R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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.
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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(5.5
+ ,6
+ ,5.33
+ ,12
+ ,3.5
+ ,4
+ ,5.56
+ ,11
+ ,8.5
+ ,4
+ ,3.78
+ ,14
+ ,5
+ ,4
+ ,4.00
+ ,12
+ ,6
+ ,4.5
+ ,4.00
+ ,21
+ ,6
+ ,3.5
+ ,3.56
+ ,12
+ ,5.5
+ ,2
+ ,4.44
+ ,22
+ ,5.5
+ ,5.5
+ ,3.56
+ ,11
+ ,6
+ ,3.5
+ ,4.00
+ ,10
+ ,6.5
+ ,3.5
+ ,3.78
+ ,13
+ ,7
+ ,6
+ ,5.11
+ ,10
+ ,8
+ ,5
+ ,6.67
+ ,8
+ ,5.5
+ ,5
+ ,5.11
+ ,15
+ ,5
+ ,4
+ ,4.00
+ ,14
+ ,5.5
+ ,4
+ ,3.33
+ ,10
+ ,7.5
+ ,2
+ ,2.67
+ ,14
+ ,4.5
+ ,4.5
+ ,4.67
+ ,14
+ ,5.5
+ ,4
+ ,3.33
+ ,11
+ ,8.5
+ ,3.5
+ ,4.44
+ ,10
+ ,8.5
+ ,5.5
+ ,6.89
+ ,13
+ ,5.5
+ ,4.5
+ ,6.00
+ ,7
+ ,9
+ ,5.5
+ ,7.56
+ ,14
+ ,7
+ ,6.5
+ ,4.67
+ ,12
+ ,5
+ ,4
+ ,6.89
+ ,14
+ ,5.5
+ ,4
+ ,4.22
+ ,11
+ ,7.5
+ ,4.5
+ ,3.56
+ ,9
+ ,7.5
+ ,3
+ ,4.44
+ ,11
+ ,6.5
+ ,4.5
+ ,4.67
+ ,15
+ ,8
+ ,4.5
+ ,4.89
+ ,14
+ ,6.5
+ ,3
+ ,3.78
+ ,13
+ ,4.5
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+ ,9
+ ,9
+ ,8
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+ ,6
+ ,3.5
+ ,5.56
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+ ,4.5
+ ,3.78
+ ,13
+ ,4.5
+ ,3
+ ,7.11
+ ,8
+ ,4.5
+ ,3
+ ,7.33
+ ,20
+ ,6
+ ,2.5
+ ,2.89
+ ,12
+ ,9
+ ,6
+ ,7.11
+ ,10
+ ,6
+ ,3.5
+ ,5.56
+ ,10
+ ,9
+ ,5
+ ,6.44
+ ,9
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+ ,4
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+ ,8
+ ,8
+ ,2.5
+ ,3.78
+ ,14
+ ,5
+ ,4
+ ,4.44
+ ,11
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+ ,4
+ ,3.33
+ ,13
+ ,7
+ ,5
+ ,4.44
+ ,9
+ ,4.5
+ ,3
+ ,7.33
+ ,11
+ ,6
+ ,4
+ ,6.44
+ ,15
+ ,8.5
+ ,3.5
+ ,5.11
+ ,11
+ ,2.5
+ ,2
+ ,5.78
+ ,10
+ ,6
+ ,4
+ ,4.00
+ ,14
+ ,6
+ ,4
+ ,4.44
+ ,18
+ ,3
+ ,2
+ ,2.44
+ ,14
+ ,12
+ ,10
+ ,6.22
+ ,11
+ ,6
+ ,4
+ ,5.78
+ ,12
+ ,6
+ ,4
+ ,4.89
+ ,13
+ ,7
+ ,3
+ ,3.78
+ ,9
+ ,3.5
+ ,2
+ ,2.67
+ ,10
+ ,6.5
+ ,4
+ ,3.11
+ ,15
+ ,6
+ ,4.5
+ ,3.78
+ ,20
+ ,6.5
+ ,3
+ ,4.67
+ ,12
+ ,7
+ ,3.5
+ ,4.22
+ ,12
+ ,4
+ ,4.5
+ ,4.00
+ ,14
+ ,5.5
+ ,2.5
+ ,2.22
+ ,13
+ ,4.5
+ ,2.5
+ ,6.44
+ ,11
+ ,5.5
+ ,4
+ ,6.89
+ ,17
+ ,6.5
+ ,4
+ ,4.22
+ ,12
+ ,5
+ ,3
+ ,2.00
+ ,13
+ ,5.5
+ ,4
+ ,4.44
+ ,14
+ ,6
+ ,3.5
+ ,6.22
+ ,13
+ ,4.5
+ ,3.5
+ ,4.22
+ ,15
+ ,7.5
+ ,4.5
+ ,6.67
+ ,13
+ ,9
+ ,5.5
+ ,6.44
+ ,10
+ ,7.5
+ ,3
+ ,5.78
+ ,11
+ ,6
+ ,4
+ ,5.11
+ ,19
+ ,6.5
+ ,3
+ ,2.89
+ ,13
+ ,7
+ ,4.5
+ ,4.67
+ ,17
+ ,5
+ ,4
+ ,4.22
+ ,13
+ ,6.5
+ ,3
+ ,6.22
+ ,9
+ ,6.5
+ ,5
+ ,5.11
+ ,11
+ ,5.5
+ ,4
+ ,4.00
+ ,10
+ ,6.5
+ ,4
+ ,4.67
+ ,9
+ ,8
+ ,5
+ ,4.44
+ ,12
+ ,4
+ ,2.5
+ ,5.11
+ ,12
+ ,8
+ ,3.5
+ ,4.67
+ ,13
+ ,5.5
+ ,2.5
+ ,4.67
+ ,13
+ ,4.5
+ ,4
+ ,3.33
+ ,12
+ ,8
+ ,7
+ ,6.22
+ ,15
+ ,6
+ ,3.5
+ ,4.22
+ ,22
+ ,7
+ ,4
+ ,5.78
+ ,13
+ ,4
+ ,3
+ ,2.22
+ ,15
+ ,4.5
+ ,2.5
+ ,3.56
+ ,13
+ ,7.5
+ ,3
+ ,4.89
+ ,15
+ ,5.5
+ ,5
+ ,4.22
+ ,10
+ ,10.5
+ ,6
+ ,6.89
+ ,11
+ ,7
+ ,4.5
+ ,6.89
+ ,16
+ ,9
+ ,6
+ ,6.44
+ ,11
+ ,6
+ ,3.5
+ ,4.22
+ ,11
+ ,6.5
+ ,4
+ ,4.89
+ ,10
+ ,7.5
+ ,5
+ ,5.11
+ ,10
+ ,6
+ ,3
+ ,3.33
+ ,16
+ ,9.5
+ ,5
+ ,4.44
+ ,12
+ ,7.5
+ ,5
+ ,4.00
+ ,11
+ ,5.5
+ ,5
+ ,5.11
+ ,16
+ ,5.5
+ ,2.5
+ ,5.56
+ ,19
+ ,5
+ ,3.5
+ ,4.67
+ ,11
+ ,6.5
+ ,5
+ ,5.33
+ ,16
+ ,7.5
+ ,5.5
+ ,5.56
+ ,15
+ ,6
+ ,3
+ ,3.78
+ ,24
+ ,6
+ ,3.5
+ ,2.89
+ ,14
+ ,8
+ ,6
+ ,6.22
+ ,15
+ ,4.5
+ ,5.5
+ ,4.67
+ ,11
+ ,9
+ ,5.5
+ ,5.56
+ ,15
+ ,4
+ ,5.5
+ ,2.00
+ ,12
+ ,6.5
+ ,2.5
+ ,3.56
+ ,10
+ ,8.5
+ ,4
+ ,4.22
+ ,14
+ ,4.5
+ ,3
+ ,3.78
+ ,13
+ ,7.5
+ ,4.5
+ ,5.56
+ ,9
+ ,4
+ ,2
+ ,4.44
+ ,15
+ ,3.5
+ ,2
+ ,6.44
+ ,15
+ ,6
+ ,3.5
+ ,3.11
+ ,14
+ ,7
+ ,5.5
+ ,4.89
+ ,11
+ ,3
+ ,3
+ ,3.33
+ ,8
+ ,4
+ ,3.5
+ ,4.22
+ ,11
+ ,8.5
+ ,4
+ ,4.44
+ ,11
+ ,5
+ ,2
+ ,3.33
+ ,8
+ ,5.5
+ ,4
+ ,4.44
+ ,10
+ ,7
+ ,4.5
+ ,4.00
+ ,11
+ ,5.5
+ ,4
+ ,7.33
+ ,13
+ ,6.5
+ ,5.5
+ ,4.89
+ ,11
+ ,6
+ ,4
+ ,3.56
+ ,20
+ ,5.5
+ ,2.5
+ ,3.78
+ ,10
+ ,4.5
+ ,2
+ ,3.56
+ ,15
+ ,6
+ ,4
+ ,4.67
+ ,12
+ ,10
+ ,5
+ ,5.78
+ ,14
+ ,6
+ ,3
+ ,4.00
+ ,23
+ ,6.5
+ ,4.5
+ ,4.00
+ ,14
+ ,6
+ ,4.5
