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(1,0,0,0,0,1,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,1,0,0,1,0,0,0,0,0,0,1,0,0,0,1,1,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,1,1,1,1,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,0,1,0,0,0,1,0,1,1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,1,1,0,0,1,0,1,1,1,0,1,0,1,0,0,0),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 = '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
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
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 Useful Outcome
0.0298690 0.0005804 -0.1487291 0.2345955 -0.0103240 -0.0186833
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.26504 -0.03045 -0.02987 -0.01119 0.75422
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0298690 0.0368579 0.810 0.420820
UseLimit 0.0005804 0.0501236 0.012 0.990798
T20 -0.1487291 0.0583749 -2.548 0.013333 *
Used 0.2345955 0.0621132 3.777 0.000358 ***
Useful -0.0103240 0.0651772 -0.158 0.874657
Outcome -0.0186833 0.0508163 -0.368 0.714376
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1898 on 62 degrees of freedom
Multiple R-squared: 0.2208, Adjusted R-squared: 0.158
F-statistic: 3.514 on 5 and 62 DF, p-value: 0.007341
> 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,] 0.000000e+00 0.000000e+00 1.0000000
[2,] 0.000000e+00 0.000000e+00 1.0000000
[3,] 0.000000e+00 0.000000e+00 1.0000000
[4,] 0.000000e+00 0.000000e+00 1.0000000
[5,] 0.000000e+00 0.000000e+00 1.0000000
[6,] 0.000000e+00 0.000000e+00 1.0000000
[7,] 0.000000e+00 0.000000e+00 1.0000000
[8,] 0.000000e+00 0.000000e+00 1.0000000
[9,] 0.000000e+00 0.000000e+00 1.0000000
[10,] 0.000000e+00 0.000000e+00 1.0000000
[11,] 0.000000e+00 0.000000e+00 1.0000000
[12,] 0.000000e+00 0.000000e+00 1.0000000
[13,] 0.000000e+00 0.000000e+00 1.0000000
[14,] 0.000000e+00 0.000000e+00 1.0000000
[15,] 0.000000e+00 0.000000e+00 1.0000000
[16,] 0.000000e+00 0.000000e+00 1.0000000
[17,] 0.000000e+00 0.000000e+00 1.0000000
[18,] 0.000000e+00 0.000000e+00 1.0000000
[19,] 0.000000e+00 0.000000e+00 1.0000000
[20,] 0.000000e+00 0.000000e+00 1.0000000
[21,] 0.000000e+00 0.000000e+00 1.0000000
[22,] 0.000000e+00 0.000000e+00 1.0000000
[23,] 0.000000e+00 0.000000e+00 1.0000000
[24,] 0.000000e+00 0.000000e+00 1.0000000
[25,] 0.000000e+00 0.000000e+00 1.0000000
[26,] 0.000000e+00 0.000000e+00 1.0000000
[27,] 0.000000e+00 0.000000e+00 1.0000000
[28,] 0.000000e+00 0.000000e+00 1.0000000
[29,] 0.000000e+00 0.000000e+00 1.0000000
[30,] 0.000000e+00 0.000000e+00 1.0000000
[31,] 0.000000e+00 0.000000e+00 1.0000000
[32,] 0.000000e+00 0.000000e+00 1.0000000
[33,] 0.000000e+00 0.000000e+00 1.0000000
[34,] 0.000000e+00 0.000000e+00 1.0000000
[35,] 0.000000e+00 0.000000e+00 1.0000000
[36,] 0.000000e+00 0.000000e+00 1.0000000
[37,] 0.000000e+00 0.000000e+00 1.0000000
[38,] 0.000000e+00 0.000000e+00 1.0000000
[39,] 0.000000e+00 0.000000e+00 1.0000000
[40,] 0.000000e+00 0.000000e+00 1.0000000
[41,] 0.000000e+00 0.000000e+00 1.0000000
[42,] 0.000000e+00 0.000000e+00 1.0000000
[43,] 0.000000e+00 0.000000e+00 1.0000000
[44,] 0.000000e+00 0.000000e+00 1.0000000
[45,] 0.000000e+00 0.000000e+00 1.0000000
[46,] 0.000000e+00 0.000000e+00 1.0000000
[47,] 1.452116e-05 2.904233e-05 0.9999855
[48,] 1.519893e-05 3.039786e-05 0.9999848
