R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-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(6.3
+ ,2
+ ,4.5
+ ,1
+ ,6.6
+ ,42
+ ,3
+ ,1
+ ,3
+ ,2.1
+ ,1.8
+ ,69
+ ,2547
+ ,4603
+ ,624
+ ,3
+ ,5
+ ,4
+ ,9.1
+ ,0.7
+ ,27
+ ,10.55
+ ,179.5
+ ,180
+ ,4
+ ,4
+ ,4
+ ,15.8
+ ,3.9
+ ,19
+ ,0.023
+ ,0.3
+ ,35
+ ,1
+ ,1
+ ,1
+ ,5.2
+ ,1
+ ,30.4
+ ,160
+ ,169
+ ,392
+ ,4
+ ,5
+ ,4
+ ,10.9
+ ,3.6
+ ,28
+ ,3.3
+ ,25.6
+ ,63
+ ,1
+ ,2
+ ,1
+ ,8.3
+ ,1.4
+ ,50
+ ,52.16
+ ,440
+ ,230
+ ,1
+ ,1
+ ,1
+ ,11
+ ,1.5
+ ,7
+ ,0.42
+ ,6.4
+ ,112
+ ,5
+ ,4
+ ,4
+ ,3.2
+ ,0.7
+ ,30
+ ,465
+ ,423
+ ,281
+ ,5
+ ,5
+ ,5
+ ,6.3
+ ,2.1
+ ,3.5
+ ,0.075
+ ,1.2
+ ,42
+ ,1
+ ,1
+ ,1
+ ,6.6
+ ,4.1
+ ,6
+ ,0.785
+ ,3.5
+ ,42
+ ,2
+ ,2
+ ,2
+ ,9.5
+ ,1.2
+ ,10.4
+ ,0.2
+ ,5
+ ,120
+ ,2
+ ,2
+ ,2
+ ,3.3
+ ,0.5
+ ,20
+ ,27.66
+ ,115
+ ,148
+ ,5
+ ,5
+ ,5
+ ,11
+ ,3.4
+ ,3.9
+ ,0.12
+ ,1
+ ,16
+ ,3
+ ,1
+ ,2
+ ,4.7
+ ,1.5
+ ,41
+ ,85
+ ,325
+ ,310
+ ,1
+ ,3
+ ,1
+ ,10.4
+ ,3.4
+ ,9
+ ,0.101
+ ,4
+ ,28
+ ,5
+ ,1
+ ,3
+ ,7.4
+ ,0.8
+ ,7.6
+ ,1.04
+ ,5.5
+ ,68
+ ,5
+ ,3
+ ,4
+ ,2.1
+ ,0.8
+ ,46
+ ,521
+ ,655
+ ,336
+ ,5
+ ,5
+ ,5
+ ,17.9
+ ,2
+ ,24
+ ,0.1
+ ,0.25
+ ,50
+ ,1
+ ,1
+ ,1
+ ,6.1
+ ,1.9
+ ,100
+ ,62
+ ,1320
+ ,267
+ ,1
+ ,1
+ ,1
+ ,11.9
+ ,1.3
+ ,3.2
+ ,0.023
+ ,0.4
+ ,19
+ ,4
+ ,1
+ ,3
+ ,13.8
+ ,5.6
+ ,5
+ ,1.7
+ ,6.3
+ ,12
+ ,2
+ ,1
+ ,1
+ ,14.3
+ ,14.3
+ ,6.5
+ ,3.5
+ ,10.8
+ ,120
+ ,2
+ ,1
+ ,1
+ ,15.2
+ ,1.8
+ ,12
+ ,0.48
+ ,15.5
+ ,140
+ ,2
+ ,2
+ ,2
+ ,10
+ ,0.9
+ ,20.2
+ ,10
+ ,115
+ ,170
+ ,4
+ ,4
+ ,4
+ ,11.9
+ ,1.8
+ ,13
+ ,1.62
+ ,11.4
+ ,17
+ ,2
+ ,1
+ ,2
+ ,6.5
+ ,1.9
+ ,27
+ ,192
+ ,180
+ ,115
+ ,4
+ ,4
+ ,4
+ ,7.5
+ ,0.9
+ ,18
+ ,2.5
+ ,12.1
+ ,31
+ ,5
+ ,5
+ ,5
+ ,10.6
+ ,2.6
+ ,4.7
+ ,0.28
+ ,1.9
+ ,21
+ ,3
+ ,1
+ ,3
+ ,7.4
+ ,2.4
+ ,9.8
+ ,4.235
+ ,50.4
+ ,52
+ ,1
+ ,1
+ ,1
+ ,8.4
+ ,1.2
+ ,29
+ ,6.8
+ ,179
+ ,164
+ ,2
+ ,3
+ ,2
+ ,5.7
+ ,0.9
+ ,7
+ ,0.75
+ ,12.3
+ ,225
+ ,2
+ ,2
+ ,2
+ ,4.9
+ ,0.5
+ ,6
+ ,3.6
+ ,21
+ ,225
+ ,3
+ ,2
+ ,3
+ ,3.2
+ ,0.6
+ ,20
+ ,55.5
+ ,175
+ ,151
+ ,5
+ ,5
+ ,5
+ ,11
+ ,2.3
+ ,4.5
+ ,0.9
+ ,2.6
+ ,60
+ ,2
+ ,1
+ ,2
+ ,4.9
+ ,0.5
+ ,7.5
+ ,2
+ ,12.3
+ ,200
+ ,3
+ ,1
+ ,3
+ ,13.2
+ ,2.6
+ ,2.3
+ ,0.104
+ ,2.5
+ ,46
+ ,3
+ ,2
+ ,2
+ ,9.7
+ ,0.6
+ ,24
+ ,4.19
+ ,58
+ ,210
+ ,4
+ ,3
+ ,4
+ ,12.8
+ ,6.6
+ ,3
+ ,3.5
+ ,3.9
+ ,14
+ ,2
+ ,1
+ ,1)
+ ,dim=c(9
+ ,39)
+ ,dimnames=list(c('SWS'
+ ,'PS'
+ ,'L'
+ ,'BW'
+ ,'BRW'
+ ,'Tg'
+ ,'P'
+ ,'S'
+ ,'D')
+ ,1:39))
> y <- array(NA,dim=c(9,39),dimnames=list(c('SWS','PS','L','BW','BRW','Tg','P','S','D'),1:39))
> 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'
> #'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
> 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
SWS PS L BW BRW Tg P S D
1 6.3 2.0 4.5 1.000 6.60 42 3 1 3
2 2.1 1.8 69.0 2547.000 4603.00 624 3 5 4
3 9.1 0.7 27.0 10.550 179.50 180 4 4 4
4 15.8 3.9 19.0 0.023 0.30 35 1 1 1
5 5.2 1.0 30.4 160.000 169.00 392 4 5 4
