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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+ ,11
+ ,6
+ ,6
+ ,27
+ ,27
+ ,24
+ ,24
+ ,1
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+ ,9
+ ,9
+ ,17
+ ,17
+ ,9
+ ,9
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+ ,25
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+ ,1
+ ,38
+ ,11
+ ,11
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+ ,21
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+ ,7
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+ ,29
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+ ,0
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+ ,0
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+ ,0
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+ ,0
+ ,1
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+ ,11
+ ,11
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+ ,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
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+ ,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
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
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 6 11 0 6
55 8 28 8 24
56 13 26 13 12
57 10 22 0 12
58 8 17 8 14
59 7 12 0 7
60 15 14 15 13
61 9 17 9 12
62 10 21 10 13
63 12 19 12 14
64 13 18 13 8
65 10 10 0 11
66 11 29 0 9
67 8 31 8 11
68 9 19 0 13
69 13 9 13 10
70 11 20 11 11
71 8 28 8 12
72 9 19 0 9
73 9 30 0 15
74 15 29 0 18
75 9 26 0 15
76 10 23 0 12
77 14 13 14 13
78 12 21 12 14
79 12 19 12 10
80 11 28 11 13
81 14 23 14 13
82 6 18 6 11
83 12 21 0 13
84 8 20 8 16
85 14 23 14 8
86 11 21 11 16
87 10 21 10 11
88 14 15 14 9
89 12 28 12 16
90 10 19 10 12
91 14 26 14 14
92 5 10 5 8
93 11 16 0 9
94 10 22 10 15
95 9 19 9 11
96 10 31 10 21
97 16 31 0 14
98 13 29 13 18
99 9 19 0 12
100 10 22 10 13
101 10 23 10 15
102 7 15 0 12
103 9 20 0 19
104 8 18 8 15
105 14 23 14 11
106 14 25 14 11
107 8 21 8 10
108 9 24 9 13
109 14 25 14 15
110 14 17 14 12
111 8 13 8 12
112 8 28 8 16
113 8 21 0 9
114 7 25 7 18
115 6 9 0 8
116 8 16 8 13
117 6 19 6 17
118 11 17 11 9
119 14 25 14 15
120 11 20 11 8
121 11 29 11 7
122 11 14 11 12
123 14 22 14 14
124 8 15 8 6
125 20 19 0 8
126 11 20 11 17
127 8 15 0 10
128 11 20 11 11
129 10 18 10 14
130 14 33 14 11
131 11 22 11 13
132 9 16 9 12
133 9 17 9 11
134 8 16 8 9
135 10 21 0 12
136 13 26 0 20
137 13 18 13 12
138 12 18 12 13
139 8 17 8 12
140 13 22 13 12
141 14 30 14 9
142 12 30 0 15
143 14 24 14 24
144 15 21 15 7
145 13 21 13 17
146 16 29 16 11
147 9 31 9 17
148 9 20 9 11
149 9 16 0 12
150 8 22 0 14
151 7 20 7 11
152 16 28 16 16
153 11 38 11 21
154 9 22 0 14
155 11 20 11 20
156 9 17 0 13
157 14 28 14 11
158 13 22 13 15
159 16 31 0 19
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 0 4 0
55 24 20 20
56 12 8 8
57 0 8 0
58 14 6 6
59 0 4 0
60 13 8 8
61 12 9 9
62 13 6 6
63 14 7 7
64 8 9 9
65 0 5 0
66 0 5 0
67 11 8 8
68 0 8 0
69 10 6 6
70 11 8 8
71 12 7 7
72 0 7 0
73 0 9 0
74 0 11 0
75 0 6 0
76 0 8 0
77 13 6 6
78 14 9 9
79 10 8 8
80 13 6 6
81 13 10 10
82 11 8 8
83 0 8 0
84 16 10 10
85 8 5 5
86 16 7 7
87 11 5 5
88 9 8 8
89 16 14 14
90 12 7 7
91 14 8 8
92 8 6 6
93 0 5 0
94 15 6 6
95 11 10 10
96 21 12 12
97 0 9 0
98 18 12 12
99 0 7 0
100 13 8 8
101 15 10 10
102 0 6 0
103 0 10 0
104 15 10 10
105 11 10 10
106 11 5 5
107 10 7 7
108 13 10 10
109 15 11 11
110 12 6 6
111 12 7 7
112 16 12 12
113 0 11 0
114 18 11 11
115 0 11 0
116 13 5 5
117 17 8 8
118 9 6 6
119 15 9 9
120 8 4 4
121 7 4 4
122 12 7 7
123 14 11 11
124 6 6 6
