R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(4
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+ ,dim=c(7
+ ,152)
+ ,dimnames=list(c('y'
+ ,'x1'
+ ,'x2'
+ ,'x3'
+ ,'x4'
+ ,'x5'
+ ,'x6')
+ ,1:152))
> y <- array(NA,dim=c(7,152),dimnames=list(c('y','x1','x2','x3','x4','x5','x6'),1:152))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
y x1 x2 x3 x4 x5 x6 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 4 4 5 4 4 4 4 1 0 0 0 0 0 0 0 0 0 0
2 4 4 4 4 3 4 4 0 1 0 0 0 0 0 0 0 0 0
3 5 5 4 4 5 5 4 0 0 1 0 0 0 0 0 0 0 0
4 3 3 2 3 4 4 3 0 0 0 1 0 0 0 0 0 0 0
5 2 3 2 3 2 4 3 0 0 0 0 1 0 0 0 0 0 0
6 5 4 3 3 4 5 4 0 0 0 0 0 1 0 0 0 0 0
7 4 3 3 3 3 4 4 0 0 0 0 0 0 1 0 0 0 0
8 2 3 4 4 2 4 2 0 0 0 0 0 0 0 1 0 0 0
9 4 4 3 4 4 5 3 0 0 0 0 0 0 0 0 1 0 0
10 4 3 2 3 2 2 3 0 0 0 0 0 0 0 0 0 1 0
11 4 3 2 4 4 4 4 0 0 0 0 0 0 0 0 0 0 1
12 2 3 2 4 2 3 2 0 0 0 0 0 0 0 0 0 0 0
13 5 4 2 5 5 5 4 1 0 0 0 0 0 0 0 0 0 0
14 3 4 2 3 3 4 4 0 1 0 0 0 0 0 0 0 0 0
15 4 3 4 4 4 4 4 0 0 1 0 0 0 0 0 0 0 0
16 4 3 3 4 4 5 4 0 0 0 1 0 0 0 0 0 0 0
17 3 2 3 3 3 3 3 0 0 0 0 1 0 0 0 0 0 0
18 4 4 4 4 4 4 4 0 0 0 0 0 1 0 0 0 0 0
19 2 3 2 2 2 4 2 0 0 0 0 0 0 1 0 0 0 0
20 4 2 4 4 3 4 4 0 0 0 0 0 0 0 1 0 0 0
21 3 3 2 4 4 4 3 0 0 0 0 0 0 0 0 1 0 0
22 3 2 4 4 2 3 4 0 0 0 0 0 0 0 0 0 1 0
23 4 4 2 4 4 4 4 0 0 0 0 0 0 0 0 0 0 1
24 4 4 3 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0
25 4 4 4 4 4 4 4 1 0 0 0 0 0 0 0 0 0 0
26 4 3 3 4 3 4 3 0 1 0 0 0 0 0 0 0 0 0
27 5 4 4 4 4 4 4 0 0 1 0 0 0 0 0 0 0 0
28 3 4 3 2 4 4 4 0 0 0 1 0 0 0 0 0 0 0
29 1 4 4 4 4 4 4 0 0 0 0 1 0 0 0 0 0 0
30 4 2 4 4 4 3 4 0 0 0 0 0 1 0 0 0 0 0
31 4 2 4 4 4 4 4 0 0 0 0 0 0 1 0 0 0 0
32 3 4 3 2 4 4 4 0 0 0 0 0 0 0 1 0 0 0
33 3 2 4 4 4 3 4 0 0 0 0 0 0 0 0 1 0 0
34 4 5 4 4 5 4 4 0 0 0 0 0 0 0 0 0 1 0
35 4 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 1
36 4 4 4 4 4 4 5 0 0 0 0 0 0 0 0 0 0 0
37 3 2 3 3 5 4 4 1 0 0 0 0 0 0 0 0 0 0
38 4 2 4 4 4 4 4 0 1 0 0 0 0 0 0 0 0 0
39 3 3 3 3 4 4 4 0 0 1 0 0 0 0 0 0 0 0
40 4 3 4 3 4 4 3 0 0 0 1 0 0 0 0 0 0 0
41 3 4 4 3 3 3 4 0 0 0 0 1 0 0 0 0 0 0
42 4 4 4 3 4 4 2 0 0 0 0 0 1 0 0 0 0 0
43 3 2 3 2 3 2 2 0 0 0 0 0 0 1 0 0 0 0
44 2 4 2 2 5 2 4 0 0 0 0 0 0 0 1 0 0 0
45 3 4 4 4 5 4 4 0 0 0 0 0 0 0 0 1 0 0
46 4 4 4 2 4 4 5 0 0 0 0 0 0 0 0 0 1 0
47 4 4 4 4 5 5 4 0 0 0 0 0 0 0 0 0 0 1
48 3 2 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0
49 3 3 4 3 4 3 4 1 0 0 0 0 0 0 0 0 0 0
50 4 2 4 4 4 4 5 0 1 0 0 0 0 0 0 0 0 0
51 4 2 4 4 4 4 3 0 0 1 0 0 0 0 0 0 0 0
52 3 4 3 3 4 3 2 0 0 0 1 0 0 0 0 0 0 0
53 2 4 2 1 4 4 4 0 0 0 0 1 0 0 0 0 0 0
54 4 4 4 4 4 4 4 0 0 0 0 0 1 0 0 0 0 0
55 4 3 4 4 4 3 2 0 0 0 0 0 0 1 0 0 0 0
56 3 4 4 2 4 3 2 0 0 0 0 0 0 0 1 0 0 0
57 2 5 2 2 4 2 4 0 0 0 0 0 0 0 0 1 0 0
58 4 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 1 0
59 3 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 1
60 3 4 4 3 4 4 3 0 0 0 0 0 0 0 0 0 0 0
61 4 4 4 3 4 4 2 1 0 0 0 0 0 0 0 0 0 0
62 3 2 3 1 4 3 4 0 1 0 0 0 0 0 0 0 0 0
63 4 4 4 4 4 4 5 0 0 1 0 0 0 0 0 0 0 0
64 3 4 4 2 4 4 4 0 0 0 1 0 0 0 0 0 0 0
65 4 3 4 4 4 4 5 0 0 0 0 1 0 0 0 0 0 0
66 4 4 5 5 5 5 4 0 0 0 0 0 1 0 0 0 0 0
67 4 2 4 3 4 4 3 0 0 0 0 0 0 1 0 0 0 0
68 3 2 3 3 4 3 3 0 0 0 0 0 0 0 1 0 0 0
69 3 2 3 2 3 2 4 0 0 0 0 0 0 0 0 1 0 0
70 3 4 4 4 4 4 3 0 0 0 0 0 0 0 0 0 1 0
71 4 4 3 2 4 2 2 0 0 0 0 0 0 0 0 0 0 1
72 3 3 3 2 2 2 4 0 0 0 0 0 0 0 0 0 0 0
73 2 2 2 2 4 2 3 1 0 0 0 0 0 0 0 0 0 0
74 4 2 4 4 5 4 5 0 1 0 0 0 0 0 0 0 0 0
75 4 2 4 5 4 4 5 0 0 1 0 0 0 0 0 0 0 0
76 4 5 4 4 5 5 4 0 0 0 1 0 0 0 0 0 0 0
77 3 4 2 2 3 2 5 0 0 0 0 1 0 0 0 0 0 0
78 5 4 4 5 4 5 4 0 0 0 0 0 1 0 0 0 0 0
79 3 2 4 2 4 4 3 0 0 0 0 0 0 1 0 0 0 0
80 2 2 3 3 3 3 3 0 0 0 0 0 0 0 1 0 0 0
81 3 4 3 4 4 3 4 0 0 0 0 0 0 0 0 1 0 0
82 3 4 3 3 4 4 4 0 0 0 0 0 0 0 0 0 1 0
83 4 4 4 2 4 4 3 0 0 0 0 0 0 0 0 0 0 1
