R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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+ ,11
+ ,6
+ ,6
+ ,27
+ ,27
+ ,24
+ ,24
+ ,1
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+ ,24
+ ,24
+ ,1
+ ,38
+ ,11
+ ,11
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+ ,21
+ ,7
+ ,7
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+ ,29
+ ,29
+ ,29
+ ,0
+ ,22
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+ ,0
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+ ,0
+ ,6
+ ,0
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+ ,0
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+ ,0
+ ,1
+ ,20
+ ,11
+ ,11
+ ,20
+ ,20
+ ,13
+ ,13
+ ,24
+ ,24
+ ,20
+ ,20
+ ,0
+ ,17
+ ,9
+ ,0
+ ,13
+ ,0
+ ,6
+ ,0
+ ,19
+ ,0
+ ,21
+ ,0
+ ,1
+ ,28
+ ,14
+ ,14
+ ,11
+ ,11
+ ,8
+ ,8
+ ,24
+ ,24
+ ,24
+ ,24
+ ,1
+ ,22
+ ,13
+ ,13
+ ,15
+ ,15
+ ,10
+ ,10
+ ,22
+ ,22
+ ,22
+ ,22
+ ,0
+ ,31
+ ,16
+ ,0
+ ,19
+ ,0
+ ,16
+ ,0
+ ,17
+ ,0
+ ,20
+ ,0)
+ ,dim=c(12
+ ,159)
+ ,dimnames=list(c('Gender'
+ ,'Concernovermistakes'
+ ,'Doubtsaboutactions'
+ ,'DoubtsaboutactionsMale'
+ ,'Parentalexpectations'
+ ,'ParentalexpectationsMale'
+ ,'Parentalcritism'
+ ,'ParentalcritismMale'
+ ,'Personalstandards'
+ ,'PersonalstandarsMale'
+ ,'Organization'
+ ,'OrganizationMale')
+ ,1:159))
> y <- array(NA,dim=c(12,159),dimnames=list(c('Gender','Concernovermistakes','Doubtsaboutactions','DoubtsaboutactionsMale','Parentalexpectations','ParentalexpectationsMale','Parentalcritism','ParentalcritismMale','Personalstandards','PersonalstandarsMale','Organization','OrganizationMale'),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 = '2'
> #'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
Concernovermistakes Gender Doubtsaboutactions DoubtsaboutactionsMale
1 24 0 14 0
2 25 0 11 0
3 17 0 6 0
4 18 1 12 12
5 18 1 8 8
6 16 1 10 10
7 20 1 10 10
8 16 1 11 11
9 18 1 16 16
10 17 1 11 11
11 23 0 13 0
12 30 0 12 0
13 23 1 8 8
14 18 1 12 12
15 15 1 11 11
16 12 1 4 4
17 21 0 9 0
18 15 1 8 8
19 20 1 8 8
20 31 0 14 0
21 27 0 15 0
22 34 1 16 16
23 21 1 9 9
24 31 1 14 14
25 19 1 11 11
26 16 0 8 0
27 20 1 9 9
28 21 1 9 9
29 22 1 9 9
30 17 1 9 9
31 24 1 10 10
32 25 0 16 0
33 26 0 11 0
34 25 1 8 8
35 17 1 9 9
36 32 1 16 16
37 33 1 11 11
38 13 1 16 16
39 32 1 12 12
40 25 1 12 12
41 29 1 14 14
42 22 1 9 9
43 18 1 10 10
44 17 1 9 9
45 20 0 10 0
46 15 1 12 12
47 20 1 14 14
48 33 1 14 14
49 29 0 10 0
50 23 1 14 14
51 26 0 16 0
52 18 1 9 9
53 20 0 10 0
54 11 1 6 6
55 28 1 8 8
56 26 1 13 13
57 22 0 10 0
58 17 1 8 8
59 12 0 7 0
60 14 1 15 15
61 17 1 9 9
62 21 1 10 10
63 19 1 12 12
64 18 1 13 13
65 10 0 10 0
66 29 0 11 0
67 31 1 8 8
68 19 0 9 0
69 9 1 13 13
70 20 1 11 11
71 28 1 8 8
72 19 0 9 0
73 30 0 9 0
74 29 0 15 0
75 26 0 9 0
76 23 0 10 0
77 13 1 14 14
78 21 1 12 12
79 19 1 12 12
80 28 1 11 11
81 23 1 14 14
82 18 1 6 6
83 21 0 12 0
84 20 1 8 8
85 23 1 14 14
86 21 1 11 11
87 21 1 10 10
88 15 1 14 14
89 28 1 12 12
90 19 1 10 10
91 26 1 14 14
92 10 1 5 5
93 16 0 11 0
94 22 1 10 10
95 19 1 9 9
96 31 1 10 10
97 31 0 16 0
98 29 1 13 13
99 19 0 9 0
100 22 1 10 10
101 23 1 10 10
102 15 0 7 0
103 20 0 9 0
104 18 1 8 8
105 23 1 14 14
106 25 1 14 14
107 21 1 8 8
108 24 1 9 9
109 25 1 14 14
110 17 1 14 14
111 13 1 8 8
112 28 1 8 8
113 21 0 8 0
114 25 1 7 7
115 9 0 6 0
116 16 1 8 8
117 19 1 6 6
118 17 1 11 11
119 25 1 14 14
120 20 1 11 11
121 29 1 11 11
122 14 1 11 11
123 22 1 14 14
124 15 1 8 8
125 19 0 20 0
126 20 1 11 11
127 15 0 8 0
128 20 1 11 11
129 18 1 10 10
130 33 1 14 14
131 22 1 11 11
132 16 1 9 9
133 17 1 9 9
134 16 1 8 8
135 21 0 10 0
136 26 0 13 0
137 18 1 13 13
138 18 1 12 12
139 17 1 8 8
140 22 1 13 13
141 30 1 14 14
142 30 0 12 0
143 24 1 14 14
144 21 1 15 15
145 21 1 13 13
146 29 1 16 16
147 31 1 9 9
148 20 1 9 9
149 16 0 9 0
150 22 0 8 0
151 20 1 7 7
152 28 1 16 16
153 38 1 11 11
154 22 0 9 0
155 20 1 11 11
156 17 0 9 0
157 28 1 14 14
158 22 1 13 13
159 31 0 16 0
Parentalexpectations ParentalexpectationsMale Parentalcritism
1 11 0 12
2 7 0 8
3 17 0 8
4 10 10 8
5 12 12 9
6 12 12 7
7 11 11 4
8 11 11 11
9 12 12 7
10 13 13 7
11 14 0 12
12 16 0 10
13 11 11 10
14 10 10 8
15 11 11 8
