R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(19,74,18,76,19,69.6,19,77.3,22,75.2,23,75.8,20,77.6,14,76.7,14,77,14,77.9,15,76.7,11,71.9,17,73.4,16,72.5,20,73.7,24,69.5,23,74.7,20,72.5,21,72.1,19,70.7,23,71.4,23,69.5,23,73.5,23,72.4,27,74.5,26,72.2,17,73,24,73.3,26,71.3,24,73.6,27,71.3,27,71.2,26,81.4,24,76.1,23,71.1,23,75.7,24,70,17,68.5,21,56.7,19,57.9,22,58.8,22,59.3,18,61.3,16,62.9,14,61.4,12,64.5,14,63.8,16,61.6,8,64.7),dim=c(2,49),dimnames=list(c('indcvtr','dzcg
'),1:49))
> y <- array(NA,dim=c(2,49),dimnames=list(c('indcvtr','dzcg
'),1:49))
> 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 = '2'
> #'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.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
dzcg\r indcvtr M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 74.0 19 1 0 0 0 0 0 0 0 0 0 0 1
2 76.0 18 0 1 0 0 0 0 0 0 0 0 0 2
3 69.6 19 0 0 1 0 0 0 0 0 0 0 0 3
4 77.3 19 0 0 0 1 0 0 0 0 0 0 0 4
5 75.2 22 0 0 0 0 1 0 0 0 0 0 0 5
6 75.8 23 0 0 0 0 0 1 0 0 0 0 0 6
7 77.6 20 0 0 0 0 0 0 1 0 0 0 0 7
8 76.7 14 0 0 0 0 0 0 0 1 0 0 0 8
9 77.0 14 0 0 0 0 0 0 0 0 1 0 0 9
10 77.9 14 0 0 0 0 0 0 0 0 0 1 0 10
11 76.7 15 0 0 0 0 0 0 0 0 0 0 1 11
12 71.9 11 0 0 0 0 0 0 0 0 0 0 0 12
13 73.4 17 1 0 0 0 0 0 0 0 0 0 0 13
14 72.5 16 0 1 0 0 0 0 0 0 0 0 0 14
15 73.7 20 0 0 1 0 0 0 0 0 0 0 0 15
16 69.5 24 0 0 0 1 0 0 0 0 0 0 0 16
17 74.7 23 0 0 0 0 1 0 0 0 0 0 0 17
18 72.5 20 0 0 0 0 0 1 0 0 0 0 0 18
19 72.1 21 0 0 0 0 0 0 1 0 0 0 0 19
20 70.7 19 0 0 0 0 0 0 0 1 0 0 0 20
21 71.4 23 0 0 0 0 0 0 0 0 1 0 0 21
22 69.5 23 0 0 0 0 0 0 0 0 0 1 0 22
23 73.5 23 0 0 0 0 0 0 0 0 0 0 1 23
24 72.4 23 0 0 0 0 0 0 0 0 0 0 0 24
25 74.5 27 1 0 0 0 0 0 0 0 0 0 0 25
26 72.2 26 0 1 0 0 0 0 0 0 0 0 0 26
27 73.0 17 0 0 1 0 0 0 0 0 0 0 0 27
28 73.3 24 0 0 0 1 0 0 0 0 0 0 0 28
29 71.3 26 0 0 0 0 1 0 0 0 0 0 0 29
30 73.6 24 0 0 0 0 0 1 0 0 0 0 0 30
31 71.3 27 0 0 0 0 0 0 1 0 0 0 0 31
32 71.2 27 0 0 0 0 0 0 0 1 0 0 0 32
33 81.4 26 0 0 0 0 0 0 0 0 1 0 0 33
34 76.1 24 0 0 0 0 0 0 0 0 0 1 0 34
35 71.1 23 0 0 0 0 0 0 0 0 0 0 1 35
36 75.7 23 0 0 0 0 0 0 0 0 0 0 0 36
37 70.0 24 1 0 0 0 0 0 0 0 0 0 0 37
38 68.5 17 0 1 0 0 0 0 0 0 0 0 0 38
39 56.7 21 0 0 1 0 0 0 0 0 0 0 0 39
40 57.9 19 0 0 0 1 0 0 0 0 0 0 0 40
41 58.8 22 0 0 0 0 1 0 0 0 0 0 0 41
42 59.3 22 0 0 0 0 0 1 0 0 0 0 0 42
43 61.3 18 0 0 0 0 0 0 1 0 0 0 0 43
44 62.9 16 0 0 0 0 0 0 0 1 0 0 0 44
45 61.4 14 0 0 0 0 0 0 0 0 1 0 0 45
46 64.5 12 0 0 0 0 0 0 0 0 0 1 0 46
47 63.8 14 0 0 0 0 0 0 0 0 0 0 1 47
48 61.6 16 0 0 0 0 0 0 0 0 0 0 0 48
49 64.7 8 1 0 0 0 0 0 0 0 0 0 0 49
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) indcvtr M1 M2 M3 M4
73.0541 0.3754 -0.9459 -1.6441 -5.3772 -4.6551
M5 M6 M7 M8 M9 M10
-4.4952 -3.5029 -2.6295 -1.5740 1.0740 0.9663
M11 t
0.3704 -0.3169
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.50352 -1.68450 -0.03420 2.16871 7.96696
