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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'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,1,0,1,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,1,1,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,1,1,1,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,1,1,0,0,1,1,0,0,0,1,0,0,0,1,1,1,1,1,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0),dim=c(4,86),dimnames=list(c('Treatment','CA','Used','Outcome'),1:86))
> y <- array(NA,dim=c(4,86),dimnames=list(c('Treatment','CA','Used','Outcome'),1:86))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '4'
> par3 <- 'Linear Trend'
> par2 <- 'Include Monthly 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
Outcome Treatment CA Used M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 4
5 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 6
7 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 8
9 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 11
12 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12
13 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 13
14 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 14
15 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 15
16 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 16
17 0 1 1 1 0 0 0 0 1 0 0 0 0 0 0 17
18 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 18
19 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 19
20 1 1 1 1 0 0 0 0 0 0 0 1 0 0 0 20
21 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 22
23 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 23
24 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24
25 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 25
26 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 26
27 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 27
28 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 28
29 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 29
30 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 30
31 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 31
32 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 32
33 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 34
35 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 35
36 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36
37 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 37
38 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 38
39 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 39
40 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 40
41 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 41
42 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 42
43 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 43
44 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 44
45 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 45
46 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 46
47 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 47
48 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48
49 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 49
50 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 50
51 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 51
52 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 52
53 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 53
54 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 54
55 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 55
56 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 56
57 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 57
58 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 58
59 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 59
60 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 60