16 15 15 4
17 9 0 9
18 11 11 8
19 17 17 7
20 17 0 11
21 11 0 9
22 18 18 11
23 14 14 13
24 10 10 8
25 11 11 8
26 15 0 9
27 15 15 6
28 13 13 9
29 16 16 9
30 13 13 6
31 9 9 6
32 18 0 16
33 18 0 5
34 12 12 7
35 17 17 9
36 9 9 6
37 9 9 6
38 12 12 5
39 18 18 12
40 12 12 7
41 18 18 10
42 14 14 9
43 15 15 8
44 16 16 5
45 10 0 8
46 11 11 8
47 14 14 10
48 9 9 6
49 12 0 8
50 17 17 7
51 5 0 4
52 12 12 8
53 12 0 8
54 6 6 4
55 24 24 20
56 12 12 8
57 12 0 8
58 14 14 6
59 7 0 4
60 13 13 8
61 12 12 9
62 13 13 6
63 14 14 7
64 8 8 9
65 11 0 5
66 9 0 5
67 11 11 8
68 13 0 8
69 10 10 6
70 11 11 8
71 12 12 7
72 9 0 7
73 15 0 9
74 18 0 11
75 15 0 6
76 12 0 8
77 13 13 6
78 14 14 9
79 10 10 8
80 13 13 6
81 13 13 10
82 11 11 8
83 13 0 8
84 16 16 10
85 8 8 5
86 16 16 7
87 11 11 5
88 9 9 8
89 16 16 14
90 12 12 7
91 14 14 8
92 8 8 6
93 9 0 5
94 15 15 6
95 11 11 10
96 21 21 12
97 14 0 9
98 18 18 12
99 12 0 7
100 13 13 8
101 15 15 10
102 12 0 6
103 19 0 10
104 15 15 10
105 11 11 10
106 11 11 5
107 10 10 7
108 13 13 10
109 15 15 11
110 12 12 6
111 12 12 7
112 16 16 12
113 9 0 11
114 18 18 11
115 8 0 11
116 13 13 5
117 17 17 8
118 9 9 6
119 15 15 9
120 8 8 4
121 7 7 4
122 12 12 7
123 14 14 11
124 6 6 6
125 8 0 7
126 17 17 8
127 10 0 4
128 11 11 8
129 14 14 9
130 11 11 8
131 13 13 11
132 12 12 8
133 11 11 5
134 9 9 4
135 12 0 8
136 20 0 10
137 12 12 6
138 13 13 9
139 12 12 9
140 12 12 13
141 9 9 9
142 15 0 10
143 24 24 20
144 7 7 5
145 17 17 11
146 11 11 6
147 17 17 9
148 11 11 7
149 12 0 9
150 14 0 10
151 11 11 9
152 16 16 8
153 21 21 7
154 14 0 6
155 20 20 13
156 13 0 6
157 11 11 8
158 15 15 10
159 19 0 16
ParentalcritismMale Personalstandards PersonalstandarsMale Organization
1 0 24 0 26
2 0 25 0 23
3 0 30 0 25
4 8 19 19 23
5 9 22 22 19
6 7 22 22 29
7 4 25 25 25
8 11 23 23 21
9 7 17 17 22
10 7 21 21 25
11 0 19 0 24
12 0 19 0 18
13 10 15 15 22
14 8 16 16 15
15 8 23 23 22
16 4 27 27 28
17 0 22 0 20
18 8 14 14 12
19 7 22 22 24
20 0 23 0 20
21 0 23 0 21
22 11 21 21 20
23 13 19 19 21
24 8 18 18 23
25 8 20 20 28
26 0 23 0 24
27 6 25 25 24
28 9 19 19 24
29 9 24 24 23
30 6 22 22 23
31 6 25 25 29
32 0 26 0 24
33 0 29 0 18
34 7 32 32 25
35 9 25 25 21
36 6 29 29 26
37 6 28 28 22
38 5 17 17 22
39 12 28 28 22
40 7 29 29 23
41 10 26 26 30
42 9 25 25 23
43 8 14 14 17
44 5 25 25 23
45 0 26 0 23
46 8 20 20 25
47 10 18 18 24
48 6 32 32 24
49 0 25 0 23
50 7 25 25 21
51 0 23 0 24
52 8 21 21 24
53 0 20 0 28
54 4 15 15 16
55 20 30 30 20
56 8 24 24 29
57 0 26 0 27
58 6 24 24 22
59 0 22 0 28
60 8 14 14 16
61 9 24 24 25
62 6 24 24 24
63 7 24 24 28
64 9 24 24 24
65 0 19 0 23
66 0 31 0 30
67 8 22 22 24
68 0 27 0 21
69 6 19 19 25
70 8 25 25 25
71 7 20 20 22
72 0 21 0 23
73 0 27 0 26
74 0 23 0 23
75 0 25 0 25
76 0 20 0 21
77 6 21 21 25
78 9 22 22 24
79 8 23 23 29
80 6 25 25 22
81 10 25 25 27
82 8 17 17 26
83 0 19 0 22
84 10 25 25 24
85 5 19 19 27
86 7 20 20 24
87 5 26 26 24
88 8 23 23 29
89 14 27 27 22
90 7 17 17 21
91 8 17 17 24
92 6 19 19 24
93 0 17 0 23
94 6 22 22 20
95 10 21 21 27
96 12 32 32 26
97 0 21 0 25
98 12 21 21 21
99 0 18 0 21
100 8 18 18 19
101 10 23 23 21
102 0 19 0 21
103 0 20 0 16
104 10 21 21 22
105 10 20 20 29
106 5 17 17 15
107 7 18 18 17
108 10 19 19 15
109 11 22 22 21
110 6 15 15 21
111 7 14 14 19
112 12 18 18 24
113 0 24 0 20
114 11 35 35 17
115 0 29 0 23
116 5 21 21 24
117 8 25 25 14
118 6 20 20 19
119 9 22 22 24
120 4 13 13 13
121 4 26 26 22
122 7 17 17 16
123 11 25 25 19
124 6 20 20 25
125 0 19 0 25
126 8 21 21 23
127 0 22 0 24
128 8 24 24 26
129 9 21 21 26
130 8 26 26 25
131 11 24 24 18
132 8 16 16 21
133 5 23 23 26
134 4 18 18 23
135 0 16 0 23
136 0 26 0 22
137 6 19 19 20
138 9 21 21 13
139 9 21 21 24
140 13 22 22 15
141 9 23 23 14
142 0 29 0 22
143 20 21 21 10
144 5 21 21 24
145 11 23 23 22
146 6 27 27 24
147 9 25 25 19
148 7 21 21 20
149 0 10 0 13
150 0 20 0 20
151 9 26 26 22
152 8 24 24 24
153 7 29 29 29
154 0 19 0 12
155 13 24 24 20
156 0 19 0 21
157 8 24 24 24
158 10 22 22 22
159 0 17 0 20
OrganizationMale
1 0
2 0
3 0
4 23
5 19
6 29
7 25
8 21
9 22
10 25
11 0
12 0
13 22
14 15
15 22
16 28
17 0
18 12
19 24
20 0
21 0
22 20
23 21
24 23
25 28
26 0
27 24
28 24
29 23
30 23
31 29
32 0
