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(22,0,22,0,20,0,21,0,20,0,21,0,21,0,21,0,19,0,21,0,21,0,22,0,19,0,24,0,22,0,22,0,22,0,24,0,22,0,23,0,24,0,21,0,20,0,22,0,23,0,23,0,22,0,20,0,21,1,21,1,20,1,20,1,17,1,18,1,19,1,19,1,20,1,21,1,20,1,21,1,19,1,22,1,20,1,18,1,16,1,17,1,18,1,19,1,18,1,20,1,21,1,18,1,19,1,19,1,19,1,21,1,19,1,19,1,17,1,16,1,16,1,17,1,16,1,15,1,16,1,16,1,16,1,18,1,19,1,16,1,16,1,16,1),dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> y <- array(NA,dim=c(2,72),dimnames=list(c('Y','X'),1:72))
> 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 = '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
1 22 0 1 0 0 0 0 0 0 0 0 0 0
2 22 0 0 1 0 0 0 0 0 0 0 0 0
3 20 0 0 0 1 0 0 0 0 0 0 0 0
4 21 0 0 0 0 1 0 0 0 0 0 0 0
5 20 0 0 0 0 0 1 0 0 0 0 0 0
6 21 0 0 0 0 0 0 1 0 0 0 0 0
7 21 0 0 0 0 0 0 0 1 0 0 0 0
8 21 0 0 0 0 0 0 0 0 1 0 0 0
9 19 0 0 0 0 0 0 0 0 0 1 0 0
10 21 0 0 0 0 0 0 0 0 0 0 1 0
11 21 0 0 0 0 0 0 0 0 0 0 0 1
12 22 0 0 0 0 0 0 0 0 0 0 0 0
13 19 0 1 0 0 0 0 0 0 0 0 0 0
14 24 0 0 1 0 0 0 0 0 0 0 0 0
15 22 0 0 0 1 0 0 0 0 0 0 0 0
16 22 0 0 0 0 1 0 0 0 0 0 0 0
17 22 0 0 0 0 0 1 0 0 0 0 0 0
18 24 0 0 0 0 0 0 1 0 0 0 0 0
19 22 0 0 0 0 0 0 0 1 0 0 0 0
20 23 0 0 0 0 0 0 0 0 1 0 0 0
21 24 0 0 0 0 0 0 0 0 0 1 0 0
22 21 0 0 0 0 0 0 0 0 0 0 1 0
23 20 0 0 0 0 0 0 0 0 0 0 0 1
24 22 0 0 0 0 0 0 0 0 0 0 0 0
25 23 0 1 0 0 0 0 0 0 0 0 0 0
26 23 0 0 1 0 0 0 0 0 0 0 0 0
27 22 0 0 0 1 0 0 0 0 0 0 0 0
28 20 0 0 0 0 1 0 0 0 0 0 0 0
29 21 1 0 0 0 0 1 0 0 0 0 0 0
30 21 1 0 0 0 0 0 1 0 0 0 0 0
31 20 1 0 0 0 0 0 0 1 0 0 0 0
32 20 1 0 0 0 0 0 0 0 1 0 0 0
33 17 1 0 0 0 0 0 0 0 0 1 0 0
34 18 1 0 0 0 0 0 0 0 0 0 1 0
35 19 1 0 0 0 0 0 0 0 0 0 0 1
36 19 1 0 0 0 0 0 0 0 0 0 0 0
37 20 1 1 0 0 0 0 0 0 0 0 0 0
38 21 1 0 1 0 0 0 0 0 0 0 0 0
39 20 1 0 0 1 0 0 0 0 0 0 0 0
40 21 1 0 0 0 1 0 0 0 0 0 0 0
41 19 1 0 0 0 0 1 0 0 0 0 0 0
42 22 1 0 0 0 0 0 1 0 0 0 0 0
43 20 1 0 0 0 0 0 0 1 0 0 0 0
44 18 1 0 0 0 0 0 0 0 1 0 0 0
45 16 1 0 0 0 0 0 0 0 0 1 0 0
46 17 1 0 0 0 0 0 0 0 0 0 1 0
47 18 1 0 0 0 0 0 0 0 0 0 0 1
48 19 1 0 0 0 0 0 0 0 0 0 0 0
49 18 1 1 0 0 0 0 0 0 0 0 0 0
50 20 1 0 1 0 0 0 0 0 0 0 0 0
51 21 1 0 0 1 0 0 0 0 0 0 0 0
52 18 1 0 0 0 1 0 0 0 0 0 0 0
53 19 1 0 0 0 0 1 0 0 0 0 0 0
54 19 1 0 0 0 0 0 1 0 0 0 0 0
55 19 1 0 0 0 0 0 0 1 0 0 0 0
56 21 1 0 0 0 0 0 0 0 1 0 0 0
57 19 1 0 0 0 0 0 0 0 0 1 0 0
58 19 1 0 0 0 0 0 0 0 0 0 1 0
59 17 1 0 0 0 0 0 0 0 0 0 0 1
60 16 1 0 0 0 0 0 0 0 0 0 0 0
61 16 1 1 0 0 0 0 0 0 0 0 0 0
62 17 1 0 1 0 0 0 0 0 0 0 0 0
63 16 1 0 0 1 0 0 0 0 0 0 0 0
64 15 1 0 0 0 1 0 0 0 0 0 0 0
65 16 1 0 0 0 0 1 0 0 0 0 0 0
66 16 1 0 0 0 0 0 1 0 0 0 0 0
67 16 1 0 0 0 0 0 0 1 0 0 0 0
68 18 1 0 0 0 0 0 0 0 1 0 0 0
69 19 1 0 0 0 0 0 0 0 0 1 0 0
70 16 1 0 0 0 0 0 0 0 0 0 1 0
71 16 1 0 0 0 0 0 0 0 0 0 0 1
72 16 1 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
