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(1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,1,1,0,1,1,1,1,1,0,1,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,1,1,0,0,0,1,0,0,0,1,1,0,1,1,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,1,1,0,0,0,1,1,0,0,0,0,1,1,1,0,0,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,1,0,1,1,1,0,0,0,0,1,0,1,1,0,0,0,0,0,1,0,1,1,0,0,1,1,0,0,0,1,0,0,0,1,1,1,1,1,1,0,0,1,0,0,1,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,1,0,0,0,0,1,1,0,0,1,0,0,0,1,0,0,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,1,1,0,0,0,0,1,0,0,0,0),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 = '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
Outcome Treatment CA Used
1 1 1 0 0
2 0 0 0 0
3 0 0 0 0
4 0 0 0 0
5 0 0 0 0
6 1 0 0 0
7 0 0 0 0
8 0 1 0 0
9 1 0 0 0
10 0 0 0 0
11 0 1 0 0
12 0 0 0 0
13 0 0 0 1
14 0 1 0 0
15 1 0 0 1
16 1 1 0 1
17 0 1 1 1
18 0 1 0 0
19 1 0 0 0
20 1 1 1 1
21 0 0 0 0
22 1 0 0 1
23 1 0 0 0
24 1 0 0 0
25 1 1 0 1
26 0 0 0 1
27 1 0 0 0
28 0 0 0 1
29 1 0 0 0
30 0 0 0 0
31 0 0 0 0
32 0 0 0 0
33 0 0 0 0
34 1 1 0 0
35 0 0 0 0
36 0 0 0 0
37 0 1 0 1
38 1 0 0 1
39 1 0 0 0
40 0 1 0 0
41 1 0 1 1
42 1 0 0 1
43 1 0 0 0
44 0 1 0 0
45 0 0 0 0
46 1 0 0 0
47 0 0 0 0
48 1 0 0 0
49 1 0 0 0
50 0 0 0 0
51 0 1 0 1
52 0 1 1 1
53 1 0 0 0
54 0 0 1 1
55 0 0 0 0
56 1 1 0 1
57 1 0 0 1
58 1 0 0 0
59 1 0 0 0
60 1 1 1 1
61 1 1 0 0
62 0 0 0 1
63 0 0 0 0
64 1 1 0 0
65 0 0 0 0
66 0 0 0 0
67 0 1 1 1
68 0 0 0 0
69 1 0 0 0
70 0 0 0 1
71 0 0 0 0
72 1 0 0 0
73 1 0 0 1
74 0 0 0 1
75 1 0 0 0
76 1 1 0 0
77 1 0 0 0
78 1 0 0 1
79 1 1 1 1
80 0 1 0 0
81 0 0 0 0
82 1 0 0 1
83 0 0 0 0
84 0 0 1 1
85 1 0 0 0
86 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Treatment CA Used
0.42741 0.01751 -0.14157 0.14693
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.5918 -0.4274 -0.4274 0.5551 0.5726
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.42741 0.07180 5.953 6.26e-08 ***
Treatment 0.01751 0.13002 0.135 0.893
CA -0.14157 0.21174 -0.669 0.506
Used 0.14693 0.13420 1.095 0.277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5069 on 82 degrees of freedom
Multiple R-squared: 0.01505, Adjusted R-squared: -0.02098
F-statistic: 0.4177 on 3 and 82 DF, p-value: 0.7407
> 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,] 0.6331621 0.7336758 0.3668379
[2,] 0.7143870 0.5712260 0.2856130
[3,] 0.7944267 0.4111466 0.2055733
[4,] 0.7220506 0.5558988 0.2779494
[5,] 0.6772877 0.6454246 0.3227123
[6,] 0.5995695 0.8008609 0.4004305
[7,] 0.5095446 0.9809109 0.4904554
[8,] 0.4477282 0.8954565 0.5522718
[9,] 0.5206126 0.9587748 0.4793874
[10,] 0.4719040 0.9438080 0.5280960
[11,] 0.3945126 0.7890252 0.6054874
[12,] 0.3485889 0.6971778 0.6514111
[13,] 0.4425386 0.8850772 0.5574614
[14,] 0.5020869 0.9958261 0.4979131
[15,] 0.4523562 0.9047124 0.5476438
[16,] 0.4087021 0.8174042 0.5912979
[17,] 0.4677298 0.9354596 0.5322702
[18,] 0.5063465 0.9873070 0.4936535
