R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(9.5,0,9.1,0,9,0,9.3,0,9.9,0,9.8,0,9.4,0,8.3,0,8,0,8.5,0,10.4,0,11.1,0,10.9,0,9.9,0,9.2,0,9.2,0,9.5,1,9.6,1,9.5,1,9.1,1,8.9,1,9,1,10.1,1,10.3,1,10.2,1,9.6,1,9.2,1,9.3,1,9.4,1,9.4,1,9.2,1,9,1,9,1,9,1,9.8,1,10,1,9.9,1,9.3,1,9,1,9,1,9.1,1,9.1,1,9.1,1,9.2,1,8.8,1,8.3,1,8.4,1,8.1,1,7.8,1,7.9,1,7.9,1,8,1,7.9,1,7.5,1,7.2,1,6.9,1,6.6,1,6.7,1,7.3,1,7.5,1,7.2,1),dim=c(2,61),dimnames=list(c('y','x'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('y','x'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
y x M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 9.5 0 1 0 0 0 0 0 0 0 0 0 0 1
2 9.1 0 0 1 0 0 0 0 0 0 0 0 0 2
3 9.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 9.3 0 0 0 0 1 0 0 0 0 0 0 0 4
5 9.9 0 0 0 0 0 1 0 0 0 0 0 0 5
6 9.8 0 0 0 0 0 0 1 0 0 0 0 0 6
7 9.4 0 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 0 0 0 0 0 0 0 0 1 0 0 0 8
9 8.0 0 0 0 0 0 0 0 0 0 1 0 0 9
10 8.5 0 0 0 0 0 0 0 0 0 0 1 0 10
11 10.4 0 0 0 0 0 0 0 0 0 0 0 1 11
12 11.1 0 0 0 0 0 0 0 0 0 0 0 0 12
13 10.9 0 1 0 0 0 0 0 0 0 0 0 0 13
14 9.9 0 0 1 0 0 0 0 0 0 0 0 0 14
15 9.2 0 0 0 1 0 0 0 0 0 0 0 0 15
16 9.2 0 0 0 0 1 0 0 0 0 0 0 0 16
17 9.5 1 0 0 0 0 1 0 0 0 0 0 0 17
18 9.6 1 0 0 0 0 0 1 0 0 0 0 0 18
19 9.5 1 0 0 0 0 0 0 1 0 0 0 0 19
20 9.1 1 0 0 0 0 0 0 0 1 0 0 0 20
21 8.9 1 0 0 0 0 0 0 0 0 1 0 0 21
22 9.0 1 0 0 0 0 0 0 0 0 0 1 0 22
23 10.1 1 0 0 0 0 0 0 0 0 0 0 1 23
24 10.3 1 0 0 0 0 0 0 0 0 0 0 0 24
25 10.2 1 1 0 0 0 0 0 0 0 0 0 0 25
26 9.6 1 0 1 0 0 0 0 0 0 0 0 0 26
27 9.2 1 0 0 1 0 0 0 0 0 0 0 0 27
28 9.3 1 0 0 0 1 0 0 0 0 0 0 0 28
29 9.4 1 0 0 0 0 1 0 0 0 0 0 0 29
30 9.4 1 0 0 0 0 0 1 0 0 0 0 0 30
31 9.2 1 0 0 0 0 0 0 1 0 0 0 0 31
32 9.0 1 0 0 0 0 0 0 0 1 0 0 0 32
33 9.0 1 0 0 0 0 0 0 0 0 1 0 0 33
34 9.0 1 0 0 0 0 0 0 0 0 0 1 0 34
35 9.8 1 0 0 0 0 0 0 0 0 0 0 1 35
36 10.0 1 0 0 0 0 0 0 0 0 0 0 0 36
37 9.9 1 1 0 0 0 0 0 0 0 0 0 0 37
38 9.3 1 0 1 0 0 0 0 0 0 0 0 0 38
39 9.0 1 0 0 1 0 0 0 0 0 0 0 0 39
40 9.0 1 0 0 0 1 0 0 0 0 0 0 0 40
41 9.1 1 0 0 0 0 1 0 0 0 0 0 0 41
42 9.1 1 0 0 0 0 0 1 0 0 0 0 0 42
43 9.1 1 0 0 0 0 0 0 1 0 0 0 0 43
44 9.2 1 0 0 0 0 0 0 0 1 0 0 0 44
45 8.8 1 0 0 0 0 0 0 0 0 1 0 0 45
46 8.3 1 0 0 0 0 0 0 0 0 0 1 0 46
47 8.4 1 0 0 0 0 0 0 0 0 0 0 1 47
48 8.1 1 0 0 0 0 0 0 0 0 0 0 0 48
49 7.8 1 1 0 0 0 0 0 0 0 0 0 0 49
50 7.9 1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.9 1 0 0 1 0 0 0 0 0 0 0 0 51
52 8.0 1 0 0 0 1 0 0 0 0 0 0 0 52
53 7.9 1 0 0 0 0 1 0 0 0 0 0 0 53
54 7.5 1 0 0 0 0 0 1 0 0 0 0 0 54
55 7.2 1 0 0 0 0 0 0 1 0 0 0 0 55
56 6.9 1 0 0 0 0 0 0 0 1 0 0 0 56
57 6.6 1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.7 1 0 0 0 0 0 0 0 0 0 1 0 58
59 7.3 1 0 0 0 0 0 0 0 0 0 0 1 59
60 7.5 1 0 0 0 0 0 0 0 0 0 0 0 60
61 7.2 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x M1 M2 M3 M4
10.68245 1.14917 -0.30258 -0.62177 -0.86061 -0.69945
M5 M6 M7 M8 M9 M10
-0.66813 -0.68696 -0.82580 -1.14464 -1.32348 -1.22232
M11 t
-0.26116 -0.06116
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.83836 -0.36376 -0.02198 0.37017 1.31522
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 10.682450 0.317696 33.625 < 2e-16 ***
x 1.149172 0.279101 4.117 0.000154 ***