84 4 4 3 3 4 3 4 0 0 0 0 0 0 0 0 0 0 0
85 3 2 3 4 4 4 3 1 0 0 0 0 0 0 0 0 0 0
86 2 2 2 1 4 2 3 0 1 0 0 0 0 0 0 0 0 0
87 4 4 4 2 5 4 3 0 0 1 0 0 0 0 0 0 0 0
88 4 3 4 2 4 3 2 0 0 0 1 0 0 0 0 0 0 0
89 3 2 2 3 4 2 5 0 0 0 0 1 0 0 0 0 0 0
90 4 2 4 3 4 4 3 0 0 0 0 0 1 0 0 0 0 0
91 3 4 3 2 4 4 4 0 0 0 0 0 0 1 0 0 0 0
92 2 4 2 2 5 4 4 0 0 0 0 0 0 0 1 0 0 0
93 3 3 4 4 4 3 3 0 0 0 0 0 0 0 0 1 0 0
94 3 4 3 3 4 3 3 0 0 0 0 0 0 0 0 0 1 0
95 3 3 3 3 3 2 4 0 0 0 0 0 0 0 0 0 0 1
96 4 3 3 4 4 3 4 0 0 0 0 0 0 0 0 0 0 0
97 4 4 5 4 4 3 3 1 0 0 0 0 0 0 0 0 0 0
98 4 4 4 2 4 2 3 0 1 0 0 0 0 0 0 0 0 0
99 3 4 2 2 5 4 4 0 0 1 0 0 0 0 0 0 0 0
100 4 4 4 4 5 4 2 0 0 0 1 0 0 0 0 0 0 0
101 4 3 3 3 4 3 4 0 0 0 0 1 0 0 0 0 0 0
102 3 4 2 2 4 2 4 0 0 0 0 0 1 0 0 0 0 0
103 4 2 4 4 5 4 4 0 0 0 0 0 0 1 0 0 0 0
104 3 3 4 3 5 4 5 0 0 0 0 0 0 0 1 0 0 0
105 4 4 3 3 4 5 5 0 0 0 0 0 0 0 0 1 0 0
106 4 3 4 4 5 5 5 0 0 0 0 0 0 0 0 0 1 0
107 3 3 4 3 4 4 4 0 0 0 0 0 0 0 0 0 0 1
108 3 2 4 4 4 3 4 0 0 0 0 0 0 0 0 0 0 0
109 3 2 4 3 4 4 3 1 0 0 0 0 0 0 0 0 0 0
110 3 2 4 3 4 4 2 0 1 0 0 0 0 0 0 0 0 0
111 3 2 4 3 2 3 2 0 0 1 0 0 0 0 0 0 0 0
112 2 4 2 2 4 2 4 0 0 0 1 0 0 0 0 0 0 0
113 4 2 4 2 5 5 2 0 0 0 0 1 0 0 0 0 0 0
114 2 3 3 1 4 3 3 0 0 0 0 0 1 0 0 0 0 0
115 3 4 3 2 4 4 4 0 0 0 0 0 0 1 0 0 0 0
116 3 3 4 3 4 3 4 0 0 0 0 0 0 0 1 0 0 0
117 3 3 3 3 4 3 4 0 0 0 0 0 0 0 0 1 0 0
118 4 4 4 3 4 5 4 0 0 0 0 0 0 0 0 0 1 0
119 4 3 3 3 3 4 3 0 0 0 0 0 0 0 0 0 0 1
120 3 2 3 2 4 3 4 0 0 0 0 0 0 0 0 0 0 0
121 4 3 4 4 4 4 3 1 0 0 0 0 0 0 0 0 0 0
122 3 2 3 2 3 4 4 0 1 0 0 0 0 0 0 0 0 0
123 3 3 4 3 4 4 4 0 0 1 0 0 0 0 0 0 0 0
124 3 4 3 3 5 4 4 0 0 0 1 0 0 0 0 0 0 0
125 4 3 4 4 5 4 2 0 0 0 0 1 0 0 0 0 0 0
126 2 3 2 3 3 4 5 0 0 0 0 0 1 0 0 0 0 0
127 4 4 3 3 5 4 5 0 0 0 0 0 0 1 0 0 0 0
128 3 2 4 3 4 4 3 0 0 0 0 0 0 0 1 0 0 0
129 3 2 3 4 4 2 3 0 0 0 0 0 0 0 0 1 0 0
130 4 3 4 4 3 5 3 0 0 0 0 0 0 0 0 0 1 0
131 4 3 3 3 3 4 4 0 0 0 0 0 0 0 0 0 0 1
132 4 3 4 4 4 4 3 0 0 0 0 0 0 0 0 0 0 0
133 3 5 1 5 5 4 2 1 0 0 0 0 0 0 0 0 0 0
134 2 4 2 2 2 1 5 0 1 0 0 0 0 0 0 0 0 0
135 4 4 4 4 4 4 4 0 0 1 0 0 0 0 0 0 0 0
136 2 4 4 4 4 4 2 0 0 0 1 0 0 0 0 0 0 0
137 3 3 3 3 4 4 4 0 0 0 0 1 0 0 0 0 0 0
138 4 4 4 3 5 4 3 0 0 0 0 0 1 0 0 0 0 0
139 3 3 4 4 4 2 2 0 0 0 0 0 0 1 0 0 0 0
140 3 2 2 3 4 4 3 0 0 0 0 0 0 0 1 0 0 0
141 3 4 4 2 4 4 3 0 0 0 0 0 0 0 0 1 0 0
142 3 4 4 4 4 3 4 0 0 0 0 0 0 0 0 0 1 0
143 4 4 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 1
144 3 2 4 4 4 4 4 0 0 0 0 0 0 0 0 0 0 0
145 3 4 4 3 5 4 2 1 0 0 0 0 0 0 0 0 0 0
146 2 2 2 4 3 3 5 0 1 0 0 0 0 0 0 0 0 0
147 2 4 4 4 4 4 4 0 0 1 0 0 0 0 0 0 0 0
148 3 3 3 4 4 2 4 0 0 0 1 0 0 0 0 0 0 0
149 4 2 4 4 4 4 3 0 0 0 0 1 0 0 0 0 0 0
150 3 3 3 3 4 4 3 0 0 0 0 0 1 0 0 0 0 0
151 4 2 4 3 4 4 5 0 0 0 0 0 0 1 0 0 0 0
152 3 5 5 5 5 5 4 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x1 x2 x3 x4 x5
0.798651 0.004725 0.207077 0.157772 0.149637 0.173327
x6 M1 M2 M3 M4 M5
0.031980 -0.038711 0.076874 0.112509 -0.132102 -0.157713
M6 M7 M8 M9 M10 M11
0.217054 0.161410 -0.554040 -0.206281 0.107726 0.377412
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.53901 -0.41049 0.05538 0.37671 1.45011
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.798651 0.446895 1.787 0.07618 .
x1 0.004725 0.063896 0.074 0.94116
x2 0.207077 0.072053 2.874 0.00472 **
x3 0.157772 0.066102 2.387 0.01839 *
x4 0.149637 0.083208 1.798 0.07437 .
x5 0.173327 0.073206 2.368 0.01933 *
x6 0.031980 0.063865 0.501 0.61738
M1 -0.038711 0.259658 -0.149 0.88171
M2 0.076874 0.253947 0.303 0.76258
M3 0.112509 0.254616 0.442 0.65929
M4 -0.132102 0.261175 -0.506 0.61383
M5 -0.157713 0.253138 -0.623 0.53432
M6 0.217054 0.255342 0.850 0.39681
M7 0.161410 0.255943 0.631 0.52935
M8 -0.554040 0.254923 -2.173 0.03151 *
M9 -0.206281 0.258109 -0.799 0.42559
M10 0.107726 0.260012 0.414 0.67931
