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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Type 'q()' to quit R.
> x <- array(list(1.4
+ ,1.9
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-0.8
+ ,1
+ ,1
+ ,1.6
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-0.8
+ ,-0.8
+ ,0
+ ,3.2
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-2.9
+ ,-1.3
+ ,3.1
+ ,3.2
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-0.7
+ ,-0.4
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-0.3
+ ,1
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3
+ ,1.5
+ ,1.5
+ ,1.4
+ ,1.3
+ ,1
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3
+ ,3
+ ,2.6
+ ,0.8
+ ,1.3
+ ,1
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3.2
+ ,2.8
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1
+ ,3.9
+ ,3.1
+ ,3.1
+ ,2.6
+ ,2.9
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1
+ ,3.9
+ ,3.9
+ ,3.4
+ ,3.9
+ ,2.9
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1
+ ,1
+ ,1.7
+ ,4.5
+ ,3.9
+ ,2.9
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1.3
+ ,1.2
+ ,4.5
+ ,4.5
+ ,3.9
+ ,2.9
+ ,1.2
+ ,0.8
+ ,0.8
+ ,0
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.9
+ ,2.9
+ ,1.2
+ ,1.2
+ ,0
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.9
+ ,2.9
+ ,2.9
+ ,1.6
+ ,1.5
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.9
+ ,3.9
+ ,2.5
+ ,1
+ ,1.5
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,4.5
+ ,3.2
+ ,2.1
+ ,1
+ ,1.5
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.4
+ ,3
+ ,2.1
+ ,1
+ ,1.5
+ ,2
+ ,3.3
+ ,3.3
+ ,2.3
+ ,4
+ ,3
+ ,2.1
+ ,1
+ ,1.5
+ ,2
+ ,2
+ ,1.9
+ ,5.1
+ ,4
+ ,3
+ ,2.1
+ ,1
+ ,1.5
+ ,1.5
+ ,1.7
+ ,4.5
+ ,5.1
+ ,4
+ ,3
+ ,2.1
+ ,1
+ ,1
+ ,1.9
+ ,4.2
+ ,4.5
+ ,5.1
+ ,4
+ ,3
+ ,2.1
+ ,2.1
+ ,3.3
+ ,3.3
+ ,4.2
+ ,4.5
+ ,5.1
+ ,4
+ ,3
+ ,3
+ ,3.8
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.5
+ ,5.1
+ ,4
+ ,4
+ ,4.4
+ ,1.8
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.5
+ ,5.1
+ ,5.1
+ ,4.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.5
+ ,4.5
+ ,3.5
+ ,0.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.2
+ ,3
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,3.3
+ ,3.3
+ ,2.8
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,2.7
+ ,2.9
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.8
+ ,1.8
+ ,2.6
+ ,1.9
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.4
+ ,2.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,0.5
+ ,1.5
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,-0.4
+ ,1.1
+ ,0.9
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.8
+ ,0.8
+ ,1.5
+ ,0.4
+ ,0.9
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.7
+ ,1.7
+ ,0.7
+ ,0.4
+ ,0.9
+ ,1.1
+ ,2
+ ,1.9
+ ,1.9
+ ,2.3
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.9
+ ,1.1
+ ,2
+ ,2
+ ,2.3
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.9
+ ,1.1
+ ,1.1
+ ,1.9
+ ,3.9
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.9
