R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(24
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+ ,6
+ ,27
+ ,27
+ ,24
+ ,24
+ ,31
+ ,9
+ ,9
+ ,17
+ ,17
+ ,9
+ ,9
+ ,25
+ ,25
+ ,19
+ ,19
+ ,20
+ ,9
+ ,9
+ ,11
+ ,11
+ ,7
+ ,7
+ ,21
+ ,21
+ ,20
+ ,20
+ ,16
+ ,9
+ ,0
+ ,12
+ ,0
+ ,9
+ ,0
+ ,10
+ ,0
+ ,13
+ ,0
+ ,22
+ ,8
+ ,0
+ ,14
+ ,0
+ ,10
+ ,0
+ ,20
+ ,0
+ ,20
+ ,0
+ ,20
+ ,7
+ ,7
+ ,11
+ ,11
+ ,9
+ ,9
+ ,26
+ ,26
+ ,22
+ ,22
+ ,28
+ ,16
+ ,16
+ ,16
+ ,16
+ ,8
+ ,8
+ ,24
+ ,24
+ ,24
+ ,24
+ ,38
+ ,11
+ ,11
+ ,21
+ ,21
+ ,7
+ ,7
+ ,29
+ ,29
+ ,29
+ ,29
+ ,22
+ ,9
+ ,0
+ ,14
+ ,0
+ ,6
+ ,0
+ ,19
+ ,0
+ ,12
+ ,0
+ ,20
+ ,11
+ ,11
+ ,20
+ ,20
+ ,13
+ ,13
+ ,24
+ ,24
+ ,20
+ ,20
+ ,17
+ ,9
+ ,0
+ ,13
+ ,0
+ ,6
+ ,0
+ ,19
+ ,0
+ ,21
+ ,0
+ ,28
+ ,14
+ ,14
+ ,11
+ ,11
+ ,8
+ ,8
+ ,24
+ ,24
+ ,24
+ ,24
+ ,22
+ ,13
+ ,13
+ ,15
+ ,15
+ ,10
+ ,10
+ ,22
+ ,22
+ ,22
+ ,22
+ ,31
+ ,16
+ ,0
+ ,19
+ ,0
+ ,16
+ ,0
+ ,17
+ ,0
+ ,20
+ ,0)
+ ,dim=c(11
+ ,159)
+ ,dimnames=list(c('Concernovermistakes'
+ ,'DoubtsaboutactionsFemale'
+ ,'DoubtsaboutactionsMale'
+ ,'ParentalexpectationsFemale'
+ ,'ParentalexpectationsMale'
+ ,'ParentalcritismFemale'
+ ,'ParentalcritismMale'
+ ,'PersonalstandardsFemale'
+ ,'PersonalstandarsMale'
+ ,'OrganizationFemale'
+ ,'OrganizationMale')
+ ,1:159))
> y <- array(NA,dim=c(11,159),dimnames=list(c('Concernovermistakes','DoubtsaboutactionsFemale','DoubtsaboutactionsMale','ParentalexpectationsFemale','ParentalexpectationsMale','ParentalcritismFemale','ParentalcritismMale','PersonalstandardsFemale','PersonalstandarsMale','OrganizationFemale','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 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Concernovermistakes DoubtsaboutactionsFemale DoubtsaboutactionsMale
1 24 14 0
2 25 11 0
3 17 6 0
4 18 12 12
5 18 8 8
6 16 10 10
7 20 10 10
8 16 11 11
9 18 16 16
10 17 11 11
11 23 13 0
12 30 12 0
13 23 8 8
14 18 12 12
15 15 11 11
16 12 4 4
17 21 9 0
18 15 8 8
19 20 8 8
20 31 14 0
21 27 15 0
22 34 16 16
23 21 9 9
24 31 14 14
25 19 11 11
26 16 8 0
27 20 9 9
28 21 9 9
29 22 9 9
30 17 9 9
31 24 10 10
32 25 16 0
33 26 11 0
34 25 8 8
35 17 9 9
36 32 16 16
37 33 11 11
38 13 16 16
39 32 12 12
40 25 12 12
41 29 14 14
42 22 9 9
43 18 10 10
44 17 9 9
45 20 10 0
46 15 12 12
47 20 14 14
48 33 14 14
49 29 10 0
50 23 14 14
51 26 16 0
52 18 9 9
53 20 10 0
54 11 6 0
55 28 8 8
56 26 13 13
57 22 10 0
58 17 8 8
59 12 7 0
60 14 15 15
61 17 9 9
62 21 10 10
63 19 12 12
64 18 13 13
65 10 10 0
66 29 11 0
67 31 8 8
68 19 9 0
69 9 13 13
70 20 11 11
71 28 8 8
72 19 9 0
73 30 9 0
74 29 15 0
75 26 9 0
76 23 10 0
77 13 14 14
78 21 12 12
79 19 12 12
80 28 11 11
81 23 14 14
82 18 6 6
83 21 12 0
84 20 8 8
85 23 14 14
86 21 11 11
87 21 10 10
88 15 14 14
89 28 12 12
90 19 10 10
91 26 14 14
92 10 5 5
93 16 11 0
94 22 10 10
95 19 9 9
96 31 10 10
97 31 16 0
98 29 13 13
99 19 9 0
100 22 10 10
101 23 10 10
102 15 7 0
103 20 9 0
104 18 8 8
105 23 14 14
106 25 14 14
107 21 8 8
108 24 9 9
109 25 14 14
110 17 14 14
111 13 8 8
112 28 8 8
113 21 8 0
114 25 7 7
115 9 6 0
116 16 8 8
117 19 6 6
118 17 11 11
119 25 14 14
120 20 11 11
121 29 11 11
122 14 11 11
123 22 14 14
124 15 8 8
125 19 20 0
126 20 11 11
127 15 8 0
128 20 11 11
129 18 10 10
130 33 14 14
131 22 11 11
132 16 9 9
133 17 9 9
134 16 8 8
135 21 10 0
136 26 13 0
137 18 13 13
138 18 12 12
139 17 8 8
140 22 13 13
141 30 14 14
142 30 12 0
143 24 14 14
144 21 15 15
145 21 13 13
146 29 16 16
147 31 9 9
148 20 9 9
149 16 9 0
150 22 8 0
151 20 7 7
152 28 16 16
153 38 11 11
154 22 9 0
155 20 11 11
156 17 9 0
157 28 14 14
158 22 13 13
159 31 16 0
ParentalexpectationsFemale ParentalexpectationsMale ParentalcritismFemale
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 0 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 PersonalstandardsFemale PersonalstandarsMale
1 0 24 0
2 0 25 0
3 0 30 0
4 8 19 19
5 9 22 22
6 7 22 22
7 4 25 25
8 11 23 23
9 7 17 17
