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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+ ,36
+ ,35
+ ,11
+ ,17
+ ,66
+ ,15
+ ,12
+ ,11
+ ,8
+ ,20
+ ,25
+ ,1
+ ,33
+ ,36
+ ,15
+ ,11
+ ,79
+ ,21
+ ,9
+ ,13
+ ,9
+ ,19
+ ,24
+ ,1
+ ,34
+ ,33
+ ,11
+ ,13
+ ,67
+ ,17
+ ,14
+ ,12
+ ,6
+ ,15
+ ,21
+ ,1
+ ,37
+ ,32
+ ,15
+ ,9
+ ,74
+ ,17
+ ,9
+ ,11
+ ,5
+ ,23
+ ,26
+ ,1
+ ,37
+ ,32
+ ,16
+ ,10
+ ,86
+ ,33
+ ,14
+ ,11
+ ,8
+ ,26
+ ,25
+ ,2
+ ,39
+ ,40
+ ,15
+ ,13
+ ,63
+ ,17
+ ,11
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+ ,2
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+ ,35
+ ,12
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+ ,69
+ ,20
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+ ,1
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+ ,73
+ ,17
+ ,9
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+ ,1
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+ ,37
+ ,15
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+ ,69
+ ,16
+ ,11
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+ ,5
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+ ,23
+ ,1
+ ,32
+ ,33
+ ,13
+ ,11
+ ,71
+ ,18
+ ,10
+ ,14
+ ,9
+ ,21
+ ,26
+ ,2
+ ,33
+ ,31
+ ,14
+ ,13
+ ,77
+ ,32
+ ,12
+ ,18
+ ,12
+ ,28
+ ,22
+ ,2
+ ,31
+ ,33
+ ,11
+ ,15
+ ,74
+ ,22
+ ,10
+ ,15
+ ,6
+ ,22
+ ,20
+ ,2
+ ,30
+ ,34
+ ,15
+ ,14
+ ,82
+ ,19
+ ,6
+ ,17
+ ,8
+ ,25
+ ,14
+ ,2
+ ,32
+ ,35
+ ,12
+ ,18
+ ,84
+ ,29
+ ,16
+ ,11
+ ,6
+ ,27
+ ,24
+ ,2
+ ,33
+ ,40
+ ,14
+ ,14
+ ,54
+ ,23
+ ,14
+ ,17
+ ,7
+ ,25
+ ,21
+ ,2
+ ,29
+ ,30
+ ,13
+ ,10
+ ,80
+ ,17
+ ,8
+ ,12
+ ,9
+ ,21
+ ,24
+ ,2
+ ,37
+ ,38
+ ,15
+ ,8
+ ,76)
+ ,dim=c(12
+ ,150)
+ ,dimnames=list(c('Mistakes'
+ ,'Doubts'
+ ,'P-Expectations'
+ ,'P-Criticism'
+ ,'Person-Standards'
+ ,'Organization'
+ ,'Gender'
+ ,'connected'
+ ,'separate'
+ ,'hapiness'
+ ,'depression'
+ ,'sport')
+ ,1:150))
> y <- array(NA,dim=c(12,150),dimnames=list(c('Mistakes','Doubts','P-Expectations','P-Criticism','Person-Standards','Organization','Gender','connected','separate','hapiness','depression','sport'),1:150))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par6 = '0'
> par5 = '0'
> par4 = '0'
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> par6 <- '0'
> par5 <- '0'
> par4 <- '0'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, 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:
> #Technical description:
> 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
Person-Standards Mistakes Doubts P-Expectations P-Criticism Organization
1 15 11 6 6 4 16
2 23 26 16 5 4 24
3 26 26 13 20 10 22
4 19 15 7 12 6 21
5 19 10 10 11 5 23
6 16 21 10 12 8 23
7 23 27 15 11 9 21
8 22 21 9 9 9 20
9 19 21 12 13 8 22
10 24 21 8 9 11 20
11 19 22 9 14 6 12
12 25 29 10 12 8 23
13 23 29 15 18 11 23
14 31 29 11 9 5 30
15 29 30 12 15 10 22
16 18 19 9 12 7 21
17 17 19 10 12 7 21
18 22 22 13 12 13 15
19 21 18 8 15 10 22
20 24 28 14 11 8 24
21 22 17 9 13 6 23
22 16 18 12 10 8 15
23 22 20 8 17 7 24
24 21 16 8 13 5 24
25 25 17 9 17 9 21
26 22 25 14 15 11 21
27 24 22 11 13 11 18
28 21 34 16 18 11 20
29 25 31 9 17 9 19
30 29 38 11 21 7 29
31 19 18 13 12 6 20
32 29 25 12 12 7 23
33 25 20 9 15 6 24
34 19 23 14 8 5 27
35 27 12 4 15 4 28
36 25 20 8 16 10 24
37 23 15 14 9 8 29
38 24 21 10 13 6 24
39 23 21 13 17 11 22
40 25 20 10 11 4 25
41 23 30 14 9 9 14
42 22 22 13 15 10 22
43 32 33 14 9 6 24
44 22 25 14 15 9 24
45 18 20 14 14 10 24
46 19 10 5 8 6 24
47 23 15 11 11 8 22
48 19 21 9 14 13 21
49 16 16 9 12 8 21
50 23 23 10 15 10 21
51 17 25 14 11 5 15
52 17 18 6 11 8 26
53 28 33 11 9 6 22
54 24 18 13 8 9 24
55 21 18 12 13 9 13
56 14 13 8 12 7 19
57 21 24 14 24 20 10
58 20 19 11 11 8 28
59 25 20 11 11 8 25
60 20 21 11 16 7 24
61 17 18 16 12 7 22
62 26 29 14 18 10 30
63 17 13 16 12 5 22
64 17 26 14 14 8 24
65 24 22 9 16 9 23
66 30 28 8 24 20 20
67 25 28 11 13 6 22
68 15 23 8 11 10 22
69 25 22 14 14 11 19
70 18 28 8 16 12 24