+ ,0.9
+ ,2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.4
+ ,1.6
+ ,2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.7
+ ,1.2
+ ,2
+ ,2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.1
+ ,2.1
+ ,1.9
+ ,1.5
+ ,2
+ ,2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.8
+ ,2.1
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,3.5
+ ,3.9
+ ,3.9
+ ,2.4
+ ,3.1
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,3.5
+ ,3.5
+ ,2.9
+ ,2.7
+ ,3.1
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,2
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,2.3
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,2.5
+ ,1.5
+ ,1.5
+ ,2.5
+ ,3
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,2.5
+ ,2.5
+ ,2.6
+ ,3.2
+ ,3
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,3.1
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3
+ ,2.5
+ ,2.8
+ ,2.7
+ ,2.7
+ ,2.5
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3
+ ,2.5
+ ,2.8
+ ,2.8
+ ,2.1
+ ,2
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3
+ ,2.5
+ ,2.5
+ ,2.2
+ ,1.8
+ ,2
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3
+ ,3
+ ,2.7
+ ,1.1
+ ,1.8
+ ,2
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3.2
+ ,3
+ ,-1.5
+ ,1.1
+ ,1.8
+ ,2
+ ,2.4
+ ,2.8
+ ,2.8
+ ,3.2
+ ,-3.7
+ ,-1.5
+ ,1.1
+ ,1.8
+ ,2
+ ,2.4)
+ ,dim=c(8
+ ,58)
+ ,dimnames=list(c('bbp'
+ ,'dnst'
+ ,'y1'
+ ,'y2'
+ ,'y3'
+ ,'y4'
+ ,'y5'
+ ,'y6')
+ ,1:58))
> y <- array(NA,dim=c(8,58),dimnames=list(c('bbp','dnst','y1','y2','y3','y4','y5','y6'),1:58))
> 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
bbp dnst y1 y2 y3 y4 y5 y6 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.4 1.9 1.5 -0.7 -0.7 -2.9 -0.8 1.0 1 0 0 0 0 0 0 0 0 0 0
2 1.0 1.6 3.0 1.5 -0.7 -0.7 -2.9 -0.8 0 1 0 0 0 0 0 0 0 0 0
3 -0.8 0.0 3.2 3.0 1.5 -0.7 -0.7 -2.9 0 0 1 0 0 0 0 0 0 0 0
4 -2.9 -1.3 3.1 3.2 3.0 1.5 -0.7 -0.7 0 0 0 1 0 0 0 0 0 0 0
5 -0.7 -0.4 3.9 3.1 3.2 3.0 1.5 -0.7 0 0 0 0 1 0 0 0 0 0 0
6 -0.7 -0.3 1.0 3.9 3.1 3.2 3.0 1.5 0 0 0 0 0 1 0 0 0 0 0
7 1.5 1.4 1.3 1.0 3.9 3.1 3.2 3.0 0 0 0 0 0 0 1 0 0 0 0
8 3.0 2.6 0.8 1.3 1.0 3.9 3.1 3.2 0 0 0 0 0 0 0 1 0 0 0
9 3.2 2.8 1.2 0.8 1.3 1.0 3.9 3.1 0 0 0 0 0 0 0 0 1 0 0
10 3.1 2.6 2.9 1.2 0.8 1.3 1.0 3.9 0 0 0 0 0 0 0 0 0 1 0
11 3.9 3.4 3.9 2.9 1.2 0.8 1.3 1.0 0 0 0 0 0 0 0 0 0 0 1
12 1.0 1.7 4.5 3.9 2.9 1.2 0.8 1.3 0 0 0 0 0 0 0 0 0 0 0
13 1.3 1.2 4.5 4.5 3.9 2.9 1.2 0.8 1 0 0 0 0 0 0 0 0 0 0
14 0.8 0.0 3.3 4.5 4.5 3.9 2.9 1.2 0 1 0 0 0 0 0 0 0 0 0
15 1.2 0.0 2.0 3.3 4.5 4.5 3.9 2.9 0 0 1 0 0 0 0 0 0 0 0
16 2.9 1.6 1.5 2.0 3.3 4.5 4.5 3.9 0 0 0 1 0 0 0 0 0 0 0
17 3.9 2.5 1.0 1.5 2.0 3.3 4.5 4.5 0 0 0 0 1 0 0 0 0 0 0
18 4.5 3.2 2.1 1.0 1.5 2.0 3.3 4.5 0 0 0 0 0 1 0 0 0 0 0
19 4.5 3.4 3.0 2.1 1.0 1.5 2.0 3.3 0 0 0 0 0 0 1 0 0 0 0
20 3.3 2.3 4.0 3.0 2.1 1.0 1.5 2.0 0 0 0 0 0 0 0 1 0 0 0
21 2.0 1.9 5.1 4.0 3.0 2.1 1.0 1.5 0 0 0 0 0 0 0 0 1 0 0
22 1.5 1.7 4.5 5.1 4.0 3.0 2.1 1.0 0 0 0 0 0 0 0 0 0 1 0
