R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(4
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+ ,dim=c(5
+ ,159)
+ ,dimnames=list(c('neat'
+ ,'fail'
+ ,'performance'
+ ,'goals'
+ ,'organized
')
+ ,1:159))
> y <- array(NA,dim=c(5,159),dimnames=list(c('neat','fail','performance','goals','organized
'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly 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
neat fail performance goals organized\r\r M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 4 4 1 4 5 1 0 0 0 0 0 0 0 0 0
2 4 2 1 4 4 0 1 0 0 0 0 0 0 0 0
3 4 3 2 5 5 0 0 1 0 0 0 0 0 0 0
4 4 2 1 3 4 0 0 0 1 0 0 0 0 0 0
5 4 2 2 4 3 0 0 0 0 1 0 0 0 0 0
6 5 2 1 3 5 0 0 0 0 0 1 0 0 0 0
7 4 1 3 4 4 0 0 0 0 0 0 1 0 0 0
8 3 1 1 3 4 0 0 0 0 0 0 0 1 0 0
9 4 1 1 2 4 0 0 0 0 0 0 0 0 1 0
10 4 2 1 4 4 0 0 0 0 0 0 0 0 0 1
11 4 2 2 2 4 0 0 0 0 0 0 0 0 0 0
12 4 2 4 2 4 0 0 0 0 0 0 0 0 0 0
13 4 2 2 2 4 1 0 0 0 0 0 0 0 0 0
14 2 2 1 1 3 0 1 0 0 0 0 0 0 0 0
15 3 1 1 4 4 0 0 1 0 0 0 0 0 0 0
16 4 3 3 4 5 0 0 0 1 0 0 0 0 0 0
17 3 2 2 2 4 0 0 0 0 1 0 0 0 0 0
18 2 2 2 2 2 0 0 0 0 0 1 0 0 0 0
19 4 2 3 3 4 0 0 0 0 0 0 1 0 0 0
20 3 2 3 3 4 0 0 0 0 0 0 0 1 0 0
21 3 3 1 3 4 0 0 0 0 0 0 0 0 1 0
22 4 4 2 4 4 0 0 0 0 0 0 0 0 0 1
23 3 2 2 3 4 0 0 0 0 0 0 0 0 0 0
24 3 2 2 2 4 0 0 0 0 0 0 0 0 0 0
25 4 2 2 2 5 1 0 0 0 0 0 0 0 0 0
26 4 1 3 4 4 0 1 0 0 0 0 0 0 0 0
27 4 2 2 4 4 0 0 1 0 0 0 0 0 0 0
28 4 2 2 3 4 0 0 0 1 0 0 0 0 0 0
29 4 2 2 4 4 0 0 0 0 1 0 0 0 0 0
30 4 2 2 2 4 0 0 0 0 0 1 0 0 0 0
31 5 4 2 4 5 0 0 0 0 0 0 1 0 0 0
32 4 2 3 4 4 0 0 0 0 0 0 0 1 0 0
33 4 4 2 5 2 0 0 0 0 0 0 0 0 1 0
34 4 3 2 5 5 0 0 0 0 0 0 0 0 0 1
35 3 1 2 4 4 0 0 0 0 0 0 0 0 0 0
36 4 4 2 4 5 0 0 0 0 0 0 0 0 0 0
37 3 3 2 4 4 1 0 0 0 0 0 0 0 0 0
38 4 2 1 2 4 0 1 0 0 0 0 0 0 0 0
39 4 4 2 4 4 0 0 1 0 0 0 0 0 0 0
40 3 2 1 4 4 0 0 0 1 0 0 0 0 0 0
41 5 3 2 4 5 0 0 0 0 1 0 0 0 0 0
42 4 3 2 3 4 0 0 0 0 0 1 0 0 0 0
43 3 2 2 2 4 0 0 0 0 0 0 1 0 0 0
44 3 1 2 3 5 0 0 0 0 0 0 0 1 0 0
45 3 2 2 4 4 0 0 0 0 0 0 0 0 1 0
46 4 1 3 3 4 0 0 0 0 0 0 0 0 0 1
47 4 2 2 2 4 0 0 0 0 0 0 0 0 0 0
48 4 4 2 4 4 0 0 0 0 0 0 0 0 0 0
49 4 2 2 4 4 1 0 0 0 0 0 0 0 0 0
50 4 2 4 3 4 0 1 0 0 0 0 0 0 0 0
51 4 2 1 4 4 0 0 1 0 0 0 0 0 0 0
52 4 2 2 3 4 0 0 0 1 0 0 0 0 0 0
53 5 2 2 4 5 0 0 0 0 1 0 0 0 0 0
54 3 1 1 2 3 0 0 0 0 0 1 0 0 0 0
55 3 2 5 4 4 0 0 0 0 0 0 1 0 0 0
56 5 3 2 4 5 0 0 0 0 0 0 0 1 0 0
57 5 2 2 4 5 0 0 0 0 0 0 0 0 1 0
58 4 2 2 4 4 0 0 0 0 0 0 0 0 0 1
59 4 1 1 3 5 0 0 0 0 0 0 0 0 0 0
60 3 1 2 1 2 0 0 0 0 0 0 0 0 0 0
61 4 2 2 3 4 1 0 0 0 0 0 0 0 0 0
62 4 2 2 3 4 0 1 0 0 0 0 0 0 0 0
63 5 1 2 4 4 0 0 1 0 0 0 0 0 0 0
64 4 2 2 2 4 0 0 0 1 0 0 0 0 0 0
65 4 1 1 3 4 0 0 0 0 1 0 0 0 0 0
66 5 4 1 5 5 0 0 0 0 0 1 0 0 0 0
67 4 4 2 4 4 0 0 0 0 0 0 1 0 0 0
68 3 1 2 4 4 0 0 0 0 0 0 0 1 0 0
69 4 1 1 3 4 0 0 0 0 0 0 0 0 1 0
70 4 3 2 4 4 0 0 0 0 0 0 0 0 0 1
71 4 4 2 2 3 0 0 0 0 0 0 0 0 0 0
72 4 2 1 3 4 0 0 0 0 0 0 0 0 0 0
73 4 4 3 4 5 1 0 0 0 0 0 0 0 0 0
74 4 4 3 3 5 0 1 0 0 0 0 0 0 0 0
75 4 3 3 4 4 0 0 1 0 0 0 0 0 0 0
76 3 4 2 4 4 0 0 0 1 0 0 0 0 0 0
77 4 2 2 3 5 0 0 0 0 1 0 0 0 0 0
78 3 2 2 3 4 0 0 0 0 0 1 0 0 0 0
79 5 2 1 2 5 0 0 0 0 0 0 1 0 0 0
80 4 2 4 4 3 0 0 0 0 0 0 0 1 0 0
81 5 2 3 3 4 0 0 0 0 0 0 0 0 1 0
82 5 2 2 2 4 0 0 0 0 0 0 0 0 0 1
83 4 2 2 2 4 0 0 0 0 0 0 0 0 0 0
84 4 1 2 3 4 0 0 0 0 0 0 0 0 0 0
85 4 3 1 2 5 1 0 0 0 0 0 0 0 0 0
86 4 3 2 2 4 0 1 0 0 0 0 0 0 0 0
87 4 2 3 4 4 0 0 1 0 0 0 0 0 0 0
88 5 4 1 4 5 0 0 0 1 0 0 0 0 0 0
89 4 4 2 4 3 0 0 0 0 1 0 0 0 0 0
90 3 2 2 2 4 0 0 0 0 0 1 0 0 0 0
91 4 2 2 2 4 0 0 0 0 0 0 1 0 0 0
92 3 1 1 4 5 0 0 0 0 0 0 0 1 0 0
93 4 1 1 2 4 0 0 0 0 0 0 0 0 1 0
94 4 1 2 3 3 0 0 0 0 0 0 0 0 0 1
95 4 2 2 3 5 0 0 0 0 0 0 0 0 0 0
96 4 2 4 5 5 0 0 0 0 0 0 0 0 0 0
97 4 3 2 3 5 1 0 0 0 0 0 0 0 0 0
98 3 4 4 4 4 0 1 0 0 0 0 0 0 0 0
99 3 2 1 3 4 0 0 1 0 0 0 0 0 0 0
100 3 2 3 2 4 0 0 0 1 0 0 0 0 0 0
101 3 4 2 4 4 0 0 0 0 1 0 0 0 0 0