M1 -0.302581 0.368681 -0.821 0.415953
M2 -0.621773 0.386816 -1.607 0.114661
M3 -0.860613 0.386398 -2.227 0.030756 *
M4 -0.699452 0.386104 -1.812 0.076446 .
M5 -0.668125 0.387252 -1.725 0.091044 .
M6 -0.686965 0.386444 -1.778 0.081931 .
M7 -0.825804 0.385759 -2.141 0.037514 *
M8 -1.144643 0.385198 -2.972 0.004658 **
M9 -1.323482 0.384760 -3.440 0.001231 **
M10 -1.222322 0.384448 -3.179 0.002612 **
M11 -0.261161 0.384260 -0.680 0.500063
t -0.061161 0.006934 -8.820 1.57e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6075 on 47 degrees of freedom
Multiple R-squared: 0.7109, Adjusted R-squared: 0.6309
F-statistic: 8.889 on 13 and 47 DF, p-value: 8.872e-09
> 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,] 0.49806926 0.9961385 0.5019307
[2,] 0.34742135 0.6948427 0.6525787
[3,] 0.25560908 0.5112182 0.7443909
[4,] 0.35969728 0.7193946 0.6403027
[5,] 0.43847924 0.8769585 0.5615208
[6,] 0.39871472 0.7974294 0.6012853
[7,] 0.36161444 0.7232289 0.6383856
[8,] 0.42729790 0.8545958 0.5727021
[9,] 0.34744407 0.6948881 0.6525559
[10,] 0.28210026 0.5642005 0.7178997
[11,] 0.25780659 0.5156132 0.7421934
[12,] 0.24623516 0.4924703 0.7537648
[13,] 0.32316473 0.6463295 0.6768353
[14,] 0.34976705 0.6995341 0.6502330
[15,] 0.38304838 0.7660968 0.6169516
[16,] 0.46939743 0.9387949 0.5306026
[17,] 0.51197459 0.9760508 0.4880254
[18,] 0.50202643 0.9959471 0.4979736
[19,] 0.47685401 0.9537080 0.5231460
[20,] 0.47314858 0.9462972 0.5268514
[21,] 0.41151246 0.8230249 0.5884875
[22,] 0.31720224 0.6344045 0.6827978
[23,] 0.23971029 0.4794206 0.7602897
[24,] 0.19000215 0.3800043 0.8099978
[25,] 0.13999867 0.2799973 0.8600013
[26,] 0.08819341 0.1763868 0.9118066
[27,] 0.05756486 0.1151297 0.9424351
[28,] 0.10444544 0.2088909 0.8955546
> postscript(file="/var/www/html/rcomp/tmp/1anwc1227536788.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/299v21227536788.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r1an1227536788.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ug551227536788.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5zox01227536788.ps",horizontal=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 = 61
Frequency = 1
1 2 3 4 5 6
-0.81870861 -0.83835541 -0.63835541 -0.43835541 0.19147903 0.17147903
7 8 9 10 11 12
-0.02852097 -0.74852097 -0.80852097 -0.34852097 0.65147903 1.15147903
13 14 15 16 17 18
1.31522075 0.69557395 0.29557395 0.19557395 -0.62376380 -0.44376380
19 20 21 22 23 24
-0.34376380 -0.36376380 -0.32376380 -0.26376380 -0.06376380 -0.06376380
25 26 27 28 29 30
0.19997792 -0.01966887 -0.11966887 -0.11966887 0.01016556 0.09016556
31 32 33 34 35 36
0.09016556 0.27016556 0.51016556 0.47016556 0.37016556 0.37016556
37 38 39 40 41 42
0.63390728 0.41426049 0.41426049 0.31426049 0.44409492 0.52409492
43 44 45 46 47 48
0.72409492 1.20409492 1.04409492 0.50409492 -0.29590508 -0.79590508
49 50 51 52 53 54
-0.73216336 -0.25181015 0.04818985 0.04818985 -0.02197572 -0.34197572
55 56 57 58 59 60
-0.44197572 -0.36197572 -0.42197572 -0.36197572 -0.66197572 -0.66197572
61
-0.59823400
> postscript(file="/var/www/html/rcomp/tmp/6cubv1227536788.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.81870861 NA
1 -0.83835541 -0.81870861