[19,] 0.4614329 0.9228657 0.5385671
[20,] 0.5162666 0.9674668 0.4837334
[21,] 0.5439104 0.9121792 0.4560896
[22,] 0.5657427 0.8685146 0.4342573
[23,] 0.5843115 0.8313771 0.4156885
[24,] 0.5583528 0.8832945 0.4416472
[25,] 0.5310416 0.9379169 0.4689584
[26,] 0.5031522 0.9936955 0.4968478
[27,] 0.4754028 0.9508056 0.5245972
[28,] 0.4885492 0.9770984 0.5114508
[29,] 0.4620316 0.9240631 0.5379684
[30,] 0.4368295 0.8736590 0.5631705
[31,] 0.4587675 0.9175351 0.5412325
[32,] 0.4431161 0.8862322 0.5568839
[33,] 0.4637837 0.9275675 0.5362163
[34,] 0.4473379 0.8946758 0.5526621
[35,] 0.4526888 0.9053776 0.5473112
[36,] 0.4325349 0.8650699 0.5674651
[37,] 0.4487046 0.8974092 0.5512954
[38,] 0.4488274 0.8976549 0.5511726
[39,] 0.4324110 0.8648219 0.5675890
[40,] 0.4463635 0.8927269 0.5536365
[41,] 0.4311369 0.8622737 0.5688631
[42,] 0.4430702 0.8861405 0.5569298
[43,] 0.4552372 0.9104743 0.5447628
[44,] 0.4390557 0.8781115 0.5609443
[45,] 0.5007479 0.9985042 0.4992521
[46,] 0.5024661 0.9950679 0.4975339
[47,] 0.5202809 0.9594382 0.4797191
[48,] 0.4882889 0.9765779 0.5117111
[49,] 0.4687199 0.9374398 0.5312801
[50,] 0.4253284 0.8506569 0.5746716
[51,] 0.4045782 0.8091564 0.5954218
[52,] 0.4202383 0.8404767 0.5797617
[53,] 0.4434218 0.8868436 0.5565782
[54,] 0.4406503 0.8813006 0.5593497
[55,] 0.4153505 0.8307010 0.5846495
[56,] 0.4284434 0.8568868 0.5715566
[57,] 0.3986372 0.7972744 0.6013628
[58,] 0.3723587 0.7447175 0.6276413
[59,] 0.3451624 0.6903248 0.6548376
[60,] 0.3235451 0.6470902 0.6764549
[61,] 0.3090886 0.6181772 0.6909114
[62,] 0.2921503 0.5843006 0.7078497
[63,] 0.2900835 0.5801670 0.7099165
[64,] 0.3249965 0.6499931 0.6750035
[65,] 0.3058374 0.6116748 0.6941626
[66,] 0.3033336 0.6066672 0.6966664
[67,] 0.2489451 0.4978902 0.7510549
[68,] 0.3441089 0.6882178 0.6558911
[69,] 0.3733331 0.7466661 0.6266669
[70,] 0.3379003 0.6758006 0.6620997
[71,] 0.4592431 0.9184862 0.5407569
[72,] 0.3264912 0.6529823 0.6735088
[73,] 0.4171363 0.8342727 0.5828637
> postscript(file="/var/wessaorg/rcomp/tmp/1tr1t1356028856.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2fwq01356028856.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/343621356028856.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/42mzg1356028856.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ntpx1356028856.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 7
0.5550780 -0.4274117 -0.4274117 -0.4274117 -0.4274117 0.5725883 -0.4274117
8 9 10 11 12 13 14
-0.4449220 0.5725883 -0.4274117 -0.4449220 -0.4274117 -0.5743394 -0.4449220
15 16 17 18 19 20 21
0.4256606 0.4081503 -0.4502812 -0.4449220 0.5725883 0.5497188 -0.4274117
22 23 24 25 26 27 28
0.4256606 0.5725883 0.5725883 0.4081503 -0.5743394 0.5725883 -0.5743394
29 30 31 32 33 34 35
0.5725883 -0.4274117 -0.4274117 -0.4274117 -0.4274117 0.5550780 -0.4274117
36 37 38 39 40 41 42
-0.4274117 -0.5918497 0.4256606 0.5725883 -0.4449220 0.5672291 0.4256606
43 44 45 46 47 48 49