M11 0.377412 0.258872 1.458 0.14720
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.6249 on 134 degrees of freedom
Multiple R-squared: 0.3993, Adjusted R-squared: 0.3231
F-statistic: 5.239 on 17 and 134 DF, p-value: 1.010e-08
> 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,] 0.12059671 0.24119343 0.879403287
[2,] 0.81985618 0.36028765 0.180143824
[3,] 0.72740167 0.54519665 0.272598326
[4,] 0.73584813 0.52830375 0.264151875
[5,] 0.63888171 0.72223658 0.361118288
[6,] 0.64376114 0.71247771 0.356238855
[7,] 0.73866997 0.52266007 0.261330034
[8,] 0.67257488 0.65485023 0.327425117
[9,] 0.99330950 0.01338101 0.006690505
[10,] 0.99048207 0.01903586 0.009517930
[11,] 0.98457331 0.03085339 0.015426694
[12,] 0.98313875 0.03372250 0.016861248
[13,] 0.97675998 0.04648004 0.023240020
[14,] 0.96645528 0.06708945 0.033544723
[15,] 0.95529341 0.08941319 0.044706594
[16,] 0.93857247 0.12285506 0.061427528
[17,] 0.97015210 0.05969580 0.029847899
[18,] 0.95939433 0.08121134 0.040605669
[19,] 0.97698121 0.04603759 0.023018794
[20,] 0.98416639 0.03166722 0.015833611
[21,] 0.98614695 0.02770610 0.013853048
[22,] 0.98225934 0.03548132 0.017740662
[23,] 0.97717047 0.04565907 0.022829535
[24,] 0.97549588 0.04900824 0.024504121
[25,] 0.97390452 0.05219096 0.026095481
[26,] 0.96714660 0.06570680 0.032853398
[27,] 0.95708317 0.08583367 0.042916834
[28,] 0.95218286 0.09563428 0.047817139
[29,] 0.94152450 0.11695100 0.058475498
[30,] 0.92604961 0.14790077 0.073950386
[31,] 0.90842010 0.18315981 0.091579904
[32,] 0.88323066 0.23353869 0.116769343
[33,] 0.88341130 0.23317741 0.116588703
[34,] 0.86924869 0.26150262 0.130751309
[35,] 0.85648358 0.28703284 0.143516422
[36,] 0.84915574 0.30168851 0.150844257
[37,] 0.83271123 0.33457754 0.167288769
[38,] 0.80741199 0.38517603 0.192588014
[39,] 0.85936964 0.28126073 0.140630363
[40,] 0.84749528 0.30500944 0.152504722
[41,] 0.84676258 0.30647483 0.153237416
[42,] 0.81511373 0.36977253 0.184886267
[43,] 0.79223041 0.41553918 0.207769591
[44,] 0.75728620 0.48542760 0.242713801
[45,] 0.78393727 0.43212546 0.216062729
[46,] 0.80578791 0.38842419 0.194212093
[47,] 0.78298209 0.43403581 0.217017905
[48,] 0.76532330 0.46935341 0.234676703
[49,] 0.77140165 0.45719670 0.228598350
[50,] 0.79679886 0.40640229 0.203201145
[51,] 0.84449707 0.31100587 0.155502933
[52,] 0.82538536 0.34922928 0.174614640
[53,] 0.83185564 0.33628873 0.168144364
[54,] 0.80169051 0.39661899 0.198309493
[55,] 0.78789953 0.42420094 0.212100470
[56,] 0.75094519 0.49810962 0.249054809
[57,] 0.74164531 0.51670937 0.258354687
[58,] 0.80140560 0.39718879 0.198594396
[59,] 0.78762822 0.42474356 0.212371778
[60,] 0.76985271 0.46029457 0.230147286
[61,] 0.72959141 0.54081717 0.270408587
[62,] 0.71005975 0.57988051 0.289940255
[63,] 0.67478805 0.65042391 0.325211953
[64,] 0.70294764 0.59410472 0.297052361
[65,] 0.66654717 0.66690565 0.333452827
[66,] 0.66221741 0.67556518 0.337782588
[67,] 0.64804375 0.70391251 0.351956254
[68,] 0.73588026 0.52823947 0.264119737
[69,] 0.70399999 0.59200001 0.296000006
[70,] 0.70365914 0.59268172 0.296340861
[71,] 0.67794962 0.64410076 0.322050380
[72,] 0.68339459 0.63321081 0.316605405
[73,] 0.64389771 0.71220458 0.356102288
[74,] 0.60688223 0.78623554 0.393117771
[75,] 0.57320402 0.85359196 0.426795980
[76,] 0.60257380 0.79485240 0.397426199
[77,] 0.58669712 0.82660576 0.413302880
[78,] 0.71056936 0.57886128 0.289430642
[79,] 0.67014346 0.65971307 0.329856537
[80,] 0.72442639 0.55114722 0.275573611
[81,] 0.76469760 0.47060480 0.235302398
[82,] 0.77931427 0.44137145 0.220685727
[83,] 0.73223385 0.53553231 0.267766155
[84,] 0.68053282 0.63893436 0.319467182
[85,] 0.69873360 0.60253280 0.301266400
[86,] 0.64248721 0.71502558 0.357512789
[87,] 0.74878466 0.50243068 0.251215341