33 0
34 25
35 21
36 26
37 22
38 22
39 22
40 23
41 30
42 23
43 17
44 23
45 0
46 25
47 24
48 24
49 0
50 21
51 0
52 24
53 0
54 16
55 20
56 29
57 0
58 22
59 0
60 16
61 25
62 24
63 28
64 24
65 0
66 0
67 24
68 0
69 25
70 25
71 22
72 0
73 0
74 0
75 0
76 0
77 25
78 24
79 29
80 22
81 27
82 26
83 0
84 24
85 27
86 24
87 24
88 29
89 22
90 21
91 24
92 24
93 0
94 20
95 27
96 26
97 0
98 21
99 0
100 19
101 21
102 0
103 0
104 22
105 29
106 15
107 17
108 15
109 21
110 21
111 19
112 24
113 0
114 17
115 0
116 24
117 14
118 19
119 24
120 13
121 22
122 16
123 19
124 25
125 0
126 23
127 0
128 26
129 26
130 25
131 18
132 21
133 26
134 23
135 0
136 0
137 20
138 13
139 24
140 15
141 14
142 0
143 10
144 24
145 22
146 24
147 19
148 20
149 0
150 0
151 22
152 24
153 29
154 0
155 20
156 0
157 24
158 22
159 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Doubtsaboutactions
-1.14209 -0.52449 1.06324
DoubtsaboutactionsMale Parentalexpectations ParentalexpectationsMale
-0.38085 0.43918 -0.31621
Parentalcritism ParentalcritismMale Personalstandards
0.04921 0.19389 0.43426
PersonalstandarsMale Organization OrganizationMale
0.21102 -0.17864 0.08046
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.6986 -2.4578 -0.5484 2.6878 12.0701
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.14209 6.20375 -0.184 0.8542
Gender -0.52449 7.15909 -0.073 0.9417
Doubtsaboutactions 1.06324 0.24599 4.322 2.83e-05 ***
DoubtsaboutactionsMale -0.38085 0.29189 -1.305 0.1940
Parentalexpectations 0.43918 0.23573 1.863 0.0645 .
ParentalexpectationsMale -0.31621 0.28846 -1.096 0.2748
Parentalcritism 0.04921 0.30912 0.159 0.8737
ParentalcritismMale 0.19389 0.37143 0.522 0.6025
Personalstandards 0.43426 0.18852 2.304 0.0226 *
PersonalstandarsMale 0.21102 0.22013 0.959 0.3393
Organization -0.17864 0.24157 -0.740 0.4608
OrganizationMale 0.08046 0.26771 0.301 0.7642
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.489 on 147 degrees of freedom
Multiple R-squared: 0.4276, Adjusted R-squared: 0.3847
F-statistic: 9.982 on 11 and 147 DF, p-value: 2.081e-13
> 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,] 0.16733067 0.33466135 0.83266933
[2,] 0.18130068 0.36260137 0.81869932
[3,] 0.09208548 0.18417096 0.90791452
[4,] 0.09489469 0.18978939 0.90510531
[5,] 0.14711477 0.29422953 0.85288523
[6,] 0.09084625 0.18169250 0.90915375
[7,] 0.10180115 0.20360230 0.89819885
[8,] 0.27938263 0.55876525 0.72061737
[9,] 0.21778925 0.43557851 0.78221075
[10,] 0.63705652 0.72588696 0.36294348
[11,] 0.55792369 0.88415263 0.44207631
[12,] 0.49620353 0.99240706 0.50379647
[13,] 0.43076442 0.86152884 0.56923558
[14,] 0.36250532 0.72501064 0.63749468
[15,] 0.29614803 0.59229606 0.70385197
[16,] 0.23772980 0.47545961 0.76227020
[17,] 0.35858384 0.71716768 0.64141616
[18,] 0.34306441 0.68612881 0.65693559
[19,] 0.30537376 0.61074752 0.69462624
[20,] 0.36071847 0.72143694 0.63928153
[21,] 0.37699139 0.75398278 0.62300861
[22,] 0.38658865 0.77317731 0.61341135
[23,] 0.60548878 0.78902243 0.39451122
[24,] 0.71684795 0.56630410 0.28315205
[25,] 0.68551149 0.62897701 0.31448851
[26,] 0.63750435 0.72499131 0.36249565
[27,] 0.58571849 0.82856303 0.41428151
[28,] 0.52798896 0.94402209 0.47201104
[29,] 0.49823148 0.99646295 0.50176852
[30,] 0.46929346 0.93858692 0.53070654
[31,] 0.41813799 0.83627598 0.58186201
[32,] 0.45846008 0.91692016 0.54153992
[33,] 0.42017851 0.84035703 0.57982149
[34,] 0.39666927 0.79333854 0.60333073
[35,] 0.47973567 0.95947133 0.52026433
[36,] 0.44208252 0.88416504 0.55791748
[37,] 0.42428668 0.84857337 0.57571332
[38,] 0.37417896 0.74835793 0.62582104
[39,] 0.32912715 0.65825429 0.67087285
[40,] 0.28862436 0.57724873 0.71137564
[41,] 0.27498838 0.54997677 0.72501162
[42,] 0.24377537 0.48755075 0.75622463
[43,] 0.21351910 0.42703821 0.78648090
[44,] 0.19217120 0.38434240 0.80782880
[45,] 0.17993742 0.35987483 0.82006258
[46,] 0.19805167 0.39610333 0.80194833
[47,] 0.20014419 0.40028838 0.79985581
[48,] 0.16913768 0.33827536 0.83086232
[49,] 0.15830606 0.31661211 0.84169394
[50,] 0.20491535 0.40983069 0.79508465
[51,] 0.36461192 0.72922384 0.63538808
[52,] 0.44043171 0.88086341 0.55956829
[53,] 0.73901937 0.52196126 0.26098063
