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(7.2,102.9,7.4,97.4,8.8,111.4,9.3,87.4,9.3,96.8,8.7,114.1,8.2,110.3,8.3,103.9,8.5,101.6,8.6,94.6,8.5,95.9,8.2,104.7,8.1,102.8,7.9,98.1,8.6,113.9,8.7,80.9,8.7,95.7,8.5,113.2,8.4,105.9,8.5,108.8,8.7,102.3,8.7,99,8.6,100.7,8.5,115.5,8.3,100.7,8,109.9,8.2,114.6,8.1,85.4,8.1,100.5,8,114.8,7.9,116.5,7.9,112.9,8,102,8,106,7.9,105.3,8,118.8,7.7,106.1,7.2,109.3,7.5,117.2,7.3,92.5,7,104.2,7,112.5,7,122.4,7.2,113.3,7.3,100,7.1,110.7,6.8,112.8,6.4,109.8,6.1,117.3,6.5,109.1,7.7,115.9,7.9,96,7.5,99.8,6.9,116.8,6.6,115.7,6.9,99.4,7.7,94.3,8,91,8,93.2,7.7,103.1,7.3,94.1,7.4,91.8,8.1,102.7,8.3,82.6,8.2,89.1),dim=c(2,65),dimnames=list(c('Werkl.graad','Industr.prod.'),1:65))
> y <- array(NA,dim=c(2,65),dimnames=list(c('Werkl.graad','Industr.prod.'),1:65))
> 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
Werkl.graad Industr.prod. M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.2 102.9 1 0 0 0 0 0 0 0 0 0 0 1
2 7.4 97.4 0 1 0 0 0 0 0 0 0 0 0 2
3 8.8 111.4 0 0 1 0 0 0 0 0 0 0 0 3
4 9.3 87.4 0 0 0 1 0 0 0 0 0 0 0 4
5 9.3 96.8 0 0 0 0 1 0 0 0 0 0 0 5
6 8.7 114.1 0 0 0 0 0 1 0 0 0 0 0 6
7 8.2 110.3 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 103.9 0 0 0 0 0 0 0 1 0 0 0 8
9 8.5 101.6 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 94.6 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 95.9 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 104.7 0 0 0 0 0 0 0 0 0 0 0 12
13 8.1 102.8 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 98.1 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 113.9 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 80.9 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 95.7 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 113.2 0 0 0 0 0 1 0 0 0 0 0 18
19 8.4 105.9 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 108.8 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 102.3 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 99.0 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 100.7 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 115.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 100.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 109.9 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 114.6 0 0 1 0 0 0 0 0 0 0 0 27
28 8.1 85.4 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 100.5 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 114.8 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 116.5 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 112.9 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 102.0 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 106.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 105.3 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 118.8 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 106.1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.2 109.3 0 1 0 0 0 0 0 0 0 0 0 38
39 7.5 117.2 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 92.5 0 0 0 1 0 0 0 0 0 0 0 40
41 7.0 104.2 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 112.5 0 0 0 0 0 1 0 0 0 0 0 42
43 7.0 122.4 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 113.3 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 100.0 0 0 0 0 0 0 0 0 1 0 0 45
46 7.1 110.7 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 112.8 0 0 0 0 0 0 0 0 0 0 1 47
48 6.4 109.8 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 117.3 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 109.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 115.9 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 96.0 0 0 0 1 0 0 0 0 0 0 0 52
53 7.5 99.8 0 0 0 0 1 0 0 0 0 0 0 53
54 6.9 116.8 0 0 0 0 0 1 0 0 0 0 0 54
55 6.6 115.7 0 0 0 0 0 0 1 0 0 0 0 55
