R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3.75
+ ,0
+ ,3.51
+ ,3.37
+ ,3.21
+ ,3
+ ,4.11
+ ,0
+ ,3.75
+ ,3.51
+ ,3.37
+ ,3.21
+ ,4.25
+ ,0
+ ,4.11
+ ,3.75
+ ,3.51
+ ,3.37
+ ,4.25
+ ,0
+ ,4.25
+ ,4.11
+ ,3.75
+ ,3.51
+ ,4.5
+ ,0
+ ,4.25
+ ,4.25
+ ,4.11
+ ,3.75
+ ,4.7
+ ,0
+ ,4.5
+ ,4.25
+ ,4.25
+ ,4.11
+ ,4.75
+ ,0
+ ,4.7
+ ,4.5
+ ,4.25
+ ,4.25
+ ,4.75
+ ,0
+ ,4.75
+ ,4.7
+ ,4.5
+ ,4.25
+ ,4.75
+ ,0
+ ,4.75
+ ,4.75
+ ,4.7
+ ,4.5
+ ,4.75
+ ,0
+ ,4.75
+ ,4.75
+ ,4.75
+ ,4.7
+ ,4.75
+ ,0
+ ,4.75
+ ,4.75
+ ,4.75
+ ,4.75
+ ,4.75
+ ,0
+ ,4.75
+ ,4.75
+ ,4.75
+ ,4.75
+ ,4.58
+ ,0
+ ,4.75
+ ,4.75
+ ,4.75
+ ,4.75
+ ,4.5
+ ,0
+ ,4.58
+ ,4.75
+ ,4.75
+ ,4.75
+ ,4.5
+ ,0
+ ,4.5
+ ,4.58
+ ,4.75
+ ,4.75
+ ,4.49
+ ,0
+ ,4.5
+ ,4.5
+ ,4.58
+ ,4.75
+ ,4.03
+ ,0
+ ,4.49
+ ,4.5
+ ,4.5
+ ,4.58
+ ,3.75
+ ,0
+ ,4.03
+ ,4.49
+ ,4.5
+ ,4.5
+ ,3.39
+ ,0
+ ,3.75
+ ,4.03
+ ,4.49
+ ,4.5
+ ,3.25
+ ,0
+ ,3.39
+ ,3.75
+ ,4.03
+ ,4.49
+ ,3.25
+ ,0
+ ,3.25
+ ,3.39
+ ,3.75
+ ,4.03
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.39
+ ,3.75
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.39
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,2.85
+ ,0
+ ,3.25
+ ,3.25
+ ,3.25
+ ,3.25
+ ,2.75
+ ,0
+ ,2.85
+ ,3.25
+ ,3.25
+ ,3.25
+ ,2.75
+ ,0
+ ,2.75
+ ,2.85
+ ,3.25
+ ,3.25
+ ,2.55
+ ,0
+ ,2.75
+ ,2.75
+ ,2.85
+ ,3.25
+ ,2.5
+ ,0
+ ,2.55
+ ,2.75
+ ,2.75
+ ,2.85
+ ,2.5
+ ,0
+ ,2.5
+ ,2.55
+ ,2.75
+ ,2.75
+ ,2.1
+ ,0
+ ,2.5
+ ,2.5
+ ,2.55
+ ,2.75
+ ,2
+ ,0
+ ,2.1
+ ,2.5
+ ,2.5
+ ,2.55
+ ,2
+ ,0
+ ,2
+ ,2.1
+ ,2.5
+ ,2.5
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2.1
+ ,2.5
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2.1
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2.21
+ ,0
+ ,2
+ ,2
+ ,2
+ ,2
+ ,2.25
+ ,0
+ ,2.21
+ ,2
+ ,2
+ ,2
+ ,2.25
+ ,0
+ ,2.25
+ ,2.21
+ ,2
+ ,2
+ ,2.45
+ ,0
+ ,2.25
+ ,2.25
+ ,2.21
+ ,2
+ ,2.5
+ ,0
+ ,2.45
+ ,2.25
+ ,2.25
+ ,2.21
+ ,2.5
+ ,0
+ ,2.5
+ ,2.45
+ ,2.25
+ ,2.25
+ ,2.64
+ ,0
+ ,2.5
+ ,2.5
+ ,2.45
+ ,2.25
+ ,2.75
+ ,0
+ ,2.64
+ ,2.5
+ ,2.5
+ ,2.45
+ ,2.93
+ ,0
+ ,2.75
+ ,2.64
+ ,2.5
+ ,2.5
+ ,3
+ ,0
+ ,2.93
+ ,2.75
+ ,2.64
+ ,2.5
+ ,3.17
+ ,0
+ ,3
+ ,2.93
+ ,2.75
+ ,2.64
+ ,3.25
+ ,0
+ ,3.17
+ ,3
+ ,2.93
+ ,2.75
+ ,3.39
+ ,0
+ ,3.25
+ ,3.17
+ ,3
+ ,2.93
+ ,3.5
+ ,0
+ ,3.39
+ ,3.25
+ ,3.17
+ ,3
+ ,3.5
+ ,0
+ ,3.5
+ ,3.39
+ ,3.25
+ ,3.17
+ ,3.65
+ ,0
+ ,3.5
+ ,3.5
+ ,3.39
+ ,3.25
+ ,3.75
+ ,0
+ ,3.65
+ ,3.5
+ ,3.5
+ ,3.39
+ ,3.75
+ ,0
+ ,3.75
+ ,3.65
+ ,3.5
+ ,3.5
+ ,3.9
+ ,0
+ ,3.75
+ ,3.75
+ ,3.65
+ ,3.5
+ ,4
+ ,0
+ ,3.9
+ ,3.75
+ ,3.75
+ ,3.65
+ ,4
+ ,0
+ ,4
+ ,3.9
+ ,3.75
+ ,3.75
+ ,4
+ ,0
+ ,4
+ ,4
+ ,3.9
+ ,3.75
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,3.9
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4.18
+ ,0
+ ,4
+ ,4
+ ,4
+ ,4
+ ,4.25
+ ,0
+ ,4.18
+ ,4
+ ,4
+ ,4
+ ,4.25
+ ,0
+ ,4.25
+ ,4.18
+ ,4
+ ,4
+ ,3.97
+ ,1
+ ,4.25
+ ,4.25
+ ,4.18
+ ,4
+ ,3.42
+ ,1
+ ,3.97
+ ,4.25
+ ,4.25
+ ,4.18
+ ,2.75
+ ,1
+ ,3.42
+ ,3.97
+ ,4.25
+ ,4.25
+ ,2.31
+ ,1
+ ,2.75
+ ,3.42
+ ,3.97
+ ,4.25
+ ,2
+ ,1
+ ,2.31
+ ,2.75
+ ,3.42
+ ,3.97
+ ,1.66
+ ,1
+ ,2
+ ,2.31
+ ,2.75
+ ,3.42
+ ,1.31
+ ,1
+ ,1.66
+ ,2
+ ,2.31
+ ,2.75
+ ,1.09
+ ,1
+ ,1.31
+ ,1.66
+ ,2
+ ,2.31
+ ,1
+ ,1
+ ,1.09
+ ,1.31
+ ,1.66
+ ,2
+ ,1
+ ,1
+ ,1
+ ,1.09