10 7 21 21
11 0 19 0
12 0 19 0
13 10 15 15
14 8 16 16
15 8 23 23
16 4 27 27
17 0 22 0
18 8 14 14
19 7 22 22
20 0 23 0
21 0 23 0
22 11 21 21
23 13 19 19
24 8 18 18
25 8 20 20
26 0 23 0
27 6 25 25
28 9 19 19
29 9 24 24
30 6 22 22
31 6 25 25
32 0 26 0
33 0 29 0
34 7 32 32
35 9 25 25
36 6 29 29
37 6 28 28
38 5 17 17
39 12 28 28
40 7 29 29
41 10 26 26
42 9 25 25
43 8 14 14
44 5 25 25
45 0 26 0
46 8 20 20
47 10 18 18
48 6 32 32
49 0 25 0
50 7 25 25
51 0 23 0
52 8 21 21
53 0 20 0
54 0 15 0
55 20 30 30
56 8 24 24
57 0 26 0
58 6 24 24
59 0 22 0
60 8 14 14
61 9 24 24
62 6 24 24
63 7 24 24
64 9 24 24
65 0 19 0
66 0 31 0
67 8 22 22
68 0 27 0
69 6 19 19
70 8 25 25
71 7 20 20
72 0 21 0
73 0 27 0
74 0 23 0
75 0 25 0
76 0 20 0
77 6 21 21
78 9 22 22
79 8 23 23
80 6 25 25
81 10 25 25
82 8 17 17
83 0 19 0
84 10 25 25
85 5 19 19
86 7 20 20
87 5 26 26
88 8 23 23
89 14 27 27
90 7 17 17
91 8 17 17
92 6 19 19
93 0 17 0
94 6 22 22
95 10 21 21
96 12 32 32
97 0 21 0
98 12 21 21
99 0 18 0
100 8 18 18
101 10 23 23
102 0 19 0
103 0 20 0
104 10 21 21
105 10 20 20
106 5 17 17
107 7 18 18
108 10 19 19
109 11 22 22
110 6 15 15
111 7 14 14
112 12 18 18
113 0 24 0
114 11 35 35
115 0 29 0
116 5 21 21
117 8 25 25
118 6 20 20
119 9 22 22
120 4 13 13
121 4 26 26
122 7 17 17
123 11 25 25
124 6 20 20
125 0 19 0
126 8 21 21
127 0 22 0
128 8 24 24
129 9 21 21
130 8 26 26
131 11 24 24
132 8 16 16
133 5 23 23
134 4 18 18
135 0 16 0
136 0 26 0
137 6 19 19
138 9 21 21
139 9 21 21
140 13 22 22
141 9 23 23
142 0 29 0
143 20 21 21
144 5 21 21
145 11 23 23
146 6 27 27
147 9 25 25
148 7 21 21
149 0 10 0
150 0 20 0
151 9 26 26
152 8 24 24
153 7 29 29
154 0 19 0
155 13 24 24
156 0 19 0
157 8 24 24
158 10 22 22
159 0 17 0
OrganizationFemale OrganizationMale
1 26 0
2 23 0
3 25 0
4 23 23
5 19 19
6 29 29
7 25 25
8 21 21
9 22 22
10 25 25
11 24 0
12 18 0
13 22 22
14 15 15
15 22 22
16 28 28
17 20 0
18 12 12
19 24 24
20 20 0
21 21 0
22 20 20
23 21 21
24 23 23
25 28 28
26 24 0
27 24 24
28 24 24
29 23 23
30 23 23
31 29 29
32 24 0
33 18 0
34 25 25
35 21 21
36 26 26
37 22 22
38 22 22
39 22 22
40 23 23
41 30 30
42 23 23
43 17 17
44 23 23
45 23 0
46 25 25
47 24 24
48 24 24
49 23 0
50 21 21
51 24 0
52 24 24
53 28 0
54 16 0
55 20 20
56 29 29
57 27 0
58 22 22
59 28 0
60 16 16
61 25 25
62 24 24
63 28 28
64 24 24
65 23 0
66 30 0
67 24 24
68 21 0
69 25 25
70 25 25
71 22 22
72 23 0
73 26 0
74 23 0
75 25 0
76 21 0
77 25 25
78 24 24
79 29 29
80 22 22
81 27 27
82 26 26
83 22 0
84 24 24
85 27 27
86 24 24
87 24 24
88 29 29
89 22 22
90 21 21
91 24 24
92 24 24
93 23 0
94 20 20
95 27 27
96 26 26
97 25 0
98 21 21
99 21 0
100 19 19
101 21 21
102 21 0
103 16 0
104 22 22
105 29 29
106 15 15
107 17 17
108 15 15
109 21 21
110 21 21
111 19 19
112 24 24
113 20 0
114 17 17
115 23 0
116 24 24
117 14 14
118 19 19
119 24 24
120 13 13
121 22 22
122 16 16
123 19 19
124 25 25
125 25 0
126 23 23
127 24 0
128 26 26
129 26 26
130 25 25
131 18 18
132 21 21
133 26 26
134 23 23
135 23 0
136 22 0
137 20 20
138 13 13
139 24 24
140 15 15
141 14 14
142 22 0
143 10 10
144 24 24
145 22 22
146 24 24
147 19 19
148 20 20
149 13 0
150 20 0
151 22 22
152 24 24
153 29 29
154 12 0
155 20 20
156 21 0
157 24 24
158 22 22
159 20 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) DoubtsaboutactionsFemale
-1.39516 1.06624
DoubtsaboutactionsMale ParentalexpectationsFemale
-0.39034 0.44439
ParentalexpectationsMale ParentalcritismFemale
-0.32676 0.05124
ParentalcritismMale PersonalstandardsFemale
0.18976 0.43572
PersonalstandarsMale OrganizationFemale
0.20857 -0.17450
OrganizationMale
0.07267
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.709 -2.495 -0.601 2.734 12.072
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.39516 3.09607 -0.451 0.6529
DoubtsaboutactionsFemale 1.06624 0.24107 4.423 1.88e-05 ***
DoubtsaboutactionsMale -0.39034 0.28101 -1.389 0.1669
ParentalexpectationsFemale 0.44439 0.21820 2.037 0.0435 *