23 1.0 1.9 4.2 4.5 5.1 4.0 3.0 2.1 0 0 0 0 0 0 0 0 0 0 1
24 2.1 3.3 3.3 4.2 4.5 5.1 4.0 3.0 0 0 0 0 0 0 0 0 0 0 0
25 3.0 3.8 2.7 3.3 4.2 4.5 5.1 4.0 1 0 0 0 0 0 0 0 0 0 0
26 4.0 4.4 1.8 2.7 3.3 4.2 4.5 5.1 0 1 0 0 0 0 0 0 0 0 0
27 5.1 4.5 1.4 1.8 2.7 3.3 4.2 4.5 0 0 1 0 0 0 0 0 0 0 0
28 4.5 3.5 0.5 1.4 1.8 2.7 3.3 4.2 0 0 0 1 0 0 0 0 0 0 0
29 4.2 3.0 -0.4 0.5 1.4 1.8 2.7 3.3 0 0 0 0 1 0 0 0 0 0 0
30 3.3 2.8 0.8 -0.4 0.5 1.4 1.8 2.7 0 0 0 0 0 1 0 0 0 0 0
31 2.7 2.9 0.7 0.8 -0.4 0.5 1.4 1.8 0 0 0 0 0 0 1 0 0 0 0
32 1.8 2.6 1.9 0.7 0.8 -0.4 0.5 1.4 0 0 0 0 0 0 0 1 0 0 0
33 1.4 2.1 2.0 1.9 0.7 0.8 -0.4 0.5 0 0 0 0 0 0 0 0 1 0 0
34 0.5 1.5 1.1 2.0 1.9 0.7 0.8 -0.4 0 0 0 0 0 0 0 0 0 1 0
35 -0.4 1.1 0.9 1.1 2.0 1.9 0.7 0.8 0 0 0 0 0 0 0 0 0 0 1
36 0.8 1.5 0.4 0.9 1.1 2.0 1.9 0.7 0 0 0 0 0 0 0 0 0 0 0
37 0.7 1.7 0.7 0.4 0.9 1.1 2.0 1.9 1 0 0 0 0 0 0 0 0 0 0
38 1.9 2.3 2.1 0.7 0.4 0.9 1.1 2.0 0 1 0 0 0 0 0 0 0 0 0
39 2.0 2.3 2.8 2.1 0.7 0.4 0.9 1.1 0 0 1 0 0 0 0 0 0 0 0
40 1.1 1.9 3.9 2.8 2.1 0.7 0.4 0.9 0 0 0 1 0 0 0 0 0 0 0
41 0.9 2.0 3.5 3.9 2.8 2.1 0.7 0.4 0 0 0 0 1 0 0 0 0 0 0
42 0.4 1.6 2.0 3.5 3.9 2.8 2.1 0.7 0 0 0 0 0 1 0 0 0 0 0
43 0.7 1.2 2.0 2.0 3.5 3.9 2.8 2.1 0 0 0 0 0 0 1 0 0 0 0
44 2.1 1.9 1.5 2.0 2.0 3.5 3.9 2.8 0 0 0 0 0 0 0 1 0 0 0
45 2.8 2.1 2.5 1.5 2.0 2.0 3.5 3.9 0 0 0 0 0 0 0 0 1 0 0
46 3.9 2.4 3.1 2.5 1.5 2.0 2.0 3.5 0 0 0 0 0 0 0 0 0 1 0
47 3.5 2.9 2.7 3.1 2.5 1.5 2.0 2.0 0 0 0 0 0 0 0 0 0 0 1
48 2.0 2.5 2.8 2.7 3.1 2.5 1.5 2.0 0 0 0 0 0 0 0 0 0 0 0
49 2.0 2.3 2.5 2.8 2.7 3.1 2.5 1.5 1 0 0 0 0 0 0 0 0 0 0
50 1.5 2.5 3.0 2.5 2.8 2.7 3.1 2.5 0 1 0 0 0 0 0 0 0 0 0
51 2.5 2.6 3.2 3.0 2.5 2.8 2.7 3.1 0 0 1 0 0 0 0 0 0 0 0
52 3.1 2.4 2.8 3.2 3.0 2.5 2.8 2.7 0 0 0 1 0 0 0 0 0 0 0
53 2.7 2.5 2.4 2.8 3.2 3.0 2.5 2.8 0 0 0 0 1 0 0 0 0 0 0
54 2.8 2.1 2.0 2.4 2.8 3.2 3.0 2.5 0 0 0 0 0 1 0 0 0 0 0
55 2.5 2.2 1.8 2.0 2.4 2.8 3.2 3.0 0 0 0 0 0 0 1 0 0 0 0
56 3.0 2.7 1.1 1.8 2.0 2.4 2.8 3.2 0 0 0 0 0 0 0 1 0 0 0
57 3.2 3.0 -1.5 1.1 1.8 2.0 2.4 2.8 0 0 0 0 0 0 0 0 1 0 0
58 2.8 3.2 -3.7 -1.5 1.1 1.8 2.0 2.4 0 0 0 0 0 0 0 0 0 1 0
t
1 1
2 2
3 3
4 4
5 5
6 6
7 7
8 8
9 9
10 10
11 11
12 12
13 13
14 14
15 15
16 16
17 17
18 18
19 19
20 20
21 21
22 22
23 23
24 24
25 25
26 26
27 27
28 28
29 29
30 30
31 31
32 32
33 33
34 34
35 35
36 36
37 37
38 38
39 39
40 40
41 41
42 42
43 43
44 44
45 45
46 46
47 47
48 48
49 49
50 50
51 51
52 52
53 53
54 54
55 55
56 56
57 57
58 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dnst y1 y2 y3 y4
-0.760450 0.774259 0.036296 0.219063 -0.323223 -0.095315
y5 y6 M1 M2 M3 M4
0.157223 0.427847 0.093122 0.164205 0.621520 0.538781
M5 M6 M7 M8 M9 M10
0.811416 0.545048 0.510790 0.465193 0.481377 0.660971
M11 t
0.500163 -0.004118
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.94665 -0.42840 -0.04646 0.39785 1.20521
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.760450 0.474901 -1.601 0.11760
dnst 0.774259 0.132288 5.853 9.06e-07 ***
y1 0.036296 0.118406 0.307 0.76087
