R version 2.11.1 (2010-05-31)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24
+ ,24
+ ,14
+ ,11
+ ,12
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+ ,16)
+ ,dim=c(5
+ ,159)
+ ,dimnames=list(c('PS'
+ ,'CM'
+ ,'D'
+ ,'PE'
+ ,'PC')
+ ,1:159))
> y <- array(NA,dim=c(5,159),dimnames=list(c('PS','CM','D','PE','PC'),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 = '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
> 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
PS CM D PE PC M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 24 24 14 11 12 1 0 0 0 0 0 0 0 0 0 0
2 25 25 11 7 8 0 1 0 0 0 0 0 0 0 0 0
3 30 17 6 17 8 0 0 1 0 0 0 0 0 0 0 0
4 19 18 12 10 8 0 0 0 1 0 0 0 0 0 0 0
5 22 18 8 12 9 0 0 0 0 1 0 0 0 0 0 0
6 22 16 10 12 7 0 0 0 0 0 1 0 0 0 0 0
7 25 20 10 11 4 0 0 0 0 0 0 1 0 0 0 0
8 23 16 11 11 11 0 0 0 0 0 0 0 1 0 0 0
9 17 18 16 12 7 0 0 0 0 0 0 0 0 1 0 0
10 21 17 11 13 7 0 0 0 0 0 0 0 0 0 1 0
11 19 23 13 14 12 0 0 0 0 0 0 0 0 0 0 1
12 19 30 12 16 10 0 0 0 0 0 0 0 0 0 0 0
13 15 23 8 11 10 1 0 0 0 0 0 0 0 0 0 0
14 16 18 12 10 8 0 1 0 0 0 0 0 0 0 0 0
15 23 15 11 11 8 0 0 1 0 0 0 0 0 0 0 0
16 27 12 4 15 4 0 0 0 1 0 0 0 0 0 0 0
17 22 21 9 9 9 0 0 0 0 1 0 0 0 0 0 0
18 14 15 8 11 8 0 0 0 0 0 1 0 0 0 0 0
19 22 20 8 17 7 0 0 0 0 0 0 1 0 0 0 0
20 23 31 14 17 11 0 0 0 0 0 0 0 1 0 0 0
21 23 27 15 11 9 0 0 0 0 0 0 0 0 1 0 0
22 21 34 16 18 11 0 0 0 0 0 0 0 0 0 1 0
23 19 21 9 14 13 0 0 0 0 0 0 0 0 0 0 1
24 18 31 14 10 8 0 0 0 0 0 0 0 0 0 0 0
25 20 19 11 11 8 1 0 0 0 0 0 0 0 0 0 0
26 23 16 8 15 9 0 1 0 0 0 0 0 0 0 0 0
27 25 20 9 15 6 0 0 1 0 0 0 0 0 0 0 0
28 19 21 9 13 9 0 0 0 1 0 0 0 0 0 0 0
29 24 22 9 16 9 0 0 0 0 1 0 0 0 0 0 0
30 22 17 9 13 6 0 0 0 0 0 1 0 0 0 0 0
31 25 24 10 9 6 0 0 0 0 0 0 1 0 0 0 0
32 26 25 16 18 16 0 0 0 0 0 0 0 1 0 0 0
33 29 26 11 18 5 0 0 0 0 0 0 0 0 1 0 0
34 32 25 8 12 7 0 0 0 0 0 0 0 0 0 1 0
35 25 17 9 17 9 0 0 0 0 0 0 0 0 0 0 1
36 29 32 16 9 6 0 0 0 0 0 0 0 0 0 0 0
37 28 33 11 9 6 1 0 0 0 0 0 0 0 0 0 0
38 17 13 16 12 5 0 1 0 0 0 0 0 0 0 0 0
39 28 32 12 18 12 0 0 1 0 0 0 0 0 0 0 0
40 29 25 12 12 7 0 0 0 1 0 0 0 0 0 0 0
41 26 29 14 18 10 0 0 0 0 1 0 0 0 0 0 0
42 25 22 9 14 9 0 0 0 0 0 1 0 0 0 0 0
43 14 18 10 15 8 0 0 0 0 0 0 1 0 0 0 0
44 25 17 9 16 5 0 0 0 0 0 0 0 1 0 0 0
45 26 20 10 10 8 0 0 0 0 0 0 0 0 1 0 0
46 20 15 12 11 8 0 0 0 0 0 0 0 0 0 1 0
47 18 20 14 14 10 0 0 0 0 0 0 0 0 0 0 1
48 32 33 14 9 6 0 0 0 0 0 0 0 0 0 0 0
49 25 29 10 12 8 1 0 0 0 0 0 0 0 0 0 0
50 25 23 14 17 7 0 1 0 0 0 0 0 0 0 0 0
51 23 26 16 5 4 0 0 1 0 0 0 0 0 0 0 0
52 21 18 9 12 8 0 0 0 1 0 0 0 0 0 0 0
53 20 20 10 12 8 0 0 0 0 1 0 0 0 0 0 0
54 15 11 6 6 4 0 0 0 0 0 1 0 0 0 0 0
55 30 28 8 24 20 0 0 0 0 0 0 1 0 0 0 0
56 24 26 13 12 8 0 0 0 0 0 0 0 1 0 0 0
57 26 22 10 12 8 0 0 0 0 0 0 0 0 1 0 0
58 24 17 8 14 6 0 0 0 0 0 0 0 0 0 1 0
59 22 12 7 7 4 0 0 0 0 0 0 0 0 0 0 1
60 14 14 15 13 8 0 0 0 0 0 0 0 0 0 0 0
61 24 17 9 12 9 1 0 0 0 0 0 0 0 0 0 0
62 24 21 10 13 6 0 1 0 0 0 0 0 0 0 0 0
63 24 19 12 14 7 0 0 1 0 0 0 0 0 0 0 0
64 24 18 13 8 9 0 0 0 1 0 0 0 0 0 0 0
65 19 10 10 11 5 0 0 0 0 1 0 0 0 0 0 0
66 31 29 11 9 5 0 0 0 0 0 1 0 0 0 0 0
67 22 31 8 11 8 0 0 0 0 0 0 1 0 0 0 0
68 27 19 9 13 8 0 0 0 0 0 0 0 1 0 0 0
69 19 9 13 10 6 0 0 0 0 0 0 0 0 1 0 0
70 25 20 11 11 8 0 0 0 0 0 0 0 0 0 1 0
71 20 28 8 12 7 0 0 0 0 0 0 0 0 0 0 1
72 21 19 9 9 7 0 0 0 0 0 0 0 0 0 0 0
73 27 30 9 15 9 1 0 0 0 0 0 0 0 0 0 0
74 23 29 15 18 11 0 1 0 0 0 0 0 0 0 0 0
75 25 26 9 15 6 0 0 1 0 0 0 0 0 0 0 0
76 20 23 10 12 8 0 0 0 1 0 0 0 0 0 0 0
77 21 13 14 13 6 0 0 0 0 1 0 0 0 0 0 0
78 22 21 12 14 9 0 0 0 0 0 1 0 0 0 0 0
79 23 19 12 10 8 0 0 0 0 0 0 1 0 0 0 0
80 25 28 11 13 6 0 0 0 0 0 0 0 1 0 0 0
81 25 23 14 13 10 0 0 0 0 0 0 0 0 1 0 0
82 17 18 6 11 8 0 0 0 0 0 0 0 0 0 1 0
83 19 21 12 13 8 0 0 0 0 0 0 0 0 0 0 1
84 25 20 8 16 10 0 0 0 0 0 0 0 0 0 0 0
85 19 23 14 8 5 1 0 0 0 0 0 0 0 0 0 0
86 20 21 11 16 7 0 1 0 0 0 0 0 0 0 0 0
87 26 21 10 11 5 0 0 1 0 0 0 0 0 0 0 0
88 23 15 14 9 8 0 0 0 1 0 0 0 0 0 0 0
89 27 28 12 16 14 0 0 0 0 1 0 0 0 0 0 0
90 17 19 10 12 7 0 0 0 0 0 1 0 0 0 0 0
