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(184
+ ,0
+ ,155
+ ,0
+ ,201.8
+ ,0
+ ,224.6
+ ,0
+ ,204.9
+ ,0
+ ,190.8
+ ,0
+ ,199
+ ,0
+ ,179.9
+ ,0
+ ,211.9
+ ,0
+ ,200.1
+ ,0
+ ,208.6
+ ,0
+ ,232.6
+ ,0
+ ,199.5
+ ,0
+ ,169.1
+ ,0
+ ,194.4
+ ,0
+ ,227.9
+ ,0
+ ,224
+ ,0
+ ,258.1
+ ,0
+ ,207.6
+ ,0
+ ,228
+ ,0
+ ,221
+ ,0
+ ,247.3
+ ,0
+ ,214.3
+ ,0
+ ,252.5
+ ,0
+ ,256.7
+ ,0
+ ,194.9
+ ,0
+ ,264.6
+ ,0
+ ,277.1
+ ,0
+ ,236.6
+ ,0
+ ,271.6
+ ,0
+ ,216.3
+ ,0
+ ,241.1
+ ,0
+ ,265.8
+ ,0
+ ,280.6
+ ,0
+ ,276.8
+ ,0
+ ,263.7
+ ,0
+ ,231.3
+ ,0
+ ,190.9
+ ,0
+ ,250.9
+ ,0
+ ,252.8
+ ,0
+ ,214.4
+ ,0
+ ,268.2
+ ,0
+ ,178
+ ,0
+ ,215.6
+ ,0
+ ,241.3
+ ,0
+ ,228.3
+ ,0
+ ,236.5
+ ,0
+ ,263.5
+ ,0
+ ,238.8
+ ,0
+ ,215.1
+ ,0
+ ,244.6
+ ,0
+ ,263.5
+ ,0
+ ,242.7
+ ,0
+ ,253.4
+ ,0
+ ,197.3
+ ,0
+ ,250.5
+ ,0
+ ,290.8
+ ,0
+ ,245.9
+ ,0
+ ,299.5
+ ,0
+ ,295.8
+ ,0
+ ,264.1
+ ,0
+ ,262.7
+ ,0
+ ,297.1
+ ,0
+ ,345.1
+ ,0
+ ,293.9
+ ,0
+ ,269.4
+ ,0
+ ,244.9
+ ,0
+ ,274.2
+ ,0
+ ,312.5
+ ,0
+ ,279
+ ,0
+ ,327.3
+ ,0
+ ,289.2
+ ,0
+ ,285.4
+ ,0
+ ,248.9
+ ,0
+ ,240.6
+ ,0
+ ,308.5
+ ,0
+ ,285.6
+ ,0
+ ,284.4
+ ,0
+ ,253.6
+ ,0
+ ,286.3
+ ,0
+ ,302.2
+ ,0
+ ,278
+ ,0
+ ,304.3
+ ,0
+ ,304.6
+ ,0
+ ,283.7
+ ,0
+ ,253.8
+ ,0
+ ,266.6
+ ,0
+ ,345.7
+ ,0
+ ,287
+ ,0
+ ,282.1
+ ,0
+ ,268.1
+ ,0
+ ,274.6
+ ,0
+ ,275.9
+ ,0
+ ,287.5
+ ,0
+ ,276
+ ,0
+ ,270.8
+ ,0
+ ,295.3
+ ,0
+ ,246.5
+ ,0
+ ,271.8
+ ,0
+ ,335.2
+ ,0
+ ,253.3
+ ,0
+ ,297.2
+ ,0
+ ,245.4
+ ,0
+ ,271.6
+ ,0
+ ,316.1
+ ,0
+ ,304.4
+ ,0
+ ,289.1
+ ,0
+ ,370.6
+ ,0
+ ,300
+ ,0
+ ,269.6
+ ,0
+ ,346.3
+ ,0
+ ,348.2
+ ,0
+ ,317.9
+ ,0
+ ,365.8
+ ,0
+ ,260.4
+ ,0
+ ,292.8
+ ,0
+ ,404.3
+ ,1
+ ,341.4
+ ,1
+ ,351.1
+ ,1
+ ,384.7
+ ,1
+ ,358.8
+ ,1
+ ,332.8
+ ,1
+ ,381.1
+ ,1
+ ,340.8
+ ,1
+ ,348.6
+ ,1
+ ,356.9
+ ,1
+ ,321.7
+ ,1
+ ,360.1
+ ,1
+ ,399.4
+ ,1
+ ,340.4
+ ,1
+ ,430.4
+ ,1
+ ,463.1
+ ,1
+ ,423
+ ,1
+ ,416.1
+ ,1
+ ,364
+ ,1
+ ,379.9
+ ,1
+ ,395.8
+ ,1
+ ,418.8
+ ,1
+ ,396.4
+ ,1
+ ,407.9
+ ,1
+ ,487.9
+ ,1
+ ,458.2
+ ,1
+ ,432.1
+ ,1
+ ,498.5
+ ,1
+ ,448.3
+ ,1
+ ,410.8
+ ,1
+ ,406
+ ,1
+ ,441
+ ,1
+ ,388.9
+ ,1
+ ,390.5
+ ,1
+ ,427.8
+ ,1
+ ,442.1
+ ,1
+ ,427
+ ,1
+ ,526.7
+ ,1
+ ,464.4
+ ,1
+ ,574.4
+ ,1
+ ,727
+ ,1
+ ,506
+ ,1
+ ,581.2
+ ,1)
+ ,dim=c(2
+ ,159)
+ ,dimnames=list(c('Uitvoer'
+ ,'X')
+ ,1:159))