102 3 2 2 3 4 0 0 0 0 0 1 0 0 0 0
103 2 3 4 3 2 0 0 0 0 0 0 1 0 0 0
104 3 2 3 3 4 0 0 0 0 0 0 0 1 0 0
105 5 2 2 4 5 0 0 0 0 0 0 0 0 1 0
106 2 4 1 1 2 0 0 0 0 0 0 0 0 0 1
107 2 2 1 3 3 0 0 0 0 0 0 0 0 0 0
108 3 3 2 2 2 0 0 0 0 0 0 0 0 0 0
109 3 2 3 3 4 1 0 0 0 0 0 0 0 0 0
110 2 2 2 4 4 0 1 0 0 0 0 0 0 0 0
111 2 1 2 3 4 0 0 1 0 0 0 0 0 0 0
112 4 2 2 4 3 0 0 0 1 0 0 0 0 0 0
113 3 2 4 4 2 0 0 0 0 1 0 0 0 0 0
114 1 2 5 3 3 0 0 0 0 0 1 0 0 0 0
115 1 1 5 5 4 0 0 0 0 0 0 1 0 0 0
116 1 2 3 4 2 0 0 0 0 0 0 0 1 0 0
117 2 3 4 2 4 0 0 0 0 0 0 0 0 1 0
118 2 2 4 3 4 0 0 0 0 0 0 0 0 0 1
119 3 2 3 4 2 0 0 0 0 0 0 0 0 0 0
120 1 1 2 2 3 0 0 0 0 0 0 0 0 0 0
121 3 1 4 4 3 1 0 0 0 0 0 0 0 0 0
122 1 2 2 3 3 0 1 0 0 0 0 0 0 0 0
123 2 2 3 4 4 0 0 1 0 0 0 0 0 0 0
124 1 1 2 4 4 0 0 0 1 0 0 0 0 0 0
125 2 2 2 4 4 0 0 0 0 1 0 0 0 0 0
126 2 3 2 4 3 0 0 0 0 0 1 0 0 0 0
127 3 1 4 5 4 0 0 0 0 0 0 1 0 0 0
128 2 2 4 5 4 0 0 0 0 0 0 0 1 0 0
129 2 2 4 5 4 0 0 0 0 0 0 0 0 1 0
130 4 2 4 5 2 0 0 0 0 0 0 0 0 0 1
131 2 2 3 4 4 0 0 0 0 0 0 0 0 0 0
132 3 2 2 4 5 0 0 0 0 0 0 0 0 0 0
133 2 1 4 5 4 1 0 0 0 0 0 0 0 0 0
134 1 1 3 4 4 0 1 0 0 0 0 0 0 0 0
135 2 2 2 4 4 0 0 1 0 0 0 0 0 0 0
136 3 4 4 4 3 0 0 0 1 0 0 0 0 0 0
137 1 2 3 4 1 0 0 0 0 1 0 0 0 0 0
138 2 2 3 2 4 0 0 0 0 0 1 0 0 0 0
139 2 2 3 4 3 0 0 0 0 0 0 1 0 0 0
140 3 2 3 2 3 0 0 0 0 0 0 0 1 0 0
141 3 2 3 3 3 0 0 0 0 0 0 0 0 1 0
142 3 2 4 4 1 0 0 0 0 0 0 0 0 0 1
143 4 5 5 1 4 0 0 0 0 0 0 0 0 0 0
144 4 1 2 4 5 0 0 0 0 0 0 0 0 0 0
145 2 4 2 3 4 1 0 0 0 0 0 0 0 0 0
146 3 2 4 4 3 0 1 0 0 0 0 0 0 0 0
147 3 3 3 4 4 0 0 1 0 0 0 0 0 0 0
148 2 2 3 4 3 0 0 0 1 0 0 0 0 0 0
149 1 2 1 1 4 0 0 0 0 1 0 0 0 0 0
150 2 2 2 2 4 0 0 0 0 0 1 0 0 0 0
151 2 1 4 4 4 0 0 0 0 0 0 1 0 0 0
152 4 2 4 4 5 0 0 0 0 0 0 0 1 0 0
153 4 5 5 5 2 0 0 0 0 0 0 0 0 1 0
154 2 2 2 2 3 0 0 0 0 0 0 0 0 0 1
155 3 3 4 2 3 0 0 0 0 0 0 0 0 0 0
156 2 2 3 4 4 0 0 0 0 0 0 0 0 0 0
157 4 2 2 4 4 1 0 0 0 0 0 0 0 0 0
158 2 2 4 4 3 0 1 0 0 0 0 0 0 0 0
159 4 4 2 4 4 0 0 1 0 0 0 0 0 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
57 0 57
58 0 58
59 1 59
60 0 60
61 0 61
62 0 62
63 0 63
64 0 64
65 0 65
66 0 66
67 0 67
68 0 68
69 0 69
70 0 70
71 1 71
72 0 72
73 0 73
74 0 74
75 0 75
76 0 76
77 0 77
78 0 78
79 0 79
80 0 80
81 0 81
82 0 82
83 1 83
84 0 84
85 0 85
86 0 86
87 0 87
88 0 88
89 0 89
90 0 90
91 0 91
92 0 92
93 0 93
94 0 94
95 1 95
96 0 96
97 0 97
98 0 98
99 0 99
100 0 100
101 0 101
102 0 102
103 0 103
104 0 104
105 0 105
106 0 106
107 1 107
108 0 108
109 0 109
110 0 110
111 0 111
112 0 112
113 0 113
114 0 114
115 0 115
116 0 116
117 0 117
118 0 118
119 1 119
120 0 120
121 0 121
122 0 122
123 0 123
124 0 124
125 0 125
126 0 126
127 0 127
128 0 128
129 0 129
130 0 130
131 1 131
132 0 132
133 0 133
134 0 134
135 0 135
136 0 136
137 0 137
138 0 138
139 0 139
140 0 140
141 0 141
142 0 142
143 1 143
144 0 144
145 0 145
146 0 146
147 0 147
148 0 148
149 0 149
150 0 150
151 0 151
152 0 152
153 0 153
154 0 154
155 1 155
156 0 156
157 0 157
158 0 158
159 0 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) fail performance goals
1.912489 0.225827 -0.015170 0.040702
`organized\r\r` M1 M2 M3
0.408125 -0.108520 -0.320678 -0.052393
M4 M5 M6 M7
-0.100794 -0.040850 -0.325278 -0.129585
M8 M9 M10 M11
-0.168273 0.353475 0.368840 0.095310
t
-0.009109
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.72135 -0.67005 0.09738 0.59278 1.72298
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.912489 0.506351 3.777 0.000233 ***
fail 0.225827 0.070489 3.204 0.001675 **
performance -0.015170 0.078959 -0.192 0.847924
goals 0.040702 0.074355 0.547 0.584962
`organized\r\r` 0.408125 0.086822 4.701 6.07e-06 ***
M1 -0.108520 0.316100 -0.343 0.731873
M2 -0.320678 0.314246 -1.020 0.309242
M3 -0.052393 0.320743 -0.163 0.870477
M4 -0.100794 0.321759 -0.313 0.754542
M5 -0.040850 0.322792 -0.127 0.899474
M6 -0.325278 0.320407 -1.015 0.311736
