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 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(1.4
+ ,1.9
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-0.8
+ ,1
+ ,1
+ ,1.6
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-0.8
+ ,-0.8
+ ,0
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-2.9
+ ,-2.9
+ ,-1.3
+ ,3.2
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-0.7
+ ,-0.4
+ ,3.1
+ ,3.2
+ ,3
+ ,1.5
+ ,-0.7
+ ,-0.7
+ ,-0.3
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3
+ ,1.5
+ ,1.5
+ ,1.4
+ ,1
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3
+ ,3
+ ,2.6
+ ,1.3
+ ,1
+ ,3.9
+ ,3.1
+ ,3.2
+ ,3.2
+ ,2.8
+ ,0.8
+ ,1.3
+ ,1
+ ,3.9
+ ,3.1
+ ,3.1
+ ,2.6
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1
+ ,3.9
+ ,3.9
+ ,3.4
+ ,2.9
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1
+ ,1
+ ,1.7
+ ,3.9
+ ,2.9
+ ,1.2
+ ,0.8
+ ,1.3
+ ,1.3
+ ,1.2
+ ,4.5
+ ,3.9
+ ,2.9
+ ,1.2
+ ,0.8
+ ,0.8
+ ,0
+ ,4.5
+ ,4.5
+ ,3.9
+ ,2.9
+ ,1.2
+ ,1.2
+ ,0
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.9
+ ,2.9
+ ,2.9
+ ,1.6
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.9
+ ,3.9
+ ,2.5
+ ,1.5
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,4.5
+ ,3.2
+ ,1
+ ,1.5
+ ,2
+ ,3.3
+ ,4.5
+ ,4.5
+ ,3.4
+ ,2.1
+ ,1
+ ,1.5
+ ,2
+ ,3.3
+ ,3.3
+ ,2.3
+ ,3
+ ,2.1
+ ,1
+ ,1.5
+ ,2
+ ,2
+ ,1.9
+ ,4
+ ,3
+ ,2.1
+ ,1
+ ,1.5
+ ,1.5
+ ,1.7
+ ,5.1
+ ,4
+ ,3
+ ,2.1
+ ,1
+ ,1
+ ,1.9
+ ,4.5
+ ,5.1
+ ,4
+ ,3
+ ,2.1
+ ,2.1
+ ,3.3
+ ,4.2
+ ,4.5
+ ,5.1
+ ,4
+ ,3
+ ,3
+ ,3.8
+ ,3.3
+ ,4.2
+ ,4.5
+ ,5.1
+ ,4
+ ,4
+ ,4.4
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.5
+ ,5.1
+ ,5.1
+ ,4.5
+ ,1.8
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.5
+ ,4.5
+ ,3.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,3.3
+ ,4.2
+ ,4.2
+ ,3
+ ,0.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,3.3
+ ,3.3
+ ,2.8
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.8
+ ,2.7
+ ,2.7
+ ,2.9
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.8
+ ,1.8
+ ,2.6
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,1.4
+ ,1.4
+ ,2.1
+ ,1.9
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,0.5
+ ,0.5
+ ,1.5
+ ,2
+ ,1.9
+ ,0.7
+ ,0.8
+ ,-0.4
+ ,-0.4
+ ,1.1
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.8
+ ,0.8
+ ,1.5
+ ,0.9
+ ,1.1
+ ,2
+ ,1.9
+ ,0.7
+ ,0.7
+ ,1.7
+ ,0.4
+ ,0.9
+ ,1.1
+ ,2
+ ,1.9
+ ,1.9
+ ,2.3
+ ,0.7
+ ,0.4
+ ,0.9
+ ,1.1
+ ,2
+ ,2
+ ,2.3
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.9
+ ,1.1
+ ,1.1
+ ,1.9
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.9
+ ,0.9
+ ,2
+ ,3.9
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.4
+ ,0.4
+ ,1.6
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.1
+ ,0.7
+ ,0.7
+ ,1.2
+ ,2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.1
+ ,2.1
+ ,1.9
+ ,2
+ ,2
+ ,3.5