> y <- array(NA,dim=c(2,159),dimnames=list(c('Uitvoer','X'),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
Uitvoer X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 184.0 0 1 0 0 0 0 0 0 0 0 0 0 1
2 155.0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 201.8 0 0 0 1 0 0 0 0 0 0 0 0 3
4 224.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 204.9 0 0 0 0 0 1 0 0 0 0 0 0 5
6 190.8 0 0 0 0 0 0 1 0 0 0 0 0 6
7 199.0 0 0 0 0 0 0 0 1 0 0 0 0 7
8 179.9 0 0 0 0 0 0 0 0 1 0 0 0 8
9 211.9 0 0 0 0 0 0 0 0 0 1 0 0 9
10 200.1 0 0 0 0 0 0 0 0 0 0 1 0 10
11 208.6 0 0 0 0 0 0 0 0 0 0 0 1 11
12 232.6 0 0 0 0 0 0 0 0 0 0 0 0 12
13 199.5 0 1 0 0 0 0 0 0 0 0 0 0 13
14 169.1 0 0 1 0 0 0 0 0 0 0 0 0 14
15 194.4 0 0 0 1 0 0 0 0 0 0 0 0 15
16 227.9 0 0 0 0 1 0 0 0 0 0 0 0 16
17 224.0 0 0 0 0 0 1 0 0 0 0 0 0 17
18 258.1 0 0 0 0 0 0 1 0 0 0 0 0 18
19 207.6 0 0 0 0 0 0 0 1 0 0 0 0 19
20 228.0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 221.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 247.3 0 0 0 0 0 0 0 0 0 0 1 0 22
23 214.3 0 0 0 0 0 0 0 0 0 0 0 1 23
24 252.5 0 0 0 0 0 0 0 0 0 0 0 0 24
25 256.7 0 1 0 0 0 0 0 0 0 0 0 0 25
26 194.9 0 0 1 0 0 0 0 0 0 0 0 0 26
27 264.6 0 0 0 1 0 0 0 0 0 0 0 0 27
28 277.1 0 0 0 0 1 0 0 0 0 0 0 0 28
29 236.6 0 0 0 0 0 1 0 0 0 0 0 0 29
30 271.6 0 0 0 0 0 0 1 0 0 0 0 0 30
31 216.3 0 0 0 0 0 0 0 1 0 0 0 0 31
32 241.1 0 0 0 0 0 0 0 0 1 0 0 0 32
33 265.8 0 0 0 0 0 0 0 0 0 1 0 0 33
34 280.6 0 0 0 0 0 0 0 0 0 0 1 0 34
35 276.8 0 0 0 0 0 0 0 0 0 0 0 1 35
36 263.7 0 0 0 0 0 0 0 0 0 0 0 0 36
37 231.3 0 1 0 0 0 0 0 0 0 0 0 0 37
38 190.9 0 0 1 0 0 0 0 0 0 0 0 0 38
39 250.9 0 0 0 1 0 0 0 0 0 0 0 0 39
40 252.8 0 0 0 0 1 0 0 0 0 0 0 0 40
41 214.4 0 0 0 0 0 1 0 0 0 0 0 0 41
42 268.2 0 0 0 0 0 0 1 0 0 0 0 0 42
43 178.0 0 0 0 0 0 0 0 1 0 0 0 0 43
44 215.6 0 0 0 0 0 0 0 0 1 0 0 0 44
45 241.3 0 0 0 0 0 0 0 0 0 1 0 0 45
46 228.3 0 0 0 0 0 0 0 0 0 0 1 0 46
47 236.5 0 0 0 0 0 0 0 0 0 0 0 1 47
48 263.5 0 0 0 0 0 0 0 0 0 0 0 0 48
49 238.8 0 1 0 0 0 0 0 0 0 0 0 0 49
50 215.1 0 0 1 0 0 0 0 0 0 0 0 0 50
51 244.6 0 0 0 1 0 0 0 0 0 0 0 0 51
52 263.5 0 0 0 0 1 0 0 0 0 0 0 0 52
53 242.7 0 0 0 0 0 1 0 0 0 0 0 0 53
54 253.4 0 0 0 0 0 0 1 0 0 0 0 0 54
55 197.3 0 0 0 0 0 0 0 1 0 0 0 0 55
56 250.5 0 0 0 0 0 0 0 0 1 0 0 0 56
57 290.8 0 0 0 0 0 0 0 0 0 1 0 0 57
