R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(25.6,8.1,23.7,7.7,22,7.5,21.3,7.6,20.7,7.8,20.4,7.8,20.3,7.8,20.4,7.5,19.8,7.5,19.5,7.1,23.1,7.5,23.5,7.5,23.5,7.6,22.9,7.7,21.9,7.7,21.5,7.9,20.5,8.1,20.2,8.2,19.4,8.2,19.2,8.2,18.8,7.9,18.8,7.3,22.6,6.9,23.3,6.6,23,6.7,21.4,6.9,19.9,7,18.8,7.1,18.6,7.2,18.4,7.1,18.6,6.9,19.9,7,19.2,6.8,18.4,6.4,21.1,6.7,20.5,6.6,19.1,6.4,18.1,6.3,17,6.2,17.1,6.5,17.4,6.8,16.8,6.8,15.3,6.4,14.3,6.1,13.4,5.8,15.3,6.1,22.1,7.2,23.7,7.3,22.2,6.9,19.5,6.1,16.6,5.8,17.3,6.2,19.8,7.1,21.2,7.7,21.5,7.9,20.6,7.7,19.1,7.4,19.6,7.5,23.5,8,24,8.1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> 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 25.6 8.1 1 0 0 0 0 0 0 0 0 0 0 1
2 23.7 7.7 0 1 0 0 0 0 0 0 0 0 0 2
3 22.0 7.5 0 0 1 0 0 0 0 0 0 0 0 3
4 21.3 7.6 0 0 0 1 0 0 0 0 0 0 0 4
5 20.7 7.8 0 0 0 0 1 0 0 0 0 0 0 5
6 20.4 7.8 0 0 0 0 0 1 0 0 0 0 0 6
7 20.3 7.8 0 0 0 0 0 0 1 0 0 0 0 7
8 20.4 7.5 0 0 0 0 0 0 0 1 0 0 0 8
9 19.8 7.5 0 0 0 0 0 0 0 0 1 0 0 9
10 19.5 7.1 0 0 0 0 0 0 0 0 0 1 0 10
11 23.1 7.5 0 0 0 0 0 0 0 0 0 0 1 11
12 23.5 7.5 0 0 0 0 0 0 0 0 0 0 0 12
13 23.5 7.6 1 0 0 0 0 0 0 0 0 0 0 13
14 22.9 7.7 0 1 0 0 0 0 0 0 0 0 0 14
15 21.9 7.7 0 0 1 0 0 0 0 0 0 0 0 15
16 21.5 7.9 0 0 0 1 0 0 0 0 0 0 0 16
17 20.5 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 20.2 8.2 0 0 0 0 0 1 0 0 0 0 0 18
19 19.4 8.2 0 0 0 0 0 0 1 0 0 0 0 19
20 19.2 8.2 0 0 0 0 0 0 0 1 0 0 0 20
21 18.8 7.9 0 0 0 0 0 0 0 0 1 0 0 21
22 18.8 7.3 0 0 0 0 0 0 0 0 0 1 0 22
23 22.6 6.9 0 0 0 0 0 0 0 0 0 0 1 23
24 23.3 6.6 0 0 0 0 0 0 0 0 0 0 0 24
25 23.0 6.7 1 0 0 0 0 0 0 0 0 0 0 25
26 21.4 6.9 0 1 0 0 0 0 0 0 0 0 0 26
27 19.9 7.0 0 0 1 0 0 0 0 0 0 0 0 27
28 18.8 7.1 0 0 0 1 0 0 0 0 0 0 0 28
29 18.6 7.2 0 0 0 0 1 0 0 0 0 0 0 29
30 18.4 7.1 0 0 0 0 0 1 0 0 0 0 0 30
31 18.6 6.9 0 0 0 0 0 0 1 0 0 0 0 31
32 19.9 7.0 0 0 0 0 0 0 0 1 0 0 0 32
33 19.2 6.8 0 0 0 0 0 0 0 0 1 0 0 33
34 18.4 6.4 0 0 0 0 0 0 0 0 0 1 0 34
35 21.1 6.7 0 0 0 0 0 0 0 0 0 0 1 35
36 20.5 6.6 0 0 0 0 0 0 0 0 0 0 0 36
37 19.1 6.4 1 0 0 0 0 0 0 0 0 0 0 37
38 18.1 6.3 0 1 0 0 0 0 0 0 0 0 0 38
39 17.0 6.2 0 0 1 0 0 0 0 0 0 0 0 39
40 17.1 6.5 0 0 0 1 0 0 0 0 0 0 0 40
41 17.4 6.8 0 0 0 0 1 0 0 0 0 0 0 41
42 16.8 6.8 0 0 0 0 0 1 0 0 0 0 0 42
43 15.3 6.4 0 0 0 0 0 0 1 0 0 0 0 43
44 14.3 6.1 0 0 0 0 0 0 0 1 0 0 0 44
45 13.4 5.8 0 0 0 0 0 0 0 0 1 0 0 45
46 15.3 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 22.1 7.2 0 0 0 0 0 0 0 0 0 0 1 47
48 23.7 7.3 0 0 0 0 0 0 0 0 0 0 0 48
49 22.2 6.9 1 0 0 0 0 0 0 0 0 0 0 49
50 19.5 6.1 0 1 0 0 0 0 0 0 0 0 0 50
51 16.6 5.8 0 0 1 0 0 0 0 0 0 0 0 51
52 17.3 6.2 0 0 0 1 0 0 0 0 0 0 0 52
53 19.8 7.1 0 0 0 0 1 0 0 0 0 0 0 53
54 21.2 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 21.5 7.9 0 0 0 0 0 0 1 0 0 0 0 55
56 20.6 7.7 0 0 0 0 0 0 0 1 0 0 0 56
57 19.1 7.4 0 0 0 0 0 0 0 0 1 0 0 57
58 19.6 7.5 0 0 0 0 0 0 0 0 0 1 0 58
59 23.5 8.0 0 0 0 0 0 0 0 0 0 0 1 59
60 24.0 8.1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
5.269421 2.500074 -0.217758 -1.268856 -2.649961 -3.471089
M5 M6 M7 M8 M9 M10
-4.112227 -4.403348 -4.574454 -4.355556 -4.616653 -3.847750
M11 t
-0.628891 -0.008888
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.03672 -0.68084 0.03504 0.55415 1.84002