M7 -0.129585 0.325230 -0.398 0.690903
M8 -0.168273 0.323169 -0.521 0.603388
M9 0.353475 0.320861 1.102 0.272479
M10 0.368840 0.323868 1.139 0.256679
M11 0.095310 0.320917 0.297 0.766906
t -0.009109 0.001620 -5.623 9.62e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8129 on 142 degrees of freedom
Multiple R-squared: 0.4381, Adjusted R-squared: 0.3748
F-statistic: 6.919 on 16 and 142 DF, p-value: 1.572e-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,] 4.286681e-01 0.8573362781 0.5713319
[2,] 2.773360e-01 0.5546720916 0.7226640
[3,] 2.407609e-01 0.4815218970 0.7592391
[4,] 1.775048e-01 0.3550096360 0.8224952
[5,] 1.103886e-01 0.2207772719 0.8896114
[6,] 6.635468e-02 0.1327093579 0.9336453
[7,] 4.059481e-02 0.0811896146 0.9594052
[8,] 1.070945e-01 0.2141889261 0.8929055
[9,] 9.134332e-02 0.1826866382 0.9086567
[10,] 6.079812e-02 0.1215962486 0.9392019
[11,] 4.295512e-02 0.0859102312 0.9570449
[12,] 5.642021e-02 0.1128404159 0.9435798
[13,] 5.162019e-02 0.1032403720 0.9483798
[14,] 5.519508e-02 0.1103901525 0.9448049
[15,] 5.872681e-02 0.1174536142 0.9412732
[16,] 6.168142e-02 0.1233628412 0.9383186
[17,] 4.511355e-02 0.0902270917 0.9548865
[18,] 5.046566e-02 0.1009313127 0.9495343
[19,] 5.898361e-02 0.1179672275 0.9410164
[20,] 4.933708e-02 0.0986741602 0.9506629
[21,] 4.392834e-02 0.0878566899 0.9560717
[22,] 3.589711e-02 0.0717942131 0.9641029
[23,] 2.425397e-02 0.0485079451 0.9757460
[24,] 2.037271e-02 0.0407454222 0.9796273
[25,] 1.746959e-02 0.0349391796 0.9825304
[26,] 2.509532e-02 0.0501906422 0.9749047
[27,] 2.074795e-02 0.0414959015 0.9792520
[28,] 2.270677e-02 0.0454135459 0.9772932
[29,] 1.910275e-02 0.0382054961 0.9808973
[30,] 1.572518e-02 0.0314503624 0.9842748
[31,] 1.074757e-02 0.0214951334 0.9892524
[32,] 9.648066e-03 0.0192961314 0.9903519
[33,] 7.325018e-03 0.0146500360 0.9926750
[34,] 6.104447e-03 0.0122088938 0.9938956
[35,] 3.978339e-03 0.0079566787 0.9960217
[36,] 6.635168e-03 0.0132703357 0.9933648
[37,] 8.910671e-03 0.0178213420 0.9910893
[38,] 7.346537e-03 0.0146930745 0.9926535
[39,] 5.233747e-03 0.0104674936 0.9947663
[40,] 3.491404e-03 0.0069828073 0.9965086
[41,] 3.428984e-03 0.0068579685 0.9965710
[42,] 2.397735e-03 0.0047954705 0.9976023
[43,] 1.640338e-03 0.0032806762 0.9983597
[44,] 3.464868e-03 0.0069297358 0.9965351
[45,] 2.439101e-03 0.0048782027 0.9975609
[46,] 1.816084e-03 0.0036321687 0.9981839
[47,] 1.278678e-03 0.0025573560 0.9987213
[48,] 8.269100e-04 0.0016538200 0.9991731
[49,] 6.671784e-04 0.0013343568 0.9993328
[50,] 4.176380e-04 0.0008352761 0.9995824
[51,] 2.876583e-04 0.0005753165 0.9997123
[52,] 2.294310e-04 0.0004588620 0.9997706
[53,] 1.420610e-04 0.0002841221 0.9998579
[54,] 1.332332e-04 0.0002664664 0.9998668
[55,] 1.046433e-04 0.0002092866 0.9998954
[56,] 6.303750e-05 0.0001260750 0.9999370
[57,] 1.359598e-04 0.0002719196 0.9998640
[58,] 1.117442e-04 0.0002234883 0.9998883
[59,] 1.267163e-04 0.0002534327 0.9998733
[60,] 1.884902e-04 0.0003769805 0.9998115
[61,] 2.180887e-04 0.0004361774 0.9997819
[62,] 2.961649e-04 0.0005923299 0.9997038
[63,] 4.542708e-04 0.0009085415 0.9995457
[64,] 3.118097e-04 0.0006236193 0.9996882
[65,] 2.457079e-04 0.0004914158 0.9997543
[66,] 1.577969e-04 0.0003155938 0.9998422
[67,] 1.457016e-04 0.0002914032 0.9998543
[68,] 1.168233e-04 0.0002336466 0.9998832
[69,] 9.442606e-05 0.0001888521 0.9999056
[70,] 7.329967e-05 0.0001465993 0.9999267
[71,] 8.554923e-05 0.0001710985 0.9999145
[72,] 1.262596e-04 0.0002525192 0.9998737
[73,] 1.418169e-04 0.0002836339 0.9998582
[74,] 1.599051e-04 0.0003198101 0.9998401
[75,] 2.263228e-04 0.0004526457 0.9997737
[76,] 1.916528e-04 0.0003833057 0.9998083
[77,] 1.440136e-04 0.0002880271 0.9998560
[78,] 1.059768e-04 0.0002119535 0.9998940
[79,] 1.219466e-04 0.0002438932 0.9998781
[80,] 1.473150e-04 0.0002946301 0.9998527