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 73.0541 3.3905 21.547 < 2e-16 ***
indcvtr 0.3754 0.1350 2.781 0.00867 **
M1 -0.9459 2.6902 -0.352 0.72725
M2 -1.6441 2.8588 -0.575 0.56891
M3 -5.3772 2.8531 -1.885 0.06780 .
M4 -4.6551 2.8790 -1.617 0.11487
M5 -4.4952 2.9202 -1.539 0.13271
M6 -3.5029 2.8878 -1.213 0.23325
M7 -2.6295 2.8671 -0.917 0.36535
M8 -1.5740 2.8320 -0.556 0.58188
M9 1.0740 2.8313 0.379 0.70674
M10 0.9663 2.8264 0.342 0.73449
M11 0.3704 2.8264 0.131 0.89648
t -0.3169 0.0413 -7.671 5.31e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.995 on 35 degrees of freedom
Multiple R-squared: 0.6677, Adjusted R-squared: 0.5443
F-statistic: 5.41 on 13 and 35 DF, p-value: 3.210e-05
> 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,] 0.26552677 0.53105354 0.7344732
[2,] 0.16313489 0.32626978 0.8368651
[3,] 0.09358249 0.18716499 0.9064175
[4,] 0.04301171 0.08602343 0.9569883
[5,] 0.02541526 0.05083053 0.9745847
[6,] 0.02560956 0.05121912 0.9743904
[7,] 0.02135496 0.04270993 0.9786450
[8,] 0.12935279 0.25870559 0.8706472
[9,] 0.37050885 0.74101770 0.6294912
[10,] 0.47273327 0.94546653 0.5272667
[11,] 0.39226767 0.78453535 0.6077323
[12,] 0.30406249 0.60812499 0.6959375
[13,] 0.20448310 0.40896620 0.7955169
[14,] 0.12326518 0.24653037 0.8767348
[15,] 0.06869511 0.13739022 0.9313049
[16,] 0.03893552 0.07787103 0.9610645
> postscript(file="/var/www/html/rcomp/tmp/1fpte1260701321.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2g3eb1260701321.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/39j041260701321.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4od7h1260701321.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53qwm1260701321.ps",horizontal=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 = 49
Frequency = 1
1 2 3 4 5 6
-4.92466938 -1.53420361 -4.25964235 3.03509482 -0.03420361 -0.48508109
7 8 9 10 11 12
1.88465608 2.49869168 0.46755136 1.79211262 1.12939325 -1.48157115
13 14 15 16 17 18
-0.97145721 -0.48099143 3.26725360 -2.83976420 2.89269234 1.14356983
19 20 21 22 23 24
-0.18844797 -1.57616735 -4.70906263 -6.18450137 -1.27178200 -1.68450137
25 26 27 28 29 30
0.17649006 -0.73304417 7.49590452 4.76257049 2.16871080 4.54414955
31 32 33 34 35 36
0.56125426 -0.27734260 7.96695583 3.84239457 0.13055269 5.41783332
37 38 39 40 41 42
0.60514098 2.74823921 -6.50351576 -4.95790110 -5.02719953 -5.20263828
43 44 45 46 47 48
-2.25746236 -0.64518173 -3.72544456 0.54999418 0.01183607 -2.25176079
49
5.11449556
> postscript(file="/var/www/html/rcomp/tmp/6t3p51260701321.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 49
Frequency = 1
lag(myerror, k = 1) myerror
0 -4.92466938 NA
1 -1.53420361 -4.92466938
2 -4.25964235 -1.53420361
3 3.03509482 -4.25964235
4 -0.03420361 3.03509482