125 0 7 0
126 17 8 8
127 0 4 0
128 11 8 8
129 14 9 9
130 11 8 8
131 13 11 11
132 12 8 8
133 11 5 5
134 9 4 4
135 0 8 0
136 0 10 0
137 12 6 6
138 13 9 9
139 12 9 9
140 12 13 13
141 9 9 9
142 0 10 0
143 24 20 20
144 7 5 5
145 17 11 11
146 11 6 6
147 17 9 9
148 11 7 7
149 0 9 0
150 0 10 0
151 11 9 9
152 16 8 8
153 21 7 7
154 0 6 0
155 20 13 13
156 0 6 0
157 11 8 8
158 15 10 10
159 0 16 0
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 15 0 16 0
55 30 30 20 20
56 24 24 29 29
57 26 0 27 0
58 24 24 22 22
59 22 0 28 0
60 14 14 16 16
61 24 24 25 25
62 24 24 24 24
63 24 24 28 28
64 24 24 24 24
65 19 0 23 0
66 31 0 30 0
67 22 22 24 24
68 27 0 21 0
69 19 19 25 25
70 25 25 25 25
71 20 20 22 22
72 21 0 23 0
73 27 0 26 0
74 23 0 23 0
75 25 0 25 0
76 20 0 21 0
77 21 21 25 25
78 22 22 24 24
79 23 23 29 29
80 25 25 22 22
81 25 25 27 27
82 17 17 26 26
83 19 0 22 0
84 25 25 24 24
85 19 19 27 27
86 20 20 24 24
87 26 26 24 24
88 23 23 29 29
89 27 27 22 22
90 17 17 21 21
91 17 17 24 24
92 19 19 24 24
93 17 0 23 0
94 22 22 20 20
95 21 21 27 27
96 32 32 26 26
97 21 0 25 0
98 21 21 21 21
99 18 0 21 0
100 18 18 19 19
101 23 23 21 21
102 19 0 21 0
103 20 0 16 0
104 21 21 22 22
105 20 20 29 29
106 17 17 15 15
107 18 18 17 17
108 19 19 15 15
109 22 22 21 21
110 15 15 21 21
111 14 14 19 19
112 18 18 24 24
113 24 0 20 0
114 35 35 17 17
115 29 0 23 0
116 21 21 24 24
117 25 25 14 14
118 20 20 19 19
119 22 22 24 24
120 13 13 13 13
121 26 26 22 22
122 17 17 16 16
123 25 25 19 19
124 20 20 25 25
125 19 0 25 0
126 21 21 23 23
127 22 0 24 0
128 24 24 26 26
129 21 21 26 26
130 26 26 25 25
131 24 24 18 18
132 16 16 21 21
133 23 23 26 26
134 18 18 23 23
135 16 0 23 0
136 26 0 22 0
137 19 19 20 20
138 21 21 13 13
139 21 21 24 24
140 22 22 15 15
141 23 23 14 14
142 29 0 22 0
143 21 21 10 10
144 21 21 24 24
145 23 23 22 22
146 27 27 24 24
147 25 25 19 19
148 21 21 20 20
149 10 0 13 0
150 20 0 20 0
151 26 26 22 22
152 24 24 24 24
153 29 29 29 29
154 19 0 12 0
155 24 24 20 20
156 19 0 21 0
157 24 24 24 24
158 22 22 22 22
159 17 0 20 0
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 1
55 1
56 0
57 1
58 0
59 1
60 1
61 1
62 1
63 1
64 0
65 0
66 1
67 0
68 1
69 1
70 1
71 0
72 0
73 0
74 0
75 0
76 1
77 1
78 1
79 1
80 1
81 1
82 0
83 1
84 1
85 1
86 1
87 1
88 1
89 1
90 1
91 1
92 0
93 1
94 1
95 1
96 0
97 1
98 0
99 1
100 1
101 0
102 0
103 1
104 1
105 1
106 1
107 1
108 1
109 1
110 1
111 1
112 0
113 1
114 0
115 1
116 1
117 1
118 1
119 1
120 1
121 1
122 1
123 1
124 0
125 1
126 0
127 1
128 1
129 1
130 1
131 1
132 1
133 1
134 0
135 0
136 1
137 1
138 1
139 1
140 1
141 0
142 1
143 1
144 1
145 1
146 1
147 1
148 0
149 0
150 1
151 1
152 1
153 0
154 1
155 0
156 1
157 1
158 0
159 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Gender Doubtsaboutactions
17.70790 0.03659 0.69515
DoubtsaboutactionsMale Parentalexpectations ParentalexpectationsMale
-0.07838 -0.22316 -0.17356
Parentalcritism ParentalcritismMale Personalstandards
0.32364 -0.51176 0.32085
PersonalstandarsMale Organization OrganizationMale
0.18288 -0.52914 0.18713
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.9508 -2.3153 -0.1563 1.6761 11.8593
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.70790 1.12879 15.687 < 2e-16 ***
Gender 0.03659 0.07405 0.494 0.621910
Doubtsaboutactions 0.69515 0.11709 5.937 2.00e-08 ***
DoubtsaboutactionsMale -0.07838 0.12186 -0.643 0.521109
Parentalexpectations -0.22316 0.13824 -1.614 0.108598
ParentalexpectationsMale -0.17356 0.16425 -1.057 0.292390
Parentalcritism 0.32364 0.18380 1.761 0.080347 .
