R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1,0,0,0,0,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,1,1,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,1,1,0,0,1,0,1,1,1,0,1,0,1,0,0,0),dim=c(6,68),dimnames=list(c('UseLimit','T20','Used','CorrectAnalysis','Useful','Outcome'),1:68))
> y <- array(NA,dim=c(6,68),dimnames=list(c('UseLimit','T20','Used','CorrectAnalysis','Useful','Outcome'),1:68))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'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, 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
Useful UseLimit T20 Used CorrectAnalysis Outcome
1 0 1 0 0 0 1
2 0 1 1 1 0 1
3 0 0 0 0 0 0
4 0 0 0 0 0 1
5 1 0 0 0 0 0
6 0 1 1 0 0 0
7 1 1 0 0 0 0
8 0 0 0 0 0 0
9 0 0 1 0 0 0
10 0 0 0 0 0 1
11 0 1 1 0 0 0
12 0 0 0 0 0 0
13 0 1 0 0 0 0
14 0 0 0 0 0 1
15 0 1 0 0 0 1
16 0 0 0 0 0 0
17 0 0 0 0 0 0
18 0 0 0 0 0 0
19 0 0 1 1 0 0
20 0 0 0 0 0 0
21 0 0 0 0 0 0
22 0 1 1 1 0 0
23 0 0 0 0 0 0
24 0 1 0 0 0 0
25 1 1 1 1 0 0
26 0 0 1 0 0 0
27 0 0 0 1 0 0
28 0 1 1 1 0 0
29 0 1 0 0 0 0
30 0 0 0 0 0 0
31 0 1 0 0 0 1
32 0 1 0 0 0 0
33 0 0 0 0 0 0
34 0 0 0 0 0 1
35 0 1 0 0 0 0
36 0 0 0 0 0 0
37 0 1 1 1 0 0
38 1 0 0 1 0 1
39 0 0 0 0 0 1
40 0 0 1 0 0 0
41 1 0 0 0 0 0
42 0 0 0 0 0 1
43 0 0 0 0 0 0
44 0 0 0 0 0 1
45 0 1 0 0 0 0
46 0 1 0 0 0 1
47 0 1 0 1 0 0
48 0 0 0 0 0 0
49 0 0 0 0 0 0
50 0 0 0 0 0 0
51 1 1 0 1 0 1
52 1 1 1 1 0 1
53 0 0 1 0 0 0
54 0 0 0 0 0 0
55 0 0 0 1 1 1
56 0 0 1 1 0 1
57 0 1 0 0 0 0
58 1 0 0 0 0 1
59 1 0 0 0 0 0
60 0 0 1 0 0 1
61 0 0 1 1 0 0
62 0 0 1 0 0 0
63 0 1 0 0 0 0
64 1 0 0 0 0 1
65 0 0 0 0 0 1
66 0 1 0 1 1 0
67 1 1 0 1 1 0
68 0 1 0 1 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit T20 Used
0.111729 0.006296 -0.138030 0.227442
CorrectAnalysis Outcome
-0.039182 0.087441
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.3874 -0.1992 -0.1117 0.0200 0.8883
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.111729 0.070775 1.579 0.1195
UseLimit 0.006296 0.097645 0.064 0.9488
T20 -0.138030 0.118235 -1.167 0.2475
Used 0.227442 0.131060 1.735 0.0876 .