+ ,1.31
+ ,1.66
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1.09
+ ,1.31
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1.09
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1)
+ ,dim=c(6
+ ,114)
+ ,dimnames=list(c('Y(t)'
+ ,'X(t)'
+ ,'Y(t-1)'
+ ,'Y(t-2)'
+ ,'Y(t-3)'
+ ,'Y(t-4)')
+ ,1:114))
> y <- array(NA,dim=c(6,114),dimnames=list(c('Y(t)','X(t)','Y(t-1)','Y(t-2)','Y(t-3)','Y(t-4)'),1:114))
> 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 = 'Do not include Seasonal 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(t) X(t) Y(t-1) Y(t-2) Y(t-3) Y(t-4)
1 3.75 0 3.51 3.37 3.21 3.00
2 4.11 0 3.75 3.51 3.37 3.21
3 4.25 0 4.11 3.75 3.51 3.37
4 4.25 0 4.25 4.11 3.75 3.51
5 4.50 0 4.25 4.25 4.11 3.75
6 4.70 0 4.50 4.25 4.25 4.11
7 4.75 0 4.70 4.50 4.25 4.25
8 4.75 0 4.75 4.70 4.50 4.25
9 4.75 0 4.75 4.75 4.70 4.50
10 4.75 0 4.75 4.75 4.75 4.70
11 4.75 0 4.75 4.75 4.75 4.75
12 4.75 0 4.75 4.75 4.75 4.75
13 4.58 0 4.75 4.75 4.75 4.75
14 4.50 0 4.58 4.75 4.75 4.75
15 4.50 0 4.50 4.58 4.75 4.75
16 4.49 0 4.50 4.50 4.58 4.75
17 4.03 0 4.49 4.50 4.50 4.58
18 3.75 0 4.03 4.49 4.50 4.50
19 3.39 0 3.75 4.03 4.49 4.50
20 3.25 0 3.39 3.75 4.03 4.49
21 3.25 0 3.25 3.39 3.75 4.03
22 3.25 0 3.25 3.25 3.39 3.75
23 3.25 0 3.25 3.25 3.25 3.39
24 3.25 0 3.25 3.25 3.25 3.25
25 3.25 0 3.25 3.25 3.25 3.25
26 3.25 0 3.25 3.25 3.25 3.25
27 3.25 0 3.25 3.25 3.25 3.25
28 3.25 0 3.25 3.25 3.25 3.25
29 3.25 0 3.25 3.25 3.25 3.25
30 3.25 0 3.25 3.25 3.25 3.25
31 3.25 0 3.25 3.25 3.25 3.25
32 2.85 0 3.25 3.25 3.25 3.25
33 2.75 0 2.85 3.25 3.25 3.25
34 2.75 0 2.75 2.85 3.25 3.25
35 2.55 0 2.75 2.75 2.85 3.25
36 2.50 0 2.55 2.75 2.75 2.85
37 2.50 0 2.50 2.55 2.75 2.75
38 2.10 0 2.50 2.50 2.55 2.75
39 2.00 0 2.10 2.50 2.50 2.55
40 2.00 0 2.00 2.10 2.50 2.50
41 2.00 0 2.00 2.00 2.10 2.50
42 2.00 0 2.00 2.00 2.00 2.10
43 2.00 0 2.00 2.00 2.00 2.00
44 2.00 0 2.00 2.00 2.00 2.00
45 2.00 0 2.00 2.00 2.00 2.00
46 2.00 0 2.00 2.00 2.00 2.00
47 2.00 0 2.00 2.00 2.00 2.00
48 2.00 0 2.00 2.00 2.00 2.00
49 2.00 0 2.00 2.00 2.00 2.00
50 2.00 0 2.00 2.00 2.00 2.00
51 2.00 0 2.00 2.00 2.00 2.00
52 2.00 0 2.00 2.00 2.00 2.00
53 2.00 0 2.00 2.00 2.00 2.00
54 2.00 0 2.00 2.00 2.00 2.00
55 2.00 0 2.00 2.00 2.00 2.00
56 2.00 0 2.00 2.00 2.00 2.00
57 2.00 0 2.00 2.00 2.00 2.00
58 2.00 0 2.00 2.00 2.00 2.00
59 2.00 0 2.00 2.00 2.00 2.00
60 2.00 0 2.00 2.00 2.00 2.00
61 2.00 0 2.00 2.00 2.00 2.00
62 2.00 0 2.00 2.00 2.00 2.00
63 2.00 0 2.00 2.00 2.00 2.00
64 2.00 0 2.00 2.00 2.00 2.00
65 2.00 0 2.00 2.00 2.00 2.00
66 2.00 0 2.00 2.00 2.00 2.00
67 2.00 0 2.00 2.00 2.00 2.00
68 2.21 0 2.00 2.00 2.00 2.00
69 2.25 0 2.21 2.00 2.00 2.00
70 2.25 0 2.25 2.21 2.00 2.00
71 2.45 0 2.25 2.25 2.21 2.00
72 2.50 0 2.45 2.25 2.25 2.21
73 2.50 0 2.50 2.45 2.25 2.25
74 2.64 0 2.50 2.50 2.45 2.25
75 2.75 0 2.64 2.50 2.50 2.45
76 2.93 0 2.75 2.64 2.50 2.50
77 3.00 0 2.93 2.75 2.64 2.50
78 3.17 0 3.00 2.93 2.75 2.64
79 3.25 0 3.17 3.00 2.93 2.75
80 3.39 0 3.25 3.17 3.00 2.93
81 3.50 0 3.39 3.25 3.17 3.00
82 3.50 0 3.50 3.39 3.25 3.17
83 3.65 0 3.50 3.50 3.39 3.25
84 3.75 0 3.65 3.50 3.50 3.39
85 3.75 0 3.75 3.65 3.50 3.50
86 3.90 0 3.75 3.75 3.65 3.50
87 4.00 0 3.90 3.75 3.75 3.65
88 4.00 0 4.00 3.90 3.75 3.75