[81,] 1.610544e-04 0.0003221088 0.9998389
[82,] 2.054341e-04 0.0004108681 0.9997946
[83,] 2.716058e-04 0.0005432116 0.9997284
[84,] 2.587174e-04 0.0005174348 0.9997413
[85,] 2.034496e-04 0.0004068993 0.9997966
[86,] 1.689696e-03 0.0033793915 0.9983103
[87,] 1.633302e-03 0.0032666046 0.9983667
[88,] 1.605294e-03 0.0032105871 0.9983947
[89,] 1.362010e-03 0.0027240198 0.9986380
[90,] 1.248231e-03 0.0024964618 0.9987518
[91,] 2.006061e-03 0.0040121228 0.9979939
[92,] 2.976171e-03 0.0059523413 0.9970238
[93,] 2.906803e-02 0.0581360617 0.9709320
[94,] 6.041654e-02 0.1208330791 0.9395835
[95,] 8.595361e-02 0.1719072211 0.9140464
[96,] 1.621743e-01 0.3243485519 0.8378257
[97,] 1.912991e-01 0.3825982180 0.8087009
[98,] 1.969077e-01 0.3938153022 0.8030923
[99,] 2.005455e-01 0.4010909533 0.7994545
[100,] 2.167818e-01 0.4335636895 0.7832182
[101,] 2.430328e-01 0.4860655903 0.7569672
[102,] 2.559149e-01 0.5118298434 0.7440851
[103,] 2.417802e-01 0.4835603719 0.7582198
[104,] 2.106610e-01 0.4213220662 0.7893390
[105,] 2.207157e-01 0.4414313393 0.7792843
[106,] 2.269855e-01 0.4539709298 0.7730145
[107,] 1.787036e-01 0.3574072191 0.8212964
[108,] 1.985129e-01 0.3970258931 0.8014871
[109,] 2.359411e-01 0.4718821422 0.7640589
[110,] 2.460132e-01 0.4920263233 0.7539868
[111,] 3.276804e-01 0.6553608526 0.6723196
[112,] 3.247574e-01 0.6495147030 0.6752426
[113,] 2.495474e-01 0.4990947490 0.7504526
[114,] 2.105854e-01 0.4211708050 0.7894146
[115,] 2.940354e-01 0.5880707572 0.7059646
[116,] 4.424716e-01 0.8849431512 0.5575284
[117,] 3.623254e-01 0.7246507613 0.6376746
[118,] 3.034954e-01 0.6069907386 0.6965046
[119,] 1.973705e-01 0.3947410298 0.8026295
[120,] 1.282288e-01 0.2564576214 0.8717712
> postscript(file="/var/www/html/freestat/rcomp/tmp/1r1yq1291387490.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/22axs1291387490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/32axs1291387490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/42axs1291387490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/52axs1291387490.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 = 159
Frequency = 1
1 2 3 4 5 6
-0.88643494 0.19461187 -0.72404949 0.03364854 0.36540608 0.86822460
7 8 9 10 11 12
0.30523087 -0.63660896 -0.10854628 -0.42203361 -0.04282107 0.09193700
13 14 15 16 17 18
0.17922740 -1.16584835 -0.72942807 -0.50135853 -0.85200689 -0.74221904
19 20 21 22 23 24
0.22941397 -0.72278871 -1.49159443 -0.74921034 -0.97421428 -0.82909336
25 26 27 28 29 30
-0.11958935 0.66939573 0.16922265 0.26743542 0.17589801 0.55083879
31 32 33 34 35 36
0.42307088 0.34581807 0.14190338 -0.86290152 -0.67978003 -0.66096882
37 38 39 40 41 42
-0.90938649 0.60394169 -0.17312360 -0.67912731 0.65125380 0.39361811
43 44 45 46 47 48
-0.52643630 -0.70163884 -1.07268198 0.20276082 0.28510497 -0.14353472
49 50 51 52 53 54
0.42574965 0.71805703 0.37267049 0.48605277 0.98638994 0.38823952
55 56 57 58 59 60
-0.45302284 0.91531302 0.62850128 0.03037062 0.15624428 0.58161289
61 62 63 64 65 66
0.57576022 0.79702667 1.72297616 0.63606334 0.75518388 0.88170927
67 68 69 70 71 72
0.15912235 -0.11559794 0.39729522 -0.08614816 0.46019282 0.55226995
73 74 75 76 77 78
-0.20024383 0.06172451 0.39579942 -0.78768669 0.24570919 -0.05262839
79 80 81 82 83 84
1.37819479 1.20634773 1.31111547 1.33039176 0.61303100 0.90257561
85 86 87 88 89 90
0.18595706 0.83051845 0.73093556 0.89832704 0.67891189 0.09738218
91 92 93 94 95 96
0.91079842 -0.32027553 0.65661447 1.03295144 0.27351236 0.32686665
97 98 99 100 101 102
0.26973336 -0.33706508 -0.14939290 -0.02084111 -0.61990485 0.16598897
103 104 105 106 107 108
-0.39983237 0.04237203 1.06573599 -1.06086259 -0.81609762 0.52649078
109 110 111 112 113 114
0.02816445 -0.80644051 -0.79908724 1.40001970 0.78764865 -1.27106837
115 116 117 118 119 120
-1.72135388 -1.07277033 -1.53091456 -1.35204506 0.69097363 -1.32067104
