R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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.
Natural language support but running in an English locale
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(13
+ ,13
+ ,14
+ ,13
+ ,3
+ ,1
+ ,12
+ ,12
+ ,8
+ ,13
+ ,5
+ ,1
+ ,15
+ ,10
+ ,12
+ ,16
+ ,6
+ ,1
+ ,12
+ ,9
+ ,7
+ ,12
+ ,6
+ ,1
+ ,10
+ ,10
+ ,10
+ ,11
+ ,5
+ ,1
+ ,12
+ ,12
+ ,7
+ ,12
+ ,3
+ ,1
+ ,15
+ ,13
+ ,16
+ ,18
+ ,8
+ ,1
+ ,9
+ ,12
+ ,11
+ ,11
+ ,4
+ ,1
+ ,12
+ ,12
+ ,14
+ ,14
+ ,4
+ ,1
+ ,11
+ ,6
+ ,6
+ ,9
+ ,4
+ ,1
+ ,11
+ ,5
+ ,16
+ ,14
+ ,6
+ ,1
+ ,11
+ ,12
+ ,11
+ ,12
+ ,6
+ ,1
+ ,15
+ ,11
+ ,16
+ ,11
+ ,5
+ ,1
+ ,7
+ ,14
+ ,12
+ ,12
+ ,4
+ ,1
+ ,11
+ ,14
+ ,7
+ ,13
+ ,6
+ ,1
+ ,11
+ ,12
+ ,13
+ ,11
+ ,4
+ ,1
+ ,10
+ ,12
+ ,11
+ ,12
+ ,6
+ ,1
+ ,14
+ ,11
+ ,15
+ ,16
+ ,6
+ ,1
+ ,10
+ ,11
+ ,7
+ ,9
+ ,4
+ ,2
+ ,6
+ ,7
+ ,9
+ ,11
+ ,4
+ ,2
+ ,11
+ ,9
+ ,7
+ ,13
+ ,2
+ ,2
+ ,15
+ ,11
+ ,14
+ ,15
+ ,7
+ ,2
+ ,11
+ ,11
+ ,15
+ ,10
+ ,5
+ ,2
+ ,12
+ ,12
+ ,7
+ ,11
+ ,4
+ ,2
+ ,14
+ ,12
+ ,15
+ ,13
+ ,6
+ ,2
+ ,15
+ ,11
+ ,17
+ ,16
+ ,6
+ ,2
+ ,9
+ ,11
+ ,15
+ ,15
+ ,7
+ ,2
+ ,13
+ ,8
+ ,14
+ ,14
+ ,5
+ ,2
+ ,13
+ ,9
+ ,14
+ ,14
+ ,6
+ ,2
+ ,16
+ ,12
+ ,8
+ ,14
+ ,4
+ ,2
+ ,13
+ ,10
+ ,8
+ ,8
+ ,4
+ ,2
+ ,12
+ ,10
+ ,14
+ ,13
+ ,7
+ ,2
+ ,14
+ ,12
+ ,14
+ ,15
+ ,7
+ ,2
+ ,11
+ ,8
+ ,8
+ ,13
+ ,4
+ ,3
+ ,9
+ ,12
+ ,11
+ ,11
+ ,4
+ ,3
+ ,16
+ ,11
+ ,16
+ ,15
+ ,6
+ ,3
+ ,12
+ ,12
+ ,10
+ ,15
+ ,6
+ ,3
+ ,10
+ ,7
+ ,8
+ ,9
+ ,5
+ ,3
+ ,13
+ ,11
+ ,14
+ ,13
+ ,6
+ ,3
+ ,16
+ ,11
+ ,16
+ ,16
+ ,7
+ ,3
+ ,14
+ ,12
+ ,13
+ ,13
+ ,6
+ ,3
+ ,15
+ ,9
+ ,5
+ ,11
+ ,3
+ ,3
+ ,5
+ ,15
+ ,8
+ ,12
+ ,3
+ ,3
+ ,8
+ ,11
+ ,10
+ ,12
+ ,4
+ ,3
+ ,11
+ ,11
+ ,8
+ ,12
+ ,6
+ ,3
+ ,16
+ ,11
+ ,13
+ ,14
+ ,7
+ ,3
+ ,17
+ ,11
+ ,15
+ ,14
+ ,5
+ ,3
+ ,9
+ ,15
+ ,6
+ ,8
+ ,4
+ ,3
+ ,9
+ ,11
+ ,12
+ ,13
+ ,5
+ ,3
+ ,13
+ ,12
+ ,16
+ ,16
+ ,6
+ ,3
+ ,10
+ ,12
+ ,5
+ ,13
+ ,6
+ ,3
+ ,6
+ ,9
+ ,15
+ ,11
+ ,6
+ ,4
+ ,12
+ ,12
+ ,12
+ ,14
+ ,5
+ ,4
+ ,8
+ ,12
+ ,8
+ ,13
+ ,4
+ ,4
+ ,14
+ ,13
+ ,13
+ ,13
+ ,5
+ ,4
+ ,12
+ ,11
+ ,14
+ ,13
+ ,5
+ ,4
+ ,11
+ ,9
+ ,12
+ ,12
+ ,4
+ ,4
+ ,16
+ ,9
+ ,16
+ ,16
+ ,6
+ ,4
+ ,8
+ ,11
+ ,10
+ ,15
+ ,2
+ ,4
+ ,15
+ ,11
+ ,15
+ ,15
+ ,8
+ ,4
+ ,7
+ ,12
+ ,8
+ ,12
+ ,3
+ ,4
+ ,16
+ ,12
+ ,16
+ ,14
+ ,6
+ ,4
+ ,14
+ ,9
+ ,19
+ ,12
+ ,6
+ ,4
+ ,16
+ ,11
+ ,14
+ ,15
+ ,6
+ ,4
+ ,9
+ ,9
+ ,6
+ ,12
+ ,5
+ ,4
+ ,14
+ ,12
+ ,13
+ ,13
+ ,5
+ ,4
+ ,11
+ ,12
+ ,15
+ ,12
+ ,6
+ ,4
+ ,13
+ ,12
+ ,7
+ ,12
+ ,5
+ ,4
+ ,15
+ ,12
+ ,13
+ ,13
+ ,6
+ ,5
+ ,5
+ ,14
+ ,4
+ ,5
+ ,2
+ ,5
+ ,15
+ ,11
+ ,14
+ ,13
+ ,5
+ ,5
+ ,13
+ ,12
+ ,13
+ ,13
+ ,5
+ ,5
+ ,11
+ ,11
+ ,11
+ ,14
+ ,5
+ ,5
+ ,11
+ ,6
+ ,14
+ ,17
+ ,6
+ ,5
+ ,12
+ ,10
+ ,12
+ ,13
+ ,6
+ ,5
+ ,12
+ ,12
+ ,15
+ ,13
+ ,6
+ ,5
+ ,12
+ ,13
+ ,14
+ ,12
+ ,5
+ ,5
+ ,12
+ ,8
+ ,13
+ ,13
+ ,5
+ ,5
+ ,14
+ ,12
+ ,8
+ ,14
+ ,4
+ ,5
+ ,6
+ ,12
+ ,6
+ ,11
+ ,2
+ ,5
+ ,7
+ ,12
+ ,7
+ ,12
+ ,4
+ ,5
+ ,14
+ ,6
+ ,13
+ ,12
+ ,6
+ ,5
+ ,14
+ ,11
+ ,13
+ ,16
+ ,6
+ ,5
+ ,10
+ ,10
+ ,11
+ ,12
+ ,5
+ ,5
+ ,13
+ ,12
+ ,5
+ ,12
+ ,3
+ ,5
+ ,12
+ ,13
+ ,12
+ ,12
+ ,6
+ ,5
+ ,9
+ ,11
+ ,8
+ ,10
+ ,4
+ ,6
+ ,12
+ ,7
+ ,11
+ ,15
+ ,5
+ ,6
+ ,16
+ ,11
+ ,14
+ ,15
+ ,8
+ ,6
+ ,10
+ ,11
+ ,9
+ ,12
+ ,4
+ ,6
+ ,14
+ ,11
+ ,10
+ ,16
+ ,6
+ ,6
+ ,10
+ ,11
+ ,13
+ ,15
+ ,6
+ ,6
+ ,16
+ ,12
+ ,16
+ ,16
+ ,7
+ ,6
+ ,15
+ ,10
+ ,16
+ ,13
+ ,6
+ ,6
+ ,12
+ ,11
+ ,11
+ ,12
+ ,5
+ ,6
+ ,10
+ ,12
+ ,8
+ ,11
+ ,4
+ ,6
+ ,8
+ ,7
+ ,4
+ ,13
+ ,6
+ ,6
+ ,8
+ ,13
+ ,7
+ ,10
+ ,3
+ ,6
+ ,11
+ ,8
+ ,14
+ ,15
+ ,5
+ ,6
+ ,13
+ ,12
+ ,11
+ ,13
+ ,6
+ ,6
+ ,16
+ ,11
+ ,17
+ ,16
+ ,7
+ ,6
+ ,16
+ ,12
+ ,15
+ ,15
+ ,7
+ ,6
+ ,14
+ ,14
+ ,17
+ ,18
+ ,6
+ ,6
+ ,11
+ ,10
+ ,5
+ ,13
+ ,3
+ ,6
+ ,4
+ ,10
+ ,4
+ ,10
+ ,2
+ ,6
+ ,14
+ ,13
+ ,10
+ ,16
+ ,8
+ ,6
+ ,9
+ ,10
+ ,11
+ ,13
+ ,3
+ ,7
+ ,14
+ ,11
+ ,15
+ ,15
+ ,8
+ ,7
+ ,8
+ ,10
+ ,10
+ ,14
+ ,3
+ ,7
+ ,8
+ ,7
+ ,9
+ ,15
+ ,4
+ ,7
+ ,11
+ ,10
+ ,12
+ ,14
+ ,5
+ ,7
+ ,12
+ ,8
+ ,15
+ ,13
+ ,7
+ ,7
+ ,11
+ ,12
+ ,7
+ ,13
+ ,6
+ ,7
+ ,14
+ ,12
+ ,13
+ ,15
+ ,6
+ ,7
+ ,15
+ ,12
+ ,12
+ ,16
+ ,7
+ ,7
+ ,16
+ ,11
+ ,14
+ ,14
+ ,6
+ ,7
+ ,16
+ ,12
+ ,14
+ ,14
+ ,6
+ ,7
+ ,11
+ ,12
+ ,8
+ ,16
+ ,6
+ ,7
+ ,14
+ ,12
+ ,15
+ ,14
+ ,6
