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(113,14.3,110,14.2,107,15.9,103,15.3,98,15.5,98,15.1,137,15,148,12.1,147,15.8,139,16.9,130,15.1,128,13.7,127,14.8,123,14.7,118,16,114,15.4,108,15,111,15.5,151,15.1,159,11.7,158,16.3,148,16.7,138,15,137,14.9,136,14.6,133,15.3,126,17.9,120,16.4,114,15.4,116,17.9,153,15.9,162,13.9,161,17.8,149,17.9,139,17.4,135,16.7,130,16,127,16.6,122,19.1,117,17.8,112,17.2,113,18.6,149,16.3,157,15.1,157,19.2,147,17.7,137,19.1,132,18,125,17.5,123,17.8,117,21.1,114,17.2,111,19.4,112,19.8,144,17.6,150,16.2,149,19.5,134,19.9,123,20,116,17.3),dim=c(2,60),dimnames=list(c('WK<25j','ExpBE'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('WK<25j','ExpBE'),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
WK<25j ExpBE M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 113 14.3 1 0 0 0 0 0 0 0 0 0 0 1
2 110 14.2 0 1 0 0 0 0 0 0 0 0 0 2
3 107 15.9 0 0 1 0 0 0 0 0 0 0 0 3
4 103 15.3 0 0 0 1 0 0 0 0 0 0 0 4
5 98 15.5 0 0 0 0 1 0 0 0 0 0 0 5
6 98 15.1 0 0 0 0 0 1 0 0 0 0 0 6
7 137 15.0 0 0 0 0 0 0 1 0 0 0 0 7
8 148 12.1 0 0 0 0 0 0 0 1 0 0 0 8
9 147 15.8 0 0 0 0 0 0 0 0 1 0 0 9
10 139 16.9 0 0 0 0 0 0 0 0 0 1 0 10
11 130 15.1 0 0 0 0 0 0 0 0 0 0 1 11
12 128 13.7 0 0 0 0 0 0 0 0 0 0 0 12
13 127 14.8 1 0 0 0 0 0 0 0 0 0 0 13
14 123 14.7 0 1 0 0 0 0 0 0 0 0 0 14
15 118 16.0 0 0 1 0 0 0 0 0 0 0 0 15
16 114 15.4 0 0 0 1 0 0 0 0 0 0 0 16
17 108 15.0 0 0 0 0 1 0 0 0 0 0 0 17
18 111 15.5 0 0 0 0 0 1 0 0 0 0 0 18
19 151 15.1 0 0 0 0 0 0 1 0 0 0 0 19
20 159 11.7 0 0 0 0 0 0 0 1 0 0 0 20
21 158 16.3 0 0 0 0 0 0 0 0 1 0 0 21
22 148 16.7 0 0 0 0 0 0 0 0 0 1 0 22
23 138 15.0 0 0 0 0 0 0 0 0 0 0 1 23
24 137 14.9 0 0 0 0 0 0 0 0 0 0 0 24
25 136 14.6 1 0 0 0 0 0 0 0 0 0 0 25
26 133 15.3 0 1 0 0 0 0 0 0 0 0 0 26
27 126 17.9 0 0 1 0 0 0 0 0 0 0 0 27
28 120 16.4 0 0 0 1 0 0 0 0 0 0 0 28
29 114 15.4 0 0 0 0 1 0 0 0 0 0 0 29
30 116 17.9 0 0 0 0 0 1 0 0 0 0 0 30
31 153 15.9 0 0 0 0 0 0 1 0 0 0 0 31
32 162 13.9 0 0 0 0 0 0 0 1 0 0 0 32
33 161 17.8 0 0 0 0 0 0 0 0 1 0 0 33
34 149 17.9 0 0 0 0 0 0 0 0 0 1 0 34
35 139 17.4 0 0 0 0 0 0 0 0 0 0 1 35
36 135 16.7 0 0 0 0 0 0 0 0 0 0 0 36
37 130 16.0 1 0 0 0 0 0 0 0 0 0 0 37
38 127 16.6 0 1 0 0 0 0 0 0 0 0 0 38
39 122 19.1 0 0 1 0 0 0 0 0 0 0 0 39
40 117 17.8 0 0 0 1 0 0 0 0 0 0 0 40
41 112 17.2 0 0 0 0 1 0 0 0 0 0 0 41
42 113 18.6 0 0 0 0 0 1 0 0 0 0 0 42
43 149 16.3 0 0 0 0 0 0 1 0 0 0 0 43
44 157 15.1 0 0 0 0 0 0 0 1 0 0 0 44
45 157 19.2 0 0 0 0 0 0 0 0 1 0 0 45
46 147 17.7 0 0 0 0 0 0 0 0 0 1 0 46
47 137 19.1 0 0 0 0 0 0 0 0 0 0 1 47
48 132 18.0 0 0 0 0 0 0 0 0 0 0 0 48
49 125 17.5 1 0 0 0 0 0 0 0 0 0 0 49
50 123 17.8 0 1 0 0 0 0 0 0 0 0 0 50
51 117 21.1 0 0 1 0 0 0 0 0 0 0 0 51
52 114 17.2 0 0 0 1 0 0 0 0 0 0 0 52
53 111 19.4 0 0 0 0 1 0 0 0 0 0 0 53
54 112 19.8 0 0 0 0 0 1 0 0 0 0 0 54
55 144 17.6 0 0 0 0 0 0 1 0 0 0 0 55
56 150 16.2 0 0 0 0 0 0 0 1 0 0 0 56
57 149 19.5 0 0 0 0 0 0 0 0 1 0 0 57
58 134 19.9 0 0 0 0 0 0 0 0 0 1 0 58
59 123 20.0 0 0 0 0 0 0 0 0 0 0 1 59
60 116 17.3 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) ExpBE M1 M2 M3 M4
165.7337 -2.9889 -1.7514 -4.2491 -2.9691 -12.4262
M5 M6 M7 M8 M9 M10