ParentalcritismMale -0.51176 0.09829 -5.207 6.37e-07 ***
Personalstandards 0.32085 0.08990 3.569 0.000484 ***
PersonalstandarsMale 0.18288 0.08723 2.097 0.037741 *
Organization -0.52914 0.09470 -5.587 1.09e-07 ***
OrganizationMale 0.18713 0.08845 2.116 0.036064 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.689 on 147 degrees of freedom
Multiple R-squared: 0.7137, Adjusted R-squared: 0.6923
F-statistic: 33.32 on 11 and 147 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,] 0.2991972 5.983943e-01 7.008028e-01
[2,] 0.3609508 7.219016e-01 6.390492e-01
[3,] 0.2268251 4.536502e-01 7.731749e-01
[4,] 0.2647185 5.294370e-01 7.352815e-01
[5,] 0.4014865 8.029730e-01 5.985135e-01
[6,] 0.3343432 6.686864e-01 6.656568e-01
[7,] 0.3957985 7.915971e-01 6.042015e-01
[8,] 0.7759977 4.480047e-01 2.240023e-01
[9,] 0.7305070 5.389859e-01 2.694930e-01
[10,] 0.9801151 3.976970e-02 1.988485e-02
[11,] 0.9693397 6.132057e-02 3.066029e-02
[12,] 0.9565731 8.685377e-02 4.342688e-02
[13,] 0.9420070 1.159861e-01 5.799305e-02
[14,] 0.9243451 1.513098e-01 7.565492e-02
[15,] 0.8998909 2.002182e-01 1.001091e-01
[16,] 0.8736853 2.526294e-01 1.263147e-01
[17,] 0.9447732 1.104536e-01 5.522680e-02
[18,] 0.9423096 1.153808e-01 5.769042e-02
[19,] 0.9439951 1.120098e-01 5.600492e-02
[20,] 0.9646226 7.075478e-02 3.537739e-02
[21,] 0.9695731 6.085388e-02 3.042694e-02
[22,] 0.9728391 5.432173e-02 2.716087e-02
[23,] 0.9951461 9.707787e-03 4.853893e-03
[24,] 0.9994663 1.067442e-03 5.337209e-04
[25,] 0.9998482 3.036058e-04 1.518029e-04
[26,] 0.9997613 4.773641e-04 2.386820e-04
[27,] 0.9998729 2.542618e-04 1.271309e-04
[28,] 0.9998281 3.437944e-04 1.718972e-04
[29,] 0.9998188 3.623148e-04 1.811574e-04
[30,] 0.9997478 5.044962e-04 2.522481e-04
[31,] 0.9996330 7.339919e-04 3.669959e-04
[32,] 0.9998571 2.857841e-04 1.428920e-04
[33,] 0.9998036 3.927114e-04 1.963557e-04
[34,] 0.9998185 3.630445e-04 1.815222e-04
[35,] 0.9999999 2.224059e-07 1.112029e-07
[36,] 0.9999998 3.871051e-07 1.935525e-07
[37,] 1.0000000 2.130340e-08 1.065170e-08
[38,] 1.0000000 3.755226e-08 1.877613e-08
[39,] 1.0000000 2.748281e-20 1.374141e-20
[40,] 1.0000000 1.017888e-21 5.089439e-22
[41,] 1.0000000 3.342562e-21 1.671281e-21
[42,] 1.0000000 3.407694e-21 1.703847e-21
[43,] 1.0000000 1.077935e-21 5.389677e-22
[44,] 1.0000000 2.713663e-22 1.356832e-22
[45,] 1.0000000 5.242407e-23 2.621204e-23
[46,] 1.0000000 1.248693e-22 6.243464e-23
[47,] 1.0000000 2.619931e-22 1.309966e-22
[48,] 1.0000000 7.777455e-22 3.888728e-22
[49,] 1.0000000 2.664172e-21 1.332086e-21
[50,] 1.0000000 2.922080e-21 1.461040e-21
[51,] 1.0000000 5.818442e-21 2.909221e-21
[52,] 1.0000000 7.457907e-21 3.728953e-21
[53,] 1.0000000 2.427779e-20 1.213890e-20
[54,] 1.0000000 3.333023e-20 1.666512e-20
[55,] 1.0000000 8.880256e-20 4.440128e-20
[56,] 1.0000000 2.375101e-19 1.187550e-19
[57,] 1.0000000 7.545136e-19 3.772568e-19
[58,] 1.0000000 2.053773e-18 1.026886e-18
[59,] 1.0000000 6.093965e-19 3.046982e-19
[60,] 1.0000000 4.071706e-20 2.035853e-20
[61,] 1.0000000 6.435410e-20 3.217705e-20
[62,] 1.0000000 1.753500e-19 8.767498e-20
[63,] 1.0000000 4.485211e-19 2.242606e-19
[64,] 1.0000000 1.465227e-18 7.326133e-19
[65,] 1.0000000 4.617607e-18 2.308804e-18
[66,] 1.0000000 1.159101e-17 5.795505e-18
[67,] 1.0000000 3.669311e-17 1.834656e-17
[68,] 1.0000000 1.148243e-16 5.741216e-17
[69,] 1.0000000 3.152033e-16 1.576017e-16
[70,] 1.0000000 9.541406e-16 4.770703e-16
[71,] 1.0000000 2.830065e-15 1.415032e-15
[72,] 1.0000000 8.272919e-15 4.136460e-15
[73,] 1.0000000 2.329525e-14 1.164763e-14
[74,] 1.0000000 5.807645e-14 2.903822e-14
[75,] 1.0000000 1.382765e-13 6.913826e-14
[76,] 1.0000000 3.439257e-13 1.719628e-13
[77,] 1.0000000 9.034201e-13 4.517101e-13
[78,] 1.0000000 1.949452e-12 9.747258e-13
[79,] 1.0000000 5.209442e-12 2.604721e-12
[80,] 1.0000000 1.312740e-11 6.563698e-12
[81,] 1.0000000 3.422481e-11 1.711240e-11
[82,] 1.0000000 8.245183e-11 4.122592e-11
[83,] 1.0000000 7.697459e-11 3.848729e-11
[84,] 1.0000000 1.900463e-10 9.502313e-11
[85,] 1.0000000 2.574058e-10 1.287029e-10
[86,] 1.0000000 5.183346e-10 2.591673e-10
[87,] 1.0000000 1.211892e-09 6.059461e-10