91 17 26 14 14 8 0 0 0 0 0 0 1 0 0 0 0
92 19 10 5 8 6 0 0 0 0 0 0 0 1 0 0 0
93 17 16 11 9 5 0 0 0 0 0 0 0 0 1 0 0
94 22 22 10 15 6 0 0 0 0 0 0 0 0 0 1 0
95 21 19 9 11 10 0 0 0 0 0 0 0 0 0 0 1
96 32 31 10 21 12 0 0 0 0 0 0 0 0 0 0 0
97 21 31 16 14 9 1 0 0 0 0 0 0 0 0 0 0
98 21 29 13 18 12 0 1 0 0 0 0 0 0 0 0 0
99 18 19 9 12 7 0 0 1 0 0 0 0 0 0 0 0
100 18 22 10 13 8 0 0 0 1 0 0 0 0 0 0 0
101 23 23 10 15 10 0 0 0 0 1 0 0 0 0 0 0
102 19 15 7 12 6 0 0 0 0 0 1 0 0 0 0 0
103 20 20 9 19 10 0 0 0 0 0 0 1 0 0 0 0
104 21 18 8 15 10 0 0 0 0 0 0 0 1 0 0 0
105 20 23 14 11 10 0 0 0 0 0 0 0 0 1 0 0
106 17 25 14 11 5 0 0 0 0 0 0 0 0 0 1 0
107 18 21 8 10 7 0 0 0 0 0 0 0 0 0 0 1
108 19 24 9 13 10 0 0 0 0 0 0 0 0 0 0 0
109 22 25 14 15 11 1 0 0 0 0 0 0 0 0 0 0
110 15 17 14 12 6 0 1 0 0 0 0 0 0 0 0 0
111 14 13 8 12 7 0 0 1 0 0 0 0 0 0 0 0
112 18 28 8 16 12 0 0 0 1 0 0 0 0 0 0 0
113 24 21 8 9 11 0 0 0 0 1 0 0 0 0 0 0
114 35 25 7 18 11 0 0 0 0 0 1 0 0 0 0 0
115 29 9 6 8 11 0 0 0 0 0 0 1 0 0 0 0
116 21 16 8 13 5 0 0 0 0 0 0 0 1 0 0 0
117 25 19 6 17 8 0 0 0 0 0 0 0 0 1 0 0
118 20 17 11 9 6 0 0 0 0 0 0 0 0 0 1 0
119 22 25 14 15 9 0 0 0 0 0 0 0 0 0 0 1
120 13 20 11 8 4 0 0 0 0 0 0 0 0 0 0 0
121 26 29 11 7 4 1 0 0 0 0 0 0 0 0 0 0
122 17 14 11 12 7 0 1 0 0 0 0 0 0 0 0 0
123 25 22 14 14 11 0 0 1 0 0 0 0 0 0 0 0
124 20 15 8 6 6 0 0 0 1 0 0 0 0 0 0 0
125 19 19 20 8 7 0 0 0 0 1 0 0 0 0 0 0
126 21 20 11 17 8 0 0 0 0 0 1 0 0 0 0 0
127 22 15 8 10 4 0 0 0 0 0 0 1 0 0 0 0
128 24 20 11 11 8 0 0 0 0 0 0 0 1 0 0 0
129 21 18 10 14 9 0 0 0 0 0 0 0 0 1 0 0
130 26 33 14 11 8 0 0 0 0 0 0 0 0 0 1 0
131 24 22 11 13 11 0 0 0 0 0 0 0 0 0 0 1
132 16 16 9 12 8 0 0 0 0 0 0 0 0 0 0 0
133 23 17 9 11 5 1 0 0 0 0 0 0 0 0 0 0
134 18 16 8 9 4 0 1 0 0 0 0 0 0 0 0 0
135 16 21 10 12 8 0 0 1 0 0 0 0 0 0 0 0
136 26 26 13 20 10 0 0 0 1 0 0 0 0 0 0 0
137 19 18 13 12 6 0 0 0 0 1 0 0 0 0 0 0
138 21 18 12 13 9 0 0 0 0 0 1 0 0 0 0 0
139 21 17 8 12 9 0 0 0 0 0 0 1 0 0 0 0
140 22 22 13 12 13 0 0 0 0 0 0 0 1 0 0 0
141 23 30 14 9 9 0 0 0 0 0 0 0 0 1 0 0
142 29 30 12 15 10 0 0 0 0 0 0 0 0 0 1 0
143 21 24 14 24 20 0 0 0 0 0 0 0 0 0 0 1
144 21 21 15 7 5 0 0 0 0 0 0 0 0 0 0 0
145 23 21 13 17 11 1 0 0 0 0 0 0 0 0 0 0
146 27 29 16 11 6 0 1 0 0 0 0 0 0 0 0 0
147 25 31 9 17 9 0 0 1 0 0 0 0 0 0 0 0
148 21 20 9 11 7 0 0 0 1 0 0 0 0 0 0 0
149 10 16 9 12 9 0 0 0 0 1 0 0 0 0 0 0
150 20 22 8 14 10 0 0 0 0 0 1 0 0 0 0 0
151 26 20 7 11 9 0 0 0 0 0 0 1 0 0 0 0
152 24 28 16 16 8 0 0 0 0 0 0 0 1 0 0 0
153 29 38 11 21 7 0 0 0 0 0 0 0 0 1 0 0
154 19 22 9 14 6 0 0 0 0 0 0 0 0 0 1 0
155 24 20 11 20 13 0 0 0 0 0 0 0 0 0 0 1
156 19 17 9 13 6 0 0 0 0 0 0 0 0 0 0 0
157 24 28 14 11 8 1 0 0 0 0 0 0 0 0 0 0
158 22 22 13 15 10 0 1 0 0 0 0 0 0 0 0 0
159 17 31 16 19 16 0 0 1 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CM D PE PC M1
15.43232 0.36429 -0.32173 0.14266 -0.08496 1.18754
M2 M3 M4 M5 M6 M7
0.58294 1.41429 1.37573 1.13169 1.21436 1.63067
M8 M9 M10 M11
2.61302 2.20956 1.17050 0.13069
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.4444 -2.2125 0.1621 2.3226 10.3821
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.43232 2.04663 7.540 4.95e-12 ***
CM 0.36429 0.06480 5.622 9.59e-08 ***
D -0.32173 0.12643 -2.545 0.012 *
PE 0.14266 0.11623 1.227 0.222
PC -0.08496 0.14890 -0.571 0.569
M1 1.18754 1.46755 0.809 0.420
M2 0.58294 1.49280 0.391 0.697
M3 1.41429 1.47771 0.957 0.340
M4 1.37573 1.51090 0.911 0.364
M5 1.13169 1.51828 0.745 0.457
M6 1.21436 1.52834 0.795 0.428
M7 1.63067 1.53217 1.064 0.289
M8 2.61302 1.51142 1.729 0.086 .
M9 2.20956 1.49828 1.475 0.142
M10 1.17050 1.49510 0.783 0.435
M11 0.13069 1.54495 0.085 0.933
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.796 on 143 degrees of freedom
Multiple R-squared: 0.2667, Adjusted R-squared: 0.1897
F-statistic: 3.467 on 15 and 143 DF, p-value: 4.769e-05
> 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,] 0.6086084 0.78278312 0.39139156
[2,] 0.9593463 0.08130733 0.04065366
[3,] 0.9463858 0.10722846 0.05361423
[4,] 0.9143318 0.17133633 0.08566816
[5,] 0.8653233 0.26935334 0.13467667
