R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(68
+ ,13
+ ,5
+ ,20
+ ,0
+ ,17
+ ,26
+ ,7
+ ,28
+ ,0
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,114
+ ,37
+ ,12
+ ,40
+ ,0
+ ,95
+ ,47
+ ,15
+ ,60
+ ,0
+ ,148
+ ,80
+ ,16
+ ,60
+ ,1
+ ,56
+ ,21
+ ,12
+ ,44
+ ,0
+ ,26
+ ,36
+ ,13
+ ,52
+ ,0
+ ,63
+ ,35
+ ,15
+ ,60
+ ,0
+ ,96
+ ,40
+ ,13
+ ,52
+ ,1
+ ,74
+ ,35
+ ,6
+ ,24
+ ,1
+ ,65
+ ,46
+ ,16
+ ,64
+ ,0
+ ,40
+ ,20
+ ,7
+ ,26
+ ,0
+ ,173
+ ,24
+ ,12
+ ,48
+ ,4
+ ,28
+ ,19
+ ,9
+ ,36
+ ,3
+ ,55
+ ,15
+ ,10
+ ,40
+ ,3
+ ,58
+ ,48
+ ,16
+ ,64
+ ,0
+ ,25
+ ,0
+ ,5
+ ,20
+ ,4
+ ,103
+ ,38
+ ,20
+ ,79
+ ,0
+ ,29
+ ,12
+ ,7
+ ,16
+ ,0
+ ,31
+ ,10
+ ,13
+ ,52
+ ,0
+ ,43
+ ,51
+ ,13
+ ,52
+ ,0
+ ,74
+ ,4
+ ,11
+ ,44
+ ,0
+ ,99
+ ,24
+ ,9
+ ,29
+ ,1
+ ,25
+ ,39
+ ,10
+ ,40
+ ,1
+ ,69
+ ,19
+ ,7
+ ,28
+ ,0
+ ,62
+ ,23
+ ,13
+ ,49
+ ,0
+ ,25
+ ,39
+ ,15
+ ,60
+ ,0
+ ,38
+ ,37
+ ,13
+ ,52
+ ,0
+ ,57
+ ,20
+ ,7
+ ,28
+ ,0
+ ,52
+ ,20
+ ,14
+ ,56
+ ,0
+ ,91
+ ,41
+ ,11
+ ,35
+ ,2
+ ,48
+ ,26
+ ,3
+ ,12
+ ,4
+ ,52
+ ,0
+ ,8
+ ,32
+ ,0
+ ,35
+ ,31
+ ,12
+ ,48
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,31
+ ,8
+ ,12
+ ,48
+ ,0
+ ,107
+ ,35
+ ,8
+ ,31
+ ,3
+ ,242
+ ,3
+ ,20
+ ,64
+ ,4
+ ,41
+ ,47
+ ,18
+ ,72
+ ,0
+ ,57
+ ,42
+ ,9
+ ,36
+ ,1
+ ,32
+ ,11
+ ,14
+ ,56
+ ,0
+ ,17
+ ,10
+ ,7
+ ,28
+ ,2
+ ,36
+ ,26
+ ,13
+ ,52
+ ,1
+ ,29
+ ,27
+ ,11
+ ,44
+ ,1
+ ,22
+ ,0
+ ,11
+ ,44
+ ,2
+ ,21
+ ,15
+ ,14
+ ,55
+ ,1
+ ,41
+ ,32
+ ,9
+ ,36
+ ,0
+ ,64
+ ,13
+ ,12
+ ,48
+ ,1
+ ,71
+ ,24
+ ,11
+ ,44
+ ,0
+ ,28
+ ,10
+ ,17
+ ,66
+ ,0
+ ,36
+ ,14
+ ,10
+ ,40
+ ,0
+ ,45
+ ,24
+ ,11
+ ,44
+ ,0
+ ,22
+ ,29
+ ,12
+ ,48
+ ,0
+ ,27
+ ,40
+ ,17
+ ,68
+ ,1
+ ,38
+ ,22
+ ,6
+ ,24
+ ,3
+ ,26
+ ,27
+ ,8
+ ,32
+ ,0
+ ,41
+ ,8
+ ,12
+ ,44
+ ,0
+ ,21
+ ,27
+ ,13
+ ,52
+ ,0
+ ,28
+ ,0
+ ,14
+ ,56
+ ,4
+ ,36
+ ,0
+ ,17
+ ,68
+ ,0
+ ,58
+ ,17
+ ,8
+ ,32
+ ,0
+ ,65
+ ,7
+ ,9
+ ,34
+ ,4
+ ,29
+ ,18
+ ,9
+ ,36
+ ,3
+ ,21
+ ,7
+ ,9
+ ,34
+ ,0
+ ,19
+ ,24
+ ,15
+ ,56
+ ,0
+ ,55
+ ,18
+ ,16
+ ,64
+ ,4
+ ,119
+ ,39
+ ,13
+ ,52
+ ,2
+ ,34
+ ,17
+ ,12
+ ,48
+ ,0
+ ,25
+ ,0
+ ,10
+ ,40
+ ,0
+ ,113
+ ,39
+ ,9
+ ,36
+ ,2
+ ,46
+ ,20
+ ,3
+ ,10
+ ,0
+ ,28
+ ,29
+ ,12
+ ,48
+ ,1
+ ,63
+ ,27
+ ,8
+ ,25
+ ,0
+ ,52
+ ,23
+ ,17
+ ,68
+ ,0
+ ,35
+ ,0
+ ,9
+ ,36
+ ,1
+ ,32
+ ,31
+ ,8
+ ,32
+ ,0
+ ,45
+ ,19
+ ,9
+ ,36
+ ,0
+ ,42
+ ,12
+ ,12
+ ,43
+ ,0
+ ,28
+ ,23
+ ,5
+ ,17
+ ,0
+ ,32
+ ,33
+ ,14
+ ,52
+ ,0
+ ,32
+ ,21
+ ,14
+ ,56
+ ,0
+ ,27
+ ,17
+ ,10
+ ,40
+ ,0
+ ,69
+ ,27
+ ,12
+ ,48
+ ,0
+ ,30
+ ,14
+ ,10
+ ,40
+ ,0
+ ,48
+ ,12
+ ,12
+ ,48
+ ,0
+ ,57
+ ,21
+ ,17
+ ,68
+ ,0
+ ,36
+ ,14
+ ,11
+ ,44
+ ,0
+ ,20
+ ,14
+ ,10
+ ,40
+ ,2
+ ,54
+ ,22
+ ,11
+ ,40
+ ,0
+ ,26
+ ,25
+ ,7
+ ,28
+ ,0
+ ,58
+ ,36
+ ,10
+ ,40
+ ,1
+ ,35
+ ,10
+ ,11
+ ,44
+ ,0
+ ,28
+ ,16
+ ,5
+ ,20
+ ,0
+ ,8
+ ,12
+ ,6
+ ,22
+ ,0
+ ,96
+ ,20
+ ,14
+ ,56
+ ,0
+ ,50
+ ,38
+ ,13
+ ,52
+ ,0
+ ,15
+ ,13
+ ,1
+ ,2
+ ,0
+ ,65
+ ,12
+ ,13
+ ,52
+ ,0
+ ,33
+ ,11
+ ,9
+ ,30
+ ,0
+ ,7
+ ,8
+ ,1
+ ,3
+ ,0
+ ,17
+ ,22
+ ,6
+ ,20
+ ,0
+ ,55
+ ,14
+ ,12
+ ,48
+ ,0
+ ,32
+ ,7
+ ,9
+ ,32
+ ,1
+ ,22
+ ,14
+ ,9
+ ,36
+ ,0
+ ,41
+ ,2
+ ,12
+ ,45
+ ,0
+ ,50
+ ,35
+ ,10
+ ,40
+ ,0
+ ,7
+ ,5
+ ,2
+ ,8
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,26
+ ,34
+ ,8
+ ,32
+ ,0
+ ,22
+ ,12
+ ,7
+ ,28
+ ,0
+ ,26
+ ,34
+ ,11
+ ,44
+ ,0
+ ,37
+ ,30
+ ,14
+ ,56
+ ,0
+ ,29
+ ,21
+ ,4
+ ,13
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,42
+ ,28
+ ,13
+ ,52
+ ,0
+ ,51
+ ,16
+ ,17
+ ,51
+ ,0
+ ,77
+ ,12
+ ,13
+ ,52
+ ,1
+ ,32
+ ,14
+ ,12
+ ,48
+ ,0
+ ,63
+ ,7
+ ,1
+ ,3
+ ,0
+ ,50
+ ,41
+ ,12
+ ,48
+ ,1
+ ,18
+ ,21
+ ,6
+ ,24
+ ,0
+ ,37
+ ,28
+ ,11
+ ,37
+ ,0
+ ,23
+ ,1
+ ,8
+ ,32
+ ,3
+ ,19
+ ,10
+ ,2
+ ,8
+ ,1
+ ,39