6 10.9 3.6 28.0 3.300 25.60 63 1 2 1
7 8.3 1.4 50.0 52.160 440.00 230 1 1 1
8 11.0 1.5 7.0 0.420 6.40 112 5 4 4
9 3.2 0.7 30.0 465.000 423.00 281 5 5 5
10 6.3 2.1 3.5 0.075 1.20 42 1 1 1
11 6.6 4.1 6.0 0.785 3.50 42 2 2 2
12 9.5 1.2 10.4 0.200 5.00 120 2 2 2
13 3.3 0.5 20.0 27.660 115.00 148 5 5 5
14 11.0 3.4 3.9 0.120 1.00 16 3 1 2
15 4.7 1.5 41.0 85.000 325.00 310 1 3 1
16 10.4 3.4 9.0 0.101 4.00 28 5 1 3
17 7.4 0.8 7.6 1.040 5.50 68 5 3 4
18 2.1 0.8 46.0 521.000 655.00 336 5 5 5
19 17.9 2.0 24.0 0.100 0.25 50 1 1 1
20 6.1 1.9 100.0 62.000 1320.00 267 1 1 1
21 11.9 1.3 3.2 0.023 0.40 19 4 1 3
22 13.8 5.6 5.0 1.700 6.30 12 2 1 1
23 14.3 14.3 6.5 3.500 10.80 120 2 1 1
24 15.2 1.8 12.0 0.480 15.50 140 2 2 2
25 10.0 0.9 20.2 10.000 115.00 170 4 4 4
26 11.9 1.8 13.0 1.620 11.40 17 2 1 2
27 6.5 1.9 27.0 192.000 180.00 115 4 4 4
28 7.5 0.9 18.0 2.500 12.10 31 5 5 5
29 10.6 2.6 4.7 0.280 1.90 21 3 1 3
30 7.4 2.4 9.8 4.235 50.40 52 1 1 1
31 8.4 1.2 29.0 6.800 179.00 164 2 3 2
32 5.7 0.9 7.0 0.750 12.30 225 2 2 2
33 4.9 0.5 6.0 3.600 21.00 225 3 2 3
34 3.2 0.6 20.0 55.500 175.00 151 5 5 5
35 11.0 2.3 4.5 0.900 2.60 60 2 1 2
36 4.9 0.5 7.5 2.000 12.30 200 3 1 3
37 13.2 2.6 2.3 0.104 2.50 46 3 2 2
38 9.7 0.6 24.0 4.190 58.00 210 4 3 4
39 12.8 6.6 3.0 3.500 3.90 14 2 1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PS L BW BRW Tg
12.431998 0.200038 0.024060 0.004612 -0.002237 -0.015701
P S D
1.054753 0.047305 -2.103307
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.9732 -1.5946 -0.2586 1.3018 6.3524
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.431998 1.763786 7.048 7.78e-08 ***
PS 0.200038 0.268482 0.745 0.4620
L 0.024060 0.054049 0.445 0.6594
BW 0.004612 0.006426 0.718 0.4785
BRW -0.002237 0.003823 -0.585 0.5627
Tg -0.015701 0.007327 -2.143 0.0404 *
P 1.054753 1.203594 0.876 0.3878
S 0.047305 0.698752 0.068 0.9465
D -2.103307 1.570874 -1.339 0.1906
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.964 on 30 degrees of freedom
Multiple R-squared: 0.5595, Adjusted R-squared: 0.4421
F-statistic: 4.764 on 8 and 30 DF, p-value: 0.0007568
> 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,] 0.9105852 0.1788296 0.08941480
[2,] 0.8759969 0.2480061 0.12400305
[3,] 0.8041011 0.3917979 0.19589894
[4,] 0.8382550 0.3234900 0.16174499
[5,] 0.8320440 0.3359120 0.16795598
[6,] 0.7760165 0.4479670 0.22398352
[7,] 0.7595675 0.4808649 0.24043247
[8,] 0.8654105 0.2691790 0.13458952
[9,] 0.8099098 0.3801803 0.19009017
[10,] 0.7198335 0.5603329 0.28016646
[11,] 0.6086508 0.7826983 0.39134916
[12,] 0.5047466 0.9905067 0.49525336
[13,] 0.8402211 0.3195577 0.15977887
[14,] 0.9051068 0.1897864 0.09489318
[15,] 0.8075799 0.3848401 0.19242006
[16,] 0.6624762 0.6750477 0.33752385
