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.
R is a collaborative project with many contributors.
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),dim=c(6,67),dimnames=list(c('UseLimit','T20','Used','CorrectAnalysis','Useful','Outcome
'),1:67))
> y <- array(NA,dim=c(6,67),dimnames=list(c('UseLimit','T20','Used','CorrectAnalysis','Useful','Outcome
'),1:67))
> 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 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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
CorrectAnalysis UseLimit T20 Used Useful Outcome\r
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 0 0 0 0 1 0
6 0 1 1 0 0 0
7 0 1 0 0 1 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 0 1 1 1 1 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 0 0 0 1 1 1
39 0 0 0 0 0 1
40 0 0 1 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 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 0 1 0 1 1 1
52 0 1 1 1 1 1
53 0 0 1 0 0 0
54 0 0 0 0 0 0
55 1 0 0 1 0 1
56 0 0 1 1 0 1
57 0 1 0 0 0 0
58 0 0 0 0 1 1
59 0 0 0 0 1 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 0 0 0 0 1 1
65 0 0 0 0 0 1
66 1 1 0 1 0 0
67 1 1 0 1 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) UseLimit T20 Used Useful
0.034209 0.007035 -0.165833 0.258868 -0.022182
`Outcome\\r`
-0.026029
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.30011 -0.04124 -0.03421 -0.00818 0.73295
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.034209 0.036606 0.935 0.353717
UseLimit 0.007035 0.049811 0.141 0.888155
T20 -0.165833 0.058908 -2.815 0.006556 **
Used 0.258868 0.063587 4.071 0.000137 ***
Useful -0.022182 0.065011 -0.341 0.734120
`Outcome\\r` -0.026029 0.050549 -0.515 0.608469
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.188 on 61 degrees of freedom
Multiple R-squared: 0.248, Adjusted R-squared: 0.1864
F-statistic: 4.024 on 5 and 61 DF, p-value: 0.003205
> 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,] 0.0000000000 0.000000000 1.0000000
[2,] 0.0000000000 0.000000000 1.0000000
[3,] 0.0000000000 0.000000000 1.0000000
[4,] 0.0000000000 0.000000000 1.0000000
[5,] 0.0000000000 0.000000000 1.0000000
[6,] 0.0000000000 0.000000000 1.0000000
[7,] 0.0000000000 0.000000000 1.0000000
[8,] 0.0000000000 0.000000000 1.0000000
[9,] 0.0000000000 0.000000000 1.0000000
[10,] 0.0000000000 0.000000000 1.0000000
[11,] 0.0000000000 0.000000000 1.0000000
[12,] 0.0000000000 0.000000000 1.0000000
[13,] 0.0000000000 0.000000000 1.0000000
[14,] 0.0000000000 0.000000000 1.0000000
[15,] 0.0000000000 0.000000000 1.0000000
[16,] 0.0000000000 0.000000000 1.0000000
[17,] 0.0000000000 0.000000000 1.0000000
[18,] 0.0000000000 0.000000000 1.0000000
[19,] 0.0000000000 0.000000000 1.0000000
[20,] 0.0000000000 0.000000000 1.0000000
[21,] 0.0000000000 0.000000000 1.0000000
[22,] 0.0000000000 0.000000000 1.0000000
[23,] 0.0000000000 0.000000000 1.0000000
[24,] 0.0000000000 0.000000000 1.0000000
[25,] 0.0000000000 0.000000000 1.0000000
[26,] 0.0000000000 0.000000000 1.0000000
[27,] 0.0000000000 0.000000000 1.0000000
[28,] 0.0000000000 0.000000000 1.0000000
[29,] 0.0000000000 0.000000000 1.0000000
[30,] 0.0000000000 0.000000000 1.0000000
[31,] 0.0000000000 0.000000000 1.0000000
[32,] 0.0000000000 0.000000000 1.0000000