61 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 61
62 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 62
63 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 63
64 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 64
65 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 65
66 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 66
67 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 67
68 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 68
69 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 69
70 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 70
71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 71
72 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 72
73 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 73
74 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 74
75 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 75
76 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 76
77 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 77
78 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 78
79 1 1 1 1 0 0 0 0 0 0 1 0 0 0 0 79
80 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 80
81 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 81
82 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 82
83 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 83
84 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 84
85 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 85
86 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 86
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Treatment CA Used M1 M2
0.478652 0.064777 -0.227074 0.141564 0.071468 -0.531489
M3 M4 M5 M6 M7 M8
-0.044637 -0.214543 0.015743 -0.182026 -0.140866 -0.346173
M9 M10 M11 t
-0.171511 0.062253 -0.307897 0.002249
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.8399 -0.3967 -0.1001 0.4118 0.8433
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.478652 0.224046 2.136 0.0361 *
Treatment 0.064777 0.156564 0.414 0.6803
CA -0.227074 0.249830 -0.909 0.3665
Used 0.141564 0.149502 0.947 0.3469
M1 0.071468 0.288905 0.247 0.8053
M2 -0.531489 0.278768 -1.907 0.0607 .
M3 -0.044637 0.281240 -0.159 0.8744
M4 -0.214543 0.293979 -0.730 0.4680
M5 0.015743 0.272323 0.058 0.9541
M6 -0.182026 0.276712 -0.658 0.5128
M7 -0.140866 0.272898 -0.516 0.6074
M8 -0.346173 0.291651 -1.187 0.2393
M9 -0.171511 0.277978 -0.617 0.5392
M10 0.062253 0.284690 0.219 0.8275
M11 -0.307897 0.278441 -1.106 0.2726
t 0.002249 0.002268 0.992 0.3247
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5086 on 70 degrees of freedom
Multiple R-squared: 0.1537, Adjusted R-squared: -0.02768
F-statistic: 0.8474 on 15 and 70 DF, p-value: 0.6232
> 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,] 0.9476996 0.1046007 0.05230035
[2,] 0.9473902 0.1052196 0.05260978
[3,] 0.9314093 0.1371813 0.06859066
[4,] 0.9019550 0.1960899 0.09804497
[5,] 0.9386284 0.1227433 0.06137164
[6,] 0.9351436 0.1297128 0.06485638
[7,] 0.8978192 0.2043617 0.10218085
[8,] 0.8718131 0.2563738 0.12818692
[9,] 0.8368997 0.3262007 0.16310035
[10,] 0.8459778 0.3080444 0.15402221
[11,] 0.8382883 0.3234235 0.16171175
[12,] 0.8380557 0.3238887 0.16194435
[13,] 0.8324492 0.3351015 0.16755076
[14,] 0.7951146 0.4097708 0.20488540
[15,] 0.7712976 0.4574048 0.22870239
[16,] 0.7248829 0.5502343 0.27511713
[17,] 0.6792548 0.6414903 0.32074517
[18,] 0.6898284 0.6203432 0.31017160
[19,] 0.7839528 0.4320943 0.21604717
[20,] 0.8272400 0.3455200 0.17276002
[21,] 0.8181703 0.3636595 0.18182974
[22,] 0.8169726 0.3660548 0.18302739
[23,] 0.8194381 0.3611238 0.18056188
[24,] 0.7840590 0.4318819 0.21594095
[25,] 0.7710452 0.4579095 0.22895475
[26,] 0.7284213 0.5431575 0.27157874
[27,] 0.7159389 0.5681222 0.28406111