y2 0.219063 0.178540 1.227 0.22738
y3 -0.323223 0.164078 -1.970 0.05617 .
y4 -0.095315 0.151490 -0.629 0.53300
y5 0.157223 0.152997 1.028 0.31063
y6 0.427847 0.143527 2.981 0.00499 **
M1 0.093122 0.437042 0.213 0.83241
M2 0.164205 0.432991 0.379 0.70663
M3 0.621520 0.440206 1.412 0.16612
M4 0.538781 0.445565 1.209 0.23405
M5 0.811416 0.432863 1.875 0.06856 .
M6 0.545048 0.446300 1.221 0.22951
M7 0.510790 0.443234 1.152 0.25635
M8 0.465193 0.447887 1.039 0.30554
M9 0.481377 0.448655 1.073 0.29007
M10 0.660971 0.442265 1.495 0.14330
M11 0.500163 0.450604 1.110 0.27398
t -0.004118 0.005713 -0.721 0.47541
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6318 on 38 degrees of freedom
Multiple R-squared: 0.889, Adjusted R-squared: 0.8335
F-statistic: 16.02 on 19 and 38 DF, p-value: 1.109e-12
> 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,] 0.4951012 0.99020236 0.50489882
[2,] 0.3924436 0.78488710 0.60755645
[3,] 0.9186739 0.16265212 0.08132606
[4,] 0.9386010 0.12279800 0.06139900
[5,] 0.9067186 0.18656276 0.09328138
[6,] 0.9249679 0.15006411 0.07503205
[7,] 0.9600263 0.07994739 0.03997369
[8,] 0.9383290 0.12334206 0.06167103
[9,] 0.9626040 0.07479193 0.03739596
[10,] 0.9333234 0.13335313 0.06667656
[11,] 0.8758778 0.24824442 0.12412221
[12,] 0.8775435 0.24491295 0.12245648
[13,] 0.7566318 0.48673646 0.24336823
> postscript(file="/var/www/html/rcomp/tmp/1egvi1258646252.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28vuf1258646252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3t1421258646252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4f77r1258646252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57t9l1258646252.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 = 58
Frequency = 1
1 2 3 4 5 6
-0.10548375 0.43343161 0.34687540 -0.94664991 0.14259517 -0.92469236
7 8 9 10 11 12
0.19766952 -0.16023978 -0.29458230 -0.58388487 0.62815318 -0.15430379
13 14 15 16 17 18
0.94867633 1.20520997 0.63469994 0.57562445 -0.05332267 0.24793449
19 20 21 22 23 24
0.36636005 0.77700795 0.20393966 -0.08589135 -0.59476149 -0.60743047
25 26 27 28 29 30
-0.71957741 -0.78278557 0.02242475 0.32559778 0.63835642 0.38647735
31 32 33 34 35 36
-0.44055088 -0.46548319 -0.14830578 -0.17373477 -0.74568271 0.28360328
37 38 39 40 41 42
-0.64117661 -0.17118839 -0.39066427 -0.44208550 -0.68802630 -0.39196679
43 44 45 46 47 48
-0.14877757 -0.21829418 -0.16269024 0.53407620 0.71229102 0.47813098
49 50 51 52 53 54
0.51756144 -0.68466763 -0.61333583 0.48751317 -0.03960261 0.68224730
55 56 57 58
0.02529889 0.06700920 0.40163865 0.30943480
> postscript(file="/var/www/html/rcomp/tmp/612o61258646252.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.10548375 NA
1 0.43343161 -0.10548375
2 0.34687540 0.43343161
3 -0.94664991 0.34687540
4 0.14259517 -0.94664991
5 -0.92469236 0.14259517
6 0.19766952 -0.92469236
7 -0.16023978 0.19766952
8 -0.29458230 -0.16023978
9 -0.58388487 -0.29458230
10 0.62815318 -0.58388487