121 122 123 124 125 126
0.74589365 -1.24830451 -0.94113840 -1.67296958 -0.94963256 -0.47379761
127 128 129 130 131 132
0.37278528 -0.80524487 -1.31788408 1.49211069 -1.01596855 -0.33484446
133 134 135 136 137 138
-0.59362499 -1.34682616 -0.84699924 0.19732116 -0.60077809 -0.45021359
139 140 141 142 143 144
-0.31007571 0.81912538 0.26578428 1.05024669 0.56830243 1.00029168
145 146 147 148 149 150
-1.11073396 0.95994999 0.05165149 -0.25688475 -1.62407906 -0.35607443
151 152 153 154 155 156
-0.36789547 1.04594895 1.05467124 -0.60563074 0.48152007 -0.69293216
157 158 159
1.40952776 0.06925867 0.91996318
> postscript(file="/var/www/html/freestat/rcomp/tmp/6v2fv1291387490.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.88643494 NA
1 0.19461187 -0.88643494
2 -0.72404949 0.19461187
3 0.03364854 -0.72404949
4 0.36540608 0.03364854
5 0.86822460 0.36540608
6 0.30523087 0.86822460
7 -0.63660896 0.30523087
8 -0.10854628 -0.63660896
9 -0.42203361 -0.10854628
10 -0.04282107 -0.42203361
11 0.09193700 -0.04282107
12 0.17922740 0.09193700
13 -1.16584835 0.17922740
14 -0.72942807 -1.16584835
15 -0.50135853 -0.72942807
16 -0.85200689 -0.50135853
17 -0.74221904 -0.85200689
18 0.22941397 -0.74221904
19 -0.72278871 0.22941397
20 -1.49159443 -0.72278871
21 -0.74921034 -1.49159443
22 -0.97421428 -0.74921034
23 -0.82909336 -0.97421428
24 -0.11958935 -0.82909336
25 0.66939573 -0.11958935
26 0.16922265 0.66939573
27 0.26743542 0.16922265
28 0.17589801 0.26743542
29 0.55083879 0.17589801
30 0.42307088 0.55083879
31 0.34581807 0.42307088
32 0.14190338 0.34581807
33 -0.86290152 0.14190338
34 -0.67978003 -0.86290152
35 -0.66096882 -0.67978003
36 -0.90938649 -0.66096882
37 0.60394169 -0.90938649
38 -0.17312360 0.60394169
39 -0.67912731 -0.17312360
40 0.65125380 -0.67912731
41 0.39361811 0.65125380
42 -0.52643630 0.39361811
43 -0.70163884 -0.52643630
44 -1.07268198 -0.70163884
45 0.20276082 -1.07268198
46 0.28510497 0.20276082
47 -0.14353472 0.28510497
48 0.42574965 -0.14353472
49 0.71805703 0.42574965
50 0.37267049 0.71805703
51 0.48605277 0.37267049
52 0.98638994 0.48605277
53 0.38823952 0.98638994
54 -0.45302284 0.38823952
55 0.91531302 -0.45302284
56 0.62850128 0.91531302
57 0.03037062 0.62850128
58 0.15624428 0.03037062
59 0.58161289 0.15624428
60 0.57576022 0.58161289
61 0.79702667 0.57576022
62 1.72297616 0.79702667
63 0.63606334 1.72297616
64 0.75518388 0.63606334
65 0.88170927 0.75518388
66 0.15912235 0.88170927
67 -0.11559794 0.15912235
68 0.39729522 -0.11559794
69 -0.08614816 0.39729522
70 0.46019282 -0.08614816
71 0.55226995 0.46019282
72 -0.20024383 0.55226995
73 0.06172451 -0.20024383
74 0.39579942 0.06172451
75 -0.78768669 0.39579942
76 0.24570919 -0.78768669
77 -0.05262839 0.24570919
78 1.37819479 -0.05262839
79 1.20634773 1.37819479
80 1.31111547 1.20634773
81 1.33039176 1.31111547
82 0.61303100 1.33039176
83 0.90257561 0.61303100
84 0.18595706 0.90257561
85 0.83051845 0.18595706
86 0.73093556 0.83051845
87 0.89832704 0.73093556
88 0.67891189 0.89832704
89 0.09738218 0.67891189
90 0.91079842 0.09738218
91 -0.32027553 0.91079842
92 0.65661447 -0.32027553
93 1.03295144 0.65661447
94 0.27351236 1.03295144
95 0.32686665 0.27351236
96 0.26973336 0.32686665
97 -0.33706508 0.26973336
98 -0.14939290 -0.33706508
99 -0.02084111 -0.14939290
100 -0.61990485 -0.02084111
101 0.16598897 -0.61990485
102 -0.39983237 0.16598897
103 0.04237203 -0.39983237
104 1.06573599 0.04237203
105 -1.06086259 1.06573599
106 -0.81609762 -1.06086259
107 0.52649078 -0.81609762
108 0.02816445 0.52649078
109 -0.80644051 0.02816445
110 -0.79908724 -0.80644051
111 1.40001970 -0.79908724
112 0.78764865 1.40001970
113 -1.27106837 0.78764865
114 -1.72135388 -1.27106837
115 -1.07277033 -1.72135388
116 -1.53091456 -1.07277033
117 -1.35204506 -1.53091456
118 0.69097363 -1.35204506
119 -1.32067104 0.69097363
120 0.74589365 -1.32067104
