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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, 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
UseLimit T20 Used CorrectAnalysis Useful Outcome\r
1 1 0 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 1 0 0 0 1 0
8 0 0 0 0 0 0
9 0 1 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 1 0 0 0 0 0
14 0 0 0 0 0 1
15 1 0 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 1 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 1 0 0 0 0 0
25 1 1 1 0 1 0
26 0 1 0 0 0 0
27 0 0 1 0 0 0
28 1 1 1 0 0 0
29 1 0 0 0 0 0
30 0 0 0 0 0 0
31 1 0 0 0 0 1
32 1 0 0 0 0 0
33 0 0 0 0 0 0
34 0 0 0 0 0 1
35 1 0 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 0 1 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 1 0 0 0 0 0
46 1 0 0 0 0 1
47 1 0 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 1 0 1 0 1 1
52 1 1 1 0 1 1
53 0 1 0 0 0 0
54 0 0 0 0 0 0
55 0 0 1 1 0 1
56 0 1 1 0 0 1
57 1 0 0 0 0 0
58 0 0 0 0 1 1
59 0 0 0 0 1 0
60 0 1 0 0 0 1
61 0 1 1 0 0 0
62 0 1 0 0 0 0
63 1 0 0 0 0 0
64 0 0 0 0 1 1
65 0 0 0 0 0 1
66 1 0 1 1 0 0
67 1 0 1 1 1 0
68 1 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T20 Used CorrectAnalysis
0.324555 -0.026995 0.365952 0.003726
Useful `Outcome\\r`
0.010650 -0.093351
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6905 -0.3246 -0.2312 0.3989 0.7688
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.324555 0.084349 3.848 0.000284 ***
T20 -0.026995 0.155419 -0.174 0.862676
Used 0.365952 0.168246 2.175 0.033445 *
CorrectAnalysis 0.003726 0.321785 0.012 0.990798
Useful 0.010650 0.165170 0.064 0.948797
`Outcome\\r` -0.093351 0.128349 -0.727 0.469766
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.481 on 62 degrees of freedom
Multiple R-squared: 0.1067, Adjusted R-squared: 0.0347
F-statistic: 1.482 on 5 and 62 DF, p-value: 0.2087
> 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,] 0.9005523 0.1988954 0.09944772
[2,] 0.8571339 0.2857321 0.14286607
[3,] 0.8343262 0.3313477 0.16567383
[4,] 0.7490071 0.5019857 0.25099285
[5,] 0.8616826 0.2766347 0.13831735
[6,] 0.8121084 0.3757831 0.18789157
[7,] 0.8599929 0.2800142 0.14000708
[8,] 0.8125802 0.3748397 0.18741983
[9,] 0.7557834 0.4884331 0.24421657
[10,] 0.6916412 0.6167177 0.30835884
[11,] 0.7003620 0.5992761 0.29963803
[12,] 0.6328835 0.7342331 0.36711653
[13,] 0.5636330 0.8727340 0.43636700
[14,] 0.5699230 0.8601540 0.43007698
[15,] 0.5045642 0.9908716 0.49543580
[16,] 0.6397170 0.7205660 0.36028298
[17,] 0.5840931 0.8318137 0.41590687
[18,] 0.5778019 0.8443961 0.42219807
[19,] 0.6055670 0.7888660 0.39443302
[20,] 0.5805467 0.8389065 0.41945327
[21,] 0.6783471 0.6433059 0.32165294
[22,] 0.6342982 0.7314036 0.36570180
[23,] 0.7126117 0.5747766 0.28738828
[24,] 0.7876795 0.4246411 0.21232054
[25,] 0.7534772 0.4930456 0.24652278
[26,] 0.7216587 0.5566826 0.27834128
[27,] 0.7905636 0.4188728 0.20943639
[28,] 0.7563062 0.4873875 0.24369376
[29,] 0.7363240 0.5273520 0.26367600
