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.
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(1778.8
+ ,0
+ ,1264.9
+ ,0
+ ,1749.1
+ ,0
+ ,1795.6
+ ,0
+ ,1759
+ ,0
+ ,1645.1
+ ,0
+ ,1589.9
+ ,0
+ ,1712.6
+ ,0
+ ,1782.5
+ ,0
+ ,1606.6
+ ,0
+ ,1882.1
+ ,0
+ ,1846.9
+ ,0
+ ,1873.2
+ ,0
+ ,1368.3
+ ,0
+ ,1843.5
+ ,0
+ ,2074.5
+ ,0
+ ,1848.5
+ ,0
+ ,1909.3
+ ,0
+ ,1932.9
+ ,0
+ ,2119.1
+ ,0
+ ,2202
+ ,0
+ ,2260.8
+ ,0
+ ,2097.1
+ ,0
+ ,2026.2
+ ,0
+ ,2475.2
+ ,0
+ ,1732.3
+ ,0
+ ,2385.2
+ ,0
+ ,2362.2
+ ,0
+ ,2119
+ ,0
+ ,2260.3
+ ,0
+ ,2006.5
+ ,0
+ ,2073.2
+ ,0
+ ,2207.8
+ ,0
+ ,2018.9
+ ,0
+ ,2082.8
+ ,0
+ ,2314.3
+ ,0
+ ,2252.7
+ ,0
+ ,1633.1
+ ,0
+ ,2161.1
+ ,0
+ ,1987.9
+ ,0
+ ,1870.3
+ ,0
+ ,1984.6
+ ,0
+ ,1735.9
+ ,0
+ ,1910
+ ,0
+ ,2410.1
+ ,0
+ ,1994.6
+ ,0
+ ,2152.3
+ ,0
+ ,2554
+ ,0
+ ,2754.5
+ ,0
+ ,1812.3
+ ,0
+ ,2549.9
+ ,0
+ ,2558.4
+ ,0
+ ,2279.2
+ ,0
+ ,2591.8
+ ,0
+ ,2442.4
+ ,0
+ ,2607.7
+ ,0
+ ,3106.7
+ ,0
+ ,2447.5
+ ,0
+ ,3129.5
+ ,0
+ ,2606.6
+ ,0
+ ,2964.4
+ ,0
+ ,2211.6
+ ,0
+ ,3246.1
+ ,0
+ ,3141.8
+ ,0
+ ,3125.9
+ ,0
+ ,2890.5
+ ,0
+ ,2554.3
+ ,0
+ ,2771.1
+ ,0
+ ,2950
+ ,0
+ ,2512.1
+ ,0
+ ,2800
+ ,0
+ ,2877.2
+ ,0
+ ,3048.7
+ ,0
+ ,2082.7
+ ,0
+ ,2454.8
+ ,0
+ ,2807.8
+ ,0
+ ,2627.6
+ ,0
+ ,2515.9
+ ,0
+ ,2690.3
+ ,0
+ ,2770.8
+ ,0
+ ,2907.7
+ ,0
+ ,2906.3
+ ,0
+ ,3104.6
+ ,0
+ ,2862.1
+ ,0
+ ,3189.1
+ ,0
+ ,2071.8
+ ,0
+ ,2907.7
+ ,0
+ ,3194.5
+ ,0
+ ,2722.9
+ ,0
+ ,2854.8
+ ,0
+ ,2803
+ ,0
+ ,2744.9
+ ,0
+ ,2574.2
+ ,0
+ ,2740.9
+ ,0
+ ,2635.9
+ ,0
+ ,2612.7
+ ,0
+ ,3094.2
+ ,0
+ ,2029
+ ,0
+ ,2931.1
+ ,0
+ ,2952.2
+ ,0
+ ,2601.9
+ ,0
+ ,2874
+ ,0
+ ,2570.9
+ ,0
+ ,2849.8
+ ,0
+ ,3171.5
+ ,0
+ ,2843.6
+ ,0
+ ,2831.5
+ ,0
+ ,3284.4
+ ,0
+ ,3230.1
+ ,0
+ ,2412.2
+ ,0
+ ,3052.7
+ ,0
+ ,3048.9
+ ,0
+ ,2819.9
+ ,0
+ ,2962.7
+ ,0
+ ,2796.6
+ ,0
+ ,2857.2
+ ,0
+ ,3213.1
+ ,0
+ ,3116.2
+ ,0
+ ,3340.1
+ ,0
+ ,3602
+ ,0
+ ,3626.4
+ ,0
+ ,2741.6
+ ,1
+ ,3756.2
+ ,1
+ ,3140
+ ,1
+ ,3421.6
+ ,1
+ ,3243.7
+ ,1
+ ,3085.2
+ ,1
+ ,3152.8
+ ,1
+ ,3543.6
+ ,1
+ ,2959.3
+ ,1
+ ,3594.1
+ ,1
+ ,3207.9
+ ,1
+ ,3366.7
+ ,1
+ ,2658.4
+ ,1
+ ,3340.4
+ ,1
+ ,3368.4
+ ,1
+ ,3422.1
+ ,1
+ ,3268
+ ,1
+ ,3234.4
+ ,1
+ ,3365.1
+ ,1
+ ,3923.6
+ ,1
+ ,3147.3
+ ,1
+ ,3447.7
+ ,1
+ ,3719.8
+ ,1
+ ,4090.4
+ ,1
+ ,3386.7
+ ,1
+ ,3436.8
+ ,1
+ ,3744.9
+ ,1
+ ,3325.8
+ ,1
+ ,3322.1
+ ,1
+ ,3338.6
+ ,1
+ ,3464.2
+ ,1
+ ,3404.1
+ ,1
+ ,3942
+ ,1
+ ,3859.9
+ ,1
+ ,3895.4
+ ,1
+ ,4472.2
+ ,1
+ ,3025.5
+ ,1
+ ,4285.9
+ ,1)
+ ,dim=c(2
+ ,159)
+ ,dimnames=list(c('x'
+ ,'y')
+ ,1:159))
> y <- array(NA,dim=c(2,159),dimnames=list(c('x','y'),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
x y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 1778.8 0 1 0 0 0 0 0 0 0 0 0 0 1
2 1264.9 0 0 1 0 0 0 0 0 0 0 0 0 2
3 1749.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 1795.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 1759.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 1645.1 0 0 0 0 0 0 1 0 0 0 0 0 6
7 1589.9 0 0 0 0 0 0 0 1 0 0 0 0 7
8 1712.6 0 0 0 0 0 0 0 0 1 0 0 0 8
9 1782.5 0 0 0 0 0 0 0 0 0 1 0 0 9
10 1606.6 0 0 0 0 0 0 0 0 0 0 1 0 10
11 1882.1 0 0 0 0 0 0 0 0 0 0 0 1 11
