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(0,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,1,0,0,0,0,0,1,0,0,1,0,1,0,0,1,0,1,0,1,0,0,1,0,0,0,0,0,1,0,0,0),dim=c(2,68),dimnames=list(c('CorrectAnalysis','T20'),1:68))
> y <- array(NA,dim=c(2,68),dimnames=list(c('CorrectAnalysis','T20'),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
CorrectAnalysis T20
1 0 0
2 0 1
3 0 0
4 0 0
5 1 0
6 0 1
7 1 0
8 0 0
9 0 1
10 0 0
11 0 1
12 0 0
13 0 0
14 0 0
15 0 0
16 0 0
17 0 0
18 0 0
19 0 1
20 0 0
21 0 0
22 0 1
23 0 0
24 0 0
25 1 1
26 0 1
27 0 0
28 0 1
29 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 0 0
35 0 0
36 0 0
37 0 1
38 1 0
39 0 0
40 0 1
41 1 0
42 0 0
43 0 0
44 0 0
45 0 0
46 0 0
47 0 0
48 0 0
49 0 0
50 0 0
51 1 0
52 1 1
53 0 1
54 0 0
55 0 0
56 0 1
57 0 0
58 1 0
59 1 0
60 0 1
61 0 1
62 0 1
63 0 0
64 1 0
65 0 0
66 0 0
67 1 0
68 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) T20
0.17647 -0.05882
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.1765 -0.1765 -0.1765 -0.1177 0.8823
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.17647 0.05221 3.380 0.00122 **
T20 -0.05882 0.10443 -0.563 0.57514
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3729 on 66 degrees of freedom
Multiple R-squared: 0.004785, Adjusted R-squared: -0.01029
F-statistic: 0.3173 on 1 and 66 DF, p-value: 0.5751
> 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,] 0.846679422 0.306641157 0.1533206
[2,] 0.735735319 0.528529361 0.2642647
[3,] 0.882322150 0.235355700 0.1176778
[4,] 0.859276213 0.281447575 0.1407238
[5,] 0.786198814 0.427602372 0.2138012
[6,] 0.745139915 0.509720170 0.2548601
[7,] 0.656292455 0.687415090 0.3437075
[8,] 0.600864788 0.798270424 0.3991352
[9,] 0.537654587 0.924690827 0.4623454
[10,] 0.470267547 0.940535095 0.5297325
[11,] 0.401992236 0.803984471 0.5980078
[12,] 0.335756251 0.671512501 0.6642437
[13,] 0.273972045 0.547944090 0.7260280
[14,] 0.218405344 0.436810688 0.7815947
[15,] 0.162902707 0.325805415 0.8370973
[16,] 0.124009709 0.248019418 0.8759903
[17,] 0.092253858 0.184507716 0.9077461
[18,] 0.064120143 0.128240286 0.9358799
[19,] 0.045598493 0.091196986 0.9544015
[20,] 0.031726533 0.063453066 0.9682735
[21,] 0.214448198 0.428896396 0.7855518
[22,] 0.168982034 0.337964068 0.8310180
[23,] 0.132328785 0.264657570 0.8676712
[24,] 0.099816797 0.199633595 0.9001832
[25,] 0.075356945 0.150713889 0.9246431
[26,] 0.055946331 0.111892663 0.9440537
[27,] 0.040879090 0.081758179 0.9591209
[28,] 0.029426777 0.058853554 0.9705732
[29,] 0.020893764 0.041787527 0.9791062
[30,] 0.014653791 0.029307582 0.9853462
[31,] 0.010169444 0.020338888 0.9898306
[32,] 0.006997921 0.013995842 0.9930021
[33,] 0.004415315 0.008830631 0.9955847
[34,] 0.034944263 0.069888527 0.9650557
[35,] 0.025825857 0.051651714 0.9741741
[36,] 0.017590527 0.035181054 0.9824095
[37,] 0.077113980 0.154227960 0.9228860
[38,] 0.059457143 0.118914285 0.9405429
[39,] 0.045395151 0.090790302 0.9546048
[40,] 0.034401005 0.068802010 0.9655990
[41,] 0.025957022 0.051914045 0.9740430