+ ,3.78
+ ,16
+ ,6
+ ,6.5
+ ,4.89
+ ,11
+ ,4.5
+ ,4.5
+ ,6.67
+ ,12
+ ,7.5
+ ,5
+ ,6.67
+ ,10
+ ,12
+ ,10
+ ,5.33
+ ,14
+ ,3.5
+ ,2.5
+ ,4.67
+ ,12
+ ,8.5
+ ,5.5
+ ,4.67
+ ,12
+ ,5.5
+ ,3
+ ,6.44
+ ,11
+ ,8.5
+ ,4.5
+ ,6.89
+ ,12
+ ,5.5
+ ,3.5
+ ,4.44
+ ,13
+ ,6
+ ,4.5
+ ,3.56
+ ,11
+ ,7
+ ,5
+ ,4.89
+ ,19
+ ,5.5
+ ,4.5
+ ,4.44
+ ,12
+ ,8
+ ,4
+ ,6.22
+ ,17
+ ,10.5
+ ,3.5
+ ,8.44
+ ,9
+ ,7
+ ,3
+ ,4.89
+ ,12
+ ,10
+ ,6.5
+ ,4.44
+ ,19
+ ,6.5
+ ,3
+ ,3.78
+ ,18
+ ,5.5
+ ,4
+ ,6.22
+ ,15
+ ,7.5
+ ,5
+ ,4.89
+ ,14
+ ,9.5
+ ,8
+ ,6.89
+ ,11)
+ ,dim=c(4
+ ,159)
+ ,dimnames=list(c('Intercept'
+ ,'Expect'
+ ,'Critisism'
+ ,'Concerns')
+ ,1:159))
> y <- array(NA,dim=c(4,159),dimnames=list(c('Intercept','Expect','Critisism','Concerns'),1:159))
> 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
> 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
Intercept Expect Critisism Concerns
1 5.5 6.0 5.33 12
2 3.5 4.0 5.56 11
3 8.5 4.0 3.78 14
4 5.0 4.0 4.00 12
5 6.0 4.5 4.00 21
6 6.0 3.5 3.56 12
7 5.5 2.0 4.44 22
8 5.5 5.5 3.56 11
9 6.0 3.5 4.00 10
10 6.5 3.5 3.78 13
11 7.0 6.0 5.11 10
12 8.0 5.0 6.67 8
13 5.5 5.0 5.11 15
14 5.0 4.0 4.00 14
15 5.5 4.0 3.33 10
16 7.5 2.0 2.67 14
17 4.5 4.5 4.67 14
18 5.5 4.0 3.33 11
19 8.5 3.5 4.44 10
20 8.5 5.5 6.89 13
21 5.5 4.5 6.00 7
22 9.0 5.5 7.56 14
23 7.0 6.5 4.67 12
24 5.0 4.0 6.89 14
25 5.5 4.0 4.22 11
26 7.5 4.5 3.56 9
27 7.5 3.0 4.44 11
28 6.5 4.5 4.67 15
29 8.0 4.5 4.89 14
30 6.5 3.0 3.78 13
31 4.5 3.0 5.33 9
32 9.0 8.0 5.56 15
33 9.0 2.5 5.78 10
34 6.0 3.5 5.56 11
35 8.5 4.5 3.78 13
36 4.5 3.0 7.11 8
37 4.5 3.0 7.33 20
38 6.0 2.5 2.89 12
39 9.0 6.0 7.11 10
40 6.0 3.5 5.56 10
41 9.0 5.0 6.44 9
42 7.0 4.5 4.89 14
43 7.5 4.0 4.00 8
44 8.0 2.5 3.78 14
45 5.0 4.0 4.44 11
46 5.5 4.0 3.33 13
47 7.0 5.0 4.44 9
48 4.5 3.0 7.33 11
49 6.0 4.0 6.44 15
50 8.5 3.5 5.11 11
51 2.5 2.0 5.78 10
52 6.0 4.0 4.00 14
53 6.0 4.0 4.44 18
54 3.0 2.0 2.44 14
55 12.0 10.0 6.22 11
56 6.0 4.0 5.78 12
57 6.0 4.0 4.89 13
58 7.0 3.0 3.78 9
59 3.5 2.0 2.67 10
60 6.5 4.0 3.11 15
61 6.0 4.5 3.78 20
62 6.5 3.0 4.67 12
63 7.0 3.5 4.22 12
64 4.0 4.5 4.00 14
65 5.5 2.5 2.22 13
66 4.5 2.5 6.44 11
67 5.5 4.0 6.89 17
68 6.5 4.0 4.22 12
69 5.0 3.0 2.00 13
70 5.5 4.0 4.44 14
71 6.0 3.5 6.22 13
72 4.5 3.5 4.22 15
73 7.5 4.5 6.67 13
74 9.0 5.5 6.44 10
75 7.5 3.0 5.78 11
76 6.0 4.0 5.11 19
77 6.5 3.0 2.89 13
78 7.0 4.5 4.67 17
79 5.0 4.0 4.22 13
80 6.5 3.0 6.22 9
81 6.5 5.0 5.11 11
82 5.5 4.0 4.00 10
83 6.5 4.0 4.67 9
84 8.0 5.0 4.44 12
85 4.0 2.5 5.11 12
86 8.0 3.5 4.67 13
87 5.5 2.5 4.67 13
88 4.5 4.0 3.33 12
89 8.0 7.0 6.22 15
90 6.0 3.5 4.22 22
91 7.0 4.0 5.78 13
92 4.0 3.0 2.22 15
93 4.5 2.5 3.56 13
94 7.5 3.0 4.89 15
95 5.5 5.0 4.22 10
96 10.5 6.0 6.89 11
97 7.0 4.5 6.89 16
98 9.0 6.0 6.44 11
99 6.0 3.5 4.22 11
100 6.5 4.0 4.89 10
101 7.5 5.0 5.11 10
102 6.0 3.0 3.33 16
103 9.5 5.0 4.44 12
104 7.5 5.0 4.00 11
105 5.5 5.0 5.11 16
106 5.5 2.5 5.56 19
107 5.0 3.5 4.67 11
108 6.5 5.0 5.33 16
109 7.5 5.5 5.56 15
110 6.0 3.0 3.78 24
111 6.0 3.5 2.89 14
112 8.0 6.0 6.22 15
113 4.5 5.5 4.67 11
114 9.0 5.5 5.56 15
115 4.0 5.5 2.00 12
116 6.5 2.5 3.56 10
117 8.5 4.0 4.22 14
118 4.5 3.0 3.78 13
119 7.5 4.5 5.56 9
120 4.0 2.0 4.44 15
121 3.5 2.0 6.44 15
122 6.0 3.5 3.11 14
123 7.0 5.5 4.89 11
124 3.0 3.0 3.33 8
125 4.0 3.5 4.22 11
126 8.5 4.0 4.44 11
127 5.0 2.0 3.33 8
128 5.5 4.0 4.44 10
129 7.0 4.5 4.00 11
130 5.5 4.0 7.33 13
131 6.5 5.5 4.89 11
132 6.0 4.0 3.56 20
133 5.5 2.5 3.78 10
134 4.5 2.0 3.56 15
135 6.0 4.0 4.67 12
136 10.0 5.0 5.78 14
137 6.0 3.0 4.00 23
138 6.5 4.5 4.00 14
139 6.0 4.5 3.78 16
140 6.0 6.5 4.89 11
141 4.5 4.5 6.67 12
142 7.5 5.0 6.67 10
143 12.0 10.0 5.33 14
144 3.5 2.5 4.67 12
145 8.5 5.5 4.67 12
146 5.5 3.0 6.44 11
147 8.5 4.5 6.89 12
148 5.5 3.5 4.44 13
149 6.0 4.5 3.56 11
150 7.0 5.0 4.89 19
151 5.5 4.5 4.44 12
152 8.0 4.0 6.22 17
153 10.5 3.5 8.44 9
154 7.0 3.0 4.89 12
155 10.0 6.5 4.44 19
156 6.5 3.0 3.78 18
157 5.5 4.0 6.22 15
158 7.5 5.0 4.89 14