[33,] 0.0000000000 0.000000000 1.0000000
[34,] 0.0000000000 0.000000000 1.0000000
[35,] 0.0000000000 0.000000000 1.0000000
[36,] 0.0000000000 0.000000000 1.0000000
[37,] 0.0000000000 0.000000000 1.0000000
[38,] 0.0000000000 0.000000000 1.0000000
[39,] 0.0000000000 0.000000000 1.0000000
[40,] 0.0000000000 0.000000000 1.0000000
[41,] 0.0000000000 0.000000000 1.0000000
[42,] 0.0000000000 0.000000000 1.0000000
[43,] 0.0000000000 0.000000000 1.0000000
[44,] 0.0000000000 0.000000000 1.0000000
[45,] 0.0000000000 0.000000000 1.0000000
[46,] 0.0000000000 0.000000000 1.0000000
[47,] 0.0008662066 0.001732413 0.9991338
[48,] 0.0035353260 0.007070652 0.9964647
[49,] 0.0036901946 0.007380389 0.9963098
[50,] 0.0015606119 0.003121224 0.9984394
> postscript(file="/var/wessaorg/rcomp/tmp/10zrd1355671680.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/21onz1355671680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31e0k1355671680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4wa1n1355671680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5as7n1355671680.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 = 67
Frequency = 1
1 2 3 4 5 6
-0.015215381 -0.108250057 -0.034209179 -0.008180649 -0.012026855 0.124589490
7 8 9 10 11 12
-0.019061587 -0.034209179 0.131624222 -0.008180649 0.124589490 -0.034209179
13 14 15 16 17 18
-0.041243911 -0.008180649 -0.015215381 -0.034209179 -0.034209179 -0.034209179
19 20 21 22 23 24
-0.127243854 -0.034209179 -0.034209179 -0.134278586 -0.034209179 -0.041243911
25 26 27 28 29 30
-0.112096263 0.131624222 -0.293077255 -0.134278586 -0.041243911 -0.034209179
31 32 33 34 35 36
-0.015215381 -0.041243911 -0.034209179 -0.008180649 -0.041243911 -0.034209179
37 38 39 40 41 42
-0.134278586 -0.244866402 -0.008180649 0.131624222 -0.012026855 -0.008180649
43 44 45 46 47 48
-0.034209179 -0.008180649 -0.041243911 -0.015215381 -0.300111987 -0.034209179
49 50 51 52 53 54
-0.034209179 -0.034209179 -0.251901134 -0.086067733 0.131624222 -0.034209179
55 56 57 58 59 60
0.732951274 -0.101215325 -0.041243911 0.014001674 -0.012026855 0.157652752
61 62 63 64 65 66
-0.127243854 0.131624222 -0.041243911 0.014001674 -0.008180649 0.699888013
67
0.722070336
> postscript(file="/var/wessaorg/rcomp/tmp/6g6dw1355671680.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.015215381 NA
1 -0.108250057 -0.015215381
2 -0.034209179 -0.108250057
3 -0.008180649 -0.034209179
4 -0.012026855 -0.008180649
5 0.124589490 -0.012026855
6 -0.019061587 0.124589490
7 -0.034209179 -0.019061587
8 0.131624222 -0.034209179
9 -0.008180649 0.131624222
10 0.124589490 -0.008180649
11 -0.034209179 0.124589490
12 -0.041243911 -0.034209179
13 -0.008180649 -0.041243911
14 -0.015215381 -0.008180649
15 -0.034209179 -0.015215381
16 -0.034209179 -0.034209179
17 -0.034209179 -0.034209179
18 -0.127243854 -0.034209179
19 -0.034209179 -0.127243854
20 -0.034209179 -0.034209179
21 -0.134278586 -0.034209179
22 -0.034209179 -0.134278586
23 -0.041243911 -0.034209179
24 -0.112096263 -0.041243911
25 0.131624222 -0.112096263
26 -0.293077255 0.131624222
27 -0.134278586 -0.293077255
28 -0.041243911 -0.134278586
29 -0.034209179 -0.041243911
30 -0.015215381 -0.034209179
31 -0.041243911 -0.015215381
32 -0.034209179 -0.041243911
33 -0.008180649 -0.034209179
34 -0.041243911 -0.008180649
35 -0.034209179 -0.041243911