[54,] 0.72010184 0.55979632 0.27989816
[55,] 0.87428875 0.25142249 0.12571125
[56,] 0.85886092 0.28227816 0.14113908
[57,] 0.94625759 0.10748482 0.05374241
[58,] 0.93367770 0.13264460 0.06632230
[59,] 0.95589859 0.08820282 0.04410141
[60,] 0.94367998 0.11264004 0.05632002
[61,] 0.94342596 0.11314807 0.05657404
[62,] 0.93699982 0.12600037 0.06300018
[63,] 0.97543429 0.04913141 0.02456571
[64,] 0.96859369 0.06281262 0.03140631
[65,] 0.96304377 0.07391246 0.03695623
[66,] 0.96518664 0.06962672 0.03481336
[67,] 0.95844808 0.08310383 0.04155192
[68,] 0.95659416 0.08681169 0.04340584
[69,] 0.94437284 0.11125432 0.05562716
[70,] 0.93260141 0.13479718 0.06739859
[71,] 0.92411217 0.15177566 0.07588783
[72,] 0.90969931 0.18060138 0.09030069
[73,] 0.89265224 0.21469551 0.10734776
[74,] 0.94177854 0.11644293 0.05822146
[75,] 0.92756695 0.14486610 0.07243305
[76,] 0.91128618 0.17742763 0.08871382
[77,] 0.91935269 0.16129463 0.08064731
[78,] 0.91685155 0.16629690 0.08314845
[79,] 0.89787816 0.20424368 0.10212184
[80,] 0.87606871 0.24786258 0.12393129
[81,] 0.84818027 0.30363947 0.15181973
[82,] 0.82082156 0.35835689 0.17917844
[83,] 0.83206670 0.33586659 0.16793330
[84,] 0.84146104 0.31707792 0.15853896
[85,] 0.81248074 0.37503853 0.18751926
[86,] 0.79708680 0.40582640 0.20291320
[87,] 0.75939174 0.48121653 0.24060826
[88,] 0.71820296 0.56359408 0.28179704
[89,] 0.72339871 0.55320257 0.27660129
[90,] 0.68246238 0.63507524 0.31753762
[91,] 0.63676981 0.72646038 0.36323019
[92,] 0.63661728 0.72676543 0.36338272
[93,] 0.63849902 0.72300197 0.36150098
[94,] 0.67673503 0.64652993 0.32326497
[95,] 0.62729714 0.74540572 0.37270286
[96,] 0.58548025 0.82903950 0.41451975
[97,] 0.53255891 0.93488218 0.46744109
[98,] 0.85474608 0.29050784 0.14525392
[99,] 0.86670900 0.26658200 0.13329100
[100,] 0.89390449 0.21219103 0.10609551
[101,] 0.95181478 0.09637045 0.04818522
[102,] 0.94359225 0.11281550 0.05640775
[103,] 0.95735254 0.08529491 0.04264746
[104,] 0.95123956 0.09752088 0.04876044
[105,] 0.93764861 0.12470279 0.06235139
[106,] 0.96451377 0.07097247 0.03548623
[107,] 0.95937653 0.08124695 0.04062347
[108,] 0.95125543 0.09748915 0.04874457
[109,] 0.96158139 0.07683723 0.03841861
[110,] 0.94593829 0.10812343 0.05406171
[111,] 0.94721835 0.10556329 0.05278165
[112,] 0.92851835 0.14296329 0.07148165
[113,] 0.91063185 0.17873630 0.08936815
[114,] 0.89017459 0.21965083 0.10982541
[115,] 0.85164053 0.29671894 0.14835947
[116,] 0.89470160 0.21059680 0.10529840
[117,] 0.85955210 0.28089581 0.14044790
[118,] 0.87381805 0.25236390 0.12618195
[119,] 0.88038345 0.23923309 0.11961655
[120,] 0.84819238 0.30361524 0.15180762
[121,] 0.81952196 0.36095608 0.18047804
[122,] 0.77272037 0.45455926 0.22727963
[123,] 0.70024387 0.59951225 0.29975613
[124,] 0.86533290 0.26933421 0.13466710
[125,] 0.83855677 0.32288647 0.16144323
[126,] 0.77525852 0.44948296 0.22474148
[127,] 0.72066502 0.55866996 0.27933498
[128,] 0.59686706 0.80626587 0.40313294
[129,] 0.64567714 0.70864571 0.35432286
[130,] 0.49791237 0.99582474 0.50208763
> postscript(file="/var/www/rcomp/tmp/169eq1292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2uf641292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3uf641292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4uf641292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5uf641292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
-0.94230406 4.23078198 -4.65885207 -1.69881201 -1.78689060 -3.68363814
7 8 9 10 11 12
-1.15996212 -6.64619560 -3.23883744 -3.23645295 -0.38258690 5.82884883
13 14 15 16 17 18
7.90453334 -0.54842741 -6.81872097 -6.55354334 2.19654169 0.05417444
19 20 21 22 23 24
0.57534691 2.83422204 0.68313093 8.27341717 1.44460424 10.58169374
25 26 27 28 29 30
-0.29376354 -4.09499076 -1.55385042 2.83451842 0.14098483 -2.47022929
31 32 33 34 35 36
2.99252918 -6.56569604 -2.08283927 -0.16444336 -5.82364270 4.02250060
37 38 39 40 41 42
8.68699838 -7.75264354 4.43925161 -1.15451448 2.63670638 -0.25834999
43 44 45 46 47 48
1.68841392 -4.53191229 -1.45778292 -5.27070445 -1.29821660 4.25505744
49 50 51 52 53 54