[42,] 0.019583398 0.039166797 0.9804166
[43,] 0.014856934 0.029713867 0.9851431
[44,] 0.011421365 0.022842729 0.9885786
[45,] 0.008991930 0.017983860 0.9910081
[46,] 0.007358276 0.014716552 0.9926417
[47,] 0.026979527 0.053959054 0.9730205
[48,] 0.144066014 0.288132028 0.8559340
[49,] 0.103087608 0.206175216 0.8969124
[50,] 0.091532338 0.183064675 0.9084677
[51,] 0.086176969 0.172353939 0.9138230
[52,] 0.056404632 0.112809264 0.9435954
[53,] 0.057638666 0.115277332 0.9423613
[54,] 0.109226360 0.218452719 0.8907736
[55,] 0.213801186 0.427602372 0.7861988
[56,] 0.140723787 0.281447575 0.8592762
[57,] 0.084142618 0.168285235 0.9158574
[58,] 0.044546818 0.089093635 0.9554532
[59,] 0.030563113 0.061126225 0.9694369
> postscript(file="/var/wessaorg/rcomp/tmp/144hv1354718253.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2aet61354718253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/30nop1354718253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4h1sj1354718253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5x5ny1354718253.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
-0.1764706 -0.1176471 -0.1764706 -0.1764706 0.8235294 -0.1176471 0.8235294
8 9 10 11 12 13 14
-0.1764706 -0.1176471 -0.1764706 -0.1176471 -0.1764706 -0.1764706 -0.1764706
15 16 17 18 19 20 21
-0.1764706 -0.1764706 -0.1764706 -0.1764706 -0.1176471 -0.1764706 -0.1764706
22 23 24 25 26 27 28
-0.1176471 -0.1764706 -0.1764706 0.8823529 -0.1176471 -0.1764706 -0.1176471
29 30 31 32 33 34 35
-0.1764706 -0.1764706 -0.1764706 -0.1764706 -0.1764706 -0.1764706 -0.1764706
36 37 38 39 40 41 42
-0.1764706 -0.1176471 0.8235294 -0.1764706 -0.1176471 0.8235294 -0.1764706
43 44 45 46 47 48 49
-0.1764706 -0.1764706 -0.1764706 -0.1764706 -0.1764706 -0.1764706 -0.1764706
50 51 52 53 54 55 56
-0.1764706 0.8235294 0.8823529 -0.1176471 -0.1764706 -0.1764706 -0.1176471
57 58 59 60 61 62 63
-0.1764706 0.8235294 0.8235294 -0.1176471 -0.1176471 -0.1176471 -0.1764706
64 65 66 67 68
0.8235294 -0.1764706 -0.1764706 0.8235294 -0.1764706
> postscript(file="/var/wessaorg/rcomp/tmp/6m4311354718253.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 -0.1764706 NA
1 -0.1176471 -0.1764706
2 -0.1764706 -0.1176471
3 -0.1764706 -0.1764706
4 0.8235294 -0.1764706
5 -0.1176471 0.8235294
6 0.8235294 -0.1176471
7 -0.1764706 0.8235294
8 -0.1176471 -0.1764706
9 -0.1764706 -0.1176471
10 -0.1176471 -0.1764706
11 -0.1764706 -0.1176471
12 -0.1764706 -0.1764706
13 -0.1764706 -0.1764706
14 -0.1764706 -0.1764706
15 -0.1764706 -0.1764706
16 -0.1764706 -0.1764706
17 -0.1764706 -0.1764706
18 -0.1176471 -0.1764706
19 -0.1764706 -0.1176471
20 -0.1764706 -0.1764706
21 -0.1176471 -0.1764706
22 -0.1764706 -0.1176471
23 -0.1764706 -0.1764706
24 0.8823529 -0.1764706
25 -0.1176471 0.8823529
26 -0.1764706 -0.1176471
27 -0.1176471 -0.1764706
28 -0.1764706 -0.1176471
29 -0.1764706 -0.1764706
30 -0.1764706 -0.1764706
31 -0.1764706 -0.1764706
32 -0.1764706 -0.1764706
33 -0.1764706 -0.1764706
34 -0.1764706 -0.1764706
