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.
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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
+ ,2.0
+ ,4.5
+ ,1.000
+ ,6.600
+ ,42
+ ,3
+ ,1
+ ,3
+ ,2.1
+ ,1.8
+ ,69.0
+ ,2547.000
+ ,4603.000
+ ,624
+ ,3
+ ,5
+ ,4
+ ,9.1
+ ,0.7
+ ,27.0
+ ,10.550
+ ,179.500
+ ,180
+ ,4
+ ,4
+ ,4
+ ,15.8
+ ,3.9
+ ,19.0
+ ,0.023
+ ,0.300
+ ,35
+ ,1
+ ,1
+ ,1
+ ,5.2
+ ,1.0
+ ,30.4
+ ,160.000
+ ,169.000
+ ,392
+ ,4
+ ,5
+ ,4
+ ,10.9
+ ,3.6
+ ,28.0
+ ,3.300
+ ,25.600
+ ,63
+ ,1
+ ,2
+ ,1
+ ,8.3
+ ,1.4
+ ,50.0
+ ,52.160
+ ,440.000
+ ,230
+ ,1
+ ,1
+ ,1
+ ,11.0
+ ,1.5
+ ,7.0
+ ,0.425
+ ,6400.000
+ ,112
+ ,5
+ ,4
+ ,4
+ ,3.2
+ ,0.7
+ ,30.0
+ ,46.500
+ ,423.000
+ ,281
+ ,5
+ ,5
+ ,5
+ ,6.3
+ ,2.1
+ ,3.5
+ ,0.075
+ ,1.200
+ ,42
+ ,1
+ ,1
+ ,1
+ ,6.6
+ ,4.1
+ ,6.0
+ ,0.785
+ ,3.500
+ ,42
+ ,2
+ ,2
+ ,2
+ ,9.5
+ ,1.2
+ ,10.4
+ ,0.200
+ ,5.000
+ ,120
+ ,2
+ ,2
+ ,2
+ ,3.3
+ ,0.5
+ ,20.0
+ ,27.660
+ ,115.000
+ ,148
+ ,5
+ ,5
+ ,5
+ ,11.0
+ ,3.4
+ ,3.9
+ ,0.120
+ ,1.000
+ ,16
+ ,3
+ ,1
+ ,2
+ ,4.7
+ ,1.5
+ ,41.0
+ ,85.000
+ ,325.000
+ ,310
+ ,1
+ ,3
+ ,1
+ ,10.4
+ ,3.4
+ ,9.0
+ ,0.101
+ ,4.000
+ ,28
+ ,5
+ ,1
+ ,3
+ ,7.4
+ ,0.8
+ ,7.6
+ ,1.040
+ ,5.500
+ ,68
+ ,5
+ ,3
+ ,4
+ ,2.1
+ ,0.8
+ ,46.0
+ ,521.000
+ ,655.000
+ ,336
+ ,5
+ ,5
+ ,5
+ ,17.9
+ ,2.0
+ ,24.0
+ ,0.010
+ ,0.250
+ ,50
+ ,1
+ ,1
+ ,1
+ ,6.1
+ ,1.9
+ ,100.0
+ ,62.000
+ ,1320.000
+ ,267
+ ,1
+ ,1
+ ,1
+ ,11.9
+ ,1.3
+ ,3.2
+ ,0.023
+ ,0.400
+ ,19
+ ,4
+ ,1
+ ,3
+ ,13.8
+ ,5.6
+ ,5.0
+ ,1.700
+ ,6.300
+ ,12
+ ,2
+ ,1
+ ,1
+ ,14.3
+ ,3.1
+ ,6.5
+ ,3.500
+ ,10.800
+ ,120
+ ,2
+ ,1
+ ,1
+ ,15.2
+ ,1.8
+ ,12.0
+ ,0.480
+ ,15.500
+ ,140
+ ,2
+ ,2
+ ,2
+ ,10.0
+ ,0.9
+ ,20.2
+ ,10.000
+ ,115.000
+ ,170
+ ,4
+ ,4
+ ,4
+ ,11.9
+ ,1.8
+ ,13.0
+ ,1.620
+ ,11.400
+ ,17
+ ,2
+ ,1
+ ,2
+ ,6.5
+ ,1.9
+ ,27.0
+ ,192.000
+ ,180.000
