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(9.3,96.8,9.3,114.1,8.7,110.3,8.2,103.9,8.3,101.6,8.5,94.6,8.6,95.9,8.5,104.7,8.2,102.8,8.1,98.1,7.9,113.9,8.6,80.9,8.7,95.7,8.7,113.2,8.5,105.9,8.4,108.8,8.5,102.3,8.7,99,8.7,100.7,8.6,115.5,8.5,100.7,8.3,109.9,8,114.6,8.2,85.4,8.1,100.5,8.1,114.8,8,116.5,7.9,112.9,7.9,102,8,106,8,105.3,7.9,118.8,8,106.1,7.7,109.3,7.2,117.2,7.5,92.5,7.3,104.2,7,112.5,7,122.4,7,113.3,7.2,100,7.3,110.7,7.1,112.8,6.8,109.8,6.4,117.3,6.1,109.1,6.5,115.9,7.7,96,7.9,99.8,7.5,116.8,6.9,115.7,6.6,99.4,6.9,94.3,7.7,91,8,93.2,8,103.1,7.7,94.1,7.3,91.8,7.4,102.7,8.1,82.6),dim=c(2,60),dimnames=list(c('werklh','ecogr'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('werklh','ecogr'),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
werklh ecogr M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 9.3 96.8 1 0 0 0 0 0 0 0 0 0 0
2 9.3 114.1 0 1 0 0 0 0 0 0 0 0 0
3 8.7 110.3 0 0 1 0 0 0 0 0 0 0 0
4 8.2 103.9 0 0 0 1 0 0 0 0 0 0 0
5 8.3 101.6 0 0 0 0 1 0 0 0 0 0 0
6 8.5 94.6 0 0 0 0 0 1 0 0 0 0 0
7 8.6 95.9 0 0 0 0 0 0 1 0 0 0 0
8 8.5 104.7 0 0 0 0 0 0 0 1 0 0 0
9 8.2 102.8 0 0 0 0 0 0 0 0 1 0 0
10 8.1 98.1 0 0 0 0 0 0 0 0 0 1 0
11 7.9 113.9 0 0 0 0 0 0 0 0 0 0 1
12 8.6 80.9 0 0 0 0 0 0 0 0 0 0 0
13 8.7 95.7 1 0 0 0 0 0 0 0 0 0 0
14 8.7 113.2 0 1 0 0 0 0 0 0 0 0 0
15 8.5 105.9 0 0 1 0 0 0 0 0 0 0 0
16 8.4 108.8 0 0 0 1 0 0 0 0 0 0 0
17 8.5 102.3 0 0 0 0 1 0 0 0 0 0 0
18 8.7 99.0 0 0 0 0 0 1 0 0 0 0 0
19 8.7 100.7 0 0 0 0 0 0 1 0 0 0 0
20 8.6 115.5 0 0 0 0 0 0 0 1 0 0 0
21 8.5 100.7 0 0 0 0 0 0 0 0 1 0 0
22 8.3 109.9 0 0 0 0 0 0 0 0 0 1 0
23 8.0 114.6 0 0 0 0 0 0 0 0 0 0 1
24 8.2 85.4 0 0 0 0 0 0 0 0 0 0 0
25 8.1 100.5 1 0 0 0 0 0 0 0 0 0 0
26 8.1 114.8 0 1 0 0 0 0 0 0 0 0 0
27 8.0 116.5 0 0 1 0 0 0 0 0 0 0 0
28 7.9 112.9 0 0 0 1 0 0 0 0 0 0 0
29 7.9 102.0 0 0 0 0 1 0 0 0 0 0 0
30 8.0 106.0 0 0 0 0 0 1 0 0 0 0 0
31 8.0 105.3 0 0 0 0 0 0 1 0 0 0 0
32 7.9 118.8 0 0 0 0 0 0 0 1 0 0 0
33 8.0 106.1 0 0 0 0 0 0 0 0 1 0 0
34 7.7 109.3 0 0 0 0 0 0 0 0 0 1 0
35 7.2 117.2 0 0 0 0 0 0 0 0 0 0 1
36 7.5 92.5 0 0 0 0 0 0 0 0 0 0 0
37 7.3 104.2 1 0 0 0 0 0 0 0 0 0 0
38 7.0 112.5 0 1 0 0 0 0 0 0 0 0 0
39 7.0 122.4 0 0 1 0 0 0 0 0 0 0 0
40 7.0 113.3 0 0 0 1 0 0 0 0 0 0 0
41 7.2 100.0 0 0 0 0 1 0 0 0 0 0 0
42 7.3 110.7 0 0 0 0 0 1 0 0 0 0 0
43 7.1 112.8 0 0 0 0 0 0 1 0 0 0 0
44 6.8 109.8 0 0 0 0 0 0 0 1 0 0 0
45 6.4 117.3 0 0 0 0 0 0 0 0 1 0 0
46 6.1 109.1 0 0 0 0 0 0 0 0 0 1 0
47 6.5 115.9 0 0 0 0 0 0 0 0 0 0 1
48 7.7 96.0 0 0 0 0 0 0 0 0 0 0 0
49 7.9 99.8 1 0 0 0 0 0 0 0 0 0 0
50 7.5 116.8 0 1 0 0 0 0 0 0 0 0 0
51 6.9 115.7 0 0 1 0 0 0 0 0 0 0 0
52 6.6 99.4 0 0 0 1 0 0 0 0 0 0 0
53 6.9 94.3 0 0 0 0 1 0 0 0 0 0 0
54 7.7 91.0 0 0 0 0 0 1 0 0 0 0 0
55 8.0 93.2 0 0 0 0 0 0 1 0 0 0 0
56 8.0 103.1 0 0 0 0 0 0 0 1 0 0 0
57 7.7 94.1 0 0 0 0 0 0 0 0 1 0 0
58 7.3 91.8 0 0 0 0 0 0 0 0 0 1 0
59 7.4 102.7 0 0 0 0 0 0 0 0 0 0 1
60 8.1 82.6 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) ecogr M1 M2 M3 M4
11.16019 -0.03590 0.66788 1.06201 0.75771 0.32438
M5 M6 M7 M8 M9 M10
0.19085 0.47875 0.56613 0.76202 0.34018 0.06008
M11
0.29104
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.3165 -0.4480 0.1357 0.4508 1.1735
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.16019 1.42561 7.828 4.6e-10 ***
ecogr -0.03590 0.01589 -2.258 0.0286 *
M1 0.66788 0.48340 1.382 0.1736