[6,] 0.8214060 0.35718792 0.17859396
[7,] 0.7500972 0.49980566 0.24990283
[8,] 0.6971560 0.60568802 0.30284401
[9,] 0.6564182 0.68716357 0.34358178
[10,] 0.6226969 0.75460619 0.37730309
[11,] 0.5418777 0.91624452 0.45812226
[12,] 0.4810207 0.96204134 0.51897933
[13,] 0.4176530 0.83530605 0.58234698
[14,] 0.4221322 0.84426432 0.57786784
[15,] 0.4169221 0.83384411 0.58307795
[16,] 0.5930280 0.81394393 0.40697197
[17,] 0.5856926 0.82861484 0.41430742
[18,] 0.7508274 0.49834526 0.24917263
[19,] 0.6975477 0.60490452 0.30245226
[20,] 0.6556382 0.68872358 0.34436179
[21,] 0.6067501 0.78649973 0.39324987
[22,] 0.6566253 0.68674944 0.34337472
[23,] 0.6095939 0.78081215 0.39040608
[24,] 0.6000411 0.79991776 0.39995888
[25,] 0.6758213 0.64835738 0.32417869
[26,] 0.6723552 0.65528957 0.32764478
[27,] 0.6612962 0.67740754 0.33870377
[28,] 0.6065828 0.78683432 0.39341716
[29,] 0.5613720 0.87725609 0.43862804
[30,] 0.6869029 0.62619429 0.31309715
[31,] 0.6345001 0.73099985 0.36549993
[32,] 0.6153282 0.76934355 0.38467178
[33,] 0.6366462 0.72670768 0.36335384
[34,] 0.5868464 0.82630724 0.41315362
[35,] 0.5518724 0.89625511 0.44812755
[36,] 0.5895291 0.82094170 0.41047085
[37,] 0.6768214 0.64635720 0.32317860
[38,] 0.6347080 0.73058397 0.36529199
[39,] 0.5964196 0.80716071 0.40358035
[40,] 0.5566817 0.88663666 0.44331833
[41,] 0.5371757 0.92564865 0.46282432
[42,] 0.4996978 0.99939565 0.50030217
[43,] 0.4980015 0.99600296 0.50199852
[44,] 0.4625418 0.92508350 0.53745825
[45,] 0.4401340 0.88026801 0.55986599
[46,] 0.4703029 0.94060575 0.52969712
[47,] 0.4261795 0.85235909 0.57382046
[48,] 0.5161803 0.96763942 0.48381971
[49,] 0.5603953 0.87920946 0.43960473
[50,] 0.5484805 0.90303907 0.45151954
[51,] 0.5089717 0.98205659 0.49102830
[52,] 0.5026441 0.99471188 0.49735594
[53,] 0.5489635 0.90207309 0.45103654
[54,] 0.5002926 0.99941479 0.49970740
[55,] 0.4512376 0.90247525 0.54876238
[56,] 0.4035077 0.80701539 0.59649230
[57,] 0.4004471 0.80089415 0.59955293
[58,] 0.3930131 0.78602617 0.60698691
[59,] 0.3901923 0.78038455 0.60980772
[60,] 0.3442847 0.68856949 0.65571525
[61,] 0.3200800 0.64015990 0.67992005
[62,] 0.2898456 0.57969112 0.71015444
[63,] 0.2660923 0.53218452 0.73390774
[64,] 0.3162564 0.63251288 0.68374356
[65,] 0.2770610 0.55412199 0.72293901
[66,] 0.2680756 0.53615119 0.73192441
[67,] 0.2409239 0.48184782 0.75907609
[68,] 0.2147809 0.42956188 0.78521906
[69,] 0.2595895 0.51917898 0.74041051
[70,] 0.3062720 0.61254397 0.69372801
[71,] 0.2857269 0.57145381 0.71427309
[72,] 0.3028983 0.60579660 0.69710170
[73,] 0.4121505 0.82430107 0.58784946
[74,] 0.3750221 0.75004422 0.62497789
[75,] 0.3674174 0.73483477 0.63258262
[76,] 0.3265176 0.65303516 0.67348242
[77,] 0.2831096 0.56621929 0.71689035
[78,] 0.4072356 0.81447115 0.59276443
[79,] 0.4057246 0.81144928 0.59427536
[80,] 0.3894956 0.77899130 0.61050435
[81,] 0.3941871 0.78837419 0.60581291
[82,] 0.4068782 0.81375640 0.59312180
[83,] 0.3701894 0.74037883 0.62981058
[84,] 0.3396002 0.67920040 0.66039980
[85,] 0.3626113 0.72522251 0.63738875
[86,] 0.3247478 0.64949554 0.67525223
[87,] 0.2970577 0.59411537 0.70294231
[88,] 0.3426094 0.68521880 0.65739060
[89,] 0.3427119 0.68542384 0.65728808
[90,] 0.3173565 0.63471303 0.68264349
[91,] 0.2782765 0.55655306 0.72172347
[92,] 0.2804275 0.56085503 0.71957249
[93,] 0.2954880 0.59097591 0.70451204
[94,] 0.4403371 0.88067428 0.55966286
[95,] 0.4477858 0.89557168 0.55221416
[96,] 0.8489129 0.30217410 0.15108705
[97,] 0.9734138 0.05317246 0.02658623
[98,] 0.9623524 0.07529527 0.03764764
[99,] 0.9649047 0.07019063 0.03509532
[100,] 0.9504457 0.09910860 0.04955430
[101,] 0.9506926 0.09861477 0.04930739
[102,] 0.9817799 0.03644012 0.01822006
[103,] 0.9729409 0.05411812 0.02705906
[104,] 0.9631209 0.07375811 0.03687905
[105,] 0.9893429 0.02131415 0.01065707
[106,] 0.9825457 0.03490856 0.01745428
[107,] 0.9774269 0.04514619 0.02257310
[108,] 0.9647403 0.07051940 0.03525970
[109,] 0.9520954 0.09580913 0.04790456
[110,] 0.9408560 0.11828797 0.05914398
[111,] 0.9392123 0.12157539 0.06078770