[88,] 0.71593934 0.56812133 0.284060663
[89,] 0.70079411 0.59841178 0.299205891
[90,] 0.65148839 0.69702322 0.348511612
[91,] 0.61893902 0.76212197 0.381060983
[92,] 0.58811530 0.82376939 0.411884695
[93,] 0.55865089 0.88269822 0.441349112
[94,] 0.62282526 0.75434949 0.377174745
[95,] 0.57415934 0.85168133 0.425840664
[96,] 0.50687251 0.98625499 0.493127493
[97,] 0.43355898 0.86711797 0.566441016
[98,] 0.36795830 0.73591660 0.632041701
[99,] 0.30870080 0.61740161 0.691299197
[100,] 0.26066085 0.52132171 0.739339147
[101,] 0.30862699 0.61725399 0.691373006
[102,] 0.24365047 0.48730093 0.756349533
[103,] 0.19635954 0.39271907 0.803640463
[104,] 0.14504816 0.29009632 0.854951841
[105,] 0.10080017 0.20160034 0.899199832
[106,] 0.13304409 0.26608819 0.866955905
[107,] 0.09463269 0.18926538 0.905367310
[108,] 0.05716468 0.11432936 0.942835320
[109,] 0.03135332 0.06270663 0.968646684
[110,] 0.03143631 0.06287262 0.968563690
[111,] 0.01538128 0.03076256 0.984618722
> postscript(file="/var/www/html/rcomp/tmp/1gsnv1291380810.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qj4g1291380810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3qj4g1291380810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4qj4g1291380810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/51tmj1291380810.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 = 152
Frequency = 1
1 2 3 4 5 6
0.134913535 0.376043328 0.863081010 0.044011159 -0.631102888 1.277747015
7 8 9 10 11 12
0.661080961 -0.774720994 0.575289786 1.450111046 0.344746274 -0.741281548
13 14 15 16 17 18
1.275407419 -0.052031723 0.195495725 0.473856522 0.190235268 0.086225403
19 20 21 22 23 24
-0.760474310 1.016407960 -0.039581561 -0.322394858 0.340020959 0.510356107
25 26 27 28 29 30
0.341990118 0.619824743 1.190770410 -0.041998474 -2.539007329 0.269002788
31 32 33 34 35 36
0.151320581 0.379940148 -0.307662175 0.041190453 -0.074132208 0.271300008
37 38 39 40 41 42
-0.433348214 0.235856630 -0.439655909 0.629857992 -0.058271463 0.307956219
43 44 45 46 47 48
0.234190600 -0.215967090 -0.640076889 0.479117148 -0.397096291 -0.687269845
49 50 51 52 53 54
-0.322186029 0.203877113 0.232200557 0.037515530 -0.651538813 0.086225403
55 56 57 58 59 60
0.383881052 0.410149351 -0.418814635 0.195553098 -1.074132208 -0.506969177
61 62 63 64 65 66
0.563720934 0.089575316 0.158790893 -0.249075057 0.433738470 -0.601587047
67 68 69 70 71 72
0.341071880 0.436925267 0.537922058 -0.772467386 0.859100482 0.476553156
73 74 75 76 77 78
-0.540229494 0.054239784 0.010469742 0.107691978 0.655000725 0.755126866
79 80 81 82 83 84
-0.501156337 -0.413437404 -0.110036223 -0.439598536 0.273390874 0.841454644
85 86 87 88 89 90
-0.409503151 -0.498041831 0.388656162 0.992936045 0.357042243 0.285427334
91 92 93 94 95 96
-0.335509901 -0.562620598 -0.280407974 -0.234292265 -0.208267689 0.688408177
97 98 99 100 101 102
0.340219805 0.920582588 -0.229170187 0.349703080 1.003893107 0.162575643
103 104 105 106 107 108
0.001683252 -0.161799748 0.669102536 -0.154665186 -0.911635109 -0.513943091
109 110 111 112 113 114
-0.458807951 -0.542412555 -0.105446731 -0.488268383 0.526981818 -1.023351079
115 116 117 118 119 120
-0.335509901 0.193143851 0.052460876 0.179998127 0.477058320 0.008677058
121 122 123 124 125 126
0.378694950 -0.091885892 -0.646732492 -0.349407586 0.380039690 -1.219466519
127 128 129 130 131 132
0.325101470 0.056521929 0.104720679 0.208568506 0.445078804 0.339984356
133 134 135 136 137 138
-0.284955527 -0.256621865 0.190770410 -1.500659590 -0.169433647 0.126339373
139 140 141 142 143 144
-0.442792194 0.470675096 -0.142916478 -0.631120148 -0.074132208 -0.687269845
145 146 147 148 149 150
-0.585916396 -1.059005637 -1.809229590 -0.006163216 0.502422819 -0.512221399
151 152
0.277112848 -0.835217767