[49,] 6.408359e-06 1.281672e-05 0.9999936
[50,] 2.398514e-06 4.797028e-06 0.9999976
[51,] 5.813984e-07 1.162797e-06 0.9999994
> postscript(file="/var/fisher/rcomp/tmp/1h31x1355699318.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2dcoo1355699318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/32cpt1355699318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4vw3g1355699318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5rext1355699318.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
-0.0117661523 -0.0976325499 -0.0298690287 -0.0111857445 -0.0195449973
6 7 8 9 10
0.1182797046 -0.0201254051 -0.0298690287 0.1188601125 -0.0111857445
11 12 13 14 15
0.1182797046 -0.0298690287 -0.0304494365 -0.0111857445 -0.0117661523
16 17 18 19 20
-0.0298690287 -0.0298690287 -0.0298690287 -0.1157354263 -0.0298690287
21 22 23 24 25
-0.0298690287 -0.1163158341 -0.0298690287 -0.0304494365 -0.1059918027
26 27 28 29 30
0.1188601125 -0.2644645674 -0.1163158341 -0.0304494365 -0.0298690287
31 32 33 34 35
-0.0117661523 -0.0304494365 -0.0298690287 -0.0111857445 -0.0304494365
36 37 38 39 40
-0.0298690287 -0.1163158341 -0.2354572519 -0.0111857445 0.1188601125
41 42 43 44 45
-0.0195449973 -0.0111857445 -0.0298690287 -0.0111857445 -0.0304494365
46 47 48 49 50
-0.0117661523 -0.2650449753 -0.0298690287 -0.0298690287 -0.0298690287
51 52 53 54 55
-0.2360376597 -0.0873085185 0.1188601125 -0.0298690287 0.7542187168
56 57 58 59 60
-0.0970521421 -0.0304494365 -0.0008617131 -0.0195449973 0.1375433967
61 62 63 64 65
-0.1157354263 0.1188601125 -0.0304494365 -0.0008617131 -0.0111857445
66 67 68
0.7349550247 0.7452790561 -0.2650449753
> postscript(file="/var/fisher/rcomp/tmp/6debr1355699318.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 -0.0117661523 NA
1 -0.0976325499 -0.0117661523
2 -0.0298690287 -0.0976325499
3 -0.0111857445 -0.0298690287
4 -0.0195449973 -0.0111857445
5 0.1182797046 -0.0195449973
6 -0.0201254051 0.1182797046
7 -0.0298690287 -0.0201254051
8 0.1188601125 -0.0298690287
9 -0.0111857445 0.1188601125
10 0.1182797046 -0.0111857445
11 -0.0298690287 0.1182797046
12 -0.0304494365 -0.0298690287
13 -0.0111857445 -0.0304494365
14 -0.0117661523 -0.0111857445
15 -0.0298690287 -0.0117661523
16 -0.0298690287 -0.0298690287
17 -0.0298690287 -0.0298690287
18 -0.1157354263 -0.0298690287
19 -0.0298690287 -0.1157354263
20 -0.0298690287 -0.0298690287
21 -0.1163158341 -0.0298690287
22 -0.0298690287 -0.1163158341
23 -0.0304494365 -0.0298690287
24 -0.1059918027 -0.0304494365
25 0.1188601125 -0.1059918027
26 -0.2644645674 0.1188601125
27 -0.1163158341 -0.2644645674
28 -0.0304494365 -0.1163158341
29 -0.0298690287 -0.0304494365
30 -0.0117661523 -0.0298690287
31 -0.0304494365 -0.0117661523
32 -0.0298690287 -0.0304494365
33 -0.0111857445 -0.0298690287
34 -0.0304494365 -0.0111857445
35 -0.0298690287 -0.0304494365
36 -0.1163158341 -0.0298690287
37 -0.2354572519 -0.1163158341
38 -0.0111857445 -0.2354572519
39 0.1188601125 -0.0111857445
40 -0.0195449973 0.1188601125
41 -0.0111857445 -0.0195449973
42 -0.0298690287 -0.0111857445
43 -0.0111857445 -0.0298690287
44 -0.0304494365 -0.0111857445
45 -0.0117661523 -0.0304494365
46 -0.2650449753 -0.0117661523
47 -0.0298690287 -0.2650449753
48 -0.0298690287 -0.0298690287
49 -0.0298690287 -0.0298690287
50 -0.2360376597 -0.0298690287
51 -0.0873085185 -0.2360376597
