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(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
+ ,0.425
+ ,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
+ ,8.6
+ ,0
+ ,50
+ ,3
+ ,25
+ ,28
+ ,2
+ ,2
+ ,2
+ ,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
+ ,7.7
+ ,1.4
+ ,2.6
+ ,0.005
+ ,0.14
+ ,21.5
+ ,5
+ ,2
+ ,4
+ ,17.9
+ ,2
+ ,24
+ ,0.01
+ ,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
+ ,10.8
+ ,2
+ ,2
+ ,0.048
+ ,0.33
+ ,30
+ ,4
+ ,1
+ ,3
+ ,13.8
+ ,5.6
+ ,5
+ ,1.7
+ ,6.3
+ ,12
+ ,2
+ ,1
+ ,1
+ ,14.3
+ ,3.1
+ ,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
+ ,42)
+ ,dimnames=list(c('SWS'
+ ,'PS'
+ ,'L'
+ ,'WB'
+ ,'WBR'
+ ,'TG'
+ ,'P'
+ ,'S'
+ ,'D
')
+ ,1:42))
> y <- array(NA,dim=c(9,42),dimnames=list(c('SWS','PS','L','WB','WBR','TG','P','S','D
'),1:42))
> 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
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
SWS PS L WB WBR TG P S D\r
1 6.3 2.0 4.5 1.000 6.60 42.0 3 1 3
2 2.1 1.8 69.0 2547.000 4603.00 624.0 3 5 4
3 9.1 0.7 27.0 10.550 179.50 180.0 4 4 4
4 15.8 3.9 19.0 0.023 0.30 35.0 1 1 1
5 5.2 1.0 30.4 160.000 169.00 392.0 4 5 4
6 10.9 3.6 28.0 3.300 25.60 63.0 1 2 1
7 8.3 1.4 50.0 52.160 440.00 230.0 1 1 1
8 11.0 1.5 7.0 0.425 6.40 112.0 5 4 4
9 3.2 0.7 30.0 465.000 423.00 281.0 5 5 5
10 6.3 2.1 3.5 0.075 1.20 42.0 1 1 1
11 8.6 0.0 50.0 3.000 25.00 28.0 2 2 2
12 6.6 4.1 6.0 0.785 3.50 42.0 2 2 2
13 9.5 1.2 10.4 0.200 5.00 120.0 2 2 2
14 3.3 0.5 20.0 27.660 115.00 148.0 5 5 5
15 11.0 3.4 3.9 0.120 1.00 16.0 3 1 2
16 4.7 1.5 41.0 85.000 325.00 310.0 1 3 1
17 10.4 3.4 9.0 0.101 4.00 28.0 5 1 3
18 7.4 0.8 7.6 1.040 5.50 68.0 5 3 4
19 2.1 0.8 46.0 521.000 655.00 336.0 5 5 5
20 7.7 1.4 2.6 0.005 0.14 21.5 5 2 4
21 17.9 2.0 24.0 0.010 0.25 50.0 1 1 1
22 6.1 1.9 100.0 62.000 1320.00 267.0 1 1 1
23 11.9 1.3 3.2 0.023 0.40 19.0 4 1 3
24 10.8 2.0 2.0 0.048 0.33 30.0 4 1 3
25 13.8 5.6 5.0 1.700 6.30 12.0 2 1 1
26 14.3 3.1 6.5 3.500 10.80 120.0 2 1 1
27 15.2 1.8 12.0 0.480 15.50 140.0 2 2 2
28 10.0 0.9 20.2 10.000 115.00 170.0 4 4 4
29 11.9 1.8 13.0 1.620 11.40 17.0 2 1 2
30 6.5 1.9 27.0 192.000 180.00 115.0 4 4 4
31 7.5 0.9 18.0 2.500 12.10 31.0 5 5 5
32 10.6 2.6 4.7 0.280 1.90 21.0 3 1 3
33 7.4 2.4 9.8 4.235 50.40 52.0 1 1 1
34 8.4 1.2 29.0 6.800 179.00 164.0 2 3 2
35 5.7 0.9 7.0 0.750 12.30 225.0 2 2 2
36 4.9 0.5 6.0 3.600 21.00 225.0 3 2 3
37 3.2 0.6 20.0 55.500 175.00 151.0 5 5 5
38 11.0 2.3 4.5 0.900 2.60 60.0 2 1 2
39 4.9 0.5 7.5 2.000 12.30 200.0 3 1 3
40 13.2 2.6 2.3 0.104 2.50 46.0 3 2 2
41 9.7 0.6 24.0 4.190 58.00 210.0 4 3 4
42 12.8 6.6 3.0 3.500 3.90 14.0 2 1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PS L WB WBR TG
12.928769 0.106606 0.000247 0.003186 -0.001326 -0.013270
P S `D\r`
1.318790 0.186037 -2.613785
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.1859 -1.6862 -0.4386 1.4048 6.5249
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.928769 2.370019 5.455 4.81e-06 ***
PS 0.106606 0.528930 0.202 0.842
L 0.000247 0.044702 0.006 0.996
WB 0.003186 0.005694 0.560 0.580
WBR -0.001326 0.003393 -0.391 0.699
TG -0.013270 0.007165 -1.852 0.073 .