[88,] 1.0000000 9.492430e-10 4.746215e-10
[89,] 1.0000000 2.027366e-09 1.013683e-09
[90,] 1.0000000 4.978562e-09 2.489281e-09
[91,] 1.0000000 1.202254e-08 6.011269e-09
[92,] 1.0000000 1.956977e-08 9.784885e-09
[93,] 1.0000000 3.119598e-08 1.559799e-08
[94,] 1.0000000 4.696594e-08 2.348297e-08
[95,] 0.9999999 1.101320e-07 5.506601e-08
[96,] 0.9999999 2.545936e-07 1.272968e-07
[97,] 0.9999997 5.332462e-07 2.666231e-07
[98,] 0.9999995 1.011667e-06 5.058334e-07
[99,] 0.9999991 1.725309e-06 8.626545e-07
[100,] 0.9999985 3.017929e-06 1.508964e-06
[101,] 0.9999998 3.593747e-07 1.796873e-07
[102,] 0.9999996 8.772038e-07 4.386019e-07
[103,] 0.9999990 1.907631e-06 9.538153e-07
[104,] 0.9999980 4.026762e-06 2.013381e-06
[105,] 0.9999954 9.272044e-06 4.636022e-06
[106,] 0.9999930 1.400653e-05 7.003266e-06
[107,] 0.9999846 3.076912e-05 1.538456e-05
[108,] 0.9999693 6.145977e-05 3.072989e-05
[109,] 0.9999352 1.296034e-04 6.480171e-05
[110,] 0.9998684 2.632062e-04 1.316031e-04
[111,] 1.0000000 3.580095e-13 1.790047e-13
[112,] 1.0000000 2.070848e-12 1.035424e-12
[113,] 1.0000000 5.063087e-12 2.531543e-12
[114,] 1.0000000 2.961654e-11 1.480827e-11
[115,] 1.0000000 1.681458e-10 8.407288e-11
[116,] 1.0000000 8.892424e-10 4.446212e-10
[117,] 1.0000000 4.522445e-09 2.261222e-09
[118,] 1.0000000 2.272005e-08 1.136002e-08
[119,] 0.9999999 1.061390e-07 5.306949e-08
[120,] 0.9999997 5.108889e-07 2.554445e-07
[121,] 0.9999993 1.302933e-06 6.514663e-07
[122,] 0.9999983 3.494212e-06 1.747106e-06
[123,] 0.9999928 1.444992e-05 7.224959e-06
[124,] 0.9999765 4.695330e-05 2.347665e-05
[125,] 0.9998935 2.130871e-04 1.065435e-04
[126,] 0.9995591 8.817315e-04 4.408657e-04
[127,] 0.9985598 2.880376e-03 1.440188e-03
[128,] 0.9999598 8.038175e-05 4.019088e-05
[129,] 0.9996309 7.381691e-04 3.690845e-04
[130,] 0.9974515 5.096924e-03 2.548462e-03
> postscript(file="/var/www/html/rcomp/tmp/1i3b81290688791.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2susb1290688791.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3susb1290688791.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4susb1290688791.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/534ae1290688791.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
1.188260947 2.767364719 -0.071290098 -3.378303587 -2.808881253 -2.998599136
7 8 9 10 11 12
-2.838887936 -6.499387394 -4.574607343 -3.082942853 2.098868835 7.712784721
13 14 15 16 17 18
6.534642675 -4.603162001 -7.721739003 -5.532837270 -0.344530830 -4.757924793
19 20 21 22 23 24
2.508506521 6.996878715 2.139202412 11.859255660 3.315465216 8.891887695
25 26 27 28 29 30
-0.158515299 -1.514718815 -0.601008964 3.192286827 2.521791501 -3.225265024
31 32 33 34 35 36
2.111935138 -0.634473569 3.263949033 0.829620589 -3.269230381 3.370384861
37 38 39 40 41 42
6.589936458 -9.950845659 9.672356468 -0.810279076 8.806085861 1.224623293
43 44 45 46 47 48
0.305443722 -3.734414635 -1.188862235 -5.801302007 0.197009286 3.408722841
49 50 51 52 53 54
8.578310027 -0.729314252 2.310739407 -1.400010691 3.828271035 -6.382653263
55 56 57 58 59 60
0.406924721 2.344999249 1.997059039 -0.370519052 -3.953050125 -2.902285834
61 62 63 64 65 66
-0.267338959 0.296667266 2.516066727 -0.449790667 -0.826567572 3.995278113
67 68 69 70 71 72
-1.776916691 2.794244907 0.137458886 0.272046691 -2.289860104 -0.942023371
73 74 75 76 77 78
1.994768231 7.115187468 0.779816931 -0.012855067 1.582367986 0.375834339
79 80 81 82 83 84
1.315187020 -0.156252801 2.072254769 -2.172930250 1.444069329 0.218733130
85 86 87 88 89 90
0.169530140 0.592400622 0.225502084 1.769736529 0.234352472 -2.456933043
91 92 93 94 95 96
-0.001900119 -3.100171843 -1.413548061 -0.903727532 -0.672357820 3.849454852
97 98 99 100 101 102
5.804958149 0.101259035 -2.063564617 -2.916878773 -0.816354492 -3.391849055
103 104 105 106 107 108
0.907068163 -1.465785372 1.207150589 -3.536024089 -4.937042812 -4.789231529
109 110 111 112 113 114
-0.198256222 -1.396064547 -4.112335075 -1.523437135 0.575814207 -0.143696779
115 116 117 118 119 120
0.946756976 -0.552718559 -3.215301409 -2.953205787 1.140702160 -6.478310417