56 6.9 99.4 0 0 0 0 0 0 0 1 0 0 0 56
57 7.7 94.3 0 0 0 0 0 0 0 0 1 0 0 57
58 8.0 91.0 0 0 0 0 0 0 0 0 0 1 0 58
59 8.0 93.2 0 0 0 0 0 0 0 0 0 0 1 59
60 7.7 103.1 0 0 0 0 0 0 0 0 0 0 0 60
61 7.3 94.1 1 0 0 0 0 0 0 0 0 0 0 61
62 7.4 91.8 0 1 0 0 0 0 0 0 0 0 0 62
63 8.1 102.7 0 0 1 0 0 0 0 0 0 0 0 63
64 8.3 82.6 0 0 0 1 0 0 0 0 0 0 0 64
65 8.2 89.1 0 0 0 0 1 0 0 0 0 0 0 65
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Industr.prod. M1 M2 M3
13.09433 -0.04081 -0.68631 -0.76971 0.41211
M4 M5 M6 M7 M8
-0.47448 -0.16784 0.08081 -0.10103 -0.20322
M9 M10 M11 t
-0.21112 -0.13908 -0.18216 -0.02306
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.10700 -0.26383 0.07073 0.31931 0.75956
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.09433 1.13490 11.538 7.87e-16 ***
Industr.prod. -0.04081 0.01007 -4.053 0.000173 ***
M1 -0.68631 0.28586 -2.401 0.020043 *
M2 -0.76970 0.28917 -2.662 0.010371 *
M3 0.41210 0.27918 1.476 0.146055
M4 -0.47448 0.36137 -1.313 0.195064
M5 -0.16784 0.30610 -0.548 0.585858
M6 0.08081 0.29371 0.275 0.784311
M7 -0.10103 0.29337 -0.344 0.731986
M8 -0.20322 0.29202 -0.696 0.489641
M9 -0.21112 0.30869 -0.684 0.497130
M10 -0.13908 0.30788 -0.452 0.653371
M11 -0.18216 0.30370 -0.600 0.551295
t -0.02306 0.00305 -7.560 7.13e-10 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4593 on 51 degrees of freedom
Multiple R-squared: 0.6575, Adjusted R-squared: 0.5702
F-statistic: 7.532 on 13 and 51 DF, p-value: 5.488e-08
> 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,] 0.69932107 0.6013579 0.30067893
[2,] 0.55448053 0.8910389 0.44551947
[3,] 0.48474858 0.9694972 0.51525142
[4,] 0.36164288 0.7232858 0.63835712
[5,] 0.25916765 0.5183353 0.74083235
[6,] 0.17402384 0.3480477 0.82597616
[7,] 0.11016090 0.2203218 0.88983910
[8,] 0.07793796 0.1558759 0.92206204
[9,] 0.09449374 0.1889875 0.90550626
[10,] 0.06983127 0.1396625 0.93016873
[11,] 0.09445724 0.1889145 0.90554276
[12,] 0.23104737 0.4620947 0.76895263
[13,] 0.30603122 0.6120624 0.69396878
[14,] 0.27909341 0.5581868 0.72090659
[15,] 0.23699661 0.4739932 0.76300339
[16,] 0.24107387 0.4821477 0.75892613
[17,] 0.20078307 0.4015661 0.79921693
[18,] 0.17895297 0.3579059 0.82104703
[19,] 0.14755830 0.2951166 0.85244170
[20,] 0.32350798 0.6470160 0.67649202
[21,] 0.48498229 0.9699646 0.51501771
[22,] 0.59506245 0.8098751 0.40493755
[23,] 0.57629928 0.8474014 0.42370072
[24,] 0.64809955 0.7038009 0.35190045
[25,] 0.71986553 0.5602689 0.28013447
[26,] 0.66911714 0.6617657 0.33088286
[27,] 0.74348939 0.5130212 0.25651061
[28,] 0.93429264 0.1314147 0.06570736
[29,] 0.89694451 0.2061110 0.10305549
[30,] 0.82538291 0.3492342 0.17461709
[31,] 0.76046721 0.4790656 0.23953279
[32,] 0.95075465 0.0984907 0.04924535
> postscript(file="/var/www/html/rcomp/tmp/1ydkc1258659091.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/27ydn1258659091.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lwe01258659091.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43vpc1258659091.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5j3ip1258659091.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 = 65
Frequency = 1
1 2 3 4 5 6
-0.985883240 -0.903873807 -0.091324230 0.338937642 0.438944912 0.319314507
7 8 9 10 11 12
-0.130856818 -0.166776075 -0.029680574 -0.264310183 -0.245126332 -0.345126332
13 14 15 16 17 18
0.186706096 -0.098638531 0.087364411 -0.249640543 0.070726822 0.359257902
19 20 21 22 23 24
0.166260590 0.509850386 0.475554702 0.291912566 0.327419387 0.672263938
25 26 27 28 29 30
0.577680581 0.759559164 -0.007400313 -0.389337051 -0.056727458 0.201219861
31 32 33 34 35 36
0.375489375 0.353830908 0.039982553 0.154234620 0.091803622 0.583598520
37 38 39 40 41 42
0.474710756 0.211744788 -0.324630929 -0.622934254 -0.729069907 -0.615967138
43 44 45 46 47 48