71 20 28 8 12 7 22
72 32 31 10 21 12 26
73 14 15 8 11 8 12
74 20 15 8 6 6 25
75 25 24 10 9 6 29
76 25 22 9 14 9 23
77 25 17 9 16 5 23
78 35 25 7 18 11 17
79 29 32 16 9 6 26
80 25 23 14 13 10 27
81 21 20 11 17 8 23
82 21 20 9 11 7 20
83 24 28 16 16 8 24
84 26 20 7 11 9 22
85 24 20 11 11 8 26
86 20 23 14 11 10 29
87 24 20 11 20 13 20
88 18 21 8 10 7 17
89 17 14 11 12 7 16
90 22 31 8 11 8 24
91 22 21 12 14 9 24
92 22 18 8 12 9 19
93 24 26 13 12 8 29
94 32 25 8 12 7 25
95 19 9 13 10 6 25
96 21 18 9 12 8 24
97 23 19 12 10 8 29
98 26 29 11 7 4 22
99 18 31 14 10 8 23
100 19 24 9 13 10 15
101 22 16 10 12 7 29
102 27 19 9 13 8 21
103 21 19 9 9 7 23
104 20 22 8 14 10 20
105 21 31 16 14 9 25
106 20 20 10 12 8 28
107 29 26 11 18 5 18
108 30 17 6 17 8 25
109 10 16 9 12 9 13
110 23 16 8 15 9 24
111 29 9 6 8 11 23
112 19 19 20 8 7 25
113 26 22 10 12 8 27
114 22 15 8 10 4 24
115 26 25 16 18 16 24
116 27 30 9 15 9 26
117 19 30 12 16 10 18
118 24 24 14 11 12 26
119 26 20 10 10 8 23
120 22 12 7 7 4 28
121 23 31 14 17 11 20
122 25 25 11 7 8 23
123 19 23 13 14 12 24
124 20 23 10 12 8 21
125 25 26 9 15 6 25
126 14 14 15 13 8 16
127 19 18 12 10 8 23
128 27 28 12 16 14 22
129 21 19 9 11 10 27
130 21 21 15 7 5 24
131 14 18 10 15 8 17
132 21 29 13 18 12 21
133 23 16 11 11 11 21
134 18 22 10 13 8 19
135 20 15 12 11 8 25
136 19 21 9 13 9 24
137 15 17 14 12 6 21
138 23 17 9 11 5 26
139 26 33 14 11 8 25
140 21 17 11 13 7 25
141 13 20 11 8 4 13
142 24 17 9 12 9 25
143 17 16 11 9 5 23
144 21 18 10 14 9 26
145 28 32 12 18 12 22
146 22 22 10 15 6 20
147 25 19 6 17 8 14
148 27 29 16 11 6 24
149 25 23 14 17 7 21
150 21 17 8 12 9 24
Gender connected separate hapiness depression sport
1 2 40 37 15 10 77
2 1 29 31 9 20 63
3 1 37 35 12 16 73
4 1 32 36 15 10 76
5 1 39 32 17 8 90
6 1 32 30 14 14 67
7 2 35 34 9 19 69
8 2 35 34 12 15 70
9 1 28 22 11 23 54
10 1 37 27 13 9 54
11 2 32 27 16 12 76
12 2 34 33 16 14 75
13 2 37 38 15 13 76
14 1 35 37 10 11 80
15 2 40 31 16 11 89
16 1 37 36 12 10 73
17 1 37 38 15 12 74
18 2 33 31 13 18 78
19 2 37 34 18 12 76
20 1 35 33 13 10 69
21 2 36 38 17 15 74
22 2 32 28 14 15 82
23 1 38 34 13 12 77
24 2 34 32 13 9 84
25 2 33 34 15 11 75
26 2 33 39 15 16 79
27 2 42 37 13 17 79
28 2 33 34 14 12 69
29 2 32 41 13 11 88
30 2 32 32 16 13 57
31 2 33 35 14 9 69
32 1 35 33 12 14 52
33 2 39 32 18 11 86
34 1 28 32 9 20 66
35 1 38 32 16 8 54
36 2 36 37 16 12 85
37 1 38 31 17 10 79
38 1 34 27 13 11 84
39 2 33 31 15 11 73
40 2 37 37 17 13 70
41 2 34 31 15 13 54
42 2 34 40 14 13 70
43 1 36 35 10 15 54
44 1 31 35 13 12 69
45 2 37 35 11 13 68
46 1 36 35 16 11 76
47 1 34 38 16 9 71
48 2 30 35 11 14 66
49 2 29 34 15 9 67
50 2 35 37 15 9 71
51 2 33 37 12 15 54
52 2 29 31 17 10 76
53 1 28 31 15 13 77
54 1 32 33 16 8 71
55 2 33 37 14 15 69
56 2 31 36 17 13 73
57 2 43 42 10 24 46
58 1 32 28 11 11 66
59 2 35 41 15 13 77
60 1 31 23 15 12 77
61 2 33 33 7 22 70
62 1 39 32 17 11 86
63 1 32 33 14 15 38
64 1 32 33 18 7 66
65 1 36 32 14 14 75
66 1 39 38 14 10 64
67 2 41 32 9 9 80
68 2 30 35 14 12 86
69 2 30 35 11 16 54
70 2 32 34 15 10 54
71 2 39 34 16 13 74
72 2 38 38 17 11 88
73 2 38 39 16 12 85
74 1 32 32 12 11 63
75 2 34 39 15 13 81
76 2 36 35 15 10 74
77 2 39 36 16 11 80
78 2 31 28 16 9 80
79 1 36 36 11 13 60
80 2 34 38 12 14 62
81 1 34 35 14 14 63
82 2 38 39 15 11 89
83 2 38 36 17 10 76
84 2 33 36 19 11 81
85 2 32 34 15 12 72
86 1 30 34 16 14 84
87 2 31 27 14 14 76
88 2 34 37 16 21 76
89 2 35 33 15 13 72
90 1 37 34 17 11 81
91 2 35 39 12 12 72
92 2 35 29 18 12 78
93 2 31 33 13 11 79
94 2 31 35 14 14 52
95 1 38 36 14 13 67
96 1 34 30 14 13 74
97 1 30 27 12 12 73
98 2 32 37 14 14 69
99 1 31 33 12 12 67
100 2 37 32 15 12 76
101 2 34 35 11 12 77
102 1 32 33 11 18 63
103 2 34 37 15 11 84
104 2 38 36 14 15 90
105 1 38 39 15 13 75
106 2 38 35 16 11 76
107 2 39 31 14 22 53
108 2 33 37 18 10 87
109 2 34 36 13 16 69
110 2 35 31 14 11 78
111 2 36 32 13 15 54
112 1 32 33 14 14 58