7.09811733 -2.74939674 2.03695702 -1.08997944 1.16264300 -1.24633315
55 56 57 58 59 60
-1.00075337 2.73552628 0.37843017 -3.29956839 -2.12342107 -5.57576770
61 62 63 64 65 66
-4.17074741 -0.34500497 -3.68312106 -5.50658933 -8.70950277 6.14498661
67 68 69 70 71 72
12.07010027 -3.50363500 -10.69864015 -2.81473952 10.28442449 1.26515402
73 74 75 76 77 78
7.46201168 -0.13226642 4.29952117 2.91213784 -9.04052476 -1.27148020
79 80 81 82 83 84
-2.69084910 5.13095296 -2.39768475 3.85767165 -1.04061484 -1.96682369
85 86 87 88 89 90
3.30438486 0.94172305 -1.14652778 -7.93265322 0.84429274 1.75731633
91 92 93 94 95 96
5.83326223 -4.09175710 -2.02585699 1.30687969 -0.15864697 2.24670100
97 98 99 100 101 102
3.88544786 5.17567276 0.89310951 3.54959184 0.78739073 -1.36546485
103 104 105 106 107 108
-3.09052607 -1.45907646 1.27105756 5.04782428 4.33002559 4.70776751
109 110 111 112 113 114
0.46002035 -1.43857522 -1.13841725 10.06397693 2.29283720 -3.91361688
115 116 117 118 119 120
-8.77688190 -1.80127405 -2.22066339 -2.44527349 1.24076557 5.09178734
121 122 123 124 125 126
6.70971225 -4.41599213 -4.54922694 -1.44007692 -8.76547953 -1.16781766
127 128 129 130 131 132
-2.21877598 -2.07127081 -2.06504854 7.49280679 -1.83198196 -0.15810610
133 134 135 136 137 138
-2.33191585 0.77139413 3.00647119 -3.31636875 -2.43550913 -4.58324172
139 140 141 142 143 144
-1.65068680 -2.56396144 5.35149323 2.63998845 -4.26936426 -0.84015086
145 146 147 148 149 150
-3.65064132 1.87075280 7.97998976 0.88335747 -0.16036692 2.88319013
151 152 153 154 155 156
-1.26811436 1.70553843 12.01020228 1.02190433 -4.98263368 -1.93112267
157 158 159
3.68519287 -1.51630934 2.18890280
> postscript(file="/var/www/rcomp/tmp/6576p1292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.94230406 NA
1 4.23078198 -0.94230406
2 -4.65885207 4.23078198
3 -1.69881201 -4.65885207
4 -1.78689060 -1.69881201
5 -3.68363814 -1.78689060
6 -1.15996212 -3.68363814
7 -6.64619560 -1.15996212
8 -3.23883744 -6.64619560
9 -3.23645295 -3.23883744
10 -0.38258690 -3.23645295
11 5.82884883 -0.38258690
12 7.90453334 5.82884883
13 -0.54842741 7.90453334
14 -6.81872097 -0.54842741
15 -6.55354334 -6.81872097
16 2.19654169 -6.55354334
17 0.05417444 2.19654169
18 0.57534691 0.05417444
19 2.83422204 0.57534691
20 0.68313093 2.83422204
21 8.27341717 0.68313093
22 1.44460424 8.27341717
23 10.58169374 1.44460424
24 -0.29376354 10.58169374
25 -4.09499076 -0.29376354
26 -1.55385042 -4.09499076
27 2.83451842 -1.55385042
28 0.14098483 2.83451842
29 -2.47022929 0.14098483
30 2.99252918 -2.47022929
31 -6.56569604 2.99252918
32 -2.08283927 -6.56569604
33 -0.16444336 -2.08283927
34 -5.82364270 -0.16444336
35 4.02250060 -5.82364270
36 8.68699838 4.02250060
37 -7.75264354 8.68699838
38 4.43925161 -7.75264354
39 -1.15451448 4.43925161
40 2.63670638 -1.15451448
41 -0.25834999 2.63670638
42 1.68841392 -0.25834999
43 -4.53191229 1.68841392
44 -1.45778292 -4.53191229
45 -5.27070445 -1.45778292
46 -1.29821660 -5.27070445
47 4.25505744 -1.29821660
48 7.09811733 4.25505744
49 -2.74939674 7.09811733
50 2.03695702 -2.74939674
51 -1.08997944 2.03695702
52 1.16264300 -1.08997944
53 -1.24633315 1.16264300
54 -1.00075337 -1.24633315
55 2.73552628 -1.00075337
56 0.37843017 2.73552628
57 -3.29956839 0.37843017
58 -2.12342107 -3.29956839
59 -5.57576770 -2.12342107
60 -4.17074741 -5.57576770
61 -0.34500497 -4.17074741
62 -3.68312106 -0.34500497
63 -5.50658933 -3.68312106
64 -8.70950277 -5.50658933
65 6.14498661 -8.70950277
66 12.07010027 6.14498661
67 -3.50363500 12.07010027
68 -10.69864015 -3.50363500
69 -2.81473952 -10.69864015
70 10.28442449 -2.81473952
71 1.26515402 10.28442449
72 7.46201168 1.26515402
73 -0.13226642 7.46201168
74 4.29952117 -0.13226642
75 2.91213784 4.29952117
76 -9.04052476 2.91213784
77 -1.27148020 -9.04052476
78 -2.69084910 -1.27148020
79 5.13095296 -2.69084910
80 -2.39768475 5.13095296
81 3.85767165 -2.39768475
82 -1.04061484 3.85767165
83 -1.96682369 -1.04061484
84 3.30438486 -1.96682369
85 0.94172305 3.30438486
86 -1.14652778 0.94172305
87 -7.93265322 -1.14652778
88 0.84429274 -7.93265322
89 1.75731633 0.84429274
90 5.83326223 1.75731633
91 -4.09175710 5.83326223