+ ,3.9
+ ,2.8
+ ,2.8
+ ,2.1
+ ,1.5
+ ,2
+ ,2
+ ,3.5
+ ,3.9
+ ,3.9
+ ,2.4
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,3.5
+ ,3.5
+ ,2.9
+ ,3.1
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,2
+ ,2.5
+ ,2.7
+ ,3.1
+ ,2.5
+ ,1.5
+ ,2
+ ,2
+ ,2.3
+ ,2.8
+ ,2.7
+ ,3.1
+ ,2.5
+ ,1.5
+ ,1.5
+ ,2.5
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,2.5
+ ,2.5
+ ,2.6
+ ,3
+ ,2.5
+ ,2.8
+ ,2.7
+ ,3.1
+ ,3.1
+ ,2.4
+ ,3.2
+ ,3
+ ,2.5
+ ,2.8
+ ,2.7
+ ,2.7
+ ,2.5
+ ,2.8
+ ,3.2
+ ,3
+ ,2.5
+ ,2.8
+ ,2.8
+ ,2.1
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3
+ ,2.5
+ ,2.5
+ ,2.2
+ ,2
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3
+ ,3
+ ,2.7
+ ,1.8
+ ,2
+ ,2.4
+ ,2.8
+ ,3.2
+ ,3.2
+ ,3
+ ,1.1
+ ,1.8
+ ,2
+ ,2.4
+ ,2.8
+ ,2.8
+ ,3.2
+ ,-1.5
+ ,1.1
+ ,1.8
+ ,2
+ ,2.4
+ ,2.4
+ ,3
+ ,-3.7
+ ,-1.5
+ ,1.1
+ ,1.8
+ ,2)
+ ,dim=c(7
+ ,59)
+ ,dimnames=list(c('bbp'
+ ,'dnst'
+ ,'y1'
+ ,'y2'
+ ,'y3'
+ ,'y4'
+ ,'y5')
+ ,1:59))
> y <- array(NA,dim=c(7,59),dimnames=list(c('bbp','dnst','y1','y2','y3','y4','y5'),1:59))
> 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1.4 1.9 -0.7 -0.7 -2.9 -0.8 1.0 1 0 0 0 0 0 0 0 0 0 0 1
2 1.0 1.6 1.5 -0.7 -0.7 -2.9 -0.8 0 1 0 0 0 0 0 0 0 0 0 2
3 -0.8 0.0 3.0 1.5 -0.7 -0.7 -2.9 0 0 1 0 0 0 0 0 0 0 0 3
4 -2.9 -1.3 3.2 3.0 1.5 -0.7 -0.7 0 0 0 1 0 0 0 0 0 0 0 4
5 -0.7 -0.4 3.1 3.2 3.0 1.5 -0.7 0 0 0 0 1 0 0 0 0 0 0 5
6 -0.7 -0.3 3.9 3.1 3.2 3.0 1.5 0 0 0 0 0 1 0 0 0 0 0 6
7 1.5 1.4 1.0 3.9 3.1 3.2 3.0 0 0 0 0 0 0 1 0 0 0 0 7
8 3.0 2.6 1.3 1.0 3.9 3.1 3.2 0 0 0 0 0 0 0 1 0 0 0 8
9 3.2 2.8 0.8 1.3 1.0 3.9 3.1 0 0 0 0 0 0 0 0 1 0 0 9
10 3.1 2.6 1.2 0.8 1.3 1.0 3.9 0 0 0 0 0 0 0 0 0 1 0 10
11 3.9 3.4 2.9 1.2 0.8 1.3 1.0 0 0 0 0 0 0 0 0 0 0 1 11
12 1.0 1.7 3.9 2.9 1.2 0.8 1.3 0 0 0 0 0 0 0 0 0 0 0 12
13 1.3 1.2 4.5 3.9 2.9 1.2 0.8 1 0 0 0 0 0 0 0 0 0 0 13
14 0.8 0.0 4.5 4.5 3.9 2.9 1.2 0 1 0 0 0 0 0 0 0 0 0 14
15 1.2 0.0 3.3 4.5 4.5 3.9 2.9 0 0 1 0 0 0 0 0 0 0 0 15
16 2.9 1.6 2.0 3.3 4.5 4.5 3.9 0 0 0 1 0 0 0 0 0 0 0 16
17 3.9 2.5 1.5 2.0 3.3 4.5 4.5 0 0 0 0 1 0 0 0 0 0 0 17
18 4.5 3.2 1.0 1.5 2.0 3.3 4.5 0 0 0 0 0 1 0 0 0 0 0 18
19 4.5 3.4 2.1 1.0 1.5 2.0 3.3 0 0 0 0 0 0 1 0 0 0 0 19
20 3.3 2.3 3.0 2.1 1.0 1.5 2.0 0 0 0 0 0 0 0 1 0 0 0 20
21 2.0 1.9 4.0 3.0 2.1 1.0 1.5 0 0 0 0 0 0 0 0 1 0 0 21
22 1.5 1.7 5.1 4.0 3.0 2.1 1.0 0 0 0 0 0 0 0 0 0 1 0 22
23 1.0 1.9 4.5 5.1 4.0 3.0 2.1 0 0 0 0 0 0 0 0 0 0 1 23
24 2.1 3.3 4.2 4.5 5.1 4.0 3.0 0 0 0 0 0 0 0 0 0 0 0 24
25 3.0 3.8 3.3 4.2 4.5 5.1 4.0 1 0 0 0 0 0 0 0 0 0 0 25
26 4.0 4.4 2.7 3.3 4.2 4.5 5.1 0 1 0 0 0 0 0 0 0 0 0 26
27 5.1 4.5 1.8 2.7 3.3 4.2 4.5 0 0 1 0 0 0 0 0 0 0 0 27
28 4.5 3.5 1.4 1.8 2.7 3.3 4.2 0 0 0 1 0 0 0 0 0 0 0 28
29 4.2 3.0 0.5 1.4 1.8 2.7 3.3 0 0 0 0 1 0 0 0 0 0 0 29
30 3.3 2.8 -0.4 0.5 1.4 1.8 2.7 0 0 0 0 0 1 0 0 0 0 0 30
31 2.7 2.9 0.8 -0.4 0.5 1.4 1.8 0 0 0 0 0 0 1 0 0 0 0 31