ParentalexpectationsMale -0.32676 0.26812 -1.219 0.2249
ParentalcritismFemale 0.05124 0.30707 0.167 0.8677
ParentalcritismMale 0.18976 0.36796 0.516 0.6068
PersonalstandardsFemale 0.43572 0.18500 2.355 0.0198 *
PersonalstandarsMale 0.20857 0.21422 0.974 0.3318
OrganizationFemale -0.17450 0.20566 -0.848 0.3975
OrganizationMale 0.07267 0.21510 0.338 0.7360
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.473 on 148 degrees of freedom
Multiple R-squared: 0.4278, Adjusted R-squared: 0.3892
F-statistic: 11.07 on 10 and 148 DF, p-value: 5.854e-14
> 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,] 0.13618891 0.27237782 0.86381109
[2,] 0.07253538 0.14507076 0.92746462
[3,] 0.09419798 0.18839596 0.90580202
[4,] 0.06407793 0.12815586 0.93592207
[5,] 0.05510652 0.11021305 0.94489348
[6,] 0.09179379 0.18358759 0.90820621
[7,] 0.05835800 0.11671600 0.94164200
[8,] 0.06623623 0.13247246 0.93376377
[9,] 0.29341849 0.58683697 0.70658151
[10,] 0.22625535 0.45251070 0.77374465
[11,] 0.62913168 0.74173664 0.37086832
[12,] 0.55156273 0.89687455 0.44843727
[13,] 0.48177997 0.96355995 0.51822003
[14,] 0.42050115 0.84100231 0.57949885
[15,] 0.35460438 0.70920875 0.64539562
[16,] 0.29338956 0.58677911 0.70661044
[17,] 0.23752280 0.47504561 0.76247720
[18,] 0.36399891 0.72799782 0.63600109
[19,] 0.44237062 0.88474124 0.55762938
[20,] 0.42437932 0.84875864 0.57562068
[21,] 0.51177359 0.97645281 0.48822641
[22,] 0.51021882 0.97956235 0.48978118
[23,] 0.53973468 0.92053063 0.46026532
[24,] 0.70607041 0.58785919 0.29392959
[25,] 0.80845865 0.38308270 0.19154135
[26,] 0.80182373 0.39635255 0.19817627
[27,] 0.76282070 0.47435860 0.23717930
[28,] 0.73420026 0.53159949 0.26579974
[29,] 0.68515127 0.62969746 0.31484873
[30,] 0.64554515 0.70890969 0.35445485
[31,] 0.62133801 0.75732398 0.37866199
[32,] 0.57132776 0.85734448 0.42867224
[33,] 0.60319926 0.79360147 0.39680074
[34,] 0.55927779 0.88144441 0.44072221
[35,] 0.53513166 0.92973668 0.46486834
[36,] 0.60471760 0.79056479 0.39528240
[37,] 0.56745688 0.86508625 0.43254312
[38,] 0.57565194 0.84869612 0.42434806
[39,] 0.52561627 0.94876745 0.47438373
[40,] 0.47328141 0.94656281 0.52671859
[41,] 0.42230615 0.84461231 0.57769385
[42,] 0.38895524 0.77791048 0.61104476
[43,] 0.35535581 0.71071162 0.64464419
[44,] 0.30829419 0.61658838 0.69170581
[45,] 0.28268917 0.56537833 0.71731083
[46,] 0.25419434 0.50838869 0.74580566
[47,] 0.27786381 0.55572762 0.72213619
[48,] 0.27837607 0.55675214 0.72162393
[49,] 0.24073859 0.48147719 0.75926141
[50,] 0.22611686 0.45223373 0.77388314
[51,] 0.27641862 0.55283724 0.72358138
[52,] 0.39513085 0.79026171 0.60486915
[53,] 0.43226344 0.86452687 0.56773656
[54,] 0.72957827 0.54084346 0.27042173
[55,] 0.70725264 0.58549473 0.29274736
[56,] 0.86647800 0.26704401 0.13352200
[57,] 0.85093006 0.29813989 0.14906994
[58,] 0.94255889 0.11488222 0.05744111
[59,] 0.92941470 0.14117061 0.07058530
[60,] 0.95499999 0.09000002 0.04500001
[61,] 0.94242077 0.11515847 0.05757923
[62,] 0.94355205 0.11289590 0.05644795
[63,] 0.93762363 0.12475275 0.06237637
[64,] 0.97604845 0.04790310 0.02395155
[65,] 0.96943138 0.06113724 0.03056862
[66,] 0.96413611 0.07172777 0.03586389
[67,] 0.96630511 0.06738977 0.03369489
[68,] 0.95986885 0.08026230 0.04013115
[69,] 0.95811442 0.08377115 0.04188558
[70,] 0.94629770 0.10740460 0.05370230
[71,] 0.93499055 0.13001890 0.06500945
[72,] 0.92680596 0.14638807 0.07319404
[73,] 0.91299671 0.17400658 0.08700329
[74,] 0.89661837 0.20676326 0.10338163
[75,] 0.94461726 0.11076549 0.05538274
[76,] 0.93119374 0.13761253 0.06880626
[77,] 0.91565082 0.16869836 0.08434918
[78,] 0.92385874 0.15228252 0.07614126
[79,] 0.92206922 0.15586156 0.07793078
[80,] 0.90405528 0.19188943 0.09594472
[81,] 0.88343961 0.23312078 0.11656039
[82,] 0.85690678 0.28618643 0.14309322
[83,] 0.83081985 0.33836029 0.16918015
[84,] 0.84077417 0.31845167 0.15922583
[85,] 0.85096783 0.29806435 0.14903217