92 -2.02585699 -4.09175710
93 1.30687969 -2.02585699
94 -0.15864697 1.30687969
95 2.24670100 -0.15864697
96 3.88544786 2.24670100
97 5.17567276 3.88544786
98 0.89310951 5.17567276
99 3.54959184 0.89310951
100 0.78739073 3.54959184
101 -1.36546485 0.78739073
102 -3.09052607 -1.36546485
103 -1.45907646 -3.09052607
104 1.27105756 -1.45907646
105 5.04782428 1.27105756
106 4.33002559 5.04782428
107 4.70776751 4.33002559
108 0.46002035 4.70776751
109 -1.43857522 0.46002035
110 -1.13841725 -1.43857522
111 10.06397693 -1.13841725
112 2.29283720 10.06397693
113 -3.91361688 2.29283720
114 -8.77688190 -3.91361688
115 -1.80127405 -8.77688190
116 -2.22066339 -1.80127405
117 -2.44527349 -2.22066339
118 1.24076557 -2.44527349
119 5.09178734 1.24076557
120 6.70971225 5.09178734
121 -4.41599213 6.70971225
122 -4.54922694 -4.41599213
123 -1.44007692 -4.54922694
124 -8.76547953 -1.44007692
125 -1.16781766 -8.76547953
126 -2.21877598 -1.16781766
127 -2.07127081 -2.21877598
128 -2.06504854 -2.07127081
129 7.49280679 -2.06504854
130 -1.83198196 7.49280679
131 -0.15810610 -1.83198196
132 -2.33191585 -0.15810610
133 0.77139413 -2.33191585
134 3.00647119 0.77139413
135 -3.31636875 3.00647119
136 -2.43550913 -3.31636875
137 -4.58324172 -2.43550913
138 -1.65068680 -4.58324172
139 -2.56396144 -1.65068680
140 5.35149323 -2.56396144
141 2.63998845 5.35149323
142 -4.26936426 2.63998845
143 -0.84015086 -4.26936426
144 -3.65064132 -0.84015086
145 1.87075280 -3.65064132
146 7.97998976 1.87075280
147 0.88335747 7.97998976
148 -0.16036692 0.88335747
149 2.88319013 -0.16036692
150 -1.26811436 2.88319013
151 1.70553843 -1.26811436
152 12.01020228 1.70553843
153 1.02190433 12.01020228
154 -4.98263368 1.02190433
155 -1.93112267 -4.98263368
156 3.68519287 -1.93112267
157 -1.51630934 3.68519287
158 2.18890280 -1.51630934
159 NA 2.18890280
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.23078198 -0.94230406
[2,] -4.65885207 4.23078198
[3,] -1.69881201 -4.65885207
[4,] -1.78689060 -1.69881201
[5,] -3.68363814 -1.78689060
[6,] -1.15996212 -3.68363814
[7,] -6.64619560 -1.15996212
[8,] -3.23883744 -6.64619560
[9,] -3.23645295 -3.23883744
[10,] -0.38258690 -3.23645295
[11,] 5.82884883 -0.38258690
[12,] 7.90453334 5.82884883
[13,] -0.54842741 7.90453334
[14,] -6.81872097 -0.54842741
[15,] -6.55354334 -6.81872097
[16,] 2.19654169 -6.55354334
[17,] 0.05417444 2.19654169
[18,] 0.57534691 0.05417444
[19,] 2.83422204 0.57534691
[20,] 0.68313093 2.83422204
[21,] 8.27341717 0.68313093
[22,] 1.44460424 8.27341717
[23,] 10.58169374 1.44460424
[24,] -0.29376354 10.58169374
[25,] -4.09499076 -0.29376354
[26,] -1.55385042 -4.09499076
[27,] 2.83451842 -1.55385042
[28,] 0.14098483 2.83451842
[29,] -2.47022929 0.14098483
[30,] 2.99252918 -2.47022929
[31,] -6.56569604 2.99252918
[32,] -2.08283927 -6.56569604
[33,] -0.16444336 -2.08283927
[34,] -5.82364270 -0.16444336
[35,] 4.02250060 -5.82364270
[36,] 8.68699838 4.02250060
[37,] -7.75264354 8.68699838
[38,] 4.43925161 -7.75264354
[39,] -1.15451448 4.43925161
[40,] 2.63670638 -1.15451448
[41,] -0.25834999 2.63670638
[42,] 1.68841392 -0.25834999
[43,] -4.53191229 1.68841392
[44,] -1.45778292 -4.53191229
[45,] -5.27070445 -1.45778292
[46,] -1.29821660 -5.27070445
[47,] 4.25505744 -1.29821660
[48,] 7.09811733 4.25505744
[49,] -2.74939674 7.09811733
[50,] 2.03695702 -2.74939674
[51,] -1.08997944 2.03695702
[52,] 1.16264300 -1.08997944
[53,] -1.24633315 1.16264300
[54,] -1.00075337 -1.24633315
[55,] 2.73552628 -1.00075337
[56,] 0.37843017 2.73552628
[57,] -3.29956839 0.37843017
[58,] -2.12342107 -3.29956839
[59,] -5.57576770 -2.12342107
[60,] -4.17074741 -5.57576770
[61,] -0.34500497 -4.17074741
[62,] -3.68312106 -0.34500497
[63,] -5.50658933 -3.68312106
[64,] -8.70950277 -5.50658933
[65,] 6.14498661 -8.70950277
[66,] 12.07010027 6.14498661
[67,] -3.50363500 12.07010027
[68,] -10.69864015 -3.50363500
[69,] -2.81473952 -10.69864015
[70,] 10.28442449 -2.81473952
[71,] 1.26515402 10.28442449
[72,] 7.46201168 1.26515402
[73,] -0.13226642 7.46201168
[74,] 4.29952117 -0.13226642
[75,] 2.91213784 4.29952117
[76,] -9.04052476 2.91213784
[77,] -1.27148020 -9.04052476