32 1.8 2.6 0.7 0.8 -0.4 0.5 1.4 0 0 0 0 0 0 0 1 0 0 0 32
33 1.4 2.1 1.9 0.7 0.8 -0.4 0.5 0 0 0 0 0 0 0 0 1 0 0 33
34 0.5 1.5 2.0 1.9 0.7 0.8 -0.4 0 0 0 0 0 0 0 0 0 1 0 34
35 -0.4 1.1 1.1 2.0 1.9 0.7 0.8 0 0 0 0 0 0 0 0 0 0 1 35
36 0.8 1.5 0.9 1.1 2.0 1.9 0.7 0 0 0 0 0 0 0 0 0 0 0 36
37 0.7 1.7 0.4 0.9 1.1 2.0 1.9 1 0 0 0 0 0 0 0 0 0 0 37
38 1.9 2.3 0.7 0.4 0.9 1.1 2.0 0 1 0 0 0 0 0 0 0 0 0 38
39 2.0 2.3 2.1 0.7 0.4 0.9 1.1 0 0 1 0 0 0 0 0 0 0 0 39
40 1.1 1.9 2.8 2.1 0.7 0.4 0.9 0 0 0 1 0 0 0 0 0 0 0 40
41 0.9 2.0 3.9 2.8 2.1 0.7 0.4 0 0 0 0 1 0 0 0 0 0 0 41
42 0.4 1.6 3.5 3.9 2.8 2.1 0.7 0 0 0 0 0 1 0 0 0 0 0 42
43 0.7 1.2 2.0 3.5 3.9 2.8 2.1 0 0 0 0 0 0 1 0 0 0 0 43
44 2.1 1.9 2.0 2.0 3.5 3.9 2.8 0 0 0 0 0 0 0 1 0 0 0 44
45 2.8 2.1 1.5 2.0 2.0 3.5 3.9 0 0 0 0 0 0 0 0 1 0 0 45
46 3.9 2.4 2.5 1.5 2.0 2.0 3.5 0 0 0 0 0 0 0 0 0 1 0 46
47 3.5 2.9 3.1 2.5 1.5 2.0 2.0 0 0 0 0 0 0 0 0 0 0 1 47
48 2.0 2.5 2.7 3.1 2.5 1.5 2.0 0 0 0 0 0 0 0 0 0 0 0 48
49 2.0 2.3 2.8 2.7 3.1 2.5 1.5 1 0 0 0 0 0 0 0 0 0 0 49
50 1.5 2.5 2.5 2.8 2.7 3.1 2.5 0 1 0 0 0 0 0 0 0 0 0 50
51 2.5 2.6 3.0 2.5 2.8 2.7 3.1 0 0 1 0 0 0 0 0 0 0 0 51
52 3.1 2.4 3.2 3.0 2.5 2.8 2.7 0 0 0 1 0 0 0 0 0 0 0 52
53 2.7 2.5 2.8 3.2 3.0 2.5 2.8 0 0 0 0 1 0 0 0 0 0 0 53
54 2.8 2.1 2.4 2.8 3.2 3.0 2.5 0 0 0 0 0 1 0 0 0 0 0 54
55 2.5 2.2 2.0 2.4 2.8 3.2 3.0 0 0 0 0 0 0 1 0 0 0 0 55
56 3.0 2.7 1.8 2.0 2.4 2.8 3.2 0 0 0 0 0 0 0 1 0 0 0 56
57 3.2 3.0 1.1 1.8 2.0 2.4 2.8 0 0 0 0 0 0 0 0 1 0 0 57
58 2.8 3.2 -1.5 1.1 1.8 2.0 2.4 0 0 0 0 0 0 0 0 0 1 0 58
59 2.4 3.0 -3.7 -1.5 1.1 1.8 2.0 0 0 0 0 0 0 0 0 0 0 1 59
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) dnst y1 y2 y3 y4
-0.729217 0.771548 0.259591 -0.324288 -0.107508 0.144420
y5 M1 M2 M3 M4 M5
0.443965 0.098382 0.165758 0.616462 0.527373 0.806866
M6 M7 M8 M9 M10 M11
0.530454 0.516266 0.463812 0.469251 0.639151 0.504141
t
-0.004445
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.00539 -0.41005 -0.04167 0.39043 1.18454
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.729217 0.444382 -1.641 0.10865
dnst 0.771548 0.128777 5.991 4.86e-07 ***
y1 0.259591 0.106780 2.431 0.01963 *
y2 -0.324288 0.158240 -2.049 0.04703 *
y3 -0.107508 0.137156 -0.784 0.43775
y4 0.144420 0.143499 1.006 0.32026
y5 0.443965 0.127899 3.471 0.00126 **
M1 0.098382 0.426031 0.231 0.81855
M2 0.165758 0.422258 0.393 0.69674
M3 0.616462 0.429299 1.436 0.15879
M4 0.527373 0.433325 1.217 0.23072
M5 0.806866 0.421667 1.914 0.06286 .
M6 0.530454 0.432543 1.226 0.22723
M7 0.516266 0.430148 1.200 0.23712
M8 0.463812 0.435468 1.065 0.29322
M9 0.469251 0.436026 1.076 0.28829
M10 0.639151 0.425767 1.501 0.14116
M11 0.504141 0.421890 1.195 0.23914
t -0.004445 0.005431 -0.819 0.41791
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6166 on 40 degrees of freedom