2.110e+01 -3.150e+00 1.417e-01 1.642e+00 6.417e-01 -2.500e-02
M5 M6 M7 M8 M9 M10
5.000e-01 1.500e+00 6.667e-01 1.167e+00 -1.243e-15 -3.333e-01
M11
-5.000e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.4500 -1.1542 0.2583 1.0500 3.0750
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.110e+01 7.444e-01 28.343 < 2e-16 ***
X -3.150e+00 4.147e-01 -7.596 2.65e-10 ***
M1 1.417e-01 9.799e-01 0.145 0.8855
M2 1.642e+00 9.799e-01 1.675 0.0992 .
M3 6.417e-01 9.799e-01 0.655 0.5151
M4 -2.500e-02 9.799e-01 -0.026 0.9797
M5 5.000e-01 9.775e-01 0.512 0.6109
M6 1.500e+00 9.775e-01 1.535 0.1302
M7 6.667e-01 9.775e-01 0.682 0.4979
M8 1.167e+00 9.775e-01 1.194 0.2374
M9 -1.243e-15 9.775e-01 -1.27e-15 1.0000
M10 -3.333e-01 9.775e-01 -0.341 0.7343
M11 -5.000e-01 9.775e-01 -0.512 0.6109
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.693 on 59 degrees of freedom
Multiple R-squared: 0.5488, Adjusted R-squared: 0.4571
F-statistic: 5.981 on 12 and 59 DF, p-value: 1.093e-06
> 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,] 0.57056470 0.85887060 0.4294353
[2,] 0.49445189 0.98890378 0.5055481
[3,] 0.52903969 0.94192062 0.4709603
[4,] 0.41143857 0.82287713 0.5885614
[5,] 0.35703586 0.71407173 0.6429641
[6,] 0.61990942 0.76018116 0.3800906
[7,] 0.51118270 0.97763460 0.4888173
[8,] 0.42180574 0.84361148 0.5781943
[9,] 0.32741393 0.65482785 0.6725861
[10,] 0.33937812 0.67875625 0.6606219
[11,] 0.25807566 0.51615132 0.7419243
[12,] 0.19870782 0.39741564 0.8012922
[13,] 0.15903946 0.31807891 0.8409605
[14,] 0.13235706 0.26471411 0.8676429
[15,] 0.10609009 0.21218019 0.8939099
[16,] 0.07774296 0.15548593 0.9222570
[17,] 0.05464132 0.10928264 0.9453587
[18,] 0.06567044 0.13134089 0.9343296
[19,] 0.04579346 0.09158693 0.9542065
[20,] 0.03337644 0.06675288 0.9666236
[21,] 0.02517962 0.05035924 0.9748204
[22,] 0.02179098 0.04358196 0.9782090
[23,] 0.01641405 0.03282810 0.9835859
[24,] 0.01085365 0.02170731 0.9891463
[25,] 0.02160935 0.04321869 0.9783907
[26,] 0.01504820 0.03009641 0.9849518
[27,] 0.02914005 0.05828010 0.9708600
[28,] 0.02532249 0.05064497 0.9746775
[29,] 0.02487587 0.04975174 0.9751241
[30,] 0.04361247 0.08722495 0.9563875
[31,] 0.03212408 0.06424815 0.9678759
[32,] 0.02258359 0.04516717 0.9774164
[33,] 0.02564866 0.05129732 0.9743513
[34,] 0.02094911 0.04189822 0.9790509
[35,] 0.02145518 0.04291035 0.9785448
[36,] 0.07841212 0.15682425 0.9215879
[37,] 0.09857899 0.19715799 0.9014210
[38,] 0.12154204 0.24308409 0.8784580
[39,] 0.17648107 0.35296214 0.8235189
[40,] 0.25003128 0.50006256 0.7499687
[41,] 0.41238052 0.82476104 0.5876195
> postscript(file="/var/www/html/rcomp/tmp/162al1258726134.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/20i1k1258726134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/361md1258726134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4k1721258726134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5i8iu1258726134.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 = 72
