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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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
T20 UseLimit Used CorrectAnalysis Useful Outcome
1 0 1 0 0 0 1
2 1 1 1 0 0 1
3 0 0 0 0 0 0
4 0 0 0 0 0 1
5 0 0 0 0 1 0
6 1 1 0 0 0 0
7 0 1 0 0 1 0
8 0 0 0 0 0 0
9 1 0 0 0 0 0
10 0 0 0 0 0 1
11 1 1 0 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 1 0 1 0 0 0
20 0 0 0 0 0 0
21 0 0 0 0 0 0
22 1 1 1 0 0 0
23 0 0 0 0 0 0
24 0 1 0 0 0 0
25 1 1 1 0 1 0
26 1 0 0 0 0 0
27 0 0 1 0 0 0
28 1 1 1 0 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 1 1 1 0 0 0
38 0 0 1 0 1 1
39 0 0 0 0 0 1
40 1 0 0 0 0 0
41 0 0 0 0 1 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 1 0 0 0
48 0 0 0 0 0 0
49 0 0 0 0 0 0
50 0 0 0 0 0 0
51 0 1 1 0 1 1
52 1 1 1 0 1 1
53 1 0 0 0 0 0
54 0 0 0 0 0 0
55 0 0 1 1 0 1
56 1 0 1 0 0 1
57 0 1 0 0 0 0
58 0 0 0 0 1 1
59 0 0 0 0 1 0
60 1 0 0 0 0 1
61 1 0 1 0 0 0
62 1 0 0 0 0 0
63 0 1 0 0 0 0
64 0 0 0 0 1 1
65 0 0 0 0 0 1
66 0 1 1 1 0 0
67 0 1 1 1 1 0
68 0 1 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit Used CorrectAnalysis
0.20816 -0.01802 0.52439 -0.63725
Useful Outcome
-0.15583 -0.09406
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.7326 -0.2082 -0.1141 0.1258 0.8859
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.20816 0.07200 2.891 0.00528 **
UseLimit -0.01802 0.10373 -0.174 0.86268
Used 0.52439 0.12609 4.159 0.00010 ***
CorrectAnalysis -0.63725 0.25011 -2.548 0.01333 *
Useful -0.15583 0.13348 -1.167 0.24751
Outcome -0.09406 0.10462 -0.899 0.37212
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.393 on 62 degrees of freedom
Multiple R-squared: 0.2491, Adjusted R-squared: 0.1886
F-statistic: 4.114 on 5 and 62 DF, p-value: 0.00273
> 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,] 0.8593210 0.2813580 0.1406790
[2,] 0.7599550 0.4800900 0.2400450
[3,] 0.7322613 0.5354773 0.2677387
[4,] 0.7207129 0.5585743 0.2792871
[5,] 0.8154798 0.3690403 0.1845202
[6,] 0.7362980 0.5274041 0.2637020
[7,] 0.6534025 0.6931949 0.3465975
[8,] 0.6060283 0.7879433 0.3939717
[9,] 0.5468229 0.9063541 0.4531771
[10,] 0.4814152 0.9628303 0.5185848
[11,] 0.3970780 0.7941561 0.6029220
[12,] 0.3344778 0.6689555 0.6655222
[13,] 0.2753999 0.5507997 0.7246001
[14,] 0.2442827 0.4885653 0.7557173
[15,] 0.1955527 0.3911055 0.8044473
[16,] 0.1908335 0.3816669 0.8091665
[17,] 0.1726796 0.3453591 0.8273204
[18,] 0.4102598 0.8205196 0.5897402
[19,] 0.6507177 0.6985646 0.3492823
[20,] 0.6160229 0.7679543 0.3839771
[21,] 0.5876013 0.8247973 0.4123987
[22,] 0.5301620 0.9396761 0.4698380
[23,] 0.4612261 0.9224522 0.5387739
[24,] 0.4175649 0.8351298 0.5824351
[25,] 0.3635249 0.7270498 0.6364751
[26,] 0.3032630 0.6065260 0.6967370
[27,] 0.2593943 0.5187887 0.7406057
[28,] 0.2182168 0.4364336 0.7817832
[29,] 0.2104422 0.4208844 0.7895578
[30,] 0.2352701 0.4705403 0.7647299
[31,] 0.1941095 0.3882191 0.8058905
[32,] 0.3965198 0.7930395 0.6034802
[33,] 0.3288452 0.6576903 0.6711548
[34,] 0.2810834 0.5621668 0.7189166
[35,] 0.2394627 0.4789253 0.7605373
[36,] 0.2069640 0.4139280 0.7930360
[37,] 0.1671907 0.3343813 0.8328093
[38,] 0.1217065 0.2434129 0.8782935
[39,] 0.1896995 0.3793990 0.8103005