[112,] 0.9146383 0.17072349 0.08536174
[113,] 0.8747313 0.25053744 0.12526872
[114,] 0.8249443 0.35011139 0.17505570
[115,] 0.7819607 0.43607867 0.21803934
[116,] 0.7133498 0.57330047 0.28665023
[117,] 0.6378200 0.72436010 0.36218005
[118,] 0.5494737 0.90105262 0.45052631
[119,] 0.6641387 0.67172262 0.33586131
[120,] 0.7144506 0.57109879 0.28554939
[121,] 0.5839985 0.83200306 0.41600153
[122,] 0.4150036 0.83000721 0.58499639
> postscript(file="/var/www/rcomp/tmp/1wxbq1290530559.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2pobt1290530559.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3pobt1290530559.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4pobt1290530559.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ifsw1290530559.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 = 159
Frequency = 1
1 2 3 4 5
2.591599e+00 3.097489e+00 7.145303e+00 -1.251473e+00 5.053010e-01
6 7 8 9 10
1.624759e+00 2.639034e+00 2.030323e+00 -3.168661e+00 4.833928e-01
11 12 13 14 15
-1.736952e+00 -4.933287e+00 -8.144400e+00 -3.458685e+00 3.338465e+00
16 17 18 19 20
5.307346e+00 1.621104e-01 -6.426787e+00 -1.605468e+00 -3.324842e+00
21 22 23 24 25
-4.564636e-01 -4.474403e+00 -2.210315e+00 -4.968118e+00 -8.919593e-01
26 27 28 29 30
2.354678e+00 2.132991e+00 -3.652546e+00 7.992304e-01 7.111188e-01
31 32 33 34 35
1.637089e+00 2.786536e+00 3.282485e+00 7.746505e+00 4.479052e+00
36 37 38 39 40
6.283775e+00 2.123302e+00 1.095072e-01 1.808445e+00 5.828192e+00
41 42 43 44 45
1.657461e+00 2.001875e+00 -7.863150e+00 1.799526e+00 3.542651e+00
46 47 48 49 50
9.039824e-01 -1.492263e+00 8.276024e+00 7.102962e-04 3.279752e+00
51 52 53 54 55
1.455948e+00 -5.019681e-01 -1.664793e+00 -4.239636e+00 3.586092e+00
56 57 58 59 60
-3.667073e-01 2.528751e+00 2.290591e+00 3.658810e+00 -2.881345e+00
61 62 63 64 65
3.135480e+00 2.207086e+00 2.690088e+00 4.440527e+00 8.659234e-01
66 67 68 69 70
6.468698e+00 -4.671818e+00 3.753787e+00 1.345154e+00 3.760781e+00
71 72 73 74 75
-4.306566e+00 8.524694e-01 9.716838e-01 -3.870961e-01 -5.277727e-02
76 77 78 79 80
-3.001713e+00 2.859605e+00 3.313554e-01 2.129287e+00 -1.051332e+00
81 82 83 84 85
2.478638e+00 -5.119272e+00 -1.527283e+00 3.422746e+00 -2.210873e+00
86 87 88 89 90
-1.814189e+00 3.576083e+00 4.627523e+00 3.003456e+00 -4.468125e+00
91 92 93 94 95
-6.347939e+00 -1.711123e+00 -3.790672e+00 -1.030081e+00 6.913545e-01
96 97 98 99 100
6.515610e+00 -2.997857e+00 -2.945591e+00 -3.989788e+00 -4.780074e+00
101 102 103 104 105
-1.571910e-02 -2.061094e+00 -3.314164e+00 -2.319033e+00 -2.236051e+00
106 107 108 109 110
-5.350393e+00 -3.471193e+00 -3.284739e+00 -4.282773e-01 -3.906167e+00
111 112 113 114 115
-6.125748e+00 -7.697417e+00 2.010305e+00 9.864838e+00 1.038206e+01
116 117 118 119 120
-1.729943e+00 6.214465e-01 -3.094875e-02 4.586464e-01 -6.980599e+00
121 122 123 124 125
1.695867e+00 -1.693506e+00 3.580507e+00 -4.480569e-02 1.402444e+00
126 127 128 129 130
-1.139005e+00 9.597056e-01 1.318259e+00 -1.214418e+00 9.901347e-01
131 132 133 134 135
3.041579e+00 -3.397650e+00 1.938288e+00 -2.214201e+00 -6.311687e+00
136 137 138 139 140
1.899271e+00 -1.140942e+00 5.668945e-01 -6.293852e-01 -4.847198e-01
141 142 143 144 145
-1.585766e+00 4.038865e+00 -5.263751e-01 2.169637e+00 1.421863e+00
146 147 148 149 150
4.508409e+00 -1.904676e+00 -1.172864e+00 -1.044438e+01 -3.234891e+00
151 152 153 154 155
3.098657e+00 -7.007317e-01 -1.347093e+00 -4.209154e+00 2.941506e+00
156 157 158 159
-1.074523e+00 7.945736e-01 8.625133e-01 -7.343155e+00
> postscript(file="/var/www/rcomp/tmp/6ifsw1290530559.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 2.591599e+00 NA
1 3.097489e+00 2.591599e+00
2 7.145303e+00 3.097489e+00
3 -1.251473e+00 7.145303e+00
4 5.053010e-01 -1.251473e+00
5 1.624759e+00 5.053010e-01
6 2.639034e+00 1.624759e+00
7 2.030323e+00 2.639034e+00
8 -3.168661e+00 2.030323e+00
9 4.833928e-01 -3.168661e+00
10 -1.736952e+00 4.833928e-01
11 -4.933287e+00 -1.736952e+00
12 -8.144400e+00 -4.933287e+00
13 -3.458685e+00 -8.144400e+00
14 3.338465e+00 -3.458685e+00