P 1.318790 1.145813 1.151 0.258
S 0.186037 0.680124 0.274 0.786
`D\r` -2.613785 1.587452 -1.647 0.109
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.898 on 33 degrees of freedom
Multiple R-squared: 0.541, Adjusted R-squared: 0.4297
F-statistic: 4.861 on 8 and 33 DF, p-value: 0.0005084
> 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,] 0.8898140 0.2203720 0.1101860
[2,] 0.8676323 0.2647354 0.1323677
[3,] 0.8212060 0.3575881 0.1787940
[4,] 0.7478502 0.5042997 0.2521498
[5,] 0.8220351 0.3559298 0.1779649
[6,] 0.8364839 0.3270323 0.1635161
[7,] 0.7756937 0.4486127 0.2243063
[8,] 0.7585907 0.4828185 0.2414093
[9,] 0.6931284 0.6137432 0.3068716
[10,] 0.8707333 0.2585334 0.1292667
[11,] 0.8174937 0.3650125 0.1825063
[12,] 0.7454430 0.5091140 0.2545570
[13,] 0.6423459 0.7153082 0.3576541
[14,] 0.5278719 0.9442563 0.4721281
[15,] 0.4260661 0.8521322 0.5739339
[16,] 0.7901212 0.4197577 0.2098788
[17,] 0.8779816 0.2440368 0.1220184
[18,] 0.7692777 0.4614445 0.2307223
[19,] 0.6151825 0.7696350 0.3848175
> postscript(file="/var/www/html/rcomp/tmp/1r12s1292937175.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jt1d1292937175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3jt1d1292937175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jt1d1292937175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5uk1g1292937175.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 = 42
Frequency = 1
1 2 3 4 5 6 7
-2.5812395 0.7983594 3.1187053 4.0245086 1.3230360 -0.6371121 -0.2122275
8 9 10 11 12 13 14
2.5200094 -1.4578846 -5.1858545 -1.7280599 -3.9899395 0.2570468 -2.1139391
15 16 17 18 19 20 21
-0.9937652 -3.3882038 -1.4555415 -1.4065115 -1.7135259 -1.6040674 6.5248510
22 23 24 25 26 27 28
-0.8517366 1.4645994 0.4360696 0.2253462 2.4248858 6.1711153 3.7826162
29 30 31 32 33 34 35
1.4156235 -1.0492035 0.4350749 1.3721402 -3.9347274 -0.2400808 -2.1088527
36 37 38 39 40 41 42
-1.5685157 -2.1939523 1.0256606 -1.7210348 1.5060181 4.1734514 -0.8631419
> postscript(file="/var/www/html/rcomp/tmp/6uk1g1292937175.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 = 42
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.5812395 NA
1 0.7983594 -2.5812395
2 3.1187053 0.7983594
3 4.0245086 3.1187053
4 1.3230360 4.0245086
5 -0.6371121 1.3230360
6 -0.2122275 -0.6371121
7 2.5200094 -0.2122275
8 -1.4578846 2.5200094
9 -5.1858545 -1.4578846
10 -1.7280599 -5.1858545
11 -3.9899395 -1.7280599
12 0.2570468 -3.9899395
13 -2.1139391 0.2570468
14 -0.9937652 -2.1139391
15 -3.3882038 -0.9937652
16 -1.4555415 -3.3882038
17 -1.4065115 -1.4555415
18 -1.7135259 -1.4065115
19 -1.6040674 -1.7135259
20 6.5248510 -1.6040674
21 -0.8517366 6.5248510
22 1.4645994 -0.8517366
23 0.4360696 1.4645994
24 0.2253462 0.4360696
25 2.4248858 0.2253462
26 6.1711153 2.4248858
27 3.7826162 6.1711153
28 1.4156235 3.7826162
29 -1.0492035 1.4156235
30 0.4350749 -1.0492035
31 1.3721402 0.4350749
32 -3.9347274 1.3721402
33 -0.2400808 -3.9347274
34 -2.1088527 -0.2400808
35 -1.5685157 -2.1088527
36 -2.1939523 -1.5685157
37 1.0256606 -2.1939523
38 -1.7210348 1.0256606
39 1.5060181 -1.7210348
40 4.1734514 1.5060181
41 -0.8631419 4.1734514
42 NA -0.8631419
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.7983594 -2.5812395
[2,] 3.1187053 0.7983594
[3,] 4.0245086 3.1187053
[4,] 1.3230360 4.0245086
[5,] -0.6371121 1.3230360
[6,] -0.2122275 -0.6371121
[7,] 2.5200094 -0.2122275
[8,] -1.4578846 2.5200094
[9,] -5.1858545 -1.4578846
[10,] -1.7280599 -5.1858545