+ ,31
+ ,12
+ ,44
+ ,2
+ ,38
+ ,7
+ ,12
+ ,48
+ ,0
+ ,55
+ ,26
+ ,14
+ ,56
+ ,0
+ ,22
+ ,1
+ ,2
+ ,8
+ ,0
+ ,7
+ ,0
+ ,0
+ ,0
+ ,0
+ ,21
+ ,12
+ ,9
+ ,25
+ ,0
+ ,5
+ ,0
+ ,1
+ ,4
+ ,0
+ ,21
+ ,17
+ ,3
+ ,12
+ ,0
+ ,1
+ ,5
+ ,0
+ ,0
+ ,0
+ ,22
+ ,4
+ ,2
+ ,6
+ ,1
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,31
+ ,6
+ ,12
+ ,48
+ ,0
+ ,25
+ ,0
+ ,14
+ ,52
+ ,0
+ ,0
+ ,0
+ ,0
+ ,0
+ ,1
+ ,4
+ ,0
+ ,0
+ ,0
+ ,0
+ ,20
+ ,15
+ ,4
+ ,12
+ ,1
+ ,29
+ ,0
+ ,7
+ ,28
+ ,0
+ ,33
+ ,12
+ ,10
+ ,40
+ ,0)
+ ,dim=c(5
+ ,144)
+ ,dimnames=list(c('CompendiumViews'
+ ,'BloggedComputations'
+ ,'ReviewedCompendiums'
+ ,'submittedfeedback'
+ ,'Sharedcompendiums')
+ ,1:144))
> y <- array(NA,dim=c(5,144),dimnames=list(c('CompendiumViews','BloggedComputations','ReviewedCompendiums','submittedfeedback','Sharedcompendiums'),1:144))
> 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
> 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
CompendiumViews BloggedComputations ReviewedCompendiums submittedfeedback
1 68 13 5 20
2 17 26 7 28
3 1 0 0 0
4 114 37 12 40
5 95 47 15 60
6 148 80 16 60
7 56 21 12 44
8 26 36 13 52
9 63 35 15 60
10 96 40 13 52
11 74 35 6 24
12 65 46 16 64
13 40 20 7 26
14 173 24 12 48
15 28 19 9 36
16 55 15 10 40
17 58 48 16 64
18 25 0 5 20
19 103 38 20 79
20 29 12 7 16
21 31 10 13 52
22 43 51 13 52
23 74 4 11 44
24 99 24 9 29
25 25 39 10 40
26 69 19 7 28
27 62 23 13 49
28 25 39 15 60
29 38 37 13 52
30 57 20 7 28
31 52 20 14 56
32 91 41 11 35
33 48 26 3 12
34 52 0 8 32
35 35 31 12 48
36 0 0 0 0
37 31 8 12 48
38 107 35 8 31
39 242 3 20 64
40 41 47 18 72
41 57 42 9 36
42 32 11 14 56
43 17 10 7 28
44 36 26 13 52
45 29 27 11 44
46 22 0 11 44
47 21 15 14 55
48 41 32 9 36
49 64 13 12 48
50 71 24 11 44
51 28 10 17 66
52 36 14 10 40
53 45 24 11 44
54 22 29 12 48
55 27 40 17 68
56 38 22 6 24
57 26 27 8 32
58 41 8 12 44
59 21 27 13 52
60 28 0 14 56
61 36 0 17 68
62 58 17 8 32
63 65 7 9 34
64 29 18 9 36
65 21 7 9 34
66 19 24 15 56
67 55 18 16 64
68 119 39 13 52
69 34 17 12 48
70 25 0 10 40
71 113 39 9 36
72 46 20 3 10
73 28 29 12 48
74 63 27 8 25
75 52 23 17 68
76 35 0 9 36
77 32 31 8 32
78 45 19 9 36
79 42 12 12 43
80 28 23 5 17
81 32 33 14 52
82 32 21 14 56
83 27 17 10 40
84 69 27 12 48
85 30 14 10 40
86 48 12 12 48
87 57 21 17 68
88 36 14 11 44
89 20 14 10 40
90 54 22 11 40
91 26 25 7 28
92 58 36 10 40
93 35 10 11 44
94 28 16 5 20
95 8 12 6 22
96 96 20 14 56
97 50 38 13 52
98 15 13 1 2
99 65 12 13 52
100 33 11 9 30
101 7 8 1 3
102 17 22 6 20
103 55 14 12 48
104 32 7 9 32
105 22 14 9 36
106 41 2 12 45
107 50 35 10 40
108 7 5 2 8
109 0 0 0 0
110 26 34 8 32
111 22 12 7 28
112 26 34 11 44
113 37 30 14 56
114 29 21 4 13
115 0 0 0 0
116 0 0 0 0
117 42 28 13 52
118 51 16 17 51
119 77 12 13 52
120 32 14 12 48
121 63 7 1 3
122 50 41 12 48
123 18 21 6 24
124 37 28 11 37
125 23 1 8 32
126 19 10 2 8
127 39 31 12 44
128 38 7 12 48
129 55 26 14 56
130 22 1 2 8
131 7 0 0 0
132 21 12 9 25
133 5 0 1 4
134 21 17 3 12
135 1 5 0 0
136 22 4 2 6
137 0 0 0 0
138 31 6 12 48
139 25 0 14 52
140 0 0 0 0
141 4 0 0 0
142 20 15 4 12
143 29 0 7 28
144 33 12 10 40
Sharedcompendiums
1 0
2 0
3 0
4 0
5 0
6 1
7 0
8 0
9 0
10 1
11 1
12 0
13 0
14 4
15 3
16 3
17 0
18 4
19 0
20 0
21 0
22 0
23 0
24 1
25 1
26 0
27 0
28 0
29 0
30 0
31 0
32 2
33 4
34 0
35 1
36 0
37 0
38 3
39 4
40 0
41 1
42 0
43 2
44 1
45 1
46 2
47 1
48 0
49 1
50 0
51 0
52 0
53 0
54 0
55 1
56 3
57 0
58 0
59 0
60 4
61 0
62 0
63 4
64 3
65 0
66 0
67 4
68 2
69 0
70 0
71 2
72 0
73 1
74 0
75 0
76 1
77 0
78 0
79 0
80 0
81 0
82 0
83 0
84 0
85 0
86 0
87 0
88 0
89 2
90 0
91 0
92 1
93 0
94 0
95 0
96 0
97 0
98 0
99 0
100 0
101 0
102 0
103 0
104 1
105 0
106 0
107 0
108 0
109 0
110 0
111 0
112 0
113 0
114 0
115 0