Frequency = 1
1 2 3 4 5 6
0.75833333 -0.74166667 -1.74166667 -0.07500000 -1.60000000 -1.60000000
7 8 9 10 11 12
-0.76666667 -1.26666667 -2.10000000 0.23333333 0.40000000 0.90000000
13 14 15 16 17 18
-2.24166667 1.25833333 0.25833333 0.92500000 0.40000000 1.40000000
19 20 21 22 23 24
0.23333333 0.73333333 2.90000000 0.23333333 -0.60000000 0.90000000
25 26 27 28 29 30
1.75833333 0.25833333 0.25833333 -1.07500000 2.55000000 1.55000000
31 32 33 34 35 36
1.38333333 0.88333333 -0.95000000 0.38333333 1.55000000 1.05000000
37 38 39 40 41 42
1.90833333 1.40833333 1.40833333 3.07500000 0.55000000 2.55000000
43 44 45 46 47 48
1.38333333 -1.11666667 -1.95000000 -0.61666667 0.55000000 1.05000000
49 50 51 52 53 54
-0.09166667 0.40833333 2.40833333 0.07500000 0.55000000 -0.45000000
55 56 57 58 59 60
0.38333333 1.88333333 1.05000000 1.38333333 -0.45000000 -1.95000000
61 62 63 64 65 66
-2.09166667 -2.59166667 -2.59166667 -2.92500000 -2.45000000 -3.45000000
67 68 69 70 71 72
-2.61666667 -1.11666667 1.05000000 -1.61666667 -1.45000000 -1.95000000
> postscript(file="/var/www/html/rcomp/tmp/6sy4m1258726134.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 = 72
Frequency = 1
lag(myerror, k = 1) myerror
0 0.75833333 NA
1 -0.74166667 0.75833333
2 -1.74166667 -0.74166667
3 -0.07500000 -1.74166667
4 -1.60000000 -0.07500000
5 -1.60000000 -1.60000000
6 -0.76666667 -1.60000000
7 -1.26666667 -0.76666667
8 -2.10000000 -1.26666667
9 0.23333333 -2.10000000
10 0.40000000 0.23333333
11 0.90000000 0.40000000
12 -2.24166667 0.90000000
13 1.25833333 -2.24166667
14 0.25833333 1.25833333
15 0.92500000 0.25833333
16 0.40000000 0.92500000
17 1.40000000 0.40000000
18 0.23333333 1.40000000
19 0.73333333 0.23333333
20 2.90000000 0.73333333
21 0.23333333 2.90000000
22 -0.60000000 0.23333333
23 0.90000000 -0.60000000
24 1.75833333 0.90000000
25 0.25833333 1.75833333
26 0.25833333 0.25833333
27 -1.07500000 0.25833333
28 2.55000000 -1.07500000
29 1.55000000 2.55000000
30 1.38333333 1.55000000
31 0.88333333 1.38333333
32 -0.95000000 0.88333333
33 0.38333333 -0.95000000
34 1.55000000 0.38333333
35 1.05000000 1.55000000
36 1.90833333 1.05000000
37 1.40833333 1.90833333
38 1.40833333 1.40833333
39 3.07500000 1.40833333
40 0.55000000 3.07500000
41 2.55000000 0.55000000
42 1.38333333 2.55000000
43 -1.11666667 1.38333333
44 -1.95000000 -1.11666667
45 -0.61666667 -1.95000000
46 0.55000000 -0.61666667
47 1.05000000 0.55000000
48 -0.09166667 1.05000000
49 0.40833333 -0.09166667
50 2.40833333 0.40833333
51 0.07500000 2.40833333
52 0.55000000 0.07500000
53 -0.45000000 0.55000000
54 0.38333333 -0.45000000
55 1.88333333 0.38333333
56 1.05000000 1.88333333
57 1.38333333 1.05000000
58 -0.45000000 1.38333333
59 -1.95000000 -0.45000000
60 -2.09166667 -1.95000000