2 -0.63835541 -0.83835541
3 -0.43835541 -0.63835541
4 0.19147903 -0.43835541
5 0.17147903 0.19147903
6 -0.02852097 0.17147903
7 -0.74852097 -0.02852097
8 -0.80852097 -0.74852097
9 -0.34852097 -0.80852097
10 0.65147903 -0.34852097
11 1.15147903 0.65147903
12 1.31522075 1.15147903
13 0.69557395 1.31522075
14 0.29557395 0.69557395
15 0.19557395 0.29557395
16 -0.62376380 0.19557395
17 -0.44376380 -0.62376380
18 -0.34376380 -0.44376380
19 -0.36376380 -0.34376380
20 -0.32376380 -0.36376380
21 -0.26376380 -0.32376380
22 -0.06376380 -0.26376380
23 -0.06376380 -0.06376380
24 0.19997792 -0.06376380
25 -0.01966887 0.19997792
26 -0.11966887 -0.01966887
27 -0.11966887 -0.11966887
28 0.01016556 -0.11966887
29 0.09016556 0.01016556
30 0.09016556 0.09016556
31 0.27016556 0.09016556
32 0.51016556 0.27016556
33 0.47016556 0.51016556
34 0.37016556 0.47016556
35 0.37016556 0.37016556
36 0.63390728 0.37016556
37 0.41426049 0.63390728
38 0.41426049 0.41426049
39 0.31426049 0.41426049
40 0.44409492 0.31426049
41 0.52409492 0.44409492
42 0.72409492 0.52409492
43 1.20409492 0.72409492
44 1.04409492 1.20409492
45 0.50409492 1.04409492
46 -0.29590508 0.50409492
47 -0.79590508 -0.29590508
48 -0.73216336 -0.79590508
49 -0.25181015 -0.73216336
50 0.04818985 -0.25181015
51 0.04818985 0.04818985
52 -0.02197572 0.04818985
53 -0.34197572 -0.02197572
54 -0.44197572 -0.34197572
55 -0.36197572 -0.44197572
56 -0.42197572 -0.36197572
57 -0.36197572 -0.42197572
58 -0.66197572 -0.36197572
59 -0.66197572 -0.66197572
60 -0.59823400 -0.66197572
61 NA -0.59823400
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.83835541 -0.81870861
[2,] -0.63835541 -0.83835541
[3,] -0.43835541 -0.63835541
[4,] 0.19147903 -0.43835541
[5,] 0.17147903 0.19147903
[6,] -0.02852097 0.17147903
[7,] -0.74852097 -0.02852097
[8,] -0.80852097 -0.74852097
[9,] -0.34852097 -0.80852097
[10,] 0.65147903 -0.34852097
[11,] 1.15147903 0.65147903
[12,] 1.31522075 1.15147903
[13,] 0.69557395 1.31522075
[14,] 0.29557395 0.69557395
[15,] 0.19557395 0.29557395
[16,] -0.62376380 0.19557395
[17,] -0.44376380 -0.62376380
[18,] -0.34376380 -0.44376380
[19,] -0.36376380 -0.34376380
[20,] -0.32376380 -0.36376380
[21,] -0.26376380 -0.32376380
[22,] -0.06376380 -0.26376380
[23,] -0.06376380 -0.06376380
[24,] 0.19997792 -0.06376380
[25,] -0.01966887 0.19997792
[26,] -0.11966887 -0.01966887
[27,] -0.11966887 -0.11966887
[28,] 0.01016556 -0.11966887
[29,] 0.09016556 0.01016556
[30,] 0.09016556 0.09016556
[31,] 0.27016556 0.09016556
[32,] 0.51016556 0.27016556
[33,] 0.47016556 0.51016556
[34,] 0.37016556 0.47016556
[35,] 0.37016556 0.37016556
[36,] 0.63390728 0.37016556
[37,] 0.41426049 0.63390728
[38,] 0.41426049 0.41426049
[39,] 0.31426049 0.41426049
[40,] 0.44409492 0.31426049
[41,] 0.52409492 0.44409492
[42,] 0.72409492 0.52409492
[43,] 1.20409492 0.72409492
[44,] 1.04409492 1.20409492
[45,] 0.50409492 1.04409492
[46,] -0.29590508 0.50409492
[47,] -0.79590508 -0.29590508
[48,] -0.73216336 -0.79590508
[49,] -0.25181015 -0.73216336
[50,] 0.04818985 -0.25181015
[51,] 0.04818985 0.04818985
[52,] -0.02197572 0.04818985
[53,] -0.34197572 -0.02197572
[54,] -0.44197572 -0.34197572
[55,] -0.36197572 -0.44197572