[86,] 0.82345319 0.35309362 0.17654681
[87,] 0.80864517 0.38270966 0.19135483
[88,] 0.77253992 0.45492017 0.22746008
[89,] 0.73287672 0.53424656 0.26712328
[90,] 0.73895912 0.52208176 0.26104088
[91,] 0.69957212 0.60085576 0.30042788
[92,] 0.65552015 0.68895970 0.34447985
[93,] 0.65516728 0.68966544 0.34483272
[94,] 0.65414119 0.69171762 0.34585881
[95,] 0.68538876 0.62922248 0.31461124
[96,] 0.63764012 0.72471976 0.36235988
[97,] 0.59815166 0.80369669 0.40184834
[98,] 0.54699758 0.90600484 0.45300242
[99,] 0.86082723 0.27834555 0.13917277
[100,] 0.87916355 0.24167290 0.12083645
[101,] 0.90252847 0.19494307 0.09747153
[102,] 0.94756914 0.10486173 0.05243086
[103,] 0.94067318 0.11865364 0.05932682
[104,] 0.96019520 0.07960960 0.03980480
[105,] 0.95608901 0.08782197 0.04391099
[106,] 0.94472048 0.11055905 0.05527952
[107,] 0.95683601 0.08632798 0.04316399
[108,] 0.95038467 0.09923066 0.04961533
[109,] 0.94934455 0.10131091 0.05065545
[110,] 0.95651246 0.08697508 0.04348754
[111,] 0.93925282 0.12149436 0.06074718
[112,] 0.93865336 0.12269328 0.06134664
[113,] 0.92028148 0.15943704 0.07971852
[114,] 0.89815085 0.20369830 0.10184915
[115,] 0.86996177 0.26007645 0.13003823
[116,] 0.82795712 0.34408575 0.17204288
[117,] 0.89989092 0.20021816 0.10010908
[118,] 0.86619926 0.26760148 0.13380074
[119,] 0.85612323 0.28775354 0.14387677
[120,] 0.86753005 0.26493991 0.13246995
[121,] 0.81693596 0.36612807 0.18306404
[122,] 0.79192382 0.41615236 0.20807618
[123,] 0.81112038 0.37775925 0.18887962
[124,] 0.75898594 0.48202811 0.24101406
[125,] 0.89981034 0.20037931 0.10018966
[126,] 0.88533130 0.22933739 0.11466870
[127,] 0.83451539 0.33096921 0.16548461
[128,] 0.80313001 0.39373998 0.19686999
[129,] 0.71477905 0.57044190 0.28522095
[130,] 0.77308721 0.45382557 0.22691279
[131,] 0.66189549 0.67620903 0.33810451
[132,] 0.50597304 0.98805391 0.49402696
> postscript(file="/var/www/html/rcomp/tmp/18mbm1290612933.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/28mbm1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31dtp1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41dtp1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/51dtp1290612933.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
-0.95563174 4.26638656 -4.67597186 -1.71921566 -1.83206567 -3.68356051
7 8 9 10 11 12
-1.18313296 -6.66474281 -3.23033162 -3.24012699 -0.39297669 5.83995483
13 14 15 16 17 18
7.86007448 -0.60100401 -6.83992820 -6.58134787 2.24249643 -0.03196178
19 20 21 22 23 24
0.57092616 2.81797026 0.69507086 8.31909544 1.42932114 10.57327540
25 26 27 28 29 30
-0.29607926 -4.09535565 -1.56157767 2.81642333 0.14026425 -2.49528606
31 32 33 34 35 36
2.97746437 -6.62424731 -2.08360501 -0.18196111 -5.82531605 4.03943361
37 38 39 40 41 42
8.65588614 -7.74834270 4.47534545 -1.15635428 2.70876248 -0.26876162
43 44 45 46 47 48
1.65487566 -4.54004510 -1.43627845 -5.27747102 -1.27740490 4.25470573
49 50 51 52 53 54
7.11065337 -2.72281743 2.07488109 -1.11352591 1.16174430 -0.61738390
55 56 57 58 59 60
-0.94703874 2.75917650 0.37292835 -3.32742425 -2.08406719 -5.59118456
61 62 63 64 65 66
-4.18555067 -0.35792724 -3.66102353 -5.52045152 -8.67692308 6.13848430
67 68 69 70 71 72
12.03571562 -3.48794072 -10.70946233 -2.82300885 10.24399131 1.30418679
73 74 75 76 77 78
7.44452726 -0.16916386 4.29518009 2.94025386 -9.02682670 -1.26176303
79 80 81 82 83 84
-2.68537696 5.11822506 -2.36429078 3.81261009 -1.02639412 -1.96728810
85 86 87 88 89 90
3.29455785 0.94943699 -1.17024583 -7.91954242 0.87290501 1.72322549
91 92 93 94 95 96
5.84887120 -4.16884779 -1.98293239 1.28806087 -0.17239047 2.30442583
97 98 99 100 101 102
3.86508674 5.20762639 0.92916658 3.51665073 0.78162709 -1.32284322
103 104 105 106 107 108
-3.07923514 -1.47617120 1.29606889 5.00826542 4.25867080 4.65894775
109 110 111 112 113 114
0.48132758 -1.45079793 -1.19577933 10.06073323 2.33481857 -3.92333622