61 -2.59166667 -2.09166667
62 -2.59166667 -2.59166667
63 -2.92500000 -2.59166667
64 -2.45000000 -2.92500000
65 -3.45000000 -2.45000000
66 -2.61666667 -3.45000000
67 -1.11666667 -2.61666667
68 1.05000000 -1.11666667
69 -1.61666667 1.05000000
70 -1.45000000 -1.61666667
71 -1.95000000 -1.45000000
72 NA -1.95000000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.74166667 0.75833333
[2,] -1.74166667 -0.74166667
[3,] -0.07500000 -1.74166667
[4,] -1.60000000 -0.07500000
[5,] -1.60000000 -1.60000000
[6,] -0.76666667 -1.60000000
[7,] -1.26666667 -0.76666667
[8,] -2.10000000 -1.26666667
[9,] 0.23333333 -2.10000000
[10,] 0.40000000 0.23333333
[11,] 0.90000000 0.40000000
[12,] -2.24166667 0.90000000
[13,] 1.25833333 -2.24166667
[14,] 0.25833333 1.25833333
[15,] 0.92500000 0.25833333
[16,] 0.40000000 0.92500000
[17,] 1.40000000 0.40000000
[18,] 0.23333333 1.40000000
[19,] 0.73333333 0.23333333
[20,] 2.90000000 0.73333333
[21,] 0.23333333 2.90000000
[22,] -0.60000000 0.23333333
[23,] 0.90000000 -0.60000000
[24,] 1.75833333 0.90000000
[25,] 0.25833333 1.75833333
[26,] 0.25833333 0.25833333
[27,] -1.07500000 0.25833333
[28,] 2.55000000 -1.07500000
[29,] 1.55000000 2.55000000
[30,] 1.38333333 1.55000000
[31,] 0.88333333 1.38333333
[32,] -0.95000000 0.88333333
[33,] 0.38333333 -0.95000000
[34,] 1.55000000 0.38333333
[35,] 1.05000000 1.55000000
[36,] 1.90833333 1.05000000
[37,] 1.40833333 1.90833333
[38,] 1.40833333 1.40833333
[39,] 3.07500000 1.40833333
[40,] 0.55000000 3.07500000
[41,] 2.55000000 0.55000000
[42,] 1.38333333 2.55000000
[43,] -1.11666667 1.38333333
[44,] -1.95000000 -1.11666667
[45,] -0.61666667 -1.95000000
[46,] 0.55000000 -0.61666667
[47,] 1.05000000 0.55000000
[48,] -0.09166667 1.05000000
[49,] 0.40833333 -0.09166667
[50,] 2.40833333 0.40833333
[51,] 0.07500000 2.40833333
[52,] 0.55000000 0.07500000
[53,] -0.45000000 0.55000000
[54,] 0.38333333 -0.45000000
[55,] 1.88333333 0.38333333
[56,] 1.05000000 1.88333333
[57,] 1.38333333 1.05000000
[58,] -0.45000000 1.38333333
[59,] -1.95000000 -0.45000000
[60,] -2.09166667 -1.95000000
[61,] -2.59166667 -2.09166667
[62,] -2.59166667 -2.59166667
[63,] -2.92500000 -2.59166667
[64,] -2.45000000 -2.92500000
[65,] -3.45000000 -2.45000000
[66,] -2.61666667 -3.45000000
[67,] -1.11666667 -2.61666667
[68,] 1.05000000 -1.11666667
[69,] -1.61666667 1.05000000
[70,] -1.45000000 -1.61666667
[71,] -1.95000000 -1.45000000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.74166667 0.75833333
2 -1.74166667 -0.74166667
3 -0.07500000 -1.74166667
4 -1.60000000 -0.07500000
5 -1.60000000 -1.60000000
6 -0.76666667 -1.60000000
7 -1.26666667 -0.76666667
8 -2.10000000 -1.26666667
9 0.23333333 -2.10000000
10 0.40000000 0.23333333
11 0.90000000 0.40000000
12 -2.24166667 0.90000000
13 1.25833333 -2.24166667
14 0.25833333 1.25833333
15 0.92500000 0.25833333
16 0.40000000 0.92500000