[78,] -2.69084910 -1.27148020
[79,] 5.13095296 -2.69084910
[80,] -2.39768475 5.13095296
[81,] 3.85767165 -2.39768475
[82,] -1.04061484 3.85767165
[83,] -1.96682369 -1.04061484
[84,] 3.30438486 -1.96682369
[85,] 0.94172305 3.30438486
[86,] -1.14652778 0.94172305
[87,] -7.93265322 -1.14652778
[88,] 0.84429274 -7.93265322
[89,] 1.75731633 0.84429274
[90,] 5.83326223 1.75731633
[91,] -4.09175710 5.83326223
[92,] -2.02585699 -4.09175710
[93,] 1.30687969 -2.02585699
[94,] -0.15864697 1.30687969
[95,] 2.24670100 -0.15864697
[96,] 3.88544786 2.24670100
[97,] 5.17567276 3.88544786
[98,] 0.89310951 5.17567276
[99,] 3.54959184 0.89310951
[100,] 0.78739073 3.54959184
[101,] -1.36546485 0.78739073
[102,] -3.09052607 -1.36546485
[103,] -1.45907646 -3.09052607
[104,] 1.27105756 -1.45907646
[105,] 5.04782428 1.27105756
[106,] 4.33002559 5.04782428
[107,] 4.70776751 4.33002559
[108,] 0.46002035 4.70776751
[109,] -1.43857522 0.46002035
[110,] -1.13841725 -1.43857522
[111,] 10.06397693 -1.13841725
[112,] 2.29283720 10.06397693
[113,] -3.91361688 2.29283720
[114,] -8.77688190 -3.91361688
[115,] -1.80127405 -8.77688190
[116,] -2.22066339 -1.80127405
[117,] -2.44527349 -2.22066339
[118,] 1.24076557 -2.44527349
[119,] 5.09178734 1.24076557
[120,] 6.70971225 5.09178734
[121,] -4.41599213 6.70971225
[122,] -4.54922694 -4.41599213
[123,] -1.44007692 -4.54922694
[124,] -8.76547953 -1.44007692
[125,] -1.16781766 -8.76547953
[126,] -2.21877598 -1.16781766
[127,] -2.07127081 -2.21877598
[128,] -2.06504854 -2.07127081
[129,] 7.49280679 -2.06504854
[130,] -1.83198196 7.49280679
[131,] -0.15810610 -1.83198196
[132,] -2.33191585 -0.15810610
[133,] 0.77139413 -2.33191585
[134,] 3.00647119 0.77139413
[135,] -3.31636875 3.00647119
[136,] -2.43550913 -3.31636875
[137,] -4.58324172 -2.43550913
[138,] -1.65068680 -4.58324172
[139,] -2.56396144 -1.65068680
[140,] 5.35149323 -2.56396144
[141,] 2.63998845 5.35149323
[142,] -4.26936426 2.63998845
[143,] -0.84015086 -4.26936426
[144,] -3.65064132 -0.84015086
[145,] 1.87075280 -3.65064132
[146,] 7.97998976 1.87075280
[147,] 0.88335747 7.97998976
[148,] -0.16036692 0.88335747
[149,] 2.88319013 -0.16036692
[150,] -1.26811436 2.88319013
[151,] 1.70553843 -1.26811436
[152,] 12.01020228 1.70553843
[153,] 1.02190433 12.01020228
[154,] -4.98263368 1.02190433
[155,] -1.93112267 -4.98263368
[156,] 3.68519287 -1.93112267
[157,] -1.51630934 3.68519287
[158,] 2.18890280 -1.51630934
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.23078198 -0.94230406
2 -4.65885207 4.23078198
3 -1.69881201 -4.65885207
4 -1.78689060 -1.69881201
5 -3.68363814 -1.78689060
6 -1.15996212 -3.68363814
7 -6.64619560 -1.15996212
8 -3.23883744 -6.64619560
9 -3.23645295 -3.23883744
10 -0.38258690 -3.23645295
11 5.82884883 -0.38258690
12 7.90453334 5.82884883
13 -0.54842741 7.90453334
14 -6.81872097 -0.54842741
15 -6.55354334 -6.81872097
16 2.19654169 -6.55354334
17 0.05417444 2.19654169
18 0.57534691 0.05417444
19 2.83422204 0.57534691
20 0.68313093 2.83422204
21 8.27341717 0.68313093
22 1.44460424 8.27341717
23 10.58169374 1.44460424
24 -0.29376354 10.58169374
25 -4.09499076 -0.29376354
26 -1.55385042 -4.09499076
27 2.83451842 -1.55385042
28 0.14098483 2.83451842
29 -2.47022929 0.14098483
30 2.99252918 -2.47022929
31 -6.56569604 2.99252918
32 -2.08283927 -6.56569604
33 -0.16444336 -2.08283927
34 -5.82364270 -0.16444336
35 4.02250060 -5.82364270
36 8.68699838 4.02250060
37 -7.75264354 8.68699838
38 4.43925161 -7.75264354
39 -1.15451448 4.43925161
40 2.63670638 -1.15451448
41 -0.25834999 2.63670638
42 1.68841392 -0.25834999
43 -4.53191229 1.68841392
44 -1.45778292 -4.53191229
45 -5.27070445 -1.45778292
46 -1.29821660 -5.27070445
47 4.25505744 -1.29821660
48 7.09811733 4.25505744
49 -2.74939674 7.09811733
50 2.03695702 -2.74939674
51 -1.08997944 2.03695702
52 1.16264300 -1.08997944
53 -1.24633315 1.16264300
54 -1.00075337 -1.24633315
55 2.73552628 -1.00075337
56 0.37843017 2.73552628
57 -3.29956839 0.37843017
58 -2.12342107 -3.29956839
59 -5.57576770 -2.12342107
60 -4.17074741 -5.57576770
61 -0.34500497 -4.17074741
62 -3.68312106 -0.34500497
63 -5.50658933 -3.68312106
64 -8.70950277 -5.50658933