115 116 117 118 119 120
-8.74341712 -1.83227513 -2.26944607 -2.49531015 1.26881020 5.00333225
121 122 123 124 125 126
6.66171072 -4.46182874 -4.53756577 -1.50373660 -8.75958064 -1.15530653
127 128 129 130 131 132
-2.18147948 -2.07689044 -2.06201727 7.50500980 -1.84978500 -0.19758373
133 134 135 136 137 138
-2.35782377 0.71027224 3.03212934 -3.35589913 -2.45387981 -4.61998879
139 140 141 142 143 144
-1.67862231 -2.58285873 5.31199459 2.62514770 -4.22215484 -0.85777762
145 146 147 148 149 150
-3.62049161 1.88908419 7.97102147 0.83777426 -0.08354098 2.90696489
151 152 153 154 155 156
-1.31019196 1.75180353 12.07185530 1.08540904 -4.95152602 -1.89970939
157 158 159
3.69175292 -1.49994867 2.15484026
> postscript(file="/var/www/html/rcomp/tmp/6b4aa1290612933.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 -0.95563174 NA
1 4.26638656 -0.95563174
2 -4.67597186 4.26638656
3 -1.71921566 -4.67597186
4 -1.83206567 -1.71921566
5 -3.68356051 -1.83206567
6 -1.18313296 -3.68356051
7 -6.66474281 -1.18313296
8 -3.23033162 -6.66474281
9 -3.24012699 -3.23033162
10 -0.39297669 -3.24012699
11 5.83995483 -0.39297669
12 7.86007448 5.83995483
13 -0.60100401 7.86007448
14 -6.83992820 -0.60100401
15 -6.58134787 -6.83992820
16 2.24249643 -6.58134787
17 -0.03196178 2.24249643
18 0.57092616 -0.03196178
19 2.81797026 0.57092616
20 0.69507086 2.81797026
21 8.31909544 0.69507086
22 1.42932114 8.31909544
23 10.57327540 1.42932114
24 -0.29607926 10.57327540
25 -4.09535565 -0.29607926
26 -1.56157767 -4.09535565
27 2.81642333 -1.56157767
28 0.14026425 2.81642333
29 -2.49528606 0.14026425
30 2.97746437 -2.49528606
31 -6.62424731 2.97746437
32 -2.08360501 -6.62424731
33 -0.18196111 -2.08360501
34 -5.82531605 -0.18196111
35 4.03943361 -5.82531605
36 8.65588614 4.03943361
37 -7.74834270 8.65588614
38 4.47534545 -7.74834270
39 -1.15635428 4.47534545
40 2.70876248 -1.15635428
41 -0.26876162 2.70876248
42 1.65487566 -0.26876162
43 -4.54004510 1.65487566
44 -1.43627845 -4.54004510
45 -5.27747102 -1.43627845
46 -1.27740490 -5.27747102
47 4.25470573 -1.27740490
48 7.11065337 4.25470573
49 -2.72281743 7.11065337
50 2.07488109 -2.72281743
51 -1.11352591 2.07488109
52 1.16174430 -1.11352591
53 -0.61738390 1.16174430
54 -0.94703874 -0.61738390
55 2.75917650 -0.94703874
56 0.37292835 2.75917650
57 -3.32742425 0.37292835
58 -2.08406719 -3.32742425
59 -5.59118456 -2.08406719
60 -4.18555067 -5.59118456
61 -0.35792724 -4.18555067
62 -3.66102353 -0.35792724
63 -5.52045152 -3.66102353
64 -8.67692308 -5.52045152
65 6.13848430 -8.67692308
66 12.03571562 6.13848430
67 -3.48794072 12.03571562
68 -10.70946233 -3.48794072
69 -2.82300885 -10.70946233
70 10.24399131 -2.82300885
71 1.30418679 10.24399131
72 7.44452726 1.30418679
73 -0.16916386 7.44452726
74 4.29518009 -0.16916386
75 2.94025386 4.29518009
76 -9.02682670 2.94025386
77 -1.26176303 -9.02682670
78 -2.68537696 -1.26176303
79 5.11822506 -2.68537696
80 -2.36429078 5.11822506
81 3.81261009 -2.36429078
82 -1.02639412 3.81261009
83 -1.96728810 -1.02639412
84 3.29455785 -1.96728810
85 0.94943699 3.29455785
86 -1.17024583 0.94943699
87 -7.91954242 -1.17024583
88 0.87290501 -7.91954242
89 1.72322549 0.87290501
90 5.84887120 1.72322549
91 -4.16884779 5.84887120
92 -1.98293239 -4.16884779
93 1.28806087 -1.98293239
94 -0.17239047 1.28806087
95 2.30442583 -0.17239047
96 3.86508674 2.30442583
97 5.20762639 3.86508674
98 0.92916658 5.20762639
99 3.51665073 0.92916658
100 0.78162709 3.51665073
101 -1.32284322 0.78162709
102 -3.07923514 -1.32284322
103 -1.47617120 -3.07923514
104 1.29606889 -1.47617120
105 5.00826542 1.29606889
106 4.25867080 5.00826542
107 4.65894775 4.25867080
108 0.48132758 4.65894775
109 -1.45079793 0.48132758
110 -1.19577933 -1.45079793
111 10.06073323 -1.19577933
112 2.33481857 10.06073323
113 -3.92333622 2.33481857
114 -8.74341712 -3.92333622
115 -1.83227513 -8.74341712
116 -2.26944607 -1.83227513
