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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+ ,2
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+ ,40
+ ,10
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+ ,26
+ ,52
+ ,22
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+ ,2
+ ,17
+ ,34
+ ,8
+ ,16
+ ,12
+ ,24
+ ,9
+ ,18
+ ,21
+ ,42
+ ,24
+ ,48
+ ,2
+ ,21
+ ,42
+ ,15
+ ,30
+ ,7
+ ,14
+ ,5
+ ,10
+ ,21
+ ,42
+ ,24
+ ,48
+ ,2
+ ,28
+ ,56
+ ,14
+ ,28
+ ,11
+ ,22
+ ,8
+ ,16
+ ,24
+ ,48
+ ,24
+ ,48)
+ ,dim=c(13
+ ,159)
+ ,dimnames=list(c('Gender'
+ ,'CM'
+ ,'CM_G'
+ ,'D'
+ ,'D_G'
+ ,'PE'
+ ,'PE_G'
+ ,'PC'
+ ,'PC_G'
+ ,'PS'
+ ,'PS_G'
+ ,'O'
+ ,'O_G')
+ ,1:159))
> y <- array(NA,dim=c(13,159),dimnames=list(c('Gender','CM','CM_G','D','D_G','PE','PE_G','PC','PC_G','PS','PS_G','O','O_G'),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 = '10'
> #'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
PS Gender CM CM_G D D_G PE PE_G PC PC_G PS_G O O_G
1 25 1 25 25 11 11 7 7 8 8 25 23 23
2 30 1 17 17 6 6 17 17 8 8 30 25 25
3 22 1 18 18 8 8 12 12 9 9 22 19 19
4 22 1 16 16 10 10 12 12 7 7 22 29 29
5 25 1 20 20 10 10 11 11 4 4 25 25 25
6 23 1 16 16 11 11 11 11 11 11 23 21 21
7 17 1 18 18 16 16 12 12 7 7 17 22 22
8 21 1 17 17 11 11 13 13 7 7 21 25 25
9 19 1 30 30 12 12 16 16 10 10 19 18 18
10 15 1 23 23 8 8 11 11 10 10 15 22 22
11 16 1 18 18 12 12 10 10 8 8 16 15 15
12 22 1 21 21 9 9 9 9 9 9 22 20 20
13 23 1 31 31 14 14 17 17 11 11 23 20 20
14 23 1 27 27 15 15 11 11 9 9 23 21 21
15 19 1 21 21 9 9 14 14 13 13 19 21 21
16 23 1 16 16 8 8 15 15 9 9 23 24 24
17 25 1 20 20 9 9 15 15 6 6 25 24 24
18 22 1 17 17 9 9 13 13 6 6 22 23 23
19 26 1 25 25 16 16 18 18 16 16 26 24 24
20 29 1 26 26 11 11 18 18 5 5 29 18 18
21 32 1 25 25 8 8 12 12 7 7 32 25 25
22 25 1 17 17 9 9 17 17 9 9 25 21 21
23 28 1 32 32 12 12 18 18 12 12 28 22 22
24 25 1 22 22 9 9 14 14 9 9 25 23 23
25 25 1 17 17 9 9 16 16 5 5 25 23 23
26 18 1 20 20 14 14 14 14 10 10 18 24 24
27 25 1 29 29 10 10 12 12 8 8 25 23 23
28 25 1 23 23 14 14 17 17 7 7 25 21 21
29 20 1 20 20 10 10 12 12 8 8 20 28 28
30 15 1 11 11 6 6 6 6 4 4 15 16 16
31 24 1 26 26 13 13 12 12 8 8 24 29 29
32 26 1 22 22 10 10 12 12 8 8 26 27 27
33 14 1 14 14 15 15 13 13 8 8 14 16 16
34 24 1 19 19 12 12 14 14 7 7 24 28 28
35 25 1 20 20 11 11 11 11 8 8 25 25 25
36 20 1 28 28 8 8 12 12 7 7 20 22 22
37 21 1 19 19 9 9 9 9 7 7 21 23 23
38 27 1 30 30 9 9 15 15 9 9 27 26 26
39 23 1 29 29 15 15 18 18 11 11 23 23 23
40 25 1 26 26 9 9 15 15 6 6 25 25 25
41 20 1 23 23 10 10 12 12 8 8 20 21 21
42 22 1 21 21 12 12 14 14 9 9 22 24 24
43 25 1 28 28 11 11 13 13 6 6 25 22 22
44 25 1 23 23 14 14 13 13 10 10 25 27 27
45 17 1 18 18 6 6 11 11 8 8 17 26 26
46 25 1 20 20 8 8 16 16 10 10 25 24 24
47 26 1 21 21 10 10 11 11 5 5 26 24 24
48 27 1 28 28 12 12 16 16 14 14 27 22 22
49 19 1 10 10 5 5 8 8 6 6 19 24 24
50 22 1 22 22 10 10 15 15 6 6 22 20 20
51 32 1 31 31 10 10 21 21 12 12 32 26 26
52 21 1 29 29 13 13 18 18 12 12 21 21 21
53 18 1 22 22 10 10 13 13 8 8 18 19 19
54 23 1 23 23 10 10 15 15 10 10 23 21 21
55 20 1 20 20 9 9 19 19 10 10 20 16 16
56 21 1 18 18 8 8 15 15 10 10 21 22 22
57 17 1 25 25 14 14 11 11 5 5 17 15 15
58 18 1 21 21 8 8 10 10 7 7 18 17 17
59 19 1 24 24 9 9 13 13 10 10 19 15 15
60 22 1 25 25 14 14 15 15 11 11 22 21 21
61 14 1 13 13 8 8 12 12 7 7 14 19 19
62 18 1 28 28 8 8 16 16 12 12 18 24 24
63 35 1 25 25 7 7 18 18 11 11 35 17 17
64 29 1 9 9 6 6 8 8 11 11 29 23 23
65 21 1 16 16 8 8 13 13 5 5 21 24 24
66 25 1 19 19 6 6 17 17 8 8 25 14 14
67 26 1 29 29 11 11 7 7 4 4 26 22 22
68 17 1 14 14 11 11 12 12 7 7 17 16 16
69 25 1 22 22 14 14 14 14 11 11 25 19 19
70 20 1 15 15 8 8 6 6 6 6 20 25 25
71 22 1 15 15 8 8 10 10 4 4 22 24 24
72 24 1 20 20 11 11 11 11 8 8 24 26 26
73 21 1 18 18 10 10 14 14 9 9 21 26 26