12 1846.9 0 0 0 0 0 0 0 0 0 0 0 0 12
13 1873.2 0 1 0 0 0 0 0 0 0 0 0 0 13
14 1368.3 0 0 1 0 0 0 0 0 0 0 0 0 14
15 1843.5 0 0 0 1 0 0 0 0 0 0 0 0 15
16 2074.5 0 0 0 0 1 0 0 0 0 0 0 0 16
17 1848.5 0 0 0 0 0 1 0 0 0 0 0 0 17
18 1909.3 0 0 0 0 0 0 1 0 0 0 0 0 18
19 1932.9 0 0 0 0 0 0 0 1 0 0 0 0 19
20 2119.1 0 0 0 0 0 0 0 0 1 0 0 0 20
21 2202.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 2260.8 0 0 0 0 0 0 0 0 0 0 1 0 22
23 2097.1 0 0 0 0 0 0 0 0 0 0 0 1 23
24 2026.2 0 0 0 0 0 0 0 0 0 0 0 0 24
25 2475.2 0 1 0 0 0 0 0 0 0 0 0 0 25
26 1732.3 0 0 1 0 0 0 0 0 0 0 0 0 26
27 2385.2 0 0 0 1 0 0 0 0 0 0 0 0 27
28 2362.2 0 0 0 0 1 0 0 0 0 0 0 0 28
29 2119.0 0 0 0 0 0 1 0 0 0 0 0 0 29
30 2260.3 0 0 0 0 0 0 1 0 0 0 0 0 30
31 2006.5 0 0 0 0 0 0 0 1 0 0 0 0 31
32 2073.2 0 0 0 0 0 0 0 0 1 0 0 0 32
33 2207.8 0 0 0 0 0 0 0 0 0 1 0 0 33
34 2018.9 0 0 0 0 0 0 0 0 0 0 1 0 34
35 2082.8 0 0 0 0 0 0 0 0 0 0 0 1 35
36 2314.3 0 0 0 0 0 0 0 0 0 0 0 0 36
37 2252.7 0 1 0 0 0 0 0 0 0 0 0 0 37
38 1633.1 0 0 1 0 0 0 0 0 0 0 0 0 38
39 2161.1 0 0 0 1 0 0 0 0 0 0 0 0 39
40 1987.9 0 0 0 0 1 0 0 0 0 0 0 0 40
41 1870.3 0 0 0 0 0 1 0 0 0 0 0 0 41
42 1984.6 0 0 0 0 0 0 1 0 0 0 0 0 42
43 1735.9 0 0 0 0 0 0 0 1 0 0 0 0 43
44 1910.0 0 0 0 0 0 0 0 0 1 0 0 0 44
45 2410.1 0 0 0 0 0 0 0 0 0 1 0 0 45
46 1994.6 0 0 0 0 0 0 0 0 0 0 1 0 46
47 2152.3 0 0 0 0 0 0 0 0 0 0 0 1 47
48 2554.0 0 0 0 0 0 0 0 0 0 0 0 0 48
49 2754.5 0 1 0 0 0 0 0 0 0 0 0 0 49
50 1812.3 0 0 1 0 0 0 0 0 0 0 0 0 50
51 2549.9 0 0 0 1 0 0 0 0 0 0 0 0 51
52 2558.4 0 0 0 0 1 0 0 0 0 0 0 0 52
53 2279.2 0 0 0 0 0 1 0 0 0 0 0 0 53
54 2591.8 0 0 0 0 0 0 1 0 0 0 0 0 54
55 2442.4 0 0 0 0 0 0 0 1 0 0 0 0 55
56 2607.7 0 0 0 0 0 0 0 0 1 0 0 0 56
57 3106.7 0 0 0 0 0 0 0 0 0 1 0 0 57
58 2447.5 0 0 0 0 0 0 0 0 0 0 1 0 58
59 3129.5 0 0 0 0 0 0 0 0 0 0 0 1 59
60 2606.6 0 0 0 0 0 0 0 0 0 0 0 0 60
61 2964.4 0 1 0 0 0 0 0 0 0 0 0 0 61
62 2211.6 0 0 1 0 0 0 0 0 0 0 0 0 62
63 3246.1 0 0 0 1 0 0 0 0 0 0 0 0 63
64 3141.8 0 0 0 0 1 0 0 0 0 0 0 0 64
65 3125.9 0 0 0 0 0 1 0 0 0 0 0 0 65
66 2890.5 0 0 0 0 0 0 1 0 0 0 0 0 66
67 2554.3 0 0 0 0 0 0 0 1 0 0 0 0 67
68 2771.1 0 0 0 0 0 0 0 0 1 0 0 0 68
69 2950.0 0 0 0 0 0 0 0 0 0 1 0 0 69
70 2512.1 0 0 0 0 0 0 0 0 0 0 1 0 70
71 2800.0 0 0 0 0 0 0 0 0 0 0 0 1 71
72 2877.2 0 0 0 0 0 0 0 0 0 0 0 0 72
73 3048.7 0 1 0 0 0 0 0 0 0 0 0 0 73
74 2082.7 0 0 1 0 0 0 0 0 0 0 0 0 74
75 2454.8 0 0 0 1 0 0 0 0 0 0 0 0 75
76 2807.8 0 0 0 0 1 0 0 0 0 0 0 0 76
77 2627.6 0 0 0 0 0 1 0 0 0 0 0 0 77
78 2515.9 0 0 0 0 0 0 1 0 0 0 0 0 78
79 2690.3 0 0 0 0 0 0 0 1 0 0 0 0 79
80 2770.8 0 0 0 0 0 0 0 0 1 0 0 0 80
81 2907.7 0 0 0 0 0 0 0 0 0 1 0 0 81
82 2906.3 0 0 0 0 0 0 0 0 0 0 1 0 82
83 3104.6 0 0 0 0 0 0 0 0 0 0 0 1 83
84 2862.1 0 0 0 0 0 0 0 0 0 0 0 0 84
85 3189.1 0 1 0 0 0 0 0 0 0 0 0 0 85
86 2071.8 0 0 1 0 0 0 0 0 0 0 0 0 86
87 2907.7 0 0 0 1 0 0 0 0 0 0 0 0 87
88 3194.5 0 0 0 0 1 0 0 0 0 0 0 0 88
89 2722.9 0 0 0 0 0 1 0 0 0 0 0 0 89
90 2854.8 0 0 0 0 0 0 1 0 0 0 0 0 90
91 2803.0 0 0 0 0 0 0 0 1 0 0 0 0 91
92 2744.9 0 0 0 0 0 0 0 0 1 0 0 0 92
93 2574.2 0 0 0 0 0 0 0 0 0 1 0 0 93
94 2740.9 0 0 0 0 0 0 0 0 0 0 1 0 94
95 2635.9 0 0 0 0 0 0 0 0 0 0 0 1 95