117 -2.49531015 -2.26944607
118 1.26881020 -2.49531015
119 5.00333225 1.26881020
120 6.66171072 5.00333225
121 -4.46182874 6.66171072
122 -4.53756577 -4.46182874
123 -1.50373660 -4.53756577
124 -8.75958064 -1.50373660
125 -1.15530653 -8.75958064
126 -2.18147948 -1.15530653
127 -2.07689044 -2.18147948
128 -2.06201727 -2.07689044
129 7.50500980 -2.06201727
130 -1.84978500 7.50500980
131 -0.19758373 -1.84978500
132 -2.35782377 -0.19758373
133 0.71027224 -2.35782377
134 3.03212934 0.71027224
135 -3.35589913 3.03212934
136 -2.45387981 -3.35589913
137 -4.61998879 -2.45387981
138 -1.67862231 -4.61998879
139 -2.58285873 -1.67862231
140 5.31199459 -2.58285873
141 2.62514770 5.31199459
142 -4.22215484 2.62514770
143 -0.85777762 -4.22215484
144 -3.62049161 -0.85777762
145 1.88908419 -3.62049161
146 7.97102147 1.88908419
147 0.83777426 7.97102147
148 -0.08354098 0.83777426
149 2.90696489 -0.08354098
150 -1.31019196 2.90696489
151 1.75180353 -1.31019196
152 12.07185530 1.75180353
153 1.08540904 12.07185530
154 -4.95152602 1.08540904
155 -1.89970939 -4.95152602
156 3.69175292 -1.89970939
157 -1.49994867 3.69175292
158 2.15484026 -1.49994867
159 NA 2.15484026
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.26638656 -0.95563174
[2,] -4.67597186 4.26638656
[3,] -1.71921566 -4.67597186
[4,] -1.83206567 -1.71921566
[5,] -3.68356051 -1.83206567
[6,] -1.18313296 -3.68356051
[7,] -6.66474281 -1.18313296
[8,] -3.23033162 -6.66474281
[9,] -3.24012699 -3.23033162
[10,] -0.39297669 -3.24012699
[11,] 5.83995483 -0.39297669
[12,] 7.86007448 5.83995483
[13,] -0.60100401 7.86007448
[14,] -6.83992820 -0.60100401
[15,] -6.58134787 -6.83992820
[16,] 2.24249643 -6.58134787
[17,] -0.03196178 2.24249643
[18,] 0.57092616 -0.03196178
[19,] 2.81797026 0.57092616
[20,] 0.69507086 2.81797026
[21,] 8.31909544 0.69507086
[22,] 1.42932114 8.31909544
[23,] 10.57327540 1.42932114
[24,] -0.29607926 10.57327540
[25,] -4.09535565 -0.29607926
[26,] -1.56157767 -4.09535565
[27,] 2.81642333 -1.56157767
[28,] 0.14026425 2.81642333
[29,] -2.49528606 0.14026425
[30,] 2.97746437 -2.49528606
[31,] -6.62424731 2.97746437
[32,] -2.08360501 -6.62424731
[33,] -0.18196111 -2.08360501
[34,] -5.82531605 -0.18196111
[35,] 4.03943361 -5.82531605
[36,] 8.65588614 4.03943361
[37,] -7.74834270 8.65588614
[38,] 4.47534545 -7.74834270
[39,] -1.15635428 4.47534545
[40,] 2.70876248 -1.15635428
[41,] -0.26876162 2.70876248
[42,] 1.65487566 -0.26876162
[43,] -4.54004510 1.65487566
[44,] -1.43627845 -4.54004510
[45,] -5.27747102 -1.43627845
[46,] -1.27740490 -5.27747102
[47,] 4.25470573 -1.27740490
[48,] 7.11065337 4.25470573
[49,] -2.72281743 7.11065337
[50,] 2.07488109 -2.72281743
[51,] -1.11352591 2.07488109
[52,] 1.16174430 -1.11352591
[53,] -0.61738390 1.16174430
[54,] -0.94703874 -0.61738390
[55,] 2.75917650 -0.94703874
[56,] 0.37292835 2.75917650
[57,] -3.32742425 0.37292835
[58,] -2.08406719 -3.32742425
[59,] -5.59118456 -2.08406719
[60,] -4.18555067 -5.59118456
[61,] -0.35792724 -4.18555067
[62,] -3.66102353 -0.35792724
[63,] -5.52045152 -3.66102353
[64,] -8.67692308 -5.52045152
[65,] 6.13848430 -8.67692308
[66,] 12.03571562 6.13848430
[67,] -3.48794072 12.03571562
[68,] -10.70946233 -3.48794072
[69,] -2.82300885 -10.70946233
[70,] 10.24399131 -2.82300885
[71,] 1.30418679 10.24399131
[72,] 7.44452726 1.30418679
[73,] -0.16916386 7.44452726
[74,] 4.29518009 -0.16916386
[75,] 2.94025386 4.29518009
[76,] -9.02682670 2.94025386
[77,] -1.26176303 -9.02682670
[78,] -2.68537696 -1.26176303
[79,] 5.11822506 -2.68537696
[80,] -2.36429078 5.11822506
[81,] 3.81261009 -2.36429078
[82,] -1.02639412 3.81261009
[83,] -1.96728810 -1.02639412
[84,] 3.29455785 -1.96728810
[85,] 0.94943699 3.29455785
[86,] -1.17024583 0.94943699
[87,] -7.91954242 -1.17024583
[88,] 0.87290501 -7.91954242
[89,] 1.72322549 0.87290501
[90,] 5.84887120 1.72322549
[91,] -4.16884779 5.84887120
[92,] -1.98293239 -4.16884779