113 2 34 36 14 11 80
114 2 44 39 17 10 74
115 2 37 39 12 14 56
116 2 32 29 16 14 82
117 2 35 34 15 11 64
118 1 38 35 10 15 67
119 1 38 32 13 11 75
120 1 38 41 15 10 69
121 2 32 38 16 10 72
122 2 39 38 14 12 54
123 1 27 32 13 15 54
124 2 37 31 17 10 71
125 2 41 38 14 12 53
126 2 31 38 16 15 54
127 1 36 33 15 12 71
128 2 38 28 12 11 69
129 1 37 38 16 10 30
130 1 30 28 8 20 53
131 1 40 32 9 19 68
132 2 34 31 13 17 69
133 2 36 34 19 8 54
134 2 36 35 11 17 66
135 1 33 36 15 11 79
136 1 34 33 11 13 67
137 1 37 32 15 9 74
138 1 37 32 16 10 86
139 2 39 40 15 13 63
140 2 37 35 12 16 69
141 1 37 33 16 12 73
142 1 35 37 15 14 69
143 1 32 33 13 11 71
144 2 33 31 14 13 77
145 2 31 33 11 15 74
146 2 30 34 15 14 82
147 2 32 35 12 18 84
148 2 33 40 14 14 54
149 2 29 30 13 10 80
150 2 37 38 15 8 76
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Mistakes Doubts `P-Expectations`
3.84738 0.32845 -0.35450 0.16370
`P-Criticism` Organization Gender connected
0.05056 0.44054 0.96940 0.16218
separate hapiness depression sport
-0.05305 -0.05541 0.02298 -0.03057
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.07933 -2.24729 -0.01444 1.95172 12.07560
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.84738 5.90561 0.651 0.51582
Mistakes 0.32845 0.05799 5.664 8.26e-08 ***
Doubts -0.35450 0.12082 -2.934 0.00392 **
`P-Expectations` 0.16370 0.10801 1.516 0.13192
`P-Criticism` 0.05056 0.13601 0.372 0.71066
Organization 0.44054 0.08120 5.425 2.52e-07 ***
Gender 0.96940 0.68158 1.422 0.15720
connected 0.16218 0.09363 1.732 0.08547 .
separate -0.05305 0.08832 -0.601 0.54902
hapiness -0.05541 0.15546 -0.356 0.72209
depression 0.02298 0.11630 0.198 0.84365
sport -0.03057 0.02901 -1.054 0.29385
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.432 on 138 degrees of freedom
Multiple R-squared: 0.4031, Adjusted R-squared: 0.3555
F-statistic: 8.472 on 11 and 138 DF, p-value: 2.730e-11
> 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,] 0.50570434 0.98859132 0.49429566
[2,] 0.56674173 0.86651655 0.43325827
[3,] 0.45229881 0.90459763 0.54770119
[4,] 0.33211081 0.66422163 0.66788919
[5,] 0.26730391 0.53460782 0.73269609
[6,] 0.18038947 0.36077895 0.81961053
[7,] 0.24368973 0.48737946 0.75631027
[8,] 0.20138178 0.40276357 0.79861822
[9,] 0.15138352 0.30276704 0.84861648
[10,] 0.10177454 0.20354907 0.89822546
[11,] 0.17839270 0.35678540 0.82160730
[12,] 0.12753662 0.25507323 0.87246338
[13,] 0.09634596 0.19269192 0.90365404
[14,] 0.10177314 0.20354627 0.89822686
[15,] 0.07087322 0.14174643 0.92912678
[16,] 0.05038890 0.10077780 0.94961110
[17,] 0.03719846 0.07439691 0.96280154
[18,] 0.13995783 0.27991566 0.86004217
[19,] 0.11020783 0.22041567 0.88979217
[20,] 0.10256842 0.20513684 0.89743158
[21,] 0.10413247 0.20826493 0.89586753
[22,] 0.07851486 0.15702973 0.92148514
[23,] 0.05929690 0.11859379 0.94070310
[24,] 0.04367127 0.08734255 0.95632873
[25,] 0.03088475 0.06176950 0.96911525
[26,] 0.02475977 0.04951954 0.97524023
[27,] 0.02082505 0.04165009 0.97917495
[28,] 0.01401229 0.02802459 0.98598771
[29,] 0.02626480 0.05252961 0.97373520
[30,] 0.01815812 0.03631624 0.98184188
[31,] 0.03436770 0.06873539 0.96563230
[32,] 0.02480171 0.04960342 0.97519829
[33,] 0.03302213 0.06604426 0.96697787
[34,] 0.02746507 0.05493014 0.97253493
[35,] 0.02420335 0.04840671 0.97579665
[36,] 0.01707472 0.03414945 0.98292528
[37,] 0.01412494 0.02824988 0.98587506
[38,] 0.02259058 0.04518116 0.97740942
[39,] 0.02604225 0.05208449 0.97395775
[40,] 0.03190354 0.06380708 0.96809646
[41,] 0.04593659 0.09187319 0.95406341
[42,] 0.04740046 0.09480093 0.95259954
[43,] 0.03547909 0.07095818 0.96452091
[44,] 0.03111874 0.06223747 0.96888126
[45,] 0.02825622 0.05651245 0.97174378
[46,] 0.02296215 0.04592429 0.97703785
[47,] 0.01795803 0.03591606 0.98204197
[48,] 0.01750864 0.03501729 0.98249136
[49,] 0.01270622 0.02541245 0.98729378
[50,] 0.02943272 0.05886545 0.97056728
