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(79.8,109.87,83.4,95.74,113.6,123.06,112.9,123.39,104,120.28,109.9,115.33,99,110.4,106.3,114.49,128.9,132.03,111.1,123.16,102.9,118.82,130,128.32,87,112.24,87.5,104.53,117.6,132.57,103.4,122.52,110.8,131.8,112.6,124.55,102.5,120.96,112.4,122.6,135.6,145.52,105.1,118.57,127.7,134.25,137,136.7,91,121.37,90.5,111.63,122.4,134.42,123.3,137.65,124.3,137.86,120,119.77,118.1,130.69,119,128.28,142.7,147.45,123.6,128.42,129.6,136.9,151.6,143.95,110.4,135.64,99.2,122.48,130.5,136.83,136.2,153.04,129.7,142.71,128,123.46,121.6,144.37,135.8,146.15,143.8,147.61,147.5,158.51,136.2,147.4,156.6,165.05,123.3,154.64,104.5,126.2,139.8,157.36,136.5,154.15,112.1,123.21,118.5,113.07,94.4,110.45,102.3,113.57,111.4,122.44,99.2,114.93,87.8,111.85,115.8,126.04),dim=c(2,60),dimnames=list(c('Investgoed','Uitvoer'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Investgoed','Uitvoer'),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
Investgoed Uitvoer M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 79.8 109.87 1 0 0 0 0 0 0 0 0 0 0 1
2 83.4 95.74 0 1 0 0 0 0 0 0 0 0 0 2
3 113.6 123.06 0 0 1 0 0 0 0 0 0 0 0 3
4 112.9 123.39 0 0 0 1 0 0 0 0 0 0 0 4
5 104.0 120.28 0 0 0 0 1 0 0 0 0 0 0 5
6 109.9 115.33 0 0 0 0 0 1 0 0 0 0 0 6
7 99.0 110.40 0 0 0 0 0 0 1 0 0 0 0 7
8 106.3 114.49 0 0 0 0 0 0 0 1 0 0 0 8
9 128.9 132.03 0 0 0 0 0 0 0 0 1 0 0 9
10 111.1 123.16 0 0 0 0 0 0 0 0 0 1 0 10
11 102.9 118.82 0 0 0 0 0 0 0 0 0 0 1 11
12 130.0 128.32 0 0 0 0 0 0 0 0 0 0 0 12
13 87.0 112.24 1 0 0 0 0 0 0 0 0 0 0 13
14 87.5 104.53 0 1 0 0 0 0 0 0 0 0 0 14
15 117.6 132.57 0 0 1 0 0 0 0 0 0 0 0 15
16 103.4 122.52 0 0 0 1 0 0 0 0 0 0 0 16
17 110.8 131.80 0 0 0 0 1 0 0 0 0 0 0 17
18 112.6 124.55 0 0 0 0 0 1 0 0 0 0 0 18
19 102.5 120.96 0 0 0 0 0 0 1 0 0 0 0 19
20 112.4 122.60 0 0 0 0 0 0 0 1 0 0 0 20
21 135.6 145.52 0 0 0 0 0 0 0 0 1 0 0 21
22 105.1 118.57 0 0 0 0 0 0 0 0 0 1 0 22
23 127.7 134.25 0 0 0 0 0 0 0 0 0 0 1 23
24 137.0 136.70 0 0 0 0 0 0 0 0 0 0 0 24
25 91.0 121.37 1 0 0 0 0 0 0 0 0 0 0 25
26 90.5 111.63 0 1 0 0 0 0 0 0 0 0 0 26
27 122.4 134.42 0 0 1 0 0 0 0 0 0 0 0 27
28 123.3 137.65 0 0 0 1 0 0 0 0 0 0 0 28
29 124.3 137.86 0 0 0 0 1 0 0 0 0 0 0 29
30 120.0 119.77 0 0 0 0 0 1 0 0 0 0 0 30
31 118.1 130.69 0 0 0 0 0 0 1 0 0 0 0 31
32 119.0 128.28 0 0 0 0 0 0 0 1 0 0 0 32
33 142.7 147.45 0 0 0 0 0 0 0 0 1 0 0 33
34 123.6 128.42 0 0 0 0 0 0 0 0 0 1 0 34
35 129.6 136.90 0 0 0 0 0 0 0 0 0 0 1 35
36 151.6 143.95 0 0 0 0 0 0 0 0 0 0 0 36
37 110.4 135.64 1 0 0 0 0 0 0 0 0 0 0 37
38 99.2 122.48 0 1 0 0 0 0 0 0 0 0 0 38
39 130.5 136.83 0 0 1 0 0 0 0 0 0 0 0 39
40 136.2 153.04 0 0 0 1 0 0 0 0 0 0 0 40
41 129.7 142.71 0 0 0 0 1 0 0 0 0 0 0 41
42 128.0 123.46 0 0 0 0 0 1 0 0 0 0 0 42
43 121.6 144.37 0 0 0 0 0 0 1 0 0 0 0 43
44 135.8 146.15 0 0 0 0 0 0 0 1 0 0 0 44
45 143.8 147.61 0 0 0 0 0 0 0 0 1 0 0 45
46 147.5 158.51 0 0 0 0 0 0 0 0 0 1 0 46
47 136.2 147.40 0 0 0 0 0 0 0 0 0 0 1 47
48 156.6 165.05 0 0 0 0 0 0 0 0 0 0 0 48
49 123.3 154.64 1 0 0 0 0 0 0 0 0 0 0 49
50 104.5 126.20 0 1 0 0 0 0 0 0 0 0 0 50
51 139.8 157.36 0 0 1 0 0 0 0 0 0 0 0 51
52 136.5 154.15 0 0 0 1 0 0 0 0 0 0 0 52
53 112.1 123.21 0 0 0 0 1 0 0 0 0 0 0 53
54 118.5 113.07 0 0 0 0 0 1 0 0 0 0 0 54
55 94.4 110.45 0 0 0 0 0 0 1 0 0 0 0 55
56 102.3 113.57 0 0 0 0 0 0 0 1 0 0 0 56
