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(2.00
+ ,4.50
+ ,1000.00
+ ,6600.00
+ ,42.00
+ ,3.00
+ ,1.00
+ ,3.00
+ ,1.80
+ ,69.00
+ ,2547000.00
+ ,4603000.00
+ ,624.00
+ ,3.00
+ ,5.00
+ ,4.00
+ ,0.70
+ ,27.00
+ ,10550.00
+ ,179500.00
+ ,180.00
+ ,4.00
+ ,4.00
+ ,4.00
+ ,3.90
+ ,19.00
+ ,23.00
+ ,300.00
+ ,35.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,30.40
+ ,160000.00
+ ,169000.00
+ ,392.00
+ ,4.00
+ ,5.00
+ ,4.00
+ ,3.60
+ ,28.00
+ ,3300.00
+ ,25600.00
+ ,63.00
+ ,1.00
+ ,2.00
+ ,1.00
+ ,1.40
+ ,50.00
+ ,52160.00
+ ,440000.00
+ ,230.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.50
+ ,7.00
+ ,425.00
+ ,6400.00
+ ,112.00
+ ,5.00
+ ,4.00
+ ,4.00
+ ,0.70
+ ,30.00
+ ,465000.00
+ ,423000.00
+ ,281.00
+ ,5.00
+ ,5.00
+ ,5.00
+ ,2.10
+ ,3.50
+ ,75.00
+ ,1200.00
+ ,42.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,0.00
+ ,50.00
+ ,3000.00
+ ,25000.00
+ ,28.00
+ ,2.00
+ ,2.00
+ ,2.00
+ ,4.10
+ ,6.00
+ ,785.00
+ ,3500.00
+ ,42.00
+ ,2.00
+ ,2.00
+ ,2.00
+ ,1.20
+ ,10.40
+ ,200.00
+ ,5000.00
+ ,120.00
+ ,2.00
+ ,2.00
+ ,2.00
+ ,0.50
+ ,20.00
+ ,27660.00
+ ,115000.00
+ ,148.00
+ ,5.00
+ ,5.00
+ ,5.00
+ ,3.40
+ ,3.90
+ ,120.00
+ ,1000.00
+ ,16.00
+ ,3.00
+ ,1.00
+ ,2.00
+ ,1.50
+ ,41.00
+ ,85000.00
+ ,325000.00
+ ,310.00
+ ,1.00
+ ,3.00
+ ,1.00
+ ,3.40
+ ,9.00
+ ,101.00
+ ,4000.00
+ ,28.00
+ ,5.00
+ ,1.00
+ ,3.00
+ ,0.80
+ ,7.60
+ ,1040.00
+ ,5500.00
+ ,68.00
+ ,5.00
+ ,3.00
+ ,4.00
+ ,0.80
+ ,46.00
+ ,521000.00
+ ,655000.00
+ ,336.00
+ ,5.00
+ ,5.00
+ ,5.00
+ ,1.40
+ ,2.60
+ ,5.00
+ ,140.00
+ ,21.50
+ ,5.00
+ ,2.00
+ ,4.00
+ ,2.00
+ ,24.00
+ ,10.00
+ ,250.00
+ ,50.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.90
+ ,100.00
+ ,62000.00
+ ,1320000.00
+ ,267.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.30
+ ,3.20
+ ,23.00
+ ,400.00
+ ,19.00
+ ,4.00
+ ,1.00
+ ,3.00
+ ,2.00
+ ,2.00
+ ,48.00
+ ,330.00
+ ,30.00
+ ,4.00
+ ,1.00
+ ,3.00
+ ,5.60
+ ,5.00
+ ,1700.00
+ ,6300.00
+ ,12.00
+ ,2.00
+ ,1.00
+ ,1.00
+ ,3.10
+ ,6.50
+ ,3500.00
+ ,10800.00
+ ,120.00
+ ,2.00
+ ,1.00
+ ,1.00
+ ,1.80
+ ,12.00
+ ,480.00
+ ,15500.00
+ ,140.00
+ ,2.00
+ ,2.00
+ ,2.00
+ ,0.90
+ ,20.20
+ ,10000.00
+ ,115000.00
+ ,170.00
+ ,4.00
+ ,4.00
+ ,4.00
+ ,1.80
+ ,13.00
+ ,1620.00
+ ,11400.00
+ ,17.00
+ ,2.00
+ ,1.00
+ ,2.00
+ ,1.90
+ ,27.00
+ ,192000.00
+ ,180000.00
+ ,115.00
+ ,4.00
+ ,4.00
+ ,4.00
+ ,0.90
+ ,18.00
+ ,2500.00
+ ,12100.00
+ ,31.00
+ ,5.00
+ ,5.00
+ ,5.00
+ ,2.60
+ ,4.70
+ ,280.00
+ ,1900.00
+ ,21.00
+ ,3.00
+ ,1.00
+ ,3.00
+ ,2.40
+ ,9.80
+ ,4235.00
+ ,50400.00
+ ,52.00
+ ,1.00
+ ,1.00
+ ,1.00
+ ,1.20
+ ,29.00
+ ,6800.00
+ ,179000.00
+ ,164.00
+ ,2.00
+ ,3.00
+ ,2.00
+ ,0.90
+ ,7.00
+ ,750.00
+ ,12300.00
+ ,225.00
+ ,2.00
+ ,2.00
+ ,2.00
+ ,0.50
+ ,6.00
+ ,3600.00