M2 1.06201 0.61582 1.725 0.0912 .
M3 0.75771 0.61450 1.233 0.2237
M4 0.32438 0.54833 0.592 0.5570
M5 0.19085 0.48747 0.392 0.6972
M6 0.47875 0.48892 0.979 0.3325
M7 0.56613 0.49800 1.137 0.2614
M8 0.76202 0.57469 1.326 0.1913
M9 0.34018 0.51808 0.657 0.5146
M10 0.06008 0.51357 0.117 0.9074
M11 0.29104 0.60043 0.485 0.6301
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7032 on 47 degrees of freedom
Multiple R-squared: 0.2144, Adjusted R-squared: 0.01387
F-statistic: 1.069 on 12 and 47 DF, p-value: 0.4065
> 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,] 0.115094199 0.230188398 0.88490580
[2,] 0.051227787 0.102455573 0.94877221
[3,] 0.020379763 0.040759526 0.97962024
[4,] 0.008610490 0.017220980 0.99138951
[5,] 0.005149652 0.010299303 0.99485035
[6,] 0.003720604 0.007441209 0.99627940
[7,] 0.002280610 0.004561220 0.99771939
[8,] 0.001189330 0.002378660 0.99881067
[9,] 0.001219030 0.002438059 0.99878097
[10,] 0.012728401 0.025456803 0.98727160
[11,] 0.040201037 0.080402073 0.95979896
[12,] 0.049331309 0.098662618 0.95066869
[13,] 0.062191689 0.124383378 0.93780831
[14,] 0.081209278 0.162418556 0.91879072
[15,] 0.073584448 0.147168895 0.92641555
[16,] 0.065412305 0.130824611 0.93458769
[17,] 0.073333878 0.146667756 0.92666612
[18,] 0.092585415 0.185170831 0.90741458
[19,] 0.265924611 0.531849222 0.73407539
[20,] 0.322283841 0.644567681 0.67771616
[21,] 0.283154201 0.566308403 0.71684580
[22,] 0.395456062 0.790912125 0.60454394
[23,] 0.729001398 0.541997203 0.27099860
[24,] 0.686513603 0.626972793 0.31348640
[25,] 0.845330424 0.309339152 0.15466958
[26,] 0.873683133 0.252633734 0.12631687
[27,] 0.890492958 0.219014084 0.10950704
[28,] 0.816207598 0.367584805 0.18379240
[29,] 0.960362050 0.079275900 0.03963795
> postscript(file="/var/www/html/rcomp/tmp/1spsp1261058029.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2lc3f1261058029.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ivnk1261058029.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qvwu1261058029.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5m8vn1261058029.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
0.946670264 1.173538711 0.741441238 0.445030843 0.595997842 0.256828344
7 8 9 10 11 12
0.316110423 0.336110423 0.389745527 0.401135870 0.537331894 0.343803976
13 14 15 16 17 18
0.307184607 0.541232264 0.383498608 0.820921500 0.821125078 0.614770974
19 20 21 22 23 24
0.588411474 0.823787788 0.614363817 1.024709287 0.662459131 0.105336211
25 26 27 28 29 30
-0.120514342 -0.001334053 0.263996762 0.468095314 0.210356262 0.166043340
31 32 33 34 35 36
0.053533315 0.242244760 0.308202499 0.403171656 -0.044211133 -0.339801817
37 38 39 40 41 42
-0.787698949 -1.183894973 -0.524216529 -0.417546265 -0.561435842 -0.365245214
43 44 45 46 47 48
-0.577246293 -1.180819710 -0.889761715 -1.204007554 -0.790876001 -0.014165634
49 50 51 52 53 54
-0.345641579 -0.529541948 -0.864720079 -1.316501392 -1.066043340 -0.672397444
55 56 57 58 59 60
-0.380808918 -0.221323261 -0.422550128 -0.625009259 -0.364703891 -0.095172735
> postscript(file="/var/www/html/rcomp/tmp/6h5891261058029.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 0.946670264 NA
1 1.173538711 0.946670264
2 0.741441238 1.173538711
3 0.445030843 0.741441238
4 0.595997842 0.445030843
5 0.256828344 0.595997842
6 0.316110423 0.256828344