96 2612.7 0 0 0 0 0 0 0 0 0 0 0 0 96
97 3094.2 0 1 0 0 0 0 0 0 0 0 0 0 97
98 2029.0 0 0 1 0 0 0 0 0 0 0 0 0 98
99 2931.1 0 0 0 1 0 0 0 0 0 0 0 0 99
100 2952.2 0 0 0 0 1 0 0 0 0 0 0 0 100
101 2601.9 0 0 0 0 0 1 0 0 0 0 0 0 101
102 2874.0 0 0 0 0 0 0 1 0 0 0 0 0 102
103 2570.9 0 0 0 0 0 0 0 1 0 0 0 0 103
104 2849.8 0 0 0 0 0 0 0 0 1 0 0 0 104
105 3171.5 0 0 0 0 0 0 0 0 0 1 0 0 105
106 2843.6 0 0 0 0 0 0 0 0 0 0 1 0 106
107 2831.5 0 0 0 0 0 0 0 0 0 0 0 1 107
108 3284.4 0 0 0 0 0 0 0 0 0 0 0 0 108
109 3230.1 0 1 0 0 0 0 0 0 0 0 0 0 109
110 2412.2 0 0 1 0 0 0 0 0 0 0 0 0 110
111 3052.7 0 0 0 1 0 0 0 0 0 0 0 0 111
112 3048.9 0 0 0 0 1 0 0 0 0 0 0 0 112
113 2819.9 0 0 0 0 0 1 0 0 0 0 0 0 113
114 2962.7 0 0 0 0 0 0 1 0 0 0 0 0 114
115 2796.6 0 0 0 0 0 0 0 1 0 0 0 0 115
116 2857.2 0 0 0 0 0 0 0 0 1 0 0 0 116
117 3213.1 0 0 0 0 0 0 0 0 0 1 0 0 117
118 3116.2 0 0 0 0 0 0 0 0 0 0 1 0 118
119 3340.1 0 0 0 0 0 0 0 0 0 0 0 1 119
120 3602.0 0 0 0 0 0 0 0 0 0 0 0 0 120
121 3626.4 0 1 0 0 0 0 0 0 0 0 0 0 121
122 2741.6 1 0 1 0 0 0 0 0 0 0 0 0 122
123 3756.2 1 0 0 1 0 0 0 0 0 0 0 0 123
124 3140.0 1 0 0 0 1 0 0 0 0 0 0 0 124
125 3421.6 1 0 0 0 0 1 0 0 0 0 0 0 125
126 3243.7 1 0 0 0 0 0 1 0 0 0 0 0 126
127 3085.2 1 0 0 0 0 0 0 1 0 0 0 0 127
128 3152.8 1 0 0 0 0 0 0 0 1 0 0 0 128
129 3543.6 1 0 0 0 0 0 0 0 0 1 0 0 129
130 2959.3 1 0 0 0 0 0 0 0 0 0 1 0 130
131 3594.1 1 0 0 0 0 0 0 0 0 0 0 1 131
132 3207.9 1 0 0 0 0 0 0 0 0 0 0 0 132
133 3366.7 1 1 0 0 0 0 0 0 0 0 0 0 133
134 2658.4 1 0 1 0 0 0 0 0 0 0 0 0 134
135 3340.4 1 0 0 1 0 0 0 0 0 0 0 0 135
136 3368.4 1 0 0 0 1 0 0 0 0 0 0 0 136
137 3422.1 1 0 0 0 0 1 0 0 0 0 0 0 137
138 3268.0 1 0 0 0 0 0 1 0 0 0 0 0 138
139 3234.4 1 0 0 0 0 0 0 1 0 0 0 0 139
140 3365.1 1 0 0 0 0 0 0 0 1 0 0 0 140
141 3923.6 1 0 0 0 0 0 0 0 0 1 0 0 141
142 3147.3 1 0 0 0 0 0 0 0 0 0 1 0 142
143 3447.7 1 0 0 0 0 0 0 0 0 0 0 1 143
144 3719.8 1 0 0 0 0 0 0 0 0 0 0 0 144
145 4090.4 1 1 0 0 0 0 0 0 0 0 0 0 145
146 3386.7 1 0 1 0 0 0 0 0 0 0 0 0 146
147 3436.8 1 0 0 1 0 0 0 0 0 0 0 0 147
148 3744.9 1 0 0 0 1 0 0 0 0 0 0 0 148
149 3325.8 1 0 0 0 0 1 0 0 0 0 0 0 149
150 3322.1 1 0 0 0 0 0 1 0 0 0 0 0 150
151 3338.6 1 0 0 0 0 0 0 1 0 0 0 0 151
152 3464.2 1 0 0 0 0 0 0 0 1 0 0 0 152
153 3404.1 1 0 0 0 0 0 0 0 0 1 0 0 153
154 3942.0 1 0 0 0 0 0 0 0 0 0 1 0 154
155 3859.9 1 0 0 0 0 0 0 0 0 0 0 1 155
156 3895.4 1 0 0 0 0 0 0 0 0 0 0 0 156
157 4472.2 1 1 0 0 0 0 0 0 0 0 0 0 157
158 3025.5 1 0 1 0 0 0 0 0 0 0 0 0 158
159 4285.9 1 0 0 1 0 0 0 0 0 0 0 0 159
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) y M1 M2 M3 M4
1860.374 31.818 198.457 -657.710 20.989 1.385
M5 M6 M7 M8 M9 M10
-182.439 -165.300 -295.931 -183.523 35.100 -200.062
M11 t
-22.738 12.023
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-439.42 -159.88 -17.07 126.20 666.47
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1860.3737 74.9187 24.832 < 2e-16 ***
y 31.8182 63.3521 0.502 0.61626
M1 198.4574 88.1338 2.252 0.02584 *
M2 -657.7098 88.2732 -7.451 7.68e-12 ***
M3 20.9885 88.2423 0.238 0.81233
M4 1.3845 89.8445 0.015 0.98773
M5 -182.4385 89.8155 -2.031 0.04406 *
M6 -165.3001 89.7905 -1.841 0.06767 .