Multiple R-squared: 0.8888, Adjusted R-squared: 0.8388
F-statistic: 17.77 on 18 and 40 DF, p-value: 1.056e-13
> 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,] 0.5999296 0.800140708 0.400070354
[2,] 0.8124835 0.375033007 0.187516504
[3,] 0.8326925 0.334614964 0.167307482
[4,] 0.9815786 0.036842720 0.018421360
[5,] 0.9965862 0.006827562 0.003413781
[6,] 0.9916481 0.016703732 0.008351866
[7,] 0.9868762 0.026247540 0.013123770
[8,] 0.9854928 0.029014460 0.014507230
[9,] 0.9762131 0.047573720 0.023786860
[10,] 0.9889686 0.022062735 0.011031367
[11,] 0.9792702 0.041459584 0.020729792
[12,] 0.9572651 0.085469861 0.042734931
[13,] 0.9342453 0.131509421 0.065754711
[14,] 0.8870595 0.225880998 0.112940499
[15,] 0.8439359 0.312128186 0.156064093
[16,] 0.7730835 0.453833099 0.226916550
> postscript(file="/var/www/html/rcomp/tmp/1zzsz1258645931.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2z5w21258645931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3aso21258645931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4trie1258645931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5xb9c1258645931.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 = 59
Frequency = 1
1 2 3 4 5 6
-0.11615085 0.42022091 0.34708846 -0.96205649 0.20285591 -1.00539441
7 8 9 10 11 12
0.20826889 -0.16734693 -0.27848030 -0.55970697 0.64133964 -0.16471933
13 14 15 16 17 18
0.94262938 1.18453686 0.61513125 0.59189387 -0.06471513 0.27725467
19 20 21 22 23 24
0.36063500 0.78493281 0.23727907 -0.07525128 -0.58846573 -0.60247194
25 26 27 28 29 30
-0.71316950 -0.80909484 0.01949872 0.29522089 0.59932663 0.42962053
31 32 33 34 35 36
-0.47168970 -0.45741410 -0.15801442 -0.18184119 -0.75701237 0.28485675
37 38 39 40 41 42
-0.64240891 -0.14420878 -0.38190922 -0.41421037 -0.69579404 -0.40588630
43 44 45 46 47 48
-0.12334951 -0.20560720 -0.12296943 0.55259254 0.68700141 0.48233452
49 50 51 52 53 54
0.52909988 -0.65145414 -0.59980921 0.48915211 -0.04167337 0.70440551
55 56 57 58 59
0.02613533 0.04543543 0.32218508 0.26420691 0.01713706
> postscript(file="/var/www/html/rcomp/tmp/6s0dx1258645931.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 = 59
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.11615085 NA
1 0.42022091 -0.11615085
2 0.34708846 0.42022091
3 -0.96205649 0.34708846
4 0.20285591 -0.96205649
5 -1.00539441 0.20285591
6 0.20826889 -1.00539441
7 -0.16734693 0.20826889
8 -0.27848030 -0.16734693
9 -0.55970697 -0.27848030
10 0.64133964 -0.55970697
11 -0.16471933 0.64133964
12 0.94262938 -0.16471933
13 1.18453686 0.94262938
14 0.61513125 1.18453686
15 0.59189387 0.61513125
16 -0.06471513 0.59189387
17 0.27725467 -0.06471513
18 0.36063500 0.27725467
19 0.78493281 0.36063500
20 0.23727907 0.78493281
21 -0.07525128 0.23727907
22 -0.58846573 -0.07525128
23 -0.60247194 -0.58846573
24 -0.71316950 -0.60247194