65 6.14498661 -8.70950277
66 12.07010027 6.14498661
67 -3.50363500 12.07010027
68 -10.69864015 -3.50363500
69 -2.81473952 -10.69864015
70 10.28442449 -2.81473952
71 1.26515402 10.28442449
72 7.46201168 1.26515402
73 -0.13226642 7.46201168
74 4.29952117 -0.13226642
75 2.91213784 4.29952117
76 -9.04052476 2.91213784
77 -1.27148020 -9.04052476
78 -2.69084910 -1.27148020
79 5.13095296 -2.69084910
80 -2.39768475 5.13095296
81 3.85767165 -2.39768475
82 -1.04061484 3.85767165
83 -1.96682369 -1.04061484
84 3.30438486 -1.96682369
85 0.94172305 3.30438486
86 -1.14652778 0.94172305
87 -7.93265322 -1.14652778
88 0.84429274 -7.93265322
89 1.75731633 0.84429274
90 5.83326223 1.75731633
91 -4.09175710 5.83326223
92 -2.02585699 -4.09175710
93 1.30687969 -2.02585699
94 -0.15864697 1.30687969
95 2.24670100 -0.15864697
96 3.88544786 2.24670100
97 5.17567276 3.88544786
98 0.89310951 5.17567276
99 3.54959184 0.89310951
100 0.78739073 3.54959184
101 -1.36546485 0.78739073
102 -3.09052607 -1.36546485
103 -1.45907646 -3.09052607
104 1.27105756 -1.45907646
105 5.04782428 1.27105756
106 4.33002559 5.04782428
107 4.70776751 4.33002559
108 0.46002035 4.70776751
109 -1.43857522 0.46002035
110 -1.13841725 -1.43857522
111 10.06397693 -1.13841725
112 2.29283720 10.06397693
113 -3.91361688 2.29283720
114 -8.77688190 -3.91361688
115 -1.80127405 -8.77688190
116 -2.22066339 -1.80127405
117 -2.44527349 -2.22066339
118 1.24076557 -2.44527349
119 5.09178734 1.24076557
120 6.70971225 5.09178734
121 -4.41599213 6.70971225
122 -4.54922694 -4.41599213
123 -1.44007692 -4.54922694
124 -8.76547953 -1.44007692
125 -1.16781766 -8.76547953
126 -2.21877598 -1.16781766
127 -2.07127081 -2.21877598
128 -2.06504854 -2.07127081
129 7.49280679 -2.06504854
130 -1.83198196 7.49280679
131 -0.15810610 -1.83198196
132 -2.33191585 -0.15810610
133 0.77139413 -2.33191585
134 3.00647119 0.77139413
135 -3.31636875 3.00647119
136 -2.43550913 -3.31636875
137 -4.58324172 -2.43550913
138 -1.65068680 -4.58324172
139 -2.56396144 -1.65068680
140 5.35149323 -2.56396144
141 2.63998845 5.35149323
142 -4.26936426 2.63998845
143 -0.84015086 -4.26936426
144 -3.65064132 -0.84015086
145 1.87075280 -3.65064132
146 7.97998976 1.87075280
147 0.88335747 7.97998976
148 -0.16036692 0.88335747
149 2.88319013 -0.16036692
150 -1.26811436 2.88319013
151 1.70553843 -1.26811436
152 12.01020228 1.70553843
153 1.02190433 12.01020228
154 -4.98263368 1.02190433
155 -1.93112267 -4.98263368
156 3.68519287 -1.93112267
157 -1.51630934 3.68519287
158 2.18890280 -1.51630934
> 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/7yy5a1292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8yy5a1292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9yy5a1292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10q74v1292768664.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11bpk01292768664.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/12f8161292768664.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/13bizx1292768664.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/14mrg01292768664.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/157sfo1292768664.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/163kdf1292768664.tab")
+ }
>
> try(system("convert tmp/169eq1292768664.ps tmp/169eq1292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uf641292768664.ps tmp/2uf641292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/3uf641292768664.ps tmp/3uf641292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/4uf641292768664.ps tmp/4uf641292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/5uf641292768664.ps tmp/5uf641292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/6576p1292768664.ps tmp/6576p1292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/7yy5a1292768664.ps tmp/7yy5a1292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/8yy5a1292768664.ps tmp/8yy5a1292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yy5a1292768664.ps tmp/9yy5a1292768664.png",intern=TRUE))
character(0)
> try(system("convert tmp/10q74v1292768664.ps tmp/10q74v1292768664.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.35 1.70 6.99