-17.5217 -13.8262 18.4548 20.0044 30.5861 19.5504
M11 t
7.7213 0.3346
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-18.105 -4.362 1.413 4.229 8.545
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 165.7337 18.8010 8.815 1.92e-11 ***
ExpBE -2.9889 1.4037 -2.129 0.038618 *
M1 -1.7514 4.2647 -0.411 0.683221
M2 -4.2491 4.2878 -0.991 0.326884
M3 -2.9691 5.6135 -0.529 0.599402
M4 -12.4262 4.4440 -2.796 0.007522 **
M5 -17.5217 4.4387 -3.947 0.000268 ***
M6 -13.8262 4.8916 -2.827 0.006941 **
M7 18.4548 4.2410 4.351 7.45e-05 ***
M8 20.0044 5.0626 3.951 0.000265 ***
M9 30.5861 4.9518 6.177 1.57e-07 ***
M10 19.5504 4.9636 3.939 0.000276 ***
M11 7.7213 4.5847 1.684 0.098927 .
t 0.3346 0.1261 2.654 0.010873 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.667 on 46 degrees of freedom
Multiple R-squared: 0.8836, Adjusted R-squared: 0.8507
F-statistic: 26.85 on 13 and 46 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,] 0.005895579 0.011791157 0.9941044
[2,] 0.001112661 0.002225322 0.9988873
[3,] 0.003497892 0.006995784 0.9965021
[4,] 0.001010013 0.002020026 0.9989900
[5,] 0.001672112 0.003344225 0.9983279
[6,] 0.002026152 0.004052304 0.9979738
[7,] 0.003744225 0.007488449 0.9962558
[8,] 0.013363065 0.026726129 0.9866369
[9,] 0.006215464 0.012430928 0.9937845
[10,] 0.002700447 0.005400894 0.9972996
[11,] 0.003078732 0.006157463 0.9969213
[12,] 0.009235835 0.018471669 0.9907642
[13,] 0.026542267 0.053084534 0.9734577
[14,] 0.031016103 0.062032206 0.9689839
[15,] 0.076095157 0.152190315 0.9239048
[16,] 0.075280661 0.150561321 0.9247193
[17,] 0.069760924 0.139521848 0.9302391
[18,] 0.149803650 0.299607300 0.8501963
[19,] 0.134625996 0.269251992 0.8653740
[20,] 0.138711674 0.277423349 0.8612883
[21,] 0.281776440 0.563552880 0.7182236
[22,] 0.308722429 0.617444858 0.6912776
[23,] 0.245844774 0.491689548 0.7541552
[24,] 0.364377503 0.728755006 0.6356225
[25,] 0.344811488 0.689622977 0.6551885
[26,] 0.513126601 0.973746799 0.4868734
[27,] 0.619203462 0.761593076 0.3807965
> postscript(file="/var/www/html/rcomp/tmp/1p6mo1261159021.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2w9dt1261159021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33bty1261159021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cuni1261159021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5t4xu1261159021.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
-8.5759281 -9.7117049 -9.2452589 -5.9161504 -5.5574840 -10.7832585
7 8 9 10 11 12
-4.6977063 -4.2497044 -5.1072594 0.8816269 -2.0039253 -0.8017025
13 14 15 16 17 18
2.9028139 0.7670371 -1.9620714 1.3670371 -1.0676283 -0.6034051
19 20 21 22 23 24
5.5854812 1.5390400 3.3714826 5.2681485 1.6814850 7.7692599
25 26 27 28 29 30
7.2893355 8.5446678 7.7011114 6.3402223 2.1122251 7.5542209
31 32 33 34 35 36
5.9608891 7.0988886 6.8391109 5.8391109 5.8391109 7.1335540
37 38 39 40 41 42
1.4580752 2.4145188 3.2720738 3.5089619 1.4765193 2.6307401
43 44 45 46 47 48
-0.8592575 1.6698510 3.0078506 -0.7743675 4.9045164 4.0034051
49 50 51 52 53 54
-3.0742965 -2.0145188 0.2341452 -5.3000709 3.0363679 1.2017025
55 56 57 58 59 60
-5.9894065 -6.0580752 -8.1111847 -11.2145188 -10.4211870 -18.1045164