[30,] 0.7947659 0.4104682 0.20523410
[31,] 0.7528849 0.4942302 0.24711509
[32,] 0.7273993 0.5452015 0.27260074
[33,] 0.6995635 0.6008730 0.30043652
[34,] 0.6439749 0.7120501 0.35602506
[35,] 0.6047736 0.7904527 0.39522635
[36,] 0.5450010 0.9099980 0.45499902
[37,] 0.6174838 0.7650325 0.38251623
[38,] 0.7841482 0.4317036 0.21585178
[39,] 0.7381201 0.5237598 0.26187989
[40,] 0.6891231 0.6217537 0.31087687
[41,] 0.6417613 0.7164775 0.35823875
[42,] 0.6024295 0.7951410 0.39757048
[43,] 0.5464609 0.9070783 0.45353913
[44,] 0.7820440 0.4359120 0.21795601
[45,] 0.7102605 0.5794790 0.28973949
[46,] 0.8151681 0.3696638 0.18483189
[47,] 0.8948192 0.2103617 0.10518083
[48,] 0.8482366 0.3035269 0.15176343
[49,] 0.8095212 0.3809575 0.19047877
[50,] 0.6874701 0.6250598 0.31252991
[51,] 0.6691419 0.6617162 0.33085812
> postscript(file="/var/fisher/rcomp/tmp/1cec81356127996.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2jlbq1356127996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3f73s1356127996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4zfkw1356127996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/53pko1356127996.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 7
0.7687958 0.4298380 -0.3245554 -0.2312042 -0.3352052 0.7024392 0.6647948
8 9 10 11 12 13 14
-0.3245554 -0.2975608 -0.2312042 0.7024392 -0.3245554 0.6754446 -0.2312042
15 16 17 18 19 20 21
0.7687958 -0.3245554 -0.3245554 -0.3245554 -0.6635131 -0.3245554 -0.3245554
22 23 24 25 26 27 28
0.3364869 -0.3245554 0.6754446 0.3258371 -0.2975608 -0.6905077 0.3364869
29 30 31 32 33 34 35
0.6754446 -0.3245554 0.7687958 0.6754446 -0.3245554 -0.2312042 0.6754446
36 37 38 39 40 41 42
-0.3245554 0.3364869 -0.6078063 -0.2312042 -0.2975608 -0.3352052 -0.2312042
43 44 45 46 47 48 49
-0.3245554 -0.2312042 0.6754446 0.7687958 0.3094923 -0.3245554 -0.3245554
50 51 52 53 54 55 56
-0.3245554 0.3921937 0.4191882 -0.2975608 -0.3245554 -0.6008826 -0.5701620
57 58 59 60 61 62 63
0.6754446 -0.2418541 -0.3352052 -0.2042097 -0.6635131 -0.2975608 0.6754446
64 65 66 67 68
-0.2418541 -0.2312042 0.3057662 0.2951164 0.3094923
> postscript(file="/var/fisher/rcomp/tmp/6zqhi1356127996.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 0.7687958 NA
1 0.4298380 0.7687958
2 -0.3245554 0.4298380
3 -0.2312042 -0.3245554
4 -0.3352052 -0.2312042
5 0.7024392 -0.3352052
6 0.6647948 0.7024392
7 -0.3245554 0.6647948
8 -0.2975608 -0.3245554
9 -0.2312042 -0.2975608
10 0.7024392 -0.2312042
11 -0.3245554 0.7024392
12 0.6754446 -0.3245554
13 -0.2312042 0.6754446
14 0.7687958 -0.2312042
15 -0.3245554 0.7687958
16 -0.3245554 -0.3245554
17 -0.3245554 -0.3245554
18 -0.6635131 -0.3245554
19 -0.3245554 -0.6635131
20 -0.3245554 -0.3245554
21 0.3364869 -0.3245554
22 -0.3245554 0.3364869
23 0.6754446 -0.3245554
24 0.3258371 0.6754446
25 -0.2975608 0.3258371
26 -0.6905077 -0.2975608
27 0.3364869 -0.6905077
28 0.6754446 0.3364869
29 -0.3245554 0.6754446
30 0.7687958 -0.3245554
31 0.6754446 0.7687958
32 -0.3245554 0.6754446
33 -0.2312042 -0.3245554