[51,] 0.02178592 0.04357185 0.97821408
[52,] 0.02325696 0.04651392 0.97674304
[53,] 0.01804928 0.03609855 0.98195072
[54,] 0.04814817 0.09629635 0.95185183
[55,] 0.07692069 0.15384138 0.92307931
[56,] 0.25439679 0.50879357 0.74560321
[57,] 0.35713623 0.71427245 0.64286377
[58,] 0.37498173 0.74996345 0.62501827
[59,] 0.36014814 0.72029627 0.63985186
[60,] 0.31891890 0.63783779 0.68108110
[61,] 0.27687010 0.55374020 0.72312990
[62,] 0.24614571 0.49229142 0.75385429
[63,] 0.23056699 0.46113398 0.76943301
[64,] 0.80189170 0.39621659 0.19810830
[65,] 0.84972292 0.30055415 0.15027708
[66,] 0.82349847 0.35300307 0.17650153
[67,] 0.79196867 0.41606266 0.20803133
[68,] 0.75988718 0.48022563 0.24011282
[69,] 0.72715597 0.54568806 0.27284403
[70,] 0.73934459 0.52131082 0.26065541
[71,] 0.70583057 0.58833885 0.29416943
[72,] 0.68783472 0.62433056 0.31216528
[73,] 0.66035959 0.67928081 0.33964041
[74,] 0.65593471 0.68813058 0.34406529
[75,] 0.60690569 0.78618863 0.39309431
[76,] 0.61747127 0.76505746 0.38252873
[77,] 0.56704553 0.86590894 0.43295447
[78,] 0.51918648 0.96162705 0.48081352
[79,] 0.48163204 0.96326408 0.51836796
[80,] 0.57196456 0.85607089 0.42803544
[81,] 0.52591620 0.94816761 0.47408380
[82,] 0.47464676 0.94929351 0.52535324
[83,] 0.42353562 0.84707125 0.57646438
[84,] 0.40051404 0.80102807 0.59948596
[85,] 0.43063021 0.86126042 0.56936979
[86,] 0.38990492 0.77980984 0.61009508
[87,] 0.36646713 0.73293427 0.63353287
[88,] 0.47299258 0.94598515 0.52700742
[89,] 0.42110496 0.84220992 0.57889504
[90,] 0.39824374 0.79648748 0.60175626
[91,] 0.37218996 0.74437991 0.62781004
[92,] 0.50315513 0.99368973 0.49684487
[93,] 0.68083308 0.63833383 0.31916692
[94,] 0.76958690 0.46082620 0.23041310
[95,] 0.92577769 0.14844461 0.07422231
[96,] 0.90197387 0.19605225 0.09802613
[97,] 0.95741069 0.08517862 0.04258931
[98,] 0.94173696 0.11652609 0.05826304
[99,] 0.92337526 0.15324948 0.07662474
[100,] 0.89737451 0.20525098 0.10262549
[101,] 0.87818708 0.24362583 0.12181292
[102,] 0.84394712 0.31210576 0.15605288
[103,] 0.85579848 0.28840304 0.14420152
[104,] 0.81782491 0.36435018 0.18217509
[105,] 0.89094843 0.21810314 0.10905157
[106,] 0.85366275 0.29267449 0.14633725
[107,] 0.82145364 0.35709272 0.17854636
[108,] 0.77325011 0.45349979 0.22674989
[109,] 0.78773236 0.42453527 0.21226764
[110,] 0.77089458 0.45821085 0.22910542
[111,] 0.71429807 0.57140385 0.28570193
[112,] 0.68292625 0.63414749 0.31707375
[113,] 0.59822154 0.80355692 0.40177846
[114,] 0.70153482 0.59693036 0.29846518
[115,] 0.65464262 0.69071476 0.34535738
[116,] 0.78020316 0.43959369 0.21979684
[117,] 0.70529945 0.58940110 0.29470055
[118,] 0.73283100 0.53433801 0.26716900
[119,] 0.74239497 0.51521007 0.25760503
[120,] 0.63252826 0.73494348 0.36747174
[121,] 0.49934320 0.99868640 0.50065680
> postscript(file="/var/www/html/rcomp/tmp/153uz1292159043.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/253uz1292159043.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/353uz1292159043.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4yub21292159043.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yub21292159043.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 = 150
Frequency = 1
1 2 3 4 5 6
-2.07408062 2.62846482 2.16579618 -0.13613758 1.14009002 -5.76631064
7 8 9 10 11 12
0.45521487 0.35553272 -2.32665480 1.87769175 0.47377046 -0.17308916
13 14 15 16 17 18
-1.75759320 4.64920759 3.47355403 -2.86033588 -3.24889316 3.35072320
19 20 21 22 23 24
-1.66701394 0.84600731 0.29393041 -0.86013223 -1.81995619 -0.89406790
25 26 27 28 29 30
3.65455282 0.29843620 2.16973268 -3.53362010 0.75757764 -3.09661449
31 32 33 34 35 36
0.01500271 5.78265981 1.24999391 -2.65152477 2.55536898 1.11700223
37 38 39 40 41 42
2.17521882 1.78032402 0.99681724 1.91896413 2.54130199 0.16853344
43 44 45 46 47 48
6.83648959 -0.16513523 -4.51663349 -0.53908999 4.61117672 -3.39649361
49 50 51 52 53 54
