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(6.3,2.7,6.1,2.5,6.1,2.2,6.3,2.9,6.3,3.1,6,3,6.2,2.8,6.4,2.5,6.8,1.9,7.5,1.9,7.5,1.8,7.6,2,7.6,2.6,7.4,2.5,7.3,2.5,7.1,1.6,6.9,1.4,6.8,0.8,7.5,1.1,7.6,1.3,7.8,1.2,8,1.3,8.1,1.1,8.2,1.3,8.3,1.2,8.2,1.6,8,1.7,7.9,1.5,7.6,0.9,7.6,1.5,8.3,1.4,8.4,1.6,8.4,1.7,8.4,1.4,8.4,1.8,8.6,1.7,8.9,1.4,8.8,1.2,8.3,1,7.5,1.7,7.2,2.4,7.4,2,8.8,2.1,9.3,2,9.3,1.8,8.7,2.7,8.2,2.3,8.3,1.9,8.5,2,8.6,2.3,8.5,2.8,8.2,2.4,8.1,2.3,7.9,2.7,8.6,2.7,8.7,2.9,8.7,3,8.5,2.2,8.4,2.3,8.5,2.8,8.7,2.8),dim=c(2,61),dimnames=list(c('Werkl','Inflatie'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Werkl','Inflatie'),1:61))
> 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
Werkl Inflatie M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 6.3 2.7 1 0 0 0 0 0 0 0 0 0 0
2 6.1 2.5 0 1 0 0 0 0 0 0 0 0 0
3 6.1 2.2 0 0 1 0 0 0 0 0 0 0 0
4 6.3 2.9 0 0 0 1 0 0 0 0 0 0 0
5 6.3 3.1 0 0 0 0 1 0 0 0 0 0 0
6 6.0 3.0 0 0 0 0 0 1 0 0 0 0 0
7 6.2 2.8 0 0 0 0 0 0 1 0 0 0 0
8 6.4 2.5 0 0 0 0 0 0 0 1 0 0 0
9 6.8 1.9 0 0 0 0 0 0 0 0 1 0 0
10 7.5 1.9 0 0 0 0 0 0 0 0 0 1 0
11 7.5 1.8 0 0 0 0 0 0 0 0 0 0 1
12 7.6 2.0 0 0 0 0 0 0 0 0 0 0 0
13 7.6 2.6 1 0 0 0 0 0 0 0 0 0 0
14 7.4 2.5 0 1 0 0 0 0 0 0 0 0 0
15 7.3 2.5 0 0 1 0 0 0 0 0 0 0 0
16 7.1 1.6 0 0 0 1 0 0 0 0 0 0 0
17 6.9 1.4 0 0 0 0 1 0 0 0 0 0 0
18 6.8 0.8 0 0 0 0 0 1 0 0 0 0 0
19 7.5 1.1 0 0 0 0 0 0 1 0 0 0 0
20 7.6 1.3 0 0 0 0 0 0 0 1 0 0 0
21 7.8 1.2 0 0 0 0 0 0 0 0 1 0 0
22 8.0 1.3 0 0 0 0 0 0 0 0 0 1 0
23 8.1 1.1 0 0 0 0 0 0 0 0 0 0 1
24 8.2 1.3 0 0 0 0 0 0 0 0 0 0 0
25 8.3 1.2 1 0 0 0 0 0 0 0 0 0 0
26 8.2 1.6 0 1 0 0 0 0 0 0 0 0 0
27 8.0 1.7 0 0 1 0 0 0 0 0 0 0 0
28 7.9 1.5 0 0 0 1 0 0 0 0 0 0 0
29 7.6 0.9 0 0 0 0 1 0 0 0 0 0 0
30 7.6 1.5 0 0 0 0 0 1 0 0 0 0 0
31 8.3 1.4 0 0 0 0 0 0 1 0 0 0 0
32 8.4 1.6 0 0 0 0 0 0 0 1 0 0 0
33 8.4 1.7 0 0 0 0 0 0 0 0 1 0 0
34 8.4 1.4 0 0 0 0 0 0 0 0 0 1 0
35 8.4 1.8 0 0 0 0 0 0 0 0 0 0 1
36 8.6 1.7 0 0 0 0 0 0 0 0 0 0 0
37 8.9 1.4 1 0 0 0 0 0 0 0 0 0 0
38 8.8 1.2 0 1 0 0 0 0 0 0 0 0 0
39 8.3 1.0 0 0 1 0 0 0 0 0 0 0 0
40 7.5 1.7 0 0 0 1 0 0 0 0 0 0 0
41 7.2 2.4 0 0 0 0 1 0 0 0 0 0 0
42 7.4 2.0 0 0 0 0 0 1 0 0 0 0 0
43 8.8 2.1 0 0 0 0 0 0 1 0 0 0 0
44 9.3 2.0 0 0 0 0 0 0 0 1 0 0 0
45 9.3 1.8 0 0 0 0 0 0 0 0 1 0 0
46 8.7 2.7 0 0 0 0 0 0 0 0 0 1 0
47 8.2 2.3 0 0 0 0 0 0 0 0 0 0 1
48 8.3 1.9 0 0 0 0 0 0 0 0 0 0 0
49 8.5 2.0 1 0 0 0 0 0 0 0 0 0 0
50 8.6 2.3 0 1 0 0 0 0 0 0 0 0 0
51 8.5 2.8 0 0 1 0 0 0 0 0 0 0 0
52 8.2 2.4 0 0 0 1 0 0 0 0 0 0 0
53 8.1 2.3 0 0 0 0 1 0 0 0 0 0 0
54 7.9 2.7 0 0 0 0 0 1 0 0 0 0 0
55 8.6 2.7 0 0 0 0 0 0 1 0 0 0 0
56 8.7 2.9 0 0 0 0 0 0 0 1 0 0 0
57 8.7 3.0 0 0 0 0 0 0 0 0 1 0 0
58 8.5 2.2 0 0 0 0 0 0 0 0 0 1 0
59 8.4 2.3 0 0 0 0 0 0 0 0 0 0 1
60 8.5 2.8 0 0 0 0 0 0 0 0 0 0 0
61 8.7 2.8 1 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) Inflatie M1 M2 M3 M4
8.70743 -0.24094 -0.14743 -0.40072 -0.57591 -0.82072
M5 M6 M7 M8 M9 M10
-1.00072 -1.08554 -0.34072 -0.13109 -0.04482 -0.02964
M11
-0.13928
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.6094 -0.5735 0.1470 0.4672 1.2055
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.70743 0.50542 17.228 <2e-16 ***
Inflatie -0.24094 0.17528 -1.375 0.1756