159 9.5 8.0 6.89 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Expect Critisism Concerns
2.5529074 0.6916955 0.2165736 -0.0007089
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.0160 -0.9574 -0.0519 0.8895 3.7047
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.5529074 0.6878758 3.711 0.000287 ***
Expect 0.6916955 0.0850450 8.133 1.26e-13 ***
Critisism 0.2165736 0.0908753 2.383 0.018374 *
Concerns -0.0007089 0.0349526 -0.020 0.983845
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.375 on 155 degrees of freedom
Multiple R-squared: 0.375, Adjusted R-squared: 0.3629
F-statistic: 31 on 3 and 155 DF, p-value: 9.34e-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,] 0.6786424 0.64271523 0.32135762
[2,] 0.6538219 0.69235629 0.34617814
[3,] 0.5258284 0.94834317 0.47417158
[4,] 0.4058960 0.81179201 0.59410399
[5,] 0.4815939 0.96318789 0.51840605
[6,] 0.7360189 0.52796227 0.26398114
[7,] 0.6695528 0.66089443 0.33044721
[8,] 0.6220507 0.75589864 0.37794932
[9,] 0.5428156 0.91436877 0.45718438
[10,] 0.5929662 0.81406762 0.40703381
[11,] 0.6051997 0.78960052 0.39480026
[12,] 0.5413673 0.91726538 0.45863269
[13,] 0.6690081 0.66198376 0.33099188
[14,] 0.7489407 0.50211865 0.25105932
[15,] 0.7447197 0.51056056 0.25528028
[16,] 0.7857699 0.42846016 0.21423008
[17,] 0.7581565 0.48368690 0.24184345
[18,] 0.7987056 0.40258877 0.20129439
[19,] 0.7600334 0.47993319 0.23996659
[20,] 0.7543785 0.49124294 0.24562147
[21,] 0.7542818 0.49143632 0.24571816
[22,] 0.7040791 0.59184181 0.29592091
[23,] 0.7186767 0.56264669 0.28132334
[24,] 0.6746098 0.65078035 0.32539018
[25,] 0.7095570 0.58088604 0.29044302
[26,] 0.7143488 0.57130249 0.28565125
[27,] 0.8619782 0.27604351 0.13802176
[28,] 0.8319405 0.33611896 0.16805948
[29,] 0.8708063 0.25838743 0.12919372
[30,] 0.8880853 0.22382933 0.11191466
[31,] 0.8959632 0.20807353 0.10403677
[32,] 0.8767075 0.24658505 0.12329253
[33,] 0.8864808 0.22703844 0.11351922
[34,] 0.8595603 0.28087941 0.14043970
[35,] 0.8824312 0.23513759 0.11756880
[36,] 0.8565864 0.28682727 0.14341363
[37,] 0.8449353 0.31012934 0.15506467
[38,] 0.9029769 0.19404622 0.09702311
[39,] 0.9042778 0.19144438 0.09572219
[40,] 0.8885532 0.22289356 0.11144678
[41,] 0.8627664 0.27446723 0.13723362
[42,] 0.8762082 0.24758366 0.12379183
[43,] 0.8540821 0.29183582 0.14591791
[44,] 0.8989066 0.20218684 0.10109342
[45,] 0.9555396 0.08892071 0.04446035
[46,] 0.9436320 0.11273610 0.05636805
[47,] 0.9291059 0.14178818 0.07089409
[48,] 0.9442616 0.11147690 0.05573845
[49,] 0.9491205 0.10175894 0.05087947
[50,] 0.9373776 0.12524473 0.06262236
[51,] 0.9223979 0.15520427 0.07760213
[52,] 0.9238638 0.15227243 0.07613621
[53,] 0.9218536 0.15629277 0.07814638
[54,] 0.9057609 0.18847813 0.09423906
[55,] 0.8866269 0.22674622 0.11337311
[56,] 0.8721340 0.25573201 0.12786601
[57,] 0.8627873 0.27442541 0.13721270
[58,] 0.9140561 0.17188786 0.08594393
[59,] 0.9017032 0.19659366 0.09829683
[60,] 0.8948536 0.21029290 0.10514645
[61,] 0.8936121 0.21277571 0.10638785
[62,] 0.8718761 0.25624784 0.12812392
[63,] 0.8512961 0.29740771 0.14870386
[64,] 0.8311862 0.33762762 0.16881381
[65,] 0.8039062 0.39218770 0.19609385
[66,] 0.8024207 0.39515860 0.19757930
[67,] 0.7765092 0.44698155 0.22349077
[68,] 0.7722032 0.45559369 0.22779685
[69,] 0.7857316 0.42853686 0.21426843
[70,] 0.7563357 0.48732862 0.24366431
[71,] 0.7565082 0.48698353 0.24349176
[72,] 0.7227291 0.55454186 0.27727093
[73,] 0.7135395 0.57292099 0.28646049
[74,] 0.6784529 0.64309416 0.32154708
[75,] 0.6442748 0.71145033 0.35572516
[76,] 0.6117773 0.77644537 0.38822268
[77,] 0.5686707 0.86265863 0.43132931
[78,] 0.5516700 0.89665998 0.44832999
[79,] 0.5525996 0.89480080 0.44740040
[80,] 0.6095893 0.78082144 0.39041072
[81,] 0.5658138 0.86837236 0.43418618
[82,] 0.5725406 0.85491877 0.42745939
[83,] 0.5484536 0.90309279 0.45154640
[84,] 0.5054002 0.98919952 0.49459976
[85,] 0.4638144 0.92762876 0.53618562
[86,] 0.4428424 0.88568484 0.55715758
[87,] 0.4029425 0.80588495 0.59705752
[88,] 0.4379422 0.87588441 0.56205779