-3.69783167 0.07367859 -2.11385479 -6.25339485 4.76550498 4.97639103
55 56 57 58 59 60
4.39712361 -4.15128626 -0.38799601 -3.02787226 2.71101080 -2.25867367
61 62 63 64 65 66
-2.61525625 -1.35608837 -0.16995876 -5.24739312 0.54733803 3.26540687
67 68 69 70 71 72
-0.42060229 -7.38199943 4.61692934 -9.07933295 -5.82800892 3.31119314
73 74 75 76 77 78
-2.25293131 -0.36045898 -0.11254619 1.18125216 2.48068672 12.07560343
79 80 81 82 83 84
4.33072443 0.83480416 -1.08326094 0.07544812 -0.12463974 4.01304641
85 86 87 88 89 90
1.25576565 -2.41870448 1.98464575 -2.15140577 -0.04388531 -3.97213436
91 92 93 94 95 96
-0.76626702 1.31651564 -1.25591473 6.37982173 1.15418857 -0.66338696
97 98 99 100 101 102
0.56754699 2.88501404 -5.04887203 -2.14433706 -1.55978927 6.03217859
103 104 105 106 107 108
-0.20056203 -2.85463942 -3.35544602 -4.86288486 4.65462552 6.59468572
109 110 111 112 113 114
-7.13941658 0.18706534 8.27228600 1.14389073 1.63402906 -0.38151462
115 116 117 118 119 120
1.46974331 -0.39314751 -4.77765466 0.35376172 4.28066244 0.76319091
121 122 123 124 125 126
-0.47069586 1.06115124 -2.88868848 -2.88895280 -2.42080803 -1.46700989
127 128 129 130 131 132
-1.01047867 1.09662219 -3.47847413 0.85574978 -6.18156394 -3.93764802
133 134 135 136 137 138
2.73506773 -4.11490722 0.84335799 -4.08406263 -3.37280493 1.26554870
139 140 141 142 143 144
-0.62509821 -1.70292779 -3.13247087 2.26270630 -1.83181184 -2.23036744
145 146 147 148 149 150
1.96264078 0.91806456 5.23179996 3.55890319 4.05906418 -1.54007523
> postscript(file="/var/www/html/rcomp/tmp/6yub21292159043.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 = 150
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.07408062 NA
1 2.62846482 -2.07408062
2 2.16579618 2.62846482
3 -0.13613758 2.16579618
4 1.14009002 -0.13613758
5 -5.76631064 1.14009002
6 0.45521487 -5.76631064
7 0.35553272 0.45521487
8 -2.32665480 0.35553272
9 1.87769175 -2.32665480
10 0.47377046 1.87769175
11 -0.17308916 0.47377046
12 -1.75759320 -0.17308916
13 4.64920759 -1.75759320
14 3.47355403 4.64920759
15 -2.86033588 3.47355403
16 -3.24889316 -2.86033588
17 3.35072320 -3.24889316
18 -1.66701394 3.35072320
19 0.84600731 -1.66701394
20 0.29393041 0.84600731
21 -0.86013223 0.29393041
22 -1.81995619 -0.86013223
23 -0.89406790 -1.81995619
24 3.65455282 -0.89406790
25 0.29843620 3.65455282
26 2.16973268 0.29843620
27 -3.53362010 2.16973268
28 0.75757764 -3.53362010
29 -3.09661449 0.75757764
30 0.01500271 -3.09661449
31 5.78265981 0.01500271
32 1.24999391 5.78265981
33 -2.65152477 1.24999391
34 2.55536898 -2.65152477
35 1.11700223 2.55536898
36 2.17521882 1.11700223
37 1.78032402 2.17521882
38 0.99681724 1.78032402
39 1.91896413 0.99681724
40 2.54130199 1.91896413
41 0.16853344 2.54130199
42 6.83648959 0.16853344
43 -0.16513523 6.83648959
44 -4.51663349 -0.16513523
45 -0.53908999 -4.51663349
46 4.61117672 -0.53908999
47 -3.39649361 4.61117672
48 -3.69783167 -3.39649361
49 0.07367859 -3.69783167
50 -2.11385479 0.07367859
51 -6.25339485 -2.11385479
52 4.76550498 -6.25339485
53 4.97639103 4.76550498
54 4.39712361 4.97639103
55 -4.15128626 4.39712361
56 -0.38799601 -4.15128626
57 -3.02787226 -0.38799601
58 2.71101080 -3.02787226
59 -2.25867367 2.71101080
60 -2.61525625 -2.25867367
61 -1.35608837 -2.61525625
62 -0.16995876 -1.35608837
63 -5.24739312 -0.16995876
64 0.54733803 -5.24739312
65 3.26540687 0.54733803
66 -0.42060229 3.26540687
67 -7.38199943 -0.42060229
68 4.61692934 -7.38199943
69 -9.07933295 4.61692934
70 -5.82800892 -9.07933295
71 3.31119314 -5.82800892
72 -2.25293131 3.31119314
73 -0.36045898 -2.25293131
74 -0.11254619 -0.36045898
75 1.18125216 -0.11254619
76 2.48068672 1.18125216
77 12.07560343 2.48068672
78 4.33072443 12.07560343
79 0.83480416 4.33072443
80 -1.08326094 0.83480416
81 0.07544812 -1.08326094
82 -0.12463974 0.07544812
83 4.01304641 -0.12463974
84 1.25576565 4.01304641
85 -2.41870448 1.25576565
86 1.98464575 -2.41870448