M1 -0.14743 0.50725 -0.291 0.7726
M2 -0.40072 0.52900 -0.758 0.4524
M3 -0.57591 0.52911 -1.088 0.2818
M4 -0.82072 0.52900 -1.551 0.1274
M5 -1.00072 0.52900 -1.892 0.0646 .
M6 -1.08554 0.52892 -2.052 0.0456 *
M7 -0.34072 0.52900 -0.644 0.5226
M8 -0.13109 0.52924 -0.248 0.8054
M9 -0.04482 0.52883 -0.085 0.9328
M10 -0.02964 0.52887 -0.056 0.9555
M11 -0.13928 0.52900 -0.263 0.7935
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8361 on 48 degrees of freedom
Multiple R-squared: 0.2293, Adjusted R-squared: 0.03665
F-statistic: 1.19 on 12 and 48 DF, p-value: 0.3170
> 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,] 0.9972622 0.005475668 0.002737834
[2,] 0.9943348 0.011330393 0.005665197
[3,] 0.9886215 0.022757018 0.011378509
[4,] 0.9901041 0.019791716 0.009895858
[5,] 0.9956641 0.008671704 0.004335852
[6,] 0.9979944 0.004011162 0.002005581
[7,] 0.9969363 0.006127315 0.003063658
[8,] 0.9938713 0.012257370 0.006128685
[9,] 0.9888153 0.022369345 0.011184673
[10,] 0.9859814 0.028037212 0.014018606
[11,] 0.9914127 0.017174626 0.008587313
[12,] 0.9933083 0.013383470 0.006691735
[13,] 0.9908180 0.018364071 0.009182036
[14,] 0.9827343 0.034531400 0.017265700
[15,] 0.9814759 0.037048224 0.018524112
[16,] 0.9844485 0.031103095 0.015551547
[17,] 0.9919100 0.016180031 0.008090015
[18,] 0.9963523 0.007295466 0.003647733
[19,] 0.9938190 0.012362047 0.006181023
[20,] 0.9904185 0.019163076 0.009581538
[21,] 0.9845342 0.030931577 0.015465788
[22,] 0.9791011 0.041797857 0.020898928
[23,] 0.9662180 0.067563962 0.033781981
[24,] 0.9397682 0.120463638 0.060231819
[25,] 0.9473795 0.105240926 0.052620463
[26,] 0.9784950 0.043009987 0.021504994
[27,] 0.9846585 0.030682988 0.015341494
[28,] 0.9735165 0.052967016 0.026483508
[29,] 0.9740914 0.051817263 0.025908632
[30,] 0.9978285 0.004343057 0.002171528
> postscript(file="/var/www/html/rcomp/tmp/1egh01260024600.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kt9c1260024600.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lcj51260024600.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rktl1260024600.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gzva1260024600.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 = 61
Frequency = 1
1 2 3 4 5 6
-1.60944938 -1.60434692 -1.50144897 -0.88796936 -0.65978057 -0.89905609
7 8 9 10 11 12
-1.49206375 -1.57398468 -1.40481888 -0.72000000 -0.63445663 -0.62554337
13 14 15 16 17 18
-0.33354378 -0.30434692 -0.22916580 -0.40119644 -0.46938523 -0.62913270
19 20 21 22 23 24
-0.60166840 -0.66311737 -0.57347962 -0.36456635 -0.20311737 -0.19420410
25 26 27 28 29 30
0.02913475 0.27880356 0.27807907 0.37470917 0.11014282 0.33952804
31 32 33 34 35 36
0.27061477 0.20916580 0.14699234 0.05952804 0.26554337 0.30217346
37 38 39 40 41 42
0.67732353 0.78242599 0.40941833 0.02289795 0.07155869 0.26000000
43 44 45 46 47 48
0.93927551 1.20554337 1.07108673 0.67275513 0.18601532 0.05036224
49 50 51 52 53 54
0.42188988 0.84746430 1.04311737 0.89155869 0.94746430 0.92866074
55 56 57 58 59 60
0.88384186 0.82239289 0.76021943 0.35228317 0.38601532 0.46721177
61
0.81464501
> postscript(file="/var/www/html/rcomp/tmp/6jejp1260024600.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.60944938 NA