74 26 1 33 33 14 14 11 11 8 8 26 25 25
75 24 1 22 22 11 11 13 13 11 11 24 18 18
76 16 1 16 16 9 9 12 12 8 8 16 21 21
77 18 1 16 16 8 8 9 9 4 4 18 23 23
78 19 1 18 18 13 13 12 12 6 6 19 20 20
79 21 1 18 18 12 12 13 13 9 9 21 13 13
80 22 1 22 22 13 13 12 12 13 13 22 15 15
81 23 1 30 30 14 14 9 9 9 9 23 14 14
82 29 1 30 30 12 12 15 15 10 10 29 22 22
83 21 1 24 24 14 14 24 24 20 20 21 10 10
84 23 1 21 21 13 13 17 17 11 11 23 22 22
85 27 1 29 29 16 16 11 11 6 6 27 24 24
86 25 1 31 31 9 9 17 17 9 9 25 19 19
87 21 1 20 20 9 9 11 11 7 7 21 20 20
88 10 1 16 16 9 9 12 12 9 9 10 13 13
89 20 1 22 22 8 8 14 14 10 10 20 20 20
90 26 1 20 20 7 7 11 11 9 9 26 22 22
91 24 1 28 28 16 16 16 16 8 8 24 24 24
92 29 1 38 38 11 11 21 21 7 7 29 29 29
93 19 1 22 22 9 9 14 14 6 6 19 12 12
94 24 1 20 20 11 11 20 20 13 13 24 20 20
95 19 1 17 17 9 9 13 13 6 6 19 21 21
96 22 1 22 22 13 13 15 15 10 10 22 22 22
97 17 1 31 31 16 16 19 19 16 16 17 20 20
98 24 2 24 48 14 28 11 22 12 24 48 26 52
99 19 2 18 36 12 24 10 20 8 16 38 23 46
100 19 2 23 46 13 26 14 28 12 24 38 24 48
101 23 2 15 30 11 22 11 22 8 16 46 22 44
102 27 2 12 24 4 8 15 30 4 8 54 28 56
103 14 2 15 30 8 16 11 22 8 16 28 12 24
104 22 2 20 40 8 16 17 34 7 14 44 24 48
105 21 2 34 68 16 32 18 36 11 22 42 20 40
106 18 2 31 62 14 28 10 20 8 16 36 23 46
107 20 2 19 38 11 22 11 22 8 16 40 28 56
108 19 2 21 42 9 18 13 26 9 18 38 24 48
109 24 2 22 44 9 18 16 32 9 18 48 23 46
110 25 2 24 48 10 20 9 18 6 12 50 29 58
111 29 2 32 64 16 32 9 18 6 12 58 26 52
112 28 2 33 66 11 22 9 18 6 12 56 22 44
113 17 2 13 26 16 32 12 24 5 10 34 22 44
114 29 2 25 50 12 24 12 24 7 14 58 23 46
115 26 2 29 58 14 28 18 36 10 20 52 30 60
116 14 2 18 36 10 20 15 30 8 16 28 17 34
117 26 2 20 40 10 20 10 20 8 16 52 23 46
118 20 2 15 30 12 24 11 22 8 16 40 25 50
119 32 2 33 66 14 28 9 18 6 12 64 24 48
120 23 2 26 52 16 32 5 10 4 8 46 24 48
121 21 2 18 36 9 18 12 24 8 16 42 24 48
122 30 2 28 56 8 16 24 48 20 40 60 20 40
123 24 2 17 34 8 16 14 28 6 12 48 22 44
124 22 2 12 24 7 14 7 14 4 8 44 28 56
125 24 2 17 34 9 18 12 24 9 18 48 25 50
126 24 2 21 42 10 20 13 26 6 12 48 24 48
127 24 2 18 36 13 26 8 16 9 18 48 24 48
128 19 2 10 20 10 20 11 22 5 10 38 23 46
129 31 2 29 58 11 22 9 18 5 10 62 30 60
130 22 2 31 62 8 16 11 22 8 16 44 24 48
131 27 2 19 38 9 18 13 26 8 16 54 21 42
132 19 2 9 18 13 26 10 20 6 12 38 25 50
133 21 2 13 26 14 28 13 26 6 12 42 25 50
134 23 2 19 38 12 24 10 20 8 16 46 29 58
135 19 2 21 42 12 24 13 26 8 16 38 22 44
136 19 2 23 46 14 28 8 16 5 10 38 27 54
137 20 2 21 42 11 22 16 32 7 14 40 24 48
138 23 2 15 30 14 28 9 18 8 16 46 29 58
139 17 2 19 38 10 20 12 24 7 14 34 21 42
140 17 2 26 52 14 28 14 28 8 16 34 24 48
141 17 2 16 32 11 22 9 18 5 10 34 23 46
142 21 2 19 38 9 18 11 22 10 20 42 27 54
143 21 2 31 62 16 32 14 28 9 18 42 25 50
144 18 2 19 38 9 18 12 24 7 14 36 21 42
145 19 2 15 30 7 14 12 24 6 12 38 21 42
146 20 2 23 46 14 28 11 22 10 20 40 29 58
147 15 2 17 34 14 28 12 24 6 12 30 21 42
148 24 2 21 42 8 16 9 18 11 22 48 20 40
149 20 2 17 34 11 22 9 18 6 12 40 19 38
150 22 2 25 50 14 28 15 30 9 18 44 24 48
151 13 2 20 40 11 22 8 16 4 8 26 13 26
152 19 2 19 38 20 40 8 16 7 14 38 25 50
153 21 2 20 40 11 22 17 34 8 16 42 23 46
154 23 2 17 34 9 18 11 22 5 10 46 26 52
155 16 2 21 42 10 20 12 24 8 16 32 23 46
156 26 2 26 52 13 26 20 40 10 20 52 22 44
157 21 2 17 34 8 16 12 24 9 18 42 24 48
158 21 2 21 42 15 30 7 14 5 10 42 24 48
159 24 2 28 56 14 28 11 22 8 16 48 24 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender CM CM_G D D_G
7.2619934 -4.6405404 0.3113310 -0.2094683 -0.3550908 0.2474875
PE PE_G PC PC_G PS_G O
0.1990055 -0.1001171 0.0007385 -0.0123780 0.6573026 0.4208484
O_G
-0.2940868
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.92905 -0.65258 -0.03514 0.82573 3.77271