87 -2.15140577 1.98464575
88 -0.04388531 -2.15140577
89 -3.97213436 -0.04388531
90 -0.76626702 -3.97213436
91 1.31651564 -0.76626702
92 -1.25591473 1.31651564
93 6.37982173 -1.25591473
94 1.15418857 6.37982173
95 -0.66338696 1.15418857
96 0.56754699 -0.66338696
97 2.88501404 0.56754699
98 -5.04887203 2.88501404
99 -2.14433706 -5.04887203
100 -1.55978927 -2.14433706
101 6.03217859 -1.55978927
102 -0.20056203 6.03217859
103 -2.85463942 -0.20056203
104 -3.35544602 -2.85463942
105 -4.86288486 -3.35544602
106 4.65462552 -4.86288486
107 6.59468572 4.65462552
108 -7.13941658 6.59468572
109 0.18706534 -7.13941658
110 8.27228600 0.18706534
111 1.14389073 8.27228600
112 1.63402906 1.14389073
113 -0.38151462 1.63402906
114 1.46974331 -0.38151462
115 -0.39314751 1.46974331
116 -4.77765466 -0.39314751
117 0.35376172 -4.77765466
118 4.28066244 0.35376172
119 0.76319091 4.28066244
120 -0.47069586 0.76319091
121 1.06115124 -0.47069586
122 -2.88868848 1.06115124
123 -2.88895280 -2.88868848
124 -2.42080803 -2.88895280
125 -1.46700989 -2.42080803
126 -1.01047867 -1.46700989
127 1.09662219 -1.01047867
128 -3.47847413 1.09662219
129 0.85574978 -3.47847413
130 -6.18156394 0.85574978
131 -3.93764802 -6.18156394
132 2.73506773 -3.93764802
133 -4.11490722 2.73506773
134 0.84335799 -4.11490722
135 -4.08406263 0.84335799
136 -3.37280493 -4.08406263
137 1.26554870 -3.37280493
138 -0.62509821 1.26554870
139 -1.70292779 -0.62509821
140 -3.13247087 -1.70292779
141 2.26270630 -3.13247087
142 -1.83181184 2.26270630
143 -2.23036744 -1.83181184
144 1.96264078 -2.23036744
145 0.91806456 1.96264078
146 5.23179996 0.91806456
147 3.55890319 5.23179996
148 4.05906418 3.55890319
149 -1.54007523 4.05906418
150 NA -1.54007523
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.62846482 -2.07408062
[2,] 2.16579618 2.62846482
[3,] -0.13613758 2.16579618
[4,] 1.14009002 -0.13613758
[5,] -5.76631064 1.14009002
[6,] 0.45521487 -5.76631064
[7,] 0.35553272 0.45521487
[8,] -2.32665480 0.35553272
[9,] 1.87769175 -2.32665480
[10,] 0.47377046 1.87769175
[11,] -0.17308916 0.47377046
[12,] -1.75759320 -0.17308916
[13,] 4.64920759 -1.75759320
[14,] 3.47355403 4.64920759
[15,] -2.86033588 3.47355403
[16,] -3.24889316 -2.86033588
[17,] 3.35072320 -3.24889316
[18,] -1.66701394 3.35072320
[19,] 0.84600731 -1.66701394
[20,] 0.29393041 0.84600731
[21,] -0.86013223 0.29393041
[22,] -1.81995619 -0.86013223
[23,] -0.89406790 -1.81995619
[24,] 3.65455282 -0.89406790
[25,] 0.29843620 3.65455282
[26,] 2.16973268 0.29843620
[27,] -3.53362010 2.16973268
[28,] 0.75757764 -3.53362010
[29,] -3.09661449 0.75757764
[30,] 0.01500271 -3.09661449
[31,] 5.78265981 0.01500271
[32,] 1.24999391 5.78265981
[33,] -2.65152477 1.24999391
[34,] 2.55536898 -2.65152477
[35,] 1.11700223 2.55536898
[36,] 2.17521882 1.11700223
[37,] 1.78032402 2.17521882
[38,] 0.99681724 1.78032402
[39,] 1.91896413 0.99681724
[40,] 2.54130199 1.91896413
[41,] 0.16853344 2.54130199
[42,] 6.83648959 0.16853344
[43,] -0.16513523 6.83648959
[44,] -4.51663349 -0.16513523
[45,] -0.53908999 -4.51663349
[46,] 4.61117672 -0.53908999
[47,] -3.39649361 4.61117672
[48,] -3.69783167 -3.39649361
[49,] 0.07367859 -3.69783167
[50,] -2.11385479 0.07367859
[51,] -6.25339485 -2.11385479
[52,] 4.76550498 -6.25339485
[53,] 4.97639103 4.76550498
[54,] 4.39712361 4.97639103
[55,] -4.15128626 4.39712361
[56,] -0.38799601 -4.15128626
[57,] -3.02787226 -0.38799601
[58,] 2.71101080 -3.02787226
[59,] -2.25867367 2.71101080
[60,] -2.61525625 -2.25867367
[61,] -1.35608837 -2.61525625
[62,] -0.16995876 -1.35608837
[63,] -5.24739312 -0.16995876
[64,] 0.54733803 -5.24739312
[65,] 3.26540687 0.54733803
[66,] -0.42060229 3.26540687
[67,] -7.38199943 -0.42060229
[68,] 4.61692934 -7.38199943
[69,] -9.07933295 4.61692934
[70,] -5.82800892 -9.07933295
[71,] 3.31119314 -5.82800892
[72,] -2.25293131 3.31119314
[73,] -0.36045898 -2.25293131
[74,] -0.11254619 -0.36045898
[75,] 1.18125216 -0.11254619