+ ,7
+ ,14
+ ,11
+ ,12
+ ,12
+ ,4
+ ,7
+ ,12
+ ,12
+ ,12
+ ,13
+ ,4
+ ,7
+ ,14
+ ,11
+ ,16
+ ,12
+ ,5
+ ,7
+ ,8
+ ,11
+ ,9
+ ,12
+ ,4
+ ,7
+ ,13
+ ,13
+ ,15
+ ,14
+ ,6
+ ,7
+ ,16
+ ,12
+ ,15
+ ,14
+ ,6
+ ,7
+ ,12
+ ,12
+ ,6
+ ,14
+ ,5
+ ,7
+ ,16
+ ,12
+ ,14
+ ,16
+ ,8
+ ,7
+ ,12
+ ,12
+ ,15
+ ,13
+ ,6
+ ,7
+ ,11
+ ,8
+ ,10
+ ,14
+ ,5
+ ,7
+ ,4
+ ,8
+ ,6
+ ,4
+ ,4
+ ,7
+ ,16
+ ,12
+ ,14
+ ,16
+ ,8
+ ,7
+ ,15
+ ,11
+ ,12
+ ,13
+ ,6
+ ,7
+ ,10
+ ,12
+ ,8
+ ,16
+ ,4
+ ,7
+ ,13
+ ,13
+ ,11
+ ,15
+ ,6
+ ,7
+ ,15
+ ,12
+ ,13
+ ,14
+ ,6
+ ,7
+ ,12
+ ,12
+ ,9
+ ,13
+ ,4
+ ,7
+ ,14
+ ,11
+ ,15
+ ,14
+ ,6
+ ,7
+ ,7
+ ,12
+ ,13
+ ,12
+ ,3
+ ,8
+ ,19
+ ,12
+ ,15
+ ,15
+ ,6
+ ,8
+ ,12
+ ,10
+ ,14
+ ,14
+ ,5
+ ,8
+ ,12
+ ,11
+ ,16
+ ,13
+ ,4
+ ,8
+ ,13
+ ,12
+ ,14
+ ,14
+ ,6
+ ,8
+ ,15
+ ,12
+ ,14
+ ,16
+ ,4
+ ,8
+ ,8
+ ,10
+ ,10
+ ,6
+ ,4
+ ,8
+ ,12
+ ,12
+ ,10
+ ,13
+ ,4
+ ,8
+ ,10
+ ,13
+ ,4
+ ,13
+ ,6
+ ,8
+ ,8
+ ,12
+ ,8
+ ,14
+ ,5
+ ,8
+ ,10
+ ,15
+ ,15
+ ,15
+ ,6
+ ,8
+ ,15
+ ,11
+ ,16
+ ,14
+ ,6
+ ,8
+ ,16
+ ,12
+ ,12
+ ,15
+ ,8
+ ,9
+ ,13
+ ,11
+ ,12
+ ,13
+ ,7
+ ,10
+ ,16
+ ,12
+ ,15
+ ,16
+ ,7
+ ,10
+ ,9
+ ,11
+ ,9
+ ,12
+ ,4
+ ,14
+ ,14
+ ,10
+ ,12
+ ,15
+ ,6
+ ,14
+ ,14
+ ,11
+ ,14
+ ,12
+ ,6
+ ,14
+ ,12
+ ,11
+ ,11
+ ,14
+ ,2
+ ,14)
+ ,dim=c(6
+ ,156)
+ ,dimnames=list(c('Popularity'
+ ,'FindingFriends'
+ ,'KnowingPeople'
+ ,'Liked'
+ ,'Celebrity'
+ ,'Date
')
+ ,1:156))
> y <- array(NA,dim=c(6,156),dimnames=list(c('Popularity','FindingFriends','KnowingPeople','Liked','Celebrity','Date
'),1:156))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = '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
Popularity FindingFriends KnowingPeople Liked Celebrity Date\r t
1 13 13 14 13 3 1 1
2 12 12 8 13 5 1 2
3 15 10 12 16 6 1 3
4 12 9 7 12 6 1 4
5 10 10 10 11 5 1 5
6 12 12 7 12 3 1 6
7 15 13 16 18 8 1 7
8 9 12 11 11 4 1 8
9 12 12 14 14 4 1 9
10 11 6 6 9 4 1 10
11 11 5 16 14 6 1 11
12 11 12 11 12 6 1 12
13 15 11 16 11 5 1 13
14 7 14 12 12 4 1 14
15 11 14 7 13 6 1 15
16 11 12 13 11 4 1 16
17 10 12 11 12 6 1 17
18 14 11 15 16 6 1 18
19 10 11 7 9 4 2 19
20 6 7 9 11 4 2 20
21 11 9 7 13 2 2 21
22 15 11 14 15 7 2 22
23 11 11 15 10 5 2 23
24 12 12 7 11 4 2 24
25 14 12 15 13 6 2 25
26 15 11 17 16 6 2 26
27 9 11 15 15 7 2 27
28 13 8 14 14 5 2 28
29 13 9 14 14 6 2 29
30 16 12 8 14 4 2 30
31 13 10 8 8 4 2 31
32 12 10 14 13 7 2 32
33 14 12 14 15 7 2 33
34 11 8 8 13 4 3 34
35 9 12 11 11 4 3 35
36 16 11 16 15 6 3 36
37 12 12 10 15 6 3 37
38 10 7 8 9 5 3 38
39 13 11 14 13 6 3 39
40 16 11 16 16 7 3 40
41 14 12 13 13 6 3 41
42 15 9 5 11 3 3 42
43 5 15 8 12 3 3 43
44 8 11 10 12 4 3 44
45 11 11 8 12 6 3 45
46 16 11 13 14 7 3 46
47 17 11 15 14 5 3 47
48 9 15 6 8 4 3 48
49 9 11 12 13 5 3 49
50 13 12 16 16 6 3 50
51 10 12 5 13 6 3 51
52 6 9 15 11 6 4 52
53 12 12 12 14 5 4 53
54 8 12 8 13 4 4 54
55 14 13 13 13 5 4 55
56 12 11 14 13 5 4 56
57 11 9 12 12 4 4 57
58 16 9 16 16 6 4 58
59 8 11 10 15 2 4 59
60 15 11 15 15 8 4 60
61 7 12 8 12 3 4 61
62 16 12 16 14 6 4 62
63 14 9 19 12 6 4 63
64 16 11 14 15 6 4 64
65 9 9 6 12 5 4 65
66 14 12 13 13 5 4 66
67 11 12 15 12 6 4 67
68 13 12 7 12 5 4 68
69 15 12 13 13 6 5 69
70 5 14 4 5 2 5 70
71 15 11 14 13 5 5 71
72 13 12 13 13 5 5 72
73 11 11 11 14 5 5 73
74 11 6 14 17 6 5 74
75 12 10 12 13 6 5 75
76 12 12 15 13 6 5 76
77 12 13 14 12 5 5 77
78 12 8 13 13 5 5 78
79 14 12 8 14 4 5 79
80 6 12 6 11 2 5 80
81 7 12 7 12 4 5 81
82 14 6 13 12 6 5 82
83 14 11 13 16 6 5 83
84 10 10 11 12 5 5 84
85 13 12 5 12 3 5 85
86 12 13 12 12 6 5 86
87 9 11 8 10 4 6 87
88 12 7 11 15 5 6 88
89 16 11 14 15 8 6 89
90 10 11 9 12 4 6 90
91 14 11 10 16 6 6 91
92 10 11 13 15 6 6 92
93 16 12 16 16 7 6 93
94 15 10 16 13 6 6 94
95 12 11 11 12 5 6 95
96 10 12 8 11 4 6 96
97 8 7 4 13 6 6 97
98 8 13 7 10 3 6 98
99 11 8 14 15 5 6 99
100 13 12 11 13 6 6 100
101 16 11 17 16 7 6 101
102 16 12 15 15 7 6 102
103 14 14 17 18 6 6 103
104 11 10 5 13 3 6 104
105 4 10 4 10 2 6 105
106 14 13 10 16 8 6 106
107 9 10 11 13 3 7 107
108 14 11 15 15 8 7 108
109 8 10 10 14 3 7 109
110 8 7 9 15 4 7 110
111 11 10 12 14 5 7 111
112 12 8 15 13 7 7 112
113 11 12 7 13 6 7 113
114 14 12 13 15 6 7 114
115 15 12 12 16 7 7 115
116 16 11 14 14 6 7 116