89 4.00 0 4.00 4.00 3.90 3.75
90 4.00 0 4.00 4.00 4.00 3.90
91 4.00 0 4.00 4.00 4.00 4.00
92 4.00 0 4.00 4.00 4.00 4.00
93 4.00 0 4.00 4.00 4.00 4.00
94 4.00 0 4.00 4.00 4.00 4.00
95 4.00 0 4.00 4.00 4.00 4.00
96 4.00 0 4.00 4.00 4.00 4.00
97 4.00 0 4.00 4.00 4.00 4.00
98 4.00 0 4.00 4.00 4.00 4.00
99 4.18 0 4.00 4.00 4.00 4.00
100 4.25 0 4.18 4.00 4.00 4.00
101 4.25 0 4.25 4.18 4.00 4.00
102 3.97 1 4.25 4.25 4.18 4.00
103 3.42 1 3.97 4.25 4.25 4.18
104 2.75 1 3.42 3.97 4.25 4.25
105 2.31 1 2.75 3.42 3.97 4.25
106 2.00 1 2.31 2.75 3.42 3.97
107 1.66 1 2.00 2.31 2.75 3.42
108 1.31 1 1.66 2.00 2.31 2.75
109 1.09 1 1.31 1.66 2.00 2.31
110 1.00 1 1.09 1.31 1.66 2.00
111 1.00 1 1.00 1.09 1.31 1.66
112 1.00 1 1.00 1.00 1.09 1.31
113 1.00 1 1.00 1.00 1.00 1.09
114 1.00 1 1.00 1.00 1.00 1.00
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `X(t)` `Y(t-1)` `Y(t-2)` `Y(t-3)` `Y(t-4)`
0.1035 -0.1195 1.5219 -0.6194 0.2548 -0.1900
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.397131 -0.038027 0.002869 0.054341 0.226248
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.10346 0.03524 2.936 0.004060 **
`X(t)` -0.11945 0.03938 -3.033 0.003030 **
`Y(t-1)` 1.52191 0.09409 16.175 < 2e-16 ***
`Y(t-2)` -0.61940 0.17246 -3.592 0.000496 ***
`Y(t-3)` 0.25479 0.17186 1.483 0.141114
`Y(t-4)` -0.19002 0.09177 -2.071 0.040770 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1071 on 108 degrees of freedom
Multiple R-squared: 0.99, Adjusted R-squared: 0.9896
F-statistic: 2142 on 5 and 108 DF, p-value: < 2.2e-16
> 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,] 0.5961928 8.076144e-01 4.038072e-01
[2,] 0.4643812 9.287624e-01 5.356188e-01
[3,] 0.3219265 6.438530e-01 6.780735e-01
[4,] 0.2096482 4.192964e-01 7.903518e-01
[5,] 0.3645149 7.290299e-01 6.354851e-01
[6,] 0.2645652 5.291305e-01 7.354348e-01
[7,] 0.2041205 4.082410e-01 7.958795e-01
[8,] 0.1451660 2.903320e-01 8.548340e-01
[9,] 0.9581990 8.360197e-02 4.180098e-02
[10,] 0.9359320 1.281360e-01 6.406802e-02
[11,] 0.9919790 1.604205e-02 8.021027e-03
[12,] 0.9961582 7.683577e-03 3.841789e-03
[13,] 0.9945626 1.087482e-02 5.437408e-03
[14,] 0.9913770 1.724595e-02 8.622977e-03
[15,] 0.9877239 2.455217e-02 1.227608e-02
[16,] 0.9841288 3.174250e-02 1.587125e-02
[17,] 0.9786753 4.264940e-02 2.132470e-02
[18,] 0.9709876 5.802479e-02 2.901240e-02
[19,] 0.9605671 7.886577e-02 3.943289e-02
[20,] 0.9468528 1.062944e-01 5.314719e-02
[21,] 0.9292689 1.414623e-01 7.073113e-02
[22,] 0.9072749 1.854502e-01 9.272510e-02
[23,] 0.8804200 2.391599e-01 1.195800e-01
[24,] 0.9985718 2.856326e-03 1.428163e-03
[25,] 0.9984331 3.133819e-03 1.566910e-03
[26,] 0.9974797 5.040614e-03 2.520307e-03
[27,] 0.9980898 3.820397e-03 1.910199e-03
[28,] 0.9972525 5.495022e-03 2.747511e-03
[29,] 0.9957494 8.501127e-03 4.250564e-03
[30,] 0.9999912 1.767072e-05 8.835358e-06
[31,] 0.9999918 1.646445e-05 8.232224e-06
[32,] 0.9999846 3.088643e-05 1.544321e-05
[33,] 0.9999784 4.320628e-05 2.160314e-05
[34,] 0.9999620 7.605173e-05 3.802587e-05
[35,] 0.9999361 1.277892e-04 6.389460e-05
[36,] 0.9998942 2.116663e-04 1.058332e-04
[37,] 0.9998275 3.450491e-04 1.725246e-04
[38,] 0.9997235 5.530464e-04 2.765232e-04
[39,] 0.9995645 8.710335e-04 4.355167e-04
[40,] 0.9993262 1.347525e-03 6.737627e-04