[28,] 0.6734345 0.6531310 0.32656552
[29,] 0.6267238 0.7465524 0.37327622
[30,] 0.5775035 0.8449930 0.42249649
[31,] 0.5220259 0.9559483 0.47797414
[32,] 0.4525636 0.9051272 0.54743641
[33,] 0.5909842 0.8180316 0.40901578
[34,] 0.5718297 0.8563406 0.42817032
[35,] 0.5359599 0.9280802 0.46404010
[36,] 0.4828118 0.9656235 0.51718824
[37,] 0.4617261 0.9234523 0.53827387
[38,] 0.4306109 0.8612217 0.56938913
[39,] 0.3691674 0.7383349 0.63083257
[40,] 0.3668102 0.7336204 0.63318980
[41,] 0.5353882 0.9292235 0.46461176
[42,] 0.6126549 0.7746902 0.38734512
[43,] 0.6228313 0.7543375 0.37716874
[44,] 0.5596729 0.8806541 0.44032706
[45,] 0.5278585 0.9442831 0.47214153
[46,] 0.4594944 0.9189887 0.54050563
[47,] 0.4209768 0.8419536 0.57902318
[48,] 0.3103509 0.6207019 0.68964907
[49,] 0.3205587 0.6411174 0.67944132
> postscript(file="/var/fisher/rcomp/tmp/161ds1356038450.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/24ki71356038450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31tkh1356038450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4w7xv1356038450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/589q01356038450.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 = 86
Frequency = 1
1 2 3 4 5 6
0.38285339 0.04833894 -0.44076235 -0.27310557 -0.50564072 0.68987908
7 8 9 10 11 12
-0.35352973 -0.21524876 0.67261750 -0.56339602 -0.26027185 -0.50564072
13 14 15 16 17 18
-0.72092247 -0.04342642 0.39068492 0.49356483 -0.51189602 -0.40188628
19 20 21 22 23 24
0.61948178 0.84327281 -0.35437099 0.26805124 0.77751653 0.46737080
25 26 27 28 29 30
0.18731218 -0.14720228 0.50526068 -0.46864679 0.44038231 -0.36409790
31 32 33 34 35 36
-0.40750671 -0.20444887 -0.38135948 0.31785013 -0.24947196 -0.55961769
37 38 39 40 41 42
-0.83967631 0.82580923 0.47827219 -0.41884790 0.49890388 0.46734937
43 44 45 46 47 48
0.56550480 -0.29621422 -0.40834797 0.35563851 -0.27646045 0.41339382
49 50 51 52 53 54
0.33967631 -0.05961501 -0.75505740 -0.36032633 0.38640534 -0.33256482
55 56 57 58 59 60
-0.46148368 0.53523305 0.42309930 0.32865003 0.69655107 0.40713852
61 62 63 64 65 66
0.24791096 -0.22816774 -0.57570478 0.52717513 -0.64058315 -0.44506336
67 68 69 70 71 72
-0.46773898 -0.28541433 0.53767506 -0.83990270 -0.33043742 0.35941685
73 74 75 76 77 78
0.14413509 -0.25515623 0.39730673 0.50018664 0.33242836 0.38638391
79 80 81 82 83 84
0.50527253 -0.37717968 -0.48931343 0.13310881 -0.35742591 -0.58206158
85 86
0.25871085 -0.14058047
> postscript(file="/var/fisher/rcomp/tmp/6jceq1356038450.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 = 86
Frequency = 1
lag(myerror, k = 1) myerror
0 0.38285339 NA
1 0.04833894 0.38285339
2 -0.44076235 0.04833894
3 -0.27310557 -0.44076235
4 -0.50564072 -0.27310557
5 0.68987908 -0.50564072
6 -0.35352973 0.68987908
7 -0.21524876 -0.35352973
8 0.67261750 -0.21524876
9 -0.56339602 0.67261750
10 -0.26027185 -0.56339602
11 -0.50564072 -0.26027185
12 -0.72092247 -0.50564072
13 -0.04342642 -0.72092247
14 0.39068492 -0.04342642
15 0.49356483 0.39068492
16 -0.51189602 0.49356483
17 -0.40188628 -0.51189602
18 0.61948178 -0.40188628
19 0.84327281 0.61948178
20 -0.35437099 0.84327281
21 0.26805124 -0.35437099
22 0.77751653 0.26805124
23 0.46737080 0.77751653
24 0.18731218 0.46737080
25 -0.14720228 0.18731218
26 0.50526068 -0.14720228
27 -0.46864679 0.50526068
28 0.44038231 -0.46864679
29 -0.36409790 0.44038231
30 -0.40750671 -0.36409790
31 -0.20444887 -0.40750671
32 -0.38135948 -0.20444887
33 0.31785013 -0.38135948
34 -0.24947196 0.31785013
35 -0.55961769 -0.24947196
36 -0.83967631 -0.55961769
37 0.82580923 -0.83967631
38 0.47827219 0.82580923
39 -0.41884790 0.47827219
40 0.49890388 -0.41884790
41 0.46734937 0.49890388
42 0.56550480 0.46734937
43 -0.29621422 0.56550480