-0.007076738 -0.053176044 -0.464962219 -0.277300403 -0.425470611 -1.106998228
49 50 51 52 53 54
-0.391576004 -0.219746619 0.098989497 0.396561812 -0.131952499 -0.263825132
55 56 57 58 59 60
-0.403816408 -0.643729175 -0.020894464 0.095463400 0.251373934 0.196262102
61 62 63 64 65
0.138361811 0.250955004 0.237001563 0.526412394 0.408078130
> postscript(file="/var/www/html/rcomp/tmp/6vocf1258659091.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 = 65
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.985883240 NA
1 -0.903873807 -0.985883240
2 -0.091324230 -0.903873807
3 0.338937642 -0.091324230
4 0.438944912 0.338937642
5 0.319314507 0.438944912
6 -0.130856818 0.319314507
7 -0.166776075 -0.130856818
8 -0.029680574 -0.166776075
9 -0.264310183 -0.029680574
10 -0.245126332 -0.264310183
11 -0.345126332 -0.245126332
12 0.186706096 -0.345126332
13 -0.098638531 0.186706096
14 0.087364411 -0.098638531
15 -0.249640543 0.087364411
16 0.070726822 -0.249640543
17 0.359257902 0.070726822
18 0.166260590 0.359257902
19 0.509850386 0.166260590
20 0.475554702 0.509850386
21 0.291912566 0.475554702
22 0.327419387 0.291912566
23 0.672263938 0.327419387
24 0.577680581 0.672263938
25 0.759559164 0.577680581
26 -0.007400313 0.759559164
27 -0.389337051 -0.007400313
28 -0.056727458 -0.389337051
29 0.201219861 -0.056727458
30 0.375489375 0.201219861
31 0.353830908 0.375489375
32 0.039982553 0.353830908
33 0.154234620 0.039982553
34 0.091803622 0.154234620
35 0.583598520 0.091803622
36 0.474710756 0.583598520
37 0.211744788 0.474710756
38 -0.324630929 0.211744788
39 -0.622934254 -0.324630929
40 -0.729069907 -0.622934254
41 -0.615967138 -0.729069907
42 -0.007076738 -0.615967138
43 -0.053176044 -0.007076738
44 -0.464962219 -0.053176044
45 -0.277300403 -0.464962219
46 -0.425470611 -0.277300403
47 -1.106998228 -0.425470611
48 -0.391576004 -1.106998228
49 -0.219746619 -0.391576004
50 0.098989497 -0.219746619
51 0.396561812 0.098989497
52 -0.131952499 0.396561812
53 -0.263825132 -0.131952499
54 -0.403816408 -0.263825132
55 -0.643729175 -0.403816408
56 -0.020894464 -0.643729175
57 0.095463400 -0.020894464
58 0.251373934 0.095463400
59 0.196262102 0.251373934
60 0.138361811 0.196262102
61 0.250955004 0.138361811
62 0.237001563 0.250955004
63 0.526412394 0.237001563
64 0.408078130 0.526412394
65 NA 0.408078130
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.903873807 -0.985883240
[2,] -0.091324230 -0.903873807
[3,] 0.338937642 -0.091324230
[4,] 0.438944912 0.338937642
[5,] 0.319314507 0.438944912
[6,] -0.130856818 0.319314507
[7,] -0.166776075 -0.130856818
[8,] -0.029680574 -0.166776075
[9,] -0.264310183 -0.029680574
[10,] -0.245126332 -0.264310183
[11,] -0.345126332 -0.245126332
[12,] 0.186706096 -0.345126332
[13,] -0.098638531 0.186706096
[14,] 0.087364411 -0.098638531
[15,] -0.249640543 0.087364411
[16,] 0.070726822 -0.249640543
[17,] 0.359257902 0.070726822
[18,] 0.166260590 0.359257902
[19,] 0.509850386 0.166260590
[20,] 0.475554702 0.509850386
[21,] 0.291912566 0.475554702
[22,] 0.327419387 0.291912566
[23,] 0.672263938 0.327419387
[24,] 0.577680581 0.672263938
[25,] 0.759559164 0.577680581
[26,] -0.007400313 0.759559164
[27,] -0.389337051 -0.007400313
[28,] -0.056727458 -0.389337051
[29,] 0.201219861 -0.056727458
[30,] 0.375489375 0.201219861
[31,] 0.353830908 0.375489375
[32,] 0.039982553 0.353830908
[33,] 0.154234620 0.039982553
[34,] 0.091803622 0.154234620
[35,] 0.583598520 0.091803622
[36,] 0.474710756 0.583598520
[37,] 0.211744788 0.474710756
[38,] -0.324630929 0.211744788
[39,] -0.622934254 -0.324630929
[40,] -0.729069907 -0.622934254
[41,] -0.615967138 -0.729069907
[42,] -0.007076738 -0.615967138
[43,] -0.053176044 -0.007076738
[44,] -0.464962219 -0.053176044
[45,] -0.277300403 -0.464962219
[46,] -0.425470611 -0.277300403
[47,] -1.106998228 -0.425470611