34 0.6754446 -0.2312042
35 -0.3245554 0.6754446
36 0.3364869 -0.3245554
37 -0.6078063 0.3364869
38 -0.2312042 -0.6078063
39 -0.2975608 -0.2312042
40 -0.3352052 -0.2975608
41 -0.2312042 -0.3352052
42 -0.3245554 -0.2312042
43 -0.2312042 -0.3245554
44 0.6754446 -0.2312042
45 0.7687958 0.6754446
46 0.3094923 0.7687958
47 -0.3245554 0.3094923
48 -0.3245554 -0.3245554
49 -0.3245554 -0.3245554
50 0.3921937 -0.3245554
51 0.4191882 0.3921937
52 -0.2975608 0.4191882
53 -0.3245554 -0.2975608
54 -0.6008826 -0.3245554
55 -0.5701620 -0.6008826
56 0.6754446 -0.5701620
57 -0.2418541 0.6754446
58 -0.3352052 -0.2418541
59 -0.2042097 -0.3352052
60 -0.6635131 -0.2042097
61 -0.2975608 -0.6635131
62 0.6754446 -0.2975608
63 -0.2418541 0.6754446
64 -0.2312042 -0.2418541
65 0.3057662 -0.2312042
66 0.2951164 0.3057662
67 0.3094923 0.2951164
68 NA 0.3094923
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.4298380 0.7687958
[2,] -0.3245554 0.4298380
[3,] -0.2312042 -0.3245554
[4,] -0.3352052 -0.2312042
[5,] 0.7024392 -0.3352052
[6,] 0.6647948 0.7024392
[7,] -0.3245554 0.6647948
[8,] -0.2975608 -0.3245554
[9,] -0.2312042 -0.2975608
[10,] 0.7024392 -0.2312042
[11,] -0.3245554 0.7024392
[12,] 0.6754446 -0.3245554
[13,] -0.2312042 0.6754446
[14,] 0.7687958 -0.2312042
[15,] -0.3245554 0.7687958
[16,] -0.3245554 -0.3245554
[17,] -0.3245554 -0.3245554
[18,] -0.6635131 -0.3245554
[19,] -0.3245554 -0.6635131
[20,] -0.3245554 -0.3245554
[21,] 0.3364869 -0.3245554
[22,] -0.3245554 0.3364869
[23,] 0.6754446 -0.3245554
[24,] 0.3258371 0.6754446
[25,] -0.2975608 0.3258371
[26,] -0.6905077 -0.2975608
[27,] 0.3364869 -0.6905077
[28,] 0.6754446 0.3364869
[29,] -0.3245554 0.6754446
[30,] 0.7687958 -0.3245554
[31,] 0.6754446 0.7687958
[32,] -0.3245554 0.6754446
[33,] -0.2312042 -0.3245554
[34,] 0.6754446 -0.2312042
[35,] -0.3245554 0.6754446
[36,] 0.3364869 -0.3245554
[37,] -0.6078063 0.3364869
[38,] -0.2312042 -0.6078063
[39,] -0.2975608 -0.2312042
[40,] -0.3352052 -0.2975608
[41,] -0.2312042 -0.3352052
[42,] -0.3245554 -0.2312042
[43,] -0.2312042 -0.3245554
[44,] 0.6754446 -0.2312042
[45,] 0.7687958 0.6754446
[46,] 0.3094923 0.7687958
[47,] -0.3245554 0.3094923
[48,] -0.3245554 -0.3245554
[49,] -0.3245554 -0.3245554
[50,] 0.3921937 -0.3245554
[51,] 0.4191882 0.3921937
[52,] -0.2975608 0.4191882
[53,] -0.3245554 -0.2975608
[54,] -0.6008826 -0.3245554
[55,] -0.5701620 -0.6008826
[56,] 0.6754446 -0.5701620
[57,] -0.2418541 0.6754446
[58,] -0.3352052 -0.2418541
[59,] -0.2042097 -0.3352052
[60,] -0.6635131 -0.2042097
[61,] -0.2975608 -0.6635131
[62,] 0.6754446 -0.2975608
[63,] -0.2418541 0.6754446
[64,] -0.2312042 -0.2418541
[65,] 0.3057662 -0.2312042
[66,] 0.2951164 0.3057662
[67,] 0.3094923 0.2951164
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.4298380 0.7687958
2 -0.3245554 0.4298380
3 -0.2312042 -0.3245554
4 -0.3352052 -0.2312042
5 0.7024392 -0.3352052
6 0.6647948 0.7024392
7 -0.3245554 0.6647948
8 -0.2975608 -0.3245554
9 -0.2312042 -0.2975608
10 0.7024392 -0.2312042