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.2619934 2.2887866 3.173 0.00184 **
Gender -4.6405404 1.6330750 -2.842 0.00513 **
CM 0.3113310 0.0585005 5.322 3.79e-07 ***
CM_G -0.2094683 0.0387833 -5.401 2.63e-07 ***
D -0.3550908 0.1156697 -3.070 0.00255 **
D_G 0.2474875 0.0763817 3.240 0.00148 **
PE 0.1990055 0.1058787 1.880 0.06216 .
PE_G -0.1001171 0.0721089 -1.388 0.16713
PC 0.0007385 0.1311500 0.006 0.99552
PC_G -0.0123780 0.0928157 -0.133 0.89409
PS_G 0.6573026 0.0192153 34.207 < 2e-16 ***
O 0.4208484 0.0752523 5.593 1.07e-07 ***
O_G -0.2940868 0.0549594 -5.351 3.31e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.144 on 146 degrees of freedom
Multiple R-squared: 0.9319, Adjusted R-squared: 0.9263
F-statistic: 166.6 on 12 and 146 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 1.297569e-46 2.595138e-46 1.000000e+00
[2,] 1.366227e-59 2.732455e-59 1.000000e+00
[3,] 1.679058e-76 3.358116e-76 1.000000e+00
[4,] 4.485546e-89 8.971092e-89 1.000000e+00
[5,] 5.209157e-102 1.041831e-101 1.000000e+00
[6,] 8.826116e-119 1.765223e-118 1.000000e+00
[7,] 6.916569e-137 1.383314e-136 1.000000e+00
[8,] 6.111754e-146 1.222351e-145 1.000000e+00
[9,] 2.019695e-165 4.039390e-165 1.000000e+00
[10,] 2.663514e-184 5.327029e-184 1.000000e+00
[11,] 1.207295e-201 2.414590e-201 1.000000e+00
[12,] 9.922889e-206 1.984578e-205 1.000000e+00
[13,] 2.554514e-246 5.109028e-246 1.000000e+00
[14,] 1.362898e-239 2.725795e-239 1.000000e+00
[15,] 7.700323e-250 1.540065e-249 1.000000e+00
[16,] 1.092406e-279 2.184812e-279 1.000000e+00
[17,] 1.646471e-280 3.292941e-280 1.000000e+00
[18,] 6.045426e-304 1.209085e-303 1.000000e+00
[19,] 3.216901e-315 6.433802e-315 1.000000e+00
[20,] 6.225227e-322 1.245045e-321 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 0.000000e+00 0.000000e+00 1.000000e+00
[55,] 0.000000e+00 0.000000e+00 1.000000e+00
[56,] 0.000000e+00 0.000000e+00 1.000000e+00
[57,] 0.000000e+00 0.000000e+00 1.000000e+00
[58,] 0.000000e+00 0.000000e+00 1.000000e+00
[59,] 0.000000e+00 0.000000e+00 1.000000e+00
[60,] 0.000000e+00 0.000000e+00 1.000000e+00
[61,] 0.000000e+00 0.000000e+00 1.000000e+00
[62,] 0.000000e+00 0.000000e+00 1.000000e+00
[63,] 0.000000e+00 0.000000e+00 1.000000e+00
[64,] 0.000000e+00 0.000000e+00 1.000000e+00
[65,] 0.000000e+00 0.000000e+00 1.000000e+00
[66,] 0.000000e+00 0.000000e+00 1.000000e+00
[67,] 0.000000e+00 0.000000e+00 1.000000e+00
[68,] 0.000000e+00 0.000000e+00 1.000000e+00
[69,] 0.000000e+00 0.000000e+00 1.000000e+00
[70,] 0.000000e+00 0.000000e+00 1.000000e+00
[71,] 0.000000e+00 0.000000e+00 1.000000e+00
[72,] 0.000000e+00 0.000000e+00 1.000000e+00
[73,] 0.000000e+00 0.000000e+00 1.000000e+00
[74,] 0.000000e+00 0.000000e+00 1.000000e+00
[75,] 0.000000e+00 0.000000e+00 1.000000e+00
[76,] 1.415257e-35 2.830513e-35 1.000000e+00
[77,] 8.821486e-30 1.764297e-29 1.000000e+00
[78,] 1.404558e-87 2.809116e-87 1.000000e+00
[79,] 9.728018e-01 5.439636e-02 2.719818e-02
[80,] 1.321174e-04 2.642348e-04 9.998679e-01
[81,] 1.449390e-13 2.898780e-13 1.000000e+00
[82,] 6.558024e-24 1.311605e-23 1.000000e+00
[83,] 1.000000e+00 1.902838e-74 9.514192e-75
[84,] 1.000000e+00 7.418664e-21 3.709332e-21
[85,] 1.000000e+00 2.590403e-61 1.295202e-61
[86,] 1.000000e+00 3.014247e-70 1.507123e-70
[87,] 1.000000e+00 1.542189e-69 7.710945e-70
[88,] 1.000000e+00 9.327618e-37 4.663809e-37
[89,] 1.000000e+00 0.000000e+00 0.000000e+00
[90,] 1.000000e+00 0.000000e+00 0.000000e+00
[91,] 1.000000e+00 0.000000e+00 0.000000e+00
[92,] 1.000000e+00 0.000000e+00 0.000000e+00
[93,] 1.000000e+00 0.000000e+00 0.000000e+00
[94,] 1.000000e+00 0.000000e+00 0.000000e+00
[95,] 1.000000e+00 0.000000e+00 0.000000e+00
[96,] 1.000000e+00 0.000000e+00 0.000000e+00
[97,] 1.000000e+00 0.000000e+00 0.000000e+00
[98,] 1.000000e+00 0.000000e+00 0.000000e+00