116 0
117 0
118 0
119 1
120 0
121 0
122 1
123 0
124 0
125 3
126 1
127 2
128 0
129 0
130 0
131 0
132 0
133 0
134 0
135 0
136 1
137 0
138 0
139 0
140 1
141 0
142 1
143 0
144 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) BloggedComputations ReviewedCompendiums
1.1404 0.6575 13.7596
submittedfeedback Sharedcompendiums
-2.9244 9.7061
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-45.428 -15.928 -1.508 7.235 112.033
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1404 4.9526 0.230 0.818218
BloggedComputations 0.6575 0.1680 3.914 0.000142 ***
ReviewedCompendiums 13.7596 2.9358 4.687 6.54e-06 ***
submittedfeedback -2.9244 0.7450 -3.925 0.000136 ***
Sharedcompendiums 9.7061 1.9425 4.997 1.72e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 25.21 on 139 degrees of freedom
Multiple R-squared: 0.4429, Adjusted R-squared: 0.4268
F-statistic: 27.62 on 4 and 139 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,] 0.9106352 1.787296e-01 8.936479e-02
[2,] 0.8409102 3.181795e-01 1.590898e-01
[3,] 0.7650387 4.699225e-01 2.349613e-01
[4,] 0.6691248 6.617504e-01 3.308752e-01
[5,] 0.5606150 8.787699e-01 4.393850e-01
[6,] 0.4600757 9.201514e-01 5.399243e-01
[7,] 0.4563327 9.126655e-01 5.436673e-01
[8,] 0.9716409 5.671814e-02 2.835907e-02
[9,] 0.9729443 5.411144e-02 2.705572e-02
[10,] 0.9618801 7.623988e-02 3.811994e-02
[11,] 0.9764849 4.703014e-02 2.351507e-02
[12,] 0.9765459 4.690816e-02 2.345408e-02
[13,] 0.9827950 3.441003e-02 1.720502e-02
[14,] 0.9736937 5.261255e-02 2.630627e-02
[15,] 0.9785363 4.292740e-02 2.146370e-02
[16,] 0.9906605 1.867902e-02 9.339508e-03
[17,] 0.9908358 1.832843e-02 9.164215e-03
[18,] 0.9940181 1.196382e-02 5.981910e-03
[19,] 0.9963016 7.396713e-03 3.698356e-03
[20,] 0.9943988 1.120245e-02 5.601225e-03
[21,] 0.9966316 6.736823e-03 3.368412e-03
[22,] 0.9957535 8.492913e-03 4.246456e-03
[23,] 0.9956648 8.670468e-03 4.335234e-03
[24,] 0.9935601 1.287981e-02 6.439906e-03
[25,] 0.9915823 1.683542e-02 8.417709e-03
[26,] 0.9895435 2.091291e-02 1.045645e-02
[27,] 0.9899057 2.018851e-02 1.009426e-02
[28,] 0.9896669 2.066616e-02 1.033308e-02
[29,] 0.9852262 2.954763e-02 1.477382e-02
[30,] 0.9800923 3.981548e-02 1.990774e-02
[31,] 0.9840033 3.199340e-02 1.599670e-02
[32,] 0.9999954 9.271641e-06 4.635820e-06
[33,] 0.9999973 5.343558e-06 2.671779e-06
[34,] 0.9999952 9.668664e-06 4.834332e-06
[35,] 0.9999930 1.398752e-05 6.993762e-06
[36,] 0.9999940 1.207771e-05 6.038856e-06
[37,] 0.9999933 1.335409e-05 6.677045e-06
[38,] 0.9999931 1.387126e-05 6.935629e-06
[39,] 0.9999937 1.264776e-05 6.323879e-06
[40,] 0.9999966 6.772305e-06 3.386153e-06
[41,] 0.9999940 1.208712e-05 6.043559e-06
[42,] 0.9999931 1.385853e-05 6.929263e-06
[43,] 0.9999955 9.009419e-06 4.504709e-06
[44,] 0.9999958 8.315497e-06 4.157749e-06
[45,] 0.9999927 1.459112e-05 7.295558e-06
[46,] 0.9999875 2.500769e-05 1.250385e-05
[47,] 0.9999881 2.384938e-05 1.192469e-05
[48,] 0.9999975 5.019577e-06 2.509789e-06
[49,] 0.9999964 7.153136e-06 3.576568e-06
[50,] 0.9999945 1.099279e-05 5.496397e-06
[51,] 0.9999915 1.698163e-05 8.490814e-06
[52,] 0.9999934 1.329354e-05 6.646771e-06
[53,] 0.9999965 6.915366e-06 3.457683e-06
[54,] 0.9999940 1.209575e-05 6.047873e-06
[55,] 0.9999954 9.196001e-06 4.598000e-06
[56,] 0.9999940 1.193260e-05 5.966302e-06
[57,] 0.9999945 1.107867e-05 5.539337e-06
[58,] 0.9999913 1.749351e-05 8.746754e-06
[59,] 0.9999973 5.383564e-06 2.691782e-06
[60,] 0.9999971 5.726324e-06 2.863162e-06
[61,] 0.9999998 3.408313e-07 1.704157e-07
[62,] 0.9999997 6.157161e-07 3.078581e-07
[63,] 0.9999994 1.136436e-06 5.682181e-07
[64,] 1.0000000 3.621266e-09 1.810633e-09
[65,] 1.0000000 2.503432e-09 1.251716e-09
[66,] 1.0000000 2.544876e-09 1.272438e-09