117 16 12 14 14 6 7 117
118 11 12 8 16 6 7 118
119 14 12 15 14 6 7 119
120 14 11 12 12 4 7 120
121 12 12 12 13 4 7 121
122 14 11 16 12 5 7 122
123 8 11 9 12 4 7 123
124 13 13 15 14 6 7 124
125 16 12 15 14 6 7 125
126 12 12 6 14 5 7 126
127 16 12 14 16 8 7 127
128 12 12 15 13 6 7 128
129 11 8 10 14 5 7 129
130 4 8 6 4 4 7 130
131 16 12 14 16 8 7 131
132 15 11 12 13 6 7 132
133 10 12 8 16 4 7 133
134 13 13 11 15 6 7 134
135 15 12 13 14 6 7 135
136 12 12 9 13 4 7 136
137 14 11 15 14 6 7 137
138 7 12 13 12 3 8 138
139 19 12 15 15 6 8 139
140 12 10 14 14 5 8 140
141 12 11 16 13 4 8 141
142 13 12 14 14 6 8 142
143 15 12 14 16 4 8 143
144 8 10 10 6 4 8 144
145 12 12 10 13 4 8 145
146 10 13 4 13 6 8 146
147 8 12 8 14 5 8 147
148 10 15 15 15 6 8 148
149 15 11 16 14 6 8 149
150 16 12 12 15 8 9 150
151 13 11 12 13 7 10 151
152 16 12 15 16 7 10 152
153 9 11 9 12 4 14 153
154 14 10 12 15 6 14 154
155 14 11 14 12 6 14 155
156 12 11 11 14 2 14 156
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FindingFriends KnowingPeople Liked Celebrity
0.184728 0.102459 0.241835 0.349829 0.637259
`Date\r` t
0.098801 -0.006413
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.46780 -1.29639 -0.05226 1.27491 6.89700
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.184728 1.456114 0.127 0.899219
FindingFriends 0.102459 0.097707 1.049 0.296043
KnowingPeople 0.241835 0.061846 3.910 0.000140 ***
Liked 0.349829 0.097950 3.571 0.000478 ***
Celebrity 0.637259 0.158123 4.030 8.86e-05 ***
`Date\r` 0.098801 0.196706 0.502 0.616215
t -0.006413 0.011948 -0.537 0.592247
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 2.118 on 149 degrees of freedom
Multiple R-squared: 0.5002, Adjusted R-squared: 0.48
F-statistic: 24.85 on 6 and 149 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,] 0.07385393 0.147707868 0.926146066
[2,] 0.11141217 0.222824337 0.888587831
[3,] 0.07345787 0.146915748 0.926542126
[4,] 0.54882735 0.902345309 0.451172655
[5,] 0.71869921 0.562601583 0.281300791
[6,] 0.65209041 0.695819175 0.347909588
[7,] 0.57611249 0.847775016 0.423887508
[8,] 0.48920417 0.978408337 0.510795831
[9,] 0.45187949 0.903758985 0.548120507
[10,] 0.36600042 0.732000833 0.633999583
[11,] 0.51003874 0.979922519 0.489961259
[12,] 0.51642667 0.967146651 0.483573325
[13,] 0.54584659 0.908306828 0.454153414
[14,] 0.47236154 0.944723072 0.527638464
[15,] 0.47794543 0.955890863 0.522054568
[16,] 0.43473137 0.869462732 0.565268634
[17,] 0.37438615 0.748772302 0.625613849
[18,] 0.62007360 0.759852804 0.379926402
[19,] 0.57495464 0.850090714 0.425045357
[20,] 0.51976054 0.960478927 0.480239464
[21,] 0.71176397 0.576472060 0.288236030
[22,] 0.81257060 0.374858803 0.187429401
[23,] 0.77682572 0.446348566 0.223174283
[24,] 0.73078310 0.538433810 0.269216905
[25,] 0.69786199 0.604276028 0.302138014
[26,] 0.69575087 0.608498257 0.304249128
[27,] 0.70844533 0.583109343 0.291554671
[28,] 0.67539471 0.649210588 0.324605294
[29,] 0.62633492 0.747330166 0.373665083
[30,] 0.57289741 0.854205177 0.427102589
[31,] 0.54355096 0.912898070 0.456449035
[32,] 0.50209156 0.995816882 0.497908441
[33,] 0.75752882 0.484942369 0.242471184
[34,] 0.95630705 0.087385905 0.043692952
[35,] 0.96668342 0.066633158 0.033316579
[36,] 0.95629027 0.087419452 0.043709726
[37,] 0.96122545 0.077549106 0.038774553
[38,] 0.98065098 0.038698035 0.019349018
[39,] 0.97421653 0.051566949 0.025783475
[40,] 0.98213664 0.035726717 0.017863358
[41,] 0.97921015 0.041579698 0.020789849
[42,] 0.97446675 0.051066508 0.025533254
[43,] 0.99759746 0.004805077 0.002402539
[44,] 0.99653455 0.006930891 0.003465446
[45,] 0.99712135 0.005757308 0.002878654
[46,] 0.99710451 0.005790972 0.002895486
[47,] 0.99591307 0.008173859 0.004086930
[48,] 0.99422923 0.011541532 0.005770766
[49,] 0.99356498 0.012870039 0.006435019
[50,] 0.99514768 0.009704646 0.004852323
[51,] 0.99366228 0.012675447 0.006337723
[52,] 0.99448678 0.011026439 0.005513219
[53,] 0.99504169 0.009916624 0.004958312
[54,] 0.99335491 0.013290179 0.006645090
[55,] 0.99371541 0.012569188 0.006284594
[56,] 0.99207410 0.015851797 0.007925899
[57,] 0.99123301 0.017533973 0.008766987
[58,] 0.99119366 0.017612689 0.008806345