[89,] 0.4359065 0.87181301 0.56409349
[90,] 0.5113014 0.97739728 0.48869864
[91,] 0.4703809 0.94076178 0.52961911
[92,] 0.4407277 0.88145541 0.55927230
[93,] 0.3959990 0.79199795 0.60400102
[94,] 0.3520421 0.70408421 0.64795789
[95,] 0.3129663 0.62593265 0.68703368
[96,] 0.2833828 0.56676557 0.71661721
[97,] 0.3954948 0.79098952 0.60450524
[98,] 0.3662586 0.73251724 0.63374138
[99,] 0.3886263 0.77725254 0.61137373
[100,] 0.3468826 0.69376510 0.65311745
[101,] 0.3194562 0.63891244 0.68054378
[102,] 0.2914379 0.58287586 0.70856207
[103,] 0.2522828 0.50456570 0.74771715
[104,] 0.2193425 0.43868502 0.78065749
[105,] 0.1920764 0.38415278 0.80792361
[106,] 0.1629594 0.32591879 0.83704061
[107,] 0.2711844 0.54236886 0.72881557
[108,] 0.2631814 0.52636272 0.73681864
[109,] 0.3680858 0.73617162 0.63191419
[110,] 0.4010654 0.80213074 0.59893463
[111,] 0.4967860 0.99357210 0.50321395
[112,] 0.4580673 0.91613450 0.54193275
[113,] 0.4207817 0.84156333 0.57921833
[114,] 0.3839282 0.76785645 0.61607177
[115,] 0.4458008 0.89160152 0.55419924
[116,] 0.4026800 0.80536007 0.59731996
[117,] 0.3522446 0.70448911 0.64775544
[118,] 0.3924910 0.78498204 0.60750898
[119,] 0.4252667 0.85053343 0.57473329
[120,] 0.5286706 0.94265886 0.47132943
[121,] 0.5046918 0.99061642 0.49530821
[122,] 0.4519309 0.90386188 0.54806906
[123,] 0.4134345 0.82686903 0.58656549
[124,] 0.4875211 0.97504219 0.51247890
[125,] 0.4436882 0.88737645 0.55631177
[126,] 0.3855280 0.77105597 0.61447201
[127,] 0.3715357 0.74307135 0.62846433
[128,] 0.3141217 0.62824347 0.68587827
[129,] 0.2578091 0.51561823 0.74219088
[130,] 0.3644974 0.72899475 0.63550262
[131,] 0.3056544 0.61130883 0.69434558
[132,] 0.2489342 0.49786837 0.75106582
[133,] 0.1977861 0.39557224 0.80221388
[134,] 0.2409976 0.48199511 0.75900244
[135,] 0.5548562 0.89028757 0.44514378
[136,] 0.4928716 0.98574319 0.50712841
[137,] 0.4266822 0.85336447 0.57331777
[138,] 0.4886319 0.97726382 0.51136809
[139,] 0.4665703 0.93314068 0.53342966
[140,] 0.4955348 0.99106953 0.50446524
[141,] 0.4006969 0.80139388 0.59930306
[142,] 0.3237591 0.64751811 0.67624094
[143,] 0.2296412 0.45928241 0.77035880
[144,] 0.1705471 0.34109426 0.82945287
[145,] 0.1501689 0.30033788 0.84983106
[146,] 0.0818324 0.16366480 0.91816760
> postscript(file="/var/www/rcomp/tmp/1f3z31321981523.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2wmfu1321981523.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/320q41321981523.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4s8dr1321981523.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5z8bc1321981523.ps",horizontal=F,onefile=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 = 159
Frequency = 1
1 2 3 4 5 6
-2.34891147 -3.01604122 2.37158640 -1.17747753 -0.51694548 0.26366262
7 8 9 10 11 12
0.61770981 -1.62043730 0.16695250 0.71672529 -0.80268301 0.54973995
13 14 15 16 17 18
-1.60744315 -1.17605979 -0.53379095 2.99537415 -2.16701187 -0.53308208
19 20 21 22 23 24
2.57166011 0.65979034 -1.46001684 1.01539489 -1.05182065 -1.80195751
25 26 27 28 29 30
-0.72583259 1.06984049 1.91821674 -0.16630300 1.28534194 1.06257306
31 32 33 34 35 36
-1.27595150 -0.27998784 3.47314701 -0.17019346 2.02502977 -1.66216139
37 38 39 40 41 42
-1.70130117 1.10046246 0.76416978 -0.17090232 1.60026075 0.28534194
43 44 45 46 47 48
1.31968700 2.90912968 -1.27347878 -0.53166435 0.03340796 -1.70768098
49 50 51 52 53 54
-0.70379052 2.42726466 -2.68100523 -0.17605979 -0.26851671 -1.45481392
55 56 57 58 59 60
1.19084706 -0.56297854 -0.36951917 1.55973759 -1.00746132 0.51739958
61 62 63 64 65 66
-0.47000816 0.86911368 1.12072404 -2.52190756 0.74627564 -1.16908271
67 68 69 70 71 72
-1.29983091 0.27487628 -0.05192593 -0.77135218 -0.31171430 -1.37714936
73 74 75 76 77 78
0.39913205 1.25512186 1.62800811 -0.41291216 1.25532356 0.33511473
79 80 81 82 83 84
-1.22441485 0.53129799 -0.61027862 -0.67889526 0.17529155 1.03553456
85 86 87 88 89 90
-1.38033094 2.02397479 0.21567031 -1.53237321 -0.73123090 0.12781272