[67,] 1.0000000 9.044829e-10 4.522414e-10
[68,] 1.0000000 1.855946e-09 9.279729e-10
[69,] 1.0000000 3.909139e-09 1.954569e-09
[70,] 1.0000000 8.222106e-09 4.111053e-09
[71,] 1.0000000 1.425234e-08 7.126170e-09
[72,] 1.0000000 2.703713e-08 1.351857e-08
[73,] 1.0000000 5.046432e-08 2.523216e-08
[74,] 1.0000000 3.622697e-08 1.811348e-08
[75,] 1.0000000 4.092322e-08 2.046161e-08
[76,] 1.0000000 6.749504e-08 3.374752e-08
[77,] 1.0000000 4.759758e-08 2.379879e-08
[78,] 1.0000000 9.067553e-08 4.533776e-08
[79,] 0.9999999 1.675896e-07 8.379479e-08
[80,] 0.9999998 3.334603e-07 1.667302e-07
[81,] 0.9999997 6.518631e-07 3.259315e-07
[82,] 0.9999997 5.798399e-07 2.899200e-07
[83,] 0.9999996 8.263401e-07 4.131700e-07
[84,] 0.9999992 1.582188e-06 7.910940e-07
[85,] 0.9999989 2.182216e-06 1.091108e-06
[86,] 0.9999980 4.083548e-06 2.041774e-06
[87,] 0.9999962 7.682807e-06 3.841404e-06
[88,] 0.9999957 8.627954e-06 4.313977e-06
[89,] 0.9999999 1.326390e-07 6.631951e-08
[90,] 0.9999999 2.920002e-07 1.460001e-07
[91,] 0.9999997 6.099683e-07 3.049841e-07
[92,] 0.9999998 3.462657e-07 1.731329e-07
[93,] 0.9999997 6.655881e-07 3.327940e-07
[94,] 0.9999993 1.416028e-06 7.080138e-07
[95,] 0.9999989 2.244929e-06 1.122465e-06
[96,] 0.9999988 2.456329e-06 1.228164e-06
[97,] 0.9999975 4.935682e-06 2.467841e-06
[98,] 0.9999956 8.729842e-06 4.364921e-06
[99,] 0.9999916 1.679952e-05 8.399758e-06
[100,] 0.9999868 2.630861e-05 1.315430e-05
[101,] 0.9999748 5.038966e-05 2.519483e-05
[102,] 0.9999548 9.034942e-05 4.517471e-05
[103,] 0.9999208 1.583179e-04 7.915893e-05
[104,] 0.9998499 3.001152e-04 1.500576e-04
[105,] 0.9998271 3.457131e-04 1.728565e-04
[106,] 0.9997550 4.899694e-04 2.449847e-04
[107,] 0.9995521 8.957018e-04 4.478509e-04
[108,] 0.9992428 1.514456e-03 7.572278e-04
[109,] 0.9987638 2.472416e-03 1.236208e-03
[110,] 0.9978605 4.279089e-03 2.139544e-03
[111,] 0.9979262 4.147524e-03 2.073762e-03
[112,] 0.9997170 5.660818e-04 2.830409e-04
[113,] 0.9994416 1.116824e-03 5.584118e-04
[114,] 1.0000000 1.722364e-08 8.611820e-09
[115,] 1.0000000 7.139544e-08 3.569772e-08
[116,] 1.0000000 6.535110e-08 3.267555e-08
[117,] 0.9999999 2.643546e-07 1.321773e-07
[118,] 0.9999995 1.066473e-06 5.332363e-07
[119,] 0.9999980 3.911128e-06 1.955564e-06
[120,] 0.9999962 7.564138e-06 3.782069e-06
[121,] 0.9999854 2.919801e-05 1.459900e-05
[122,] 0.9999509 9.819867e-05 4.909933e-05
[123,] 0.9999644 7.115451e-05 3.557725e-05
[124,] 0.9998707 2.586575e-04 1.293287e-04
[125,] 0.9995253 9.494642e-04 4.747321e-04
[126,] 0.9980860 3.827950e-03 1.913975e-03
[127,] 0.9929560 1.408792e-02 7.043958e-03
[128,] 0.9786775 4.264500e-02 2.132250e-02
[129,] 0.9721315 5.573705e-02 2.786853e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1mnvb1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2crhy1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3gxyz1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4xq841322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5jda31322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 144
Frequency = 1
1 2 3 4 5 6
48.0026941 -15.6683131 -0.1404419 40.3944757 32.0292105 39.8669929
7 8 9 10 11 12
4.6116297 -25.6147211 7.9188438 32.0492702 27.7702833 0.6247277
13 14 15 16 17 18
5.4276820 92.5123408 -33.3083243 -5.7403988 -7.6902112 -25.2747268
19 20 21 22 23 24
32.7122634 -29.5566700 -3.5205157 -18.4767627 47.5482055 33.3457151
25 26 27 28 29 30
-32.1074035 40.9339729 10.1591493 -32.7110340 -14.2721906 28.2765035
31 32 33 34 35 36
8.8428376 -5.5101223 -15.2450277 34.3639400 -20.9715526 -1.1404419
37 38 39 40 41 42
-0.1436246 34.3097062 112.0330089 -28.1566463 -0.0178596 -5.2399374
43 44 45 46 47 48
