15 0.44840278 0.57479165
16 0.49361114 0.44840278
17 0.45787038 0.49361114
18 0.39260418 0.45787038
19 0.47686342 0.39260418
20 0.65372684 0.47686342
21 0.54307873 0.65372684
22 0.71680557 0.54307873
23 0.81372684 0.71680557
24 0.55692127 0.81372684
25 0.58112266 0.55692127
26 0.51680557 0.58112266
27 0.80106481 0.51680557
28 0.82526620 0.80106481
29 0.50520835 0.82526620
30 -0.08638886 0.50520835
31 -0.28112266 -0.08638886
32 -0.23591430 -0.28112266
33 -0.08857633 -0.23591430
34 0.13781253 -0.08857633
35 0.02940975 0.13781253
36 -0.43271987 0.02940975
37 -0.38218747 -0.43271987
38 -0.28851848 -0.38218747
39 0.09041671 -0.28851848
40 -0.16969899 0.09041671
41 -0.61076380 -0.16969899
42 -1.07603000 -0.61076380
43 -1.37076380 -1.07603000
44 -1.46756937 -1.37076380
45 -0.95692127 -1.46756937
46 -0.52520835 -0.95692127
47 -0.40728013 -0.52520835
48 -0.65876165 -0.40728013
49 -0.80822924 -0.65876165
50 -0.70391215 -0.80822924
51 -0.90397000 -0.70391215
52 -0.76912051 -0.90397000
53 -0.12586823 -0.76912051
54 0.33519658 -0.12586823
55 0.33513873 0.33519658
56 0.11200215 0.33513873
57 -0.37231494 0.11200215
58 -0.84621532 -0.37231494
59 -0.81260418 -0.84621532
60 NA -0.81260418
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.25982646 0.38828709
[2,] -0.09916658 0.25982646
[3,] -0.43591430 -0.09916658
[4,] -0.38005785 -0.43591430
[5,] -0.22644671 -0.38005785
[6,] 0.43461810 -0.22644671
[7,] 0.83988430 0.43461810
[8,] 0.93775468 0.83988430
[9,] 0.87473380 0.93775468
[10,] 0.51680557 0.87473380
[11,] 0.37674772 0.51680557
[12,] 0.14627316 0.37674772
[13,] 0.34946759 0.14627316
[14,] 0.57479165 0.34946759
[15,] 0.44840278 0.57479165
[16,] 0.49361114 0.44840278
[17,] 0.45787038 0.49361114
[18,] 0.39260418 0.45787038
[19,] 0.47686342 0.39260418
[20,] 0.65372684 0.47686342
[21,] 0.54307873 0.65372684
[22,] 0.71680557 0.54307873
[23,] 0.81372684 0.71680557
[24,] 0.55692127 0.81372684
[25,] 0.58112266 0.55692127
[26,] 0.51680557 0.58112266
[27,] 0.80106481 0.51680557
[28,] 0.82526620 0.80106481
[29,] 0.50520835 0.82526620
[30,] -0.08638886 0.50520835
[31,] -0.28112266 -0.08638886
[32,] -0.23591430 -0.28112266
[33,] -0.08857633 -0.23591430
[34,] 0.13781253 -0.08857633
[35,] 0.02940975 0.13781253
[36,] -0.43271987 0.02940975
[37,] -0.38218747 -0.43271987
[38,] -0.28851848 -0.38218747
[39,] 0.09041671 -0.28851848
[40,] -0.16969899 0.09041671
[41,] -0.61076380 -0.16969899
[42,] -1.07603000 -0.61076380
[43,] -1.37076380 -1.07603000
[44,] -1.46756937 -1.37076380
[45,] -0.95692127 -1.46756937
[46,] -0.52520835 -0.95692127
[47,] -0.40728013 -0.52520835
[48,] -0.65876165 -0.40728013
[49,] -0.80822924 -0.65876165
[50,] -0.70391215 -0.80822924
[51,] -0.90397000 -0.70391215
[52,] -0.76912051 -0.90397000
[53,] -0.12586823 -0.76912051
[54,] 0.33519658 -0.12586823
[55,] 0.33513873 0.33519658
[56,] 0.11200215 0.33513873
[57,] -0.37231494 0.11200215
[58,] -0.84621532 -0.37231494
[59,] -0.81260418 -0.84621532