[11,] -3.9899395 -1.7280599
[12,] 0.2570468 -3.9899395
[13,] -2.1139391 0.2570468
[14,] -0.9937652 -2.1139391
[15,] -3.3882038 -0.9937652
[16,] -1.4555415 -3.3882038
[17,] -1.4065115 -1.4555415
[18,] -1.7135259 -1.4065115
[19,] -1.6040674 -1.7135259
[20,] 6.5248510 -1.6040674
[21,] -0.8517366 6.5248510
[22,] 1.4645994 -0.8517366
[23,] 0.4360696 1.4645994
[24,] 0.2253462 0.4360696
[25,] 2.4248858 0.2253462
[26,] 6.1711153 2.4248858
[27,] 3.7826162 6.1711153
[28,] 1.4156235 3.7826162
[29,] -1.0492035 1.4156235
[30,] 0.4350749 -1.0492035
[31,] 1.3721402 0.4350749
[32,] -3.9347274 1.3721402
[33,] -0.2400808 -3.9347274
[34,] -2.1088527 -0.2400808
[35,] -1.5685157 -2.1088527
[36,] -2.1939523 -1.5685157
[37,] 1.0256606 -2.1939523
[38,] -1.7210348 1.0256606
[39,] 1.5060181 -1.7210348
[40,] 4.1734514 1.5060181
[41,] -0.8631419 4.1734514
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.7983594 -2.5812395
2 3.1187053 0.7983594
3 4.0245086 3.1187053
4 1.3230360 4.0245086
5 -0.6371121 1.3230360
6 -0.2122275 -0.6371121
7 2.5200094 -0.2122275
8 -1.4578846 2.5200094
9 -5.1858545 -1.4578846
10 -1.7280599 -5.1858545
11 -3.9899395 -1.7280599
12 0.2570468 -3.9899395
13 -2.1139391 0.2570468
14 -0.9937652 -2.1139391
15 -3.3882038 -0.9937652
16 -1.4555415 -3.3882038
17 -1.4065115 -1.4555415
18 -1.7135259 -1.4065115
19 -1.6040674 -1.7135259
20 6.5248510 -1.6040674
21 -0.8517366 6.5248510
22 1.4645994 -0.8517366
23 0.4360696 1.4645994
24 0.2253462 0.4360696
25 2.4248858 0.2253462
26 6.1711153 2.4248858
27 3.7826162 6.1711153
28 1.4156235 3.7826162
29 -1.0492035 1.4156235
30 0.4350749 -1.0492035
31 1.3721402 0.4350749
32 -3.9347274 1.3721402
33 -0.2400808 -3.9347274
34 -2.1088527 -0.2400808
35 -1.5685157 -2.1088527
36 -2.1939523 -1.5685157
37 1.0256606 -2.1939523
38 -1.7210348 1.0256606
39 1.5060181 -1.7210348
40 4.1734514 1.5060181
41 -0.8631419 4.1734514
> 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/7nbi11292937175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nbi11292937175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9nbi11292937175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xlh41292937175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111lfa1292937175.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/12m4ef1292937175.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/130dc61292937175.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/144wsu1292937175.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/157fr01292937175.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/163op91292937175.tab")
+ }
>
> try(system("convert tmp/1r12s1292937175.ps tmp/1r12s1292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jt1d1292937175.ps tmp/2jt1d1292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jt1d1292937175.ps tmp/3jt1d1292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/4jt1d1292937175.ps tmp/4jt1d1292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uk1g1292937175.ps tmp/5uk1g1292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/6uk1g1292937175.ps tmp/6uk1g1292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nbi11292937175.ps tmp/7nbi11292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nbi11292937175.ps tmp/8nbi11292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/9nbi11292937175.ps tmp/9nbi11292937175.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xlh41292937175.ps tmp/10xlh41292937175.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.330 1.603 5.279