> postscript(file="/var/www/html/rcomp/tmp/61tmj1291380810.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 = 152
Frequency = 1
lag(myerror, k = 1) myerror
0 0.134913535 NA
1 0.376043328 0.134913535
2 0.863081010 0.376043328
3 0.044011159 0.863081010
4 -0.631102888 0.044011159
5 1.277747015 -0.631102888
6 0.661080961 1.277747015
7 -0.774720994 0.661080961
8 0.575289786 -0.774720994
9 1.450111046 0.575289786
10 0.344746274 1.450111046
11 -0.741281548 0.344746274
12 1.275407419 -0.741281548
13 -0.052031723 1.275407419
14 0.195495725 -0.052031723
15 0.473856522 0.195495725
16 0.190235268 0.473856522
17 0.086225403 0.190235268
18 -0.760474310 0.086225403
19 1.016407960 -0.760474310
20 -0.039581561 1.016407960
21 -0.322394858 -0.039581561
22 0.340020959 -0.322394858
23 0.510356107 0.340020959
24 0.341990118 0.510356107
25 0.619824743 0.341990118
26 1.190770410 0.619824743
27 -0.041998474 1.190770410
28 -2.539007329 -0.041998474
29 0.269002788 -2.539007329
30 0.151320581 0.269002788
31 0.379940148 0.151320581
32 -0.307662175 0.379940148
33 0.041190453 -0.307662175
34 -0.074132208 0.041190453
35 0.271300008 -0.074132208
36 -0.433348214 0.271300008
37 0.235856630 -0.433348214
38 -0.439655909 0.235856630
39 0.629857992 -0.439655909
40 -0.058271463 0.629857992
41 0.307956219 -0.058271463
42 0.234190600 0.307956219
43 -0.215967090 0.234190600
44 -0.640076889 -0.215967090
45 0.479117148 -0.640076889
46 -0.397096291 0.479117148
47 -0.687269845 -0.397096291
48 -0.322186029 -0.687269845
49 0.203877113 -0.322186029
50 0.232200557 0.203877113
51 0.037515530 0.232200557
52 -0.651538813 0.037515530
53 0.086225403 -0.651538813
54 0.383881052 0.086225403
55 0.410149351 0.383881052
56 -0.418814635 0.410149351
57 0.195553098 -0.418814635
58 -1.074132208 0.195553098
59 -0.506969177 -1.074132208
60 0.563720934 -0.506969177
61 0.089575316 0.563720934
62 0.158790893 0.089575316
63 -0.249075057 0.158790893
64 0.433738470 -0.249075057
65 -0.601587047 0.433738470
66 0.341071880 -0.601587047
67 0.436925267 0.341071880
68 0.537922058 0.436925267
69 -0.772467386 0.537922058
70 0.859100482 -0.772467386
71 0.476553156 0.859100482
72 -0.540229494 0.476553156
73 0.054239784 -0.540229494
74 0.010469742 0.054239784
75 0.107691978 0.010469742
76 0.655000725 0.107691978
77 0.755126866 0.655000725
78 -0.501156337 0.755126866
79 -0.413437404 -0.501156337
80 -0.110036223 -0.413437404
81 -0.439598536 -0.110036223
82 0.273390874 -0.439598536
83 0.841454644 0.273390874
84 -0.409503151 0.841454644
85 -0.498041831 -0.409503151
86 0.388656162 -0.498041831
87 0.992936045 0.388656162
88 0.357042243 0.992936045
89 0.285427334 0.357042243
90 -0.335509901 0.285427334
91 -0.562620598 -0.335509901
92 -0.280407974 -0.562620598
93 -0.234292265 -0.280407974
94 -0.208267689 -0.234292265
95 0.688408177 -0.208267689
96 0.340219805 0.688408177
97 0.920582588 0.340219805
98 -0.229170187 0.920582588
99 0.349703080 -0.229170187
100 1.003893107 0.349703080
101 0.162575643 1.003893107
102 0.001683252 0.162575643
103 -0.161799748 0.001683252
104 0.669102536 -0.161799748
105 -0.154665186 0.669102536
106 -0.911635109 -0.154665186
107 -0.513943091 -0.911635109
108 -0.458807951 -0.513943091
109 -0.542412555 -0.458807951
110 -0.105446731 -0.542412555
111 -0.488268383 -0.105446731
112 0.526981818 -0.488268383
113 -1.023351079 0.526981818
114 -0.335509901 -1.023351079
115 0.193143851 -0.335509901
116 0.052460876 0.193143851
117 0.179998127 0.052460876
118 0.477058320 0.179998127
119 0.008677058 0.477058320
120 0.378694950 0.008677058
121 -0.091885892 0.378694950
122 -0.646732492 -0.091885892
123 -0.349407586 -0.646732492
124 0.380039690 -0.349407586
125 -1.219466519 0.380039690
126 0.325101470 -1.219466519
127 0.056521929 0.325101470
128 0.104720679 0.056521929
129 0.208568506 0.104720679