[41,] 0.9989764 2.047201e-03 1.023600e-03
[42,] 0.9984731 3.053727e-03 1.526863e-03
[43,] 0.9977641 4.471846e-03 2.235923e-03
[44,] 0.9967860 6.427986e-03 3.213993e-03
[45,] 0.9954658 9.068439e-03 4.534220e-03
[46,] 0.9937230 1.255398e-02 6.276991e-03
[47,] 0.9914752 1.704969e-02 8.524843e-03
[48,] 0.9886457 2.270865e-02 1.135433e-02
[49,] 0.9851758 2.964846e-02 1.482423e-02
[50,] 0.9810403 3.791938e-02 1.895969e-02
[51,] 0.9762682 4.746365e-02 2.373183e-02
[52,] 0.9709671 5.806572e-02 2.903286e-02
[53,] 0.9653531 6.929375e-02 3.464688e-02
[54,] 0.9597829 8.043422e-02 4.021711e-02
[55,] 0.9547881 9.042388e-02 4.521194e-02
[56,] 0.9511046 9.779076e-02 4.889538e-02
[57,] 0.9496824 1.006352e-01 5.031762e-02
[58,] 0.9516339 9.673211e-02 4.836605e-02
[59,] 0.9580204 8.395923e-02 4.197962e-02
[60,] 0.9641023 7.179534e-02 3.589767e-02
[61,] 0.9853401 2.931975e-02 1.465987e-02
[62,] 0.9876897 2.462054e-02 1.231027e-02
[63,] 0.9857558 2.848837e-02 1.424418e-02
[64,] 0.9955586 8.882816e-03 4.441408e-03
[65,] 0.9972451 5.509748e-03 2.754874e-03
[66,] 0.9958159 8.368145e-03 4.184073e-03
[67,] 0.9964382 7.123586e-03 3.561793e-03
[68,] 0.9953222 9.355626e-03 4.677813e-03
[69,] 0.9977820 4.436070e-03 2.218035e-03
[70,] 0.9969813 6.037469e-03 3.018735e-03
[71,] 0.9984792 3.041504e-03 1.520752e-03
[72,] 0.9977717 4.456684e-03 2.228342e-03
[73,] 0.9973156 5.368739e-03 2.684369e-03
[74,] 0.9982925 3.414912e-03 1.707456e-03
[75,] 0.9984952 3.009520e-03 1.504760e-03
[76,] 0.9985690 2.862076e-03 1.431038e-03
[77,] 0.9979816 4.036761e-03 2.018381e-03
[78,] 0.9990908 1.818332e-03 9.091662e-04
[79,] 0.9989132 2.173549e-03 1.086774e-03
[80,] 0.9979995 4.000928e-03 2.000464e-03
[81,] 0.9962757 7.448570e-03 3.724285e-03
[82,] 0.9942200 1.155994e-02 5.779970e-03
[83,] 0.9899347 2.013051e-02 1.006526e-02
[84,] 0.9830132 3.397369e-02 1.698684e-02
[85,] 0.9722637 5.547267e-02 2.773633e-02
[86,] 0.9562804 8.743915e-02 4.371958e-02
[87,] 0.9336818 1.326364e-01 6.631821e-02
[88,] 0.9036814 1.926372e-01 9.631861e-02
[89,] 0.8673888 2.652224e-01 1.326112e-01
[90,] 0.8312745 3.374511e-01 1.687255e-01
[91,] 0.9401902 1.196197e-01 5.980983e-02
[92,] 0.9700728 5.985444e-02 2.992722e-02
[93,] 0.9400161 1.199678e-01 5.998388e-02
[94,] 0.9136243 1.727514e-01 8.637570e-02
[95,] 0.9126025 1.747949e-01 8.739747e-02
[96,] 0.9357780 1.284440e-01 6.422202e-02
[97,] 0.9822251 3.554981e-02 1.777491e-02
> postscript(file="/var/www/html/rcomp/tmp/1iiti1258808125.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25d2b1258808125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3w1091258808125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u9lo1258808125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5u5ch1258808125.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 = 114
Frequency = 1
1 2 3 4 5 6
0.144188176 0.224783352 -0.039715556 -0.064347052 0.226247577 0.078506715
7 8 9 10 11 12
0.005577467 -0.010336631 0.017179313 0.042443415 0.051944355 0.051944355
13 14 15 16 17 18
-0.118055645 0.060668434 0.077123358 0.060886392 -0.395814288 0.002867153
19 20 21 22 23 24
-0.213374013 0.136385625 0.110402833 0.062207451 0.029471726 0.002869094
25 26 27 28 29 30