CorrectAnalysis -0.039182 0.247364 -0.158 0.8747
Outcome 0.087441 0.098481 0.888 0.3780
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3698 on 62 degrees of freedom
Multiple R-squared: 0.08028, Adjusted R-squared: 0.006113
F-statistic: 1.082 on 5 and 62 DF, p-value: 0.379
> 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,] 0.911933506 0.17613299 0.08806649
[2,] 0.842457794 0.31508441 0.15754221
[3,] 0.750322203 0.49935559 0.24967780
[4,] 0.732505371 0.53498926 0.26749463
[5,] 0.767219232 0.46556154 0.23278077
[6,] 0.680891360 0.63821728 0.31910864
[7,] 0.591417400 0.81716520 0.40858260
[8,] 0.533123357 0.93375329 0.46687664
[9,] 0.464399617 0.92879923 0.53560038
[10,] 0.391872712 0.78374542 0.60812729
[11,] 0.311253732 0.62250746 0.68874627
[12,] 0.248055798 0.49611160 0.75194420
[13,] 0.191617422 0.38323484 0.80838258
[14,] 0.148603715 0.29720743 0.85139628
[15,] 0.108929220 0.21785844 0.89107078
[16,] 0.089383576 0.17876715 0.91061642
[17,] 0.282209051 0.56441810 0.71779095
[18,] 0.220882553 0.44176511 0.77911745
[19,] 0.208739483 0.41747897 0.79126052
[20,] 0.175208755 0.35041751 0.82479125
[21,] 0.142727244 0.28545449 0.85727276
[22,] 0.105841916 0.21168383 0.89415808
[23,] 0.078746085 0.15749217 0.92125391
[24,] 0.059085825 0.11817165 0.94091417
[25,] 0.040788835 0.08157767 0.95921117
[26,] 0.030287200 0.06057440 0.96971280
[27,] 0.021021369 0.04204274 0.97897863
[28,] 0.013629031 0.02725806 0.98637097
[29,] 0.009347241 0.01869448 0.99065276
[30,] 0.030548448 0.06109690 0.96945155
[31,] 0.022538740 0.04507748 0.97746126
[32,] 0.014220789 0.02844158 0.98577921
[33,] 0.088343271 0.17668654 0.91165673
[34,] 0.071202269 0.14240454 0.92879773
[35,] 0.049672160 0.09934432 0.95032784
[36,] 0.041536335 0.08307267 0.95846367
[37,] 0.028645647 0.05729129 0.97135435
[38,] 0.034677387 0.06935477 0.96532261
[39,] 0.031133455 0.06226691 0.96886655
[40,] 0.020056402 0.04011280 0.97994360
[41,] 0.012554885 0.02510977 0.98744512
[42,] 0.007707617 0.01541523 0.99229238
[43,] 0.009897971 0.01979594 0.99010203
[44,] 0.052247450 0.10449490 0.94775255
[45,] 0.031454358 0.06290872 0.96854564
[46,] 0.045192746 0.09038549 0.95480725
[47,] 0.146489759 0.29297952 0.85351024
[48,] 0.099022809 0.19804562 0.90097719
[49,] 0.056485688 0.11297138 0.94351431
[50,] 0.069306258 0.13861252 0.93069374
[51,] 0.063451679 0.12690336 0.93654832
> postscript(file="/var/wessaorg/rcomp/tmp/1gkpx1356127370.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2bf3j1356127370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3mfqj1356127370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/452um1356127370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5e5rp1356127370.ps",horizontal=F,onefile=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 = 68
Frequency = 1
1 2 3 4 5 6
-0.20546624 -0.29487873 -0.11172902 -0.19917030 0.88827098 0.02000484
7 8 9 10 11 12
0.88197504 -0.11172902 0.02630078 -0.19917030 0.02000484 -0.11172902
13 14 15 16 17 18
-0.11802496 -0.19917030 -0.20546624 -0.11172902 -0.11172902 -0.11172902
19 20 21 22 23 24
-0.20114151 -0.11172902 -0.11172902 -0.20743745 -0.11172902 -0.11802496
25 26 27 28 29 30
0.79256255 0.02630078 -0.33917131 -0.20743745 -0.11802496 -0.11172902
31 32 33 34 35 36
-0.20546624 -0.11802496 -0.11172902 -0.19917030 -0.11802496 -0.11172902
37 38 39 40 41 42
-0.20743745 0.57338741 -0.19917030 0.02630078 0.88827098 -0.19917030
43 44 45 46 47 48
-0.11172902 -0.19917030 -0.11802496 -0.20546624 -0.34546725 -0.11172902
49 50 51 52 53 54
-0.11172902 -0.11172902 0.56709147 0.70512127 0.02630078 -0.11172902
55 56 57 58 59 60
-0.38743022 -0.28858279 -0.11802496 0.80082970 0.88827098 -0.06114050
61 62 63 64 65 66
-0.20114151 0.02630078 -0.11802496 0.80082970 -0.19917030 -0.30628489
67 68