91 92 93 94 95 96
0.43773032 -1.09815439 -0.54393299 1.82359409 -1.41823698 2.31252484
97 98 99 100 101 102
-0.14638754 0.90998296 0.12001517 0.12835423 0.38901251 0.66215778
103 104 105 106 107 108
2.53553456 0.63011808 -1.60673428 0.02717301 -0.97744295 -0.65438048
109 110 111 112 113 114
-0.05074903 0.57037060 0.41018467 -0.03953537 -2.86083400 1.44925097
115 116 117 118 119 120
-2.78187360 1.45394041 2.27629401 -0.93742694 0.63669328 -0.88725227
121 122 123 124 125 126
-1.82039947 0.36253848 -0.40848019 -2.34351316 -1.87998483 2.22652122
127 128 129 130 131 132
0.34818236 -0.77418765 0.47596584 -1.39795876 -0.90848019 -0.07651420
133 134 135 136 137 138
0.40629421 -0.19666749 -0.32258184 2.74674367 0.52201554 -0.02190756
139 140 141 142 143 144
-0.47284363 -2.10017571 -2.60157681 0.05115769 1.38572417 -1.78503856
145 146 147 148 149 150
1.13987487 -0.51493047 1.35077699 -0.42621329 -0.42874177 -0.05696149
151 152 153 154 155 156
-1.11861768 1.34527341 3.70465683 1.32146749 2.00295335 1.06611739
157 158 159
-1.15614433 0.43949418 -0.07086621
> postscript(file="/var/www/rcomp/tmp/6lxhl1321981523.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.34891147 NA
1 -3.01604122 -2.34891147
2 2.37158640 -3.01604122
3 -1.17747753 2.37158640
4 -0.51694548 -1.17747753
5 0.26366262 -0.51694548
6 0.61770981 0.26366262
7 -1.62043730 0.61770981
8 0.16695250 -1.62043730
9 0.71672529 0.16695250
10 -0.80268301 0.71672529
11 0.54973995 -0.80268301
12 -1.60744315 0.54973995
13 -1.17605979 -1.60744315
14 -0.53379095 -1.17605979
15 2.99537415 -0.53379095
16 -2.16701187 2.99537415
17 -0.53308208 -2.16701187
18 2.57166011 -0.53308208
19 0.65979034 2.57166011
20 -1.46001684 0.65979034
21 1.01539489 -1.46001684
22 -1.05182065 1.01539489
23 -1.80195751 -1.05182065
24 -0.72583259 -1.80195751
25 1.06984049 -0.72583259
26 1.91821674 1.06984049
27 -0.16630300 1.91821674
28 1.28534194 -0.16630300
29 1.06257306 1.28534194
30 -1.27595150 1.06257306
31 -0.27998784 -1.27595150
32 3.47314701 -0.27998784
33 -0.17019346 3.47314701
34 2.02502977 -0.17019346
35 -1.66216139 2.02502977
36 -1.70130117 -1.66216139
37 1.10046246 -1.70130117
38 0.76416978 1.10046246
39 -0.17090232 0.76416978
40 1.60026075 -0.17090232
41 0.28534194 1.60026075
42 1.31968700 0.28534194
43 2.90912968 1.31968700
44 -1.27347878 2.90912968
45 -0.53166435 -1.27347878
46 0.03340796 -0.53166435
47 -1.70768098 0.03340796
48 -0.70379052 -1.70768098
49 2.42726466 -0.70379052
50 -2.68100523 2.42726466
51 -0.17605979 -2.68100523
52 -0.26851671 -0.17605979
53 -1.45481392 -0.26851671
54 1.19084706 -1.45481392
55 -0.56297854 1.19084706
56 -0.36951917 -0.56297854
57 1.55973759 -0.36951917
58 -1.00746132 1.55973759
59 0.51739958 -1.00746132
60 -0.47000816 0.51739958
61 0.86911368 -0.47000816
62 1.12072404 0.86911368
63 -2.52190756 1.12072404
64 0.74627564 -2.52190756
65 -1.16908271 0.74627564
66 -1.29983091 -1.16908271
67 0.27487628 -1.29983091
68 -0.05192593 0.27487628
69 -0.77135218 -0.05192593
70 -0.31171430 -0.77135218
71 -1.37714936 -0.31171430
72 0.39913205 -1.37714936
73 1.25512186 0.39913205
74 1.62800811 1.25512186
75 -0.41291216 1.62800811
76 1.25532356 -0.41291216
77 0.33511473 1.25532356
78 -1.22441485 0.33511473
79 0.53129799 -1.22441485
80 -0.61027862 0.53129799
81 -0.67889526 -0.61027862
82 0.17529155 -0.67889526
83 1.03553456 0.17529155
84 -1.38033094 1.03553456
85 2.02397479 -1.38033094
86 0.21567031 2.02397479
87 -1.53237321 0.21567031
88 -0.73123090 -1.53237321
89 0.12781272 -0.73123090
90 0.43773032 0.12781272
91 -1.09815439 0.43773032
92 -0.54393299 -1.09815439
93 1.82359409 -0.54393299
94 -1.41823698 1.82359409
95 2.31252484 -1.41823698
96 -0.14638754 2.31252484
97 0.90998296 -0.14638754
98 0.12001517 0.90998296
99 0.12835423 0.12001517
100 0.38901251 0.12835423
101 0.66215778 0.38901251
102 2.53553456 0.66215778
103 0.63011808 2.53553456
104 -1.60673428 0.63011808
105 0.02717301 -1.60673428
106 -0.97744295 0.02717301
107 -0.65438048 -0.97744295
108 -0.05074903 -0.65438048