+ ,115
+ ,4
+ ,4
+ ,4
+ ,7.5
+ ,0.9
+ ,18.0
+ ,2.500
+ ,12.100
+ ,31
+ ,5
+ ,5
+ ,5
+ ,10.6
+ ,2.6
+ ,4.7
+ ,0.280
+ ,1.900
+ ,21
+ ,3
+ ,1
+ ,3
+ ,7.4
+ ,2.4
+ ,9.8
+ ,4.235
+ ,50.400
+ ,52
+ ,1
+ ,1
+ ,1
+ ,8.4
+ ,1.2
+ ,29.0
+ ,6.800
+ ,179.000
+ ,164
+ ,2
+ ,3
+ ,2
+ ,5.7
+ ,0.9
+ ,7.0
+ ,0.750
+ ,12.300
+ ,225
+ ,2
+ ,2
+ ,2
+ ,4.9
+ ,0.5
+ ,6.0
+ ,3.600
+ ,21.000
+ ,225
+ ,3
+ ,2
+ ,3
+ ,3.2
+ ,0.6
+ ,20.0
+ ,5.550
+ ,175.000
+ ,151
+ ,5
+ ,5
+ ,5
+ ,11.0
+ ,2.3
+ ,4.5
+ ,0.900
+ ,2.600
+ ,60
+ ,2
+ ,1
+ ,2
+ ,4.9
+ ,0.5
+ ,7.5
+ ,2.000
+ ,12.300
+ ,200
+ ,3
+ ,1
+ ,3
+ ,13.2
+ ,2.6
+ ,2.3
+ ,0.104
+ ,2.500
+ ,46
+ ,3
+ ,2
+ ,2
+ ,9.7
+ ,0.6
+ ,24.0
+ ,4.190
+ ,58.000
+ ,210
+ ,4
+ ,3
+ ,4
+ ,12.8
+ ,6.6
+ ,3.0
+ ,3.500
+ ,3.900
+ ,14
+ ,2
+ ,1
+ ,1)
+ ,dim=c(9
+ ,39)
+ ,dimnames=list(c('SWS'
+ ,'PS'
+ ,'L'
+ ,'Wb'
+ ,'Wbr'
+ ,'tg'
+ ,'P'
+ ,'S'
+ ,'D')
+ ,1:39))
> y <- array(NA,dim=c(9,39),dimnames=list(c('SWS','PS','L','Wb','Wbr','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 Wb Wbr 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 0.425 6400.00 112 5 4 4
9 3.2 0.7 30.0 46.500 423.00 281 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 0.010 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 3.1 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 5.550 175.00 151 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 Wb Wbr tg
13.6544237 -0.0585808 -0.0058201 0.0011052 0.0003124 -0.0164357
P S D
1.2609612 0.2412630 -2.5896412
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5.4338 -1.6871 -0.3668 1.1753 6.4115
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.6544237 2.5940606 5.264 1.11e-05 ***
PS -0.0585808 0.6058970 -0.097 0.9236
L -0.0058201 0.0352793 -0.165 0.8701
Wb 0.0011052 0.0022066 0.501 0.6201
Wbr 0.0003124 0.0004994 0.626 0.5363
tg -0.0164357 0.0083450 -1.970 0.0582 .