15 5.307346e+00 3.338465e+00
16 1.621104e-01 5.307346e+00
17 -6.426787e+00 1.621104e-01
18 -1.605468e+00 -6.426787e+00
19 -3.324842e+00 -1.605468e+00
20 -4.564636e-01 -3.324842e+00
21 -4.474403e+00 -4.564636e-01
22 -2.210315e+00 -4.474403e+00
23 -4.968118e+00 -2.210315e+00
24 -8.919593e-01 -4.968118e+00
25 2.354678e+00 -8.919593e-01
26 2.132991e+00 2.354678e+00
27 -3.652546e+00 2.132991e+00
28 7.992304e-01 -3.652546e+00
29 7.111188e-01 7.992304e-01
30 1.637089e+00 7.111188e-01
31 2.786536e+00 1.637089e+00
32 3.282485e+00 2.786536e+00
33 7.746505e+00 3.282485e+00
34 4.479052e+00 7.746505e+00
35 6.283775e+00 4.479052e+00
36 2.123302e+00 6.283775e+00
37 1.095072e-01 2.123302e+00
38 1.808445e+00 1.095072e-01
39 5.828192e+00 1.808445e+00
40 1.657461e+00 5.828192e+00
41 2.001875e+00 1.657461e+00
42 -7.863150e+00 2.001875e+00
43 1.799526e+00 -7.863150e+00
44 3.542651e+00 1.799526e+00
45 9.039824e-01 3.542651e+00
46 -1.492263e+00 9.039824e-01
47 8.276024e+00 -1.492263e+00
48 7.102962e-04 8.276024e+00
49 3.279752e+00 7.102962e-04
50 1.455948e+00 3.279752e+00
51 -5.019681e-01 1.455948e+00
52 -1.664793e+00 -5.019681e-01
53 -4.239636e+00 -1.664793e+00
54 3.586092e+00 -4.239636e+00
55 -3.667073e-01 3.586092e+00
56 2.528751e+00 -3.667073e-01
57 2.290591e+00 2.528751e+00
58 3.658810e+00 2.290591e+00
59 -2.881345e+00 3.658810e+00
60 3.135480e+00 -2.881345e+00
61 2.207086e+00 3.135480e+00
62 2.690088e+00 2.207086e+00
63 4.440527e+00 2.690088e+00
64 8.659234e-01 4.440527e+00
65 6.468698e+00 8.659234e-01
66 -4.671818e+00 6.468698e+00
67 3.753787e+00 -4.671818e+00
68 1.345154e+00 3.753787e+00
69 3.760781e+00 1.345154e+00
70 -4.306566e+00 3.760781e+00
71 8.524694e-01 -4.306566e+00
72 9.716838e-01 8.524694e-01
73 -3.870961e-01 9.716838e-01
74 -5.277727e-02 -3.870961e-01
75 -3.001713e+00 -5.277727e-02
76 2.859605e+00 -3.001713e+00
77 3.313554e-01 2.859605e+00
78 2.129287e+00 3.313554e-01
79 -1.051332e+00 2.129287e+00
80 2.478638e+00 -1.051332e+00
81 -5.119272e+00 2.478638e+00
82 -1.527283e+00 -5.119272e+00
83 3.422746e+00 -1.527283e+00
84 -2.210873e+00 3.422746e+00
85 -1.814189e+00 -2.210873e+00
86 3.576083e+00 -1.814189e+00
87 4.627523e+00 3.576083e+00
88 3.003456e+00 4.627523e+00
89 -4.468125e+00 3.003456e+00
90 -6.347939e+00 -4.468125e+00
91 -1.711123e+00 -6.347939e+00
92 -3.790672e+00 -1.711123e+00
93 -1.030081e+00 -3.790672e+00
94 6.913545e-01 -1.030081e+00
95 6.515610e+00 6.913545e-01
96 -2.997857e+00 6.515610e+00
97 -2.945591e+00 -2.997857e+00
98 -3.989788e+00 -2.945591e+00
99 -4.780074e+00 -3.989788e+00
100 -1.571910e-02 -4.780074e+00
101 -2.061094e+00 -1.571910e-02
102 -3.314164e+00 -2.061094e+00
103 -2.319033e+00 -3.314164e+00
104 -2.236051e+00 -2.319033e+00
105 -5.350393e+00 -2.236051e+00
106 -3.471193e+00 -5.350393e+00
107 -3.284739e+00 -3.471193e+00
108 -4.282773e-01 -3.284739e+00
109 -3.906167e+00 -4.282773e-01
110 -6.125748e+00 -3.906167e+00
111 -7.697417e+00 -6.125748e+00
112 2.010305e+00 -7.697417e+00
113 9.864838e+00 2.010305e+00
114 1.038206e+01 9.864838e+00
115 -1.729943e+00 1.038206e+01
116 6.214465e-01 -1.729943e+00
117 -3.094875e-02 6.214465e-01
118 4.586464e-01 -3.094875e-02
119 -6.980599e+00 4.586464e-01
120 1.695867e+00 -6.980599e+00
121 -1.693506e+00 1.695867e+00
122 3.580507e+00 -1.693506e+00
123 -4.480569e-02 3.580507e+00
124 1.402444e+00 -4.480569e-02
125 -1.139005e+00 1.402444e+00
126 9.597056e-01 -1.139005e+00
127 1.318259e+00 9.597056e-01
128 -1.214418e+00 1.318259e+00
129 9.901347e-01 -1.214418e+00
130 3.041579e+00 9.901347e-01
131 -3.397650e+00 3.041579e+00
132 1.938288e+00 -3.397650e+00
133 -2.214201e+00 1.938288e+00
134 -6.311687e+00 -2.214201e+00
135 1.899271e+00 -6.311687e+00
136 -1.140942e+00 1.899271e+00
137 5.668945e-01 -1.140942e+00
138 -6.293852e-01 5.668945e-01
139 -4.847198e-01 -6.293852e-01
140 -1.585766e+00 -4.847198e-01
141 4.038865e+00 -1.585766e+00
142 -5.263751e-01 4.038865e+00
143 2.169637e+00 -5.263751e-01
144 1.421863e+00 2.169637e+00
145 4.508409e+00 1.421863e+00
146 -1.904676e+00 4.508409e+00
147 -1.172864e+00 -1.904676e+00