M7 -295.9308 89.7692 -3.297 0.00123 **
M8 -183.5231 89.7519 -2.045 0.04268 *
M9 35.1000 89.7384 0.391 0.69627
M10 -200.0616 89.7287 -2.230 0.02731 *
M11 -22.7385 89.7229 -0.253 0.80029
t 12.0231 0.5886 20.428 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 228.7 on 145 degrees of freedom
Multiple R-squared: 0.8835, Adjusted R-squared: 0.873
F-statistic: 84.58 on 13 and 145 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,] 0.030874342 0.061748685 0.9691257
[2,] 0.016174639 0.032349277 0.9838254
[3,] 0.015025671 0.030051342 0.9849743
[4,] 0.017071399 0.034142798 0.9829286
[5,] 0.015031696 0.030063393 0.9849683
[6,] 0.058300269 0.116600538 0.9416997
[7,] 0.031864869 0.063729738 0.9681351
[8,] 0.017936154 0.035872307 0.9820638
[9,] 0.019522026 0.039044052 0.9804780
[10,] 0.010326376 0.020652753 0.9896736
[11,] 0.006983115 0.013966230 0.9930169
[12,] 0.003530012 0.007060025 0.9964700
[13,] 0.002299044 0.004598088 0.9977010
[14,] 0.001146625 0.002293251 0.9988534
[15,] 0.001031062 0.002062125 0.9989689
[16,] 0.001463535 0.002927071 0.9985365
[17,] 0.001218412 0.002436824 0.9987816
[18,] 0.002017785 0.004035570 0.9979822
[19,] 0.002660563 0.005321127 0.9973394
[20,] 0.001463725 0.002927450 0.9985363
[21,] 0.002010821 0.004021643 0.9979892
[22,] 0.002345385 0.004690770 0.9976546
[23,] 0.002708992 0.005417984 0.9972910
[24,] 0.013695250 0.027390500 0.9863048
[25,] 0.027744331 0.055488662 0.9722557
[26,] 0.032355566 0.064711133 0.9676444
[27,] 0.064481055 0.128962110 0.9355189
[28,] 0.090668271 0.181336542 0.9093317
[29,] 0.073734045 0.147468091 0.9262660
[30,] 0.079505252 0.159010504 0.9204947
[31,] 0.082122529 0.164245057 0.9178775
[32,] 0.080117550 0.160235099 0.9198825
[33,] 0.098062543 0.196125087 0.9019375
[34,] 0.077735215 0.155470430 0.9222648
[35,] 0.069052968 0.138105936 0.9309470
[36,] 0.058596249 0.117192499 0.9414038
[37,] 0.048167730 0.096335459 0.9518323
[38,] 0.049718991 0.099437981 0.9502810
[39,] 0.047589995 0.095179990 0.9524100
[40,] 0.047032179 0.094064358 0.9529678
[41,] 0.116568140 0.233136280 0.8834319
[42,] 0.093899483 0.187798965 0.9061005
[43,] 0.243194685 0.486389371 0.7568053
[44,] 0.209882406 0.419764812 0.7901176
[45,] 0.188350441 0.376700882 0.8116496
[46,] 0.163398191 0.326796381 0.8366018
[47,] 0.308271997 0.616543994 0.6917280
[48,] 0.395875165 0.791750331 0.6041248
[49,] 0.629184248 0.741631503 0.3708158
[50,] 0.656931825 0.686136350 0.3430682
[51,] 0.620877371 0.758245259 0.3791226
[52,] 0.607642171 0.784715658 0.3923578
[53,] 0.589418781 0.821162438 0.4105812
[54,] 0.559087825 0.881824351 0.4409122
[55,] 0.521335585 0.957328830 0.4786644
[56,] 0.482134676 0.964269353 0.5178653
[57,] 0.435260258 0.870520516 0.5647397
[58,] 0.439306929 0.878613858 0.5606931
[59,] 0.587981454 0.824037091 0.4120185
[60,] 0.561763234 0.876473531 0.4382368
[61,] 0.539718839 0.920562323 0.4602812
[62,] 0.549488970 0.901022061 0.4505110
[63,] 0.529572621 0.940854759 0.4704274
[64,] 0.511825852 0.976348296 0.4881741
[65,] 0.490833806 0.981667613 0.5091662
[66,] 0.505898073 0.988203853 0.4941019
[67,] 0.544435070 0.911129859 0.4555649
[68,] 0.511099009 0.977801981 0.4889010
[69,] 0.476318575 0.952637150 0.5236814
[70,] 0.491916240 0.983832481 0.5080838
[71,] 0.456410841 0.912821683 0.5435892
[72,] 0.534736345 0.930527309 0.4652637
[73,] 0.523905124 0.952189751 0.4760949
[74,] 0.529723560 0.940552881 0.4702764
[75,] 0.568547632 0.862904736 0.4314524
[76,] 0.571001014 0.857997973 0.4289990
[77,] 0.693882640 0.612234719 0.3061174
[78,] 0.672719996 0.654560009 0.3272800
[79,] 0.713765983 0.572468034 0.2862340
[80,] 0.780604870 0.438790261 0.2193951
[81,] 0.750602900 0.498794200 0.2493971
[82,] 0.784718816 0.430562368 0.2152812
[83,] 0.755213375 0.489573250 0.2447866
[84,] 0.730880723 0.538238555 0.2691193
[85,] 0.735146499 0.529707003 0.2648535
[86,] 0.705643401 0.588713198 0.2943566
[87,] 0.686071601 0.627856797 0.3139284
[88,] 0.650415032 0.699169936 0.3495850
[89,] 0.609045891 0.781908218 0.3909541
[90,] 0.561464687 0.877070625 0.4385353
[91,] 0.567643274 0.864713452 0.4323567
[92,] 0.533905468 0.932189065 0.4660945
[93,] 0.500644090 0.998711820 0.4993559
[94,] 0.451838432 0.903676864 0.5481616
[95,] 0.425350769 0.850701538 0.5746492
[96,] 0.381550898 0.763101796 0.6184491
[97,] 0.370185621 0.740371243 0.6298144
[98,] 0.321159442 0.642318885 0.6788406
[99,] 0.283192873 0.566385746 0.7168071
[100,] 0.263034020 0.526068039 0.7369660
[101,] 0.230227510 0.460455019 0.7697725
[102,] 0.187676489 0.375352978 0.8123235