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.269421 2.038176 2.585 0.012962 *
X 2.500074 0.250380 9.985 4.27e-13 ***
M1 -0.217758 0.681393 -0.320 0.750735
M2 -1.268856 0.686395 -1.849 0.070952 .
M3 -2.649961 0.689209 -3.845 0.000369 ***
M4 -3.471089 0.679409 -5.109 6.09e-06 ***
M5 -4.112227 0.674816 -6.094 2.09e-07 ***
M6 -4.403348 0.675837 -6.515 4.87e-08 ***
M7 -4.574454 0.674328 -6.784 1.92e-08 ***
M8 -4.355556 0.673047 -6.471 5.67e-08 ***
M9 -4.616653 0.674394 -6.846 1.55e-08 ***
M10 -3.847750 0.678941 -5.667 9.10e-07 ***
M11 -0.628891 0.672335 -0.935 0.354476
t -0.008888 0.009131 -0.973 0.335495
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.063 on 46 degrees of freedom
Multiple R-squared: 0.8631, Adjusted R-squared: 0.8244
F-statistic: 22.31 on 13 and 46 DF, p-value: 1.390e-15
> 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,] 0.0014981376 0.0029962751 0.9985019
[2,] 0.0009408803 0.0018817606 0.9990591
[3,] 0.0038229514 0.0076459027 0.9961770
[4,] 0.0167463318 0.0334926636 0.9832537
[5,] 0.0113303629 0.0226607257 0.9886696
[6,] 0.0051840566 0.0103681132 0.9948159
[7,] 0.0035785218 0.0071570436 0.9964215
[8,] 0.0040351211 0.0080702421 0.9959649
[9,] 0.0026422093 0.0052844186 0.9973578
[10,] 0.0015554704 0.0031109409 0.9984445
[11,] 0.0010537640 0.0021075281 0.9989462
[12,] 0.0018781588 0.0037563176 0.9981218
[13,] 0.0012449860 0.0024899719 0.9987550
[14,] 0.0005682749 0.0011365498 0.9994317
[15,] 0.0003426642 0.0006853284 0.9996573
[16,] 0.0026364954 0.0052729909 0.9973635
[17,] 0.0085543162 0.0171086324 0.9914457
[18,] 0.0262397096 0.0524794192 0.9737603
[19,] 0.0423305740 0.0846611479 0.9576694
[20,] 0.0757824248 0.1515648496 0.9242176
[21,] 0.2513027832 0.5026055663 0.7486972
[22,] 0.3404475927 0.6808951854 0.6595524
[23,] 0.2944974533 0.5889949066 0.7055025
[24,] 0.2459298431 0.4918596863 0.7540702
[25,] 0.4162511249 0.8325022498 0.5837489
[26,] 0.8896398365 0.2207203271 0.1103602
[27,] 0.8902807651 0.2194384698 0.1097192
> postscript(file="/var/www/html/rcomp/tmp/1xa5j1261767164.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wtgq1261767164.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3rsyo1261767164.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4z4fz1261767164.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5650h1261767164.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 = 60
Frequency = 1
1 2 3 4 5 6
0.30662573 0.46664053 0.65664792 0.53665680 0.08666716 0.08667604
7 8 9 10 11 12
0.16667012 0.80668196 0.47666568 0.41668048 -0.19332100 -0.41332396
13 14 15 16 17 18
-0.43668566 -0.22670785 0.16328475 0.09328623 -0.75670341 -1.00670193
19 20 21 22 23 24
-1.62670785 -2.03671821 -1.41671229 -0.67668270 0.91337501 1.74339424
25 26 27 28 29 30
1.42003255 0.38000296 0.01998816 -0.50000296 -0.29998520 0.05003107
31 32 33 34 35 36
0.93003995 1.77002219 1.84002071 1.28003551 0.02004143 -0.94995413
37 38 39 40 41 42
-1.62329363 -1.31330103 -0.77330103 -0.59330695 -0.39330399 -0.69329511
43 44 45 46 47 48
-1.01327144 -1.47325960 -1.35325368 -0.96329067 -0.12334394 0.60664571
49 50 51 52 53 54
0.33332100 0.69336539 -0.06661981 0.46336687 1.36332544 1.56328993
55 56 57 58 59 60
1.54326922 0.93327366 0.45327957 -0.05674262 -0.61675150 -0.98676186