[76,] 2.48068672 1.18125216
[77,] 12.07560343 2.48068672
[78,] 4.33072443 12.07560343
[79,] 0.83480416 4.33072443
[80,] -1.08326094 0.83480416
[81,] 0.07544812 -1.08326094
[82,] -0.12463974 0.07544812
[83,] 4.01304641 -0.12463974
[84,] 1.25576565 4.01304641
[85,] -2.41870448 1.25576565
[86,] 1.98464575 -2.41870448
[87,] -2.15140577 1.98464575
[88,] -0.04388531 -2.15140577
[89,] -3.97213436 -0.04388531
[90,] -0.76626702 -3.97213436
[91,] 1.31651564 -0.76626702
[92,] -1.25591473 1.31651564
[93,] 6.37982173 -1.25591473
[94,] 1.15418857 6.37982173
[95,] -0.66338696 1.15418857
[96,] 0.56754699 -0.66338696
[97,] 2.88501404 0.56754699
[98,] -5.04887203 2.88501404
[99,] -2.14433706 -5.04887203
[100,] -1.55978927 -2.14433706
[101,] 6.03217859 -1.55978927
[102,] -0.20056203 6.03217859
[103,] -2.85463942 -0.20056203
[104,] -3.35544602 -2.85463942
[105,] -4.86288486 -3.35544602
[106,] 4.65462552 -4.86288486
[107,] 6.59468572 4.65462552
[108,] -7.13941658 6.59468572
[109,] 0.18706534 -7.13941658
[110,] 8.27228600 0.18706534
[111,] 1.14389073 8.27228600
[112,] 1.63402906 1.14389073
[113,] -0.38151462 1.63402906
[114,] 1.46974331 -0.38151462
[115,] -0.39314751 1.46974331
[116,] -4.77765466 -0.39314751
[117,] 0.35376172 -4.77765466
[118,] 4.28066244 0.35376172
[119,] 0.76319091 4.28066244
[120,] -0.47069586 0.76319091
[121,] 1.06115124 -0.47069586
[122,] -2.88868848 1.06115124
[123,] -2.88895280 -2.88868848
[124,] -2.42080803 -2.88895280
[125,] -1.46700989 -2.42080803
[126,] -1.01047867 -1.46700989
[127,] 1.09662219 -1.01047867
[128,] -3.47847413 1.09662219
[129,] 0.85574978 -3.47847413
[130,] -6.18156394 0.85574978
[131,] -3.93764802 -6.18156394
[132,] 2.73506773 -3.93764802
[133,] -4.11490722 2.73506773
[134,] 0.84335799 -4.11490722
[135,] -4.08406263 0.84335799
[136,] -3.37280493 -4.08406263
[137,] 1.26554870 -3.37280493
[138,] -0.62509821 1.26554870
[139,] -1.70292779 -0.62509821
[140,] -3.13247087 -1.70292779
[141,] 2.26270630 -3.13247087
[142,] -1.83181184 2.26270630
[143,] -2.23036744 -1.83181184
[144,] 1.96264078 -2.23036744
[145,] 0.91806456 1.96264078
[146,] 5.23179996 0.91806456
[147,] 3.55890319 5.23179996
[148,] 4.05906418 3.55890319
[149,] -1.54007523 4.05906418
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.62846482 -2.07408062
2 2.16579618 2.62846482
3 -0.13613758 2.16579618
4 1.14009002 -0.13613758
5 -5.76631064 1.14009002
6 0.45521487 -5.76631064
7 0.35553272 0.45521487
8 -2.32665480 0.35553272
9 1.87769175 -2.32665480
10 0.47377046 1.87769175
11 -0.17308916 0.47377046
12 -1.75759320 -0.17308916
13 4.64920759 -1.75759320
14 3.47355403 4.64920759
15 -2.86033588 3.47355403
16 -3.24889316 -2.86033588
17 3.35072320 -3.24889316
18 -1.66701394 3.35072320
19 0.84600731 -1.66701394
20 0.29393041 0.84600731
21 -0.86013223 0.29393041
22 -1.81995619 -0.86013223
23 -0.89406790 -1.81995619
24 3.65455282 -0.89406790
25 0.29843620 3.65455282
26 2.16973268 0.29843620
27 -3.53362010 2.16973268
28 0.75757764 -3.53362010
29 -3.09661449 0.75757764
30 0.01500271 -3.09661449
31 5.78265981 0.01500271
32 1.24999391 5.78265981
33 -2.65152477 1.24999391
34 2.55536898 -2.65152477
35 1.11700223 2.55536898
36 2.17521882 1.11700223
37 1.78032402 2.17521882
38 0.99681724 1.78032402
39 1.91896413 0.99681724
40 2.54130199 1.91896413
41 0.16853344 2.54130199
42 6.83648959 0.16853344
43 -0.16513523 6.83648959
44 -4.51663349 -0.16513523
45 -0.53908999 -4.51663349
46 4.61117672 -0.53908999
47 -3.39649361 4.61117672
48 -3.69783167 -3.39649361
49 0.07367859 -3.69783167
50 -2.11385479 0.07367859
51 -6.25339485 -2.11385479
52 4.76550498 -6.25339485
53 4.97639103 4.76550498
54 4.39712361 4.97639103
55 -4.15128626 4.39712361
56 -0.38799601 -4.15128626
57 -3.02787226 -0.38799601
58 2.71101080 -3.02787226
59 -2.25867367 2.71101080
60 -2.61525625 -2.25867367
61 -1.35608837 -2.61525625
62 -0.16995876 -1.35608837
63 -5.24739312 -0.16995876
64 0.54733803 -5.24739312