-24.5610639 -18.7461576 -22.2797225 -21.2341786 -31.5003568 0.2629657
49 50 51 52 53 54
19.8628973 31.3988167 -20.6171463 5.0354633 5.3988167 -22.9504828
55 56 57 58 59 60
-45.1985388 -19.0948758 -9.3877349 -1.8412676 -24.6974962 -40.8322972
61 62 63 64 65 66
-0.1936304 29.1869595 -3.9736434 -31.6508549 -9.1491198 -40.5466354
67 68 69 70 71 72
-29.7906516 46.0006088 -3.0608495 3.2400355 48.2484178 19.6754910
73 74 75 76 77 78
-26.6566137 7.1413899 0.6845725 5.5958568 -6.0176126 12.8100684
79 80 81 82 83 84
-6.3955561 -7.3452326 -31.4019081 -11.8146318 -5.9369450 25.3644561
85 86 87 88 89 90
-0.9645367 14.2264977 6.9995114 2.9735111 -30.3767985 4.0161125
91 92 93 94 95 96
-6.0108437 2.8650048 4.6033888 6.0302858 -19.2506102 52.8428376
97 98 99 100 101 102
-2.9296600 -2.5983184 29.1645454 -11.4766406 -4.3865604 -22.6741261
103 104 105 106 107 108
19.9115588 -13.7040722 -6.9025844 5.0279598 5.2286051 -1.5516936
109 110 111 112 113 114
-1.1404419 -13.9900209 -1.4637410 -20.1758777 -12.7318568 -2.9683414
115 116 117 118 119 120
-1.1404419 -1.1404419 -4.3549656 -45.4281242 31.4584145 -3.0884412
121 122 123 124 125 126
52.2709090 -12.5462470 -9.3190137 -25.7019363 -24.4119221 -2.5451717
127 128 129 130 131 132
-38.3753265 7.5138449 7.8980210 16.0781842 5.8595581 -38.7561638
133 134 135 136 137 138
1.7976059 2.4967209 -3.4277891 -1.4491766 -1.1404419 1.1713143
139 140 141 142 143 144
-16.7054166 -10.8465728 2.8595581 -20.6540664 13.4258923 3.3504022
> postscript(file="/var/wessaorg/rcomp/tmp/6wxwe1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 48.0026941 NA
1 -15.6683131 48.0026941
2 -0.1404419 -15.6683131
3 40.3944757 -0.1404419
4 32.0292105 40.3944757
5 39.8669929 32.0292105
6 4.6116297 39.8669929
7 -25.6147211 4.6116297
8 7.9188438 -25.6147211
9 32.0492702 7.9188438
10 27.7702833 32.0492702
11 0.6247277 27.7702833
12 5.4276820 0.6247277
13 92.5123408 5.4276820
14 -33.3083243 92.5123408
15 -5.7403988 -33.3083243
16 -7.6902112 -5.7403988
17 -25.2747268 -7.6902112
18 32.7122634 -25.2747268
19 -29.5566700 32.7122634
20 -3.5205157 -29.5566700
21 -18.4767627 -3.5205157
22 47.5482055 -18.4767627
23 33.3457151 47.5482055
24 -32.1074035 33.3457151
25 40.9339729 -32.1074035
26 10.1591493 40.9339729
27 -32.7110340 10.1591493
28 -14.2721906 -32.7110340
29 28.2765035 -14.2721906
30 8.8428376 28.2765035
31 -5.5101223 8.8428376
32 -15.2450277 -5.5101223
33 34.3639400 -15.2450277
34 -20.9715526 34.3639400
35 -1.1404419 -20.9715526
36 -0.1436246 -1.1404419
37 34.3097062 -0.1436246
38 112.0330089 34.3097062
39 -28.1566463 112.0330089
40 -0.0178596 -28.1566463
41 -5.2399374 -0.0178596
42 -24.5610639 -5.2399374
43 -18.7461576 -24.5610639
44 -22.2797225 -18.7461576
45 -21.2341786 -22.2797225
46 -31.5003568 -21.2341786
47 0.2629657 -31.5003568
48 19.8628973 0.2629657
49 31.3988167 19.8628973
50 -20.6171463 31.3988167
51 5.0354633 -20.6171463
52 5.3988167 5.0354633
53 -22.9504828 5.3988167
54 -45.1985388 -22.9504828
55 -19.0948758 -45.1985388
56 -9.3877349 -19.0948758
57 -1.8412676 -9.3877349
58 -24.6974962 -1.8412676
59 -40.8322972 -24.6974962
60 -0.1936304 -40.8322972
61 29.1869595 -0.1936304
62 -3.9736434 29.1869595
63 -31.6508549 -3.9736434
64 -9.1491198 -31.6508549
65 -40.5466354 -9.1491198
66 -29.7906516 -40.5466354
67 46.0006088 -29.7906516
68 -3.0608495 46.0006088
69 3.2400355 -3.0608495
70 48.2484178 3.2400355
71 19.6754910 48.2484178
72 -26.6566137 19.6754910
73 7.1413899 -26.6566137
74 0.6845725 7.1413899
75 5.5958568 0.6845725
76 -6.0176126 5.5958568
77 12.8100684 -6.0176126
78 -6.3955561 12.8100684
79 -7.3452326 -6.3955561
80 -31.4019081 -7.3452326
81 -11.8146318 -31.4019081
82 -5.9369450 -11.8146318
83 25.3644561 -5.9369450
84 -0.9645367 25.3644561
