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(8.1,1.3,7.7,1.3,7.5,1.2,7.6,1.1,7.8,1.4,7.8,1.2,7.8,1.5,7.5,1.1,7.5,1.3,7.1,1.5,7.5,1.1,7.5,1.4,7.6,1.3,7.7,1.5,7.7,1.6,7.9,1.7,8.1,1.1,8.2,1.6,8.2,1.3,8.2,1.7,7.9,1.6,7.3,1.7,6.9,1.9,6.6,1.8,6.7,1.9,6.9,1.6,7.0,1.5,7.1,1.6,7.2,1.6,7.1,1.7,6.9,2.0,7.0,2.0,6.8,1.9,6.4,1.7,6.7,1.8,6.6,1.9,6.4,1.7,6.3,2.0,6.2,2.1,6.5,2.4,6.8,2.5,6.8,2.5,6.4,2.6,6.1,2.2,5.8,2.5,6.1,2.8,7.2,2.8,7.3,2.9,6.9,3.0,6.1,3.1,5.8,2.9,6.2,2.7,7.1,2.2,7.7,2.5,7.9,2.3,7.7,2.6,7.4,2.3,7.5,2.2,8.0,1.8,8.1,1.8),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 = '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 8.1 1.3 1 0 0 0 0 0 0 0 0 0 0 1
2 7.7 1.3 0 1 0 0 0 0 0 0 0 0 0 2
3 7.5 1.2 0 0 1 0 0 0 0 0 0 0 0 3
4 7.6 1.1 0 0 0 1 0 0 0 0 0 0 0 4
5 7.8 1.4 0 0 0 0 1 0 0 0 0 0 0 5
6 7.8 1.2 0 0 0 0 0 1 0 0 0 0 0 6
7 7.8 1.5 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 1.1 0 0 0 0 0 0 0 1 0 0 0 8
9 7.5 1.3 0 0 0 0 0 0 0 0 1 0 0 9
10 7.1 1.5 0 0 0 0 0 0 0 0 0 1 0 10
11 7.5 1.1 0 0 0 0 0 0 0 0 0 0 1 11
12 7.5 1.4 0 0 0 0 0 0 0 0 0 0 0 12
13 7.6 1.3 1 0 0 0 0 0 0 0 0 0 0 13
14 7.7 1.5 0 1 0 0 0 0 0 0 0 0 0 14
15 7.7 1.6 0 0 1 0 0 0 0 0 0 0 0 15
16 7.9 1.7 0 0 0 1 0 0 0 0 0 0 0 16
17 8.1 1.1 0 0 0 0 1 0 0 0 0 0 0 17
18 8.2 1.6 0 0 0 0 0 1 0 0 0 0 0 18
19 8.2 1.3 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 1.7 0 0 0 0 0 0 0 1 0 0 0 20
21 7.9 1.6 0 0 0 0 0 0 0 0 1 0 0 21
22 7.3 1.7 0 0 0 0 0 0 0 0 0 1 0 22
23 6.9 1.9 0 0 0 0 0 0 0 0 0 0 1 23
24 6.6 1.8 0 0 0 0 0 0 0 0 0 0 0 24
25 6.7 1.9 1 0 0 0 0 0 0 0 0 0 0 25
26 6.9 1.6 0 1 0 0 0 0 0 0 0 0 0 26
27 7.0 1.5 0 0 1 0 0 0 0 0 0 0 0 27
28 7.1 1.6 0 0 0 1 0 0 0 0 0 0 0 28
29 7.2 1.6 0 0 0 0 1 0 0 0 0 0 0 29
30 7.1 1.7 0 0 0 0 0 1 0 0 0 0 0 30
31 6.9 2.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.0 2.0 0 0 0 0 0 0 0 1 0 0 0 32
33 6.8 1.9 0 0 0 0 0 0 0 0 1 0 0 33
34 6.4 1.7 0 0 0 0 0 0 0 0 0 1 0 34
35 6.7 1.8 0 0 0 0 0 0 0 0 0 0 1 35
36 6.6 1.9 0 0 0 0 0 0 0 0 0 0 0 36
37 6.4 1.7 1 0 0 0 0 0 0 0 0 0 0 37
38 6.3 2.0 0 1 0 0 0 0 0 0 0 0 0 38
39 6.2 2.1 0 0 1 0 0 0 0 0 0 0 0 39
40 6.5 2.4 0 0 0 1 0 0 0 0 0 0 0 40
41 6.8 2.5 0 0 0 0 1 0 0 0 0 0 0 41
42 6.8 2.5 0 0 0 0 0 1 0 0 0 0 0 42
43 6.4 2.6 0 0 0 0 0 0 1 0 0 0 0 43
44 6.1 2.2 0 0 0 0 0 0 0 1 0 0 0 44
45 5.8 2.5 0 0 0 0 0 0 0 0 1 0 0 45
46 6.1 2.8 0 0 0 0 0 0 0 0 0 1 0 46
47 7.2 2.8 0 0 0 0 0 0 0 0 0 0 1 47
48 7.3 2.9 0 0 0 0 0 0 0 0 0 0 0 48
49 6.9 3.0 1 0 0 0 0 0 0 0 0 0 0 49
50 6.1 3.1 0 1 0 0 0 0 0 0 0 0 0 50
51 5.8 2.9 0 0 1 0 0 0 0 0 0 0 0 51
52 6.2 2.7 0 0 0 1 0 0 0 0 0 0 0 52
53 7.1 2.2 0 0 0 0 1 0 0 0 0 0 0 53
54 7.7 2.5 0 0 0 0 0 1 0 0 0 0 0 54
55 7.9 2.3 0 0 0 0 0 0 1 0 0 0 0 55
56 7.7 2.6 0 0 0 0 0 0 0 1 0 0 0 56
57 7.4 2.3 0 0 0 0 0 0 0 0 1 0 0 57
58 7.5 2.2 0 0 0 0 0 0 0 0 0 1 0 58
59 8.0 1.8 0 0 0 0 0 0 0 0 0 0 1 59
60 8.1 1.8 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) X M1 M2 M3 M4
8.617997 -0.784077 -0.131680 -0.288491 -0.423709 -0.176202
M5 M6 M7 M8 M9 M10
0.050172 0.276088 0.223595 0.064058 -0.159797 -0.316608
M11 t
-0.018871 0.003855
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.02672 -0.32531 -0.06494 0.50083 0.84064
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.617997 0.389086 22.149 <2e-16 ***
X -0.784077 0.249202 -3.146 0.0029 **