+ ,21000.00
+ ,225.00
+ ,3.00
+ ,2.00
+ ,3.00
+ ,0.60
+ ,20.00
+ ,55500.00
+ ,175000.00
+ ,151.00
+ ,5.00
+ ,5.00
+ ,5.00
+ ,2.30
+ ,4.50
+ ,900.00
+ ,2600.00
+ ,60.00
+ ,2.00
+ ,1.00
+ ,2.00
+ ,0.50
+ ,7.50
+ ,2000.00
+ ,12300.00
+ ,200.00
+ ,3.00
+ ,1.00
+ ,3.00
+ ,2.60
+ ,2.30
+ ,104.00
+ ,2500.00
+ ,46.00
+ ,3.00
+ ,2.00
+ ,2.00
+ ,0.60
+ ,24.00
+ ,4190.00
+ ,58000.00
+ ,210.00
+ ,4.00
+ ,3.00
+ ,4.00
+ ,6.60
+ ,3.00
+ ,3500.00
+ ,3900.00
+ ,14.00
+ ,2.00
+ ,1.00
+ ,1.00)
+ ,dim=c(8
+ ,42)
+ ,dimnames=list(c('PS'
+ ,'L'
+ ,'Wb'
+ ,'Wbr'
+ ,'Tg'
+ ,'P'
+ ,'S'
+ ,'D')
+ ,1:42))
> y <- array(NA,dim=c(8,42),dimnames=list(c('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
> 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
PS L Wb Wbr Tg P S D
1 2.0 4.5 1000 6600 42.0 3 1 3
2 1.8 69.0 2547000 4603000 624.0 3 5 4
3 0.7 27.0 10550 179500 180.0 4 4 4
4 3.9 19.0 23 300 35.0 1 1 1
5 1.0 30.4 160000 169000 392.0 4 5 4
6 3.6 28.0 3300 25600 63.0 1 2 1
7 1.4 50.0 52160 440000 230.0 1 1 1
8 1.5 7.0 425 6400 112.0 5 4 4
9 0.7 30.0 465000 423000 281.0 5 5 5
10 2.1 3.5 75 1200 42.0 1 1 1
11 0.0 50.0 3000 25000 28.0 2 2 2
12 4.1 6.0 785 3500 42.0 2 2 2
13 1.2 10.4 200 5000 120.0 2 2 2
14 0.5 20.0 27660 115000 148.0 5 5 5
15 3.4 3.9 120 1000 16.0 3 1 2
16 1.5 41.0 85000 325000 310.0 1 3 1
17 3.4 9.0 101 4000 28.0 5 1 3
18 0.8 7.6 1040 5500 68.0 5 3 4
19 0.8 46.0 521000 655000 336.0 5 5 5
20 1.4 2.6 5 140 21.5 5 2 4
21 2.0 24.0 10 250 50.0 1 1 1
22 1.9 100.0 62000 1320000 267.0 1 1 1
23 1.3 3.2 23 400 19.0 4 1 3
24 2.0 2.0 48 330 30.0 4 1 3
25 5.6 5.0 1700 6300 12.0 2 1 1
26 3.1 6.5 3500 10800 120.0 2 1 1
27 1.8 12.0 480 15500 140.0 2 2 2
28 0.9 20.2 10000 115000 170.0 4 4 4
29 1.8 13.0 1620 11400 17.0 2 1 2
30 1.9 27.0 192000 180000 115.0 4 4 4
31 0.9 18.0 2500 12100 31.0 5 5 5
32 2.6 4.7 280 1900 21.0 3 1 3
33 2.4 9.8 4235 50400 52.0 1 1 1
34 1.2 29.0 6800 179000 164.0 2 3 2
35 0.9 7.0 750 12300 225.0 2 2 2
36 0.5 6.0 3600 21000 225.0 3 2 3
37 0.6 20.0 55500 175000 151.0 5 5 5
38 2.3 4.5 900 2600 60.0 2 1 2
39 0.5 7.5 2000 12300 200.0 3 1 3
40 2.6 2.3 104 2500 46.0 3 2 2
41 0.6 24.0 4190 58000 210.0 4 3 4
42 6.6 3.0 3500 3900 14.0 2 1 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) L Wb Wbr Tg P
3.778e+00 -1.339e-02 1.370e-06 2.961e-07 -5.007e-03 9.004e-01
S D
3.600e-01 -1.738e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.02306 -0.59238 -0.06292 0.57529 2.50441
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.778e+00 4.133e-01 9.141 1.10e-10 ***
L -1.339e-02 1.431e-02 -0.936 0.355869
Wb 1.370e-06 1.831e-06 0.748 0.459434
Wbr 2.961e-07 1.099e-06 0.269 0.789227
Tg -5.007e-03 2.158e-03 -2.320 0.026497 *
P 9.004e-01 3.379e-01 2.665 0.011704 *
S 3.600e-01 2.117e-01 1.701 0.098147 .