[59,] 0.99240860 0.015182793 0.007591397
[60,] 0.99262299 0.014754022 0.007377011
[61,] 0.99003546 0.019929081 0.009964541
[62,] 0.99151234 0.016975311 0.008487655
[63,] 0.98874471 0.022510576 0.011255288
[64,] 0.98587554 0.028248929 0.014124464
[65,] 0.98968395 0.020632092 0.010316046
[66,] 0.98606498 0.027870031 0.013935015
[67,] 0.98381401 0.032371979 0.016185989
[68,] 0.97875723 0.042485539 0.021242769
[69,] 0.97189841 0.056203173 0.028101587
[70,] 0.98192884 0.036142321 0.018071160
[71,] 0.98117119 0.037657622 0.018828811
[72,] 0.98446562 0.031068762 0.015534381
[73,] 0.98553158 0.028936839 0.014468419
[74,] 0.98054811 0.038903774 0.019451887
[75,] 0.97602519 0.047949610 0.023974805
[76,] 0.99361474 0.012770514 0.006385257
[77,] 0.99118616 0.017627683 0.008813842
[78,] 0.98793741 0.024125173 0.012062587
[79,] 0.98416877 0.031662456 0.015831228
[80,] 0.98026700 0.039466009 0.019733005
[81,] 0.97393345 0.052133108 0.026066554
[82,] 0.96894738 0.062105247 0.031052623
[83,] 0.98148321 0.037033587 0.018516793
[84,] 0.97607710 0.047845802 0.023922901
[85,] 0.97282461 0.054350782 0.027175391
[86,] 0.96597904 0.068041929 0.034020964
[87,] 0.95754400 0.084912006 0.042456003
[88,] 0.95359737 0.092805260 0.046402630
[89,] 0.94102872 0.117942566 0.058971283
[90,] 0.93533203 0.129335932 0.064667966
[91,] 0.92149837 0.157003253 0.078501627
[92,] 0.90266029 0.194679421 0.097339710
[93,] 0.88868717 0.222625662 0.111312831
[94,] 0.89189046 0.216219075 0.108109538
[95,] 0.92892920 0.142141590 0.071070795
[96,] 0.92545692 0.149086167 0.074543084
[97,] 0.90562530 0.188749399 0.094374700
[98,] 0.88523091 0.229538181 0.114769090
[99,] 0.87954822 0.240903568 0.120451784
[100,] 0.87697145 0.246057103 0.123028552
[101,] 0.89671471 0.206570590 0.103285295
[102,] 0.88881883 0.222362336 0.111181168
[103,] 0.92652960 0.146940795 0.073470397
[104,] 0.90486601 0.190267983 0.095133991
[105,] 0.88276184 0.234476319 0.117238159
[106,] 0.85657865 0.286842696 0.143421348
[107,] 0.84736048 0.305279035 0.152639517
[108,] 0.84516007 0.309679864 0.154839932
[109,] 0.84621391 0.307572190 0.153786095
[110,] 0.81404065 0.371918694 0.185959347
[111,] 0.85132102 0.297357960 0.148678980
[112,] 0.81899052 0.362018968 0.181009484
[113,] 0.79045237 0.419095266 0.209547633
[114,] 0.78251078 0.434978438 0.217489219
[115,] 0.74670636 0.506587288 0.253293644
[116,] 0.73413091 0.531738181 0.265869091
[117,] 0.71539815 0.569203696 0.284601848
[118,] 0.65721142 0.685577167 0.342788584
[119,] 0.62927898 0.741442030 0.370721015
[120,] 0.63626513 0.727469742 0.363734871
[121,] 0.64008455 0.719830891 0.359915445
[122,] 0.59454615 0.810907707 0.405453853
[123,] 0.55310335 0.893793301 0.446896650
[124,] 0.54369990 0.912600197 0.456300098
[125,] 0.47134678 0.942693566 0.528653217
[126,] 0.41431560 0.828631191 0.585684404
[127,] 0.39370284 0.787405671 0.606297164
[128,] 0.32079045 0.641580902 0.679209549
[129,] 0.41161517 0.823230338 0.588384831
[130,] 0.77136457 0.457270857 0.228635429
[131,] 0.76377510 0.472449798 0.236224899
[132,] 0.70961141 0.580777182 0.290388591
[133,] 0.62334924 0.753301519 0.376650760
[134,] 0.55995156 0.880096890 0.440048445
[135,] 0.43466229 0.869324572 0.565337714
[136,] 0.69958066 0.600838689 0.300419344
[137,] 0.84924859 0.301502825 0.150751412
> postscript(file="/var/www/html/freestat/rcomp/tmp/1drtg1290354850.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2drtg1290354850.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/freestat/rcomp/tmp/3n0s01290354850.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4n0s01290354850.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5n0s01290354850.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 = 156
Frequency = 1
1 2 3 4 5 6
1.54567249 0.83103819 1.38828198 1.10564515 -0.72881948 2.72287163
7 8 9 10 11 12
-1.83496131 -1.51907532 -0.28765467 3.01733797 -2.31580620 -1.11777154
13 14 15 16 17 18
2.76901080 -4.27718083 -0.68593807 0.04855561 -2.08570793 -0.34349239
19 20 21 22 23 24
1.22212161 -3.54495806 2.31506904 0.53776241 -0.67399881 2.45206912
25 26 27 28 29 30
0.54962268 0.12533768 -5.67200941 0.50796261 -0.22534284 5.19922425
31 32 33 34 35 36