M1 -0.131680 0.364768 -0.361 0.7198
M2 -0.288491 0.365305 -0.790 0.4337
M3 -0.423709 0.362866 -1.168 0.2490
M4 -0.176202 0.362764 -0.486 0.6295
M5 0.050172 0.360405 0.139 0.8899
M6 0.276088 0.360898 0.765 0.4482
M7 0.223595 0.360859 0.620 0.5386
M8 0.064058 0.359909 0.178 0.8595
M9 -0.159797 0.359486 -0.445 0.6588
M10 -0.316608 0.359682 -0.880 0.3833
M11 -0.018871 0.359390 -0.053 0.9584
t 0.003855 0.007897 0.488 0.6277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5678 on 46 degrees of freedom
Multiple R-squared: 0.4229, Adjusted R-squared: 0.2598
F-statistic: 2.593 on 13 and 46 DF, p-value: 0.008774
> 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,] 0.10087966 0.20175931 0.89912034
[2,] 0.04954326 0.09908651 0.95045674
[3,] 0.03053545 0.06107090 0.96946455
[4,] 0.03085797 0.06171593 0.96914203
[5,] 0.02410023 0.04820047 0.97589977
[6,] 0.01505267 0.03010533 0.98494733
[7,] 0.05521177 0.11042355 0.94478823
[8,] 0.13297629 0.26595259 0.86702371
[9,] 0.29383972 0.58767944 0.70616028
[10,] 0.32349842 0.64699684 0.67650158
[11,] 0.35134697 0.70269394 0.64865303
[12,] 0.37163092 0.74326184 0.62836908
[13,] 0.35203774 0.70407548 0.64796226
[14,] 0.30824566 0.61649132 0.69175434
[15,] 0.29253460 0.58506919 0.70746540
[16,] 0.30651903 0.61303807 0.69348097
[17,] 0.39602132 0.79204264 0.60397868
[18,] 0.33382239 0.66764477 0.66617761
[19,] 0.24409055 0.48818110 0.75590945
[20,] 0.17428921 0.34857842 0.82571079
[21,] 0.14108564 0.28217127 0.85891436
[22,] 0.10257029 0.20514058 0.89742971
[23,] 0.14539950 0.29079900 0.85460050
[24,] 0.56050791 0.87898417 0.43949209
[25,] 0.85448726 0.29102548 0.14551274
[26,] 0.95840181 0.08319639 0.04159819
[27,] 0.90200226 0.19599548 0.09799774
> postscript(file="/var/www/html/rcomp/tmp/1mc2i1259256899.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2abv41259256899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jka11259256899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4v6301259256899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5e8as1259256899.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
0.629127448 0.382082825 0.235038202 0.005267416 0.210261316 -0.176324880
7 8 9 10 11 12
0.107535152 -0.350414125 0.026401284 -0.063827929 -0.279051043 -0.066554093
13 14 15 16 17 18
0.082862921 0.492633708 0.702404494 0.729449117 0.228773676 0.491041413
19 20 21 22 23 24
0.304455217 0.773767576 0.615359872 0.246722953 -0.298053933 -0.699187801
25 26 27 28 29 30
-0.392955377 -0.275223114 -0.122267737 -0.195223114 -0.325452327 -0.576815409
31 32 33 34 35 36
-0.492955377 -0.237273836 -0.295681541 -0.699541573 -0.622726164 -0.667044623
37 38 39 40 41 42
-0.896035313 -0.607856822 -0.498086035 -0.214226003 -0.066047512 -0.295818299
43 44 45 46 47 48
-0.568773676 -1.026722953 -0.871499839 -0.183321348 0.615086356 0.770767897
49 50 51 52 53 54
0.577000321 0.008363403 -0.317088925 -0.325267416 -0.047535152 0.557917175
55 56 57 58 59 60
0.649738684 0.840643339 0.525420225 0.699967897 0.584744783 0.662018620
> postscript(file="/var/www/html/rcomp/tmp/6hdva1259256899.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 0.629127448 NA
1 0.382082825 0.629127448
2 0.235038202 0.382082825
3 0.005267416 0.235038202
4 0.210261316 0.005267416
5 -0.176324880 0.210261316