D -1.738e+00 4.196e-01 -4.143 0.000214 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9396 on 34 degrees of freedom
Multiple R-squared: 0.6204, Adjusted R-squared: 0.5422
F-statistic: 7.938 on 7 and 34 DF, p-value: 1.066e-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,] 0.8677923 0.2644154 0.13220768
[2,] 0.8743541 0.2512918 0.12564588
[3,] 0.9136695 0.1726610 0.08633052
[4,] 0.8511537 0.2976927 0.14884635
[5,] 0.7863531 0.4272937 0.21364686
[6,] 0.7115912 0.5768176 0.28840880
[7,] 0.6409433 0.7181133 0.35905665
[8,] 0.6473278 0.7053444 0.35267219
[9,] 0.5731301 0.8537397 0.42686987
[10,] 0.4910234 0.9820468 0.50897662
[11,] 0.4228144 0.8456287 0.57718563
[12,] 0.4386158 0.8772316 0.56138420
[13,] 0.5092970 0.9814060 0.49070301
[14,] 0.5638430 0.8723140 0.43615699
[15,] 0.6389035 0.7221930 0.36109648
[16,] 0.5924903 0.8150194 0.40750971
[17,] 0.4819251 0.9638501 0.51807493
[18,] 0.3672351 0.7344702 0.63276489
[19,] 0.3148464 0.6296927 0.68515364
[20,] 0.3465897 0.6931793 0.65341033
[21,] 0.2249404 0.4498809 0.77505957
> postscript(file="/var/www/rcomp/tmp/1x70v1292273020.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2pyzy1292273020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3pyzy1292273020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4pyzy1292273020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5iqh11292273020.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
0.64387758 -0.32960842 0.02997877 1.03006718 0.87530424 0.61881458
7 8 9 10 11 12
-0.27997008 -0.61362575 0.35910296 -0.94284340 -2.02305677 1.56706499
13 14 15 16 17 18
-0.88309234 0.04971839 0.17006453 -0.63095279 0.23533556 -1.16645245
19 20 21 22 23 24
0.80337016 -0.50322678 -0.72782012 0.80096496 -1.08588399 -0.34689428
25 26 27 28 29 30
1.52294005 -0.42000870 -0.16501303 0.10867649 -0.40780797 0.65573142
31 32 33 34 35 36
-0.09794443 1.14378761 -0.52865474 -0.83422816 -0.70581372 -0.28759404
37 38 39 40 41 42
0.10882252 0.19722912 -0.02788694 -0.86160047 0.44471996 2.50440830
> postscript(file="/var/www/rcomp/tmp/6iqh11292273020.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 0.64387758 NA
1 -0.32960842 0.64387758
2 0.02997877 -0.32960842
3 1.03006718 0.02997877
4 0.87530424 1.03006718
5 0.61881458 0.87530424
6 -0.27997008 0.61881458
7 -0.61362575 -0.27997008
8 0.35910296 -0.61362575
9 -0.94284340 0.35910296
10 -2.02305677 -0.94284340
11 1.56706499 -2.02305677
12 -0.88309234 1.56706499
13 0.04971839 -0.88309234
14 0.17006453 0.04971839
15 -0.63095279 0.17006453
16 0.23533556 -0.63095279
17 -1.16645245 0.23533556
18 0.80337016 -1.16645245
19 -0.50322678 0.80337016
20 -0.72782012 -0.50322678
21 0.80096496 -0.72782012
22 -1.08588399 0.80096496
23 -0.34689428 -1.08588399
24 1.52294005 -0.34689428
25 -0.42000870 1.52294005
26 -0.16501303 -0.42000870
27 0.10867649 -0.16501303
28 -0.40780797 0.10867649
29 0.65573142 -0.40780797
30 -0.09794443 0.65573142
31 1.14378761 -0.09794443
32 -0.52865474 1.14378761
33 -0.83422816 -0.52865474
34 -0.70581372 -0.83422816
35 -0.28759404 -0.70581372
36 0.10882252 -0.28759404
37 0.19722912 0.10882252
38 -0.02788694 0.19722912
39 -0.86160047 -0.02788694
40 0.44471996 -0.86160047
41 2.50440830 0.44471996
42 NA 2.50440830
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.32960842 0.64387758
[2,] 0.02997877 -0.32960842
[3,] 1.03006718 0.02997877
[4,] 0.87530424 1.03006718
[5,] 0.61881458 0.87530424
[6,] -0.27997008 0.61881458
[7,] -0.61362575 -0.27997008
[8,] 0.35910296 -0.61362575
[9,] -0.94284340 0.35910296
[10,] -2.02305677 -0.94284340
[11,] 1.56706499 -2.02305677
[12,] -0.88309234 1.56706499