7 0.336110423 0.316110423
8 0.389745527 0.336110423
9 0.401135870 0.389745527
10 0.537331894 0.401135870
11 0.343803976 0.537331894
12 0.307184607 0.343803976
13 0.541232264 0.307184607
14 0.383498608 0.541232264
15 0.820921500 0.383498608
16 0.821125078 0.820921500
17 0.614770974 0.821125078
18 0.588411474 0.614770974
19 0.823787788 0.588411474
20 0.614363817 0.823787788
21 1.024709287 0.614363817
22 0.662459131 1.024709287
23 0.105336211 0.662459131
24 -0.120514342 0.105336211
25 -0.001334053 -0.120514342
26 0.263996762 -0.001334053
27 0.468095314 0.263996762
28 0.210356262 0.468095314
29 0.166043340 0.210356262
30 0.053533315 0.166043340
31 0.242244760 0.053533315
32 0.308202499 0.242244760
33 0.403171656 0.308202499
34 -0.044211133 0.403171656
35 -0.339801817 -0.044211133
36 -0.787698949 -0.339801817
37 -1.183894973 -0.787698949
38 -0.524216529 -1.183894973
39 -0.417546265 -0.524216529
40 -0.561435842 -0.417546265
41 -0.365245214 -0.561435842
42 -0.577246293 -0.365245214
43 -1.180819710 -0.577246293
44 -0.889761715 -1.180819710
45 -1.204007554 -0.889761715
46 -0.790876001 -1.204007554
47 -0.014165634 -0.790876001
48 -0.345641579 -0.014165634
49 -0.529541948 -0.345641579
50 -0.864720079 -0.529541948
51 -1.316501392 -0.864720079
52 -1.066043340 -1.316501392
53 -0.672397444 -1.066043340
54 -0.380808918 -0.672397444
55 -0.221323261 -0.380808918
56 -0.422550128 -0.221323261
57 -0.625009259 -0.422550128
58 -0.364703891 -0.625009259
59 -0.095172735 -0.364703891
60 NA -0.095172735
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.173538711 0.946670264
[2,] 0.741441238 1.173538711
[3,] 0.445030843 0.741441238
[4,] 0.595997842 0.445030843
[5,] 0.256828344 0.595997842
[6,] 0.316110423 0.256828344
[7,] 0.336110423 0.316110423
[8,] 0.389745527 0.336110423
[9,] 0.401135870 0.389745527
[10,] 0.537331894 0.401135870
[11,] 0.343803976 0.537331894
[12,] 0.307184607 0.343803976
[13,] 0.541232264 0.307184607
[14,] 0.383498608 0.541232264
[15,] 0.820921500 0.383498608
[16,] 0.821125078 0.820921500
[17,] 0.614770974 0.821125078
[18,] 0.588411474 0.614770974
[19,] 0.823787788 0.588411474
[20,] 0.614363817 0.823787788
[21,] 1.024709287 0.614363817
[22,] 0.662459131 1.024709287
[23,] 0.105336211 0.662459131
[24,] -0.120514342 0.105336211
[25,] -0.001334053 -0.120514342
[26,] 0.263996762 -0.001334053
[27,] 0.468095314 0.263996762
[28,] 0.210356262 0.468095314
[29,] 0.166043340 0.210356262
[30,] 0.053533315 0.166043340
[31,] 0.242244760 0.053533315
[32,] 0.308202499 0.242244760
[33,] 0.403171656 0.308202499
[34,] -0.044211133 0.403171656
[35,] -0.339801817 -0.044211133
[36,] -0.787698949 -0.339801817
[37,] -1.183894973 -0.787698949
[38,] -0.524216529 -1.183894973
[39,] -0.417546265 -0.524216529
[40,] -0.561435842 -0.417546265
[41,] -0.365245214 -0.561435842
[42,] -0.577246293 -0.365245214
[43,] -1.180819710 -0.577246293
[44,] -0.889761715 -1.180819710
[45,] -1.204007554 -0.889761715
[46,] -0.790876001 -1.204007554
[47,] -0.014165634 -0.790876001
[48,] -0.345641579 -0.014165634
[49,] -0.529541948 -0.345641579
[50,] -0.864720079 -0.529541948
[51,] -1.316501392 -0.864720079
[52,] -1.066043340 -1.316501392
[53,] -0.672397444 -1.066043340
[54,] -0.380808918 -0.672397444
[55,] -0.221323261 -0.380808918
[56,] -0.422550128 -0.221323261
[57,] -0.625009259 -0.422550128
[58,] -0.364703891 -0.625009259