58 245.9 0 0 0 0 0 0 0 0 0 0 1 0 58
59 299.5 0 0 0 0 0 0 0 0 0 0 0 1 59
60 295.8 0 0 0 0 0 0 0 0 0 0 0 0 60
61 264.1 0 1 0 0 0 0 0 0 0 0 0 0 61
62 262.7 0 0 1 0 0 0 0 0 0 0 0 0 62
63 297.1 0 0 0 1 0 0 0 0 0 0 0 0 63
64 345.1 0 0 0 0 1 0 0 0 0 0 0 0 64
65 293.9 0 0 0 0 0 1 0 0 0 0 0 0 65
66 269.4 0 0 0 0 0 0 1 0 0 0 0 0 66
67 244.9 0 0 0 0 0 0 0 1 0 0 0 0 67
68 274.2 0 0 0 0 0 0 0 0 1 0 0 0 68
69 312.5 0 0 0 0 0 0 0 0 0 1 0 0 69
70 279.0 0 0 0 0 0 0 0 0 0 0 1 0 70
71 327.3 0 0 0 0 0 0 0 0 0 0 0 1 71
72 289.2 0 0 0 0 0 0 0 0 0 0 0 0 72
73 285.4 0 1 0 0 0 0 0 0 0 0 0 0 73
74 248.9 0 0 1 0 0 0 0 0 0 0 0 0 74
75 240.6 0 0 0 1 0 0 0 0 0 0 0 0 75
76 308.5 0 0 0 0 1 0 0 0 0 0 0 0 76
77 285.6 0 0 0 0 0 1 0 0 0 0 0 0 77
78 284.4 0 0 0 0 0 0 1 0 0 0 0 0 78
79 253.6 0 0 0 0 0 0 0 1 0 0 0 0 79
80 286.3 0 0 0 0 0 0 0 0 1 0 0 0 80
81 302.2 0 0 0 0 0 0 0 0 0 1 0 0 81
82 278.0 0 0 0 0 0 0 0 0 0 0 1 0 82
83 304.3 0 0 0 0 0 0 0 0 0 0 0 1 83
84 304.6 0 0 0 0 0 0 0 0 0 0 0 0 84
85 283.7 0 1 0 0 0 0 0 0 0 0 0 0 85
86 253.8 0 0 1 0 0 0 0 0 0 0 0 0 86
87 266.6 0 0 0 1 0 0 0 0 0 0 0 0 87
88 345.7 0 0 0 0 1 0 0 0 0 0 0 0 88
89 287.0 0 0 0 0 0 1 0 0 0 0 0 0 89
90 282.1 0 0 0 0 0 0 1 0 0 0 0 0 90
91 268.1 0 0 0 0 0 0 0 1 0 0 0 0 91
92 274.6 0 0 0 0 0 0 0 0 1 0 0 0 92
93 275.9 0 0 0 0 0 0 0 0 0 1 0 0 93
94 287.5 0 0 0 0 0 0 0 0 0 0 1 0 94
95 276.0 0 0 0 0 0 0 0 0 0 0 0 1 95
96 270.8 0 0 0 0 0 0 0 0 0 0 0 0 96
97 295.3 0 1 0 0 0 0 0 0 0 0 0 0 97
98 246.5 0 0 1 0 0 0 0 0 0 0 0 0 98
99 271.8 0 0 0 1 0 0 0 0 0 0 0 0 99
100 335.2 0 0 0 0 1 0 0 0 0 0 0 0 100
101 253.3 0 0 0 0 0 1 0 0 0 0 0 0 101
102 297.2 0 0 0 0 0 0 1 0 0 0 0 0 102
103 245.4 0 0 0 0 0 0 0 1 0 0 0 0 103
104 271.6 0 0 0 0 0 0 0 0 1 0 0 0 104
105 316.1 0 0 0 0 0 0 0 0 0 1 0 0 105
106 304.4 0 0 0 0 0 0 0 0 0 0 1 0 106
107 289.1 0 0 0 0 0 0 0 0 0 0 0 1 107
108 370.6 0 0 0 0 0 0 0 0 0 0 0 0 108
109 300.0 0 1 0 0 0 0 0 0 0 0 0 0 109
110 269.6 0 0 1 0 0 0 0 0 0 0 0 0 110
111 346.3 0 0 0 1 0 0 0 0 0 0 0 0 111
112 348.2 0 0 0 0 1 0 0 0 0 0 0 0 112
113 317.9 0 0 0 0 0 1 0 0 0 0 0 0 113
114 365.8 0 0 0 0 0 0 1 0 0 0 0 0 114
115 260.4 0 0 0 0 0 0 0 1 0 0 0 0 115
116 292.8 0 0 0 0 0 0 0 0 1 0 0 0 116
117 404.3 1 0 0 0 0 0 0 0 0 1 0 0 117
118 341.4 1 0 0 0 0 0 0 0 0 0 1 0 118
119 351.1 1 0 0 0 0 0 0 0 0 0 0 1 119
120 384.7 1 0 0 0 0 0 0 0 0 0 0 0 120