6 0.107535152 -0.176324880
7 -0.350414125 0.107535152
8 0.026401284 -0.350414125
9 -0.063827929 0.026401284
10 -0.279051043 -0.063827929
11 -0.066554093 -0.279051043
12 0.082862921 -0.066554093
13 0.492633708 0.082862921
14 0.702404494 0.492633708
15 0.729449117 0.702404494
16 0.228773676 0.729449117
17 0.491041413 0.228773676
18 0.304455217 0.491041413
19 0.773767576 0.304455217
20 0.615359872 0.773767576
21 0.246722953 0.615359872
22 -0.298053933 0.246722953
23 -0.699187801 -0.298053933
24 -0.392955377 -0.699187801
25 -0.275223114 -0.392955377
26 -0.122267737 -0.275223114
27 -0.195223114 -0.122267737
28 -0.325452327 -0.195223114
29 -0.576815409 -0.325452327
30 -0.492955377 -0.576815409
31 -0.237273836 -0.492955377
32 -0.295681541 -0.237273836
33 -0.699541573 -0.295681541
34 -0.622726164 -0.699541573
35 -0.667044623 -0.622726164
36 -0.896035313 -0.667044623
37 -0.607856822 -0.896035313
38 -0.498086035 -0.607856822
39 -0.214226003 -0.498086035
40 -0.066047512 -0.214226003
41 -0.295818299 -0.066047512
42 -0.568773676 -0.295818299
43 -1.026722953 -0.568773676
44 -0.871499839 -1.026722953
45 -0.183321348 -0.871499839
46 0.615086356 -0.183321348
47 0.770767897 0.615086356
48 0.577000321 0.770767897
49 0.008363403 0.577000321
50 -0.317088925 0.008363403
51 -0.325267416 -0.317088925
52 -0.047535152 -0.325267416
53 0.557917175 -0.047535152
54 0.649738684 0.557917175
55 0.840643339 0.649738684
56 0.525420225 0.840643339
57 0.699967897 0.525420225
58 0.584744783 0.699967897
59 0.662018620 0.584744783
60 NA 0.662018620
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.382082825 0.629127448
[2,] 0.235038202 0.382082825
[3,] 0.005267416 0.235038202
[4,] 0.210261316 0.005267416
[5,] -0.176324880 0.210261316
[6,] 0.107535152 -0.176324880
[7,] -0.350414125 0.107535152
[8,] 0.026401284 -0.350414125
[9,] -0.063827929 0.026401284
[10,] -0.279051043 -0.063827929
[11,] -0.066554093 -0.279051043
[12,] 0.082862921 -0.066554093
[13,] 0.492633708 0.082862921
[14,] 0.702404494 0.492633708
[15,] 0.729449117 0.702404494
[16,] 0.228773676 0.729449117
[17,] 0.491041413 0.228773676
[18,] 0.304455217 0.491041413
[19,] 0.773767576 0.304455217
[20,] 0.615359872 0.773767576
[21,] 0.246722953 0.615359872
[22,] -0.298053933 0.246722953
[23,] -0.699187801 -0.298053933
[24,] -0.392955377 -0.699187801
[25,] -0.275223114 -0.392955377
[26,] -0.122267737 -0.275223114
[27,] -0.195223114 -0.122267737
[28,] -0.325452327 -0.195223114
[29,] -0.576815409 -0.325452327
[30,] -0.492955377 -0.576815409
[31,] -0.237273836 -0.492955377
[32,] -0.295681541 -0.237273836
[33,] -0.699541573 -0.295681541
[34,] -0.622726164 -0.699541573
[35,] -0.667044623 -0.622726164
[36,] -0.896035313 -0.667044623
[37,] -0.607856822 -0.896035313
[38,] -0.498086035 -0.607856822
[39,] -0.214226003 -0.498086035
[40,] -0.066047512 -0.214226003
[41,] -0.295818299 -0.066047512
[42,] -0.568773676 -0.295818299
[43,] -1.026722953 -0.568773676
[44,] -0.871499839 -1.026722953
[45,] -0.183321348 -0.871499839
[46,] 0.615086356 -0.183321348
[47,] 0.770767897 0.615086356
[48,] 0.577000321 0.770767897
[49,] 0.008363403 0.577000321
[50,] -0.317088925 0.008363403
[51,] -0.325267416 -0.317088925
[52,] -0.047535152 -0.325267416
[53,] 0.557917175 -0.047535152
[54,] 0.649738684 0.557917175