36 -0.134278586 -0.034209179
37 -0.244866402 -0.134278586
38 -0.008180649 -0.244866402
39 0.131624222 -0.008180649
40 -0.012026855 0.131624222
41 -0.008180649 -0.012026855
42 -0.034209179 -0.008180649
43 -0.008180649 -0.034209179
44 -0.041243911 -0.008180649
45 -0.015215381 -0.041243911
46 -0.300111987 -0.015215381
47 -0.034209179 -0.300111987
48 -0.034209179 -0.034209179
49 -0.034209179 -0.034209179
50 -0.251901134 -0.034209179
51 -0.086067733 -0.251901134
52 0.131624222 -0.086067733
53 -0.034209179 0.131624222
54 0.732951274 -0.034209179
55 -0.101215325 0.732951274
56 -0.041243911 -0.101215325
57 0.014001674 -0.041243911
58 -0.012026855 0.014001674
59 0.157652752 -0.012026855
60 -0.127243854 0.157652752
61 0.131624222 -0.127243854
62 -0.041243911 0.131624222
63 0.014001674 -0.041243911
64 -0.008180649 0.014001674
65 0.699888013 -0.008180649
66 0.722070336 0.699888013
67 NA 0.722070336
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.108250057 -0.015215381
[2,] -0.034209179 -0.108250057
[3,] -0.008180649 -0.034209179
[4,] -0.012026855 -0.008180649
[5,] 0.124589490 -0.012026855
[6,] -0.019061587 0.124589490
[7,] -0.034209179 -0.019061587
[8,] 0.131624222 -0.034209179
[9,] -0.008180649 0.131624222
[10,] 0.124589490 -0.008180649
[11,] -0.034209179 0.124589490
[12,] -0.041243911 -0.034209179
[13,] -0.008180649 -0.041243911
[14,] -0.015215381 -0.008180649
[15,] -0.034209179 -0.015215381
[16,] -0.034209179 -0.034209179
[17,] -0.034209179 -0.034209179
[18,] -0.127243854 -0.034209179
[19,] -0.034209179 -0.127243854
[20,] -0.034209179 -0.034209179
[21,] -0.134278586 -0.034209179
[22,] -0.034209179 -0.134278586
[23,] -0.041243911 -0.034209179
[24,] -0.112096263 -0.041243911
[25,] 0.131624222 -0.112096263
[26,] -0.293077255 0.131624222
[27,] -0.134278586 -0.293077255
[28,] -0.041243911 -0.134278586
[29,] -0.034209179 -0.041243911
[30,] -0.015215381 -0.034209179
[31,] -0.041243911 -0.015215381
[32,] -0.034209179 -0.041243911
[33,] -0.008180649 -0.034209179
[34,] -0.041243911 -0.008180649
[35,] -0.034209179 -0.041243911
[36,] -0.134278586 -0.034209179
[37,] -0.244866402 -0.134278586
[38,] -0.008180649 -0.244866402
[39,] 0.131624222 -0.008180649
[40,] -0.012026855 0.131624222
[41,] -0.008180649 -0.012026855
[42,] -0.034209179 -0.008180649
[43,] -0.008180649 -0.034209179
[44,] -0.041243911 -0.008180649
[45,] -0.015215381 -0.041243911
[46,] -0.300111987 -0.015215381
[47,] -0.034209179 -0.300111987
[48,] -0.034209179 -0.034209179
[49,] -0.034209179 -0.034209179
[50,] -0.251901134 -0.034209179
[51,] -0.086067733 -0.251901134
[52,] 0.131624222 -0.086067733
[53,] -0.034209179 0.131624222
[54,] 0.732951274 -0.034209179
[55,] -0.101215325 0.732951274
[56,] -0.041243911 -0.101215325
[57,] 0.014001674 -0.041243911
[58,] -0.012026855 0.014001674
[59,] 0.157652752 -0.012026855
[60,] -0.127243854 0.157652752
[61,] 0.131624222 -0.127243854
[62,] -0.041243911 0.131624222
[63,] 0.014001674 -0.041243911
[64,] -0.008180649 0.014001674
[65,] 0.699888013 -0.008180649
[66,] 0.722070336 0.699888013
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.108250057 -0.015215381
2 -0.034209179 -0.108250057
3 -0.008180649 -0.034209179
4 -0.012026855 -0.008180649
5 0.124589490 -0.012026855
6 -0.019061587 0.124589490
7 -0.034209179 -0.019061587
8 0.131624222 -0.034209179