35 -0.1764706 -0.1764706
36 -0.1176471 -0.1764706
37 0.8235294 -0.1176471
38 -0.1764706 0.8235294
39 -0.1176471 -0.1764706
40 0.8235294 -0.1176471
41 -0.1764706 0.8235294
42 -0.1764706 -0.1764706
43 -0.1764706 -0.1764706
44 -0.1764706 -0.1764706
45 -0.1764706 -0.1764706
46 -0.1764706 -0.1764706
47 -0.1764706 -0.1764706
48 -0.1764706 -0.1764706
49 -0.1764706 -0.1764706
50 0.8235294 -0.1764706
51 0.8823529 0.8235294
52 -0.1176471 0.8823529
53 -0.1764706 -0.1176471
54 -0.1764706 -0.1764706
55 -0.1176471 -0.1764706
56 -0.1764706 -0.1176471
57 0.8235294 -0.1764706
58 0.8235294 0.8235294
59 -0.1176471 0.8235294
60 -0.1176471 -0.1176471
61 -0.1176471 -0.1176471
62 -0.1764706 -0.1176471
63 0.8235294 -0.1764706
64 -0.1764706 0.8235294
65 -0.1764706 -0.1764706
66 0.8235294 -0.1764706
67 -0.1764706 0.8235294
68 NA -0.1764706
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1176471 -0.1764706
[2,] -0.1764706 -0.1176471
[3,] -0.1764706 -0.1764706
[4,] 0.8235294 -0.1764706
[5,] -0.1176471 0.8235294
[6,] 0.8235294 -0.1176471
[7,] -0.1764706 0.8235294
[8,] -0.1176471 -0.1764706
[9,] -0.1764706 -0.1176471
[10,] -0.1176471 -0.1764706
[11,] -0.1764706 -0.1176471
[12,] -0.1764706 -0.1764706
[13,] -0.1764706 -0.1764706
[14,] -0.1764706 -0.1764706
[15,] -0.1764706 -0.1764706
[16,] -0.1764706 -0.1764706
[17,] -0.1764706 -0.1764706
[18,] -0.1176471 -0.1764706
[19,] -0.1764706 -0.1176471
[20,] -0.1764706 -0.1764706
[21,] -0.1176471 -0.1764706
[22,] -0.1764706 -0.1176471
[23,] -0.1764706 -0.1764706
[24,] 0.8823529 -0.1764706
[25,] -0.1176471 0.8823529
[26,] -0.1764706 -0.1176471
[27,] -0.1176471 -0.1764706
[28,] -0.1764706 -0.1176471
[29,] -0.1764706 -0.1764706
[30,] -0.1764706 -0.1764706
[31,] -0.1764706 -0.1764706
[32,] -0.1764706 -0.1764706
[33,] -0.1764706 -0.1764706
[34,] -0.1764706 -0.1764706
[35,] -0.1764706 -0.1764706
[36,] -0.1176471 -0.1764706
[37,] 0.8235294 -0.1176471
[38,] -0.1764706 0.8235294
[39,] -0.1176471 -0.1764706
[40,] 0.8235294 -0.1176471
[41,] -0.1764706 0.8235294
[42,] -0.1764706 -0.1764706
[43,] -0.1764706 -0.1764706
[44,] -0.1764706 -0.1764706
[45,] -0.1764706 -0.1764706
[46,] -0.1764706 -0.1764706
[47,] -0.1764706 -0.1764706
[48,] -0.1764706 -0.1764706
[49,] -0.1764706 -0.1764706
[50,] 0.8235294 -0.1764706
[51,] 0.8823529 0.8235294
[52,] -0.1176471 0.8823529
[53,] -0.1764706 -0.1176471
[54,] -0.1764706 -0.1764706
[55,] -0.1176471 -0.1764706
[56,] -0.1764706 -0.1176471
[57,] 0.8235294 -0.1764706
[58,] 0.8235294 0.8235294
[59,] -0.1176471 0.8235294
[60,] -0.1176471 -0.1176471
[61,] -0.1176471 -0.1176471
[62,] -0.1764706 -0.1176471
[63,] 0.8235294 -0.1764706
[64,] -0.1764706 0.8235294
[65,] -0.1764706 -0.1764706
[66,] 0.8235294 -0.1764706
[67,] -0.1764706 0.8235294
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1176471 -0.1764706
2 -0.1764706 -0.1176471
3 -0.1764706 -0.1764706
4 0.8235294 -0.1764706
5 -0.1176471 0.8235294
6 0.8235294 -0.1176471
7 -0.1764706 0.8235294
8 -0.1176471 -0.1764706
9 -0.1764706 -0.1176471
10 -0.1176471 -0.1764706
11 -0.1764706 -0.1176471