[56,] -0.42197572 -0.36197572
[57,] -0.36197572 -0.42197572
[58,] -0.66197572 -0.36197572
[59,] -0.66197572 -0.66197572
[60,] -0.59823400 -0.66197572
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.83835541 -0.81870861
2 -0.63835541 -0.83835541
3 -0.43835541 -0.63835541
4 0.19147903 -0.43835541
5 0.17147903 0.19147903
6 -0.02852097 0.17147903
7 -0.74852097 -0.02852097
8 -0.80852097 -0.74852097
9 -0.34852097 -0.80852097
10 0.65147903 -0.34852097
11 1.15147903 0.65147903
12 1.31522075 1.15147903
13 0.69557395 1.31522075
14 0.29557395 0.69557395
15 0.19557395 0.29557395
16 -0.62376380 0.19557395
17 -0.44376380 -0.62376380
18 -0.34376380 -0.44376380
19 -0.36376380 -0.34376380
20 -0.32376380 -0.36376380
21 -0.26376380 -0.32376380
22 -0.06376380 -0.26376380
23 -0.06376380 -0.06376380
24 0.19997792 -0.06376380
25 -0.01966887 0.19997792
26 -0.11966887 -0.01966887
27 -0.11966887 -0.11966887
28 0.01016556 -0.11966887
29 0.09016556 0.01016556
30 0.09016556 0.09016556
31 0.27016556 0.09016556
32 0.51016556 0.27016556
33 0.47016556 0.51016556
34 0.37016556 0.47016556
35 0.37016556 0.37016556
36 0.63390728 0.37016556
37 0.41426049 0.63390728
38 0.41426049 0.41426049
39 0.31426049 0.41426049
40 0.44409492 0.31426049
41 0.52409492 0.44409492
42 0.72409492 0.52409492
43 1.20409492 0.72409492
44 1.04409492 1.20409492
45 0.50409492 1.04409492
46 -0.29590508 0.50409492
47 -0.79590508 -0.29590508
48 -0.73216336 -0.79590508
49 -0.25181015 -0.73216336
50 0.04818985 -0.25181015
51 0.04818985 0.04818985
52 -0.02197572 0.04818985
53 -0.34197572 -0.02197572
54 -0.44197572 -0.34197572
55 -0.36197572 -0.44197572
56 -0.42197572 -0.36197572
57 -0.36197572 -0.42197572
58 -0.66197572 -0.36197572
59 -0.66197572 -0.66197572
60 -0.59823400 -0.66197572
> 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/70x4r1227536788.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8johx1227536788.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91fny1227536788.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106rl71227536788.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i7j11227536788.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/12c5jr1227536788.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/13dbo01227536788.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/14n3w71227536788.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/15231g1227536788.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/16j6gk1227536788.tab")
+ }
>
> system("convert tmp/1anwc1227536788.ps tmp/1anwc1227536788.png")
> system("convert tmp/299v21227536788.ps tmp/299v21227536788.png")
> system("convert tmp/3r1an1227536788.ps tmp/3r1an1227536788.png")
> system("convert tmp/4ug551227536788.ps tmp/4ug551227536788.png")
> system("convert tmp/5zox01227536788.ps tmp/5zox01227536788.png")
> system("convert tmp/6cubv1227536788.ps tmp/6cubv1227536788.png")
> system("convert tmp/70x4r1227536788.ps tmp/70x4r1227536788.png")
> system("convert tmp/8johx1227536788.ps tmp/8johx1227536788.png")
> system("convert tmp/91fny1227536788.ps tmp/91fny1227536788.png")
> system("convert tmp/106rl71227536788.ps tmp/106rl71227536788.png")
>
>
> proc.time()
user system elapsed
2.437 1.601 2.845