11 -0.15430379 0.62815318
12 0.94867633 -0.15430379
13 1.20520997 0.94867633
14 0.63469994 1.20520997
15 0.57562445 0.63469994
16 -0.05332267 0.57562445
17 0.24793449 -0.05332267
18 0.36636005 0.24793449
19 0.77700795 0.36636005
20 0.20393966 0.77700795
21 -0.08589135 0.20393966
22 -0.59476149 -0.08589135
23 -0.60743047 -0.59476149
24 -0.71957741 -0.60743047
25 -0.78278557 -0.71957741
26 0.02242475 -0.78278557
27 0.32559778 0.02242475
28 0.63835642 0.32559778
29 0.38647735 0.63835642
30 -0.44055088 0.38647735
31 -0.46548319 -0.44055088
32 -0.14830578 -0.46548319
33 -0.17373477 -0.14830578
34 -0.74568271 -0.17373477
35 0.28360328 -0.74568271
36 -0.64117661 0.28360328
37 -0.17118839 -0.64117661
38 -0.39066427 -0.17118839
39 -0.44208550 -0.39066427
40 -0.68802630 -0.44208550
41 -0.39196679 -0.68802630
42 -0.14877757 -0.39196679
43 -0.21829418 -0.14877757
44 -0.16269024 -0.21829418
45 0.53407620 -0.16269024
46 0.71229102 0.53407620
47 0.47813098 0.71229102
48 0.51756144 0.47813098
49 -0.68466763 0.51756144
50 -0.61333583 -0.68466763
51 0.48751317 -0.61333583
52 -0.03960261 0.48751317
53 0.68224730 -0.03960261
54 0.02529889 0.68224730
55 0.06700920 0.02529889
56 0.40163865 0.06700920
57 0.30943480 0.40163865
58 NA 0.30943480
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.43343161 -0.10548375
[2,] 0.34687540 0.43343161
[3,] -0.94664991 0.34687540
[4,] 0.14259517 -0.94664991
[5,] -0.92469236 0.14259517
[6,] 0.19766952 -0.92469236
[7,] -0.16023978 0.19766952
[8,] -0.29458230 -0.16023978
[9,] -0.58388487 -0.29458230
[10,] 0.62815318 -0.58388487
[11,] -0.15430379 0.62815318
[12,] 0.94867633 -0.15430379
[13,] 1.20520997 0.94867633
[14,] 0.63469994 1.20520997
[15,] 0.57562445 0.63469994
[16,] -0.05332267 0.57562445
[17,] 0.24793449 -0.05332267
[18,] 0.36636005 0.24793449
[19,] 0.77700795 0.36636005
[20,] 0.20393966 0.77700795
[21,] -0.08589135 0.20393966
[22,] -0.59476149 -0.08589135
[23,] -0.60743047 -0.59476149
[24,] -0.71957741 -0.60743047
[25,] -0.78278557 -0.71957741
[26,] 0.02242475 -0.78278557
[27,] 0.32559778 0.02242475
[28,] 0.63835642 0.32559778
[29,] 0.38647735 0.63835642
[30,] -0.44055088 0.38647735
[31,] -0.46548319 -0.44055088
[32,] -0.14830578 -0.46548319
[33,] -0.17373477 -0.14830578
[34,] -0.74568271 -0.17373477
[35,] 0.28360328 -0.74568271
[36,] -0.64117661 0.28360328
[37,] -0.17118839 -0.64117661
[38,] -0.39066427 -0.17118839
[39,] -0.44208550 -0.39066427
[40,] -0.68802630 -0.44208550
[41,] -0.39196679 -0.68802630
[42,] -0.14877757 -0.39196679
[43,] -0.21829418 -0.14877757
[44,] -0.16269024 -0.21829418
[45,] 0.53407620 -0.16269024
[46,] 0.71229102 0.53407620
[47,] 0.47813098 0.71229102
[48,] 0.51756144 0.47813098
[49,] -0.68466763 0.51756144
[50,] -0.61333583 -0.68466763
[51,] 0.48751317 -0.61333583
[52,] -0.03960261 0.48751317
[53,] 0.68224730 -0.03960261
[54,] 0.02529889 0.68224730
[55,] 0.06700920 0.02529889
[56,] 0.40163865 0.06700920
[57,] 0.30943480 0.40163865
