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.
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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
+ ,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
+ ,41)
+ ,dimnames=list(c('SWS'
+ ,'PS'
+ ,'LS'
+ ,'BW'
+ ,'BRW'
+ ,'GT'
+ ,'PI'
+ ,'SEI'
+ ,'ODI')
+ ,1:41))
> y <- array(NA,dim=c(9,41),dimnames=list(c('SWS','PS','LS','BW','BRW','GT','PI','SEI','ODI'),1:41))
> 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 LS BW BRW GT PI SEI ODI
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 10.0 0.9 20.2 10.000 115.00 170.0 4 4 4
28 11.9 1.8 13.0 1.620 11.40 17.0 2 1 2
29 6.5 1.9 27.0 192.000 180.00 115.0 4 4 4
30 7.5 0.9 18.0 2.500 12.10 31.0 5 5 5
31 10.6 2.6 4.7 0.280 1.90 21.0 3 1 3
32 7.4 2.4 9.8 4.235 50.40 52.0 1 1 1
33 8.4 1.2 29.0 6.800 179.00 164.0 2 3 2
34 5.7 0.9 7.0 0.750 12.30 225.0 2 2 2
35 4.9 0.5 6.0 3.600 21.00 225.0 3 2 3
36 3.2 0.6 20.0 55.500 175.00 151.0 5 5 5
37 11.0 2.3 4.5 0.900 2.60 60.0 2 1 2
38 4.9 0.5 7.5 2.000 12.30 200.0 3 1 3
39 13.2 2.6 2.3 0.104 2.50 46.0 3 2 2
40 9.7 0.6 24.0 4.190 58.00 210.0 4 3 4
41 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 LS BW BRW GT
12.319709 0.142634 0.011019 0.003586 -0.001478 -0.014242
PI SEI ODI
1.510160 0.137314 -2.671409
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.7342 -1.6814 -0.4319 1.5050 6.7669
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 12.319709 2.239418 5.501 4.62e-06 ***
PS 0.142634 0.496630 0.287 0.7758
LS 0.011019 0.042204 0.261 0.7957
BW 0.003586 0.005347 0.671 0.5073
BRW -0.001478 0.003185 -0.464 0.6459
GT -0.014242 0.006737 -2.114 0.0424 *
PI 1.510160 1.078434 1.400 0.1710
SEI 0.137314 0.638624 0.215 0.8311
ODI -2.671409 1.489998 -1.793 0.0824 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.72 on 32 degrees of freedom
Multiple R-squared: 0.5781, Adjusted R-squared: 0.4726
F-statistic: 5.481 on 8 and 32 DF, p-value: 0.0002149
> 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,] 0.9382468 0.12350641 0.06175320
[2,] 0.9262009 0.14759825 0.07379913
[3,] 0.8968482 0.20630366 0.10315183
[4,] 0.8456018 0.30879639 0.15439820
[5,] 0.8959146 0.20817077 0.10408539
[6,] 0.9299183 0.14016339 0.07008170
[7,] 0.9046548 0.19069031 0.09534515
[8,] 0.9027427 0.19451466 0.09725733
[9,] 0.8895602 0.22087964 0.11043982
[10,] 0.9716687 0.05666266 0.02833133
[11,] 0.9462628 0.10747450 0.05373725
[12,] 0.9104758 0.17904833 0.08952417
[13,] 0.8877081 0.22458370 0.11229185
[14,] 0.8133914 0.37321724 0.18660862
[15,] 0.7301306 0.53973887 0.26986944
[16,] 0.8494478 0.30110450 0.15055225
[17,] 0.7319146 0.53617072 0.26808536
[18,] 0.5723245 0.85535104 0.42767552
> postscript(file="/var/www/html/rcomp/tmp/1u59l1292428979.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nf961292428979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nf961292428979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nf961292428979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5goq91292428979.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 = 41
Frequency = 1
1 2 3 4 5 6
-2.403803302 0.787388424 3.269617897 4.237421028 1.619915298 -0.431867160
7 8 9 10 11 12
-0.007651494 2.577809150 -1.470817795 -4.734209370 -1.797824258 -3.722236954
13 14 15 16 17 18
0.658097572 -2.013187095 -0.943696262 -2.945710424 -1.773399062 -1.421820245
19 20 21 22 23 24
-1.836071809 -1.681447026 6.766932286 -1.037927489 1.466984984 0.436830904
25 26 27 28 29 30
0.314336908 2.692708677 3.980265369 1.618637277 -1.077159035 0.423669669
31 32 33 34 35 36
1.504970487 -3.546216872 0.075916681 -1.557439576 -1.125481529 -2.095894270
37 38 39 40 41
1.342958216 -1.367859772 1.680256050 4.324780511 -0.787776592
> postscript(file="/var/www/html/rcomp/tmp/6goq91292428979.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 = 41
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.403803302 NA
1 0.787388424 -2.403803302
2 3.269617897 0.787388424
3 4.237421028 3.269617897
4 1.619915298 4.237421028
5 -0.431867160 1.619915298
6 -0.007651494 -0.431867160
7 2.577809150 -0.007651494
8 -1.470817795 2.577809150
9 -4.734209370 -1.470817795
10 -1.797824258 -4.734209370
11 -3.722236954 -1.797824258
12 0.658097572 -3.722236954
13 -2.013187095 0.658097572
14 -0.943696262 -2.013187095
15 -2.945710424 -0.943696262
16 -1.773399062 -2.945710424
17 -1.421820245 -1.773399062
18 -1.836071809 -1.421820245
19 -1.681447026 -1.836071809
20 6.766932286 -1.681447026
21 -1.037927489 6.766932286
22 1.466984984 -1.037927489
23 0.436830904 1.466984984
24 0.314336908 0.436830904