57 111.4 122.44 0 0 0 0 0 0 0 0 1 0 0 57
58 99.2 114.93 0 0 0 0 0 0 0 0 0 1 0 58
59 87.8 111.85 0 0 0 0 0 0 0 0 0 0 1 59
60 115.8 126.04 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) Uitvoer M1 M2 M3 M4
-0.26592 1.00324 -27.20821 -17.74915 -10.74583 -14.31649
M5 M6 M7 M8 M9 M10
-13.54028 0.11001 -14.66585 -8.21961 -4.88145 -9.68049
M11 t
-11.21457 -0.05557
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-11.1980 -2.9060 0.2374 2.5971 9.4492
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.26592 7.71488 -0.034 0.972653
Uitvoer 1.00324 0.05644 17.777 < 2e-16 ***
M1 -27.20821 3.16499 -8.597 3.97e-11 ***
M2 -17.74915 3.41172 -5.202 4.44e-06 ***
M3 -10.74583 3.10669 -3.459 0.001180 **
M4 -14.31649 3.10302 -4.614 3.17e-05 ***
M5 -13.54028 3.12240 -4.336 7.82e-05 ***
M6 0.11001 3.27575 0.034 0.973354
M7 -14.66585 3.20938 -4.570 3.67e-05 ***
M8 -8.21961 3.18823 -2.578 0.013202 *
M9 -4.88145 3.09058 -1.579 0.121084
M10 -9.68049 3.14770 -3.075 0.003532 **
M11 -11.21457 3.13852 -3.573 0.000841 ***
t -0.05557 0.04100 -1.355 0.181925
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.883 on 46 degrees of freedom
Multiple R-squared: 0.9415, Adjusted R-squared: 0.925
F-statistic: 56.98 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,] 0.38104908 0.7620982 0.6189509
[2,] 0.36912383 0.7382477 0.6308762
[3,] 0.25190626 0.5038125 0.7480937
[4,] 0.17042797 0.3408559 0.8295720
[5,] 0.11820965 0.2364193 0.8817904
[6,] 0.08072137 0.1614427 0.9192786
[7,] 0.41763525 0.8352705 0.5823647
[8,] 0.31537803 0.6307561 0.6846220
[9,] 0.25976436 0.5195287 0.7402356
[10,] 0.19757046 0.3951409 0.8024295
[11,] 0.18604908 0.3720982 0.8139509
[12,] 0.15187232 0.3037446 0.8481277
[13,] 0.22752404 0.4550481 0.7724760
[14,] 0.50080896 0.9983821 0.4991910
[15,] 0.43114264 0.8622853 0.5688574
[16,] 0.40633697 0.8126739 0.5936630
[17,] 0.35927434 0.7185487 0.6407257
[18,] 0.35418731 0.7083746 0.6458127
[19,] 0.28754702 0.5750940 0.7124530
[20,] 0.45617851 0.9123570 0.5438215
[21,] 0.37264109 0.7452822 0.6273589
[22,] 0.37521967 0.7504393 0.6247803
[23,] 0.45916668 0.9183334 0.5408333
[24,] 0.34819751 0.6963950 0.6518025
[25,] 0.25647042 0.5129408 0.7435296
[26,] 0.18763830 0.3752766 0.8123617
[27,] 0.45772349 0.9154470 0.5422765
> postscript(file="/var/www/html/rcomp/tmp/1kgye1258713863.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25j3v1258713863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3x5c81258713863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4uhyb1258713863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5j7qz1258713863.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
-2.89681739 5.47554075 1.31914324 3.91429717 -2.58625367 -5.31492175
7 8 9 10 11 12
3.56250198 0.36856523 2.08905272 -1.95756136 -4.21382535 2.19634216
13 14 15 16 17 18
2.59229041 1.42381701 -3.55491675 -4.04608191 -6.67683567 -11.19804075
19 20 21 22 23 24
-2.86496503 -1.00095205 -4.07792152 -2.68586984 5.77290552 1.45594879
25 26 27 28 29 30
-1.90053655 -2.03242303 0.05587834 1.34162239 1.41029886 1.66426728
31 32 33 34 35 36
3.64026113 0.56741550 1.75261399 6.59896695 5.68110478 9.44922203
37 38 39 40 41 42
3.84995827 -3.55083103 6.40485635 -0.53151694 2.61135959 6.62909196
43 44 45 46 47 48
-5.91732962 0.10622909 3.35889277 0.97812923 2.41383245 -6.05244505