[103,] 0.154511325 0.309022649 0.8454887
[104,] 0.154136472 0.308272945 0.8458635
[105,] 0.123764960 0.247529920 0.8762350
[106,] 0.097106275 0.194212551 0.9028937
[107,] 0.142123895 0.284247790 0.8578761
[108,] 0.127999546 0.255999092 0.8720005
[109,] 0.140718938 0.281437876 0.8592811
[110,] 0.137309655 0.274619311 0.8626903
[111,] 0.113760346 0.227520693 0.8862397
[112,] 0.089812702 0.179625405 0.9101873
[113,] 0.083376843 0.166753686 0.9166232
[114,] 0.069436643 0.138873286 0.9305634
[115,] 0.076778297 0.153556594 0.9232217
[116,] 0.060016355 0.120032710 0.9399836
[117,] 0.098281386 0.196562771 0.9017186
[118,] 0.077499680 0.154999360 0.9225003
[119,] 0.060850933 0.121701866 0.9391491
[120,] 0.043290848 0.086581697 0.9567092
[121,] 0.033504965 0.067009930 0.9664950
[122,] 0.020721438 0.041442876 0.9792786
[123,] 0.011461023 0.022922047 0.9885390
[124,] 0.005903723 0.011807447 0.9940963
[125,] 0.044705694 0.089411388 0.9552943
[126,] 0.052240470 0.104480941 0.9477595
> postscript(file="/var/www/html/freestat/rcomp/tmp/1ok661230123185.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2dn0h1230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3y46n1230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4zprv1230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/513rz1230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 159
Frequency = 1
1 2 3 4 5 6
-292.054196 38.189964 -168.331465 -114.250525 20.949475 -122.112063
7 8 9 10 11 12
-58.704371 -60.435140 -221.181294 -173.942832 -87.788986 -157.750525
13 14 15 16 17 18
-341.930971 -2.686811 -218.208239 20.372700 -33.827300 -2.188838
19 20 21 22 23 24
140.018854 201.788085 54.041931 335.980393 -17.065761 -122.727300
25 26 27 28 29 30
115.792254 217.036414 179.214986 163.795925 92.395925 204.534387
31 32 33 34 35 36
69.342079 11.611310 -84.434844 -50.196382 -175.642536 21.095925
37 38 39 40 41 42
-250.984521 -26.440361 -189.161789 -354.780850 -300.580850 -215.442388
43 44 45 46 47 48
-345.534696 -295.865465 -26.411619 -218.773157 -250.419311 116.519150
49 50 51 52 53 54
106.538704 8.482864 55.361436 71.442375 -35.957625 247.480837
55 56 57 58 59 60
216.688529 257.557760 525.911606 89.850068 582.503914 24.842375
61 62 63 64 65 66
172.161929 263.506089 607.284661 510.565600 666.465600 401.904062
67 68 69 70 71 72
184.311754 276.680985 224.934831 10.173293 108.727139 151.165600
73 74 75 76 77 78
112.185154 -9.670686 -328.292114 32.288826 23.888826 -116.972713
79 80 81 82 83 84
176.034979 132.104210 38.358056 260.096518 269.050364 -8.211174
85 86 87 88 89 90
108.308379 -164.847461 -19.668889 274.712051 -25.087949 77.650512
91 92 93 94 95 96
144.458204 -38.072565 -439.418719 -49.580257 -343.926411 -401.887949
97 98 99 100 101 102
-130.868396 -351.924236 -140.545664 -111.864724 -290.364724 -47.426263
103 104 105 106 107 108
-231.918571 -77.449340 13.604506 -91.157032 -292.603186 125.535276
109 110 111 112 113 114
-139.245171 -113.001011 -163.222439 -159.441499 -216.641499 -103.003038
115 116 117 118 119 120
-150.495345 -214.326115 -89.072269 37.166193 71.720039 298.858501
121 122 123 124 125 126
112.778054 40.303971 364.182542 -244.436518 208.963482 1.901943
127 128 129 130 131 132
-37.990364 -94.821134 65.332713 -295.828826 149.625020 -271.336518
133 134 135 136 137 138
-323.016965 -187.172804 -195.894233 -160.313293 65.186707 -118.074832
139 140 141 142 143 144
-33.067139 -26.797909 301.055938 -252.105601 -141.051755 96.286707
145 146 147 148 149 150
256.406260 396.850421 -243.771008 71.909932 -175.390068 -208.251607
151 152 153 154 155 156
-73.143914 -71.974684 -362.720837 398.317624 126.871470 127.609932
157 158 159
493.929485 -108.626354 461.052217
> postscript(file="/var/www/html/freestat/rcomp/tmp/619v31230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 -292.054196 NA
1 38.189964 -292.054196
2 -168.331465 38.189964
3 -114.250525 -168.331465
4 20.949475 -114.250525
5 -122.112063 20.949475
6 -58.704371 -122.112063
7 -60.435140 -58.704371
8 -221.181294 -60.435140
9 -173.942832 -221.181294
10 -87.788986 -173.942832
11 -157.750525 -87.788986
12 -341.930971 -157.750525
13 -2.686811 -341.930971
14 -218.208239 -2.686811
15 20.372700 -218.208239
16 -33.827300 20.372700
17 -2.188838 -33.827300
18 140.018854 -2.188838
19 201.788085 140.018854
20 54.041931 201.788085
21 335.980393 54.041931
22 -17.065761 335.980393
23 -122.727300 -17.065761
24 115.792254 -122.727300
25 217.036414 115.792254
26 179.214986 217.036414
27 163.795925 179.214986