[93,] 1.28806087 -1.98293239
[94,] -0.17239047 1.28806087
[95,] 2.30442583 -0.17239047
[96,] 3.86508674 2.30442583
[97,] 5.20762639 3.86508674
[98,] 0.92916658 5.20762639
[99,] 3.51665073 0.92916658
[100,] 0.78162709 3.51665073
[101,] -1.32284322 0.78162709
[102,] -3.07923514 -1.32284322
[103,] -1.47617120 -3.07923514
[104,] 1.29606889 -1.47617120
[105,] 5.00826542 1.29606889
[106,] 4.25867080 5.00826542
[107,] 4.65894775 4.25867080
[108,] 0.48132758 4.65894775
[109,] -1.45079793 0.48132758
[110,] -1.19577933 -1.45079793
[111,] 10.06073323 -1.19577933
[112,] 2.33481857 10.06073323
[113,] -3.92333622 2.33481857
[114,] -8.74341712 -3.92333622
[115,] -1.83227513 -8.74341712
[116,] -2.26944607 -1.83227513
[117,] -2.49531015 -2.26944607
[118,] 1.26881020 -2.49531015
[119,] 5.00333225 1.26881020
[120,] 6.66171072 5.00333225
[121,] -4.46182874 6.66171072
[122,] -4.53756577 -4.46182874
[123,] -1.50373660 -4.53756577
[124,] -8.75958064 -1.50373660
[125,] -1.15530653 -8.75958064
[126,] -2.18147948 -1.15530653
[127,] -2.07689044 -2.18147948
[128,] -2.06201727 -2.07689044
[129,] 7.50500980 -2.06201727
[130,] -1.84978500 7.50500980
[131,] -0.19758373 -1.84978500
[132,] -2.35782377 -0.19758373
[133,] 0.71027224 -2.35782377
[134,] 3.03212934 0.71027224
[135,] -3.35589913 3.03212934
[136,] -2.45387981 -3.35589913
[137,] -4.61998879 -2.45387981
[138,] -1.67862231 -4.61998879
[139,] -2.58285873 -1.67862231
[140,] 5.31199459 -2.58285873
[141,] 2.62514770 5.31199459
[142,] -4.22215484 2.62514770
[143,] -0.85777762 -4.22215484
[144,] -3.62049161 -0.85777762
[145,] 1.88908419 -3.62049161
[146,] 7.97102147 1.88908419
[147,] 0.83777426 7.97102147
[148,] -0.08354098 0.83777426
[149,] 2.90696489 -0.08354098
[150,] -1.31019196 2.90696489
[151,] 1.75180353 -1.31019196
[152,] 12.07185530 1.75180353
[153,] 1.08540904 12.07185530
[154,] -4.95152602 1.08540904
[155,] -1.89970939 -4.95152602
[156,] 3.69175292 -1.89970939
[157,] -1.49994867 3.69175292
[158,] 2.15484026 -1.49994867
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.26638656 -0.95563174
2 -4.67597186 4.26638656
3 -1.71921566 -4.67597186
4 -1.83206567 -1.71921566
5 -3.68356051 -1.83206567
6 -1.18313296 -3.68356051
7 -6.66474281 -1.18313296
8 -3.23033162 -6.66474281
9 -3.24012699 -3.23033162
10 -0.39297669 -3.24012699
11 5.83995483 -0.39297669
12 7.86007448 5.83995483
13 -0.60100401 7.86007448
14 -6.83992820 -0.60100401
15 -6.58134787 -6.83992820
16 2.24249643 -6.58134787
17 -0.03196178 2.24249643
18 0.57092616 -0.03196178
19 2.81797026 0.57092616
20 0.69507086 2.81797026
21 8.31909544 0.69507086
22 1.42932114 8.31909544
23 10.57327540 1.42932114
24 -0.29607926 10.57327540
25 -4.09535565 -0.29607926
26 -1.56157767 -4.09535565
27 2.81642333 -1.56157767
28 0.14026425 2.81642333
29 -2.49528606 0.14026425
30 2.97746437 -2.49528606
31 -6.62424731 2.97746437
32 -2.08360501 -6.62424731
33 -0.18196111 -2.08360501
34 -5.82531605 -0.18196111
35 4.03943361 -5.82531605
36 8.65588614 4.03943361
37 -7.74834270 8.65588614
38 4.47534545 -7.74834270
39 -1.15635428 4.47534545
40 2.70876248 -1.15635428
41 -0.26876162 2.70876248
42 1.65487566 -0.26876162
43 -4.54004510 1.65487566
44 -1.43627845 -4.54004510
45 -5.27747102 -1.43627845
46 -1.27740490 -5.27747102
47 4.25470573 -1.27740490
48 7.11065337 4.25470573
49 -2.72281743 7.11065337
50 2.07488109 -2.72281743
51 -1.11352591 2.07488109
52 1.16174430 -1.11352591
53 -0.61738390 1.16174430
54 -0.94703874 -0.61738390
55 2.75917650 -0.94703874
56 0.37292835 2.75917650
57 -3.32742425 0.37292835
58 -2.08406719 -3.32742425
59 -5.59118456 -2.08406719
60 -4.18555067 -5.59118456
61 -0.35792724 -4.18555067
62 -3.66102353 -0.35792724
63 -5.52045152 -3.66102353
64 -8.67692308 -5.52045152
65 6.13848430 -8.67692308
66 12.03571562 6.13848430
67 -3.48794072 12.03571562
68 -10.70946233 -3.48794072
69 -2.82300885 -10.70946233
70 10.24399131 -2.82300885
71 1.30418679 10.24399131