> postscript(file="/var/www/html/rcomp/tmp/6go9j1261767164.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.30662573 NA
1 0.46664053 0.30662573
2 0.65664792 0.46664053
3 0.53665680 0.65664792
4 0.08666716 0.53665680
5 0.08667604 0.08666716
6 0.16667012 0.08667604
7 0.80668196 0.16667012
8 0.47666568 0.80668196
9 0.41668048 0.47666568
10 -0.19332100 0.41668048
11 -0.41332396 -0.19332100
12 -0.43668566 -0.41332396
13 -0.22670785 -0.43668566
14 0.16328475 -0.22670785
15 0.09328623 0.16328475
16 -0.75670341 0.09328623
17 -1.00670193 -0.75670341
18 -1.62670785 -1.00670193
19 -2.03671821 -1.62670785
20 -1.41671229 -2.03671821
21 -0.67668270 -1.41671229
22 0.91337501 -0.67668270
23 1.74339424 0.91337501
24 1.42003255 1.74339424
25 0.38000296 1.42003255
26 0.01998816 0.38000296
27 -0.50000296 0.01998816
28 -0.29998520 -0.50000296
29 0.05003107 -0.29998520
30 0.93003995 0.05003107
31 1.77002219 0.93003995
32 1.84002071 1.77002219
33 1.28003551 1.84002071
34 0.02004143 1.28003551
35 -0.94995413 0.02004143
36 -1.62329363 -0.94995413
37 -1.31330103 -1.62329363
38 -0.77330103 -1.31330103
39 -0.59330695 -0.77330103
40 -0.39330399 -0.59330695
41 -0.69329511 -0.39330399
42 -1.01327144 -0.69329511
43 -1.47325960 -1.01327144
44 -1.35325368 -1.47325960
45 -0.96329067 -1.35325368
46 -0.12334394 -0.96329067
47 0.60664571 -0.12334394
48 0.33332100 0.60664571
49 0.69336539 0.33332100
50 -0.06661981 0.69336539
51 0.46336687 -0.06661981
52 1.36332544 0.46336687
53 1.56328993 1.36332544
54 1.54326922 1.56328993
55 0.93327366 1.54326922
56 0.45327957 0.93327366
57 -0.05674262 0.45327957
58 -0.61675150 -0.05674262
59 -0.98676186 -0.61675150
60 NA -0.98676186
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.46664053 0.30662573
[2,] 0.65664792 0.46664053
[3,] 0.53665680 0.65664792
[4,] 0.08666716 0.53665680
[5,] 0.08667604 0.08666716
[6,] 0.16667012 0.08667604
[7,] 0.80668196 0.16667012
[8,] 0.47666568 0.80668196
[9,] 0.41668048 0.47666568
[10,] -0.19332100 0.41668048
[11,] -0.41332396 -0.19332100
[12,] -0.43668566 -0.41332396
[13,] -0.22670785 -0.43668566
[14,] 0.16328475 -0.22670785
[15,] 0.09328623 0.16328475
[16,] -0.75670341 0.09328623
[17,] -1.00670193 -0.75670341
[18,] -1.62670785 -1.00670193
[19,] -2.03671821 -1.62670785
[20,] -1.41671229 -2.03671821
[21,] -0.67668270 -1.41671229
[22,] 0.91337501 -0.67668270
[23,] 1.74339424 0.91337501
[24,] 1.42003255 1.74339424
[25,] 0.38000296 1.42003255
[26,] 0.01998816 0.38000296
[27,] -0.50000296 0.01998816
[28,] -0.29998520 -0.50000296
[29,] 0.05003107 -0.29998520
[30,] 0.93003995 0.05003107
[31,] 1.77002219 0.93003995
[32,] 1.84002071 1.77002219
[33,] 1.28003551 1.84002071
[34,] 0.02004143 1.28003551
[35,] -0.94995413 0.02004143
[36,] -1.62329363 -0.94995413
[37,] -1.31330103 -1.62329363
[38,] -0.77330103 -1.31330103
[39,] -0.59330695 -0.77330103
[40,] -0.39330399 -0.59330695
[41,] -0.69329511 -0.39330399
[42,] -1.01327144 -0.69329511
[43,] -1.47325960 -1.01327144
[44,] -1.35325368 -1.47325960
[45,] -0.96329067 -1.35325368
[46,] -0.12334394 -0.96329067
[47,] 0.60664571 -0.12334394
[48,] 0.33332100 0.60664571
[49,] 0.69336539 0.33332100
[50,] -0.06661981 0.69336539
[51,] 0.46336687 -0.06661981
[52,] 1.36332544 0.46336687
[53,] 1.56328993 1.36332544
[54,] 1.54326922 1.56328993
[55,] 0.93327366 1.54326922
[56,] 0.45327957 0.93327366
[57,] -0.05674262 0.45327957