121 -1.24830451 0.74589365
122 -0.94113840 -1.24830451
123 -1.67296958 -0.94113840
124 -0.94963256 -1.67296958
125 -0.47379761 -0.94963256
126 0.37278528 -0.47379761
127 -0.80524487 0.37278528
128 -1.31788408 -0.80524487
129 1.49211069 -1.31788408
130 -1.01596855 1.49211069
131 -0.33484446 -1.01596855
132 -0.59362499 -0.33484446
133 -1.34682616 -0.59362499
134 -0.84699924 -1.34682616
135 0.19732116 -0.84699924
136 -0.60077809 0.19732116
137 -0.45021359 -0.60077809
138 -0.31007571 -0.45021359
139 0.81912538 -0.31007571
140 0.26578428 0.81912538
141 1.05024669 0.26578428
142 0.56830243 1.05024669
143 1.00029168 0.56830243
144 -1.11073396 1.00029168
145 0.95994999 -1.11073396
146 0.05165149 0.95994999
147 -0.25688475 0.05165149
148 -1.62407906 -0.25688475
149 -0.35607443 -1.62407906
150 -0.36789547 -0.35607443
151 1.04594895 -0.36789547
152 1.05467124 1.04594895
153 -0.60563074 1.05467124
154 0.48152007 -0.60563074
155 -0.69293216 0.48152007
156 1.40952776 -0.69293216
157 0.06925867 1.40952776
158 0.91996318 0.06925867
159 NA 0.91996318
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.19461187 -0.88643494
[2,] -0.72404949 0.19461187
[3,] 0.03364854 -0.72404949
[4,] 0.36540608 0.03364854
[5,] 0.86822460 0.36540608
[6,] 0.30523087 0.86822460
[7,] -0.63660896 0.30523087
[8,] -0.10854628 -0.63660896
[9,] -0.42203361 -0.10854628
[10,] -0.04282107 -0.42203361
[11,] 0.09193700 -0.04282107
[12,] 0.17922740 0.09193700
[13,] -1.16584835 0.17922740
[14,] -0.72942807 -1.16584835
[15,] -0.50135853 -0.72942807
[16,] -0.85200689 -0.50135853
[17,] -0.74221904 -0.85200689
[18,] 0.22941397 -0.74221904
[19,] -0.72278871 0.22941397
[20,] -1.49159443 -0.72278871
[21,] -0.74921034 -1.49159443
[22,] -0.97421428 -0.74921034
[23,] -0.82909336 -0.97421428
[24,] -0.11958935 -0.82909336
[25,] 0.66939573 -0.11958935
[26,] 0.16922265 0.66939573
[27,] 0.26743542 0.16922265
[28,] 0.17589801 0.26743542
[29,] 0.55083879 0.17589801
[30,] 0.42307088 0.55083879
[31,] 0.34581807 0.42307088
[32,] 0.14190338 0.34581807
[33,] -0.86290152 0.14190338
[34,] -0.67978003 -0.86290152
[35,] -0.66096882 -0.67978003
[36,] -0.90938649 -0.66096882
[37,] 0.60394169 -0.90938649
[38,] -0.17312360 0.60394169
[39,] -0.67912731 -0.17312360
[40,] 0.65125380 -0.67912731
[41,] 0.39361811 0.65125380
[42,] -0.52643630 0.39361811
[43,] -0.70163884 -0.52643630
[44,] -1.07268198 -0.70163884
[45,] 0.20276082 -1.07268198
[46,] 0.28510497 0.20276082
[47,] -0.14353472 0.28510497
[48,] 0.42574965 -0.14353472
[49,] 0.71805703 0.42574965
[50,] 0.37267049 0.71805703
[51,] 0.48605277 0.37267049
[52,] 0.98638994 0.48605277
[53,] 0.38823952 0.98638994
[54,] -0.45302284 0.38823952
[55,] 0.91531302 -0.45302284
[56,] 0.62850128 0.91531302
[57,] 0.03037062 0.62850128
[58,] 0.15624428 0.03037062
[59,] 0.58161289 0.15624428
[60,] 0.57576022 0.58161289
[61,] 0.79702667 0.57576022
[62,] 1.72297616 0.79702667
[63,] 0.63606334 1.72297616
[64,] 0.75518388 0.63606334
[65,] 0.88170927 0.75518388
[66,] 0.15912235 0.88170927
[67,] -0.11559794 0.15912235
[68,] 0.39729522 -0.11559794
[69,] -0.08614816 0.39729522
[70,] 0.46019282 -0.08614816
[71,] 0.55226995 0.46019282
[72,] -0.20024383 0.55226995
[73,] 0.06172451 -0.20024383
[74,] 0.39579942 0.06172451
[75,] -0.78768669 0.39579942
[76,] 0.24570919 -0.78768669
[77,] -0.05262839 0.24570919
[78,] 1.37819479 -0.05262839
[79,] 1.20634773 1.37819479
[80,] 1.31111547 1.20634773
[81,] 1.33039176 1.31111547
[82,] 0.61303100 1.33039176
[83,] 0.90257561 0.61303100
[84,] 0.18595706 0.90257561
[85,] 0.83051845 0.18595706
[86,] 0.73093556 0.83051845
[87,] 0.89832704 0.73093556
[88,] 0.67891189 0.89832704
[89,] 0.09738218 0.67891189
[90,] 0.91079842 0.09738218
[91,] -0.32027553 0.91079842
[92,] 0.65661447 -0.32027553
[93,] 1.03295144 0.65661447
[94,] 0.27351236 1.03295144
[95,] 0.32686665 0.27351236
[96,] 0.26973336 0.32686665