5 -0.48508109 -0.03420361
6 1.88465608 -0.48508109
7 2.49869168 1.88465608
8 0.46755136 2.49869168
9 1.79211262 0.46755136
10 1.12939325 1.79211262
11 -1.48157115 1.12939325
12 -0.97145721 -1.48157115
13 -0.48099143 -0.97145721
14 3.26725360 -0.48099143
15 -2.83976420 3.26725360
16 2.89269234 -2.83976420
17 1.14356983 2.89269234
18 -0.18844797 1.14356983
19 -1.57616735 -0.18844797
20 -4.70906263 -1.57616735
21 -6.18450137 -4.70906263
22 -1.27178200 -6.18450137
23 -1.68450137 -1.27178200
24 0.17649006 -1.68450137
25 -0.73304417 0.17649006
26 7.49590452 -0.73304417
27 4.76257049 7.49590452
28 2.16871080 4.76257049
29 4.54414955 2.16871080
30 0.56125426 4.54414955
31 -0.27734260 0.56125426
32 7.96695583 -0.27734260
33 3.84239457 7.96695583
34 0.13055269 3.84239457
35 5.41783332 0.13055269
36 0.60514098 5.41783332
37 2.74823921 0.60514098
38 -6.50351576 2.74823921
39 -4.95790110 -6.50351576
40 -5.02719953 -4.95790110
41 -5.20263828 -5.02719953
42 -2.25746236 -5.20263828
43 -0.64518173 -2.25746236
44 -3.72544456 -0.64518173
45 0.54999418 -3.72544456
46 0.01183607 0.54999418
47 -2.25176079 0.01183607
48 5.11449556 -2.25176079
49 NA 5.11449556
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.53420361 -4.92466938
[2,] -4.25964235 -1.53420361
[3,] 3.03509482 -4.25964235
[4,] -0.03420361 3.03509482
[5,] -0.48508109 -0.03420361
[6,] 1.88465608 -0.48508109
[7,] 2.49869168 1.88465608
[8,] 0.46755136 2.49869168
[9,] 1.79211262 0.46755136
[10,] 1.12939325 1.79211262
[11,] -1.48157115 1.12939325
[12,] -0.97145721 -1.48157115
[13,] -0.48099143 -0.97145721
[14,] 3.26725360 -0.48099143
[15,] -2.83976420 3.26725360
[16,] 2.89269234 -2.83976420
[17,] 1.14356983 2.89269234
[18,] -0.18844797 1.14356983
[19,] -1.57616735 -0.18844797
[20,] -4.70906263 -1.57616735
[21,] -6.18450137 -4.70906263
[22,] -1.27178200 -6.18450137
[23,] -1.68450137 -1.27178200
[24,] 0.17649006 -1.68450137
[25,] -0.73304417 0.17649006
[26,] 7.49590452 -0.73304417
[27,] 4.76257049 7.49590452
[28,] 2.16871080 4.76257049
[29,] 4.54414955 2.16871080
[30,] 0.56125426 4.54414955
[31,] -0.27734260 0.56125426
[32,] 7.96695583 -0.27734260
[33,] 3.84239457 7.96695583
[34,] 0.13055269 3.84239457
[35,] 5.41783332 0.13055269
[36,] 0.60514098 5.41783332
[37,] 2.74823921 0.60514098
[38,] -6.50351576 2.74823921
[39,] -4.95790110 -6.50351576
[40,] -5.02719953 -4.95790110
[41,] -5.20263828 -5.02719953
[42,] -2.25746236 -5.20263828
[43,] -0.64518173 -2.25746236
[44,] -3.72544456 -0.64518173
[45,] 0.54999418 -3.72544456
[46,] 0.01183607 0.54999418
[47,] -2.25176079 0.01183607
[48,] 5.11449556 -2.25176079
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.53420361 -4.92466938
2 -4.25964235 -1.53420361
3 3.03509482 -4.25964235
4 -0.03420361 3.03509482
5 -0.48508109 -0.03420361
6 1.88465608 -0.48508109
7 2.49869168 1.88465608