121 358.8 1 1 0 0 0 0 0 0 0 0 0 0 121
122 332.8 1 0 1 0 0 0 0 0 0 0 0 0 122
123 381.1 1 0 0 1 0 0 0 0 0 0 0 0 123
124 340.8 1 0 0 0 1 0 0 0 0 0 0 0 124
125 348.6 1 0 0 0 0 1 0 0 0 0 0 0 125
126 356.9 1 0 0 0 0 0 1 0 0 0 0 0 126
127 321.7 1 0 0 0 0 0 0 1 0 0 0 0 127
128 360.1 1 0 0 0 0 0 0 0 1 0 0 0 128
129 399.4 1 0 0 0 0 0 0 0 0 1 0 0 129
130 340.4 1 0 0 0 0 0 0 0 0 0 1 0 130
131 430.4 1 0 0 0 0 0 0 0 0 0 0 1 131
132 463.1 1 0 0 0 0 0 0 0 0 0 0 0 132
133 423.0 1 1 0 0 0 0 0 0 0 0 0 0 133
134 416.1 1 0 1 0 0 0 0 0 0 0 0 0 134
135 364.0 1 0 0 1 0 0 0 0 0 0 0 0 135
136 379.9 1 0 0 0 1 0 0 0 0 0 0 0 136
137 395.8 1 0 0 0 0 1 0 0 0 0 0 0 137
138 418.8 1 0 0 0 0 0 1 0 0 0 0 0 138
139 396.4 1 0 0 0 0 0 0 1 0 0 0 0 139
140 407.9 1 0 0 0 0 0 0 0 1 0 0 0 140
141 487.9 1 0 0 0 0 0 0 0 0 1 0 0 141
142 458.2 1 0 0 0 0 0 0 0 0 0 1 0 142
143 432.1 1 0 0 0 0 0 0 0 0 0 0 1 143
144 498.5 1 0 0 0 0 0 0 0 0 0 0 0 144
145 448.3 1 1 0 0 0 0 0 0 0 0 0 0 145
146 410.8 1 0 1 0 0 0 0 0 0 0 0 0 146
147 406.0 1 0 0 1 0 0 0 0 0 0 0 0 147
148 441.0 1 0 0 0 1 0 0 0 0 0 0 0 148
149 388.9 1 0 0 0 0 1 0 0 0 0 0 0 149
150 390.5 1 0 0 0 0 0 1 0 0 0 0 0 150
151 427.8 1 0 0 0 0 0 0 1 0 0 0 0 151
152 442.1 1 0 0 0 0 0 0 0 1 0 0 0 152
153 427.0 1 0 0 0 0 0 0 0 0 1 0 0 153
154 526.7 1 0 0 0 0 0 0 0 0 0 1 0 154
155 464.4 1 0 0 0 0 0 0 0 0 0 0 1 155
156 574.4 1 0 0 0 0 0 0 0 0 0 0 0 156
157 727.0 1 1 0 0 0 0 0 0 0 0 0 0 157
158 506.0 1 0 1 0 0 0 0 0 0 0 0 0 158
159 581.2 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) X M1 M2 M3 M4
226.348 71.239 -15.021 -60.704 -30.493 -14.207
M5 M6 M7 M8 M9 M10
-45.855 -30.557 -69.435 -46.860 -20.288 -32.058
M11 t
-26.068 1.132
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-82.990 -21.247 -4.494 18.331 266.656
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 226.3479 13.0524 17.341 < 2e-16 ***
X 71.2389 11.2441 6.336 2.80e-09 ***
M1 -15.0215 15.4516 -0.972 0.332588
M2 -60.7038 15.4495 -3.929 0.000131 ***
M3 -30.4933 15.4482 -1.974 0.050292 .
M4 -14.2075 15.7412 -0.903 0.368254
M5 -45.8552 15.7402 -2.913 0.004144 **
M6 -30.5568 15.7399 -1.941 0.054154 .
M7 -69.4353 15.7404 -4.411 1.99e-05 ***
M8 -46.8599 15.7416 -2.977 0.003414 **
M9 -20.2876 15.7336 -1.289 0.199297
M10 -32.0584 15.7317 -2.038 0.043386 *
M11 -26.0677 15.7306 -1.657 0.099655 .