[40,] 0.1598462 0.3196925 0.8401538
[41,] 0.1384785 0.2769571 0.8615215
[42,] 0.1275582 0.2551164 0.8724418
[43,] 0.1187748 0.2375496 0.8812252
[44,] 0.2763080 0.5526161 0.7236920
[45,] 0.3556748 0.7113495 0.6443252
[46,] 0.4361370 0.8722740 0.5638630
[47,] 0.5652060 0.8695879 0.4347940
[48,] 0.5130061 0.9739879 0.4869939
[49,] 0.3877410 0.7754820 0.6122590
[50,] 0.2607569 0.5215138 0.7392431
[51,] 0.2601254 0.5202508 0.7398746
> postscript(file="/var/wessaorg/rcomp/tmp/1l2q31356113472.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2poje1356113472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3vx9k1356113472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4xyaa1356113472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5lpzz1356113472.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
-0.096085119 0.379520111 -0.208157968 -0.114101370 -0.052330698 0.809858284
7 8 9 10 11 12
-0.034314446 -0.208157968 0.791842032 -0.114101370 0.809858284 -0.208157968
13 14 15 16 17 18
-0.190141716 -0.114101370 -0.096085119 -0.208157968 -0.208157968 -0.208157968
19 20 21 22 23 24
0.267447262 -0.208157968 -0.208157968 0.285463513 -0.208157968 -0.190141716
25 26 27 28 29 30
0.441290783 0.791842032 -0.732552738 0.285463513 -0.190141716 -0.208157968
31 32 33 34 35 36
-0.096085119 -0.190141716 -0.208157968 -0.114101370 -0.190141716 -0.208157968
37 38 39 40 41 42
0.285463513 -0.482668870 -0.114101370 0.791842032 -0.052330698 -0.114101370
43 44 45 46 47 48
-0.208157968 -0.114101370 -0.190141716 -0.096085119 -0.714536487 -0.208157968
49 50 51 52 53 54
-0.208157968 -0.208157968 -0.464652619 0.535347381 0.791842032 -0.208157968
55 56 57 58 59 60
-0.001248859 0.361503860 -0.190141716 0.041725900 -0.052330698 0.885898630
61 62 63 64 65 66
0.267447262 0.791842032 -0.190141716 0.041725900 -0.114101370 -0.077289205
67 68
0.078538065 -0.714536487
> postscript(file="/var/wessaorg/rcomp/tmp/63a0t1356113472.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 -0.096085119 NA
1 0.379520111 -0.096085119
2 -0.208157968 0.379520111
3 -0.114101370 -0.208157968
4 -0.052330698 -0.114101370
5 0.809858284 -0.052330698
6 -0.034314446 0.809858284
7 -0.208157968 -0.034314446
8 0.791842032 -0.208157968
9 -0.114101370 0.791842032
10 0.809858284 -0.114101370
11 -0.208157968 0.809858284
12 -0.190141716 -0.208157968
13 -0.114101370 -0.190141716
14 -0.096085119 -0.114101370
15 -0.208157968 -0.096085119
16 -0.208157968 -0.208157968
17 -0.208157968 -0.208157968
18 0.267447262 -0.208157968
19 -0.208157968 0.267447262
20 -0.208157968 -0.208157968
21 0.285463513 -0.208157968
22 -0.208157968 0.285463513
23 -0.190141716 -0.208157968
24 0.441290783 -0.190141716
25 0.791842032 0.441290783
26 -0.732552738 0.791842032
27 0.285463513 -0.732552738
28 -0.190141716 0.285463513
29 -0.208157968 -0.190141716
30 -0.096085119 -0.208157968
31 -0.190141716 -0.096085119
32 -0.208157968 -0.190141716
33 -0.114101370 -0.208157968
34 -0.190141716 -0.114101370
35 -0.208157968 -0.190141716
36 0.285463513 -0.208157968
37 -0.482668870 0.285463513
38 -0.114101370 -0.482668870
39 0.791842032 -0.114101370
40 -0.052330698 0.791842032
41 -0.114101370 -0.052330698
42 -0.208157968 -0.114101370
43 -0.114101370 -0.208157968