0.5725883 -0.4449220 -0.4274117 0.5725883 -0.4274117 0.5725883 0.5725883
50 51 52 53 54 55 56
-0.4274117 -0.5918497 -0.4502812 0.5725883 -0.4327709 -0.4274117 0.4081503
57 58 59 60 61 62 63
0.4256606 0.5725883 0.5725883 0.5497188 0.5550780 -0.5743394 -0.4274117
64 65 66 67 68 69 70
0.5550780 -0.4274117 -0.4274117 -0.4502812 -0.4274117 0.5725883 -0.5743394
71 72 73 74 75 76 77
-0.4274117 0.5725883 0.4256606 -0.5743394 0.5725883 0.5550780 0.5725883
78 79 80 81 82 83 84
0.4256606 0.5497188 -0.4449220 -0.4274117 0.4256606 -0.4274117 -0.4327709
85 86
0.5725883 -0.4274117
> postscript(file="/var/wessaorg/rcomp/tmp/6kvdl1356028856.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 0.5550780 NA
1 -0.4274117 0.5550780
2 -0.4274117 -0.4274117
3 -0.4274117 -0.4274117
4 -0.4274117 -0.4274117
5 0.5725883 -0.4274117
6 -0.4274117 0.5725883
7 -0.4449220 -0.4274117
8 0.5725883 -0.4449220
9 -0.4274117 0.5725883
10 -0.4449220 -0.4274117
11 -0.4274117 -0.4449220
12 -0.5743394 -0.4274117
13 -0.4449220 -0.5743394
14 0.4256606 -0.4449220
15 0.4081503 0.4256606
16 -0.4502812 0.4081503
17 -0.4449220 -0.4502812
18 0.5725883 -0.4449220
19 0.5497188 0.5725883
20 -0.4274117 0.5497188
21 0.4256606 -0.4274117
22 0.5725883 0.4256606
23 0.5725883 0.5725883
24 0.4081503 0.5725883
25 -0.5743394 0.4081503
26 0.5725883 -0.5743394
27 -0.5743394 0.5725883
28 0.5725883 -0.5743394
29 -0.4274117 0.5725883
30 -0.4274117 -0.4274117
31 -0.4274117 -0.4274117
32 -0.4274117 -0.4274117
33 0.5550780 -0.4274117
34 -0.4274117 0.5550780
35 -0.4274117 -0.4274117
36 -0.5918497 -0.4274117
37 0.4256606 -0.5918497
38 0.5725883 0.4256606
39 -0.4449220 0.5725883
40 0.5672291 -0.4449220
41 0.4256606 0.5672291
42 0.5725883 0.4256606
43 -0.4449220 0.5725883
44 -0.4274117 -0.4449220
45 0.5725883 -0.4274117
46 -0.4274117 0.5725883
47 0.5725883 -0.4274117
48 0.5725883 0.5725883
49 -0.4274117 0.5725883
50 -0.5918497 -0.4274117
51 -0.4502812 -0.5918497
52 0.5725883 -0.4502812
53 -0.4327709 0.5725883
54 -0.4274117 -0.4327709
55 0.4081503 -0.4274117
56 0.4256606 0.4081503
57 0.5725883 0.4256606
58 0.5725883 0.5725883
59 0.5497188 0.5725883
60 0.5550780 0.5497188
61 -0.5743394 0.5550780
62 -0.4274117 -0.5743394
63 0.5550780 -0.4274117
64 -0.4274117 0.5550780
65 -0.4274117 -0.4274117
66 -0.4502812 -0.4274117
67 -0.4274117 -0.4502812
68 0.5725883 -0.4274117
69 -0.5743394 0.5725883
70 -0.4274117 -0.5743394
71 0.5725883 -0.4274117
72 0.4256606 0.5725883
73 -0.5743394 0.4256606
74 0.5725883 -0.5743394
75 0.5550780 0.5725883
76 0.5725883 0.5550780
77 0.4256606 0.5725883
78 0.5497188 0.4256606
79 -0.4449220 0.5497188
80 -0.4274117 -0.4449220
81 0.4256606 -0.4274117
82 -0.4274117 0.4256606
83 -0.4327709 -0.4274117
84 0.5725883 -0.4327709
85 -0.4274117 0.5725883
86 NA -0.4274117
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.4274117 0.5550780
[2,] -0.4274117 -0.4274117
[3,] -0.4274117 -0.4274117
[4,] -0.4274117 -0.4274117
[5,] 0.5725883 -0.4274117