130 0.445078804 0.208568506
131 0.339984356 0.445078804
132 -0.284955527 0.339984356
133 -0.256621865 -0.284955527
134 0.190770410 -0.256621865
135 -1.500659590 0.190770410
136 -0.169433647 -1.500659590
137 0.126339373 -0.169433647
138 -0.442792194 0.126339373
139 0.470675096 -0.442792194
140 -0.142916478 0.470675096
141 -0.631120148 -0.142916478
142 -0.074132208 -0.631120148
143 -0.687269845 -0.074132208
144 -0.585916396 -0.687269845
145 -1.059005637 -0.585916396
146 -1.809229590 -1.059005637
147 -0.006163216 -1.809229590
148 0.502422819 -0.006163216
149 -0.512221399 0.502422819
150 0.277112848 -0.512221399
151 -0.835217767 0.277112848
152 NA -0.835217767
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.376043328 0.134913535
[2,] 0.863081010 0.376043328
[3,] 0.044011159 0.863081010
[4,] -0.631102888 0.044011159
[5,] 1.277747015 -0.631102888
[6,] 0.661080961 1.277747015
[7,] -0.774720994 0.661080961
[8,] 0.575289786 -0.774720994
[9,] 1.450111046 0.575289786
[10,] 0.344746274 1.450111046
[11,] -0.741281548 0.344746274
[12,] 1.275407419 -0.741281548
[13,] -0.052031723 1.275407419
[14,] 0.195495725 -0.052031723
[15,] 0.473856522 0.195495725
[16,] 0.190235268 0.473856522
[17,] 0.086225403 0.190235268
[18,] -0.760474310 0.086225403
[19,] 1.016407960 -0.760474310
[20,] -0.039581561 1.016407960
[21,] -0.322394858 -0.039581561
[22,] 0.340020959 -0.322394858
[23,] 0.510356107 0.340020959
[24,] 0.341990118 0.510356107
[25,] 0.619824743 0.341990118
[26,] 1.190770410 0.619824743
[27,] -0.041998474 1.190770410
[28,] -2.539007329 -0.041998474
[29,] 0.269002788 -2.539007329
[30,] 0.151320581 0.269002788
[31,] 0.379940148 0.151320581
[32,] -0.307662175 0.379940148
[33,] 0.041190453 -0.307662175
[34,] -0.074132208 0.041190453
[35,] 0.271300008 -0.074132208
[36,] -0.433348214 0.271300008
[37,] 0.235856630 -0.433348214
[38,] -0.439655909 0.235856630
[39,] 0.629857992 -0.439655909
[40,] -0.058271463 0.629857992
[41,] 0.307956219 -0.058271463
[42,] 0.234190600 0.307956219
[43,] -0.215967090 0.234190600
[44,] -0.640076889 -0.215967090
[45,] 0.479117148 -0.640076889
[46,] -0.397096291 0.479117148
[47,] -0.687269845 -0.397096291
[48,] -0.322186029 -0.687269845
[49,] 0.203877113 -0.322186029
[50,] 0.232200557 0.203877113
[51,] 0.037515530 0.232200557
[52,] -0.651538813 0.037515530
[53,] 0.086225403 -0.651538813
[54,] 0.383881052 0.086225403
[55,] 0.410149351 0.383881052
[56,] -0.418814635 0.410149351
[57,] 0.195553098 -0.418814635
[58,] -1.074132208 0.195553098
[59,] -0.506969177 -1.074132208
[60,] 0.563720934 -0.506969177
[61,] 0.089575316 0.563720934
[62,] 0.158790893 0.089575316
[63,] -0.249075057 0.158790893
[64,] 0.433738470 -0.249075057
[65,] -0.601587047 0.433738470
[66,] 0.341071880 -0.601587047
[67,] 0.436925267 0.341071880
[68,] 0.537922058 0.436925267
[69,] -0.772467386 0.537922058
[70,] 0.859100482 -0.772467386
[71,] 0.476553156 0.859100482
[72,] -0.540229494 0.476553156
[73,] 0.054239784 -0.540229494
[74,] 0.010469742 0.054239784
[75,] 0.107691978 0.010469742
[76,] 0.655000725 0.107691978
[77,] 0.755126866 0.655000725
[78,] -0.501156337 0.755126866
[79,] -0.413437404 -0.501156337
[80,] -0.110036223 -0.413437404
[81,] -0.439598536 -0.110036223
[82,] 0.273390874 -0.439598536
[83,] 0.841454644 0.273390874
[84,] -0.409503151 0.841454644
[85,] -0.498041831 -0.409503151
[86,] 0.388656162 -0.498041831
[87,] 0.992936045 0.388656162
[88,] 0.357042243 0.992936045
[89,] 0.285427334 0.357042243
[90,] -0.335509901 0.285427334
[91,] -0.562620598 -0.335509901
[92,] -0.280407974 -0.562620598
[93,] -0.234292265 -0.280407974
[94,] -0.208267689 -0.234292265
[95,] 0.688408177 -0.208267689
[96,] 0.340219805 0.688408177
[97,] 0.920582588 0.340219805
[98,] -0.229170187 0.920582588
[99,] 0.349703080 -0.229170187
[100,] 1.003893107 0.349703080