85 14.2264977 -0.9645367
86 6.9995114 14.2264977
87 2.9735111 6.9995114
88 -30.3767985 2.9735111
89 4.0161125 -30.3767985
90 -6.0108437 4.0161125
91 2.8650048 -6.0108437
92 4.6033888 2.8650048
93 6.0302858 4.6033888
94 -19.2506102 6.0302858
95 52.8428376 -19.2506102
96 -2.9296600 52.8428376
97 -2.5983184 -2.9296600
98 29.1645454 -2.5983184
99 -11.4766406 29.1645454
100 -4.3865604 -11.4766406
101 -22.6741261 -4.3865604
102 19.9115588 -22.6741261
103 -13.7040722 19.9115588
104 -6.9025844 -13.7040722
105 5.0279598 -6.9025844
106 5.2286051 5.0279598
107 -1.5516936 5.2286051
108 -1.1404419 -1.5516936
109 -13.9900209 -1.1404419
110 -1.4637410 -13.9900209
111 -20.1758777 -1.4637410
112 -12.7318568 -20.1758777
113 -2.9683414 -12.7318568
114 -1.1404419 -2.9683414
115 -1.1404419 -1.1404419
116 -4.3549656 -1.1404419
117 -45.4281242 -4.3549656
118 31.4584145 -45.4281242
119 -3.0884412 31.4584145
120 52.2709090 -3.0884412
121 -12.5462470 52.2709090
122 -9.3190137 -12.5462470
123 -25.7019363 -9.3190137
124 -24.4119221 -25.7019363
125 -2.5451717 -24.4119221
126 -38.3753265 -2.5451717
127 7.5138449 -38.3753265
128 7.8980210 7.5138449
129 16.0781842 7.8980210
130 5.8595581 16.0781842
131 -38.7561638 5.8595581
132 1.7976059 -38.7561638
133 2.4967209 1.7976059
134 -3.4277891 2.4967209
135 -1.4491766 -3.4277891
136 -1.1404419 -1.4491766
137 1.1713143 -1.1404419
138 -16.7054166 1.1713143
139 -10.8465728 -16.7054166
140 2.8595581 -10.8465728
141 -20.6540664 2.8595581
142 13.4258923 -20.6540664
143 3.3504022 13.4258923
144 NA 3.3504022
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -15.6683131 48.0026941
[2,] -0.1404419 -15.6683131
[3,] 40.3944757 -0.1404419
[4,] 32.0292105 40.3944757
[5,] 39.8669929 32.0292105
[6,] 4.6116297 39.8669929
[7,] -25.6147211 4.6116297
[8,] 7.9188438 -25.6147211
[9,] 32.0492702 7.9188438
[10,] 27.7702833 32.0492702
[11,] 0.6247277 27.7702833
[12,] 5.4276820 0.6247277
[13,] 92.5123408 5.4276820
[14,] -33.3083243 92.5123408
[15,] -5.7403988 -33.3083243
[16,] -7.6902112 -5.7403988
[17,] -25.2747268 -7.6902112
[18,] 32.7122634 -25.2747268
[19,] -29.5566700 32.7122634
[20,] -3.5205157 -29.5566700
[21,] -18.4767627 -3.5205157
[22,] 47.5482055 -18.4767627
[23,] 33.3457151 47.5482055
[24,] -32.1074035 33.3457151
[25,] 40.9339729 -32.1074035
[26,] 10.1591493 40.9339729
[27,] -32.7110340 10.1591493
[28,] -14.2721906 -32.7110340
[29,] 28.2765035 -14.2721906
[30,] 8.8428376 28.2765035
[31,] -5.5101223 8.8428376
[32,] -15.2450277 -5.5101223
[33,] 34.3639400 -15.2450277
[34,] -20.9715526 34.3639400
[35,] -1.1404419 -20.9715526
[36,] -0.1436246 -1.1404419
[37,] 34.3097062 -0.1436246
[38,] 112.0330089 34.3097062
[39,] -28.1566463 112.0330089
[40,] -0.0178596 -28.1566463
[41,] -5.2399374 -0.0178596
[42,] -24.5610639 -5.2399374
[43,] -18.7461576 -24.5610639
[44,] -22.2797225 -18.7461576
[45,] -21.2341786 -22.2797225
[46,] -31.5003568 -21.2341786
[47,] 0.2629657 -31.5003568
[48,] 19.8628973 0.2629657
[49,] 31.3988167 19.8628973
[50,] -20.6171463 31.3988167
[51,] 5.0354633 -20.6171463
[52,] 5.3988167 5.0354633
[53,] -22.9504828 5.3988167
[54,] -45.1985388 -22.9504828
[55,] -19.0948758 -45.1985388
[56,] -9.3877349 -19.0948758
[57,] -1.8412676 -9.3877349
[58,] -24.6974962 -1.8412676
[59,] -40.8322972 -24.6974962
[60,] -0.1936304 -40.8322972
[61,] 29.1869595 -0.1936304
[62,] -3.9736434 29.1869595
[63,] -31.6508549 -3.9736434
[64,] -9.1491198 -31.6508549
[65,] -40.5466354 -9.1491198
[66,] -29.7906516 -40.5466354
[67,] 46.0006088 -29.7906516
[68,] -3.0608495 46.0006088
[69,] 3.2400355 -3.0608495
[70,] 48.2484178 3.2400355
[71,] 19.6754910 48.2484178
[72,] -26.6566137 19.6754910
[73,] 7.1413899 -26.6566137
[74,] 0.6845725 7.1413899