0.002869094 0.002869094 0.002869094 0.002869094 0.002869094 0.002869094
31 32 33 34 35 36
0.002869094 -0.397130906 0.111631633 0.016063246 -0.143959243 0.059893823
37 38 39 40 41 42
-0.006892251 -0.386903495 0.096594942 -0.008474385 0.031503126 -0.019025078
43 44 45 46 47 48
-0.038026958 -0.038026958 -0.038026958 -0.038026958 -0.038026958 -0.038026958
49 50 51 52 53 54
-0.038026958 -0.038026958 -0.038026958 -0.038026958 -0.038026958 -0.038026958
55 56 57 58 59 60
-0.038026958 -0.038026958 -0.038026958 -0.038026958 -0.038026958 -0.038026958
61 62 63 64 65 66
-0.038026958 -0.038026958 -0.038026958 -0.038026958 -0.038026958 -0.038026958
67 68 69 70 71 72
-0.038026958 0.171973042 -0.107627291 -0.038430058 0.132839279 -0.091829769
73 74 75 76 77 78
-0.036444824 0.083566421 0.005763634 0.114570533 -0.056909922 0.116623578
79 80 81 82 83 84
-0.043703374 0.096209565 0.012680958 -0.056093340 0.141570852 0.011860283
85 86 87 88 89 90
-0.026518650 0.147202131 0.021939682 -0.018339440 0.005381341 0.008404844
91 92 93 94 95 96
0.027406724 0.027406724 0.027406724 0.027406724 0.027406724 0.027406724
97 98 99 100 101 102
0.027406724 0.027406724 0.207406724 0.003463582 0.008421697 -0.154630447
103 104 105 106 107 108
-0.262128807 -0.255210314 0.055140370 0.086713779 0.012170904 -0.027597783
109 110 111 112 113 114
0.069851880 0.125605980 0.150881306 0.084683442 0.065810691 0.048708999
> postscript(file="/var/www/html/rcomp/tmp/63da71258808125.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 = 114
Frequency = 1
lag(myerror, k = 1) myerror
0 0.144188176 NA
1 0.224783352 0.144188176
2 -0.039715556 0.224783352
3 -0.064347052 -0.039715556
4 0.226247577 -0.064347052
5 0.078506715 0.226247577
6 0.005577467 0.078506715
7 -0.010336631 0.005577467
8 0.017179313 -0.010336631
9 0.042443415 0.017179313
10 0.051944355 0.042443415
11 0.051944355 0.051944355
12 -0.118055645 0.051944355
13 0.060668434 -0.118055645
14 0.077123358 0.060668434
15 0.060886392 0.077123358
16 -0.395814288 0.060886392
17 0.002867153 -0.395814288
18 -0.213374013 0.002867153
19 0.136385625 -0.213374013
20 0.110402833 0.136385625
21 0.062207451 0.110402833
22 0.029471726 0.062207451
23 0.002869094 0.029471726
24 0.002869094 0.002869094
25 0.002869094 0.002869094
26 0.002869094 0.002869094
27 0.002869094 0.002869094
28 0.002869094 0.002869094
29 0.002869094 0.002869094
30 0.002869094 0.002869094
31 -0.397130906 0.002869094
32 0.111631633 -0.397130906
33 0.016063246 0.111631633
34 -0.143959243 0.016063246
35 0.059893823 -0.143959243
36 -0.006892251 0.059893823
37 -0.386903495 -0.006892251
38 0.096594942 -0.386903495
39 -0.008474385 0.096594942
40 0.031503126 -0.008474385
41 -0.019025078 0.031503126
42 -0.038026958 -0.019025078
43 -0.038026958 -0.038026958
44 -0.038026958 -0.038026958
45 -0.038026958 -0.038026958
46 -0.038026958 -0.038026958
47 -0.038026958 -0.038026958
48 -0.038026958 -0.038026958
49 -0.038026958 -0.038026958
50 -0.038026958 -0.038026958
51 -0.038026958 -0.038026958
52 -0.038026958 -0.038026958
53 -0.038026958 -0.038026958
54 -0.038026958 -0.038026958
55 -0.038026958 -0.038026958
56 -0.038026958 -0.038026958
57 -0.038026958 -0.038026958
58 -0.038026958 -0.038026958
59 -0.038026958 -0.038026958
60 -0.038026958 -0.038026958
61 -0.038026958 -0.038026958