4.50952636 -1.59599426 -0.49415655 0.88573854 -1.54353351 1.68232807
37 38 39 40 41 42
-0.96270553 0.77590345 -0.11510571 0.72089111 1.03709625 6.89700415
43 44 45 46 47 48
-4.78667151 -2.49135325 -0.27578819 2.18453093 3.98179131 0.49111808
49 50 51 52 53 54
-2.93004835 -1.68018132 -0.96409306 -6.46780158 -0.45548583 -2.49464353
55 56 57 58 59 60
1.56287386 -0.46763102 0.21445826 1.57969636 -2.26893052 -0.29525053
61 62 63 64 65 66
-2.46266661 1.99762769 0.28556805 2.24675432 -0.92048663 1.73587273
67 68 69 70 71 72
-2.02881608 2.54953936 2.01905078 -0.65526965 2.52975897 0.67554821
73 74 75 76 77 78
-1.08173788 -2.97528192 -0.49571962 -1.41973102 -0.28685393 0.12386021
79 80 81 82 83 84
3.21704510 -1.96886701 -2.82863684 2.06699827 0.16180204 -1.20908183
85 86 87 88 89 90
4.31794418 -0.38272782 -0.32868069 -0.02434083 0.93495215 -0.25093514
91 92 93 94 95 96
0.83980926 -3.52945572 0.66190393 1.55997953 0.66019835 0.27674631
97 98 99 100 101 102
-2.21138037 -0.58396388 -1.78176608 0.60271519 0.57382920 1.31128247
103 104 105 106 107 108
-1.78312002 2.19607429 -2.86893251 -0.54343625 -1.33450099 -1.28384240
109 110 111 112 113 114
-2.42966870 -2.86113165 -1.17503264 -1.61389831 -0.44537854 0.41036441
115 116 117 118 119 120
0.67152471 2.63364193 2.53759574 -1.70463613 0.30858575 3.11713940
121 122 123 124 125 126
0.67126461 1.52536385 -2.13811614 -0.76180955 2.34706209 1.16725296
127 128 129 130 131 132
0.62754726 -1.28387115 -0.37101493 -2.26171529 0.65319815 2.56974496
133 134 135 136 137 138
-1.33392677 -0.08016918 1.89486018 1.49296175 0.52647368 -3.57326753
139 140 141 142 143 144
4.98821076 -0.57153540 -0.16416460 -0.40088705 2.18038701 -0.14265477
145 146 147 148 149 150
1.21003997 -0.70951216 -3.28055158 -4.26145148 1.26279007 1.41093763
151 152 153 154 155 156
-0.24207515 0.88688659 -1.63734046 0.42202058 0.89178930 1.47308818
> postscript(file="/var/www/html/freestat/rcomp/tmp/6ya9l1290354850.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 = 156
Frequency = 1
lag(myerror, k = 1) myerror
0 1.54567249 NA
1 0.83103819 1.54567249
2 1.38828198 0.83103819
3 1.10564515 1.38828198
4 -0.72881948 1.10564515
5 2.72287163 -0.72881948
6 -1.83496131 2.72287163
7 -1.51907532 -1.83496131
8 -0.28765467 -1.51907532
9 3.01733797 -0.28765467
10 -2.31580620 3.01733797
11 -1.11777154 -2.31580620
12 2.76901080 -1.11777154
13 -4.27718083 2.76901080
14 -0.68593807 -4.27718083
15 0.04855561 -0.68593807
16 -2.08570793 0.04855561
17 -0.34349239 -2.08570793
18 1.22212161 -0.34349239
19 -3.54495806 1.22212161
20 2.31506904 -3.54495806
21 0.53776241 2.31506904
22 -0.67399881 0.53776241
23 2.45206912 -0.67399881
24 0.54962268 2.45206912
25 0.12533768 0.54962268
26 -5.67200941 0.12533768
27 0.50796261 -5.67200941
28 -0.22534284 0.50796261
29 5.19922425 -0.22534284
30 4.50952636 5.19922425
31 -1.59599426 4.50952636
32 -0.49415655 -1.59599426
33 0.88573854 -0.49415655
34 -1.54353351 0.88573854
35 1.68232807 -1.54353351
36 -0.96270553 1.68232807
37 0.77590345 -0.96270553
38 -0.11510571 0.77590345
39 0.72089111 -0.11510571
40 1.03709625 0.72089111
41 6.89700415 1.03709625
42 -4.78667151 6.89700415
43 -2.49135325 -4.78667151
44 -0.27578819 -2.49135325
45 2.18453093 -0.27578819
46 3.98179131 2.18453093
47 0.49111808 3.98179131
48 -2.93004835 0.49111808
49 -1.68018132 -2.93004835
50 -0.96409306 -1.68018132
51 -6.46780158 -0.96409306
52 -0.45548583 -6.46780158
53 -2.49464353 -0.45548583
54 1.56287386 -2.49464353
55 -0.46763102 1.56287386
56 0.21445826 -0.46763102
57 1.57969636 0.21445826
58 -2.26893052 1.57969636
59 -0.29525053 -2.26893052
60 -2.46266661 -0.29525053
61 1.99762769 -2.46266661
62 0.28556805 1.99762769
63 2.24675432 0.28556805
64 -0.92048663 2.24675432
65 1.73587273 -0.92048663
66 -2.02881608 1.73587273
67 2.54953936 -2.02881608
68 2.01905078 2.54953936
69 -0.65526965 2.01905078
70 2.52975897 -0.65526965
71 0.67554821 2.52975897
72 -1.08173788 0.67554821
73 -2.97528192 -1.08173788
74 -0.49571962 -2.97528192
75 -1.41973102 -0.49571962
76 -0.28685393 -1.41973102
77 0.12386021 -0.28685393
78 3.21704510 0.12386021
79 -1.96886701 3.21704510
80 -2.82863684 -1.96886701
81 2.06699827 -2.82863684
82 0.16180204 2.06699827