> postscript(file="/var/www/html/rcomp/tmp/6r0mh1261159021.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 -8.5759281 NA
1 -9.7117049 -8.5759281
2 -9.2452589 -9.7117049
3 -5.9161504 -9.2452589
4 -5.5574840 -5.9161504
5 -10.7832585 -5.5574840
6 -4.6977063 -10.7832585
7 -4.2497044 -4.6977063
8 -5.1072594 -4.2497044
9 0.8816269 -5.1072594
10 -2.0039253 0.8816269
11 -0.8017025 -2.0039253
12 2.9028139 -0.8017025
13 0.7670371 2.9028139
14 -1.9620714 0.7670371
15 1.3670371 -1.9620714
16 -1.0676283 1.3670371
17 -0.6034051 -1.0676283
18 5.5854812 -0.6034051
19 1.5390400 5.5854812
20 3.3714826 1.5390400
21 5.2681485 3.3714826
22 1.6814850 5.2681485
23 7.7692599 1.6814850
24 7.2893355 7.7692599
25 8.5446678 7.2893355
26 7.7011114 8.5446678
27 6.3402223 7.7011114
28 2.1122251 6.3402223
29 7.5542209 2.1122251
30 5.9608891 7.5542209
31 7.0988886 5.9608891
32 6.8391109 7.0988886
33 5.8391109 6.8391109
34 5.8391109 5.8391109
35 7.1335540 5.8391109
36 1.4580752 7.1335540
37 2.4145188 1.4580752
38 3.2720738 2.4145188
39 3.5089619 3.2720738
40 1.4765193 3.5089619
41 2.6307401 1.4765193
42 -0.8592575 2.6307401
43 1.6698510 -0.8592575
44 3.0078506 1.6698510
45 -0.7743675 3.0078506
46 4.9045164 -0.7743675
47 4.0034051 4.9045164
48 -3.0742965 4.0034051
49 -2.0145188 -3.0742965
50 0.2341452 -2.0145188
51 -5.3000709 0.2341452
52 3.0363679 -5.3000709
53 1.2017025 3.0363679
54 -5.9894065 1.2017025
55 -6.0580752 -5.9894065
56 -8.1111847 -6.0580752
57 -11.2145188 -8.1111847
58 -10.4211870 -11.2145188
59 -18.1045164 -10.4211870
60 NA -18.1045164
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -9.7117049 -8.5759281
[2,] -9.2452589 -9.7117049
[3,] -5.9161504 -9.2452589
[4,] -5.5574840 -5.9161504
[5,] -10.7832585 -5.5574840
[6,] -4.6977063 -10.7832585
[7,] -4.2497044 -4.6977063
[8,] -5.1072594 -4.2497044
[9,] 0.8816269 -5.1072594
[10,] -2.0039253 0.8816269
[11,] -0.8017025 -2.0039253
[12,] 2.9028139 -0.8017025
[13,] 0.7670371 2.9028139
[14,] -1.9620714 0.7670371
[15,] 1.3670371 -1.9620714
[16,] -1.0676283 1.3670371
[17,] -0.6034051 -1.0676283
[18,] 5.5854812 -0.6034051
[19,] 1.5390400 5.5854812
[20,] 3.3714826 1.5390400
[21,] 5.2681485 3.3714826
[22,] 1.6814850 5.2681485
[23,] 7.7692599 1.6814850
[24,] 7.2893355 7.7692599
[25,] 8.5446678 7.2893355
[26,] 7.7011114 8.5446678
[27,] 6.3402223 7.7011114
[28,] 2.1122251 6.3402223
[29,] 7.5542209 2.1122251
[30,] 5.9608891 7.5542209
[31,] 7.0988886 5.9608891
[32,] 6.8391109 7.0988886
[33,] 5.8391109 6.8391109
[34,] 5.8391109 5.8391109
[35,] 7.1335540 5.8391109
[36,] 1.4580752 7.1335540
[37,] 2.4145188 1.4580752
[38,] 3.2720738 2.4145188
[39,] 3.5089619 3.2720738
[40,] 1.4765193 3.5089619
[41,] 2.6307401 1.4765193
[42,] -0.8592575 2.6307401
[43,] 1.6698510 -0.8592575
[44,] 3.0078506 1.6698510
[45,] -0.7743675 3.0078506
[46,] 4.9045164 -0.7743675
[47,] 4.0034051 4.9045164
[48,] -3.0742965 4.0034051
[49,] -2.0145188 -3.0742965
[50,] 0.2341452 -2.0145188
[51,] -5.3000709 0.2341452
[52,] 3.0363679 -5.3000709
[53,] 1.2017025 3.0363679
[54,] -5.9894065 1.2017025
[55,] -6.0580752 -5.9894065
[56,] -8.1111847 -6.0580752
[57,] -11.2145188 -8.1111847