72 7.44452726 1.30418679
73 -0.16916386 7.44452726
74 4.29518009 -0.16916386
75 2.94025386 4.29518009
76 -9.02682670 2.94025386
77 -1.26176303 -9.02682670
78 -2.68537696 -1.26176303
79 5.11822506 -2.68537696
80 -2.36429078 5.11822506
81 3.81261009 -2.36429078
82 -1.02639412 3.81261009
83 -1.96728810 -1.02639412
84 3.29455785 -1.96728810
85 0.94943699 3.29455785
86 -1.17024583 0.94943699
87 -7.91954242 -1.17024583
88 0.87290501 -7.91954242
89 1.72322549 0.87290501
90 5.84887120 1.72322549
91 -4.16884779 5.84887120
92 -1.98293239 -4.16884779
93 1.28806087 -1.98293239
94 -0.17239047 1.28806087
95 2.30442583 -0.17239047
96 3.86508674 2.30442583
97 5.20762639 3.86508674
98 0.92916658 5.20762639
99 3.51665073 0.92916658
100 0.78162709 3.51665073
101 -1.32284322 0.78162709
102 -3.07923514 -1.32284322
103 -1.47617120 -3.07923514
104 1.29606889 -1.47617120
105 5.00826542 1.29606889
106 4.25867080 5.00826542
107 4.65894775 4.25867080
108 0.48132758 4.65894775
109 -1.45079793 0.48132758
110 -1.19577933 -1.45079793
111 10.06073323 -1.19577933
112 2.33481857 10.06073323
113 -3.92333622 2.33481857
114 -8.74341712 -3.92333622
115 -1.83227513 -8.74341712
116 -2.26944607 -1.83227513
117 -2.49531015 -2.26944607
118 1.26881020 -2.49531015
119 5.00333225 1.26881020
120 6.66171072 5.00333225
121 -4.46182874 6.66171072
122 -4.53756577 -4.46182874
123 -1.50373660 -4.53756577
124 -8.75958064 -1.50373660
125 -1.15530653 -8.75958064
126 -2.18147948 -1.15530653
127 -2.07689044 -2.18147948
128 -2.06201727 -2.07689044
129 7.50500980 -2.06201727
130 -1.84978500 7.50500980
131 -0.19758373 -1.84978500
132 -2.35782377 -0.19758373
133 0.71027224 -2.35782377
134 3.03212934 0.71027224
135 -3.35589913 3.03212934
136 -2.45387981 -3.35589913
137 -4.61998879 -2.45387981
138 -1.67862231 -4.61998879
139 -2.58285873 -1.67862231
140 5.31199459 -2.58285873
141 2.62514770 5.31199459
142 -4.22215484 2.62514770
143 -0.85777762 -4.22215484
144 -3.62049161 -0.85777762
145 1.88908419 -3.62049161
146 7.97102147 1.88908419
147 0.83777426 7.97102147
148 -0.08354098 0.83777426
149 2.90696489 -0.08354098
150 -1.31019196 2.90696489
151 1.75180353 -1.31019196
152 12.07185530 1.75180353
153 1.08540904 12.07185530
154 -4.95152602 1.08540904
155 -1.89970939 -4.95152602
156 3.69175292 -1.89970939
157 -1.49994867 3.69175292
158 2.15484026 -1.49994867
> 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/7mw9d1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8mw9d1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xn8g1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xn8g1290612933.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i6731290612933.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/12l6n91290612933.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/13a72l1290612933.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/143g2o1290612933.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/15z8zf1290612933.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/16l9g31290612933.tab")
+ }
>
> try(system("convert tmp/18mbm1290612933.ps tmp/18mbm1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/28mbm1290612933.ps tmp/28mbm1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/31dtp1290612933.ps tmp/31dtp1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/41dtp1290612933.ps tmp/41dtp1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/51dtp1290612933.ps tmp/51dtp1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/6b4aa1290612933.ps tmp/6b4aa1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mw9d1290612933.ps tmp/7mw9d1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/8mw9d1290612933.ps tmp/8mw9d1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xn8g1290612933.ps tmp/9xn8g1290612933.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xn8g1290612933.ps tmp/10xn8g1290612933.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.486 1.728 10.282