[97,] -0.33706508 0.26973336
[98,] -0.14939290 -0.33706508
[99,] -0.02084111 -0.14939290
[100,] -0.61990485 -0.02084111
[101,] 0.16598897 -0.61990485
[102,] -0.39983237 0.16598897
[103,] 0.04237203 -0.39983237
[104,] 1.06573599 0.04237203
[105,] -1.06086259 1.06573599
[106,] -0.81609762 -1.06086259
[107,] 0.52649078 -0.81609762
[108,] 0.02816445 0.52649078
[109,] -0.80644051 0.02816445
[110,] -0.79908724 -0.80644051
[111,] 1.40001970 -0.79908724
[112,] 0.78764865 1.40001970
[113,] -1.27106837 0.78764865
[114,] -1.72135388 -1.27106837
[115,] -1.07277033 -1.72135388
[116,] -1.53091456 -1.07277033
[117,] -1.35204506 -1.53091456
[118,] 0.69097363 -1.35204506
[119,] -1.32067104 0.69097363
[120,] 0.74589365 -1.32067104
[121,] -1.24830451 0.74589365
[122,] -0.94113840 -1.24830451
[123,] -1.67296958 -0.94113840
[124,] -0.94963256 -1.67296958
[125,] -0.47379761 -0.94963256
[126,] 0.37278528 -0.47379761
[127,] -0.80524487 0.37278528
[128,] -1.31788408 -0.80524487
[129,] 1.49211069 -1.31788408
[130,] -1.01596855 1.49211069
[131,] -0.33484446 -1.01596855
[132,] -0.59362499 -0.33484446
[133,] -1.34682616 -0.59362499
[134,] -0.84699924 -1.34682616
[135,] 0.19732116 -0.84699924
[136,] -0.60077809 0.19732116
[137,] -0.45021359 -0.60077809
[138,] -0.31007571 -0.45021359
[139,] 0.81912538 -0.31007571
[140,] 0.26578428 0.81912538
[141,] 1.05024669 0.26578428
[142,] 0.56830243 1.05024669
[143,] 1.00029168 0.56830243
[144,] -1.11073396 1.00029168
[145,] 0.95994999 -1.11073396
[146,] 0.05165149 0.95994999
[147,] -0.25688475 0.05165149
[148,] -1.62407906 -0.25688475
[149,] -0.35607443 -1.62407906
[150,] -0.36789547 -0.35607443
[151,] 1.04594895 -0.36789547
[152,] 1.05467124 1.04594895
[153,] -0.60563074 1.05467124
[154,] 0.48152007 -0.60563074
[155,] -0.69293216 0.48152007
[156,] 1.40952776 -0.69293216
[157,] 0.06925867 1.40952776
[158,] 0.91996318 0.06925867
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.19461187 -0.88643494
2 -0.72404949 0.19461187
3 0.03364854 -0.72404949
4 0.36540608 0.03364854
5 0.86822460 0.36540608
6 0.30523087 0.86822460
7 -0.63660896 0.30523087
8 -0.10854628 -0.63660896
9 -0.42203361 -0.10854628
10 -0.04282107 -0.42203361
11 0.09193700 -0.04282107
12 0.17922740 0.09193700
13 -1.16584835 0.17922740
14 -0.72942807 -1.16584835
15 -0.50135853 -0.72942807
16 -0.85200689 -0.50135853
17 -0.74221904 -0.85200689
18 0.22941397 -0.74221904
19 -0.72278871 0.22941397
20 -1.49159443 -0.72278871
21 -0.74921034 -1.49159443
22 -0.97421428 -0.74921034
23 -0.82909336 -0.97421428
24 -0.11958935 -0.82909336
25 0.66939573 -0.11958935
26 0.16922265 0.66939573
27 0.26743542 0.16922265
28 0.17589801 0.26743542
29 0.55083879 0.17589801
30 0.42307088 0.55083879
31 0.34581807 0.42307088
32 0.14190338 0.34581807
33 -0.86290152 0.14190338
34 -0.67978003 -0.86290152
35 -0.66096882 -0.67978003
36 -0.90938649 -0.66096882
37 0.60394169 -0.90938649
38 -0.17312360 0.60394169
39 -0.67912731 -0.17312360
40 0.65125380 -0.67912731
41 0.39361811 0.65125380
42 -0.52643630 0.39361811
43 -0.70163884 -0.52643630
44 -1.07268198 -0.70163884
45 0.20276082 -1.07268198
46 0.28510497 0.20276082
47 -0.14353472 0.28510497
48 0.42574965 -0.14353472
49 0.71805703 0.42574965
50 0.37267049 0.71805703
51 0.48605277 0.37267049
52 0.98638994 0.48605277
53 0.38823952 0.98638994
54 -0.45302284 0.38823952
55 0.91531302 -0.45302284
56 0.62850128 0.91531302
57 0.03037062 0.62850128
58 0.15624428 0.03037062
59 0.58161289 0.15624428
60 0.57576022 0.58161289
61 0.79702667 0.57576022
62 1.72297616 0.79702667
63 0.63606334 1.72297616
64 0.75518388 0.63606334
65 0.88170927 0.75518388
66 0.15912235 0.88170927
67 -0.11559794 0.15912235
68 0.39729522 -0.11559794
69 -0.08614816 0.39729522
70 0.46019282 -0.08614816
71 0.55226995 0.46019282
72 -0.20024383 0.55226995
73 0.06172451 -0.20024383
74 0.39579942 0.06172451