65 3.26540687 0.54733803
66 -0.42060229 3.26540687
67 -7.38199943 -0.42060229
68 4.61692934 -7.38199943
69 -9.07933295 4.61692934
70 -5.82800892 -9.07933295
71 3.31119314 -5.82800892
72 -2.25293131 3.31119314
73 -0.36045898 -2.25293131
74 -0.11254619 -0.36045898
75 1.18125216 -0.11254619
76 2.48068672 1.18125216
77 12.07560343 2.48068672
78 4.33072443 12.07560343
79 0.83480416 4.33072443
80 -1.08326094 0.83480416
81 0.07544812 -1.08326094
82 -0.12463974 0.07544812
83 4.01304641 -0.12463974
84 1.25576565 4.01304641
85 -2.41870448 1.25576565
86 1.98464575 -2.41870448
87 -2.15140577 1.98464575
88 -0.04388531 -2.15140577
89 -3.97213436 -0.04388531
90 -0.76626702 -3.97213436
91 1.31651564 -0.76626702
92 -1.25591473 1.31651564
93 6.37982173 -1.25591473
94 1.15418857 6.37982173
95 -0.66338696 1.15418857
96 0.56754699 -0.66338696
97 2.88501404 0.56754699
98 -5.04887203 2.88501404
99 -2.14433706 -5.04887203
100 -1.55978927 -2.14433706
101 6.03217859 -1.55978927
102 -0.20056203 6.03217859
103 -2.85463942 -0.20056203
104 -3.35544602 -2.85463942
105 -4.86288486 -3.35544602
106 4.65462552 -4.86288486
107 6.59468572 4.65462552
108 -7.13941658 6.59468572
109 0.18706534 -7.13941658
110 8.27228600 0.18706534
111 1.14389073 8.27228600
112 1.63402906 1.14389073
113 -0.38151462 1.63402906
114 1.46974331 -0.38151462
115 -0.39314751 1.46974331
116 -4.77765466 -0.39314751
117 0.35376172 -4.77765466
118 4.28066244 0.35376172
119 0.76319091 4.28066244
120 -0.47069586 0.76319091
121 1.06115124 -0.47069586
122 -2.88868848 1.06115124
123 -2.88895280 -2.88868848
124 -2.42080803 -2.88895280
125 -1.46700989 -2.42080803
126 -1.01047867 -1.46700989
127 1.09662219 -1.01047867
128 -3.47847413 1.09662219
129 0.85574978 -3.47847413
130 -6.18156394 0.85574978
131 -3.93764802 -6.18156394
132 2.73506773 -3.93764802
133 -4.11490722 2.73506773
134 0.84335799 -4.11490722
135 -4.08406263 0.84335799
136 -3.37280493 -4.08406263
137 1.26554870 -3.37280493
138 -0.62509821 1.26554870
139 -1.70292779 -0.62509821
140 -3.13247087 -1.70292779
141 2.26270630 -3.13247087
142 -1.83181184 2.26270630
143 -2.23036744 -1.83181184
144 1.96264078 -2.23036744
145 0.91806456 1.96264078
146 5.23179996 0.91806456
147 3.55890319 5.23179996
148 4.05906418 3.55890319
149 -1.54007523 4.05906418
> 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/7qmt51292159043.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/81vs81292159043.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91vs81292159043.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101vs81292159043.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11f58z1292159043.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/12j56n1292159043.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/13xxme1292159043.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/140ylk1292159043.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/15ly171292159043.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/16pyzd1292159043.tab")
+ }
>
> try(system("convert tmp/153uz1292159043.ps tmp/153uz1292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/253uz1292159043.ps tmp/253uz1292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/353uz1292159043.ps tmp/353uz1292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/4yub21292159043.ps tmp/4yub21292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yub21292159043.ps tmp/5yub21292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yub21292159043.ps tmp/6yub21292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/7qmt51292159043.ps tmp/7qmt51292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/81vs81292159043.ps tmp/81vs81292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/91vs81292159043.ps tmp/91vs81292159043.png",intern=TRUE))
character(0)
> try(system("convert tmp/101vs81292159043.ps tmp/101vs81292159043.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.449 1.712 9.775