0.69371511 -0.34546725
> postscript(file="/var/wessaorg/rcomp/tmp/6vw9m1356127370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 68
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.20546624 NA
1 -0.29487873 -0.20546624
2 -0.11172902 -0.29487873
3 -0.19917030 -0.11172902
4 0.88827098 -0.19917030
5 0.02000484 0.88827098
6 0.88197504 0.02000484
7 -0.11172902 0.88197504
8 0.02630078 -0.11172902
9 -0.19917030 0.02630078
10 0.02000484 -0.19917030
11 -0.11172902 0.02000484
12 -0.11802496 -0.11172902
13 -0.19917030 -0.11802496
14 -0.20546624 -0.19917030
15 -0.11172902 -0.20546624
16 -0.11172902 -0.11172902
17 -0.11172902 -0.11172902
18 -0.20114151 -0.11172902
19 -0.11172902 -0.20114151
20 -0.11172902 -0.11172902
21 -0.20743745 -0.11172902
22 -0.11172902 -0.20743745
23 -0.11802496 -0.11172902
24 0.79256255 -0.11802496
25 0.02630078 0.79256255
26 -0.33917131 0.02630078
27 -0.20743745 -0.33917131
28 -0.11802496 -0.20743745
29 -0.11172902 -0.11802496
30 -0.20546624 -0.11172902
31 -0.11802496 -0.20546624
32 -0.11172902 -0.11802496
33 -0.19917030 -0.11172902
34 -0.11802496 -0.19917030
35 -0.11172902 -0.11802496
36 -0.20743745 -0.11172902
37 0.57338741 -0.20743745
38 -0.19917030 0.57338741
39 0.02630078 -0.19917030
40 0.88827098 0.02630078
41 -0.19917030 0.88827098
42 -0.11172902 -0.19917030
43 -0.19917030 -0.11172902
44 -0.11802496 -0.19917030
45 -0.20546624 -0.11802496
46 -0.34546725 -0.20546624
47 -0.11172902 -0.34546725
48 -0.11172902 -0.11172902
49 -0.11172902 -0.11172902
50 0.56709147 -0.11172902
51 0.70512127 0.56709147
52 0.02630078 0.70512127
53 -0.11172902 0.02630078
54 -0.38743022 -0.11172902
55 -0.28858279 -0.38743022
56 -0.11802496 -0.28858279
57 0.80082970 -0.11802496
58 0.88827098 0.80082970
59 -0.06114050 0.88827098
60 -0.20114151 -0.06114050
61 0.02630078 -0.20114151
62 -0.11802496 0.02630078
63 0.80082970 -0.11802496
64 -0.19917030 0.80082970
65 -0.30628489 -0.19917030
66 0.69371511 -0.30628489
67 -0.34546725 0.69371511
68 NA -0.34546725
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.29487873 -0.20546624
[2,] -0.11172902 -0.29487873
[3,] -0.19917030 -0.11172902
[4,] 0.88827098 -0.19917030
[5,] 0.02000484 0.88827098
[6,] 0.88197504 0.02000484
[7,] -0.11172902 0.88197504
[8,] 0.02630078 -0.11172902
[9,] -0.19917030 0.02630078
[10,] 0.02000484 -0.19917030
[11,] -0.11172902 0.02000484
[12,] -0.11802496 -0.11172902
[13,] -0.19917030 -0.11802496
[14,] -0.20546624 -0.19917030
[15,] -0.11172902 -0.20546624
[16,] -0.11172902 -0.11172902
[17,] -0.11172902 -0.11172902
[18,] -0.20114151 -0.11172902
[19,] -0.11172902 -0.20114151
[20,] -0.11172902 -0.11172902
[21,] -0.20743745 -0.11172902
[22,] -0.11172902 -0.20743745
[23,] -0.11802496 -0.11172902
[24,] 0.79256255 -0.11802496
[25,] 0.02630078 0.79256255
[26,] -0.33917131 0.02630078
[27,] -0.20743745 -0.33917131
[28,] -0.11802496 -0.20743745
[29,] -0.11172902 -0.11802496
[30,] -0.20546624 -0.11172902
[31,] -0.11802496 -0.20546624
[32,] -0.11172902 -0.11802496
[33,] -0.19917030 -0.11172902
[34,] -0.11802496 -0.19917030
[35,] -0.11172902 -0.11802496
[36,] -0.20743745 -0.11172902
[37,] 0.57338741 -0.20743745
[38,] -0.19917030 0.57338741
[39,] 0.02630078 -0.19917030
[40,] 0.88827098 0.02630078
[41,] -0.19917030 0.88827098
[42,] -0.11172902 -0.19917030
[43,] -0.19917030 -0.11172902
[44,] -0.11802496 -0.19917030
[45,] -0.20546624 -0.11802496
[46,] -0.34546725 -0.20546624
[47,] -0.11172902 -0.34546725
[48,] -0.11172902 -0.11172902
[49,] -0.11172902 -0.11172902
[50,] 0.56709147 -0.11172902
[51,] 0.70512127 0.56709147
[52,] 0.02630078 0.70512127
[53,] -0.11172902 0.02630078
[54,] -0.38743022 -0.11172902
[55,] -0.28858279 -0.38743022
[56,] -0.11802496 -0.28858279
[57,] 0.80082970 -0.11802496
[58,] 0.88827098 0.80082970
[59,] -0.06114050 0.88827098