83 -1.20908183 0.16180204
84 4.31794418 -1.20908183
85 -0.38272782 4.31794418
86 -0.32868069 -0.38272782
87 -0.02434083 -0.32868069
88 0.93495215 -0.02434083
89 -0.25093514 0.93495215
90 0.83980926 -0.25093514
91 -3.52945572 0.83980926
92 0.66190393 -3.52945572
93 1.55997953 0.66190393
94 0.66019835 1.55997953
95 0.27674631 0.66019835
96 -2.21138037 0.27674631
97 -0.58396388 -2.21138037
98 -1.78176608 -0.58396388
99 0.60271519 -1.78176608
100 0.57382920 0.60271519
101 1.31128247 0.57382920
102 -1.78312002 1.31128247
103 2.19607429 -1.78312002
104 -2.86893251 2.19607429
105 -0.54343625 -2.86893251
106 -1.33450099 -0.54343625
107 -1.28384240 -1.33450099
108 -2.42966870 -1.28384240
109 -2.86113165 -2.42966870
110 -1.17503264 -2.86113165
111 -1.61389831 -1.17503264
112 -0.44537854 -1.61389831
113 0.41036441 -0.44537854
114 0.67152471 0.41036441
115 2.63364193 0.67152471
116 2.53759574 2.63364193
117 -1.70463613 2.53759574
118 0.30858575 -1.70463613
119 3.11713940 0.30858575
120 0.67126461 3.11713940
121 1.52536385 0.67126461
122 -2.13811614 1.52536385
123 -0.76180955 -2.13811614
124 2.34706209 -0.76180955
125 1.16725296 2.34706209
126 0.62754726 1.16725296
127 -1.28387115 0.62754726
128 -0.37101493 -1.28387115
129 -2.26171529 -0.37101493
130 0.65319815 -2.26171529
131 2.56974496 0.65319815
132 -1.33392677 2.56974496
133 -0.08016918 -1.33392677
134 1.89486018 -0.08016918
135 1.49296175 1.89486018
136 0.52647368 1.49296175
137 -3.57326753 0.52647368
138 4.98821076 -3.57326753
139 -0.57153540 4.98821076
140 -0.16416460 -0.57153540
141 -0.40088705 -0.16416460
142 2.18038701 -0.40088705
143 -0.14265477 2.18038701
144 1.21003997 -0.14265477
145 -0.70951216 1.21003997
146 -3.28055158 -0.70951216
147 -4.26145148 -3.28055158
148 1.26279007 -4.26145148
149 1.41093763 1.26279007
150 -0.24207515 1.41093763
151 0.88688659 -0.24207515
152 -1.63734046 0.88688659
153 0.42202058 -1.63734046
154 0.89178930 0.42202058
155 1.47308818 0.89178930
156 NA 1.47308818
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.83103819 1.54567249
[2,] 1.38828198 0.83103819
[3,] 1.10564515 1.38828198
[4,] -0.72881948 1.10564515
[5,] 2.72287163 -0.72881948
[6,] -1.83496131 2.72287163
[7,] -1.51907532 -1.83496131
[8,] -0.28765467 -1.51907532
[9,] 3.01733797 -0.28765467
[10,] -2.31580620 3.01733797
[11,] -1.11777154 -2.31580620
[12,] 2.76901080 -1.11777154
[13,] -4.27718083 2.76901080
[14,] -0.68593807 -4.27718083
[15,] 0.04855561 -0.68593807
[16,] -2.08570793 0.04855561
[17,] -0.34349239 -2.08570793
[18,] 1.22212161 -0.34349239
[19,] -3.54495806 1.22212161
[20,] 2.31506904 -3.54495806
[21,] 0.53776241 2.31506904
[22,] -0.67399881 0.53776241
[23,] 2.45206912 -0.67399881
[24,] 0.54962268 2.45206912
[25,] 0.12533768 0.54962268
[26,] -5.67200941 0.12533768
[27,] 0.50796261 -5.67200941
[28,] -0.22534284 0.50796261
[29,] 5.19922425 -0.22534284
[30,] 4.50952636 5.19922425
[31,] -1.59599426 4.50952636
[32,] -0.49415655 -1.59599426
[33,] 0.88573854 -0.49415655
[34,] -1.54353351 0.88573854
[35,] 1.68232807 -1.54353351
[36,] -0.96270553 1.68232807
[37,] 0.77590345 -0.96270553
[38,] -0.11510571 0.77590345
[39,] 0.72089111 -0.11510571
[40,] 1.03709625 0.72089111
[41,] 6.89700415 1.03709625
[42,] -4.78667151 6.89700415
[43,] -2.49135325 -4.78667151
[44,] -0.27578819 -2.49135325
[45,] 2.18453093 -0.27578819
[46,] 3.98179131 2.18453093
[47,] 0.49111808 3.98179131
[48,] -2.93004835 0.49111808
[49,] -1.68018132 -2.93004835
[50,] -0.96409306 -1.68018132
[51,] -6.46780158 -0.96409306
[52,] -0.45548583 -6.46780158
[53,] -2.49464353 -0.45548583
[54,] 1.56287386 -2.49464353
[55,] -0.46763102 1.56287386
[56,] 0.21445826 -0.46763102
[57,] 1.57969636 0.21445826
[58,] -2.26893052 1.57969636
[59,] -0.29525053 -2.26893052
[60,] -2.46266661 -0.29525053
[61,] 1.99762769 -2.46266661
[62,] 0.28556805 1.99762769
[63,] 2.24675432 0.28556805
[64,] -0.92048663 2.24675432
[65,] 1.73587273 -0.92048663
[66,] -2.02881608 1.73587273
[67,] 2.54953936 -2.02881608
[68,] 2.01905078 2.54953936
[69,] -0.65526965 2.01905078
[70,] 2.52975897 -0.65526965
[71,] 0.67554821 2.52975897
[72,] -1.08173788 0.67554821
[73,] -2.97528192 -1.08173788
[74,] -0.49571962 -2.97528192
[75,] -1.41973102 -0.49571962