28 92.395925 163.795925
29 204.534387 92.395925
30 69.342079 204.534387
31 11.611310 69.342079
32 -84.434844 11.611310
33 -50.196382 -84.434844
34 -175.642536 -50.196382
35 21.095925 -175.642536
36 -250.984521 21.095925
37 -26.440361 -250.984521
38 -189.161789 -26.440361
39 -354.780850 -189.161789
40 -300.580850 -354.780850
41 -215.442388 -300.580850
42 -345.534696 -215.442388
43 -295.865465 -345.534696
44 -26.411619 -295.865465
45 -218.773157 -26.411619
46 -250.419311 -218.773157
47 116.519150 -250.419311
48 106.538704 116.519150
49 8.482864 106.538704
50 55.361436 8.482864
51 71.442375 55.361436
52 -35.957625 71.442375
53 247.480837 -35.957625
54 216.688529 247.480837
55 257.557760 216.688529
56 525.911606 257.557760
57 89.850068 525.911606
58 582.503914 89.850068
59 24.842375 582.503914
60 172.161929 24.842375
61 263.506089 172.161929
62 607.284661 263.506089
63 510.565600 607.284661
64 666.465600 510.565600
65 401.904062 666.465600
66 184.311754 401.904062
67 276.680985 184.311754
68 224.934831 276.680985
69 10.173293 224.934831
70 108.727139 10.173293
71 151.165600 108.727139
72 112.185154 151.165600
73 -9.670686 112.185154
74 -328.292114 -9.670686
75 32.288826 -328.292114
76 23.888826 32.288826
77 -116.972713 23.888826
78 176.034979 -116.972713
79 132.104210 176.034979
80 38.358056 132.104210
81 260.096518 38.358056
82 269.050364 260.096518
83 -8.211174 269.050364
84 108.308379 -8.211174
85 -164.847461 108.308379
86 -19.668889 -164.847461
87 274.712051 -19.668889
88 -25.087949 274.712051
89 77.650512 -25.087949
90 144.458204 77.650512
91 -38.072565 144.458204
92 -439.418719 -38.072565
93 -49.580257 -439.418719
94 -343.926411 -49.580257
95 -401.887949 -343.926411
96 -130.868396 -401.887949
97 -351.924236 -130.868396
98 -140.545664 -351.924236
99 -111.864724 -140.545664
100 -290.364724 -111.864724
101 -47.426263 -290.364724
102 -231.918571 -47.426263
103 -77.449340 -231.918571
104 13.604506 -77.449340
105 -91.157032 13.604506
106 -292.603186 -91.157032
107 125.535276 -292.603186
108 -139.245171 125.535276
109 -113.001011 -139.245171
110 -163.222439 -113.001011
111 -159.441499 -163.222439
112 -216.641499 -159.441499
113 -103.003038 -216.641499
114 -150.495345 -103.003038
115 -214.326115 -150.495345
116 -89.072269 -214.326115
117 37.166193 -89.072269
118 71.720039 37.166193
119 298.858501 71.720039
120 112.778054 298.858501
121 40.303971 112.778054
122 364.182542 40.303971
123 -244.436518 364.182542
124 208.963482 -244.436518
125 1.901943 208.963482
126 -37.990364 1.901943
127 -94.821134 -37.990364
128 65.332713 -94.821134
129 -295.828826 65.332713
130 149.625020 -295.828826
131 -271.336518 149.625020
132 -323.016965 -271.336518
133 -187.172804 -323.016965
134 -195.894233 -187.172804
135 -160.313293 -195.894233
136 65.186707 -160.313293
137 -118.074832 65.186707
138 -33.067139 -118.074832
139 -26.797909 -33.067139
140 301.055938 -26.797909
141 -252.105601 301.055938
142 -141.051755 -252.105601
143 96.286707 -141.051755
144 256.406260 96.286707
145 396.850421 256.406260
146 -243.771008 396.850421
147 71.909932 -243.771008
148 -175.390068 71.909932
149 -208.251607 -175.390068
150 -73.143914 -208.251607
151 -71.974684 -73.143914
152 -362.720837 -71.974684
153 398.317624 -362.720837
154 126.871470 398.317624
155 127.609932 126.871470
156 493.929485 127.609932
157 -108.626354 493.929485
158 461.052217 -108.626354
159 NA 461.052217
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 38.189964 -292.054196
[2,] -168.331465 38.189964
[3,] -114.250525 -168.331465
[4,] 20.949475 -114.250525
[5,] -122.112063 20.949475
[6,] -58.704371 -122.112063
[7,] -60.435140 -58.704371
[8,] -221.181294 -60.435140
[9,] -173.942832 -221.181294
[10,] -87.788986 -173.942832
[11,] -157.750525 -87.788986
[12,] -341.930971 -157.750525
[13,] -2.686811 -341.930971
[14,] -218.208239 -2.686811
[15,] 20.372700 -218.208239
[16,] -33.827300 20.372700
[17,] -2.188838 -33.827300
[18,] 140.018854 -2.188838
[19,] 201.788085 140.018854
[20,] 54.041931 201.788085
[21,] 335.980393 54.041931
[22,] -17.065761 335.980393
[23,] -122.727300 -17.065761
[24,] 115.792254 -122.727300
[25,] 217.036414 115.792254
[26,] 179.214986 217.036414
[27,] 163.795925 179.214986
[28,] 92.395925 163.795925
[29,] 204.534387 92.395925
[30,] 69.342079 204.534387
[31,] 11.611310 69.342079
[32,] -84.434844 11.611310
[33,] -50.196382 -84.434844
[34,] -175.642536 -50.196382
[35,] 21.095925 -175.642536
[36,] -250.984521 21.095925
[37,] -26.440361 -250.984521
[38,] -189.161789 -26.440361