[13,] 0.04971839 -0.88309234
[14,] 0.17006453 0.04971839
[15,] -0.63095279 0.17006453
[16,] 0.23533556 -0.63095279
[17,] -1.16645245 0.23533556
[18,] 0.80337016 -1.16645245
[19,] -0.50322678 0.80337016
[20,] -0.72782012 -0.50322678
[21,] 0.80096496 -0.72782012
[22,] -1.08588399 0.80096496
[23,] -0.34689428 -1.08588399
[24,] 1.52294005 -0.34689428
[25,] -0.42000870 1.52294005
[26,] -0.16501303 -0.42000870
[27,] 0.10867649 -0.16501303
[28,] -0.40780797 0.10867649
[29,] 0.65573142 -0.40780797
[30,] -0.09794443 0.65573142
[31,] 1.14378761 -0.09794443
[32,] -0.52865474 1.14378761
[33,] -0.83422816 -0.52865474
[34,] -0.70581372 -0.83422816
[35,] -0.28759404 -0.70581372
[36,] 0.10882252 -0.28759404
[37,] 0.19722912 0.10882252
[38,] -0.02788694 0.19722912
[39,] -0.86160047 -0.02788694
[40,] 0.44471996 -0.86160047
[41,] 2.50440830 0.44471996
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.32960842 0.64387758
2 0.02997877 -0.32960842
3 1.03006718 0.02997877
4 0.87530424 1.03006718
5 0.61881458 0.87530424
6 -0.27997008 0.61881458
7 -0.61362575 -0.27997008
8 0.35910296 -0.61362575
9 -0.94284340 0.35910296
10 -2.02305677 -0.94284340
11 1.56706499 -2.02305677
12 -0.88309234 1.56706499
13 0.04971839 -0.88309234
14 0.17006453 0.04971839
15 -0.63095279 0.17006453
16 0.23533556 -0.63095279
17 -1.16645245 0.23533556
18 0.80337016 -1.16645245
19 -0.50322678 0.80337016
20 -0.72782012 -0.50322678
21 0.80096496 -0.72782012
22 -1.08588399 0.80096496
23 -0.34689428 -1.08588399
24 1.52294005 -0.34689428
25 -0.42000870 1.52294005
26 -0.16501303 -0.42000870
27 0.10867649 -0.16501303
28 -0.40780797 0.10867649
29 0.65573142 -0.40780797
30 -0.09794443 0.65573142
31 1.14378761 -0.09794443
32 -0.52865474 1.14378761
33 -0.83422816 -0.52865474
34 -0.70581372 -0.83422816
35 -0.28759404 -0.70581372
36 0.10882252 -0.28759404
37 0.19722912 0.10882252
38 -0.02788694 0.19722912
39 -0.86160047 -0.02788694
40 0.44471996 -0.86160047
41 2.50440830 0.44471996
> 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/7shg41292273020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8shg41292273020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/93qx71292273020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/103qx71292273020.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11prwd1292273020.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/12srcj1292273020.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/13hs9u1292273020.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/14r18x1292273020.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/15ncay1292273021.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/16j4p71292273021.tab")
+ }
>
> try(system("convert tmp/1x70v1292273020.ps tmp/1x70v1292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/2pyzy1292273020.ps tmp/2pyzy1292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pyzy1292273020.ps tmp/3pyzy1292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pyzy1292273020.ps tmp/4pyzy1292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/5iqh11292273020.ps tmp/5iqh11292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/6iqh11292273020.ps tmp/6iqh11292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/7shg41292273020.ps tmp/7shg41292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/8shg41292273020.ps tmp/8shg41292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/93qx71292273020.ps tmp/93qx71292273020.png",intern=TRUE))
character(0)
> try(system("convert tmp/103qx71292273020.ps tmp/103qx71292273020.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.930 1.680 4.593