109 0.57037060 -0.05074903
110 0.41018467 0.57037060
111 -0.03953537 0.41018467
112 -2.86083400 -0.03953537
113 1.44925097 -2.86083400
114 -2.78187360 1.44925097
115 1.45394041 -2.78187360
116 2.27629401 1.45394041
117 -0.93742694 2.27629401
118 0.63669328 -0.93742694
119 -0.88725227 0.63669328
120 -1.82039947 -0.88725227
121 0.36253848 -1.82039947
122 -0.40848019 0.36253848
123 -2.34351316 -0.40848019
124 -1.87998483 -2.34351316
125 2.22652122 -1.87998483
126 0.34818236 2.22652122
127 -0.77418765 0.34818236
128 0.47596584 -0.77418765
129 -1.39795876 0.47596584
130 -0.90848019 -1.39795876
131 -0.07651420 -0.90848019
132 0.40629421 -0.07651420
133 -0.19666749 0.40629421
134 -0.32258184 -0.19666749
135 2.74674367 -0.32258184
136 0.52201554 2.74674367
137 -0.02190756 0.52201554
138 -0.47284363 -0.02190756
139 -2.10017571 -0.47284363
140 -2.60157681 -2.10017571
141 0.05115769 -2.60157681
142 1.38572417 0.05115769
143 -1.78503856 1.38572417
144 1.13987487 -1.78503856
145 -0.51493047 1.13987487
146 1.35077699 -0.51493047
147 -0.42621329 1.35077699
148 -0.42874177 -0.42621329
149 -0.05696149 -0.42874177
150 -1.11861768 -0.05696149
151 1.34527341 -1.11861768
152 3.70465683 1.34527341
153 1.32146749 3.70465683
154 2.00295335 1.32146749
155 1.06611739 2.00295335
156 -1.15614433 1.06611739
157 0.43949418 -1.15614433
158 -0.07086621 0.43949418
159 NA -0.07086621
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.01604122 -2.34891147
[2,] 2.37158640 -3.01604122
[3,] -1.17747753 2.37158640
[4,] -0.51694548 -1.17747753
[5,] 0.26366262 -0.51694548
[6,] 0.61770981 0.26366262
[7,] -1.62043730 0.61770981
[8,] 0.16695250 -1.62043730
[9,] 0.71672529 0.16695250
[10,] -0.80268301 0.71672529
[11,] 0.54973995 -0.80268301
[12,] -1.60744315 0.54973995
[13,] -1.17605979 -1.60744315
[14,] -0.53379095 -1.17605979
[15,] 2.99537415 -0.53379095
[16,] -2.16701187 2.99537415
[17,] -0.53308208 -2.16701187
[18,] 2.57166011 -0.53308208
[19,] 0.65979034 2.57166011
[20,] -1.46001684 0.65979034
[21,] 1.01539489 -1.46001684
[22,] -1.05182065 1.01539489
[23,] -1.80195751 -1.05182065
[24,] -0.72583259 -1.80195751
[25,] 1.06984049 -0.72583259
[26,] 1.91821674 1.06984049
[27,] -0.16630300 1.91821674
[28,] 1.28534194 -0.16630300
[29,] 1.06257306 1.28534194
[30,] -1.27595150 1.06257306
[31,] -0.27998784 -1.27595150
[32,] 3.47314701 -0.27998784
[33,] -0.17019346 3.47314701
[34,] 2.02502977 -0.17019346
[35,] -1.66216139 2.02502977
[36,] -1.70130117 -1.66216139
[37,] 1.10046246 -1.70130117
[38,] 0.76416978 1.10046246
[39,] -0.17090232 0.76416978
[40,] 1.60026075 -0.17090232
[41,] 0.28534194 1.60026075
[42,] 1.31968700 0.28534194
[43,] 2.90912968 1.31968700
[44,] -1.27347878 2.90912968
[45,] -0.53166435 -1.27347878
[46,] 0.03340796 -0.53166435
[47,] -1.70768098 0.03340796
[48,] -0.70379052 -1.70768098
[49,] 2.42726466 -0.70379052
[50,] -2.68100523 2.42726466
[51,] -0.17605979 -2.68100523
[52,] -0.26851671 -0.17605979
[53,] -1.45481392 -0.26851671
[54,] 1.19084706 -1.45481392
[55,] -0.56297854 1.19084706
[56,] -0.36951917 -0.56297854
[57,] 1.55973759 -0.36951917
[58,] -1.00746132 1.55973759
[59,] 0.51739958 -1.00746132
[60,] -0.47000816 0.51739958
[61,] 0.86911368 -0.47000816
[62,] 1.12072404 0.86911368
[63,] -2.52190756 1.12072404
[64,] 0.74627564 -2.52190756
[65,] -1.16908271 0.74627564
[66,] -1.29983091 -1.16908271
[67,] 0.27487628 -1.29983091
[68,] -0.05192593 0.27487628
[69,] -0.77135218 -0.05192593
[70,] -0.31171430 -0.77135218
[71,] -1.37714936 -0.31171430
[72,] 0.39913205 -1.37714936
[73,] 1.25512186 0.39913205
[74,] 1.62800811 1.25512186
[75,] -0.41291216 1.62800811
[76,] 1.25532356 -0.41291216
[77,] 0.33511473 1.25532356
[78,] -1.22441485 0.33511473
[79,] 0.53129799 -1.22441485
[80,] -0.61027862 0.53129799
[81,] -0.67889526 -0.61027862
[82,] 0.17529155 -0.67889526
[83,] 1.03553456 0.17529155
[84,] -1.38033094 1.03553456
[85,] 2.02397479 -1.38033094
[86,] 0.21567031 2.02397479
[87,] -1.53237321 0.21567031
[88,] -0.73123090 -1.53237321
[89,] 0.12781272 -0.73123090