[99,] 1.000000e+00 0.000000e+00 0.000000e+00
[100,] 1.000000e+00 0.000000e+00 0.000000e+00
[101,] 1.000000e+00 0.000000e+00 0.000000e+00
[102,] 1.000000e+00 0.000000e+00 0.000000e+00
[103,] 1.000000e+00 0.000000e+00 0.000000e+00
[104,] 1.000000e+00 0.000000e+00 0.000000e+00
[105,] 1.000000e+00 0.000000e+00 0.000000e+00
[106,] 1.000000e+00 0.000000e+00 0.000000e+00
[107,] 1.000000e+00 0.000000e+00 0.000000e+00
[108,] 1.000000e+00 0.000000e+00 0.000000e+00
[109,] 1.000000e+00 1.778636e-322 8.893182e-323
[110,] 1.000000e+00 0.000000e+00 0.000000e+00
[111,] 1.000000e+00 1.733300e-297 8.666502e-298
[112,] 1.000000e+00 2.155306e-283 1.077653e-283
[113,] 1.000000e+00 1.210289e-275 6.051443e-276
[114,] 1.000000e+00 1.367702e-255 6.838512e-256
[115,] 1.000000e+00 3.488342e-233 1.744171e-233
[116,] 1.000000e+00 1.946991e-230 9.734955e-231
[117,] 1.000000e+00 4.836635e-206 2.418318e-206
[118,] 1.000000e+00 3.726670e-195 1.863335e-195
[119,] 1.000000e+00 8.375157e-182 4.187579e-182
[120,] 1.000000e+00 1.934055e-162 9.670275e-163
[121,] 1.000000e+00 3.930199e-154 1.965099e-154
[122,] 1.000000e+00 2.257518e-136 1.128759e-136
[123,] 1.000000e+00 2.978750e-120 1.489375e-120
[124,] 1.000000e+00 3.893462e-113 1.946731e-113
[125,] 1.000000e+00 5.298166e-89 2.649083e-89
[126,] 1.000000e+00 4.041986e-74 2.020993e-74
[127,] 1.000000e+00 0.000000e+00 0.000000e+00
[128,] 1.000000e+00 1.824320e-45 9.121599e-46
> postscript(file="/var/www/html/rcomp/tmp/19u7v1290473546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/29u7v1290473546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/323py1290473546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/423py1290473546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/523py1290473546.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 6
1.06843359 1.81639997 0.45481452 -0.41714768 0.77450960 1.19269160
7 8 9 10 11 12
-0.80140999 -0.34594680 -1.62236978 -2.72313802 -0.47777376 0.42673305
13 14 15 16 17 18
-0.47900802 0.47933563 -1.17600440 0.07076485 0.42139355 0.02342760
19 20 21 22 23 24
0.82773044 1.84847814 2.38490166 0.94440821 0.57663803 0.47823645
25 26 27 28 29 30
0.74321545 -1.29402577 0.05893772 0.84796921 -1.37159249 -0.53081803
31 32 33 34 35 36
-0.41593079 0.60762824 -0.85633434 0.10685045 0.92867093 -1.65277133
37 38 39 40 41 42
-0.11580251 -0.13044345 -0.64685216 -0.31654439 -0.78985003 -0.25194481
43 44 45 46 47 48
0.27299793 0.51787190 -2.27396113 0.26145997 1.15374534 0.86244720
49 50 51 52 53 54
-0.35435862 -0.19577488 1.03037290 -1.28229107 -1.21874747 -0.03514366
55 56 57 58 59 60
-0.62699676 -0.55319296 -0.76671575 -0.79354279 -0.65705368 -0.13951392
61 62 63 64 65 66
-1.80073037 -2.92904503 3.77271097 3.46704092 -0.46341143 1.29356402
67 68 69 70 71 72
1.08388357 -0.17140634 1.54657800 -0.12714987 0.26617423 0.45921196
73 74 75 76 77 78
-0.75778366 0.26996265 1.10672054 -1.55520394 -0.98082844 -0.19694142
79 80 81 82 83 84
1.20421104 1.13898414 1.15125144 1.39644691 0.22878878 0.17849302
85 86 87 88 89 90
1.33880013 -0.22814707 -0.03515733 -2.58565692 -0.95092999 1.23287930
91 92 93 94 95 96
-0.05859208 -1.04163742 -0.21848999 0.38798371 -0.75114169 -0.07993002
97 98 99 100 101 102
-2.45956523 -0.25509674 0.35278935 1.01924334 -1.25465999 -0.94391019
103 104 105 106 107 108
0.32318566 0.33563167 0.46563811 1.78649818 1.12352937 1.29028796
109 110 111 112 113 114
-0.33877087 0.34524856 -1.39360879 -0.94127815 -0.35248522 -2.06158269
115 116 117 118 119 120
0.28358884 1.20777566 -1.35446683 0.05124701 -2.28470069 -0.53921229
121 122 123 124 125 126
0.31301479 -1.66883715 -0.97874975 0.19963207 -0.54706332 -0.49467423
127 128 129 130 131 132
-1.17123531 -0.29911019 -1.00093073 1.53593730 -1.96775740 -0.46892920
133 134 135 136 137 138
-0.80391498 0.20592629 0.51196717 1.20583981 0.65156591 -0.50549322