t 1.1323 0.1086 10.424 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 40.1 on 145 degrees of freedom
Multiple R-squared: 0.8198, Adjusted R-squared: 0.8036
F-statistic: 50.73 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,] 1.239555e-02 2.479109e-02 0.9876045
[2,] 8.039653e-02 1.607931e-01 0.9196035
[3,] 3.423030e-02 6.846060e-02 0.9657697
[4,] 2.277877e-02 4.555755e-02 0.9772212
[5,] 9.646445e-03 1.929289e-02 0.9903536
[6,] 5.953803e-03 1.190761e-02 0.9940462
[7,] 2.709741e-03 5.419482e-03 0.9972903
[8,] 9.877734e-04 1.975547e-03 0.9990122
[9,] 9.216874e-04 1.843375e-03 0.9990783
[10,] 3.469604e-04 6.939207e-04 0.9996530
[11,] 2.819194e-04 5.638389e-04 0.9997181
[12,] 1.169692e-04 2.339383e-04 0.9998830
[13,] 6.475536e-05 1.295107e-04 0.9999352
[14,] 2.649119e-05 5.298239e-05 0.9999735
[15,] 2.182448e-05 4.364896e-05 0.9999782
[16,] 8.228686e-06 1.645737e-05 0.9999918
[17,] 3.407054e-06 6.814108e-06 0.9999966
[18,] 1.970340e-06 3.940681e-06 0.9999980
[19,] 1.519472e-06 3.038944e-06 0.9999985
[20,] 8.640121e-07 1.728024e-06 0.9999991
[21,] 1.318490e-06 2.636980e-06 0.9999987
[22,] 1.401702e-06 2.803404e-06 0.9999986
[23,] 7.181292e-07 1.436258e-06 0.9999993
[24,] 9.414905e-07 1.882981e-06 0.9999991
[25,] 2.997976e-06 5.995951e-06 0.9999970
[26,] 1.560665e-06 3.121329e-06 0.9999984
[27,] 1.455132e-05 2.910264e-05 0.9999854
[28,] 1.320181e-05 2.640361e-05 0.9999868
[29,] 8.280907e-06 1.656181e-05 0.9999917
[30,] 1.134523e-05 2.269046e-05 0.9999887
[31,] 7.346407e-06 1.469281e-05 0.9999927
[32,] 3.754335e-06 7.508670e-06 0.9999962
[33,] 1.920252e-06 3.840504e-06 0.9999981
[34,] 8.976727e-07 1.795345e-06 0.9999991
[35,] 4.648639e-07 9.297279e-07 0.9999995
[36,] 2.297639e-07 4.595279e-07 0.9999998
[37,] 1.077927e-07 2.155853e-07 0.9999999
[38,] 6.939938e-08 1.387988e-07 0.9999999
[39,] 5.670579e-08 1.134116e-07 0.9999999
[40,] 2.702322e-08 5.404645e-08 1.0000000
[41,] 2.242668e-08 4.485336e-08 1.0000000
[42,] 1.306495e-08 2.612991e-08 1.0000000
[43,] 1.829721e-08 3.659441e-08 1.0000000
[44,] 9.129594e-09 1.825919e-08 1.0000000
[45,] 4.020204e-09 8.040408e-09 1.0000000
[46,] 6.482886e-09 1.296577e-08 1.0000000
[47,] 6.198411e-09 1.239682e-08 1.0000000
[48,] 4.134628e-08 8.269255e-08 1.0000000
[49,] 5.156175e-08 1.031235e-07 0.9999999
[50,] 3.787097e-08 7.574195e-08 1.0000000
[51,] 2.285558e-08 4.571116e-08 1.0000000
[52,] 1.588859e-08 3.177717e-08 1.0000000
[53,] 1.557625e-08 3.115249e-08 1.0000000
[54,] 9.102962e-09 1.820592e-08 1.0000000
[55,] 2.438323e-08 4.876646e-08 1.0000000
[56,] 1.394594e-08 2.789187e-08 1.0000000
[57,] 6.726883e-09 1.345377e-08 1.0000000
[58,] 3.667088e-09 7.334176e-09 1.0000000
[59,] 8.992138e-09 1.798428e-08 1.0000000
[60,] 7.196114e-09 1.439223e-08 1.0000000
[61,] 7.330711e-09 1.466142e-08 1.0000000
[62,] 6.593214e-09 1.318643e-08 1.0000000
[63,] 5.516271e-09 1.103254e-08 1.0000000
[64,] 6.520634e-09 1.304127e-08 1.0000000
[65,] 4.797893e-09 9.595787e-09 1.0000000
[66,] 3.493780e-09 6.987561e-09 1.0000000
[67,] 3.753984e-09 7.507968e-09 1.0000000
[68,] 2.002870e-09 4.005741e-09 1.0000000
[69,] 9.574602e-10 1.914920e-09 1.0000000
[70,] 5.559598e-10 1.111920e-09 1.0000000
[71,] 4.739948e-10 9.479895e-10 1.0000000
[72,] 1.932801e-09 3.865601e-09 1.0000000
[73,] 2.946812e-09 5.893624e-09 1.0000000
[74,] 3.642868e-09 7.285735e-09 1.0000000
[75,] 5.302095e-09 1.060419e-08 1.0000000
[76,] 6.417624e-09 1.283525e-08 1.0000000
[77,] 7.659965e-09 1.531993e-08 1.0000000
[78,] 5.173742e-09 1.034748e-08 1.0000000
[79,] 7.067477e-09 1.413495e-08 1.0000000