1 -1.60434692 -1.60944938
2 -1.50144897 -1.60434692
3 -0.88796936 -1.50144897
4 -0.65978057 -0.88796936
5 -0.89905609 -0.65978057
6 -1.49206375 -0.89905609
7 -1.57398468 -1.49206375
8 -1.40481888 -1.57398468
9 -0.72000000 -1.40481888
10 -0.63445663 -0.72000000
11 -0.62554337 -0.63445663
12 -0.33354378 -0.62554337
13 -0.30434692 -0.33354378
14 -0.22916580 -0.30434692
15 -0.40119644 -0.22916580
16 -0.46938523 -0.40119644
17 -0.62913270 -0.46938523
18 -0.60166840 -0.62913270
19 -0.66311737 -0.60166840
20 -0.57347962 -0.66311737
21 -0.36456635 -0.57347962
22 -0.20311737 -0.36456635
23 -0.19420410 -0.20311737
24 0.02913475 -0.19420410
25 0.27880356 0.02913475
26 0.27807907 0.27880356
27 0.37470917 0.27807907
28 0.11014282 0.37470917
29 0.33952804 0.11014282
30 0.27061477 0.33952804
31 0.20916580 0.27061477
32 0.14699234 0.20916580
33 0.05952804 0.14699234
34 0.26554337 0.05952804
35 0.30217346 0.26554337
36 0.67732353 0.30217346
37 0.78242599 0.67732353
38 0.40941833 0.78242599
39 0.02289795 0.40941833
40 0.07155869 0.02289795
41 0.26000000 0.07155869
42 0.93927551 0.26000000
43 1.20554337 0.93927551
44 1.07108673 1.20554337
45 0.67275513 1.07108673
46 0.18601532 0.67275513
47 0.05036224 0.18601532
48 0.42188988 0.05036224
49 0.84746430 0.42188988
50 1.04311737 0.84746430
51 0.89155869 1.04311737
52 0.94746430 0.89155869
53 0.92866074 0.94746430
54 0.88384186 0.92866074
55 0.82239289 0.88384186
56 0.76021943 0.82239289
57 0.35228317 0.76021943
58 0.38601532 0.35228317
59 0.46721177 0.38601532
60 0.81464501 0.46721177
61 NA 0.81464501
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.60434692 -1.60944938
[2,] -1.50144897 -1.60434692
[3,] -0.88796936 -1.50144897
[4,] -0.65978057 -0.88796936
[5,] -0.89905609 -0.65978057
[6,] -1.49206375 -0.89905609
[7,] -1.57398468 -1.49206375
[8,] -1.40481888 -1.57398468
[9,] -0.72000000 -1.40481888
[10,] -0.63445663 -0.72000000
[11,] -0.62554337 -0.63445663
[12,] -0.33354378 -0.62554337
[13,] -0.30434692 -0.33354378
[14,] -0.22916580 -0.30434692
[15,] -0.40119644 -0.22916580
[16,] -0.46938523 -0.40119644
[17,] -0.62913270 -0.46938523
[18,] -0.60166840 -0.62913270
[19,] -0.66311737 -0.60166840
[20,] -0.57347962 -0.66311737
[21,] -0.36456635 -0.57347962
[22,] -0.20311737 -0.36456635
[23,] -0.19420410 -0.20311737
[24,] 0.02913475 -0.19420410
[25,] 0.27880356 0.02913475
[26,] 0.27807907 0.27880356
[27,] 0.37470917 0.27807907
[28,] 0.11014282 0.37470917
[29,] 0.33952804 0.11014282
[30,] 0.27061477 0.33952804
[31,] 0.20916580 0.27061477
[32,] 0.14699234 0.20916580
[33,] 0.05952804 0.14699234
[34,] 0.26554337 0.05952804
[35,] 0.30217346 0.26554337
[36,] 0.67732353 0.30217346
[37,] 0.78242599 0.67732353
[38,] 0.40941833 0.78242599
[39,] 0.02289795 0.40941833
[40,] 0.07155869 0.02289795
[41,] 0.26000000 0.07155869
[42,] 0.93927551 0.26000000
[43,] 1.20554337 0.93927551
[44,] 1.07108673 1.20554337
[45,] 0.67275513 1.07108673
[46,] 0.18601532 0.67275513
[47,] 0.05036224 0.18601532
[48,] 0.42188988 0.05036224
[49,] 0.84746430 0.42188988
[50,] 1.04311737 0.84746430
[51,] 0.89155869 1.04311737
[52,] 0.94746430 0.89155869
[53,] 0.92866074 0.94746430
[54,] 0.88384186 0.92866074