139 140 141 142 143 144
1.01316316 1.73531611 0.83339147 0.96940237 0.92649774 0.83844229
145 146 147 148 149 150
0.34916597 1.34965911 0.84360786 -0.76903491 -0.64810205 0.07993130
151 152 153 154 155 156
0.82373479 -0.35050350 0.08727623 -0.16243160 1.90164746 -1.23548837
157 158 159
0.36931094 -0.28167049 -0.25539501
> postscript(file="/var/www/html/rcomp/tmp/6cu601290473546.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 1.06843359 NA
1 1.81639997 1.06843359
2 0.45481452 1.81639997
3 -0.41714768 0.45481452
4 0.77450960 -0.41714768
5 1.19269160 0.77450960
6 -0.80140999 1.19269160
7 -0.34594680 -0.80140999
8 -1.62236978 -0.34594680
9 -2.72313802 -1.62236978
10 -0.47777376 -2.72313802
11 0.42673305 -0.47777376
12 -0.47900802 0.42673305
13 0.47933563 -0.47900802
14 -1.17600440 0.47933563
15 0.07076485 -1.17600440
16 0.42139355 0.07076485
17 0.02342760 0.42139355
18 0.82773044 0.02342760
19 1.84847814 0.82773044
20 2.38490166 1.84847814
21 0.94440821 2.38490166
22 0.57663803 0.94440821
23 0.47823645 0.57663803
24 0.74321545 0.47823645
25 -1.29402577 0.74321545
26 0.05893772 -1.29402577
27 0.84796921 0.05893772
28 -1.37159249 0.84796921
29 -0.53081803 -1.37159249
30 -0.41593079 -0.53081803
31 0.60762824 -0.41593079
32 -0.85633434 0.60762824
33 0.10685045 -0.85633434
34 0.92867093 0.10685045
35 -1.65277133 0.92867093
36 -0.11580251 -1.65277133
37 -0.13044345 -0.11580251
38 -0.64685216 -0.13044345
39 -0.31654439 -0.64685216
40 -0.78985003 -0.31654439
41 -0.25194481 -0.78985003
42 0.27299793 -0.25194481
43 0.51787190 0.27299793
44 -2.27396113 0.51787190
45 0.26145997 -2.27396113
46 1.15374534 0.26145997
47 0.86244720 1.15374534
48 -0.35435862 0.86244720
49 -0.19577488 -0.35435862
50 1.03037290 -0.19577488
51 -1.28229107 1.03037290
52 -1.21874747 -1.28229107
53 -0.03514366 -1.21874747
54 -0.62699676 -0.03514366
55 -0.55319296 -0.62699676
56 -0.76671575 -0.55319296
57 -0.79354279 -0.76671575
58 -0.65705368 -0.79354279
59 -0.13951392 -0.65705368
60 -1.80073037 -0.13951392
61 -2.92904503 -1.80073037
62 3.77271097 -2.92904503
63 3.46704092 3.77271097
64 -0.46341143 3.46704092
65 1.29356402 -0.46341143
66 1.08388357 1.29356402
67 -0.17140634 1.08388357
68 1.54657800 -0.17140634
69 -0.12714987 1.54657800
70 0.26617423 -0.12714987
71 0.45921196 0.26617423
72 -0.75778366 0.45921196
73 0.26996265 -0.75778366
74 1.10672054 0.26996265
75 -1.55520394 1.10672054
76 -0.98082844 -1.55520394
77 -0.19694142 -0.98082844
78 1.20421104 -0.19694142
79 1.13898414 1.20421104
80 1.15125144 1.13898414
81 1.39644691 1.15125144
82 0.22878878 1.39644691
83 0.17849302 0.22878878
84 1.33880013 0.17849302
85 -0.22814707 1.33880013
86 -0.03515733 -0.22814707
87 -2.58565692 -0.03515733
88 -0.95092999 -2.58565692
89 1.23287930 -0.95092999
90 -0.05859208 1.23287930
91 -1.04163742 -0.05859208
92 -0.21848999 -1.04163742
93 0.38798371 -0.21848999
94 -0.75114169 0.38798371
95 -0.07993002 -0.75114169
96 -2.45956523 -0.07993002
97 -0.25509674 -2.45956523
98 0.35278935 -0.25509674
99 1.01924334 0.35278935
100 -1.25465999 1.01924334
101 -0.94391019 -1.25465999
102 0.32318566 -0.94391019
103 0.33563167 0.32318566
104 0.46563811 0.33563167
105 1.78649818 0.46563811
106 1.12352937 1.78649818
107 1.29028796 1.12352937
108 -0.33877087 1.29028796
109 0.34524856 -0.33877087
110 -1.39360879 0.34524856
111 -0.94127815 -1.39360879
112 -0.35248522 -0.94127815
113 -2.06158269 -0.35248522
114 0.28358884 -2.06158269
115 1.20777566 0.28358884
116 -1.35446683 1.20777566
117 0.05124701 -1.35446683
118 -2.28470069 0.05124701
119 -0.53921229 -2.28470069
120 0.31301479 -0.53921229
121 -1.66883715 0.31301479
122 -0.97874975 -1.66883715
123 0.19963207 -0.97874975
124 -0.54706332 0.19963207
125 -0.49467423 -0.54706332
126 -1.17123531 -0.49467423
127 -0.29911019 -1.17123531