52 0.1188601125 -0.0873085185
53 -0.0298690287 0.1188601125
54 0.7542187168 -0.0298690287
55 -0.0970521421 0.7542187168
56 -0.0304494365 -0.0970521421
57 -0.0008617131 -0.0304494365
58 -0.0195449973 -0.0008617131
59 0.1375433967 -0.0195449973
60 -0.1157354263 0.1375433967
61 0.1188601125 -0.1157354263
62 -0.0304494365 0.1188601125
63 -0.0008617131 -0.0304494365
64 -0.0111857445 -0.0008617131
65 0.7349550247 -0.0111857445
66 0.7452790561 0.7349550247
67 -0.2650449753 0.7452790561
68 NA -0.2650449753
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0976325499 -0.0117661523
[2,] -0.0298690287 -0.0976325499
[3,] -0.0111857445 -0.0298690287
[4,] -0.0195449973 -0.0111857445
[5,] 0.1182797046 -0.0195449973
[6,] -0.0201254051 0.1182797046
[7,] -0.0298690287 -0.0201254051
[8,] 0.1188601125 -0.0298690287
[9,] -0.0111857445 0.1188601125
[10,] 0.1182797046 -0.0111857445
[11,] -0.0298690287 0.1182797046
[12,] -0.0304494365 -0.0298690287
[13,] -0.0111857445 -0.0304494365
[14,] -0.0117661523 -0.0111857445
[15,] -0.0298690287 -0.0117661523
[16,] -0.0298690287 -0.0298690287
[17,] -0.0298690287 -0.0298690287
[18,] -0.1157354263 -0.0298690287
[19,] -0.0298690287 -0.1157354263
[20,] -0.0298690287 -0.0298690287
[21,] -0.1163158341 -0.0298690287
[22,] -0.0298690287 -0.1163158341
[23,] -0.0304494365 -0.0298690287
[24,] -0.1059918027 -0.0304494365
[25,] 0.1188601125 -0.1059918027
[26,] -0.2644645674 0.1188601125
[27,] -0.1163158341 -0.2644645674
[28,] -0.0304494365 -0.1163158341
[29,] -0.0298690287 -0.0304494365
[30,] -0.0117661523 -0.0298690287
[31,] -0.0304494365 -0.0117661523
[32,] -0.0298690287 -0.0304494365
[33,] -0.0111857445 -0.0298690287
[34,] -0.0304494365 -0.0111857445
[35,] -0.0298690287 -0.0304494365
[36,] -0.1163158341 -0.0298690287
[37,] -0.2354572519 -0.1163158341
[38,] -0.0111857445 -0.2354572519
[39,] 0.1188601125 -0.0111857445
[40,] -0.0195449973 0.1188601125
[41,] -0.0111857445 -0.0195449973
[42,] -0.0298690287 -0.0111857445
[43,] -0.0111857445 -0.0298690287
[44,] -0.0304494365 -0.0111857445
[45,] -0.0117661523 -0.0304494365
[46,] -0.2650449753 -0.0117661523
[47,] -0.0298690287 -0.2650449753
[48,] -0.0298690287 -0.0298690287
[49,] -0.0298690287 -0.0298690287
[50,] -0.2360376597 -0.0298690287
[51,] -0.0873085185 -0.2360376597
[52,] 0.1188601125 -0.0873085185
[53,] -0.0298690287 0.1188601125
[54,] 0.7542187168 -0.0298690287
[55,] -0.0970521421 0.7542187168
[56,] -0.0304494365 -0.0970521421
[57,] -0.0008617131 -0.0304494365
[58,] -0.0195449973 -0.0008617131
[59,] 0.1375433967 -0.0195449973
[60,] -0.1157354263 0.1375433967
[61,] 0.1188601125 -0.1157354263
[62,] -0.0304494365 0.1188601125
[63,] -0.0008617131 -0.0304494365
[64,] -0.0111857445 -0.0008617131
[65,] 0.7349550247 -0.0111857445
[66,] 0.7452790561 0.7349550247
[67,] -0.2650449753 0.7452790561
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0976325499 -0.0117661523
2 -0.0298690287 -0.0976325499
3 -0.0111857445 -0.0298690287
4 -0.0195449973 -0.0111857445
5 0.1182797046 -0.0195449973
6 -0.0201254051 0.1182797046
7 -0.0298690287 -0.0201254051
8 0.1188601125 -0.0298690287
9 -0.0111857445 0.1188601125
10 0.1182797046 -0.0111857445
11 -0.0298690287 0.1182797046
12 -0.0304494365 -0.0298690287
13 -0.0111857445 -0.0304494365
14 -0.0117661523 -0.0111857445
15 -0.0298690287 -0.0117661523