8 0.46755136 2.49869168
9 1.79211262 0.46755136
10 1.12939325 1.79211262
11 -1.48157115 1.12939325
12 -0.97145721 -1.48157115
13 -0.48099143 -0.97145721
14 3.26725360 -0.48099143
15 -2.83976420 3.26725360
16 2.89269234 -2.83976420
17 1.14356983 2.89269234
18 -0.18844797 1.14356983
19 -1.57616735 -0.18844797
20 -4.70906263 -1.57616735
21 -6.18450137 -4.70906263
22 -1.27178200 -6.18450137
23 -1.68450137 -1.27178200
24 0.17649006 -1.68450137
25 -0.73304417 0.17649006
26 7.49590452 -0.73304417
27 4.76257049 7.49590452
28 2.16871080 4.76257049
29 4.54414955 2.16871080
30 0.56125426 4.54414955
31 -0.27734260 0.56125426
32 7.96695583 -0.27734260
33 3.84239457 7.96695583
34 0.13055269 3.84239457
35 5.41783332 0.13055269
36 0.60514098 5.41783332
37 2.74823921 0.60514098
38 -6.50351576 2.74823921
39 -4.95790110 -6.50351576
40 -5.02719953 -4.95790110
41 -5.20263828 -5.02719953
42 -2.25746236 -5.20263828
43 -0.64518173 -2.25746236
44 -3.72544456 -0.64518173
45 0.54999418 -3.72544456
46 0.01183607 0.54999418
47 -2.25176079 0.01183607
48 5.11449556 -2.25176079
> 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/www/html/rcomp/tmp/7sqrn1260701321.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nrep1260701321.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z4391260701321.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ixgc1260701321.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/html/rcomp/tmp/119wcb1260701321.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/www/html/rcomp/tmp/126qdf1260701321.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/www/html/rcomp/tmp/13pyef1260701321.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/www/html/rcomp/tmp/149kgk1260701321.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/www/html/rcomp/tmp/1599iv1260701321.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/www/html/rcomp/tmp/16rnkw1260701321.tab")
+ }
>
> try(system("convert tmp/1fpte1260701321.ps tmp/1fpte1260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g3eb1260701321.ps tmp/2g3eb1260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/39j041260701321.ps tmp/39j041260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/4od7h1260701321.ps tmp/4od7h1260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/53qwm1260701321.ps tmp/53qwm1260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/6t3p51260701321.ps tmp/6t3p51260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sqrn1260701321.ps tmp/7sqrn1260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nrep1260701321.ps tmp/8nrep1260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/9z4391260701321.ps tmp/9z4391260701321.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ixgc1260701321.ps tmp/10ixgc1260701321.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.202 1.527 3.822