P 1.2609612 1.2751441 0.989 0.3306
S 0.2412630 0.6963650 0.346 0.7314
D -2.5896412 1.7302613 -1.497 0.1449
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.983 on 30 degrees of freedom
Multiple R-squared: 0.5539, Adjusted R-squared: 0.4349
F-statistic: 4.656 on 8 and 30 DF, p-value: 0.000892
> 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,] 0.8208631 0.3582739 0.1791369
[2,] 0.8318895 0.3362209 0.1681105
[3,] 0.7492975 0.5014049 0.2507025
[4,] 0.7943850 0.4112300 0.2056150
[5,] 0.7639786 0.4720427 0.2360214
[6,] 0.7008060 0.5983881 0.2991940
[7,] 0.6781629 0.6436741 0.3218371
[8,] 0.8181913 0.3636173 0.1818087
[9,] 0.8085496 0.3829008 0.1914504
[10,] 0.7212102 0.5575796 0.2787898
[11,] 0.6148690 0.7702620 0.3851310
[12,] 0.5193389 0.9613222 0.4806611
[13,] 0.8221449 0.3557103 0.1778551
[14,] 0.9017749 0.1964502 0.0982251
[15,] 0.8022725 0.3954550 0.1977275
[16,] 0.7761160 0.4477679 0.2238840
> postscript(file="/var/www/rcomp/tmp/1nuck1293050847.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2nuck1293050847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3y3tn1293050847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4y3tn1293050847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5y3tn1293050847.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
-2.77916275 0.32495646 2.88408576 4.14717033 2.10265619 -0.51061159
7 8 9 10 11 12
-0.30887473 0.40399471 -0.36682929 -5.43377546 -3.91614988 0.12173629
13 14 15 16 17 18
-2.40565827 -1.01488896 -3.12344017 -1.32117557 -1.71859725 -1.06078777
19 20 21 22 23 24
6.41153274 -1.86622774 1.13629897 0.52256630 2.65650415 6.19132132
25 26 27 28 29 30
3.61262463 1.11683509 -0.91463657 -0.05689085 1.21426415 -4.13514444
31 32 33 34 35 36
-0.44975080 -1.99276687 -1.49920695 -2.44479935 0.90693405 -1.65562004
37 38 39
1.38029113 4.24008445 -0.39886144
> postscript(file="/var/www/rcomp/tmp/6rcs81293050847.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 -2.77916275 NA
1 0.32495646 -2.77916275
2 2.88408576 0.32495646
3 4.14717033 2.88408576
4 2.10265619 4.14717033
5 -0.51061159 2.10265619
6 -0.30887473 -0.51061159
7 0.40399471 -0.30887473
8 -0.36682929 0.40399471
9 -5.43377546 -0.36682929
10 -3.91614988 -5.43377546
11 0.12173629 -3.91614988
12 -2.40565827 0.12173629
13 -1.01488896 -2.40565827
14 -3.12344017 -1.01488896
15 -1.32117557 -3.12344017
16 -1.71859725 -1.32117557
17 -1.06078777 -1.71859725
18 6.41153274 -1.06078777
19 -1.86622774 6.41153274
20 1.13629897 -1.86622774
21 0.52256630 1.13629897
22 2.65650415 0.52256630
23 6.19132132 2.65650415
24 3.61262463 6.19132132
25 1.11683509 3.61262463
26 -0.91463657 1.11683509
27 -0.05689085 -0.91463657
28 1.21426415 -0.05689085
29 -4.13514444 1.21426415
30 -0.44975080 -4.13514444
31 -1.99276687 -0.44975080
32 -1.49920695 -1.99276687
33 -2.44479935 -1.49920695
34 0.90693405 -2.44479935
35 -1.65562004 0.90693405
36 1.38029113 -1.65562004
37 4.24008445 1.38029113
38 -0.39886144 4.24008445
39 NA -0.39886144
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.32495646 -2.77916275
[2,] 2.88408576 0.32495646
[3,] 4.14717033 2.88408576
[4,] 2.10265619 4.14717033
[5,] -0.51061159 2.10265619
[6,] -0.30887473 -0.51061159
[7,] 0.40399471 -0.30887473
[8,] -0.36682929 0.40399471
[9,] -5.43377546 -0.36682929
[10,] -3.91614988 -5.43377546
[11,] 0.12173629 -3.91614988
[12,] -2.40565827 0.12173629
[13,] -1.01488896 -2.40565827
[14,] -3.12344017 -1.01488896
[15,] -1.32117557 -3.12344017
[16,] -1.71859725 -1.32117557