[80,] 1.967289e-08 3.934578e-08 1.0000000
[81,] 1.016627e-08 2.033254e-08 1.0000000
[82,] 5.822876e-09 1.164575e-08 1.0000000
[83,] 3.986139e-09 7.972277e-09 1.0000000
[84,] 5.805598e-09 1.161120e-08 1.0000000
[85,] 8.706729e-09 1.741346e-08 1.0000000
[86,] 5.658557e-09 1.131711e-08 1.0000000
[87,] 3.823096e-09 7.646192e-09 1.0000000
[88,] 2.385759e-09 4.771517e-09 1.0000000
[89,] 1.099746e-09 2.199492e-09 1.0000000
[90,] 4.981708e-10 9.963416e-10 1.0000000
[91,] 3.506617e-10 7.013235e-10 1.0000000
[92,] 5.533012e-10 1.106602e-09 1.0000000
[93,] 1.184446e-09 2.368892e-09 1.0000000
[94,] 9.346201e-10 1.869240e-09 1.0000000
[95,] 1.024116e-09 2.048233e-09 1.0000000
[96,] 6.243784e-10 1.248757e-09 1.0000000
[97,] 3.293627e-10 6.587253e-10 1.0000000
[98,] 1.563167e-09 3.126334e-09 1.0000000
[99,] 8.495690e-10 1.699138e-09 1.0000000
[100,] 3.901500e-10 7.803000e-10 1.0000000
[101,] 7.652193e-10 1.530439e-09 1.0000000
[102,] 6.418870e-10 1.283774e-09 1.0000000
[103,] 3.797186e-10 7.594372e-10 1.0000000
[104,] 1.669808e-10 3.339617e-10 1.0000000
[105,] 2.251799e-10 4.503598e-10 1.0000000
[106,] 9.338649e-11 1.867730e-10 1.0000000
[107,] 6.655572e-11 1.331114e-10 1.0000000
[108,] 1.137399e-10 2.274798e-10 1.0000000
[109,] 7.338496e-11 1.467699e-10 1.0000000
[110,] 5.002918e-11 1.000584e-10 1.0000000
[111,] 1.863333e-11 3.726665e-11 1.0000000
[112,] 8.584931e-12 1.716986e-11 1.0000000
[113,] 5.213834e-12 1.042767e-11 1.0000000
[114,] 6.844681e-12 1.368936e-11 1.0000000
[115,] 5.070288e-11 1.014058e-10 1.0000000
[116,] 1.335423e-10 2.670847e-10 1.0000000
[117,] 4.413774e-10 8.827547e-10 1.0000000
[118,] 1.857479e-09 3.714957e-09 1.0000000
[119,] 9.497104e-10 1.899421e-09 1.0000000
[120,] 3.975105e-10 7.950211e-10 1.0000000
[121,] 5.037906e-10 1.007581e-09 1.0000000
[122,] 2.761783e-09 5.523565e-09 1.0000000
[123,] 2.802851e-09 5.605701e-09 1.0000000
[124,] 2.238080e-09 4.476160e-09 1.0000000
[125,] 6.781001e-06 1.356200e-05 0.9999932
[126,] 9.081345e-06 1.816269e-05 0.9999909
> postscript(file="/var/www/html/rcomp/tmp/182gr1230036616.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2u1cm1230036616.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/39zv11230036616.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ziuq1230036616.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5a4gf1230036616.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
-28.4587943 -12.9087943 2.5483486 7.9301586 18.7455433 -11.7852260
7 8 9 10 11 12
34.1609279 -8.6467644 -4.3514607 -5.5129992 -4.1360761 -7.3360761
13 14 15 16 17 18
-26.5469381 -12.3969381 -18.4397952 -2.3579852 24.2573994 41.9266302
19 20 21 22 23 24
29.1727841 25.8650917 -8.8396046 28.0988570 -12.0242199 -1.0242199
25 26 27 28 29 30
17.0649181 -0.1850819 38.1720609 33.2538710 23.2692556 41.8384864
31 32 33 34 35 36
24.2846402 25.3769479 22.3722516 47.8107132 36.8876362 -3.4123638
37 38 39 40 41 42
-21.9232257 -17.7732257 10.8839171 -4.6342728 -12.5188882 24.8503426
43 44 45 46 47 48
-27.6035036 -13.7111959 -15.7158922 -18.0774306 -17.0005076 -17.2005076
49 50 51 52 53 54
-28.0113696 -7.1613696 -9.0042267 -7.5224166 2.1929680 -3.5378012
55 56 57 58 59 60
-21.8916474 7.6006603 20.1959640 -14.0655745 32.4113486 1.5113486
61 62 63 64 65 66
-16.2995134 26.8504866 29.9076295 60.4894395 39.8048242 -1.1259451
67 68 69 70 71 72
12.1202088 17.7125165 28.3078202 5.4462817 46.6232048 -18.6767952
73 74 75 76 77 78
-8.5876572 -0.5376572 -40.1805143 10.3012957 17.9166803 0.2859111
79 80 81 82 83 84