25 -0.80909484 -0.71316950
26 0.01949872 -0.80909484
27 0.29522089 0.01949872
28 0.59932663 0.29522089
29 0.42962053 0.59932663
30 -0.47168970 0.42962053
31 -0.45741410 -0.47168970
32 -0.15801442 -0.45741410
33 -0.18184119 -0.15801442
34 -0.75701237 -0.18184119
35 0.28485675 -0.75701237
36 -0.64240891 0.28485675
37 -0.14420878 -0.64240891
38 -0.38190922 -0.14420878
39 -0.41421037 -0.38190922
40 -0.69579404 -0.41421037
41 -0.40588630 -0.69579404
42 -0.12334951 -0.40588630
43 -0.20560720 -0.12334951
44 -0.12296943 -0.20560720
45 0.55259254 -0.12296943
46 0.68700141 0.55259254
47 0.48233452 0.68700141
48 0.52909988 0.48233452
49 -0.65145414 0.52909988
50 -0.59980921 -0.65145414
51 0.48915211 -0.59980921
52 -0.04167337 0.48915211
53 0.70440551 -0.04167337
54 0.02613533 0.70440551
55 0.04543543 0.02613533
56 0.32218508 0.04543543
57 0.26420691 0.32218508
58 0.01713706 0.26420691
59 NA 0.01713706
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.42022091 -0.11615085
[2,] 0.34708846 0.42022091
[3,] -0.96205649 0.34708846
[4,] 0.20285591 -0.96205649
[5,] -1.00539441 0.20285591
[6,] 0.20826889 -1.00539441
[7,] -0.16734693 0.20826889
[8,] -0.27848030 -0.16734693
[9,] -0.55970697 -0.27848030
[10,] 0.64133964 -0.55970697
[11,] -0.16471933 0.64133964
[12,] 0.94262938 -0.16471933
[13,] 1.18453686 0.94262938
[14,] 0.61513125 1.18453686
[15,] 0.59189387 0.61513125
[16,] -0.06471513 0.59189387
[17,] 0.27725467 -0.06471513
[18,] 0.36063500 0.27725467
[19,] 0.78493281 0.36063500
[20,] 0.23727907 0.78493281
[21,] -0.07525128 0.23727907
[22,] -0.58846573 -0.07525128
[23,] -0.60247194 -0.58846573
[24,] -0.71316950 -0.60247194
[25,] -0.80909484 -0.71316950
[26,] 0.01949872 -0.80909484
[27,] 0.29522089 0.01949872
[28,] 0.59932663 0.29522089
[29,] 0.42962053 0.59932663
[30,] -0.47168970 0.42962053
[31,] -0.45741410 -0.47168970
[32,] -0.15801442 -0.45741410
[33,] -0.18184119 -0.15801442
[34,] -0.75701237 -0.18184119
[35,] 0.28485675 -0.75701237
[36,] -0.64240891 0.28485675
[37,] -0.14420878 -0.64240891
[38,] -0.38190922 -0.14420878
[39,] -0.41421037 -0.38190922
[40,] -0.69579404 -0.41421037
[41,] -0.40588630 -0.69579404
[42,] -0.12334951 -0.40588630
[43,] -0.20560720 -0.12334951
[44,] -0.12296943 -0.20560720
[45,] 0.55259254 -0.12296943
[46,] 0.68700141 0.55259254
[47,] 0.48233452 0.68700141
[48,] 0.52909988 0.48233452
[49,] -0.65145414 0.52909988
[50,] -0.59980921 -0.65145414
[51,] 0.48915211 -0.59980921
[52,] -0.04167337 0.48915211
[53,] 0.70440551 -0.04167337
[54,] 0.02613533 0.70440551
[55,] 0.04543543 0.02613533
[56,] 0.32218508 0.04543543
[57,] 0.26420691 0.32218508
[58,] 0.01713706 0.26420691
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.42022091 -0.11615085
2 0.34708846 0.42022091
3 -0.96205649 0.34708846
4 0.20285591 -0.96205649
5 -1.00539441 0.20285591