[75,] 5.5958568 0.6845725
[76,] -6.0176126 5.5958568
[77,] 12.8100684 -6.0176126
[78,] -6.3955561 12.8100684
[79,] -7.3452326 -6.3955561
[80,] -31.4019081 -7.3452326
[81,] -11.8146318 -31.4019081
[82,] -5.9369450 -11.8146318
[83,] 25.3644561 -5.9369450
[84,] -0.9645367 25.3644561
[85,] 14.2264977 -0.9645367
[86,] 6.9995114 14.2264977
[87,] 2.9735111 6.9995114
[88,] -30.3767985 2.9735111
[89,] 4.0161125 -30.3767985
[90,] -6.0108437 4.0161125
[91,] 2.8650048 -6.0108437
[92,] 4.6033888 2.8650048
[93,] 6.0302858 4.6033888
[94,] -19.2506102 6.0302858
[95,] 52.8428376 -19.2506102
[96,] -2.9296600 52.8428376
[97,] -2.5983184 -2.9296600
[98,] 29.1645454 -2.5983184
[99,] -11.4766406 29.1645454
[100,] -4.3865604 -11.4766406
[101,] -22.6741261 -4.3865604
[102,] 19.9115588 -22.6741261
[103,] -13.7040722 19.9115588
[104,] -6.9025844 -13.7040722
[105,] 5.0279598 -6.9025844
[106,] 5.2286051 5.0279598
[107,] -1.5516936 5.2286051
[108,] -1.1404419 -1.5516936
[109,] -13.9900209 -1.1404419
[110,] -1.4637410 -13.9900209
[111,] -20.1758777 -1.4637410
[112,] -12.7318568 -20.1758777
[113,] -2.9683414 -12.7318568
[114,] -1.1404419 -2.9683414
[115,] -1.1404419 -1.1404419
[116,] -4.3549656 -1.1404419
[117,] -45.4281242 -4.3549656
[118,] 31.4584145 -45.4281242
[119,] -3.0884412 31.4584145
[120,] 52.2709090 -3.0884412
[121,] -12.5462470 52.2709090
[122,] -9.3190137 -12.5462470
[123,] -25.7019363 -9.3190137
[124,] -24.4119221 -25.7019363
[125,] -2.5451717 -24.4119221
[126,] -38.3753265 -2.5451717
[127,] 7.5138449 -38.3753265
[128,] 7.8980210 7.5138449
[129,] 16.0781842 7.8980210
[130,] 5.8595581 16.0781842
[131,] -38.7561638 5.8595581
[132,] 1.7976059 -38.7561638
[133,] 2.4967209 1.7976059
[134,] -3.4277891 2.4967209
[135,] -1.4491766 -3.4277891
[136,] -1.1404419 -1.4491766
[137,] 1.1713143 -1.1404419
[138,] -16.7054166 1.1713143
[139,] -10.8465728 -16.7054166
[140,] 2.8595581 -10.8465728
[141,] -20.6540664 2.8595581
[142,] 13.4258923 -20.6540664
[143,] 3.3504022 13.4258923
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -15.6683131 48.0026941
2 -0.1404419 -15.6683131
3 40.3944757 -0.1404419
4 32.0292105 40.3944757
5 39.8669929 32.0292105
6 4.6116297 39.8669929
7 -25.6147211 4.6116297
8 7.9188438 -25.6147211
9 32.0492702 7.9188438
10 27.7702833 32.0492702
11 0.6247277 27.7702833
12 5.4276820 0.6247277
13 92.5123408 5.4276820
14 -33.3083243 92.5123408
15 -5.7403988 -33.3083243
16 -7.6902112 -5.7403988
17 -25.2747268 -7.6902112
18 32.7122634 -25.2747268
19 -29.5566700 32.7122634
20 -3.5205157 -29.5566700
21 -18.4767627 -3.5205157
22 47.5482055 -18.4767627
23 33.3457151 47.5482055
24 -32.1074035 33.3457151
25 40.9339729 -32.1074035
26 10.1591493 40.9339729
27 -32.7110340 10.1591493
28 -14.2721906 -32.7110340
29 28.2765035 -14.2721906
30 8.8428376 28.2765035
31 -5.5101223 8.8428376
32 -15.2450277 -5.5101223
33 34.3639400 -15.2450277
34 -20.9715526 34.3639400
35 -1.1404419 -20.9715526
36 -0.1436246 -1.1404419
37 34.3097062 -0.1436246
38 112.0330089 34.3097062
39 -28.1566463 112.0330089
40 -0.0178596 -28.1566463
41 -5.2399374 -0.0178596
42 -24.5610639 -5.2399374
43 -18.7461576 -24.5610639
44 -22.2797225 -18.7461576
45 -21.2341786 -22.2797225
46 -31.5003568 -21.2341786
47 0.2629657 -31.5003568
48 19.8628973 0.2629657
49 31.3988167 19.8628973
50 -20.6171463 31.3988167
51 5.0354633 -20.6171463
52 5.3988167 5.0354633
53 -22.9504828 5.3988167
54 -45.1985388 -22.9504828
55 -19.0948758 -45.1985388
56 -9.3877349 -19.0948758
57 -1.8412676 -9.3877349
58 -24.6974962 -1.8412676
59 -40.8322972 -24.6974962
60 -0.1936304 -40.8322972
61 29.1869595 -0.1936304
62 -3.9736434 29.1869595
63 -31.6508549 -3.9736434
64 -9.1491198 -31.6508549
65 -40.5466354 -9.1491198
66 -29.7906516 -40.5466354