7.2320650 16.2243727 4.4196764 -9.1418621 10.0350610 -16.8649390
85 86 87 88 89 90
-23.8758010 -9.2258010 -27.7686581 33.9131519 5.7285365 -15.6022327
91 92 93 94 95 96
8.1439211 -9.0637712 -35.4684675 -13.2300059 -31.8530828 -64.2530828
97 98 99 100 101 102
-25.8639448 -30.1139448 -36.1568020 9.8250081 -41.5596073 -14.0903765
103 104 105 106 107 108
-28.1442227 -25.6519150 -8.8566113 -9.9181497 -32.3412267 21.9587733
109 110 111 112 113 114
-34.7520886 -20.6020886 24.7550542 9.2368643 9.4522489 40.9214797
115 116 117 118 119 120
-26.7323665 -18.0400588 -5.4837033 -57.7452417 -55.1683186 -48.7683186
121 122 123 124 125 126
-60.7791806 -42.2291806 -25.2720378 -82.9902277 -44.6748431 -52.8056123
127 128 129 130 131 132
-50.2594585 -35.5671508 -23.9718471 -72.3333855 10.5435375 16.0435375
133 134 135 136 137 138
-10.1673244 27.4826756 -55.9601816 -57.4783715 -11.0629869 -4.4937561
139 140 141 142 143 144
10.8523977 -1.3552946 50.9400091 31.8784706 -1.3446063 37.8553937
145 146 147 148 149 150
1.5445317 8.5945317 -27.5483254 -9.9665153 -31.5511307 -46.3819000
151 152 153 154 155 156
28.6642539 19.2565616 -23.5481347 86.7903268 17.3672499 100.1672499
157 158 159
266.6563879 90.2063879 134.0635308
> postscript(file="/var/www/html/rcomp/tmp/6l4b91230036616.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 -28.4587943 NA
1 -12.9087943 -28.4587943
2 2.5483486 -12.9087943
3 7.9301586 2.5483486
4 18.7455433 7.9301586
5 -11.7852260 18.7455433
6 34.1609279 -11.7852260
7 -8.6467644 34.1609279
8 -4.3514607 -8.6467644
9 -5.5129992 -4.3514607
10 -4.1360761 -5.5129992
11 -7.3360761 -4.1360761
12 -26.5469381 -7.3360761
13 -12.3969381 -26.5469381
14 -18.4397952 -12.3969381
15 -2.3579852 -18.4397952
16 24.2573994 -2.3579852
17 41.9266302 24.2573994
18 29.1727841 41.9266302
19 25.8650917 29.1727841
20 -8.8396046 25.8650917
21 28.0988570 -8.8396046
22 -12.0242199 28.0988570
23 -1.0242199 -12.0242199
24 17.0649181 -1.0242199
25 -0.1850819 17.0649181
26 38.1720609 -0.1850819
27 33.2538710 38.1720609
28 23.2692556 33.2538710
29 41.8384864 23.2692556
30 24.2846402 41.8384864
31 25.3769479 24.2846402
32 22.3722516 25.3769479
33 47.8107132 22.3722516
34 36.8876362 47.8107132
35 -3.4123638 36.8876362
36 -21.9232257 -3.4123638
37 -17.7732257 -21.9232257
38 10.8839171 -17.7732257
39 -4.6342728 10.8839171
40 -12.5188882 -4.6342728
41 24.8503426 -12.5188882
42 -27.6035036 24.8503426
43 -13.7111959 -27.6035036
44 -15.7158922 -13.7111959
45 -18.0774306 -15.7158922
46 -17.0005076 -18.0774306
47 -17.2005076 -17.0005076
48 -28.0113696 -17.2005076
49 -7.1613696 -28.0113696
50 -9.0042267 -7.1613696
51 -7.5224166 -9.0042267
52 2.1929680 -7.5224166
53 -3.5378012 2.1929680
54 -21.8916474 -3.5378012
55 7.6006603 -21.8916474
56 20.1959640 7.6006603
57 -14.0655745 20.1959640
58 32.4113486 -14.0655745
59 1.5113486 32.4113486
60 -16.2995134 1.5113486
61 26.8504866 -16.2995134
62 29.9076295 26.8504866
63 60.4894395 29.9076295
64 39.8048242 60.4894395
65 -1.1259451 39.8048242
66 12.1202088 -1.1259451
67 17.7125165 12.1202088
68 28.3078202 17.7125165
69 5.4462817 28.3078202
70 46.6232048 5.4462817
71 -18.6767952 46.6232048
72 -8.5876572 -18.6767952
73 -0.5376572 -8.5876572
74 -40.1805143 -0.5376572
75 10.3012957 -40.1805143
76 17.9166803 10.3012957
77 0.2859111 17.9166803
78 7.2320650 0.2859111
79 16.2243727 7.2320650
80 4.4196764 16.2243727
81 -9.1418621 4.4196764
82 10.0350610 -9.1418621
83 -16.8649390 10.0350610
84 -23.8758010 -16.8649390
85 -9.2258010 -23.8758010
86 -27.7686581 -9.2258010
87 33.9131519 -27.7686581
88 5.7285365 33.9131519