49 50 51 52 53 54
-1.64489475 -1.31610370 -4.22496119 -0.67832071 5.24143089 8.21960326
55 56 57 58 59 60
1.57953153 -0.04125778 -3.12263797 -2.93366497 -9.65401739 -7.04906793
> postscript(file="/var/www/html/rcomp/tmp/6onh21258713863.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 -2.89681739 NA
1 5.47554075 -2.89681739
2 1.31914324 5.47554075
3 3.91429717 1.31914324
4 -2.58625367 3.91429717
5 -5.31492175 -2.58625367
6 3.56250198 -5.31492175
7 0.36856523 3.56250198
8 2.08905272 0.36856523
9 -1.95756136 2.08905272
10 -4.21382535 -1.95756136
11 2.19634216 -4.21382535
12 2.59229041 2.19634216
13 1.42381701 2.59229041
14 -3.55491675 1.42381701
15 -4.04608191 -3.55491675
16 -6.67683567 -4.04608191
17 -11.19804075 -6.67683567
18 -2.86496503 -11.19804075
19 -1.00095205 -2.86496503
20 -4.07792152 -1.00095205
21 -2.68586984 -4.07792152
22 5.77290552 -2.68586984
23 1.45594879 5.77290552
24 -1.90053655 1.45594879
25 -2.03242303 -1.90053655
26 0.05587834 -2.03242303
27 1.34162239 0.05587834
28 1.41029886 1.34162239
29 1.66426728 1.41029886
30 3.64026113 1.66426728
31 0.56741550 3.64026113
32 1.75261399 0.56741550
33 6.59896695 1.75261399
34 5.68110478 6.59896695
35 9.44922203 5.68110478
36 3.84995827 9.44922203
37 -3.55083103 3.84995827
38 6.40485635 -3.55083103
39 -0.53151694 6.40485635
40 2.61135959 -0.53151694
41 6.62909196 2.61135959
42 -5.91732962 6.62909196
43 0.10622909 -5.91732962
44 3.35889277 0.10622909
45 0.97812923 3.35889277
46 2.41383245 0.97812923
47 -6.05244505 2.41383245
48 -1.64489475 -6.05244505
49 -1.31610370 -1.64489475
50 -4.22496119 -1.31610370
51 -0.67832071 -4.22496119
52 5.24143089 -0.67832071
53 8.21960326 5.24143089
54 1.57953153 8.21960326
55 -0.04125778 1.57953153
56 -3.12263797 -0.04125778
57 -2.93366497 -3.12263797
58 -9.65401739 -2.93366497
59 -7.04906793 -9.65401739
60 NA -7.04906793
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.47554075 -2.89681739
[2,] 1.31914324 5.47554075
[3,] 3.91429717 1.31914324
[4,] -2.58625367 3.91429717
[5,] -5.31492175 -2.58625367
[6,] 3.56250198 -5.31492175
[7,] 0.36856523 3.56250198
[8,] 2.08905272 0.36856523
[9,] -1.95756136 2.08905272
[10,] -4.21382535 -1.95756136
[11,] 2.19634216 -4.21382535
[12,] 2.59229041 2.19634216
[13,] 1.42381701 2.59229041
[14,] -3.55491675 1.42381701
[15,] -4.04608191 -3.55491675
[16,] -6.67683567 -4.04608191
[17,] -11.19804075 -6.67683567
[18,] -2.86496503 -11.19804075
[19,] -1.00095205 -2.86496503
[20,] -4.07792152 -1.00095205
[21,] -2.68586984 -4.07792152
[22,] 5.77290552 -2.68586984
[23,] 1.45594879 5.77290552
[24,] -1.90053655 1.45594879
[25,] -2.03242303 -1.90053655
[26,] 0.05587834 -2.03242303
[27,] 1.34162239 0.05587834
[28,] 1.41029886 1.34162239
[29,] 1.66426728 1.41029886
[30,] 3.64026113 1.66426728
[31,] 0.56741550 3.64026113
[32,] 1.75261399 0.56741550
[33,] 6.59896695 1.75261399
[34,] 5.68110478 6.59896695
[35,] 9.44922203 5.68110478
[36,] 3.84995827 9.44922203
[37,] -3.55083103 3.84995827
[38,] 6.40485635 -3.55083103
[39,] -0.53151694 6.40485635
[40,] 2.61135959 -0.53151694
[41,] 6.62909196 2.61135959
[42,] -5.91732962 6.62909196
[43,] 0.10622909 -5.91732962
[44,] 3.35889277 0.10622909
[45,] 0.97812923 3.35889277
[46,] 2.41383245 0.97812923
[47,] -6.05244505 2.41383245
[48,] -1.64489475 -6.05244505
[49,] -1.31610370 -1.64489475