128 -1.00093073 -0.29911019
129 1.53593730 -1.00093073
130 -1.96775740 1.53593730
131 -0.46892920 -1.96775740
132 -0.80391498 -0.46892920
133 0.20592629 -0.80391498
134 0.51196717 0.20592629
135 1.20583981 0.51196717
136 0.65156591 1.20583981
137 -0.50549322 0.65156591
138 1.01316316 -0.50549322
139 1.73531611 1.01316316
140 0.83339147 1.73531611
141 0.96940237 0.83339147
142 0.92649774 0.96940237
143 0.83844229 0.92649774
144 0.34916597 0.83844229
145 1.34965911 0.34916597
146 0.84360786 1.34965911
147 -0.76903491 0.84360786
148 -0.64810205 -0.76903491
149 0.07993130 -0.64810205
150 0.82373479 0.07993130
151 -0.35050350 0.82373479
152 0.08727623 -0.35050350
153 -0.16243160 0.08727623
154 1.90164746 -0.16243160
155 -1.23548837 1.90164746
156 0.36931094 -1.23548837
157 -0.28167049 0.36931094
158 -0.25539501 -0.28167049
159 NA -0.25539501
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.81639997 1.06843359
[2,] 0.45481452 1.81639997
[3,] -0.41714768 0.45481452
[4,] 0.77450960 -0.41714768
[5,] 1.19269160 0.77450960
[6,] -0.80140999 1.19269160
[7,] -0.34594680 -0.80140999
[8,] -1.62236978 -0.34594680
[9,] -2.72313802 -1.62236978
[10,] -0.47777376 -2.72313802
[11,] 0.42673305 -0.47777376
[12,] -0.47900802 0.42673305
[13,] 0.47933563 -0.47900802
[14,] -1.17600440 0.47933563
[15,] 0.07076485 -1.17600440
[16,] 0.42139355 0.07076485
[17,] 0.02342760 0.42139355
[18,] 0.82773044 0.02342760
[19,] 1.84847814 0.82773044
[20,] 2.38490166 1.84847814
[21,] 0.94440821 2.38490166
[22,] 0.57663803 0.94440821
[23,] 0.47823645 0.57663803
[24,] 0.74321545 0.47823645
[25,] -1.29402577 0.74321545
[26,] 0.05893772 -1.29402577
[27,] 0.84796921 0.05893772
[28,] -1.37159249 0.84796921
[29,] -0.53081803 -1.37159249
[30,] -0.41593079 -0.53081803
[31,] 0.60762824 -0.41593079
[32,] -0.85633434 0.60762824
[33,] 0.10685045 -0.85633434
[34,] 0.92867093 0.10685045
[35,] -1.65277133 0.92867093
[36,] -0.11580251 -1.65277133
[37,] -0.13044345 -0.11580251
[38,] -0.64685216 -0.13044345
[39,] -0.31654439 -0.64685216
[40,] -0.78985003 -0.31654439
[41,] -0.25194481 -0.78985003
[42,] 0.27299793 -0.25194481
[43,] 0.51787190 0.27299793
[44,] -2.27396113 0.51787190
[45,] 0.26145997 -2.27396113
[46,] 1.15374534 0.26145997
[47,] 0.86244720 1.15374534
[48,] -0.35435862 0.86244720
[49,] -0.19577488 -0.35435862
[50,] 1.03037290 -0.19577488
[51,] -1.28229107 1.03037290
[52,] -1.21874747 -1.28229107
[53,] -0.03514366 -1.21874747
[54,] -0.62699676 -0.03514366
[55,] -0.55319296 -0.62699676
[56,] -0.76671575 -0.55319296
[57,] -0.79354279 -0.76671575
[58,] -0.65705368 -0.79354279
[59,] -0.13951392 -0.65705368
[60,] -1.80073037 -0.13951392
[61,] -2.92904503 -1.80073037
[62,] 3.77271097 -2.92904503
[63,] 3.46704092 3.77271097
[64,] -0.46341143 3.46704092
[65,] 1.29356402 -0.46341143
[66,] 1.08388357 1.29356402
[67,] -0.17140634 1.08388357
[68,] 1.54657800 -0.17140634
[69,] -0.12714987 1.54657800
[70,] 0.26617423 -0.12714987
[71,] 0.45921196 0.26617423
[72,] -0.75778366 0.45921196
[73,] 0.26996265 -0.75778366
[74,] 1.10672054 0.26996265
[75,] -1.55520394 1.10672054
[76,] -0.98082844 -1.55520394
[77,] -0.19694142 -0.98082844
[78,] 1.20421104 -0.19694142
[79,] 1.13898414 1.20421104
[80,] 1.15125144 1.13898414
[81,] 1.39644691 1.15125144
[82,] 0.22878878 1.39644691
[83,] 0.17849302 0.22878878
[84,] 1.33880013 0.17849302
[85,] -0.22814707 1.33880013
[86,] -0.03515733 -0.22814707
[87,] -2.58565692 -0.03515733
[88,] -0.95092999 -2.58565692
[89,] 1.23287930 -0.95092999
[90,] -0.05859208 1.23287930
[91,] -1.04163742 -0.05859208
[92,] -0.21848999 -1.04163742
[93,] 0.38798371 -0.21848999
[94,] -0.75114169 0.38798371
[95,] -0.07993002 -0.75114169
[96,] -2.45956523 -0.07993002
[97,] -0.25509674 -2.45956523
[98,] 0.35278935 -0.25509674
[99,] 1.01924334 0.35278935
[100,] -1.25465999 1.01924334