[101,] 0.162575643 1.003893107
[102,] 0.001683252 0.162575643
[103,] -0.161799748 0.001683252
[104,] 0.669102536 -0.161799748
[105,] -0.154665186 0.669102536
[106,] -0.911635109 -0.154665186
[107,] -0.513943091 -0.911635109
[108,] -0.458807951 -0.513943091
[109,] -0.542412555 -0.458807951
[110,] -0.105446731 -0.542412555
[111,] -0.488268383 -0.105446731
[112,] 0.526981818 -0.488268383
[113,] -1.023351079 0.526981818
[114,] -0.335509901 -1.023351079
[115,] 0.193143851 -0.335509901
[116,] 0.052460876 0.193143851
[117,] 0.179998127 0.052460876
[118,] 0.477058320 0.179998127
[119,] 0.008677058 0.477058320
[120,] 0.378694950 0.008677058
[121,] -0.091885892 0.378694950
[122,] -0.646732492 -0.091885892
[123,] -0.349407586 -0.646732492
[124,] 0.380039690 -0.349407586
[125,] -1.219466519 0.380039690
[126,] 0.325101470 -1.219466519
[127,] 0.056521929 0.325101470
[128,] 0.104720679 0.056521929
[129,] 0.208568506 0.104720679
[130,] 0.445078804 0.208568506
[131,] 0.339984356 0.445078804
[132,] -0.284955527 0.339984356
[133,] -0.256621865 -0.284955527
[134,] 0.190770410 -0.256621865
[135,] -1.500659590 0.190770410
[136,] -0.169433647 -1.500659590
[137,] 0.126339373 -0.169433647
[138,] -0.442792194 0.126339373
[139,] 0.470675096 -0.442792194
[140,] -0.142916478 0.470675096
[141,] -0.631120148 -0.142916478
[142,] -0.074132208 -0.631120148
[143,] -0.687269845 -0.074132208
[144,] -0.585916396 -0.687269845
[145,] -1.059005637 -0.585916396
[146,] -1.809229590 -1.059005637
[147,] -0.006163216 -1.809229590
[148,] 0.502422819 -0.006163216
[149,] -0.512221399 0.502422819
[150,] 0.277112848 -0.512221399
[151,] -0.835217767 0.277112848
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.376043328 0.134913535
2 0.863081010 0.376043328
3 0.044011159 0.863081010
4 -0.631102888 0.044011159
5 1.277747015 -0.631102888
6 0.661080961 1.277747015
7 -0.774720994 0.661080961
8 0.575289786 -0.774720994
9 1.450111046 0.575289786
10 0.344746274 1.450111046
11 -0.741281548 0.344746274
12 1.275407419 -0.741281548
13 -0.052031723 1.275407419
14 0.195495725 -0.052031723
15 0.473856522 0.195495725
16 0.190235268 0.473856522
17 0.086225403 0.190235268
18 -0.760474310 0.086225403
19 1.016407960 -0.760474310
20 -0.039581561 1.016407960
21 -0.322394858 -0.039581561
22 0.340020959 -0.322394858
23 0.510356107 0.340020959
24 0.341990118 0.510356107
25 0.619824743 0.341990118
26 1.190770410 0.619824743
27 -0.041998474 1.190770410
28 -2.539007329 -0.041998474
29 0.269002788 -2.539007329
30 0.151320581 0.269002788
31 0.379940148 0.151320581
32 -0.307662175 0.379940148
33 0.041190453 -0.307662175
34 -0.074132208 0.041190453
35 0.271300008 -0.074132208
36 -0.433348214 0.271300008
37 0.235856630 -0.433348214
38 -0.439655909 0.235856630
39 0.629857992 -0.439655909
40 -0.058271463 0.629857992
41 0.307956219 -0.058271463
42 0.234190600 0.307956219
43 -0.215967090 0.234190600
44 -0.640076889 -0.215967090
45 0.479117148 -0.640076889
46 -0.397096291 0.479117148
47 -0.687269845 -0.397096291
48 -0.322186029 -0.687269845
49 0.203877113 -0.322186029
50 0.232200557 0.203877113
51 0.037515530 0.232200557
52 -0.651538813 0.037515530
53 0.086225403 -0.651538813
54 0.383881052 0.086225403
55 0.410149351 0.383881052
56 -0.418814635 0.410149351
57 0.195553098 -0.418814635
58 -1.074132208 0.195553098
59 -0.506969177 -1.074132208
60 0.563720934 -0.506969177
61 0.089575316 0.563720934
62 0.158790893 0.089575316
63 -0.249075057 0.158790893
64 0.433738470 -0.249075057
65 -0.601587047 0.433738470
66 0.341071880 -0.601587047
67 0.436925267 0.341071880
68 0.537922058 0.436925267
69 -0.772467386 0.537922058
70 0.859100482 -0.772467386
71 0.476553156 0.859100482
72 -0.540229494 0.476553156
73 0.054239784 -0.540229494
74 0.010469742 0.054239784
75 0.107691978 0.010469742
76 0.655000725 0.107691978