[50,] -4.22496119 -1.31610370
[51,] -0.67832071 -4.22496119
[52,] 5.24143089 -0.67832071
[53,] 8.21960326 5.24143089
[54,] 1.57953153 8.21960326
[55,] -0.04125778 1.57953153
[56,] -3.12263797 -0.04125778
[57,] -2.93366497 -3.12263797
[58,] -9.65401739 -2.93366497
[59,] -7.04906793 -9.65401739
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.47554075 -2.89681739
2 1.31914324 5.47554075
3 3.91429717 1.31914324
4 -2.58625367 3.91429717
5 -5.31492175 -2.58625367
6 3.56250198 -5.31492175
7 0.36856523 3.56250198
8 2.08905272 0.36856523
9 -1.95756136 2.08905272
10 -4.21382535 -1.95756136
11 2.19634216 -4.21382535
12 2.59229041 2.19634216
13 1.42381701 2.59229041
14 -3.55491675 1.42381701
15 -4.04608191 -3.55491675
16 -6.67683567 -4.04608191
17 -11.19804075 -6.67683567
18 -2.86496503 -11.19804075
19 -1.00095205 -2.86496503
20 -4.07792152 -1.00095205
21 -2.68586984 -4.07792152
22 5.77290552 -2.68586984
23 1.45594879 5.77290552
24 -1.90053655 1.45594879
25 -2.03242303 -1.90053655
26 0.05587834 -2.03242303
27 1.34162239 0.05587834
28 1.41029886 1.34162239
29 1.66426728 1.41029886
30 3.64026113 1.66426728
31 0.56741550 3.64026113
32 1.75261399 0.56741550
33 6.59896695 1.75261399
34 5.68110478 6.59896695
35 9.44922203 5.68110478
36 3.84995827 9.44922203
37 -3.55083103 3.84995827
38 6.40485635 -3.55083103
39 -0.53151694 6.40485635
40 2.61135959 -0.53151694
41 6.62909196 2.61135959
42 -5.91732962 6.62909196
43 0.10622909 -5.91732962
44 3.35889277 0.10622909
45 0.97812923 3.35889277
46 2.41383245 0.97812923
47 -6.05244505 2.41383245
48 -1.64489475 -6.05244505
49 -1.31610370 -1.64489475
50 -4.22496119 -1.31610370
51 -0.67832071 -4.22496119
52 5.24143089 -0.67832071
53 8.21960326 5.24143089
54 1.57953153 8.21960326
55 -0.04125778 1.57953153
56 -3.12263797 -0.04125778
57 -2.93366497 -3.12263797
58 -9.65401739 -2.93366497
59 -7.04906793 -9.65401739
> 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/7o4vk1258713863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mcuq1258713863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/93gg41258713863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10jxk61258713863.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111ddw1258713863.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/12czar1258713863.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/13v2if1258713863.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/14bnpg1258713863.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/15b3cr1258713863.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/16x2h31258713863.tab")
+ }
>
> system("convert tmp/1kgye1258713863.ps tmp/1kgye1258713863.png")
> system("convert tmp/25j3v1258713863.ps tmp/25j3v1258713863.png")
> system("convert tmp/3x5c81258713863.ps tmp/3x5c81258713863.png")
> system("convert tmp/4uhyb1258713863.ps tmp/4uhyb1258713863.png")
> system("convert tmp/5j7qz1258713863.ps tmp/5j7qz1258713863.png")
> system("convert tmp/6onh21258713863.ps tmp/6onh21258713863.png")
> system("convert tmp/7o4vk1258713863.ps tmp/7o4vk1258713863.png")
> system("convert tmp/8mcuq1258713863.ps tmp/8mcuq1258713863.png")
> system("convert tmp/93gg41258713863.ps tmp/93gg41258713863.png")
> system("convert tmp/10jxk61258713863.ps tmp/10jxk61258713863.png")
>
>
> proc.time()
user system elapsed
2.347 1.507 2.792