> postscript(file="/var/www/rcomp/tmp/109sr1292342204.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2airu1292342204.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3airu1292342204.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4airu1292342204.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5399f1292342204.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 = 39
Frequency = 1
1 2 3 4 5 6
-2.87237898 1.01136861 3.06255486 3.68207577 1.58940664 -0.94062479
7 8 9 10 11 12
-0.25855257 2.82068899 -1.87343788 -4.97324763 -4.13035211 0.47464473
13 14 15 16 17 18
-2.25343078 -0.95800713 -2.90928464 -1.49169634 -1.30214183 -2.25398000
19 20 21 22 23 24
6.27690082 -1.25700483 1.47367697 0.26865712 0.68973646 6.35235314
25 26 27 28 29 30
3.78735981 1.12991733 -1.63371485 -0.03658025 0.96586473 -3.83692288
31 32 33 34 35 36
-0.07043142 -1.52111523 -1.16216316 -2.32046713 0.99318891 -1.55556590
37 38 39
1.86768048 4.03055470 -0.86552976
> postscript(file="/var/www/rcomp/tmp/6399f1292342204.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 = 39
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.87237898 NA
1 1.01136861 -2.87237898
2 3.06255486 1.01136861
3 3.68207577 3.06255486
4 1.58940664 3.68207577
5 -0.94062479 1.58940664
6 -0.25855257 -0.94062479
7 2.82068899 -0.25855257
8 -1.87343788 2.82068899
9 -4.97324763 -1.87343788
10 -4.13035211 -4.97324763
11 0.47464473 -4.13035211
12 -2.25343078 0.47464473
13 -0.95800713 -2.25343078
14 -2.90928464 -0.95800713
15 -1.49169634 -2.90928464
16 -1.30214183 -1.49169634
17 -2.25398000 -1.30214183
18 6.27690082 -2.25398000
19 -1.25700483 6.27690082
20 1.47367697 -1.25700483
21 0.26865712 1.47367697
22 0.68973646 0.26865712
23 6.35235314 0.68973646
24 3.78735981 6.35235314
25 1.12991733 3.78735981
26 -1.63371485 1.12991733
27 -0.03658025 -1.63371485
28 0.96586473 -0.03658025
29 -3.83692288 0.96586473
30 -0.07043142 -3.83692288
31 -1.52111523 -0.07043142
32 -1.16216316 -1.52111523
33 -2.32046713 -1.16216316
34 0.99318891 -2.32046713
35 -1.55556590 0.99318891
36 1.86768048 -1.55556590
37 4.03055470 1.86768048
38 -0.86552976 4.03055470
39 NA -0.86552976
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.01136861 -2.87237898
[2,] 3.06255486 1.01136861
[3,] 3.68207577 3.06255486
[4,] 1.58940664 3.68207577
[5,] -0.94062479 1.58940664
[6,] -0.25855257 -0.94062479
[7,] 2.82068899 -0.25855257
[8,] -1.87343788 2.82068899
[9,] -4.97324763 -1.87343788
[10,] -4.13035211 -4.97324763
[11,] 0.47464473 -4.13035211
[12,] -2.25343078 0.47464473
[13,] -0.95800713 -2.25343078
[14,] -2.90928464 -0.95800713
[15,] -1.49169634 -2.90928464
[16,] -1.30214183 -1.49169634
[17,] -2.25398000 -1.30214183
[18,] 6.27690082 -2.25398000
[19,] -1.25700483 6.27690082
[20,] 1.47367697 -1.25700483
[21,] 0.26865712 1.47367697
[22,] 0.68973646 0.26865712
[23,] 6.35235314 0.68973646
[24,] 3.78735981 6.35235314
[25,] 1.12991733 3.78735981
[26,] -1.63371485 1.12991733
[27,] -0.03658025 -1.63371485
[28,] 0.96586473 -0.03658025
[29,] -3.83692288 0.96586473
[30,] -0.07043142 -3.83692288
[31,] -1.52111523 -0.07043142