75 -0.78768669 0.39579942
76 0.24570919 -0.78768669
77 -0.05262839 0.24570919
78 1.37819479 -0.05262839
79 1.20634773 1.37819479
80 1.31111547 1.20634773
81 1.33039176 1.31111547
82 0.61303100 1.33039176
83 0.90257561 0.61303100
84 0.18595706 0.90257561
85 0.83051845 0.18595706
86 0.73093556 0.83051845
87 0.89832704 0.73093556
88 0.67891189 0.89832704
89 0.09738218 0.67891189
90 0.91079842 0.09738218
91 -0.32027553 0.91079842
92 0.65661447 -0.32027553
93 1.03295144 0.65661447
94 0.27351236 1.03295144
95 0.32686665 0.27351236
96 0.26973336 0.32686665
97 -0.33706508 0.26973336
98 -0.14939290 -0.33706508
99 -0.02084111 -0.14939290
100 -0.61990485 -0.02084111
101 0.16598897 -0.61990485
102 -0.39983237 0.16598897
103 0.04237203 -0.39983237
104 1.06573599 0.04237203
105 -1.06086259 1.06573599
106 -0.81609762 -1.06086259
107 0.52649078 -0.81609762
108 0.02816445 0.52649078
109 -0.80644051 0.02816445
110 -0.79908724 -0.80644051
111 1.40001970 -0.79908724
112 0.78764865 1.40001970
113 -1.27106837 0.78764865
114 -1.72135388 -1.27106837
115 -1.07277033 -1.72135388
116 -1.53091456 -1.07277033
117 -1.35204506 -1.53091456
118 0.69097363 -1.35204506
119 -1.32067104 0.69097363
120 0.74589365 -1.32067104
121 -1.24830451 0.74589365
122 -0.94113840 -1.24830451
123 -1.67296958 -0.94113840
124 -0.94963256 -1.67296958
125 -0.47379761 -0.94963256
126 0.37278528 -0.47379761
127 -0.80524487 0.37278528
128 -1.31788408 -0.80524487
129 1.49211069 -1.31788408
130 -1.01596855 1.49211069
131 -0.33484446 -1.01596855
132 -0.59362499 -0.33484446
133 -1.34682616 -0.59362499
134 -0.84699924 -1.34682616
135 0.19732116 -0.84699924
136 -0.60077809 0.19732116
137 -0.45021359 -0.60077809
138 -0.31007571 -0.45021359
139 0.81912538 -0.31007571
140 0.26578428 0.81912538
141 1.05024669 0.26578428
142 0.56830243 1.05024669
143 1.00029168 0.56830243
144 -1.11073396 1.00029168
145 0.95994999 -1.11073396
146 0.05165149 0.95994999
147 -0.25688475 0.05165149
148 -1.62407906 -0.25688475
149 -0.35607443 -1.62407906
150 -0.36789547 -0.35607443
151 1.04594895 -0.36789547
152 1.05467124 1.04594895
153 -0.60563074 1.05467124
154 0.48152007 -0.60563074
155 -0.69293216 0.48152007
156 1.40952776 -0.69293216
157 0.06925867 1.40952776
158 0.91996318 0.06925867
> 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/75tey1291387490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/85tey1291387490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/95tey1291387490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10y2d11291387490.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jlu71291387490.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/1253sv1291387490.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/13umpp1291387490.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/144v6r1291387490.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/157enf1291387490.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/164o2o1291387490.tab")
+ }
>
> try(system("convert tmp/1r1yq1291387490.ps tmp/1r1yq1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/22axs1291387490.ps tmp/22axs1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/32axs1291387490.ps tmp/32axs1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/42axs1291387490.ps tmp/42axs1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/52axs1291387490.ps tmp/52axs1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/6v2fv1291387490.ps tmp/6v2fv1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/75tey1291387490.ps tmp/75tey1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/85tey1291387490.ps tmp/85tey1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/95tey1291387490.ps tmp/95tey1291387490.png",intern=TRUE))
character(0)
> try(system("convert tmp/10y2d11291387490.ps tmp/10y2d11291387490.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.884 2.708 6.283