16 -0.0298690287 -0.0298690287
17 -0.0298690287 -0.0298690287
18 -0.1157354263 -0.0298690287
19 -0.0298690287 -0.1157354263
20 -0.0298690287 -0.0298690287
21 -0.1163158341 -0.0298690287
22 -0.0298690287 -0.1163158341
23 -0.0304494365 -0.0298690287
24 -0.1059918027 -0.0304494365
25 0.1188601125 -0.1059918027
26 -0.2644645674 0.1188601125
27 -0.1163158341 -0.2644645674
28 -0.0304494365 -0.1163158341
29 -0.0298690287 -0.0304494365
30 -0.0117661523 -0.0298690287
31 -0.0304494365 -0.0117661523
32 -0.0298690287 -0.0304494365
33 -0.0111857445 -0.0298690287
34 -0.0304494365 -0.0111857445
35 -0.0298690287 -0.0304494365
36 -0.1163158341 -0.0298690287
37 -0.2354572519 -0.1163158341
38 -0.0111857445 -0.2354572519
39 0.1188601125 -0.0111857445
40 -0.0195449973 0.1188601125
41 -0.0111857445 -0.0195449973
42 -0.0298690287 -0.0111857445
43 -0.0111857445 -0.0298690287
44 -0.0304494365 -0.0111857445
45 -0.0117661523 -0.0304494365
46 -0.2650449753 -0.0117661523
47 -0.0298690287 -0.2650449753
48 -0.0298690287 -0.0298690287
49 -0.0298690287 -0.0298690287
50 -0.2360376597 -0.0298690287
51 -0.0873085185 -0.2360376597
52 0.1188601125 -0.0873085185
53 -0.0298690287 0.1188601125
54 0.7542187168 -0.0298690287
55 -0.0970521421 0.7542187168
56 -0.0304494365 -0.0970521421
57 -0.0008617131 -0.0304494365
58 -0.0195449973 -0.0008617131
59 0.1375433967 -0.0195449973
60 -0.1157354263 0.1375433967
61 0.1188601125 -0.1157354263
62 -0.0304494365 0.1188601125
63 -0.0008617131 -0.0304494365
64 -0.0111857445 -0.0008617131
65 0.7349550247 -0.0111857445
66 0.7452790561 0.7349550247
67 -0.2650449753 0.7452790561
> 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/fisher/rcomp/tmp/7n9fn1355699318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/815na1355699318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/94qw71355699318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/1047jz1355699318.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11syvv1355699318.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/fisher/rcomp/tmp/12552w1355699318.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/fisher/rcomp/tmp/131flj1355699318.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/fisher/rcomp/tmp/14g8uj1355699318.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/fisher/rcomp/tmp/150aqo1355699318.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/fisher/rcomp/tmp/169emz1355699318.tab")
+ }
>
> try(system("convert tmp/1h31x1355699318.ps tmp/1h31x1355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dcoo1355699318.ps tmp/2dcoo1355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/32cpt1355699318.ps tmp/32cpt1355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vw3g1355699318.ps tmp/4vw3g1355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/5rext1355699318.ps tmp/5rext1355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/6debr1355699318.ps tmp/6debr1355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/7n9fn1355699318.ps tmp/7n9fn1355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/815na1355699318.ps tmp/815na1355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/94qw71355699318.ps tmp/94qw71355699318.png",intern=TRUE))
character(0)
> try(system("convert tmp/1047jz1355699318.ps tmp/1047jz1355699318.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.418 1.711 8.131