[55,] 0.82239289 0.88384186
[56,] 0.76021943 0.82239289
[57,] 0.35228317 0.76021943
[58,] 0.38601532 0.35228317
[59,] 0.46721177 0.38601532
[60,] 0.81464501 0.46721177
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.60434692 -1.60944938
2 -1.50144897 -1.60434692
3 -0.88796936 -1.50144897
4 -0.65978057 -0.88796936
5 -0.89905609 -0.65978057
6 -1.49206375 -0.89905609
7 -1.57398468 -1.49206375
8 -1.40481888 -1.57398468
9 -0.72000000 -1.40481888
10 -0.63445663 -0.72000000
11 -0.62554337 -0.63445663
12 -0.33354378 -0.62554337
13 -0.30434692 -0.33354378
14 -0.22916580 -0.30434692
15 -0.40119644 -0.22916580
16 -0.46938523 -0.40119644
17 -0.62913270 -0.46938523
18 -0.60166840 -0.62913270
19 -0.66311737 -0.60166840
20 -0.57347962 -0.66311737
21 -0.36456635 -0.57347962
22 -0.20311737 -0.36456635
23 -0.19420410 -0.20311737
24 0.02913475 -0.19420410
25 0.27880356 0.02913475
26 0.27807907 0.27880356
27 0.37470917 0.27807907
28 0.11014282 0.37470917
29 0.33952804 0.11014282
30 0.27061477 0.33952804
31 0.20916580 0.27061477
32 0.14699234 0.20916580
33 0.05952804 0.14699234
34 0.26554337 0.05952804
35 0.30217346 0.26554337
36 0.67732353 0.30217346
37 0.78242599 0.67732353
38 0.40941833 0.78242599
39 0.02289795 0.40941833
40 0.07155869 0.02289795
41 0.26000000 0.07155869
42 0.93927551 0.26000000
43 1.20554337 0.93927551
44 1.07108673 1.20554337
45 0.67275513 1.07108673
46 0.18601532 0.67275513
47 0.05036224 0.18601532
48 0.42188988 0.05036224
49 0.84746430 0.42188988
50 1.04311737 0.84746430
51 0.89155869 1.04311737
52 0.94746430 0.89155869
53 0.92866074 0.94746430
54 0.88384186 0.92866074
55 0.82239289 0.88384186
56 0.76021943 0.82239289
57 0.35228317 0.76021943
58 0.38601532 0.35228317
59 0.46721177 0.38601532
60 0.81464501 0.46721177
> 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/7z1a01260024600.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xkz81260024600.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ddsv1260024600.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10hdz31260024600.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112gfg1260024600.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/12v06n1260024600.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/13dqar1260024600.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/14mavm1260024600.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/15uuiu1260024600.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/16gzrb1260024600.tab")
+ }
>
> system("convert tmp/1egh01260024600.ps tmp/1egh01260024600.png")
> system("convert tmp/2kt9c1260024600.ps tmp/2kt9c1260024600.png")
> system("convert tmp/3lcj51260024600.ps tmp/3lcj51260024600.png")
> system("convert tmp/4rktl1260024600.ps tmp/4rktl1260024600.png")
> system("convert tmp/5gzva1260024600.ps tmp/5gzva1260024600.png")
> system("convert tmp/6jejp1260024600.ps tmp/6jejp1260024600.png")
> system("convert tmp/7z1a01260024600.ps tmp/7z1a01260024600.png")
> system("convert tmp/8xkz81260024600.ps tmp/8xkz81260024600.png")
> system("convert tmp/9ddsv1260024600.ps tmp/9ddsv1260024600.png")
> system("convert tmp/10hdz31260024600.ps tmp/10hdz31260024600.png")
>
>
> proc.time()
user system elapsed
2.492 1.631 3.246