148 -1.044438e+01 -1.172864e+00
149 -3.234891e+00 -1.044438e+01
150 3.098657e+00 -3.234891e+00
151 -7.007317e-01 3.098657e+00
152 -1.347093e+00 -7.007317e-01
153 -4.209154e+00 -1.347093e+00
154 2.941506e+00 -4.209154e+00
155 -1.074523e+00 2.941506e+00
156 7.945736e-01 -1.074523e+00
157 8.625133e-01 7.945736e-01
158 -7.343155e+00 8.625133e-01
159 NA -7.343155e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.097489e+00 2.591599e+00
[2,] 7.145303e+00 3.097489e+00
[3,] -1.251473e+00 7.145303e+00
[4,] 5.053010e-01 -1.251473e+00
[5,] 1.624759e+00 5.053010e-01
[6,] 2.639034e+00 1.624759e+00
[7,] 2.030323e+00 2.639034e+00
[8,] -3.168661e+00 2.030323e+00
[9,] 4.833928e-01 -3.168661e+00
[10,] -1.736952e+00 4.833928e-01
[11,] -4.933287e+00 -1.736952e+00
[12,] -8.144400e+00 -4.933287e+00
[13,] -3.458685e+00 -8.144400e+00
[14,] 3.338465e+00 -3.458685e+00
[15,] 5.307346e+00 3.338465e+00
[16,] 1.621104e-01 5.307346e+00
[17,] -6.426787e+00 1.621104e-01
[18,] -1.605468e+00 -6.426787e+00
[19,] -3.324842e+00 -1.605468e+00
[20,] -4.564636e-01 -3.324842e+00
[21,] -4.474403e+00 -4.564636e-01
[22,] -2.210315e+00 -4.474403e+00
[23,] -4.968118e+00 -2.210315e+00
[24,] -8.919593e-01 -4.968118e+00
[25,] 2.354678e+00 -8.919593e-01
[26,] 2.132991e+00 2.354678e+00
[27,] -3.652546e+00 2.132991e+00
[28,] 7.992304e-01 -3.652546e+00
[29,] 7.111188e-01 7.992304e-01
[30,] 1.637089e+00 7.111188e-01
[31,] 2.786536e+00 1.637089e+00
[32,] 3.282485e+00 2.786536e+00
[33,] 7.746505e+00 3.282485e+00
[34,] 4.479052e+00 7.746505e+00
[35,] 6.283775e+00 4.479052e+00
[36,] 2.123302e+00 6.283775e+00
[37,] 1.095072e-01 2.123302e+00
[38,] 1.808445e+00 1.095072e-01
[39,] 5.828192e+00 1.808445e+00
[40,] 1.657461e+00 5.828192e+00
[41,] 2.001875e+00 1.657461e+00
[42,] -7.863150e+00 2.001875e+00
[43,] 1.799526e+00 -7.863150e+00
[44,] 3.542651e+00 1.799526e+00
[45,] 9.039824e-01 3.542651e+00
[46,] -1.492263e+00 9.039824e-01
[47,] 8.276024e+00 -1.492263e+00
[48,] 7.102962e-04 8.276024e+00
[49,] 3.279752e+00 7.102962e-04
[50,] 1.455948e+00 3.279752e+00
[51,] -5.019681e-01 1.455948e+00
[52,] -1.664793e+00 -5.019681e-01
[53,] -4.239636e+00 -1.664793e+00
[54,] 3.586092e+00 -4.239636e+00
[55,] -3.667073e-01 3.586092e+00
[56,] 2.528751e+00 -3.667073e-01
[57,] 2.290591e+00 2.528751e+00
[58,] 3.658810e+00 2.290591e+00
[59,] -2.881345e+00 3.658810e+00
[60,] 3.135480e+00 -2.881345e+00
[61,] 2.207086e+00 3.135480e+00
[62,] 2.690088e+00 2.207086e+00
[63,] 4.440527e+00 2.690088e+00
[64,] 8.659234e-01 4.440527e+00
[65,] 6.468698e+00 8.659234e-01
[66,] -4.671818e+00 6.468698e+00
[67,] 3.753787e+00 -4.671818e+00
[68,] 1.345154e+00 3.753787e+00
[69,] 3.760781e+00 1.345154e+00
[70,] -4.306566e+00 3.760781e+00
[71,] 8.524694e-01 -4.306566e+00
[72,] 9.716838e-01 8.524694e-01
[73,] -3.870961e-01 9.716838e-01
[74,] -5.277727e-02 -3.870961e-01
[75,] -3.001713e+00 -5.277727e-02
[76,] 2.859605e+00 -3.001713e+00
[77,] 3.313554e-01 2.859605e+00
[78,] 2.129287e+00 3.313554e-01
[79,] -1.051332e+00 2.129287e+00
[80,] 2.478638e+00 -1.051332e+00
[81,] -5.119272e+00 2.478638e+00
[82,] -1.527283e+00 -5.119272e+00
[83,] 3.422746e+00 -1.527283e+00
[84,] -2.210873e+00 3.422746e+00
[85,] -1.814189e+00 -2.210873e+00
[86,] 3.576083e+00 -1.814189e+00
[87,] 4.627523e+00 3.576083e+00
[88,] 3.003456e+00 4.627523e+00
[89,] -4.468125e+00 3.003456e+00
[90,] -6.347939e+00 -4.468125e+00
[91,] -1.711123e+00 -6.347939e+00
[92,] -3.790672e+00 -1.711123e+00
[93,] -1.030081e+00 -3.790672e+00
[94,] 6.913545e-01 -1.030081e+00
[95,] 6.515610e+00 6.913545e-01
[96,] -2.997857e+00 6.515610e+00
[97,] -2.945591e+00 -2.997857e+00
[98,] -3.989788e+00 -2.945591e+00
[99,] -4.780074e+00 -3.989788e+00
[100,] -1.571910e-02 -4.780074e+00
[101,] -2.061094e+00 -1.571910e-02
[102,] -3.314164e+00 -2.061094e+00
[103,] -2.319033e+00 -3.314164e+00
[104,] -2.236051e+00 -2.319033e+00
[105,] -5.350393e+00 -2.236051e+00
[106,] -3.471193e+00 -5.350393e+00
[107,] -3.284739e+00 -3.471193e+00
[108,] -4.282773e-01 -3.284739e+00
[109,] -3.906167e+00 -4.282773e-01
[110,] -6.125748e+00 -3.906167e+00