121 122 123 124 125 126
-1.510984431 -3.700420540 -0.509801142 -2.434479456 8.403184492 0.812228848
127 128 129 130 131 132
-2.096195060 0.427398068 0.377522745 0.901794693 -2.263074644 -2.993000845
133 134 135 136 137 138
0.186816511 -2.340642084 -2.165341576 6.739198501 -1.320130624 -3.815720148
139 140 141 142 143 144
-1.491189953 -3.675683489 -3.936070136 6.736229571 -2.798400828 -0.410928799
145 146 147 148 149 150
0.783513762 1.802699599 -1.158642051 -2.495401674 -4.050621873 -1.289511675
151 152 153 154 155 156
-1.945332555 2.474072101 5.114628712 -0.032496316 0.500358450 -1.573795093
157 158 159
0.356681013 0.290154214 6.466221414
> postscript(file="/var/www/html/rcomp/tmp/634ae1290688791.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 1.188260947 NA
1 2.767364719 1.188260947
2 -0.071290098 2.767364719
3 -3.378303587 -0.071290098
4 -2.808881253 -3.378303587
5 -2.998599136 -2.808881253
6 -2.838887936 -2.998599136
7 -6.499387394 -2.838887936
8 -4.574607343 -6.499387394
9 -3.082942853 -4.574607343
10 2.098868835 -3.082942853
11 7.712784721 2.098868835
12 6.534642675 7.712784721
13 -4.603162001 6.534642675
14 -7.721739003 -4.603162001
15 -5.532837270 -7.721739003
16 -0.344530830 -5.532837270
17 -4.757924793 -0.344530830
18 2.508506521 -4.757924793
19 6.996878715 2.508506521
20 2.139202412 6.996878715
21 11.859255660 2.139202412
22 3.315465216 11.859255660
23 8.891887695 3.315465216
24 -0.158515299 8.891887695
25 -1.514718815 -0.158515299
26 -0.601008964 -1.514718815
27 3.192286827 -0.601008964
28 2.521791501 3.192286827
29 -3.225265024 2.521791501
30 2.111935138 -3.225265024
31 -0.634473569 2.111935138
32 3.263949033 -0.634473569
33 0.829620589 3.263949033
34 -3.269230381 0.829620589
35 3.370384861 -3.269230381
36 6.589936458 3.370384861
37 -9.950845659 6.589936458
38 9.672356468 -9.950845659
39 -0.810279076 9.672356468
40 8.806085861 -0.810279076
41 1.224623293 8.806085861
42 0.305443722 1.224623293
43 -3.734414635 0.305443722
44 -1.188862235 -3.734414635
45 -5.801302007 -1.188862235
46 0.197009286 -5.801302007
47 3.408722841 0.197009286
48 8.578310027 3.408722841
49 -0.729314252 8.578310027
50 2.310739407 -0.729314252
51 -1.400010691 2.310739407
52 3.828271035 -1.400010691
53 -6.382653263 3.828271035
54 0.406924721 -6.382653263
55 2.344999249 0.406924721
56 1.997059039 2.344999249
57 -0.370519052 1.997059039
58 -3.953050125 -0.370519052
59 -2.902285834 -3.953050125
60 -0.267338959 -2.902285834
61 0.296667266 -0.267338959
62 2.516066727 0.296667266
63 -0.449790667 2.516066727
64 -0.826567572 -0.449790667
65 3.995278113 -0.826567572
66 -1.776916691 3.995278113
67 2.794244907 -1.776916691
68 0.137458886 2.794244907
69 0.272046691 0.137458886
70 -2.289860104 0.272046691
71 -0.942023371 -2.289860104
72 1.994768231 -0.942023371
73 7.115187468 1.994768231
74 0.779816931 7.115187468
75 -0.012855067 0.779816931
76 1.582367986 -0.012855067
77 0.375834339 1.582367986
78 1.315187020 0.375834339
79 -0.156252801 1.315187020
80 2.072254769 -0.156252801
81 -2.172930250 2.072254769
82 1.444069329 -2.172930250
83 0.218733130 1.444069329
84 0.169530140 0.218733130
85 0.592400622 0.169530140
86 0.225502084 0.592400622
87 1.769736529 0.225502084
88 0.234352472 1.769736529
89 -2.456933043 0.234352472
90 -0.001900119 -2.456933043
91 -3.100171843 -0.001900119
92 -1.413548061 -3.100171843
93 -0.903727532 -1.413548061
94 -0.672357820 -0.903727532
95 3.849454852 -0.672357820
96 5.804958149 3.849454852
97 0.101259035 5.804958149
98 -2.063564617 0.101259035
99 -2.916878773 -2.063564617
100 -0.816354492 -2.916878773
101 -3.391849055 -0.816354492
102 0.907068163 -3.391849055
103 -1.465785372 0.907068163
104 1.207150589 -1.465785372
105 -3.536024089 1.207150589
106 -4.937042812 -3.536024089
107 -4.789231529 -4.937042812
108 -0.198256222 -4.789231529
109 -1.396064547 -0.198256222
110 -4.112335075 -1.396064547
111 -1.523437135 -4.112335075
112 0.575814207 -1.523437135
113 -0.143696779 0.575814207
114 0.946756976 -0.143696779
115 -0.552718559 0.946756976
116 -3.215301409 -0.552718559
117 -2.953205787 -3.215301409
118 1.140702160 -2.953205787
119 -6.478310417 1.140702160