17 1.40000000 0.40000000
18 0.23333333 1.40000000
19 0.73333333 0.23333333
20 2.90000000 0.73333333
21 0.23333333 2.90000000
22 -0.60000000 0.23333333
23 0.90000000 -0.60000000
24 1.75833333 0.90000000
25 0.25833333 1.75833333
26 0.25833333 0.25833333
27 -1.07500000 0.25833333
28 2.55000000 -1.07500000
29 1.55000000 2.55000000
30 1.38333333 1.55000000
31 0.88333333 1.38333333
32 -0.95000000 0.88333333
33 0.38333333 -0.95000000
34 1.55000000 0.38333333
35 1.05000000 1.55000000
36 1.90833333 1.05000000
37 1.40833333 1.90833333
38 1.40833333 1.40833333
39 3.07500000 1.40833333
40 0.55000000 3.07500000
41 2.55000000 0.55000000
42 1.38333333 2.55000000
43 -1.11666667 1.38333333
44 -1.95000000 -1.11666667
45 -0.61666667 -1.95000000
46 0.55000000 -0.61666667
47 1.05000000 0.55000000
48 -0.09166667 1.05000000
49 0.40833333 -0.09166667
50 2.40833333 0.40833333
51 0.07500000 2.40833333
52 0.55000000 0.07500000
53 -0.45000000 0.55000000
54 0.38333333 -0.45000000
55 1.88333333 0.38333333
56 1.05000000 1.88333333
57 1.38333333 1.05000000
58 -0.45000000 1.38333333
59 -1.95000000 -0.45000000
60 -2.09166667 -1.95000000
61 -2.59166667 -2.09166667
62 -2.59166667 -2.59166667
63 -2.92500000 -2.59166667
64 -2.45000000 -2.92500000
65 -3.45000000 -2.45000000
66 -2.61666667 -3.45000000
67 -1.11666667 -2.61666667
68 1.05000000 -1.11666667
69 -1.61666667 1.05000000
70 -1.45000000 -1.61666667
71 -1.95000000 -1.45000000
> 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/70l4h1258726134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ohdk1258726134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9n2lh1258726134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fw081258726134.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119jyn1258726134.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/127aux1258726134.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/13ntb71258726134.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/14az6m1258726134.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/151doc1258726134.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/16dak91258726134.tab")
+ }
> system("convert tmp/162al1258726134.ps tmp/162al1258726134.png")
> system("convert tmp/20i1k1258726134.ps tmp/20i1k1258726134.png")
> system("convert tmp/361md1258726134.ps tmp/361md1258726134.png")
> system("convert tmp/4k1721258726134.ps tmp/4k1721258726134.png")
> system("convert tmp/5i8iu1258726134.ps tmp/5i8iu1258726134.png")
> system("convert tmp/6sy4m1258726134.ps tmp/6sy4m1258726134.png")
> system("convert tmp/70l4h1258726134.ps tmp/70l4h1258726134.png")
> system("convert tmp/8ohdk1258726134.ps tmp/8ohdk1258726134.png")
> system("convert tmp/9n2lh1258726134.ps tmp/9n2lh1258726134.png")
> system("convert tmp/10fw081258726134.ps tmp/10fw081258726134.png")
>
>
> proc.time()
user system elapsed
2.595 1.588 3.132