[58,] -10.4211870 -11.2145188
[59,] -18.1045164 -10.4211870
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -9.7117049 -8.5759281
2 -9.2452589 -9.7117049
3 -5.9161504 -9.2452589
4 -5.5574840 -5.9161504
5 -10.7832585 -5.5574840
6 -4.6977063 -10.7832585
7 -4.2497044 -4.6977063
8 -5.1072594 -4.2497044
9 0.8816269 -5.1072594
10 -2.0039253 0.8816269
11 -0.8017025 -2.0039253
12 2.9028139 -0.8017025
13 0.7670371 2.9028139
14 -1.9620714 0.7670371
15 1.3670371 -1.9620714
16 -1.0676283 1.3670371
17 -0.6034051 -1.0676283
18 5.5854812 -0.6034051
19 1.5390400 5.5854812
20 3.3714826 1.5390400
21 5.2681485 3.3714826
22 1.6814850 5.2681485
23 7.7692599 1.6814850
24 7.2893355 7.7692599
25 8.5446678 7.2893355
26 7.7011114 8.5446678
27 6.3402223 7.7011114
28 2.1122251 6.3402223
29 7.5542209 2.1122251
30 5.9608891 7.5542209
31 7.0988886 5.9608891
32 6.8391109 7.0988886
33 5.8391109 6.8391109
34 5.8391109 5.8391109
35 7.1335540 5.8391109
36 1.4580752 7.1335540
37 2.4145188 1.4580752
38 3.2720738 2.4145188
39 3.5089619 3.2720738
40 1.4765193 3.5089619
41 2.6307401 1.4765193
42 -0.8592575 2.6307401
43 1.6698510 -0.8592575
44 3.0078506 1.6698510
45 -0.7743675 3.0078506
46 4.9045164 -0.7743675
47 4.0034051 4.9045164
48 -3.0742965 4.0034051
49 -2.0145188 -3.0742965
50 0.2341452 -2.0145188
51 -5.3000709 0.2341452
52 3.0363679 -5.3000709
53 1.2017025 3.0363679
54 -5.9894065 1.2017025
55 -6.0580752 -5.9894065
56 -8.1111847 -6.0580752
57 -11.2145188 -8.1111847
58 -10.4211870 -11.2145188
59 -18.1045164 -10.4211870
> 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/7jehp1261159021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8aeuq1261159021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9epo41261159021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10s8i91261159021.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/1148mp1261159021.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/12mwhc1261159021.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/13yeh71261159021.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/14snvu1261159021.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/1501yn1261159021.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/16crbi1261159021.tab")
+ }
>
> try(system("convert tmp/1p6mo1261159021.ps tmp/1p6mo1261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w9dt1261159021.ps tmp/2w9dt1261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/33bty1261159021.ps tmp/33bty1261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cuni1261159021.ps tmp/4cuni1261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/5t4xu1261159021.ps tmp/5t4xu1261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/6r0mh1261159021.ps tmp/6r0mh1261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jehp1261159021.ps tmp/7jehp1261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/8aeuq1261159021.ps tmp/8aeuq1261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/9epo41261159021.ps tmp/9epo41261159021.png",intern=TRUE))
character(0)
> try(system("convert tmp/10s8i91261159021.ps tmp/10s8i91261159021.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.390 1.576 3.444