6 0.20826889 -1.00539441
7 -0.16734693 0.20826889
8 -0.27848030 -0.16734693
9 -0.55970697 -0.27848030
10 0.64133964 -0.55970697
11 -0.16471933 0.64133964
12 0.94262938 -0.16471933
13 1.18453686 0.94262938
14 0.61513125 1.18453686
15 0.59189387 0.61513125
16 -0.06471513 0.59189387
17 0.27725467 -0.06471513
18 0.36063500 0.27725467
19 0.78493281 0.36063500
20 0.23727907 0.78493281
21 -0.07525128 0.23727907
22 -0.58846573 -0.07525128
23 -0.60247194 -0.58846573
24 -0.71316950 -0.60247194
25 -0.80909484 -0.71316950
26 0.01949872 -0.80909484
27 0.29522089 0.01949872
28 0.59932663 0.29522089
29 0.42962053 0.59932663
30 -0.47168970 0.42962053
31 -0.45741410 -0.47168970
32 -0.15801442 -0.45741410
33 -0.18184119 -0.15801442
34 -0.75701237 -0.18184119
35 0.28485675 -0.75701237
36 -0.64240891 0.28485675
37 -0.14420878 -0.64240891
38 -0.38190922 -0.14420878
39 -0.41421037 -0.38190922
40 -0.69579404 -0.41421037
41 -0.40588630 -0.69579404
42 -0.12334951 -0.40588630
43 -0.20560720 -0.12334951
44 -0.12296943 -0.20560720
45 0.55259254 -0.12296943
46 0.68700141 0.55259254
47 0.48233452 0.68700141
48 0.52909988 0.48233452
49 -0.65145414 0.52909988
50 -0.59980921 -0.65145414
51 0.48915211 -0.59980921
52 -0.04167337 0.48915211
53 0.70440551 -0.04167337
54 0.02613533 0.70440551
55 0.04543543 0.02613533
56 0.32218508 0.04543543
57 0.26420691 0.32218508
58 0.01713706 0.26420691
> 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/7cuh11258645931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ytyu1258645931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/967bc1258645931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10qia61258645931.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11iger1258645931.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/1271tq1258645931.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/1345nr1258645931.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/144gse1258645931.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/15j6en1258645931.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/16svq61258645931.tab")
+ }
>
> system("convert tmp/1zzsz1258645931.ps tmp/1zzsz1258645931.png")
> system("convert tmp/2z5w21258645931.ps tmp/2z5w21258645931.png")
> system("convert tmp/3aso21258645931.ps tmp/3aso21258645931.png")
> system("convert tmp/4trie1258645931.ps tmp/4trie1258645931.png")
> system("convert tmp/5xb9c1258645931.ps tmp/5xb9c1258645931.png")
> system("convert tmp/6s0dx1258645931.ps tmp/6s0dx1258645931.png")
> system("convert tmp/7cuh11258645931.ps tmp/7cuh11258645931.png")
> system("convert tmp/8ytyu1258645931.ps tmp/8ytyu1258645931.png")
> system("convert tmp/967bc1258645931.ps tmp/967bc1258645931.png")
> system("convert tmp/10qia61258645931.ps tmp/10qia61258645931.png")
>
>
> proc.time()
user system elapsed
2.330 1.534 2.773