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.25982646 0.38828709
2 -0.09916658 0.25982646
3 -0.43591430 -0.09916658
4 -0.38005785 -0.43591430
5 -0.22644671 -0.38005785
6 0.43461810 -0.22644671
7 0.83988430 0.43461810
8 0.93775468 0.83988430
9 0.87473380 0.93775468
10 0.51680557 0.87473380
11 0.37674772 0.51680557
12 0.14627316 0.37674772
13 0.34946759 0.14627316
14 0.57479165 0.34946759
15 0.44840278 0.57479165
16 0.49361114 0.44840278
17 0.45787038 0.49361114
18 0.39260418 0.45787038
19 0.47686342 0.39260418
20 0.65372684 0.47686342
21 0.54307873 0.65372684
22 0.71680557 0.54307873
23 0.81372684 0.71680557
24 0.55692127 0.81372684
25 0.58112266 0.55692127
26 0.51680557 0.58112266
27 0.80106481 0.51680557
28 0.82526620 0.80106481
29 0.50520835 0.82526620
30 -0.08638886 0.50520835
31 -0.28112266 -0.08638886
32 -0.23591430 -0.28112266
33 -0.08857633 -0.23591430
34 0.13781253 -0.08857633
35 0.02940975 0.13781253
36 -0.43271987 0.02940975
37 -0.38218747 -0.43271987
38 -0.28851848 -0.38218747
39 0.09041671 -0.28851848
40 -0.16969899 0.09041671
41 -0.61076380 -0.16969899
42 -1.07603000 -0.61076380
43 -1.37076380 -1.07603000
44 -1.46756937 -1.37076380
45 -0.95692127 -1.46756937
46 -0.52520835 -0.95692127
47 -0.40728013 -0.52520835
48 -0.65876165 -0.40728013
49 -0.80822924 -0.65876165
50 -0.70391215 -0.80822924
51 -0.90397000 -0.70391215
52 -0.76912051 -0.90397000
53 -0.12586823 -0.76912051
54 0.33519658 -0.12586823
55 0.33513873 0.33519658
56 0.11200215 0.33513873
57 -0.37231494 0.11200215
58 -0.84621532 -0.37231494
59 -0.81260418 -0.84621532
> 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/7z3lc1258482869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84w4y1258482869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95j4z1258482869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qgbe1258482869.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jd071258482869.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/12j70x1258482869.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/13nvtg1258482869.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/14me8m1258482869.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/15awfx1258482869.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/16m1o61258482869.tab")
+ }
>
> system("convert tmp/1aqgj1258482869.ps tmp/1aqgj1258482869.png")
> system("convert tmp/26pz71258482869.ps tmp/26pz71258482869.png")
> system("convert tmp/3ayoy1258482869.ps tmp/3ayoy1258482869.png")
> system("convert tmp/49dfk1258482869.ps tmp/49dfk1258482869.png")
> system("convert tmp/52hko1258482869.ps tmp/52hko1258482869.png")
> system("convert tmp/62qnj1258482869.ps tmp/62qnj1258482869.png")
> system("convert tmp/7z3lc1258482869.ps tmp/7z3lc1258482869.png")
> system("convert tmp/84w4y1258482869.ps tmp/84w4y1258482869.png")
> system("convert tmp/95j4z1258482869.ps tmp/95j4z1258482869.png")
> system("convert tmp/10qgbe1258482869.ps tmp/10qgbe1258482869.png")
>
>
> proc.time()
user system elapsed
2.445 1.573 3.551