[6,] -0.4274117 0.5725883
[7,] -0.4449220 -0.4274117
[8,] 0.5725883 -0.4449220
[9,] -0.4274117 0.5725883
[10,] -0.4449220 -0.4274117
[11,] -0.4274117 -0.4449220
[12,] -0.5743394 -0.4274117
[13,] -0.4449220 -0.5743394
[14,] 0.4256606 -0.4449220
[15,] 0.4081503 0.4256606
[16,] -0.4502812 0.4081503
[17,] -0.4449220 -0.4502812
[18,] 0.5725883 -0.4449220
[19,] 0.5497188 0.5725883
[20,] -0.4274117 0.5497188
[21,] 0.4256606 -0.4274117
[22,] 0.5725883 0.4256606
[23,] 0.5725883 0.5725883
[24,] 0.4081503 0.5725883
[25,] -0.5743394 0.4081503
[26,] 0.5725883 -0.5743394
[27,] -0.5743394 0.5725883
[28,] 0.5725883 -0.5743394
[29,] -0.4274117 0.5725883
[30,] -0.4274117 -0.4274117
[31,] -0.4274117 -0.4274117
[32,] -0.4274117 -0.4274117
[33,] 0.5550780 -0.4274117
[34,] -0.4274117 0.5550780
[35,] -0.4274117 -0.4274117
[36,] -0.5918497 -0.4274117
[37,] 0.4256606 -0.5918497
[38,] 0.5725883 0.4256606
[39,] -0.4449220 0.5725883
[40,] 0.5672291 -0.4449220
[41,] 0.4256606 0.5672291
[42,] 0.5725883 0.4256606
[43,] -0.4449220 0.5725883
[44,] -0.4274117 -0.4449220
[45,] 0.5725883 -0.4274117
[46,] -0.4274117 0.5725883
[47,] 0.5725883 -0.4274117
[48,] 0.5725883 0.5725883
[49,] -0.4274117 0.5725883
[50,] -0.5918497 -0.4274117
[51,] -0.4502812 -0.5918497
[52,] 0.5725883 -0.4502812
[53,] -0.4327709 0.5725883
[54,] -0.4274117 -0.4327709
[55,] 0.4081503 -0.4274117
[56,] 0.4256606 0.4081503
[57,] 0.5725883 0.4256606
[58,] 0.5725883 0.5725883
[59,] 0.5497188 0.5725883
[60,] 0.5550780 0.5497188
[61,] -0.5743394 0.5550780
[62,] -0.4274117 -0.5743394
[63,] 0.5550780 -0.4274117
[64,] -0.4274117 0.5550780
[65,] -0.4274117 -0.4274117
[66,] -0.4502812 -0.4274117
[67,] -0.4274117 -0.4502812
[68,] 0.5725883 -0.4274117
[69,] -0.5743394 0.5725883
[70,] -0.4274117 -0.5743394
[71,] 0.5725883 -0.4274117
[72,] 0.4256606 0.5725883
[73,] -0.5743394 0.4256606
[74,] 0.5725883 -0.5743394
[75,] 0.5550780 0.5725883
[76,] 0.5725883 0.5550780
[77,] 0.4256606 0.5725883
[78,] 0.5497188 0.4256606
[79,] -0.4449220 0.5497188
[80,] -0.4274117 -0.4449220
[81,] 0.4256606 -0.4274117
[82,] -0.4274117 0.4256606
[83,] -0.4327709 -0.4274117
[84,] 0.5725883 -0.4327709
[85,] -0.4274117 0.5725883
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.4274117 0.5550780
2 -0.4274117 -0.4274117
3 -0.4274117 -0.4274117
4 -0.4274117 -0.4274117
5 0.5725883 -0.4274117
6 -0.4274117 0.5725883
7 -0.4449220 -0.4274117
8 0.5725883 -0.4449220
9 -0.4274117 0.5725883
10 -0.4449220 -0.4274117
11 -0.4274117 -0.4449220
12 -0.5743394 -0.4274117
13 -0.4449220 -0.5743394
14 0.4256606 -0.4449220
15 0.4081503 0.4256606
16 -0.4502812 0.4081503
17 -0.4449220 -0.4502812
18 0.5725883 -0.4449220
19 0.5497188 0.5725883
20 -0.4274117 0.5497188
21 0.4256606 -0.4274117
22 0.5725883 0.4256606
23 0.5725883 0.5725883
24 0.4081503 0.5725883
25 -0.5743394 0.4081503
26 0.5725883 -0.5743394
27 -0.5743394 0.5725883
28 0.5725883 -0.5743394
29 -0.4274117 0.5725883
30 -0.4274117 -0.4274117
31 -0.4274117 -0.4274117