[60,] -0.20114151 -0.06114050
[61,] 0.02630078 -0.20114151
[62,] -0.11802496 0.02630078
[63,] 0.80082970 -0.11802496
[64,] -0.19917030 0.80082970
[65,] -0.30628489 -0.19917030
[66,] 0.69371511 -0.30628489
[67,] -0.34546725 0.69371511
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.29487873 -0.20546624
2 -0.11172902 -0.29487873
3 -0.19917030 -0.11172902
4 0.88827098 -0.19917030
5 0.02000484 0.88827098
6 0.88197504 0.02000484
7 -0.11172902 0.88197504
8 0.02630078 -0.11172902
9 -0.19917030 0.02630078
10 0.02000484 -0.19917030
11 -0.11172902 0.02000484
12 -0.11802496 -0.11172902
13 -0.19917030 -0.11802496
14 -0.20546624 -0.19917030
15 -0.11172902 -0.20546624
16 -0.11172902 -0.11172902
17 -0.11172902 -0.11172902
18 -0.20114151 -0.11172902
19 -0.11172902 -0.20114151
20 -0.11172902 -0.11172902
21 -0.20743745 -0.11172902
22 -0.11172902 -0.20743745
23 -0.11802496 -0.11172902
24 0.79256255 -0.11802496
25 0.02630078 0.79256255
26 -0.33917131 0.02630078
27 -0.20743745 -0.33917131
28 -0.11802496 -0.20743745
29 -0.11172902 -0.11802496
30 -0.20546624 -0.11172902
31 -0.11802496 -0.20546624
32 -0.11172902 -0.11802496
33 -0.19917030 -0.11172902
34 -0.11802496 -0.19917030
35 -0.11172902 -0.11802496
36 -0.20743745 -0.11172902
37 0.57338741 -0.20743745
38 -0.19917030 0.57338741
39 0.02630078 -0.19917030
40 0.88827098 0.02630078
41 -0.19917030 0.88827098
42 -0.11172902 -0.19917030
43 -0.19917030 -0.11172902
44 -0.11802496 -0.19917030
45 -0.20546624 -0.11802496
46 -0.34546725 -0.20546624
47 -0.11172902 -0.34546725
48 -0.11172902 -0.11172902
49 -0.11172902 -0.11172902
50 0.56709147 -0.11172902
51 0.70512127 0.56709147
52 0.02630078 0.70512127
53 -0.11172902 0.02630078
54 -0.38743022 -0.11172902
55 -0.28858279 -0.38743022
56 -0.11802496 -0.28858279
57 0.80082970 -0.11802496
58 0.88827098 0.80082970
59 -0.06114050 0.88827098
60 -0.20114151 -0.06114050
61 0.02630078 -0.20114151
62 -0.11802496 0.02630078
63 0.80082970 -0.11802496
64 -0.19917030 0.80082970
65 -0.30628489 -0.19917030
66 0.69371511 -0.30628489
67 -0.34546725 0.69371511
> 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/wessaorg/rcomp/tmp/783hj1356127370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8641a1356127370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9luyz1356127370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10pg411356127370.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11oqeh1356127370.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/wessaorg/rcomp/tmp/12fhyh1356127370.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/wessaorg/rcomp/tmp/13pjqb1356127370.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/wessaorg/rcomp/tmp/1400uj1356127371.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/wessaorg/rcomp/tmp/15h3ut1356127371.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/wessaorg/rcomp/tmp/167zcv1356127371.tab")
+ }
>
> try(system("convert tmp/1gkpx1356127370.ps tmp/1gkpx1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bf3j1356127370.ps tmp/2bf3j1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mfqj1356127370.ps tmp/3mfqj1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/452um1356127370.ps tmp/452um1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/5e5rp1356127370.ps tmp/5e5rp1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vw9m1356127370.ps tmp/6vw9m1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/783hj1356127370.ps tmp/783hj1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/8641a1356127370.ps tmp/8641a1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/9luyz1356127370.ps tmp/9luyz1356127370.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pg411356127370.ps tmp/10pg411356127370.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.365 1.016 7.405