[111,] -7.697417e+00 -6.125748e+00
[112,] 2.010305e+00 -7.697417e+00
[113,] 9.864838e+00 2.010305e+00
[114,] 1.038206e+01 9.864838e+00
[115,] -1.729943e+00 1.038206e+01
[116,] 6.214465e-01 -1.729943e+00
[117,] -3.094875e-02 6.214465e-01
[118,] 4.586464e-01 -3.094875e-02
[119,] -6.980599e+00 4.586464e-01
[120,] 1.695867e+00 -6.980599e+00
[121,] -1.693506e+00 1.695867e+00
[122,] 3.580507e+00 -1.693506e+00
[123,] -4.480569e-02 3.580507e+00
[124,] 1.402444e+00 -4.480569e-02
[125,] -1.139005e+00 1.402444e+00
[126,] 9.597056e-01 -1.139005e+00
[127,] 1.318259e+00 9.597056e-01
[128,] -1.214418e+00 1.318259e+00
[129,] 9.901347e-01 -1.214418e+00
[130,] 3.041579e+00 9.901347e-01
[131,] -3.397650e+00 3.041579e+00
[132,] 1.938288e+00 -3.397650e+00
[133,] -2.214201e+00 1.938288e+00
[134,] -6.311687e+00 -2.214201e+00
[135,] 1.899271e+00 -6.311687e+00
[136,] -1.140942e+00 1.899271e+00
[137,] 5.668945e-01 -1.140942e+00
[138,] -6.293852e-01 5.668945e-01
[139,] -4.847198e-01 -6.293852e-01
[140,] -1.585766e+00 -4.847198e-01
[141,] 4.038865e+00 -1.585766e+00
[142,] -5.263751e-01 4.038865e+00
[143,] 2.169637e+00 -5.263751e-01
[144,] 1.421863e+00 2.169637e+00
[145,] 4.508409e+00 1.421863e+00
[146,] -1.904676e+00 4.508409e+00
[147,] -1.172864e+00 -1.904676e+00
[148,] -1.044438e+01 -1.172864e+00
[149,] -3.234891e+00 -1.044438e+01
[150,] 3.098657e+00 -3.234891e+00
[151,] -7.007317e-01 3.098657e+00
[152,] -1.347093e+00 -7.007317e-01
[153,] -4.209154e+00 -1.347093e+00
[154,] 2.941506e+00 -4.209154e+00
[155,] -1.074523e+00 2.941506e+00
[156,] 7.945736e-01 -1.074523e+00
[157,] 8.625133e-01 7.945736e-01
[158,] -7.343155e+00 8.625133e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.097489e+00 2.591599e+00
2 7.145303e+00 3.097489e+00
3 -1.251473e+00 7.145303e+00
4 5.053010e-01 -1.251473e+00
5 1.624759e+00 5.053010e-01
6 2.639034e+00 1.624759e+00
7 2.030323e+00 2.639034e+00
8 -3.168661e+00 2.030323e+00
9 4.833928e-01 -3.168661e+00
10 -1.736952e+00 4.833928e-01
11 -4.933287e+00 -1.736952e+00
12 -8.144400e+00 -4.933287e+00
13 -3.458685e+00 -8.144400e+00
14 3.338465e+00 -3.458685e+00
15 5.307346e+00 3.338465e+00
16 1.621104e-01 5.307346e+00
17 -6.426787e+00 1.621104e-01
18 -1.605468e+00 -6.426787e+00
19 -3.324842e+00 -1.605468e+00
20 -4.564636e-01 -3.324842e+00
21 -4.474403e+00 -4.564636e-01
22 -2.210315e+00 -4.474403e+00
23 -4.968118e+00 -2.210315e+00
24 -8.919593e-01 -4.968118e+00
25 2.354678e+00 -8.919593e-01
26 2.132991e+00 2.354678e+00
27 -3.652546e+00 2.132991e+00
28 7.992304e-01 -3.652546e+00
29 7.111188e-01 7.992304e-01
30 1.637089e+00 7.111188e-01
31 2.786536e+00 1.637089e+00
32 3.282485e+00 2.786536e+00
33 7.746505e+00 3.282485e+00
34 4.479052e+00 7.746505e+00
35 6.283775e+00 4.479052e+00
36 2.123302e+00 6.283775e+00
37 1.095072e-01 2.123302e+00
38 1.808445e+00 1.095072e-01
39 5.828192e+00 1.808445e+00
40 1.657461e+00 5.828192e+00
41 2.001875e+00 1.657461e+00
42 -7.863150e+00 2.001875e+00
43 1.799526e+00 -7.863150e+00
44 3.542651e+00 1.799526e+00
45 9.039824e-01 3.542651e+00
46 -1.492263e+00 9.039824e-01
47 8.276024e+00 -1.492263e+00
48 7.102962e-04 8.276024e+00
49 3.279752e+00 7.102962e-04
50 1.455948e+00 3.279752e+00
51 -5.019681e-01 1.455948e+00
52 -1.664793e+00 -5.019681e-01
53 -4.239636e+00 -1.664793e+00
54 3.586092e+00 -4.239636e+00
55 -3.667073e-01 3.586092e+00
56 2.528751e+00 -3.667073e-01
57 2.290591e+00 2.528751e+00
58 3.658810e+00 2.290591e+00
59 -2.881345e+00 3.658810e+00
60 3.135480e+00 -2.881345e+00
61 2.207086e+00 3.135480e+00
62 2.690088e+00 2.207086e+00
63 4.440527e+00 2.690088e+00
64 8.659234e-01 4.440527e+00
65 6.468698e+00 8.659234e-01
66 -4.671818e+00 6.468698e+00
67 3.753787e+00 -4.671818e+00
68 1.345154e+00 3.753787e+00
69 3.760781e+00 1.345154e+00
70 -4.306566e+00 3.760781e+00
71 8.524694e-01 -4.306566e+00
72 9.716838e-01 8.524694e-01
73 -3.870961e-01 9.716838e-01
74 -5.277727e-02 -3.870961e-01
75 -3.001713e+00 -5.277727e-02
76 2.859605e+00 -3.001713e+00
77 3.313554e-01 2.859605e+00
78 2.129287e+00 3.313554e-01
79 -1.051332e+00 2.129287e+00
80 2.478638e+00 -1.051332e+00