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.43343161 -0.10548375
2 0.34687540 0.43343161
3 -0.94664991 0.34687540
4 0.14259517 -0.94664991
5 -0.92469236 0.14259517
6 0.19766952 -0.92469236
7 -0.16023978 0.19766952
8 -0.29458230 -0.16023978
9 -0.58388487 -0.29458230
10 0.62815318 -0.58388487
11 -0.15430379 0.62815318
12 0.94867633 -0.15430379
13 1.20520997 0.94867633
14 0.63469994 1.20520997
15 0.57562445 0.63469994
16 -0.05332267 0.57562445
17 0.24793449 -0.05332267
18 0.36636005 0.24793449
19 0.77700795 0.36636005
20 0.20393966 0.77700795
21 -0.08589135 0.20393966
22 -0.59476149 -0.08589135
23 -0.60743047 -0.59476149
24 -0.71957741 -0.60743047
25 -0.78278557 -0.71957741
26 0.02242475 -0.78278557
27 0.32559778 0.02242475
28 0.63835642 0.32559778
29 0.38647735 0.63835642
30 -0.44055088 0.38647735
31 -0.46548319 -0.44055088
32 -0.14830578 -0.46548319
33 -0.17373477 -0.14830578
34 -0.74568271 -0.17373477
35 0.28360328 -0.74568271
36 -0.64117661 0.28360328
37 -0.17118839 -0.64117661
38 -0.39066427 -0.17118839
39 -0.44208550 -0.39066427
40 -0.68802630 -0.44208550
41 -0.39196679 -0.68802630
42 -0.14877757 -0.39196679
43 -0.21829418 -0.14877757
44 -0.16269024 -0.21829418
45 0.53407620 -0.16269024
46 0.71229102 0.53407620
47 0.47813098 0.71229102
48 0.51756144 0.47813098
49 -0.68466763 0.51756144
50 -0.61333583 -0.68466763
51 0.48751317 -0.61333583
52 -0.03960261 0.48751317
53 0.68224730 -0.03960261
54 0.02529889 0.68224730
55 0.06700920 0.02529889
56 0.40163865 0.06700920
57 0.30943480 0.40163865
> 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/793q31258646252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87cv11258646252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ixwr1258646252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1055lw1258646252.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11un0x1258646252.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/12pb5q1258646252.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/13cikb1258646252.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/14zfr31258646252.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/1519cr1258646252.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/160mpv1258646252.tab")
+ }
>
> system("convert tmp/1egvi1258646252.ps tmp/1egvi1258646252.png")
> system("convert tmp/28vuf1258646252.ps tmp/28vuf1258646252.png")
> system("convert tmp/3t1421258646252.ps tmp/3t1421258646252.png")
> system("convert tmp/4f77r1258646252.ps tmp/4f77r1258646252.png")
> system("convert tmp/57t9l1258646252.ps tmp/57t9l1258646252.png")
> system("convert tmp/612o61258646252.ps tmp/612o61258646252.png")
> system("convert tmp/793q31258646252.ps tmp/793q31258646252.png")
> system("convert tmp/87cv11258646252.ps tmp/87cv11258646252.png")
> system("convert tmp/9ixwr1258646252.ps tmp/9ixwr1258646252.png")
> system("convert tmp/1055lw1258646252.ps tmp/1055lw1258646252.png")
>
>
> proc.time()
user system elapsed
2.374 1.588 5.237