44 -0.190141716 -0.114101370
45 -0.096085119 -0.190141716
46 -0.714536487 -0.096085119
47 -0.208157968 -0.714536487
48 -0.208157968 -0.208157968
49 -0.208157968 -0.208157968
50 -0.464652619 -0.208157968
51 0.535347381 -0.464652619
52 0.791842032 0.535347381
53 -0.208157968 0.791842032
54 -0.001248859 -0.208157968
55 0.361503860 -0.001248859
56 -0.190141716 0.361503860
57 0.041725900 -0.190141716
58 -0.052330698 0.041725900
59 0.885898630 -0.052330698
60 0.267447262 0.885898630
61 0.791842032 0.267447262
62 -0.190141716 0.791842032
63 0.041725900 -0.190141716
64 -0.114101370 0.041725900
65 -0.077289205 -0.114101370
66 0.078538065 -0.077289205
67 -0.714536487 0.078538065
68 NA -0.714536487
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.379520111 -0.096085119
[2,] -0.208157968 0.379520111
[3,] -0.114101370 -0.208157968
[4,] -0.052330698 -0.114101370
[5,] 0.809858284 -0.052330698
[6,] -0.034314446 0.809858284
[7,] -0.208157968 -0.034314446
[8,] 0.791842032 -0.208157968
[9,] -0.114101370 0.791842032
[10,] 0.809858284 -0.114101370
[11,] -0.208157968 0.809858284
[12,] -0.190141716 -0.208157968
[13,] -0.114101370 -0.190141716
[14,] -0.096085119 -0.114101370
[15,] -0.208157968 -0.096085119
[16,] -0.208157968 -0.208157968
[17,] -0.208157968 -0.208157968
[18,] 0.267447262 -0.208157968
[19,] -0.208157968 0.267447262
[20,] -0.208157968 -0.208157968
[21,] 0.285463513 -0.208157968
[22,] -0.208157968 0.285463513
[23,] -0.190141716 -0.208157968
[24,] 0.441290783 -0.190141716
[25,] 0.791842032 0.441290783
[26,] -0.732552738 0.791842032
[27,] 0.285463513 -0.732552738
[28,] -0.190141716 0.285463513
[29,] -0.208157968 -0.190141716
[30,] -0.096085119 -0.208157968
[31,] -0.190141716 -0.096085119
[32,] -0.208157968 -0.190141716
[33,] -0.114101370 -0.208157968
[34,] -0.190141716 -0.114101370
[35,] -0.208157968 -0.190141716
[36,] 0.285463513 -0.208157968
[37,] -0.482668870 0.285463513
[38,] -0.114101370 -0.482668870
[39,] 0.791842032 -0.114101370
[40,] -0.052330698 0.791842032
[41,] -0.114101370 -0.052330698
[42,] -0.208157968 -0.114101370
[43,] -0.114101370 -0.208157968
[44,] -0.190141716 -0.114101370
[45,] -0.096085119 -0.190141716
[46,] -0.714536487 -0.096085119
[47,] -0.208157968 -0.714536487
[48,] -0.208157968 -0.208157968
[49,] -0.208157968 -0.208157968
[50,] -0.464652619 -0.208157968
[51,] 0.535347381 -0.464652619
[52,] 0.791842032 0.535347381
[53,] -0.208157968 0.791842032
[54,] -0.001248859 -0.208157968
[55,] 0.361503860 -0.001248859
[56,] -0.190141716 0.361503860
[57,] 0.041725900 -0.190141716
[58,] -0.052330698 0.041725900
[59,] 0.885898630 -0.052330698
[60,] 0.267447262 0.885898630
[61,] 0.791842032 0.267447262
[62,] -0.190141716 0.791842032
[63,] 0.041725900 -0.190141716
[64,] -0.114101370 0.041725900
[65,] -0.077289205 -0.114101370
[66,] 0.078538065 -0.077289205
[67,] -0.714536487 0.078538065
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.379520111 -0.096085119
2 -0.208157968 0.379520111
3 -0.114101370 -0.208157968
4 -0.052330698 -0.114101370
5 0.809858284 -0.052330698
6 -0.034314446 0.809858284
7 -0.208157968 -0.034314446
8 0.791842032 -0.208157968
9 -0.114101370 0.791842032
10 0.809858284 -0.114101370
11 -0.208157968 0.809858284