[90,] 0.43773032 0.12781272
[91,] -1.09815439 0.43773032
[92,] -0.54393299 -1.09815439
[93,] 1.82359409 -0.54393299
[94,] -1.41823698 1.82359409
[95,] 2.31252484 -1.41823698
[96,] -0.14638754 2.31252484
[97,] 0.90998296 -0.14638754
[98,] 0.12001517 0.90998296
[99,] 0.12835423 0.12001517
[100,] 0.38901251 0.12835423
[101,] 0.66215778 0.38901251
[102,] 2.53553456 0.66215778
[103,] 0.63011808 2.53553456
[104,] -1.60673428 0.63011808
[105,] 0.02717301 -1.60673428
[106,] -0.97744295 0.02717301
[107,] -0.65438048 -0.97744295
[108,] -0.05074903 -0.65438048
[109,] 0.57037060 -0.05074903
[110,] 0.41018467 0.57037060
[111,] -0.03953537 0.41018467
[112,] -2.86083400 -0.03953537
[113,] 1.44925097 -2.86083400
[114,] -2.78187360 1.44925097
[115,] 1.45394041 -2.78187360
[116,] 2.27629401 1.45394041
[117,] -0.93742694 2.27629401
[118,] 0.63669328 -0.93742694
[119,] -0.88725227 0.63669328
[120,] -1.82039947 -0.88725227
[121,] 0.36253848 -1.82039947
[122,] -0.40848019 0.36253848
[123,] -2.34351316 -0.40848019
[124,] -1.87998483 -2.34351316
[125,] 2.22652122 -1.87998483
[126,] 0.34818236 2.22652122
[127,] -0.77418765 0.34818236
[128,] 0.47596584 -0.77418765
[129,] -1.39795876 0.47596584
[130,] -0.90848019 -1.39795876
[131,] -0.07651420 -0.90848019
[132,] 0.40629421 -0.07651420
[133,] -0.19666749 0.40629421
[134,] -0.32258184 -0.19666749
[135,] 2.74674367 -0.32258184
[136,] 0.52201554 2.74674367
[137,] -0.02190756 0.52201554
[138,] -0.47284363 -0.02190756
[139,] -2.10017571 -0.47284363
[140,] -2.60157681 -2.10017571
[141,] 0.05115769 -2.60157681
[142,] 1.38572417 0.05115769
[143,] -1.78503856 1.38572417
[144,] 1.13987487 -1.78503856
[145,] -0.51493047 1.13987487
[146,] 1.35077699 -0.51493047
[147,] -0.42621329 1.35077699
[148,] -0.42874177 -0.42621329
[149,] -0.05696149 -0.42874177
[150,] -1.11861768 -0.05696149
[151,] 1.34527341 -1.11861768
[152,] 3.70465683 1.34527341
[153,] 1.32146749 3.70465683
[154,] 2.00295335 1.32146749
[155,] 1.06611739 2.00295335
[156,] -1.15614433 1.06611739
[157,] 0.43949418 -1.15614433
[158,] -0.07086621 0.43949418
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.01604122 -2.34891147
2 2.37158640 -3.01604122
3 -1.17747753 2.37158640
4 -0.51694548 -1.17747753
5 0.26366262 -0.51694548
6 0.61770981 0.26366262
7 -1.62043730 0.61770981
8 0.16695250 -1.62043730
9 0.71672529 0.16695250
10 -0.80268301 0.71672529
11 0.54973995 -0.80268301
12 -1.60744315 0.54973995
13 -1.17605979 -1.60744315
14 -0.53379095 -1.17605979
15 2.99537415 -0.53379095
16 -2.16701187 2.99537415
17 -0.53308208 -2.16701187
18 2.57166011 -0.53308208
19 0.65979034 2.57166011
20 -1.46001684 0.65979034
21 1.01539489 -1.46001684
22 -1.05182065 1.01539489
23 -1.80195751 -1.05182065
24 -0.72583259 -1.80195751
25 1.06984049 -0.72583259
26 1.91821674 1.06984049
27 -0.16630300 1.91821674
28 1.28534194 -0.16630300
29 1.06257306 1.28534194
30 -1.27595150 1.06257306
31 -0.27998784 -1.27595150
32 3.47314701 -0.27998784
33 -0.17019346 3.47314701
34 2.02502977 -0.17019346
35 -1.66216139 2.02502977
36 -1.70130117 -1.66216139
37 1.10046246 -1.70130117
38 0.76416978 1.10046246
39 -0.17090232 0.76416978
40 1.60026075 -0.17090232
41 0.28534194 1.60026075
42 1.31968700 0.28534194
43 2.90912968 1.31968700
44 -1.27347878 2.90912968
45 -0.53166435 -1.27347878
46 0.03340796 -0.53166435
47 -1.70768098 0.03340796
48 -0.70379052 -1.70768098
49 2.42726466 -0.70379052
50 -2.68100523 2.42726466
51 -0.17605979 -2.68100523
52 -0.26851671 -0.17605979
53 -1.45481392 -0.26851671
54 1.19084706 -1.45481392
55 -0.56297854 1.19084706
56 -0.36951917 -0.56297854
57 1.55973759 -0.36951917
58 -1.00746132 1.55973759
59 0.51739958 -1.00746132
60 -0.47000816 0.51739958
61 0.86911368 -0.47000816
62 1.12072404 0.86911368
63 -2.52190756 1.12072404
64 0.74627564 -2.52190756
65 -1.16908271 0.74627564
66 -1.29983091 -1.16908271
67 0.27487628 -1.29983091
68 -0.05192593 0.27487628
69 -0.77135218 -0.05192593
70 -0.31171430 -0.77135218
71 -1.37714936 -0.31171430
72 0.39913205 -1.37714936