62 -0.038026958 -0.038026958
63 -0.038026958 -0.038026958
64 -0.038026958 -0.038026958
65 -0.038026958 -0.038026958
66 -0.038026958 -0.038026958
67 0.171973042 -0.038026958
68 -0.107627291 0.171973042
69 -0.038430058 -0.107627291
70 0.132839279 -0.038430058
71 -0.091829769 0.132839279
72 -0.036444824 -0.091829769
73 0.083566421 -0.036444824
74 0.005763634 0.083566421
75 0.114570533 0.005763634
76 -0.056909922 0.114570533
77 0.116623578 -0.056909922
78 -0.043703374 0.116623578
79 0.096209565 -0.043703374
80 0.012680958 0.096209565
81 -0.056093340 0.012680958
82 0.141570852 -0.056093340
83 0.011860283 0.141570852
84 -0.026518650 0.011860283
85 0.147202131 -0.026518650
86 0.021939682 0.147202131
87 -0.018339440 0.021939682
88 0.005381341 -0.018339440
89 0.008404844 0.005381341
90 0.027406724 0.008404844
91 0.027406724 0.027406724
92 0.027406724 0.027406724
93 0.027406724 0.027406724
94 0.027406724 0.027406724
95 0.027406724 0.027406724
96 0.027406724 0.027406724
97 0.027406724 0.027406724
98 0.207406724 0.027406724
99 0.003463582 0.207406724
100 0.008421697 0.003463582
101 -0.154630447 0.008421697
102 -0.262128807 -0.154630447
103 -0.255210314 -0.262128807
104 0.055140370 -0.255210314
105 0.086713779 0.055140370
106 0.012170904 0.086713779
107 -0.027597783 0.012170904
108 0.069851880 -0.027597783
109 0.125605980 0.069851880
110 0.150881306 0.125605980
111 0.084683442 0.150881306
112 0.065810691 0.084683442
113 0.048708999 0.065810691
114 NA 0.048708999
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.224783352 0.144188176
[2,] -0.039715556 0.224783352
[3,] -0.064347052 -0.039715556
[4,] 0.226247577 -0.064347052
[5,] 0.078506715 0.226247577
[6,] 0.005577467 0.078506715
[7,] -0.010336631 0.005577467
[8,] 0.017179313 -0.010336631
[9,] 0.042443415 0.017179313
[10,] 0.051944355 0.042443415
[11,] 0.051944355 0.051944355
[12,] -0.118055645 0.051944355
[13,] 0.060668434 -0.118055645
[14,] 0.077123358 0.060668434
[15,] 0.060886392 0.077123358
[16,] -0.395814288 0.060886392
[17,] 0.002867153 -0.395814288
[18,] -0.213374013 0.002867153
[19,] 0.136385625 -0.213374013
[20,] 0.110402833 0.136385625
[21,] 0.062207451 0.110402833
[22,] 0.029471726 0.062207451
[23,] 0.002869094 0.029471726
[24,] 0.002869094 0.002869094
[25,] 0.002869094 0.002869094
[26,] 0.002869094 0.002869094
[27,] 0.002869094 0.002869094
[28,] 0.002869094 0.002869094
[29,] 0.002869094 0.002869094
[30,] 0.002869094 0.002869094
[31,] -0.397130906 0.002869094
[32,] 0.111631633 -0.397130906
[33,] 0.016063246 0.111631633
[34,] -0.143959243 0.016063246
[35,] 0.059893823 -0.143959243
[36,] -0.006892251 0.059893823
[37,] -0.386903495 -0.006892251
[38,] 0.096594942 -0.386903495
[39,] -0.008474385 0.096594942
[40,] 0.031503126 -0.008474385
[41,] -0.019025078 0.031503126
[42,] -0.038026958 -0.019025078
[43,] -0.038026958 -0.038026958
[44,] -0.038026958 -0.038026958
[45,] -0.038026958 -0.038026958
[46,] -0.038026958 -0.038026958
[47,] -0.038026958 -0.038026958
[48,] -0.038026958 -0.038026958
[49,] -0.038026958 -0.038026958
[50,] -0.038026958 -0.038026958
[51,] -0.038026958 -0.038026958
[52,] -0.038026958 -0.038026958
[53,] -0.038026958 -0.038026958
[54,] -0.038026958 -0.038026958
[55,] -0.038026958 -0.038026958