11 -0.3245554 0.7024392
12 0.6754446 -0.3245554
13 -0.2312042 0.6754446
14 0.7687958 -0.2312042
15 -0.3245554 0.7687958
16 -0.3245554 -0.3245554
17 -0.3245554 -0.3245554
18 -0.6635131 -0.3245554
19 -0.3245554 -0.6635131
20 -0.3245554 -0.3245554
21 0.3364869 -0.3245554
22 -0.3245554 0.3364869
23 0.6754446 -0.3245554
24 0.3258371 0.6754446
25 -0.2975608 0.3258371
26 -0.6905077 -0.2975608
27 0.3364869 -0.6905077
28 0.6754446 0.3364869
29 -0.3245554 0.6754446
30 0.7687958 -0.3245554
31 0.6754446 0.7687958
32 -0.3245554 0.6754446
33 -0.2312042 -0.3245554
34 0.6754446 -0.2312042
35 -0.3245554 0.6754446
36 0.3364869 -0.3245554
37 -0.6078063 0.3364869
38 -0.2312042 -0.6078063
39 -0.2975608 -0.2312042
40 -0.3352052 -0.2975608
41 -0.2312042 -0.3352052
42 -0.3245554 -0.2312042
43 -0.2312042 -0.3245554
44 0.6754446 -0.2312042
45 0.7687958 0.6754446
46 0.3094923 0.7687958
47 -0.3245554 0.3094923
48 -0.3245554 -0.3245554
49 -0.3245554 -0.3245554
50 0.3921937 -0.3245554
51 0.4191882 0.3921937
52 -0.2975608 0.4191882
53 -0.3245554 -0.2975608
54 -0.6008826 -0.3245554
55 -0.5701620 -0.6008826
56 0.6754446 -0.5701620
57 -0.2418541 0.6754446
58 -0.3352052 -0.2418541
59 -0.2042097 -0.3352052
60 -0.6635131 -0.2042097
61 -0.2975608 -0.6635131
62 0.6754446 -0.2975608
63 -0.2418541 0.6754446
64 -0.2312042 -0.2418541
65 0.3057662 -0.2312042
66 0.2951164 0.3057662
67 0.3094923 0.2951164
> 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/7kuue1356127996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8laha1356127996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9jfi11356127996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10jb281356127996.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11asvz1356127996.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/126tzp1356127996.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/13e61h1356127996.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/146tqo1356127996.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/15sr011356127996.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/16aw3f1356127996.tab")
+ }
>
> try(system("convert tmp/1cec81356127996.ps tmp/1cec81356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jlbq1356127996.ps tmp/2jlbq1356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/3f73s1356127996.ps tmp/3f73s1356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zfkw1356127996.ps tmp/4zfkw1356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/53pko1356127996.ps tmp/53pko1356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zqhi1356127996.ps tmp/6zqhi1356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/7kuue1356127996.ps tmp/7kuue1356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/8laha1356127996.ps tmp/8laha1356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jfi11356127996.ps tmp/9jfi11356127996.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jb281356127996.ps tmp/10jb281356127996.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.966 1.673 7.665