[101,] -0.94391019 -1.25465999
[102,] 0.32318566 -0.94391019
[103,] 0.33563167 0.32318566
[104,] 0.46563811 0.33563167
[105,] 1.78649818 0.46563811
[106,] 1.12352937 1.78649818
[107,] 1.29028796 1.12352937
[108,] -0.33877087 1.29028796
[109,] 0.34524856 -0.33877087
[110,] -1.39360879 0.34524856
[111,] -0.94127815 -1.39360879
[112,] -0.35248522 -0.94127815
[113,] -2.06158269 -0.35248522
[114,] 0.28358884 -2.06158269
[115,] 1.20777566 0.28358884
[116,] -1.35446683 1.20777566
[117,] 0.05124701 -1.35446683
[118,] -2.28470069 0.05124701
[119,] -0.53921229 -2.28470069
[120,] 0.31301479 -0.53921229
[121,] -1.66883715 0.31301479
[122,] -0.97874975 -1.66883715
[123,] 0.19963207 -0.97874975
[124,] -0.54706332 0.19963207
[125,] -0.49467423 -0.54706332
[126,] -1.17123531 -0.49467423
[127,] -0.29911019 -1.17123531
[128,] -1.00093073 -0.29911019
[129,] 1.53593730 -1.00093073
[130,] -1.96775740 1.53593730
[131,] -0.46892920 -1.96775740
[132,] -0.80391498 -0.46892920
[133,] 0.20592629 -0.80391498
[134,] 0.51196717 0.20592629
[135,] 1.20583981 0.51196717
[136,] 0.65156591 1.20583981
[137,] -0.50549322 0.65156591
[138,] 1.01316316 -0.50549322
[139,] 1.73531611 1.01316316
[140,] 0.83339147 1.73531611
[141,] 0.96940237 0.83339147
[142,] 0.92649774 0.96940237
[143,] 0.83844229 0.92649774
[144,] 0.34916597 0.83844229
[145,] 1.34965911 0.34916597
[146,] 0.84360786 1.34965911
[147,] -0.76903491 0.84360786
[148,] -0.64810205 -0.76903491
[149,] 0.07993130 -0.64810205
[150,] 0.82373479 0.07993130
[151,] -0.35050350 0.82373479
[152,] 0.08727623 -0.35050350
[153,] -0.16243160 0.08727623
[154,] 1.90164746 -0.16243160
[155,] -1.23548837 1.90164746
[156,] 0.36931094 -1.23548837
[157,] -0.28167049 0.36931094
[158,] -0.25539501 -0.28167049
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.81639997 1.06843359
2 0.45481452 1.81639997
3 -0.41714768 0.45481452
4 0.77450960 -0.41714768
5 1.19269160 0.77450960
6 -0.80140999 1.19269160
7 -0.34594680 -0.80140999
8 -1.62236978 -0.34594680
9 -2.72313802 -1.62236978
10 -0.47777376 -2.72313802
11 0.42673305 -0.47777376
12 -0.47900802 0.42673305
13 0.47933563 -0.47900802
14 -1.17600440 0.47933563
15 0.07076485 -1.17600440
16 0.42139355 0.07076485
17 0.02342760 0.42139355
18 0.82773044 0.02342760
19 1.84847814 0.82773044
20 2.38490166 1.84847814
21 0.94440821 2.38490166
22 0.57663803 0.94440821
23 0.47823645 0.57663803
24 0.74321545 0.47823645
25 -1.29402577 0.74321545
26 0.05893772 -1.29402577
27 0.84796921 0.05893772
28 -1.37159249 0.84796921
29 -0.53081803 -1.37159249
30 -0.41593079 -0.53081803
31 0.60762824 -0.41593079
32 -0.85633434 0.60762824
33 0.10685045 -0.85633434
34 0.92867093 0.10685045
35 -1.65277133 0.92867093
36 -0.11580251 -1.65277133
37 -0.13044345 -0.11580251
38 -0.64685216 -0.13044345
39 -0.31654439 -0.64685216
40 -0.78985003 -0.31654439
41 -0.25194481 -0.78985003
42 0.27299793 -0.25194481
43 0.51787190 0.27299793
44 -2.27396113 0.51787190
45 0.26145997 -2.27396113
46 1.15374534 0.26145997
47 0.86244720 1.15374534
48 -0.35435862 0.86244720
49 -0.19577488 -0.35435862
50 1.03037290 -0.19577488
51 -1.28229107 1.03037290
52 -1.21874747 -1.28229107
53 -0.03514366 -1.21874747
54 -0.62699676 -0.03514366
55 -0.55319296 -0.62699676
56 -0.76671575 -0.55319296
57 -0.79354279 -0.76671575
58 -0.65705368 -0.79354279
59 -0.13951392 -0.65705368
60 -1.80073037 -0.13951392
61 -2.92904503 -1.80073037
62 3.77271097 -2.92904503
63 3.46704092 3.77271097
64 -0.46341143 3.46704092
65 1.29356402 -0.46341143
66 1.08388357 1.29356402
67 -0.17140634 1.08388357
68 1.54657800 -0.17140634
69 -0.12714987 1.54657800
70 0.26617423 -0.12714987
71 0.45921196 0.26617423
72 -0.75778366 0.45921196
73 0.26996265 -0.75778366
74 1.10672054 0.26996265
75 -1.55520394 1.10672054
76 -0.98082844 -1.55520394
77 -0.19694142 -0.98082844