[56,] -0.038026958 -0.038026958
[57,] -0.038026958 -0.038026958
[58,] -0.038026958 -0.038026958
[59,] -0.038026958 -0.038026958
[60,] -0.038026958 -0.038026958
[61,] -0.038026958 -0.038026958
[62,] -0.038026958 -0.038026958
[63,] -0.038026958 -0.038026958
[64,] -0.038026958 -0.038026958
[65,] -0.038026958 -0.038026958
[66,] -0.038026958 -0.038026958
[67,] 0.171973042 -0.038026958
[68,] -0.107627291 0.171973042
[69,] -0.038430058 -0.107627291
[70,] 0.132839279 -0.038430058
[71,] -0.091829769 0.132839279
[72,] -0.036444824 -0.091829769
[73,] 0.083566421 -0.036444824
[74,] 0.005763634 0.083566421
[75,] 0.114570533 0.005763634
[76,] -0.056909922 0.114570533
[77,] 0.116623578 -0.056909922
[78,] -0.043703374 0.116623578
[79,] 0.096209565 -0.043703374
[80,] 0.012680958 0.096209565
[81,] -0.056093340 0.012680958
[82,] 0.141570852 -0.056093340
[83,] 0.011860283 0.141570852
[84,] -0.026518650 0.011860283
[85,] 0.147202131 -0.026518650
[86,] 0.021939682 0.147202131
[87,] -0.018339440 0.021939682
[88,] 0.005381341 -0.018339440
[89,] 0.008404844 0.005381341
[90,] 0.027406724 0.008404844
[91,] 0.027406724 0.027406724
[92,] 0.027406724 0.027406724
[93,] 0.027406724 0.027406724
[94,] 0.027406724 0.027406724
[95,] 0.027406724 0.027406724
[96,] 0.027406724 0.027406724
[97,] 0.027406724 0.027406724
[98,] 0.207406724 0.027406724
[99,] 0.003463582 0.207406724
[100,] 0.008421697 0.003463582
[101,] -0.154630447 0.008421697
[102,] -0.262128807 -0.154630447
[103,] -0.255210314 -0.262128807
[104,] 0.055140370 -0.255210314
[105,] 0.086713779 0.055140370
[106,] 0.012170904 0.086713779
[107,] -0.027597783 0.012170904
[108,] 0.069851880 -0.027597783
[109,] 0.125605980 0.069851880
[110,] 0.150881306 0.125605980
[111,] 0.084683442 0.150881306
[112,] 0.065810691 0.084683442
[113,] 0.048708999 0.065810691
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.224783352 0.144188176
2 -0.039715556 0.224783352
3 -0.064347052 -0.039715556
4 0.226247577 -0.064347052
5 0.078506715 0.226247577
6 0.005577467 0.078506715
7 -0.010336631 0.005577467
8 0.017179313 -0.010336631
9 0.042443415 0.017179313
10 0.051944355 0.042443415
11 0.051944355 0.051944355
12 -0.118055645 0.051944355
13 0.060668434 -0.118055645
14 0.077123358 0.060668434
15 0.060886392 0.077123358
16 -0.395814288 0.060886392
17 0.002867153 -0.395814288
18 -0.213374013 0.002867153
19 0.136385625 -0.213374013
20 0.110402833 0.136385625
21 0.062207451 0.110402833
22 0.029471726 0.062207451
23 0.002869094 0.029471726
24 0.002869094 0.002869094
25 0.002869094 0.002869094
26 0.002869094 0.002869094
27 0.002869094 0.002869094
28 0.002869094 0.002869094
29 0.002869094 0.002869094
30 0.002869094 0.002869094
31 -0.397130906 0.002869094
32 0.111631633 -0.397130906
33 0.016063246 0.111631633
34 -0.143959243 0.016063246
35 0.059893823 -0.143959243
36 -0.006892251 0.059893823
37 -0.386903495 -0.006892251
38 0.096594942 -0.386903495
39 -0.008474385 0.096594942
40 0.031503126 -0.008474385
41 -0.019025078 0.031503126
42 -0.038026958 -0.019025078
43 -0.038026958 -0.038026958
44 -0.038026958 -0.038026958
45 -0.038026958 -0.038026958
46 -0.038026958 -0.038026958
47 -0.038026958 -0.038026958
48 -0.038026958 -0.038026958
49 -0.038026958 -0.038026958
50 -0.038026958 -0.038026958