67 46.0006088 -29.7906516
68 -3.0608495 46.0006088
69 3.2400355 -3.0608495
70 48.2484178 3.2400355
71 19.6754910 48.2484178
72 -26.6566137 19.6754910
73 7.1413899 -26.6566137
74 0.6845725 7.1413899
75 5.5958568 0.6845725
76 -6.0176126 5.5958568
77 12.8100684 -6.0176126
78 -6.3955561 12.8100684
79 -7.3452326 -6.3955561
80 -31.4019081 -7.3452326
81 -11.8146318 -31.4019081
82 -5.9369450 -11.8146318
83 25.3644561 -5.9369450
84 -0.9645367 25.3644561
85 14.2264977 -0.9645367
86 6.9995114 14.2264977
87 2.9735111 6.9995114
88 -30.3767985 2.9735111
89 4.0161125 -30.3767985
90 -6.0108437 4.0161125
91 2.8650048 -6.0108437
92 4.6033888 2.8650048
93 6.0302858 4.6033888
94 -19.2506102 6.0302858
95 52.8428376 -19.2506102
96 -2.9296600 52.8428376
97 -2.5983184 -2.9296600
98 29.1645454 -2.5983184
99 -11.4766406 29.1645454
100 -4.3865604 -11.4766406
101 -22.6741261 -4.3865604
102 19.9115588 -22.6741261
103 -13.7040722 19.9115588
104 -6.9025844 -13.7040722
105 5.0279598 -6.9025844
106 5.2286051 5.0279598
107 -1.5516936 5.2286051
108 -1.1404419 -1.5516936
109 -13.9900209 -1.1404419
110 -1.4637410 -13.9900209
111 -20.1758777 -1.4637410
112 -12.7318568 -20.1758777
113 -2.9683414 -12.7318568
114 -1.1404419 -2.9683414
115 -1.1404419 -1.1404419
116 -4.3549656 -1.1404419
117 -45.4281242 -4.3549656
118 31.4584145 -45.4281242
119 -3.0884412 31.4584145
120 52.2709090 -3.0884412
121 -12.5462470 52.2709090
122 -9.3190137 -12.5462470
123 -25.7019363 -9.3190137
124 -24.4119221 -25.7019363
125 -2.5451717 -24.4119221
126 -38.3753265 -2.5451717
127 7.5138449 -38.3753265
128 7.8980210 7.5138449
129 16.0781842 7.8980210
130 5.8595581 16.0781842
131 -38.7561638 5.8595581
132 1.7976059 -38.7561638
133 2.4967209 1.7976059
134 -3.4277891 2.4967209
135 -1.4491766 -3.4277891
136 -1.1404419 -1.4491766
137 1.1713143 -1.1404419
138 -16.7054166 1.1713143
139 -10.8465728 -16.7054166
140 2.8595581 -10.8465728
141 -20.6540664 2.8595581
142 13.4258923 -20.6540664
143 3.3504022 13.4258923
> 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/wessaorg/rcomp/tmp/76bur1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8we9j1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/925ve1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wdcd1322146673.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11abd71322146673.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/wessaorg/rcomp/tmp/121lcl1322146673.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/wessaorg/rcomp/tmp/13ymov1322146673.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/wessaorg/rcomp/tmp/14jkaq1322146673.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/wessaorg/rcomp/tmp/15w5it1322146673.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/wessaorg/rcomp/tmp/16krdm1322146673.tab")
+ }
>
> try(system("convert tmp/1mnvb1322146673.ps tmp/1mnvb1322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/2crhy1322146673.ps tmp/2crhy1322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gxyz1322146673.ps tmp/3gxyz1322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xq841322146673.ps tmp/4xq841322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jda31322146673.ps tmp/5jda31322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wxwe1322146673.ps tmp/6wxwe1322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/76bur1322146673.ps tmp/76bur1322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/8we9j1322146673.ps tmp/8we9j1322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/925ve1322146673.ps tmp/925ve1322146673.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wdcd1322146673.ps tmp/10wdcd1322146673.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.710 0.511 5.367