[76,] -0.28685393 -1.41973102
[77,] 0.12386021 -0.28685393
[78,] 3.21704510 0.12386021
[79,] -1.96886701 3.21704510
[80,] -2.82863684 -1.96886701
[81,] 2.06699827 -2.82863684
[82,] 0.16180204 2.06699827
[83,] -1.20908183 0.16180204
[84,] 4.31794418 -1.20908183
[85,] -0.38272782 4.31794418
[86,] -0.32868069 -0.38272782
[87,] -0.02434083 -0.32868069
[88,] 0.93495215 -0.02434083
[89,] -0.25093514 0.93495215
[90,] 0.83980926 -0.25093514
[91,] -3.52945572 0.83980926
[92,] 0.66190393 -3.52945572
[93,] 1.55997953 0.66190393
[94,] 0.66019835 1.55997953
[95,] 0.27674631 0.66019835
[96,] -2.21138037 0.27674631
[97,] -0.58396388 -2.21138037
[98,] -1.78176608 -0.58396388
[99,] 0.60271519 -1.78176608
[100,] 0.57382920 0.60271519
[101,] 1.31128247 0.57382920
[102,] -1.78312002 1.31128247
[103,] 2.19607429 -1.78312002
[104,] -2.86893251 2.19607429
[105,] -0.54343625 -2.86893251
[106,] -1.33450099 -0.54343625
[107,] -1.28384240 -1.33450099
[108,] -2.42966870 -1.28384240
[109,] -2.86113165 -2.42966870
[110,] -1.17503264 -2.86113165
[111,] -1.61389831 -1.17503264
[112,] -0.44537854 -1.61389831
[113,] 0.41036441 -0.44537854
[114,] 0.67152471 0.41036441
[115,] 2.63364193 0.67152471
[116,] 2.53759574 2.63364193
[117,] -1.70463613 2.53759574
[118,] 0.30858575 -1.70463613
[119,] 3.11713940 0.30858575
[120,] 0.67126461 3.11713940
[121,] 1.52536385 0.67126461
[122,] -2.13811614 1.52536385
[123,] -0.76180955 -2.13811614
[124,] 2.34706209 -0.76180955
[125,] 1.16725296 2.34706209
[126,] 0.62754726 1.16725296
[127,] -1.28387115 0.62754726
[128,] -0.37101493 -1.28387115
[129,] -2.26171529 -0.37101493
[130,] 0.65319815 -2.26171529
[131,] 2.56974496 0.65319815
[132,] -1.33392677 2.56974496
[133,] -0.08016918 -1.33392677
[134,] 1.89486018 -0.08016918
[135,] 1.49296175 1.89486018
[136,] 0.52647368 1.49296175
[137,] -3.57326753 0.52647368
[138,] 4.98821076 -3.57326753
[139,] -0.57153540 4.98821076
[140,] -0.16416460 -0.57153540
[141,] -0.40088705 -0.16416460
[142,] 2.18038701 -0.40088705
[143,] -0.14265477 2.18038701
[144,] 1.21003997 -0.14265477
[145,] -0.70951216 1.21003997
[146,] -3.28055158 -0.70951216
[147,] -4.26145148 -3.28055158
[148,] 1.26279007 -4.26145148
[149,] 1.41093763 1.26279007
[150,] -0.24207515 1.41093763
[151,] 0.88688659 -0.24207515
[152,] -1.63734046 0.88688659
[153,] 0.42202058 -1.63734046
[154,] 0.89178930 0.42202058
[155,] 1.47308818 0.89178930
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.83103819 1.54567249
2 1.38828198 0.83103819
3 1.10564515 1.38828198
4 -0.72881948 1.10564515
5 2.72287163 -0.72881948
6 -1.83496131 2.72287163
7 -1.51907532 -1.83496131
8 -0.28765467 -1.51907532
9 3.01733797 -0.28765467
10 -2.31580620 3.01733797
11 -1.11777154 -2.31580620
12 2.76901080 -1.11777154
13 -4.27718083 2.76901080
14 -0.68593807 -4.27718083
15 0.04855561 -0.68593807
16 -2.08570793 0.04855561
17 -0.34349239 -2.08570793
18 1.22212161 -0.34349239
19 -3.54495806 1.22212161
20 2.31506904 -3.54495806
21 0.53776241 2.31506904
22 -0.67399881 0.53776241
23 2.45206912 -0.67399881
24 0.54962268 2.45206912
25 0.12533768 0.54962268
26 -5.67200941 0.12533768
27 0.50796261 -5.67200941
28 -0.22534284 0.50796261
29 5.19922425 -0.22534284
30 4.50952636 5.19922425
31 -1.59599426 4.50952636
32 -0.49415655 -1.59599426
33 0.88573854 -0.49415655
34 -1.54353351 0.88573854
35 1.68232807 -1.54353351
36 -0.96270553 1.68232807
37 0.77590345 -0.96270553
38 -0.11510571 0.77590345
39 0.72089111 -0.11510571
40 1.03709625 0.72089111
41 6.89700415 1.03709625
42 -4.78667151 6.89700415
43 -2.49135325 -4.78667151
44 -0.27578819 -2.49135325
45 2.18453093 -0.27578819
46 3.98179131 2.18453093
47 0.49111808 3.98179131
48 -2.93004835 0.49111808
49 -1.68018132 -2.93004835
50 -0.96409306 -1.68018132
51 -6.46780158 -0.96409306
52 -0.45548583 -6.46780158
53 -2.49464353 -0.45548583
54 1.56287386 -2.49464353
55 -0.46763102 1.56287386
56 0.21445826 -0.46763102
57 1.57969636 0.21445826
58 -2.26893052 1.57969636
59 -0.29525053 -2.26893052
60 -2.46266661 -0.29525053
61 1.99762769 -2.46266661
62 0.28556805 1.99762769
63 2.24675432 0.28556805
64 -0.92048663 2.24675432
65 1.73587273 -0.92048663
66 -2.02881608 1.73587273
67 2.54953936 -2.02881608