51 -0.038026958 -0.038026958
52 -0.038026958 -0.038026958
53 -0.038026958 -0.038026958
54 -0.038026958 -0.038026958
55 -0.038026958 -0.038026958
56 -0.038026958 -0.038026958
57 -0.038026958 -0.038026958
58 -0.038026958 -0.038026958
59 -0.038026958 -0.038026958
60 -0.038026958 -0.038026958
61 -0.038026958 -0.038026958
62 -0.038026958 -0.038026958
63 -0.038026958 -0.038026958
64 -0.038026958 -0.038026958
65 -0.038026958 -0.038026958
66 -0.038026958 -0.038026958
67 0.171973042 -0.038026958
68 -0.107627291 0.171973042
69 -0.038430058 -0.107627291
70 0.132839279 -0.038430058
71 -0.091829769 0.132839279
72 -0.036444824 -0.091829769
73 0.083566421 -0.036444824
74 0.005763634 0.083566421
75 0.114570533 0.005763634
76 -0.056909922 0.114570533
77 0.116623578 -0.056909922
78 -0.043703374 0.116623578
79 0.096209565 -0.043703374
80 0.012680958 0.096209565
81 -0.056093340 0.012680958
82 0.141570852 -0.056093340
83 0.011860283 0.141570852
84 -0.026518650 0.011860283
85 0.147202131 -0.026518650
86 0.021939682 0.147202131
87 -0.018339440 0.021939682
88 0.005381341 -0.018339440
89 0.008404844 0.005381341
90 0.027406724 0.008404844
91 0.027406724 0.027406724
92 0.027406724 0.027406724
93 0.027406724 0.027406724
94 0.027406724 0.027406724
95 0.027406724 0.027406724
96 0.027406724 0.027406724
97 0.027406724 0.027406724
98 0.207406724 0.027406724
99 0.003463582 0.207406724
100 0.008421697 0.003463582
101 -0.154630447 0.008421697
102 -0.262128807 -0.154630447
103 -0.255210314 -0.262128807
104 0.055140370 -0.255210314
105 0.086713779 0.055140370
106 0.012170904 0.086713779
107 -0.027597783 0.012170904
108 0.069851880 -0.027597783
109 0.125605980 0.069851880
110 0.150881306 0.125605980
111 0.084683442 0.150881306
112 0.065810691 0.084683442
113 0.048708999 0.065810691
> 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/7efu31258808125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/884401258808125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ti1h1258808125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/107agz1258808125.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11z9i91258808125.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/12m0x61258808125.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/13q0lj1258808125.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/14zomu1258808125.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/15u7pn1258808125.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/16fx0b1258808125.tab")
+ }
>
> system("convert tmp/1iiti1258808125.ps tmp/1iiti1258808125.png")
> system("convert tmp/25d2b1258808125.ps tmp/25d2b1258808125.png")
> system("convert tmp/3w1091258808125.ps tmp/3w1091258808125.png")
> system("convert tmp/4u9lo1258808125.ps tmp/4u9lo1258808125.png")
> system("convert tmp/5u5ch1258808125.ps tmp/5u5ch1258808125.png")
> system("convert tmp/63da71258808125.ps tmp/63da71258808125.png")
> system("convert tmp/7efu31258808125.ps tmp/7efu31258808125.png")
> system("convert tmp/884401258808125.ps tmp/884401258808125.png")
> system("convert tmp/9ti1h1258808125.ps tmp/9ti1h1258808125.png")
> system("convert tmp/107agz1258808125.ps tmp/107agz1258808125.png")
>
>
> proc.time()
user system elapsed
3.283 1.617 4.076