9 -0.008180649 0.131624222
10 0.124589490 -0.008180649
11 -0.034209179 0.124589490
12 -0.041243911 -0.034209179
13 -0.008180649 -0.041243911
14 -0.015215381 -0.008180649
15 -0.034209179 -0.015215381
16 -0.034209179 -0.034209179
17 -0.034209179 -0.034209179
18 -0.127243854 -0.034209179
19 -0.034209179 -0.127243854
20 -0.034209179 -0.034209179
21 -0.134278586 -0.034209179
22 -0.034209179 -0.134278586
23 -0.041243911 -0.034209179
24 -0.112096263 -0.041243911
25 0.131624222 -0.112096263
26 -0.293077255 0.131624222
27 -0.134278586 -0.293077255
28 -0.041243911 -0.134278586
29 -0.034209179 -0.041243911
30 -0.015215381 -0.034209179
31 -0.041243911 -0.015215381
32 -0.034209179 -0.041243911
33 -0.008180649 -0.034209179
34 -0.041243911 -0.008180649
35 -0.034209179 -0.041243911
36 -0.134278586 -0.034209179
37 -0.244866402 -0.134278586
38 -0.008180649 -0.244866402
39 0.131624222 -0.008180649
40 -0.012026855 0.131624222
41 -0.008180649 -0.012026855
42 -0.034209179 -0.008180649
43 -0.008180649 -0.034209179
44 -0.041243911 -0.008180649
45 -0.015215381 -0.041243911
46 -0.300111987 -0.015215381
47 -0.034209179 -0.300111987
48 -0.034209179 -0.034209179
49 -0.034209179 -0.034209179
50 -0.251901134 -0.034209179
51 -0.086067733 -0.251901134
52 0.131624222 -0.086067733
53 -0.034209179 0.131624222
54 0.732951274 -0.034209179
55 -0.101215325 0.732951274
56 -0.041243911 -0.101215325
57 0.014001674 -0.041243911
58 -0.012026855 0.014001674
59 0.157652752 -0.012026855
60 -0.127243854 0.157652752
61 0.131624222 -0.127243854
62 -0.041243911 0.131624222
63 0.014001674 -0.041243911
64 -0.008180649 0.014001674
65 0.699888013 -0.008180649
66 0.722070336 0.699888013
> 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/7gn2d1355671680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8r15b1355671680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9yt101355671680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10vkt91355671680.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114pl71355671680.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/121egd1355671680.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/13qz2n1355671680.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/14iyt31355671680.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/15wka61355671680.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/16tz031355671680.tab")
+ }
>
> try(system("convert tmp/10zrd1355671680.ps tmp/10zrd1355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/21onz1355671680.ps tmp/21onz1355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/31e0k1355671680.ps tmp/31e0k1355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wa1n1355671680.ps tmp/4wa1n1355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/5as7n1355671680.ps tmp/5as7n1355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/6g6dw1355671680.ps tmp/6g6dw1355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gn2d1355671680.ps tmp/7gn2d1355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/8r15b1355671680.ps tmp/8r15b1355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yt101355671680.ps tmp/9yt101355671680.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vkt91355671680.ps tmp/10vkt91355671680.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.732 0.992 7.725