32 -0.4274117 -0.4274117
33 0.5550780 -0.4274117
34 -0.4274117 0.5550780
35 -0.4274117 -0.4274117
36 -0.5918497 -0.4274117
37 0.4256606 -0.5918497
38 0.5725883 0.4256606
39 -0.4449220 0.5725883
40 0.5672291 -0.4449220
41 0.4256606 0.5672291
42 0.5725883 0.4256606
43 -0.4449220 0.5725883
44 -0.4274117 -0.4449220
45 0.5725883 -0.4274117
46 -0.4274117 0.5725883
47 0.5725883 -0.4274117
48 0.5725883 0.5725883
49 -0.4274117 0.5725883
50 -0.5918497 -0.4274117
51 -0.4502812 -0.5918497
52 0.5725883 -0.4502812
53 -0.4327709 0.5725883
54 -0.4274117 -0.4327709
55 0.4081503 -0.4274117
56 0.4256606 0.4081503
57 0.5725883 0.4256606
58 0.5725883 0.5725883
59 0.5497188 0.5725883
60 0.5550780 0.5497188
61 -0.5743394 0.5550780
62 -0.4274117 -0.5743394
63 0.5550780 -0.4274117
64 -0.4274117 0.5550780
65 -0.4274117 -0.4274117
66 -0.4502812 -0.4274117
67 -0.4274117 -0.4502812
68 0.5725883 -0.4274117
69 -0.5743394 0.5725883
70 -0.4274117 -0.5743394
71 0.5725883 -0.4274117
72 0.4256606 0.5725883
73 -0.5743394 0.4256606
74 0.5725883 -0.5743394
75 0.5550780 0.5725883
76 0.5725883 0.5550780
77 0.4256606 0.5725883
78 0.5497188 0.4256606
79 -0.4449220 0.5497188
80 -0.4274117 -0.4449220
81 0.4256606 -0.4274117
82 -0.4274117 0.4256606
83 -0.4327709 -0.4274117
84 0.5725883 -0.4327709
85 -0.4274117 0.5725883
> 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/7b87s1356028856.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8bchl1356028856.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9nj501356028856.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/106if01356028856.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wfh31356028856.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/125yp31356028856.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/133gab1356028856.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/14kiza1356028856.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/15522b1356028856.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/16slvj1356028856.tab")
+ }
>
> try(system("convert tmp/1tr1t1356028856.ps tmp/1tr1t1356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fwq01356028856.ps tmp/2fwq01356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/343621356028856.ps tmp/343621356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/42mzg1356028856.ps tmp/42mzg1356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ntpx1356028856.ps tmp/5ntpx1356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/6kvdl1356028856.ps tmp/6kvdl1356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/7b87s1356028856.ps tmp/7b87s1356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bchl1356028856.ps tmp/8bchl1356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nj501356028856.ps tmp/9nj501356028856.png",intern=TRUE))
character(0)
> try(system("convert tmp/106if01356028856.ps tmp/106if01356028856.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.983 0.993 7.994