[59,] -0.095172735 -0.364703891
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.173538711 0.946670264
2 0.741441238 1.173538711
3 0.445030843 0.741441238
4 0.595997842 0.445030843
5 0.256828344 0.595997842
6 0.316110423 0.256828344
7 0.336110423 0.316110423
8 0.389745527 0.336110423
9 0.401135870 0.389745527
10 0.537331894 0.401135870
11 0.343803976 0.537331894
12 0.307184607 0.343803976
13 0.541232264 0.307184607
14 0.383498608 0.541232264
15 0.820921500 0.383498608
16 0.821125078 0.820921500
17 0.614770974 0.821125078
18 0.588411474 0.614770974
19 0.823787788 0.588411474
20 0.614363817 0.823787788
21 1.024709287 0.614363817
22 0.662459131 1.024709287
23 0.105336211 0.662459131
24 -0.120514342 0.105336211
25 -0.001334053 -0.120514342
26 0.263996762 -0.001334053
27 0.468095314 0.263996762
28 0.210356262 0.468095314
29 0.166043340 0.210356262
30 0.053533315 0.166043340
31 0.242244760 0.053533315
32 0.308202499 0.242244760
33 0.403171656 0.308202499
34 -0.044211133 0.403171656
35 -0.339801817 -0.044211133
36 -0.787698949 -0.339801817
37 -1.183894973 -0.787698949
38 -0.524216529 -1.183894973
39 -0.417546265 -0.524216529
40 -0.561435842 -0.417546265
41 -0.365245214 -0.561435842
42 -0.577246293 -0.365245214
43 -1.180819710 -0.577246293
44 -0.889761715 -1.180819710
45 -1.204007554 -0.889761715
46 -0.790876001 -1.204007554
47 -0.014165634 -0.790876001
48 -0.345641579 -0.014165634
49 -0.529541948 -0.345641579
50 -0.864720079 -0.529541948
51 -1.316501392 -0.864720079
52 -1.066043340 -1.316501392
53 -0.672397444 -1.066043340
54 -0.380808918 -0.672397444
55 -0.221323261 -0.380808918
56 -0.422550128 -0.221323261
57 -0.625009259 -0.422550128
58 -0.364703891 -0.625009259
59 -0.095172735 -0.364703891
> 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/7jfcm1261058029.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xp0r1261058029.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9v7cn1261058029.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1057jb1261058029.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11beii1261058029.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/12g9dg1261058029.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/13mzep1261058029.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/14b2o61261058029.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/150rnq1261058029.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/1642wt1261058029.tab")
+ }
>
> try(system("convert tmp/1spsp1261058029.ps tmp/1spsp1261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lc3f1261058029.ps tmp/2lc3f1261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ivnk1261058029.ps tmp/3ivnk1261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qvwu1261058029.ps tmp/4qvwu1261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m8vn1261058029.ps tmp/5m8vn1261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/6h5891261058029.ps tmp/6h5891261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jfcm1261058029.ps tmp/7jfcm1261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xp0r1261058029.ps tmp/8xp0r1261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/9v7cn1261058029.ps tmp/9v7cn1261058029.png",intern=TRUE))
character(0)
> try(system("convert tmp/1057jb1261058029.ps tmp/1057jb1261058029.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.415 1.555 3.826