44 -0.40834797 -0.29621422
45 0.35563851 -0.40834797
46 -0.27646045 0.35563851
47 0.41339382 -0.27646045
48 0.33967631 0.41339382
49 -0.05961501 0.33967631
50 -0.75505740 -0.05961501
51 -0.36032633 -0.75505740
52 0.38640534 -0.36032633
53 -0.33256482 0.38640534
54 -0.46148368 -0.33256482
55 0.53523305 -0.46148368
56 0.42309930 0.53523305
57 0.32865003 0.42309930
58 0.69655107 0.32865003
59 0.40713852 0.69655107
60 0.24791096 0.40713852
61 -0.22816774 0.24791096
62 -0.57570478 -0.22816774
63 0.52717513 -0.57570478
64 -0.64058315 0.52717513
65 -0.44506336 -0.64058315
66 -0.46773898 -0.44506336
67 -0.28541433 -0.46773898
68 0.53767506 -0.28541433
69 -0.83990270 0.53767506
70 -0.33043742 -0.83990270
71 0.35941685 -0.33043742
72 0.14413509 0.35941685
73 -0.25515623 0.14413509
74 0.39730673 -0.25515623
75 0.50018664 0.39730673
76 0.33242836 0.50018664
77 0.38638391 0.33242836
78 0.50527253 0.38638391
79 -0.37717968 0.50527253
80 -0.48931343 -0.37717968
81 0.13310881 -0.48931343
82 -0.35742591 0.13310881
83 -0.58206158 -0.35742591
84 0.25871085 -0.58206158
85 -0.14058047 0.25871085
86 NA -0.14058047
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.04833894 0.38285339
[2,] -0.44076235 0.04833894
[3,] -0.27310557 -0.44076235
[4,] -0.50564072 -0.27310557
[5,] 0.68987908 -0.50564072
[6,] -0.35352973 0.68987908
[7,] -0.21524876 -0.35352973
[8,] 0.67261750 -0.21524876
[9,] -0.56339602 0.67261750
[10,] -0.26027185 -0.56339602
[11,] -0.50564072 -0.26027185
[12,] -0.72092247 -0.50564072
[13,] -0.04342642 -0.72092247
[14,] 0.39068492 -0.04342642
[15,] 0.49356483 0.39068492
[16,] -0.51189602 0.49356483
[17,] -0.40188628 -0.51189602
[18,] 0.61948178 -0.40188628
[19,] 0.84327281 0.61948178
[20,] -0.35437099 0.84327281
[21,] 0.26805124 -0.35437099
[22,] 0.77751653 0.26805124
[23,] 0.46737080 0.77751653
[24,] 0.18731218 0.46737080
[25,] -0.14720228 0.18731218
[26,] 0.50526068 -0.14720228
[27,] -0.46864679 0.50526068
[28,] 0.44038231 -0.46864679
[29,] -0.36409790 0.44038231
[30,] -0.40750671 -0.36409790
[31,] -0.20444887 -0.40750671
[32,] -0.38135948 -0.20444887
[33,] 0.31785013 -0.38135948
[34,] -0.24947196 0.31785013
[35,] -0.55961769 -0.24947196
[36,] -0.83967631 -0.55961769
[37,] 0.82580923 -0.83967631
[38,] 0.47827219 0.82580923
[39,] -0.41884790 0.47827219
[40,] 0.49890388 -0.41884790
[41,] 0.46734937 0.49890388
[42,] 0.56550480 0.46734937
[43,] -0.29621422 0.56550480
[44,] -0.40834797 -0.29621422
[45,] 0.35563851 -0.40834797
[46,] -0.27646045 0.35563851
[47,] 0.41339382 -0.27646045
[48,] 0.33967631 0.41339382
[49,] -0.05961501 0.33967631
[50,] -0.75505740 -0.05961501
[51,] -0.36032633 -0.75505740
[52,] 0.38640534 -0.36032633
[53,] -0.33256482 0.38640534
[54,] -0.46148368 -0.33256482
[55,] 0.53523305 -0.46148368
[56,] 0.42309930 0.53523305
[57,] 0.32865003 0.42309930
[58,] 0.69655107 0.32865003
[59,] 0.40713852 0.69655107
[60,] 0.24791096 0.40713852
[61,] -0.22816774 0.24791096
[62,] -0.57570478 -0.22816774
[63,] 0.52717513 -0.57570478
[64,] -0.64058315 0.52717513
[65,] -0.44506336 -0.64058315
[66,] -0.46773898 -0.44506336
[67,] -0.28541433 -0.46773898
[68,] 0.53767506 -0.28541433
[69,] -0.83990270 0.53767506
[70,] -0.33043742 -0.83990270
[71,] 0.35941685 -0.33043742
[72,] 0.14413509 0.35941685
[73,] -0.25515623 0.14413509
[74,] 0.39730673 -0.25515623
[75,] 0.50018664 0.39730673
[76,] 0.33242836 0.50018664
[77,] 0.38638391 0.33242836
[78,] 0.50527253 0.38638391
[79,] -0.37717968 0.50527253
[80,] -0.48931343 -0.37717968
[81,] 0.13310881 -0.48931343
[82,] -0.35742591 0.13310881
[83,] -0.58206158 -0.35742591
[84,] 0.25871085 -0.58206158
[85,] -0.14058047 0.25871085
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.04833894 0.38285339
2 -0.44076235 0.04833894
3 -0.27310557 -0.44076235