[39,] -354.780850 -189.161789
[40,] -300.580850 -354.780850
[41,] -215.442388 -300.580850
[42,] -345.534696 -215.442388
[43,] -295.865465 -345.534696
[44,] -26.411619 -295.865465
[45,] -218.773157 -26.411619
[46,] -250.419311 -218.773157
[47,] 116.519150 -250.419311
[48,] 106.538704 116.519150
[49,] 8.482864 106.538704
[50,] 55.361436 8.482864
[51,] 71.442375 55.361436
[52,] -35.957625 71.442375
[53,] 247.480837 -35.957625
[54,] 216.688529 247.480837
[55,] 257.557760 216.688529
[56,] 525.911606 257.557760
[57,] 89.850068 525.911606
[58,] 582.503914 89.850068
[59,] 24.842375 582.503914
[60,] 172.161929 24.842375
[61,] 263.506089 172.161929
[62,] 607.284661 263.506089
[63,] 510.565600 607.284661
[64,] 666.465600 510.565600
[65,] 401.904062 666.465600
[66,] 184.311754 401.904062
[67,] 276.680985 184.311754
[68,] 224.934831 276.680985
[69,] 10.173293 224.934831
[70,] 108.727139 10.173293
[71,] 151.165600 108.727139
[72,] 112.185154 151.165600
[73,] -9.670686 112.185154
[74,] -328.292114 -9.670686
[75,] 32.288826 -328.292114
[76,] 23.888826 32.288826
[77,] -116.972713 23.888826
[78,] 176.034979 -116.972713
[79,] 132.104210 176.034979
[80,] 38.358056 132.104210
[81,] 260.096518 38.358056
[82,] 269.050364 260.096518
[83,] -8.211174 269.050364
[84,] 108.308379 -8.211174
[85,] -164.847461 108.308379
[86,] -19.668889 -164.847461
[87,] 274.712051 -19.668889
[88,] -25.087949 274.712051
[89,] 77.650512 -25.087949
[90,] 144.458204 77.650512
[91,] -38.072565 144.458204
[92,] -439.418719 -38.072565
[93,] -49.580257 -439.418719
[94,] -343.926411 -49.580257
[95,] -401.887949 -343.926411
[96,] -130.868396 -401.887949
[97,] -351.924236 -130.868396
[98,] -140.545664 -351.924236
[99,] -111.864724 -140.545664
[100,] -290.364724 -111.864724
[101,] -47.426263 -290.364724
[102,] -231.918571 -47.426263
[103,] -77.449340 -231.918571
[104,] 13.604506 -77.449340
[105,] -91.157032 13.604506
[106,] -292.603186 -91.157032
[107,] 125.535276 -292.603186
[108,] -139.245171 125.535276
[109,] -113.001011 -139.245171
[110,] -163.222439 -113.001011
[111,] -159.441499 -163.222439
[112,] -216.641499 -159.441499
[113,] -103.003038 -216.641499
[114,] -150.495345 -103.003038
[115,] -214.326115 -150.495345
[116,] -89.072269 -214.326115
[117,] 37.166193 -89.072269
[118,] 71.720039 37.166193
[119,] 298.858501 71.720039
[120,] 112.778054 298.858501
[121,] 40.303971 112.778054
[122,] 364.182542 40.303971
[123,] -244.436518 364.182542
[124,] 208.963482 -244.436518
[125,] 1.901943 208.963482
[126,] -37.990364 1.901943
[127,] -94.821134 -37.990364
[128,] 65.332713 -94.821134
[129,] -295.828826 65.332713
[130,] 149.625020 -295.828826
[131,] -271.336518 149.625020
[132,] -323.016965 -271.336518
[133,] -187.172804 -323.016965
[134,] -195.894233 -187.172804
[135,] -160.313293 -195.894233
[136,] 65.186707 -160.313293
[137,] -118.074832 65.186707
[138,] -33.067139 -118.074832
[139,] -26.797909 -33.067139
[140,] 301.055938 -26.797909
[141,] -252.105601 301.055938
[142,] -141.051755 -252.105601
[143,] 96.286707 -141.051755
[144,] 256.406260 96.286707
[145,] 396.850421 256.406260
[146,] -243.771008 396.850421
[147,] 71.909932 -243.771008
[148,] -175.390068 71.909932
[149,] -208.251607 -175.390068
[150,] -73.143914 -208.251607
[151,] -71.974684 -73.143914
[152,] -362.720837 -71.974684
[153,] 398.317624 -362.720837
[154,] 126.871470 398.317624
[155,] 127.609932 126.871470
[156,] 493.929485 127.609932
[157,] -108.626354 493.929485
[158,] 461.052217 -108.626354
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 38.189964 -292.054196
2 -168.331465 38.189964
3 -114.250525 -168.331465
4 20.949475 -114.250525
5 -122.112063 20.949475
6 -58.704371 -122.112063
7 -60.435140 -58.704371
8 -221.181294 -60.435140
9 -173.942832 -221.181294
10 -87.788986 -173.942832
11 -157.750525 -87.788986
12 -341.930971 -157.750525
13 -2.686811 -341.930971
14 -218.208239 -2.686811
15 20.372700 -218.208239
16 -33.827300 20.372700
17 -2.188838 -33.827300
18 140.018854 -2.188838
19 201.788085 140.018854
20 54.041931 201.788085
21 335.980393 54.041931
22 -17.065761 335.980393
23 -122.727300 -17.065761
24 115.792254 -122.727300
25 217.036414 115.792254
26 179.214986 217.036414
27 163.795925 179.214986
28 92.395925 163.795925
29 204.534387 92.395925
30 69.342079 204.534387
31 11.611310 69.342079
32 -84.434844 11.611310
33 -50.196382 -84.434844
34 -175.642536 -50.196382
35 21.095925 -175.642536
36 -250.984521 21.095925
37 -26.440361 -250.984521
38 -189.161789 -26.440361
39 -354.780850 -189.161789