68 2.01905078 2.54953936
69 -0.65526965 2.01905078
70 2.52975897 -0.65526965
71 0.67554821 2.52975897
72 -1.08173788 0.67554821
73 -2.97528192 -1.08173788
74 -0.49571962 -2.97528192
75 -1.41973102 -0.49571962
76 -0.28685393 -1.41973102
77 0.12386021 -0.28685393
78 3.21704510 0.12386021
79 -1.96886701 3.21704510
80 -2.82863684 -1.96886701
81 2.06699827 -2.82863684
82 0.16180204 2.06699827
83 -1.20908183 0.16180204
84 4.31794418 -1.20908183
85 -0.38272782 4.31794418
86 -0.32868069 -0.38272782
87 -0.02434083 -0.32868069
88 0.93495215 -0.02434083
89 -0.25093514 0.93495215
90 0.83980926 -0.25093514
91 -3.52945572 0.83980926
92 0.66190393 -3.52945572
93 1.55997953 0.66190393
94 0.66019835 1.55997953
95 0.27674631 0.66019835
96 -2.21138037 0.27674631
97 -0.58396388 -2.21138037
98 -1.78176608 -0.58396388
99 0.60271519 -1.78176608
100 0.57382920 0.60271519
101 1.31128247 0.57382920
102 -1.78312002 1.31128247
103 2.19607429 -1.78312002
104 -2.86893251 2.19607429
105 -0.54343625 -2.86893251
106 -1.33450099 -0.54343625
107 -1.28384240 -1.33450099
108 -2.42966870 -1.28384240
109 -2.86113165 -2.42966870
110 -1.17503264 -2.86113165
111 -1.61389831 -1.17503264
112 -0.44537854 -1.61389831
113 0.41036441 -0.44537854
114 0.67152471 0.41036441
115 2.63364193 0.67152471
116 2.53759574 2.63364193
117 -1.70463613 2.53759574
118 0.30858575 -1.70463613
119 3.11713940 0.30858575
120 0.67126461 3.11713940
121 1.52536385 0.67126461
122 -2.13811614 1.52536385
123 -0.76180955 -2.13811614
124 2.34706209 -0.76180955
125 1.16725296 2.34706209
126 0.62754726 1.16725296
127 -1.28387115 0.62754726
128 -0.37101493 -1.28387115
129 -2.26171529 -0.37101493
130 0.65319815 -2.26171529
131 2.56974496 0.65319815
132 -1.33392677 2.56974496
133 -0.08016918 -1.33392677
134 1.89486018 -0.08016918
135 1.49296175 1.89486018
136 0.52647368 1.49296175
137 -3.57326753 0.52647368
138 4.98821076 -3.57326753
139 -0.57153540 4.98821076
140 -0.16416460 -0.57153540
141 -0.40088705 -0.16416460
142 2.18038701 -0.40088705
143 -0.14265477 2.18038701
144 1.21003997 -0.14265477
145 -0.70951216 1.21003997
146 -3.28055158 -0.70951216
147 -4.26145148 -3.28055158
148 1.26279007 -4.26145148
149 1.41093763 1.26279007
150 -0.24207515 1.41093763
151 0.88688659 -0.24207515
152 -1.63734046 0.88688659
153 0.42202058 -1.63734046
154 0.89178930 0.42202058
155 1.47308818 0.89178930
> 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/freestat/rcomp/tmp/7jtb11290354851.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8jtb11290354851.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9jtb11290354851.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10c2a41290354851.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/11f39s1290354851.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/freestat/rcomp/tmp/121l7y1290354851.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/freestat/rcomp/tmp/13xdn61290354851.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/freestat/rcomp/tmp/14idmu1290354851.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/freestat/rcomp/tmp/154ek01290354851.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/freestat/rcomp/tmp/16pxj61290354851.tab")
+ }
>
> try(system("convert tmp/1drtg1290354850.ps tmp/1drtg1290354850.png",intern=TRUE))
character(0)
> try(system("convert tmp/2drtg1290354850.ps tmp/2drtg1290354850.png",intern=TRUE))
character(0)
> try(system("convert tmp/3n0s01290354850.ps tmp/3n0s01290354850.png",intern=TRUE))
character(0)
> try(system("convert tmp/4n0s01290354850.ps tmp/4n0s01290354850.png",intern=TRUE))
character(0)
> try(system("convert tmp/5n0s01290354850.ps tmp/5n0s01290354850.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ya9l1290354850.ps tmp/6ya9l1290354850.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jtb11290354851.ps tmp/7jtb11290354851.png",intern=TRUE))
character(0)
> try(system("convert tmp/8jtb11290354851.ps tmp/8jtb11290354851.png",intern=TRUE))
character(0)
> try(system("convert tmp/9jtb11290354851.ps tmp/9jtb11290354851.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c2a41290354851.ps tmp/10c2a41290354851.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.766 2.752 7.493