[17,] -1.06078777 -1.71859725
[18,] 6.41153274 -1.06078777
[19,] -1.86622774 6.41153274
[20,] 1.13629897 -1.86622774
[21,] 0.52256630 1.13629897
[22,] 2.65650415 0.52256630
[23,] 6.19132132 2.65650415
[24,] 3.61262463 6.19132132
[25,] 1.11683509 3.61262463
[26,] -0.91463657 1.11683509
[27,] -0.05689085 -0.91463657
[28,] 1.21426415 -0.05689085
[29,] -4.13514444 1.21426415
[30,] -0.44975080 -4.13514444
[31,] -1.99276687 -0.44975080
[32,] -1.49920695 -1.99276687
[33,] -2.44479935 -1.49920695
[34,] 0.90693405 -2.44479935
[35,] -1.65562004 0.90693405
[36,] 1.38029113 -1.65562004
[37,] 4.24008445 1.38029113
[38,] -0.39886144 4.24008445
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.32495646 -2.77916275
2 2.88408576 0.32495646
3 4.14717033 2.88408576
4 2.10265619 4.14717033
5 -0.51061159 2.10265619
6 -0.30887473 -0.51061159
7 0.40399471 -0.30887473
8 -0.36682929 0.40399471
9 -5.43377546 -0.36682929
10 -3.91614988 -5.43377546
11 0.12173629 -3.91614988
12 -2.40565827 0.12173629
13 -1.01488896 -2.40565827
14 -3.12344017 -1.01488896
15 -1.32117557 -3.12344017
16 -1.71859725 -1.32117557
17 -1.06078777 -1.71859725
18 6.41153274 -1.06078777
19 -1.86622774 6.41153274
20 1.13629897 -1.86622774
21 0.52256630 1.13629897
22 2.65650415 0.52256630
23 6.19132132 2.65650415
24 3.61262463 6.19132132
25 1.11683509 3.61262463
26 -0.91463657 1.11683509
27 -0.05689085 -0.91463657
28 1.21426415 -0.05689085
29 -4.13514444 1.21426415
30 -0.44975080 -4.13514444
31 -1.99276687 -0.44975080
32 -1.49920695 -1.99276687
33 -2.44479935 -1.49920695
34 0.90693405 -2.44479935
35 -1.65562004 0.90693405
36 1.38029113 -1.65562004
37 4.24008445 1.38029113
38 -0.39886144 4.24008445
> 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/7rcs81293050847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/82mst1293050847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/92mst1293050847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
Warning messages:
1: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
2: In sqrt(crit * p * (1 - hh)/hh) : NaNs produced
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10cvrw1293050847.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113he91293050847.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/12w8wu1293050847.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/13fomh1293050847.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/14qflk1293050847.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/15bf171293050847.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/1677hy1293050847.tab")
+ }
> try(system("convert tmp/1nuck1293050847.ps tmp/1nuck1293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nuck1293050847.ps tmp/2nuck1293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y3tn1293050847.ps tmp/3y3tn1293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y3tn1293050847.ps tmp/4y3tn1293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/5y3tn1293050847.ps tmp/5y3tn1293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rcs81293050847.ps tmp/6rcs81293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rcs81293050847.ps tmp/7rcs81293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/82mst1293050847.ps tmp/82mst1293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/92mst1293050847.ps tmp/92mst1293050847.png",intern=TRUE))
character(0)
> try(system("convert tmp/10cvrw1293050847.ps tmp/10cvrw1293050847.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.850 1.340 4.164