78 1.20421104 -0.19694142
79 1.13898414 1.20421104
80 1.15125144 1.13898414
81 1.39644691 1.15125144
82 0.22878878 1.39644691
83 0.17849302 0.22878878
84 1.33880013 0.17849302
85 -0.22814707 1.33880013
86 -0.03515733 -0.22814707
87 -2.58565692 -0.03515733
88 -0.95092999 -2.58565692
89 1.23287930 -0.95092999
90 -0.05859208 1.23287930
91 -1.04163742 -0.05859208
92 -0.21848999 -1.04163742
93 0.38798371 -0.21848999
94 -0.75114169 0.38798371
95 -0.07993002 -0.75114169
96 -2.45956523 -0.07993002
97 -0.25509674 -2.45956523
98 0.35278935 -0.25509674
99 1.01924334 0.35278935
100 -1.25465999 1.01924334
101 -0.94391019 -1.25465999
102 0.32318566 -0.94391019
103 0.33563167 0.32318566
104 0.46563811 0.33563167
105 1.78649818 0.46563811
106 1.12352937 1.78649818
107 1.29028796 1.12352937
108 -0.33877087 1.29028796
109 0.34524856 -0.33877087
110 -1.39360879 0.34524856
111 -0.94127815 -1.39360879
112 -0.35248522 -0.94127815
113 -2.06158269 -0.35248522
114 0.28358884 -2.06158269
115 1.20777566 0.28358884
116 -1.35446683 1.20777566
117 0.05124701 -1.35446683
118 -2.28470069 0.05124701
119 -0.53921229 -2.28470069
120 0.31301479 -0.53921229
121 -1.66883715 0.31301479
122 -0.97874975 -1.66883715
123 0.19963207 -0.97874975
124 -0.54706332 0.19963207
125 -0.49467423 -0.54706332
126 -1.17123531 -0.49467423
127 -0.29911019 -1.17123531
128 -1.00093073 -0.29911019
129 1.53593730 -1.00093073
130 -1.96775740 1.53593730
131 -0.46892920 -1.96775740
132 -0.80391498 -0.46892920
133 0.20592629 -0.80391498
134 0.51196717 0.20592629
135 1.20583981 0.51196717
136 0.65156591 1.20583981
137 -0.50549322 0.65156591
138 1.01316316 -0.50549322
139 1.73531611 1.01316316
140 0.83339147 1.73531611
141 0.96940237 0.83339147
142 0.92649774 0.96940237
143 0.83844229 0.92649774
144 0.34916597 0.83844229
145 1.34965911 0.34916597
146 0.84360786 1.34965911
147 -0.76903491 0.84360786
148 -0.64810205 -0.76903491
149 0.07993130 -0.64810205
150 0.82373479 0.07993130
151 -0.35050350 0.82373479
152 0.08727623 -0.35050350
153 -0.16243160 0.08727623
154 1.90164746 -0.16243160
155 -1.23548837 1.90164746
156 0.36931094 -1.23548837
157 -0.28167049 0.36931094
158 -0.25539501 -0.28167049
> 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/7nl5l1290473546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nl5l1290473546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9yvmo1290473546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10yvmo1290473546.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/111v3c1290473546.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/124w101290473546.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/13bxyu1290473546.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/14fxxi1290473546.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/15iyw61290473546.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/16wqte1290473546.tab")
+ }
>
> try(system("convert tmp/19u7v1290473546.ps tmp/19u7v1290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/29u7v1290473546.ps tmp/29u7v1290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/323py1290473546.ps tmp/323py1290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/423py1290473546.ps tmp/423py1290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/523py1290473546.ps tmp/523py1290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cu601290473546.ps tmp/6cu601290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nl5l1290473546.ps tmp/7nl5l1290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nl5l1290473546.ps tmp/8nl5l1290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yvmo1290473546.ps tmp/9yvmo1290473546.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yvmo1290473546.ps tmp/10yvmo1290473546.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.711 1.785 12.964