40 -300.580850 -354.780850
41 -215.442388 -300.580850
42 -345.534696 -215.442388
43 -295.865465 -345.534696
44 -26.411619 -295.865465
45 -218.773157 -26.411619
46 -250.419311 -218.773157
47 116.519150 -250.419311
48 106.538704 116.519150
49 8.482864 106.538704
50 55.361436 8.482864
51 71.442375 55.361436
52 -35.957625 71.442375
53 247.480837 -35.957625
54 216.688529 247.480837
55 257.557760 216.688529
56 525.911606 257.557760
57 89.850068 525.911606
58 582.503914 89.850068
59 24.842375 582.503914
60 172.161929 24.842375
61 263.506089 172.161929
62 607.284661 263.506089
63 510.565600 607.284661
64 666.465600 510.565600
65 401.904062 666.465600
66 184.311754 401.904062
67 276.680985 184.311754
68 224.934831 276.680985
69 10.173293 224.934831
70 108.727139 10.173293
71 151.165600 108.727139
72 112.185154 151.165600
73 -9.670686 112.185154
74 -328.292114 -9.670686
75 32.288826 -328.292114
76 23.888826 32.288826
77 -116.972713 23.888826
78 176.034979 -116.972713
79 132.104210 176.034979
80 38.358056 132.104210
81 260.096518 38.358056
82 269.050364 260.096518
83 -8.211174 269.050364
84 108.308379 -8.211174
85 -164.847461 108.308379
86 -19.668889 -164.847461
87 274.712051 -19.668889
88 -25.087949 274.712051
89 77.650512 -25.087949
90 144.458204 77.650512
91 -38.072565 144.458204
92 -439.418719 -38.072565
93 -49.580257 -439.418719
94 -343.926411 -49.580257
95 -401.887949 -343.926411
96 -130.868396 -401.887949
97 -351.924236 -130.868396
98 -140.545664 -351.924236
99 -111.864724 -140.545664
100 -290.364724 -111.864724
101 -47.426263 -290.364724
102 -231.918571 -47.426263
103 -77.449340 -231.918571
104 13.604506 -77.449340
105 -91.157032 13.604506
106 -292.603186 -91.157032
107 125.535276 -292.603186
108 -139.245171 125.535276
109 -113.001011 -139.245171
110 -163.222439 -113.001011
111 -159.441499 -163.222439
112 -216.641499 -159.441499
113 -103.003038 -216.641499
114 -150.495345 -103.003038
115 -214.326115 -150.495345
116 -89.072269 -214.326115
117 37.166193 -89.072269
118 71.720039 37.166193
119 298.858501 71.720039
120 112.778054 298.858501
121 40.303971 112.778054
122 364.182542 40.303971
123 -244.436518 364.182542
124 208.963482 -244.436518
125 1.901943 208.963482
126 -37.990364 1.901943
127 -94.821134 -37.990364
128 65.332713 -94.821134
129 -295.828826 65.332713
130 149.625020 -295.828826
131 -271.336518 149.625020
132 -323.016965 -271.336518
133 -187.172804 -323.016965
134 -195.894233 -187.172804
135 -160.313293 -195.894233
136 65.186707 -160.313293
137 -118.074832 65.186707
138 -33.067139 -118.074832
139 -26.797909 -33.067139
140 301.055938 -26.797909
141 -252.105601 301.055938
142 -141.051755 -252.105601
143 96.286707 -141.051755
144 256.406260 96.286707
145 396.850421 256.406260
146 -243.771008 396.850421
147 71.909932 -243.771008
148 -175.390068 71.909932
149 -208.251607 -175.390068
150 -73.143914 -208.251607
151 -71.974684 -73.143914
152 -362.720837 -71.974684
153 398.317624 -362.720837
154 126.871470 398.317624
155 127.609932 126.871470
156 493.929485 127.609932
157 -108.626354 493.929485
158 461.052217 -108.626354
> 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/7vjvs1230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8p0wv1230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/995ez1230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10spdt1230123185.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11f0h51230123185.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/12032g1230123186.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/13uftd1230123186.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/14qrxu1230123186.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/15hgc91230123186.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/16s2z51230123186.tab")
+ }
>
> system("convert tmp/1ok661230123185.ps tmp/1ok661230123185.png")
> system("convert tmp/2dn0h1230123185.ps tmp/2dn0h1230123185.png")
> system("convert tmp/3y46n1230123185.ps tmp/3y46n1230123185.png")
> system("convert tmp/4zprv1230123185.ps tmp/4zprv1230123185.png")
> system("convert tmp/513rz1230123185.ps tmp/513rz1230123185.png")
> system("convert tmp/619v31230123185.ps tmp/619v31230123185.png")
> system("convert tmp/7vjvs1230123185.ps tmp/7vjvs1230123185.png")
> system("convert tmp/8p0wv1230123185.ps tmp/8p0wv1230123185.png")
> system("convert tmp/995ez1230123185.ps tmp/995ez1230123185.png")
> system("convert tmp/10spdt1230123185.ps tmp/10spdt1230123185.png")
>
>
> proc.time()
user system elapsed
5.480 2.693 6.046