4 -0.50564072 -0.27310557
5 0.68987908 -0.50564072
6 -0.35352973 0.68987908
7 -0.21524876 -0.35352973
8 0.67261750 -0.21524876
9 -0.56339602 0.67261750
10 -0.26027185 -0.56339602
11 -0.50564072 -0.26027185
12 -0.72092247 -0.50564072
13 -0.04342642 -0.72092247
14 0.39068492 -0.04342642
15 0.49356483 0.39068492
16 -0.51189602 0.49356483
17 -0.40188628 -0.51189602
18 0.61948178 -0.40188628
19 0.84327281 0.61948178
20 -0.35437099 0.84327281
21 0.26805124 -0.35437099
22 0.77751653 0.26805124
23 0.46737080 0.77751653
24 0.18731218 0.46737080
25 -0.14720228 0.18731218
26 0.50526068 -0.14720228
27 -0.46864679 0.50526068
28 0.44038231 -0.46864679
29 -0.36409790 0.44038231
30 -0.40750671 -0.36409790
31 -0.20444887 -0.40750671
32 -0.38135948 -0.20444887
33 0.31785013 -0.38135948
34 -0.24947196 0.31785013
35 -0.55961769 -0.24947196
36 -0.83967631 -0.55961769
37 0.82580923 -0.83967631
38 0.47827219 0.82580923
39 -0.41884790 0.47827219
40 0.49890388 -0.41884790
41 0.46734937 0.49890388
42 0.56550480 0.46734937
43 -0.29621422 0.56550480
44 -0.40834797 -0.29621422
45 0.35563851 -0.40834797
46 -0.27646045 0.35563851
47 0.41339382 -0.27646045
48 0.33967631 0.41339382
49 -0.05961501 0.33967631
50 -0.75505740 -0.05961501
51 -0.36032633 -0.75505740
52 0.38640534 -0.36032633
53 -0.33256482 0.38640534
54 -0.46148368 -0.33256482
55 0.53523305 -0.46148368
56 0.42309930 0.53523305
57 0.32865003 0.42309930
58 0.69655107 0.32865003
59 0.40713852 0.69655107
60 0.24791096 0.40713852
61 -0.22816774 0.24791096
62 -0.57570478 -0.22816774
63 0.52717513 -0.57570478
64 -0.64058315 0.52717513
65 -0.44506336 -0.64058315
66 -0.46773898 -0.44506336
67 -0.28541433 -0.46773898
68 0.53767506 -0.28541433
69 -0.83990270 0.53767506
70 -0.33043742 -0.83990270
71 0.35941685 -0.33043742
72 0.14413509 0.35941685
73 -0.25515623 0.14413509
74 0.39730673 -0.25515623
75 0.50018664 0.39730673
76 0.33242836 0.50018664
77 0.38638391 0.33242836
78 0.50527253 0.38638391
79 -0.37717968 0.50527253
80 -0.48931343 -0.37717968
81 0.13310881 -0.48931343
82 -0.35742591 0.13310881
83 -0.58206158 -0.35742591
84 0.25871085 -0.58206158
85 -0.14058047 0.25871085
> 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/73nc71356038450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8xt411356038450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9rcn41356038450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10rpxq1356038450.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11y7ux1356038450.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/12zzm91356038450.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/13dukp1356038450.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/14j0a11356038450.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/15p3in1356038450.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/16nf311356038450.tab")
+ }
>
> try(system("convert tmp/161ds1356038450.ps tmp/161ds1356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/24ki71356038450.ps tmp/24ki71356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/31tkh1356038450.ps tmp/31tkh1356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/4w7xv1356038450.ps tmp/4w7xv1356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/589q01356038450.ps tmp/589q01356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jceq1356038450.ps tmp/6jceq1356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/73nc71356038450.ps tmp/73nc71356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xt411356038450.ps tmp/8xt411356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rcn41356038450.ps tmp/9rcn41356038450.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rpxq1356038450.ps tmp/10rpxq1356038450.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.538 1.754 8.291