[48,] -0.391576004 -1.106998228
[49,] -0.219746619 -0.391576004
[50,] 0.098989497 -0.219746619
[51,] 0.396561812 0.098989497
[52,] -0.131952499 0.396561812
[53,] -0.263825132 -0.131952499
[54,] -0.403816408 -0.263825132
[55,] -0.643729175 -0.403816408
[56,] -0.020894464 -0.643729175
[57,] 0.095463400 -0.020894464
[58,] 0.251373934 0.095463400
[59,] 0.196262102 0.251373934
[60,] 0.138361811 0.196262102
[61,] 0.250955004 0.138361811
[62,] 0.237001563 0.250955004
[63,] 0.526412394 0.237001563
[64,] 0.408078130 0.526412394
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.903873807 -0.985883240
2 -0.091324230 -0.903873807
3 0.338937642 -0.091324230
4 0.438944912 0.338937642
5 0.319314507 0.438944912
6 -0.130856818 0.319314507
7 -0.166776075 -0.130856818
8 -0.029680574 -0.166776075
9 -0.264310183 -0.029680574
10 -0.245126332 -0.264310183
11 -0.345126332 -0.245126332
12 0.186706096 -0.345126332
13 -0.098638531 0.186706096
14 0.087364411 -0.098638531
15 -0.249640543 0.087364411
16 0.070726822 -0.249640543
17 0.359257902 0.070726822
18 0.166260590 0.359257902
19 0.509850386 0.166260590
20 0.475554702 0.509850386
21 0.291912566 0.475554702
22 0.327419387 0.291912566
23 0.672263938 0.327419387
24 0.577680581 0.672263938
25 0.759559164 0.577680581
26 -0.007400313 0.759559164
27 -0.389337051 -0.007400313
28 -0.056727458 -0.389337051
29 0.201219861 -0.056727458
30 0.375489375 0.201219861
31 0.353830908 0.375489375
32 0.039982553 0.353830908
33 0.154234620 0.039982553
34 0.091803622 0.154234620
35 0.583598520 0.091803622
36 0.474710756 0.583598520
37 0.211744788 0.474710756
38 -0.324630929 0.211744788
39 -0.622934254 -0.324630929
40 -0.729069907 -0.622934254
41 -0.615967138 -0.729069907
42 -0.007076738 -0.615967138
43 -0.053176044 -0.007076738
44 -0.464962219 -0.053176044
45 -0.277300403 -0.464962219
46 -0.425470611 -0.277300403
47 -1.106998228 -0.425470611
48 -0.391576004 -1.106998228
49 -0.219746619 -0.391576004
50 0.098989497 -0.219746619
51 0.396561812 0.098989497
52 -0.131952499 0.396561812
53 -0.263825132 -0.131952499
54 -0.403816408 -0.263825132
55 -0.643729175 -0.403816408
56 -0.020894464 -0.643729175
57 0.095463400 -0.020894464
58 0.251373934 0.095463400
59 0.196262102 0.251373934
60 0.138361811 0.196262102
61 0.250955004 0.138361811
62 0.237001563 0.250955004
63 0.526412394 0.237001563
64 0.408078130 0.526412394
> 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/7wqci1258659091.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89g9z1258659091.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9fb2c1258659091.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rdsr1258659091.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wxfd1258659091.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/12d3431258659091.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/1321le1258659091.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/14g40x1258659091.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/15gq8n1258659091.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/16zzt71258659091.tab")
+ }
>
> system("convert tmp/1ydkc1258659091.ps tmp/1ydkc1258659091.png")
> system("convert tmp/27ydn1258659091.ps tmp/27ydn1258659091.png")
> system("convert tmp/3lwe01258659091.ps tmp/3lwe01258659091.png")
> system("convert tmp/43vpc1258659091.ps tmp/43vpc1258659091.png")
> system("convert tmp/5j3ip1258659091.ps tmp/5j3ip1258659091.png")
> system("convert tmp/6vocf1258659091.ps tmp/6vocf1258659091.png")
> system("convert tmp/7wqci1258659091.ps tmp/7wqci1258659091.png")
> system("convert tmp/89g9z1258659091.ps tmp/89g9z1258659091.png")
> system("convert tmp/9fb2c1258659091.ps tmp/9fb2c1258659091.png")
> system("convert tmp/10rdsr1258659091.ps tmp/10rdsr1258659091.png")
>
>
> proc.time()
user system elapsed
2.457 1.560 2.838