25 2.692708677 0.314336908
26 3.980265369 2.692708677
27 1.618637277 3.980265369
28 -1.077159035 1.618637277
29 0.423669669 -1.077159035
30 1.504970487 0.423669669
31 -3.546216872 1.504970487
32 0.075916681 -3.546216872
33 -1.557439576 0.075916681
34 -1.125481529 -1.557439576
35 -2.095894270 -1.125481529
36 1.342958216 -2.095894270
37 -1.367859772 1.342958216
38 1.680256050 -1.367859772
39 4.324780511 1.680256050
40 -0.787776592 4.324780511
41 NA -0.787776592
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.787388424 -2.403803302
[2,] 3.269617897 0.787388424
[3,] 4.237421028 3.269617897
[4,] 1.619915298 4.237421028
[5,] -0.431867160 1.619915298
[6,] -0.007651494 -0.431867160
[7,] 2.577809150 -0.007651494
[8,] -1.470817795 2.577809150
[9,] -4.734209370 -1.470817795
[10,] -1.797824258 -4.734209370
[11,] -3.722236954 -1.797824258
[12,] 0.658097572 -3.722236954
[13,] -2.013187095 0.658097572
[14,] -0.943696262 -2.013187095
[15,] -2.945710424 -0.943696262
[16,] -1.773399062 -2.945710424
[17,] -1.421820245 -1.773399062
[18,] -1.836071809 -1.421820245
[19,] -1.681447026 -1.836071809
[20,] 6.766932286 -1.681447026
[21,] -1.037927489 6.766932286
[22,] 1.466984984 -1.037927489
[23,] 0.436830904 1.466984984
[24,] 0.314336908 0.436830904
[25,] 2.692708677 0.314336908
[26,] 3.980265369 2.692708677
[27,] 1.618637277 3.980265369
[28,] -1.077159035 1.618637277
[29,] 0.423669669 -1.077159035
[30,] 1.504970487 0.423669669
[31,] -3.546216872 1.504970487
[32,] 0.075916681 -3.546216872
[33,] -1.557439576 0.075916681
[34,] -1.125481529 -1.557439576
[35,] -2.095894270 -1.125481529
[36,] 1.342958216 -2.095894270
[37,] -1.367859772 1.342958216
[38,] 1.680256050 -1.367859772
[39,] 4.324780511 1.680256050
[40,] -0.787776592 4.324780511
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.787388424 -2.403803302
2 3.269617897 0.787388424
3 4.237421028 3.269617897
4 1.619915298 4.237421028
5 -0.431867160 1.619915298
6 -0.007651494 -0.431867160
7 2.577809150 -0.007651494
8 -1.470817795 2.577809150
9 -4.734209370 -1.470817795
10 -1.797824258 -4.734209370
11 -3.722236954 -1.797824258
12 0.658097572 -3.722236954
13 -2.013187095 0.658097572
14 -0.943696262 -2.013187095
15 -2.945710424 -0.943696262
16 -1.773399062 -2.945710424
17 -1.421820245 -1.773399062
18 -1.836071809 -1.421820245
19 -1.681447026 -1.836071809
20 6.766932286 -1.681447026
21 -1.037927489 6.766932286
22 1.466984984 -1.037927489
23 0.436830904 1.466984984
24 0.314336908 0.436830904
25 2.692708677 0.314336908
26 3.980265369 2.692708677
27 1.618637277 3.980265369
28 -1.077159035 1.618637277
29 0.423669669 -1.077159035
30 1.504970487 0.423669669
31 -3.546216872 1.504970487
32 0.075916681 -3.546216872
33 -1.557439576 0.075916681
34 -1.125481529 -1.557439576
35 -2.095894270 -1.125481529
36 1.342958216 -2.095894270
37 -1.367859772 1.342958216
38 1.680256050 -1.367859772
39 4.324780511 1.680256050
40 -0.787776592 4.324780511
> 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/7rx7c1292428979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8rx7c1292428979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rx7c1292428979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10166f1292428979.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11n7n31292428979.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/1287381292428979.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/134zji1292428979.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/14pii51292428979.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/15bigb1292428979.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/167se21292428979.tab")
+ }
>
> try(system("convert tmp/1u59l1292428979.ps tmp/1u59l1292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nf961292428979.ps tmp/2nf961292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nf961292428979.ps tmp/3nf961292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nf961292428979.ps tmp/4nf961292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/5goq91292428979.ps tmp/5goq91292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/6goq91292428979.ps tmp/6goq91292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rx7c1292428979.ps tmp/7rx7c1292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/8rx7c1292428979.ps tmp/8rx7c1292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rx7c1292428979.ps tmp/9rx7c1292428979.png",intern=TRUE))
character(0)
> try(system("convert tmp/10166f1292428979.ps tmp/10166f1292428979.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.285 1.598 5.868