77 0.755126866 0.655000725
78 -0.501156337 0.755126866
79 -0.413437404 -0.501156337
80 -0.110036223 -0.413437404
81 -0.439598536 -0.110036223
82 0.273390874 -0.439598536
83 0.841454644 0.273390874
84 -0.409503151 0.841454644
85 -0.498041831 -0.409503151
86 0.388656162 -0.498041831
87 0.992936045 0.388656162
88 0.357042243 0.992936045
89 0.285427334 0.357042243
90 -0.335509901 0.285427334
91 -0.562620598 -0.335509901
92 -0.280407974 -0.562620598
93 -0.234292265 -0.280407974
94 -0.208267689 -0.234292265
95 0.688408177 -0.208267689
96 0.340219805 0.688408177
97 0.920582588 0.340219805
98 -0.229170187 0.920582588
99 0.349703080 -0.229170187
100 1.003893107 0.349703080
101 0.162575643 1.003893107
102 0.001683252 0.162575643
103 -0.161799748 0.001683252
104 0.669102536 -0.161799748
105 -0.154665186 0.669102536
106 -0.911635109 -0.154665186
107 -0.513943091 -0.911635109
108 -0.458807951 -0.513943091
109 -0.542412555 -0.458807951
110 -0.105446731 -0.542412555
111 -0.488268383 -0.105446731
112 0.526981818 -0.488268383
113 -1.023351079 0.526981818
114 -0.335509901 -1.023351079
115 0.193143851 -0.335509901
116 0.052460876 0.193143851
117 0.179998127 0.052460876
118 0.477058320 0.179998127
119 0.008677058 0.477058320
120 0.378694950 0.008677058
121 -0.091885892 0.378694950
122 -0.646732492 -0.091885892
123 -0.349407586 -0.646732492
124 0.380039690 -0.349407586
125 -1.219466519 0.380039690
126 0.325101470 -1.219466519
127 0.056521929 0.325101470
128 0.104720679 0.056521929
129 0.208568506 0.104720679
130 0.445078804 0.208568506
131 0.339984356 0.445078804
132 -0.284955527 0.339984356
133 -0.256621865 -0.284955527
134 0.190770410 -0.256621865
135 -1.500659590 0.190770410
136 -0.169433647 -1.500659590
137 0.126339373 -0.169433647
138 -0.442792194 0.126339373
139 0.470675096 -0.442792194
140 -0.142916478 0.470675096
141 -0.631120148 -0.142916478
142 -0.074132208 -0.631120148
143 -0.687269845 -0.074132208
144 -0.585916396 -0.687269845
145 -1.059005637 -0.585916396
146 -1.809229590 -1.059005637
147 -0.006163216 -1.809229590
148 0.502422819 -0.006163216
149 -0.512221399 0.502422819
150 0.277112848 -0.512221399
151 -0.835217767 0.277112848
> 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/7u2lm1291380810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84b2p1291380810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94b2p1291380810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104b2p1291380810.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111liy1291380810.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/12tczj1291380810.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/130vwd1291380810.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/143ed01291380810.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/157wbo1291380810.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/16sfsc1291380810.tab")
+ }
>
> try(system("convert tmp/1gsnv1291380810.ps tmp/1gsnv1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qj4g1291380810.ps tmp/2qj4g1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qj4g1291380810.ps tmp/3qj4g1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qj4g1291380810.ps tmp/4qj4g1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/51tmj1291380810.ps tmp/51tmj1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/61tmj1291380810.ps tmp/61tmj1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/7u2lm1291380810.ps tmp/7u2lm1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/84b2p1291380810.ps tmp/84b2p1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/94b2p1291380810.ps tmp/94b2p1291380810.png",intern=TRUE))
character(0)
> try(system("convert tmp/104b2p1291380810.ps tmp/104b2p1291380810.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.120 1.760 9.074