[55,] 0.840643339 0.649738684
[56,] 0.525420225 0.840643339
[57,] 0.699967897 0.525420225
[58,] 0.584744783 0.699967897
[59,] 0.662018620 0.584744783
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.382082825 0.629127448
2 0.235038202 0.382082825
3 0.005267416 0.235038202
4 0.210261316 0.005267416
5 -0.176324880 0.210261316
6 0.107535152 -0.176324880
7 -0.350414125 0.107535152
8 0.026401284 -0.350414125
9 -0.063827929 0.026401284
10 -0.279051043 -0.063827929
11 -0.066554093 -0.279051043
12 0.082862921 -0.066554093
13 0.492633708 0.082862921
14 0.702404494 0.492633708
15 0.729449117 0.702404494
16 0.228773676 0.729449117
17 0.491041413 0.228773676
18 0.304455217 0.491041413
19 0.773767576 0.304455217
20 0.615359872 0.773767576
21 0.246722953 0.615359872
22 -0.298053933 0.246722953
23 -0.699187801 -0.298053933
24 -0.392955377 -0.699187801
25 -0.275223114 -0.392955377
26 -0.122267737 -0.275223114
27 -0.195223114 -0.122267737
28 -0.325452327 -0.195223114
29 -0.576815409 -0.325452327
30 -0.492955377 -0.576815409
31 -0.237273836 -0.492955377
32 -0.295681541 -0.237273836
33 -0.699541573 -0.295681541
34 -0.622726164 -0.699541573
35 -0.667044623 -0.622726164
36 -0.896035313 -0.667044623
37 -0.607856822 -0.896035313
38 -0.498086035 -0.607856822
39 -0.214226003 -0.498086035
40 -0.066047512 -0.214226003
41 -0.295818299 -0.066047512
42 -0.568773676 -0.295818299
43 -1.026722953 -0.568773676
44 -0.871499839 -1.026722953
45 -0.183321348 -0.871499839
46 0.615086356 -0.183321348
47 0.770767897 0.615086356
48 0.577000321 0.770767897
49 0.008363403 0.577000321
50 -0.317088925 0.008363403
51 -0.325267416 -0.317088925
52 -0.047535152 -0.325267416
53 0.557917175 -0.047535152
54 0.649738684 0.557917175
55 0.840643339 0.649738684
56 0.525420225 0.840643339
57 0.699967897 0.525420225
58 0.584744783 0.699967897
59 0.662018620 0.584744783
> 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/7un841259256899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81ssx1259256899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9sdjo1259256899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ki9f1259256899.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t22t1259256899.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/12j4971259256899.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/13z0ba1259256899.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/14lozx1259256899.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/15i15n1259256899.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/16qezq1259256899.tab")
+ }
>
> system("convert tmp/1mc2i1259256899.ps tmp/1mc2i1259256899.png")
> system("convert tmp/2abv41259256899.ps tmp/2abv41259256899.png")
> system("convert tmp/3jka11259256899.ps tmp/3jka11259256899.png")
> system("convert tmp/4v6301259256899.ps tmp/4v6301259256899.png")
> system("convert tmp/5e8as1259256899.ps tmp/5e8as1259256899.png")
> system("convert tmp/6hdva1259256899.ps tmp/6hdva1259256899.png")
> system("convert tmp/7un841259256899.ps tmp/7un841259256899.png")
> system("convert tmp/81ssx1259256899.ps tmp/81ssx1259256899.png")
> system("convert tmp/9sdjo1259256899.ps tmp/9sdjo1259256899.png")
> system("convert tmp/10ki9f1259256899.ps tmp/10ki9f1259256899.png")
>
>
> proc.time()
user system elapsed
2.385 1.560 4.359