12 -0.190141716 -0.208157968
13 -0.114101370 -0.190141716
14 -0.096085119 -0.114101370
15 -0.208157968 -0.096085119
16 -0.208157968 -0.208157968
17 -0.208157968 -0.208157968
18 0.267447262 -0.208157968
19 -0.208157968 0.267447262
20 -0.208157968 -0.208157968
21 0.285463513 -0.208157968
22 -0.208157968 0.285463513
23 -0.190141716 -0.208157968
24 0.441290783 -0.190141716
25 0.791842032 0.441290783
26 -0.732552738 0.791842032
27 0.285463513 -0.732552738
28 -0.190141716 0.285463513
29 -0.208157968 -0.190141716
30 -0.096085119 -0.208157968
31 -0.190141716 -0.096085119
32 -0.208157968 -0.190141716
33 -0.114101370 -0.208157968
34 -0.190141716 -0.114101370
35 -0.208157968 -0.190141716
36 0.285463513 -0.208157968
37 -0.482668870 0.285463513
38 -0.114101370 -0.482668870
39 0.791842032 -0.114101370
40 -0.052330698 0.791842032
41 -0.114101370 -0.052330698
42 -0.208157968 -0.114101370
43 -0.114101370 -0.208157968
44 -0.190141716 -0.114101370
45 -0.096085119 -0.190141716
46 -0.714536487 -0.096085119
47 -0.208157968 -0.714536487
48 -0.208157968 -0.208157968
49 -0.208157968 -0.208157968
50 -0.464652619 -0.208157968
51 0.535347381 -0.464652619
52 0.791842032 0.535347381
53 -0.208157968 0.791842032
54 -0.001248859 -0.208157968
55 0.361503860 -0.001248859
56 -0.190141716 0.361503860
57 0.041725900 -0.190141716
58 -0.052330698 0.041725900
59 0.885898630 -0.052330698
60 0.267447262 0.885898630
61 0.791842032 0.267447262
62 -0.190141716 0.791842032
63 0.041725900 -0.190141716
64 -0.114101370 0.041725900
65 -0.077289205 -0.114101370
66 0.078538065 -0.077289205
67 -0.714536487 0.078538065
> 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/73x6k1356113472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8uwe91356113472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9sesi1356113472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wwkp1356113472.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11eum41356113472.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/12kfbm1356113472.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/136x2c1356113472.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/14yl0b1356113472.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/15q7x61356113472.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/16q63r1356113472.tab")
+ }
>
> try(system("convert tmp/1l2q31356113472.ps tmp/1l2q31356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/2poje1356113472.ps tmp/2poje1356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vx9k1356113472.ps tmp/3vx9k1356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xyaa1356113472.ps tmp/4xyaa1356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lpzz1356113472.ps tmp/5lpzz1356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/63a0t1356113472.ps tmp/63a0t1356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/73x6k1356113472.ps tmp/73x6k1356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uwe91356113472.ps tmp/8uwe91356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sesi1356113472.ps tmp/9sesi1356113472.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wwkp1356113472.ps tmp/10wwkp1356113472.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.125 1.186 9.352