81 -5.119272e+00 2.478638e+00
82 -1.527283e+00 -5.119272e+00
83 3.422746e+00 -1.527283e+00
84 -2.210873e+00 3.422746e+00
85 -1.814189e+00 -2.210873e+00
86 3.576083e+00 -1.814189e+00
87 4.627523e+00 3.576083e+00
88 3.003456e+00 4.627523e+00
89 -4.468125e+00 3.003456e+00
90 -6.347939e+00 -4.468125e+00
91 -1.711123e+00 -6.347939e+00
92 -3.790672e+00 -1.711123e+00
93 -1.030081e+00 -3.790672e+00
94 6.913545e-01 -1.030081e+00
95 6.515610e+00 6.913545e-01
96 -2.997857e+00 6.515610e+00
97 -2.945591e+00 -2.997857e+00
98 -3.989788e+00 -2.945591e+00
99 -4.780074e+00 -3.989788e+00
100 -1.571910e-02 -4.780074e+00
101 -2.061094e+00 -1.571910e-02
102 -3.314164e+00 -2.061094e+00
103 -2.319033e+00 -3.314164e+00
104 -2.236051e+00 -2.319033e+00
105 -5.350393e+00 -2.236051e+00
106 -3.471193e+00 -5.350393e+00
107 -3.284739e+00 -3.471193e+00
108 -4.282773e-01 -3.284739e+00
109 -3.906167e+00 -4.282773e-01
110 -6.125748e+00 -3.906167e+00
111 -7.697417e+00 -6.125748e+00
112 2.010305e+00 -7.697417e+00
113 9.864838e+00 2.010305e+00
114 1.038206e+01 9.864838e+00
115 -1.729943e+00 1.038206e+01
116 6.214465e-01 -1.729943e+00
117 -3.094875e-02 6.214465e-01
118 4.586464e-01 -3.094875e-02
119 -6.980599e+00 4.586464e-01
120 1.695867e+00 -6.980599e+00
121 -1.693506e+00 1.695867e+00
122 3.580507e+00 -1.693506e+00
123 -4.480569e-02 3.580507e+00
124 1.402444e+00 -4.480569e-02
125 -1.139005e+00 1.402444e+00
126 9.597056e-01 -1.139005e+00
127 1.318259e+00 9.597056e-01
128 -1.214418e+00 1.318259e+00
129 9.901347e-01 -1.214418e+00
130 3.041579e+00 9.901347e-01
131 -3.397650e+00 3.041579e+00
132 1.938288e+00 -3.397650e+00
133 -2.214201e+00 1.938288e+00
134 -6.311687e+00 -2.214201e+00
135 1.899271e+00 -6.311687e+00
136 -1.140942e+00 1.899271e+00
137 5.668945e-01 -1.140942e+00
138 -6.293852e-01 5.668945e-01
139 -4.847198e-01 -6.293852e-01
140 -1.585766e+00 -4.847198e-01
141 4.038865e+00 -1.585766e+00
142 -5.263751e-01 4.038865e+00
143 2.169637e+00 -5.263751e-01
144 1.421863e+00 2.169637e+00
145 4.508409e+00 1.421863e+00
146 -1.904676e+00 4.508409e+00
147 -1.172864e+00 -1.904676e+00
148 -1.044438e+01 -1.172864e+00
149 -3.234891e+00 -1.044438e+01
150 3.098657e+00 -3.234891e+00
151 -7.007317e-01 3.098657e+00
152 -1.347093e+00 -7.007317e-01
153 -4.209154e+00 -1.347093e+00
154 2.941506e+00 -4.209154e+00
155 -1.074523e+00 2.941506e+00
156 7.945736e-01 -1.074523e+00
157 8.625133e-01 7.945736e-01
158 -7.343155e+00 8.625133e-01
> 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/7s6rz1290530559.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/83y8k1290530559.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/93y8k1290530559.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/103y8k1290530559.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11oypq1290530559.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/12ah5w1290530559.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/13hik81290530559.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/14rr1a1290530559.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/15d9iy1290530559.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/16jb0k1290530560.tab")
+ }
> try(system("convert tmp/1wxbq1290530559.ps tmp/1wxbq1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/2pobt1290530559.ps tmp/2pobt1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pobt1290530559.ps tmp/3pobt1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pobt1290530559.ps tmp/4pobt1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ifsw1290530559.ps tmp/5ifsw1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ifsw1290530559.ps tmp/6ifsw1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/7s6rz1290530559.ps tmp/7s6rz1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/83y8k1290530559.ps tmp/83y8k1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/93y8k1290530559.ps tmp/93y8k1290530559.png",intern=TRUE))
character(0)
> try(system("convert tmp/103y8k1290530559.ps tmp/103y8k1290530559.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.720 2.160 7.832