73 1.25512186 0.39913205
74 1.62800811 1.25512186
75 -0.41291216 1.62800811
76 1.25532356 -0.41291216
77 0.33511473 1.25532356
78 -1.22441485 0.33511473
79 0.53129799 -1.22441485
80 -0.61027862 0.53129799
81 -0.67889526 -0.61027862
82 0.17529155 -0.67889526
83 1.03553456 0.17529155
84 -1.38033094 1.03553456
85 2.02397479 -1.38033094
86 0.21567031 2.02397479
87 -1.53237321 0.21567031
88 -0.73123090 -1.53237321
89 0.12781272 -0.73123090
90 0.43773032 0.12781272
91 -1.09815439 0.43773032
92 -0.54393299 -1.09815439
93 1.82359409 -0.54393299
94 -1.41823698 1.82359409
95 2.31252484 -1.41823698
96 -0.14638754 2.31252484
97 0.90998296 -0.14638754
98 0.12001517 0.90998296
99 0.12835423 0.12001517
100 0.38901251 0.12835423
101 0.66215778 0.38901251
102 2.53553456 0.66215778
103 0.63011808 2.53553456
104 -1.60673428 0.63011808
105 0.02717301 -1.60673428
106 -0.97744295 0.02717301
107 -0.65438048 -0.97744295
108 -0.05074903 -0.65438048
109 0.57037060 -0.05074903
110 0.41018467 0.57037060
111 -0.03953537 0.41018467
112 -2.86083400 -0.03953537
113 1.44925097 -2.86083400
114 -2.78187360 1.44925097
115 1.45394041 -2.78187360
116 2.27629401 1.45394041
117 -0.93742694 2.27629401
118 0.63669328 -0.93742694
119 -0.88725227 0.63669328
120 -1.82039947 -0.88725227
121 0.36253848 -1.82039947
122 -0.40848019 0.36253848
123 -2.34351316 -0.40848019
124 -1.87998483 -2.34351316
125 2.22652122 -1.87998483
126 0.34818236 2.22652122
127 -0.77418765 0.34818236
128 0.47596584 -0.77418765
129 -1.39795876 0.47596584
130 -0.90848019 -1.39795876
131 -0.07651420 -0.90848019
132 0.40629421 -0.07651420
133 -0.19666749 0.40629421
134 -0.32258184 -0.19666749
135 2.74674367 -0.32258184
136 0.52201554 2.74674367
137 -0.02190756 0.52201554
138 -0.47284363 -0.02190756
139 -2.10017571 -0.47284363
140 -2.60157681 -2.10017571
141 0.05115769 -2.60157681
142 1.38572417 0.05115769
143 -1.78503856 1.38572417
144 1.13987487 -1.78503856
145 -0.51493047 1.13987487
146 1.35077699 -0.51493047
147 -0.42621329 1.35077699
148 -0.42874177 -0.42621329
149 -0.05696149 -0.42874177
150 -1.11861768 -0.05696149
151 1.34527341 -1.11861768
152 3.70465683 1.34527341
153 1.32146749 3.70465683
154 2.00295335 1.32146749
155 1.06611739 2.00295335
156 -1.15614433 1.06611739
157 0.43949418 -1.15614433
158 -0.07086621 0.43949418
> 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/rcomp/tmp/7iquo1321981523.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8h8tw1321981523.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9ct091321981523.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10ff4i1321981523.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11gun91321981523.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/rcomp/tmp/1220111321981523.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/rcomp/tmp/139cev1321981523.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/rcomp/tmp/149wgs1321981523.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/rcomp/tmp/15mbh31321981523.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/rcomp/tmp/16kb0x1321981523.tab")
+ }
>
> try(system("convert tmp/1f3z31321981523.ps tmp/1f3z31321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wmfu1321981523.ps tmp/2wmfu1321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/320q41321981523.ps tmp/320q41321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/4s8dr1321981523.ps tmp/4s8dr1321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/5z8bc1321981523.ps tmp/5z8bc1321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lxhl1321981523.ps tmp/6lxhl1321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/7iquo1321981523.ps tmp/7iquo1321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/8h8tw1321981523.ps tmp/8h8tw1321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ct091321981523.ps tmp/9ct091321981523.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ff4i1321981523.ps tmp/10ff4i1321981523.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.300 0.648 6.961