120 -1.510984431 -6.478310417
121 -3.700420540 -1.510984431
122 -0.509801142 -3.700420540
123 -2.434479456 -0.509801142
124 8.403184492 -2.434479456
125 0.812228848 8.403184492
126 -2.096195060 0.812228848
127 0.427398068 -2.096195060
128 0.377522745 0.427398068
129 0.901794693 0.377522745
130 -2.263074644 0.901794693
131 -2.993000845 -2.263074644
132 0.186816511 -2.993000845
133 -2.340642084 0.186816511
134 -2.165341576 -2.340642084
135 6.739198501 -2.165341576
136 -1.320130624 6.739198501
137 -3.815720148 -1.320130624
138 -1.491189953 -3.815720148
139 -3.675683489 -1.491189953
140 -3.936070136 -3.675683489
141 6.736229571 -3.936070136
142 -2.798400828 6.736229571
143 -0.410928799 -2.798400828
144 0.783513762 -0.410928799
145 1.802699599 0.783513762
146 -1.158642051 1.802699599
147 -2.495401674 -1.158642051
148 -4.050621873 -2.495401674
149 -1.289511675 -4.050621873
150 -1.945332555 -1.289511675
151 2.474072101 -1.945332555
152 5.114628712 2.474072101
153 -0.032496316 5.114628712
154 0.500358450 -0.032496316
155 -1.573795093 0.500358450
156 0.356681013 -1.573795093
157 0.290154214 0.356681013
158 6.466221414 0.290154214
159 NA 6.466221414
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.767364719 1.188260947
[2,] -0.071290098 2.767364719
[3,] -3.378303587 -0.071290098
[4,] -2.808881253 -3.378303587
[5,] -2.998599136 -2.808881253
[6,] -2.838887936 -2.998599136
[7,] -6.499387394 -2.838887936
[8,] -4.574607343 -6.499387394
[9,] -3.082942853 -4.574607343
[10,] 2.098868835 -3.082942853
[11,] 7.712784721 2.098868835
[12,] 6.534642675 7.712784721
[13,] -4.603162001 6.534642675
[14,] -7.721739003 -4.603162001
[15,] -5.532837270 -7.721739003
[16,] -0.344530830 -5.532837270
[17,] -4.757924793 -0.344530830
[18,] 2.508506521 -4.757924793
[19,] 6.996878715 2.508506521
[20,] 2.139202412 6.996878715
[21,] 11.859255660 2.139202412
[22,] 3.315465216 11.859255660
[23,] 8.891887695 3.315465216
[24,] -0.158515299 8.891887695
[25,] -1.514718815 -0.158515299
[26,] -0.601008964 -1.514718815
[27,] 3.192286827 -0.601008964
[28,] 2.521791501 3.192286827
[29,] -3.225265024 2.521791501
[30,] 2.111935138 -3.225265024
[31,] -0.634473569 2.111935138
[32,] 3.263949033 -0.634473569
[33,] 0.829620589 3.263949033
[34,] -3.269230381 0.829620589
[35,] 3.370384861 -3.269230381
[36,] 6.589936458 3.370384861
[37,] -9.950845659 6.589936458
[38,] 9.672356468 -9.950845659
[39,] -0.810279076 9.672356468
[40,] 8.806085861 -0.810279076
[41,] 1.224623293 8.806085861
[42,] 0.305443722 1.224623293
[43,] -3.734414635 0.305443722
[44,] -1.188862235 -3.734414635
[45,] -5.801302007 -1.188862235
[46,] 0.197009286 -5.801302007
[47,] 3.408722841 0.197009286
[48,] 8.578310027 3.408722841
[49,] -0.729314252 8.578310027
[50,] 2.310739407 -0.729314252
[51,] -1.400010691 2.310739407
[52,] 3.828271035 -1.400010691
[53,] -6.382653263 3.828271035
[54,] 0.406924721 -6.382653263
[55,] 2.344999249 0.406924721
[56,] 1.997059039 2.344999249
[57,] -0.370519052 1.997059039
[58,] -3.953050125 -0.370519052
[59,] -2.902285834 -3.953050125
[60,] -0.267338959 -2.902285834
[61,] 0.296667266 -0.267338959
[62,] 2.516066727 0.296667266
[63,] -0.449790667 2.516066727
[64,] -0.826567572 -0.449790667
[65,] 3.995278113 -0.826567572
[66,] -1.776916691 3.995278113
[67,] 2.794244907 -1.776916691
[68,] 0.137458886 2.794244907
[69,] 0.272046691 0.137458886
[70,] -2.289860104 0.272046691
[71,] -0.942023371 -2.289860104
[72,] 1.994768231 -0.942023371
[73,] 7.115187468 1.994768231
[74,] 0.779816931 7.115187468
[75,] -0.012855067 0.779816931
[76,] 1.582367986 -0.012855067
[77,] 0.375834339 1.582367986
[78,] 1.315187020 0.375834339
[79,] -0.156252801 1.315187020
[80,] 2.072254769 -0.156252801
[81,] -2.172930250 2.072254769
[82,] 1.444069329 -2.172930250
[83,] 0.218733130 1.444069329
[84,] 0.169530140 0.218733130
[85,] 0.592400622 0.169530140
[86,] 0.225502084 0.592400622
[87,] 1.769736529 0.225502084
[88,] 0.234352472 1.769736529
[89,] -2.456933043 0.234352472
[90,] -0.001900119 -2.456933043
[91,] -3.100171843 -0.001900119
[92,] -1.413548061 -3.100171843
[93,] -0.903727532 -1.413548061
[94,] -0.672357820 -0.903727532