12 -0.1764706 -0.1764706
13 -0.1764706 -0.1764706
14 -0.1764706 -0.1764706
15 -0.1764706 -0.1764706
16 -0.1764706 -0.1764706
17 -0.1764706 -0.1764706
18 -0.1176471 -0.1764706
19 -0.1764706 -0.1176471
20 -0.1764706 -0.1764706
21 -0.1176471 -0.1764706
22 -0.1764706 -0.1176471
23 -0.1764706 -0.1764706
24 0.8823529 -0.1764706
25 -0.1176471 0.8823529
26 -0.1764706 -0.1176471
27 -0.1176471 -0.1764706
28 -0.1764706 -0.1176471
29 -0.1764706 -0.1764706
30 -0.1764706 -0.1764706
31 -0.1764706 -0.1764706
32 -0.1764706 -0.1764706
33 -0.1764706 -0.1764706
34 -0.1764706 -0.1764706
35 -0.1764706 -0.1764706
36 -0.1176471 -0.1764706
37 0.8235294 -0.1176471
38 -0.1764706 0.8235294
39 -0.1176471 -0.1764706
40 0.8235294 -0.1176471
41 -0.1764706 0.8235294
42 -0.1764706 -0.1764706
43 -0.1764706 -0.1764706
44 -0.1764706 -0.1764706
45 -0.1764706 -0.1764706
46 -0.1764706 -0.1764706
47 -0.1764706 -0.1764706
48 -0.1764706 -0.1764706
49 -0.1764706 -0.1764706
50 0.8235294 -0.1764706
51 0.8823529 0.8235294
52 -0.1176471 0.8823529
53 -0.1764706 -0.1176471
54 -0.1764706 -0.1764706
55 -0.1176471 -0.1764706
56 -0.1764706 -0.1176471
57 0.8235294 -0.1764706
58 0.8235294 0.8235294
59 -0.1176471 0.8235294
60 -0.1176471 -0.1176471
61 -0.1176471 -0.1176471
62 -0.1764706 -0.1176471
63 0.8235294 -0.1764706
64 -0.1764706 0.8235294
65 -0.1764706 -0.1764706
66 0.8235294 -0.1764706
67 -0.1764706 0.8235294
> 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/7mwie1354718253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ujjb1354718253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/919jk1354718253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10kd691354718253.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11yh2l1354718253.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/12zwf71354718253.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/13yhq81354718253.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/14gswz1354718253.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/15vrmj1354718253.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/16rq4a1354718253.tab")
+ }
>
> try(system("convert tmp/144hv1354718253.ps tmp/144hv1354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/2aet61354718253.ps tmp/2aet61354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/30nop1354718253.ps tmp/30nop1354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/4h1sj1354718253.ps tmp/4h1sj1354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/5x5ny1354718253.ps tmp/5x5ny1354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/6m4311354718253.ps tmp/6m4311354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mwie1354718253.ps tmp/7mwie1354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ujjb1354718253.ps tmp/8ujjb1354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/919jk1354718253.ps tmp/919jk1354718253.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kd691354718253.ps tmp/10kd691354718253.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.183 1.110 7.348