[32,] -1.16216316 -1.52111523
[33,] -2.32046713 -1.16216316
[34,] 0.99318891 -2.32046713
[35,] -1.55556590 0.99318891
[36,] 1.86768048 -1.55556590
[37,] 4.03055470 1.86768048
[38,] -0.86552976 4.03055470
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.01136861 -2.87237898
2 3.06255486 1.01136861
3 3.68207577 3.06255486
4 1.58940664 3.68207577
5 -0.94062479 1.58940664
6 -0.25855257 -0.94062479
7 2.82068899 -0.25855257
8 -1.87343788 2.82068899
9 -4.97324763 -1.87343788
10 -4.13035211 -4.97324763
11 0.47464473 -4.13035211
12 -2.25343078 0.47464473
13 -0.95800713 -2.25343078
14 -2.90928464 -0.95800713
15 -1.49169634 -2.90928464
16 -1.30214183 -1.49169634
17 -2.25398000 -1.30214183
18 6.27690082 -2.25398000
19 -1.25700483 6.27690082
20 1.47367697 -1.25700483
21 0.26865712 1.47367697
22 0.68973646 0.26865712
23 6.35235314 0.68973646
24 3.78735981 6.35235314
25 1.12991733 3.78735981
26 -1.63371485 1.12991733
27 -0.03658025 -1.63371485
28 0.96586473 -0.03658025
29 -3.83692288 0.96586473
30 -0.07043142 -3.83692288
31 -1.52111523 -0.07043142
32 -1.16216316 -1.52111523
33 -2.32046713 -1.16216316
34 0.99318891 -2.32046713
35 -1.55556590 0.99318891
36 1.86768048 -1.55556590
37 4.03055470 1.86768048
38 -0.86552976 4.03055470
> 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/rcomp/tmp/7ej8i1292342204.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8ej8i1292342204.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/96s7k1292342204.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/106s7k1292342204.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11ran81292342204.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/rcomp/tmp/12db4w1292342204.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/rcomp/tmp/13um431292342205.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/rcomp/tmp/145v3o1292342205.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/rcomp/tmp/15qwkc1292342205.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/rcomp/tmp/16n6031292342205.tab")
+ }
>
> try(system("convert tmp/109sr1292342204.ps tmp/109sr1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/2airu1292342204.ps tmp/2airu1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/3airu1292342204.ps tmp/3airu1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/4airu1292342204.ps tmp/4airu1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/5399f1292342204.ps tmp/5399f1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/6399f1292342204.ps tmp/6399f1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ej8i1292342204.ps tmp/7ej8i1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ej8i1292342204.ps tmp/8ej8i1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/96s7k1292342204.ps tmp/96s7k1292342204.png",intern=TRUE))
character(0)
> try(system("convert tmp/106s7k1292342204.ps tmp/106s7k1292342204.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.950 1.620 4.574