[95,] 3.849454852 -0.672357820
[96,] 5.804958149 3.849454852
[97,] 0.101259035 5.804958149
[98,] -2.063564617 0.101259035
[99,] -2.916878773 -2.063564617
[100,] -0.816354492 -2.916878773
[101,] -3.391849055 -0.816354492
[102,] 0.907068163 -3.391849055
[103,] -1.465785372 0.907068163
[104,] 1.207150589 -1.465785372
[105,] -3.536024089 1.207150589
[106,] -4.937042812 -3.536024089
[107,] -4.789231529 -4.937042812
[108,] -0.198256222 -4.789231529
[109,] -1.396064547 -0.198256222
[110,] -4.112335075 -1.396064547
[111,] -1.523437135 -4.112335075
[112,] 0.575814207 -1.523437135
[113,] -0.143696779 0.575814207
[114,] 0.946756976 -0.143696779
[115,] -0.552718559 0.946756976
[116,] -3.215301409 -0.552718559
[117,] -2.953205787 -3.215301409
[118,] 1.140702160 -2.953205787
[119,] -6.478310417 1.140702160
[120,] -1.510984431 -6.478310417
[121,] -3.700420540 -1.510984431
[122,] -0.509801142 -3.700420540
[123,] -2.434479456 -0.509801142
[124,] 8.403184492 -2.434479456
[125,] 0.812228848 8.403184492
[126,] -2.096195060 0.812228848
[127,] 0.427398068 -2.096195060
[128,] 0.377522745 0.427398068
[129,] 0.901794693 0.377522745
[130,] -2.263074644 0.901794693
[131,] -2.993000845 -2.263074644
[132,] 0.186816511 -2.993000845
[133,] -2.340642084 0.186816511
[134,] -2.165341576 -2.340642084
[135,] 6.739198501 -2.165341576
[136,] -1.320130624 6.739198501
[137,] -3.815720148 -1.320130624
[138,] -1.491189953 -3.815720148
[139,] -3.675683489 -1.491189953
[140,] -3.936070136 -3.675683489
[141,] 6.736229571 -3.936070136
[142,] -2.798400828 6.736229571
[143,] -0.410928799 -2.798400828
[144,] 0.783513762 -0.410928799
[145,] 1.802699599 0.783513762
[146,] -1.158642051 1.802699599
[147,] -2.495401674 -1.158642051
[148,] -4.050621873 -2.495401674
[149,] -1.289511675 -4.050621873
[150,] -1.945332555 -1.289511675
[151,] 2.474072101 -1.945332555
[152,] 5.114628712 2.474072101
[153,] -0.032496316 5.114628712
[154,] 0.500358450 -0.032496316
[155,] -1.573795093 0.500358450
[156,] 0.356681013 -1.573795093
[157,] 0.290154214 0.356681013
[158,] 6.466221414 0.290154214
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.767364719 1.188260947
2 -0.071290098 2.767364719
3 -3.378303587 -0.071290098
4 -2.808881253 -3.378303587
5 -2.998599136 -2.808881253
6 -2.838887936 -2.998599136
7 -6.499387394 -2.838887936
8 -4.574607343 -6.499387394
9 -3.082942853 -4.574607343
10 2.098868835 -3.082942853
11 7.712784721 2.098868835
12 6.534642675 7.712784721
13 -4.603162001 6.534642675
14 -7.721739003 -4.603162001
15 -5.532837270 -7.721739003
16 -0.344530830 -5.532837270
17 -4.757924793 -0.344530830
18 2.508506521 -4.757924793
19 6.996878715 2.508506521
20 2.139202412 6.996878715
21 11.859255660 2.139202412
22 3.315465216 11.859255660
23 8.891887695 3.315465216
24 -0.158515299 8.891887695
25 -1.514718815 -0.158515299
26 -0.601008964 -1.514718815
27 3.192286827 -0.601008964
28 2.521791501 3.192286827
29 -3.225265024 2.521791501
30 2.111935138 -3.225265024
31 -0.634473569 2.111935138
32 3.263949033 -0.634473569
33 0.829620589 3.263949033
34 -3.269230381 0.829620589
35 3.370384861 -3.269230381
36 6.589936458 3.370384861
37 -9.950845659 6.589936458
38 9.672356468 -9.950845659
39 -0.810279076 9.672356468
40 8.806085861 -0.810279076
41 1.224623293 8.806085861
42 0.305443722 1.224623293
43 -3.734414635 0.305443722
44 -1.188862235 -3.734414635
45 -5.801302007 -1.188862235
46 0.197009286 -5.801302007
47 3.408722841 0.197009286
48 8.578310027 3.408722841
49 -0.729314252 8.578310027
50 2.310739407 -0.729314252
51 -1.400010691 2.310739407
52 3.828271035 -1.400010691
53 -6.382653263 3.828271035
54 0.406924721 -6.382653263
55 2.344999249 0.406924721
56 1.997059039 2.344999249
57 -0.370519052 1.997059039
58 -3.953050125 -0.370519052
59 -2.902285834 -3.953050125
60 -0.267338959 -2.902285834
61 0.296667266 -0.267338959
62 2.516066727 0.296667266
63 -0.449790667 2.516066727
64 -0.826567572 -0.449790667
65 3.995278113 -0.826567572
66 -1.776916691 3.995278113
67 2.794244907 -1.776916691
68 0.137458886 2.794244907
69 0.272046691 0.137458886
70 -2.289860104 0.272046691
71 -0.942023371 -2.289860104
72 1.994768231 -0.942023371