[58,] -0.61675150 -0.05674262
[59,] -0.98676186 -0.61675150
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.46664053 0.30662573
2 0.65664792 0.46664053
3 0.53665680 0.65664792
4 0.08666716 0.53665680
5 0.08667604 0.08666716
6 0.16667012 0.08667604
7 0.80668196 0.16667012
8 0.47666568 0.80668196
9 0.41668048 0.47666568
10 -0.19332100 0.41668048
11 -0.41332396 -0.19332100
12 -0.43668566 -0.41332396
13 -0.22670785 -0.43668566
14 0.16328475 -0.22670785
15 0.09328623 0.16328475
16 -0.75670341 0.09328623
17 -1.00670193 -0.75670341
18 -1.62670785 -1.00670193
19 -2.03671821 -1.62670785
20 -1.41671229 -2.03671821
21 -0.67668270 -1.41671229
22 0.91337501 -0.67668270
23 1.74339424 0.91337501
24 1.42003255 1.74339424
25 0.38000296 1.42003255
26 0.01998816 0.38000296
27 -0.50000296 0.01998816
28 -0.29998520 -0.50000296
29 0.05003107 -0.29998520
30 0.93003995 0.05003107
31 1.77002219 0.93003995
32 1.84002071 1.77002219
33 1.28003551 1.84002071
34 0.02004143 1.28003551
35 -0.94995413 0.02004143
36 -1.62329363 -0.94995413
37 -1.31330103 -1.62329363
38 -0.77330103 -1.31330103
39 -0.59330695 -0.77330103
40 -0.39330399 -0.59330695
41 -0.69329511 -0.39330399
42 -1.01327144 -0.69329511
43 -1.47325960 -1.01327144
44 -1.35325368 -1.47325960
45 -0.96329067 -1.35325368
46 -0.12334394 -0.96329067
47 0.60664571 -0.12334394
48 0.33332100 0.60664571
49 0.69336539 0.33332100
50 -0.06661981 0.69336539
51 0.46336687 -0.06661981
52 1.36332544 0.46336687
53 1.56328993 1.36332544
54 1.54326922 1.56328993
55 0.93327366 1.54326922
56 0.45327957 0.93327366
57 -0.05674262 0.45327957
58 -0.61675150 -0.05674262
59 -0.98676186 -0.61675150
> 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/7y3z51261767164.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89jzo1261767164.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9f09k1261767164.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106dum1261767164.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11p9451261767164.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/12datv1261767164.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/13ki0q1261767164.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/14wkno1261767164.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/15yhr61261767164.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/16vzc81261767164.tab")
+ }
>
> try(system("convert tmp/1xa5j1261767164.ps tmp/1xa5j1261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wtgq1261767164.ps tmp/2wtgq1261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rsyo1261767164.ps tmp/3rsyo1261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/4z4fz1261767164.ps tmp/4z4fz1261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/5650h1261767164.ps tmp/5650h1261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/6go9j1261767164.ps tmp/6go9j1261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/7y3z51261767164.ps tmp/7y3z51261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/89jzo1261767164.ps tmp/89jzo1261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/9f09k1261767164.ps tmp/9f09k1261767164.png",intern=TRUE))
character(0)
> try(system("convert tmp/106dum1261767164.ps tmp/106dum1261767164.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.378 1.567 3.003