73 7.115187468 1.994768231
74 0.779816931 7.115187468
75 -0.012855067 0.779816931
76 1.582367986 -0.012855067
77 0.375834339 1.582367986
78 1.315187020 0.375834339
79 -0.156252801 1.315187020
80 2.072254769 -0.156252801
81 -2.172930250 2.072254769
82 1.444069329 -2.172930250
83 0.218733130 1.444069329
84 0.169530140 0.218733130
85 0.592400622 0.169530140
86 0.225502084 0.592400622
87 1.769736529 0.225502084
88 0.234352472 1.769736529
89 -2.456933043 0.234352472
90 -0.001900119 -2.456933043
91 -3.100171843 -0.001900119
92 -1.413548061 -3.100171843
93 -0.903727532 -1.413548061
94 -0.672357820 -0.903727532
95 3.849454852 -0.672357820
96 5.804958149 3.849454852
97 0.101259035 5.804958149
98 -2.063564617 0.101259035
99 -2.916878773 -2.063564617
100 -0.816354492 -2.916878773
101 -3.391849055 -0.816354492
102 0.907068163 -3.391849055
103 -1.465785372 0.907068163
104 1.207150589 -1.465785372
105 -3.536024089 1.207150589
106 -4.937042812 -3.536024089
107 -4.789231529 -4.937042812
108 -0.198256222 -4.789231529
109 -1.396064547 -0.198256222
110 -4.112335075 -1.396064547
111 -1.523437135 -4.112335075
112 0.575814207 -1.523437135
113 -0.143696779 0.575814207
114 0.946756976 -0.143696779
115 -0.552718559 0.946756976
116 -3.215301409 -0.552718559
117 -2.953205787 -3.215301409
118 1.140702160 -2.953205787
119 -6.478310417 1.140702160
120 -1.510984431 -6.478310417
121 -3.700420540 -1.510984431
122 -0.509801142 -3.700420540
123 -2.434479456 -0.509801142
124 8.403184492 -2.434479456
125 0.812228848 8.403184492
126 -2.096195060 0.812228848
127 0.427398068 -2.096195060
128 0.377522745 0.427398068
129 0.901794693 0.377522745
130 -2.263074644 0.901794693
131 -2.993000845 -2.263074644
132 0.186816511 -2.993000845
133 -2.340642084 0.186816511
134 -2.165341576 -2.340642084
135 6.739198501 -2.165341576
136 -1.320130624 6.739198501
137 -3.815720148 -1.320130624
138 -1.491189953 -3.815720148
139 -3.675683489 -1.491189953
140 -3.936070136 -3.675683489
141 6.736229571 -3.936070136
142 -2.798400828 6.736229571
143 -0.410928799 -2.798400828
144 0.783513762 -0.410928799
145 1.802699599 0.783513762
146 -1.158642051 1.802699599
147 -2.495401674 -1.158642051
148 -4.050621873 -2.495401674
149 -1.289511675 -4.050621873
150 -1.945332555 -1.289511675
151 2.474072101 -1.945332555
152 5.114628712 2.474072101
153 -0.032496316 5.114628712
154 0.500358450 -0.032496316
155 -1.573795093 0.500358450
156 0.356681013 -1.573795093
157 0.290154214 0.356681013
158 6.466221414 0.290154214
> 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/7wd9h1290688791.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wd9h1290688791.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/975821290688791.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1075821290688791.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11snp81290688791.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/12vnne1290688791.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/13k6271290688791.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/14vgka1290688791.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/15yg0g1290688791.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/16uqg71290688791.tab")
+ }
>
> try(system("convert tmp/1i3b81290688791.ps tmp/1i3b81290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/2susb1290688791.ps tmp/2susb1290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/3susb1290688791.ps tmp/3susb1290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/4susb1290688791.ps tmp/4susb1290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/534ae1290688791.ps tmp/534ae1290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/634ae1290688791.ps tmp/634ae1290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wd9h1290688791.ps tmp/7wd9h1290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/8wd9h1290688791.ps tmp/8wd9h1290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/975821290688791.ps tmp/975821290688791.png",intern=TRUE))
character(0)
> try(system("convert tmp/1075821290688791.ps tmp/1075821290688791.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.650 1.761 10.653