89 -15.6022327 5.7285365
90 8.1439211 -15.6022327
91 -9.0637712 8.1439211
92 -35.4684675 -9.0637712
93 -13.2300059 -35.4684675
94 -31.8530828 -13.2300059
95 -64.2530828 -31.8530828
96 -25.8639448 -64.2530828
97 -30.1139448 -25.8639448
98 -36.1568020 -30.1139448
99 9.8250081 -36.1568020
100 -41.5596073 9.8250081
101 -14.0903765 -41.5596073
102 -28.1442227 -14.0903765
103 -25.6519150 -28.1442227
104 -8.8566113 -25.6519150
105 -9.9181497 -8.8566113
106 -32.3412267 -9.9181497
107 21.9587733 -32.3412267
108 -34.7520886 21.9587733
109 -20.6020886 -34.7520886
110 24.7550542 -20.6020886
111 9.2368643 24.7550542
112 9.4522489 9.2368643
113 40.9214797 9.4522489
114 -26.7323665 40.9214797
115 -18.0400588 -26.7323665
116 -5.4837033 -18.0400588
117 -57.7452417 -5.4837033
118 -55.1683186 -57.7452417
119 -48.7683186 -55.1683186
120 -60.7791806 -48.7683186
121 -42.2291806 -60.7791806
122 -25.2720378 -42.2291806
123 -82.9902277 -25.2720378
124 -44.6748431 -82.9902277
125 -52.8056123 -44.6748431
126 -50.2594585 -52.8056123
127 -35.5671508 -50.2594585
128 -23.9718471 -35.5671508
129 -72.3333855 -23.9718471
130 10.5435375 -72.3333855
131 16.0435375 10.5435375
132 -10.1673244 16.0435375
133 27.4826756 -10.1673244
134 -55.9601816 27.4826756
135 -57.4783715 -55.9601816
136 -11.0629869 -57.4783715
137 -4.4937561 -11.0629869
138 10.8523977 -4.4937561
139 -1.3552946 10.8523977
140 50.9400091 -1.3552946
141 31.8784706 50.9400091
142 -1.3446063 31.8784706
143 37.8553937 -1.3446063
144 1.5445317 37.8553937
145 8.5945317 1.5445317
146 -27.5483254 8.5945317
147 -9.9665153 -27.5483254
148 -31.5511307 -9.9665153
149 -46.3819000 -31.5511307
150 28.6642539 -46.3819000
151 19.2565616 28.6642539
152 -23.5481347 19.2565616
153 86.7903268 -23.5481347
154 17.3672499 86.7903268
155 100.1672499 17.3672499
156 266.6563879 100.1672499
157 90.2063879 266.6563879
158 134.0635308 90.2063879
159 NA 134.0635308
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -12.9087943 -28.4587943
[2,] 2.5483486 -12.9087943
[3,] 7.9301586 2.5483486
[4,] 18.7455433 7.9301586
[5,] -11.7852260 18.7455433
[6,] 34.1609279 -11.7852260
[7,] -8.6467644 34.1609279
[8,] -4.3514607 -8.6467644
[9,] -5.5129992 -4.3514607
[10,] -4.1360761 -5.5129992
[11,] -7.3360761 -4.1360761
[12,] -26.5469381 -7.3360761
[13,] -12.3969381 -26.5469381
[14,] -18.4397952 -12.3969381
[15,] -2.3579852 -18.4397952
[16,] 24.2573994 -2.3579852
[17,] 41.9266302 24.2573994
[18,] 29.1727841 41.9266302
[19,] 25.8650917 29.1727841
[20,] -8.8396046 25.8650917
[21,] 28.0988570 -8.8396046
[22,] -12.0242199 28.0988570
[23,] -1.0242199 -12.0242199
[24,] 17.0649181 -1.0242199
[25,] -0.1850819 17.0649181
[26,] 38.1720609 -0.1850819
[27,] 33.2538710 38.1720609
[28,] 23.2692556 33.2538710
[29,] 41.8384864 23.2692556
[30,] 24.2846402 41.8384864
[31,] 25.3769479 24.2846402
[32,] 22.3722516 25.3769479
[33,] 47.8107132 22.3722516
[34,] 36.8876362 47.8107132
[35,] -3.4123638 36.8876362
[36,] -21.9232257 -3.4123638
[37,] -17.7732257 -21.9232257
[38,] 10.8839171 -17.7732257
[39,] -4.6342728 10.8839171
[40,] -12.5188882 -4.6342728
[41,] 24.8503426 -12.5188882
[42,] -27.6035036 24.8503426
[43,] -13.7111959 -27.6035036
[44,] -15.7158922 -13.7111959
[45,] -18.0774306 -15.7158922
[46,] -17.0005076 -18.0774306
[47,] -17.2005076 -17.0005076
[48,] -28.0113696 -17.2005076
[49,] -7.1613696 -28.0113696
[50,] -9.0042267 -7.1613696
[51,] -7.5224166 -9.0042267
[52,] 2.1929680 -7.5224166
[53,] -3.5378012 2.1929680
[54,] -21.8916474 -3.5378012
[55,] 7.6006603 -21.8916474
[56,] 20.1959640 7.6006603
[57,] -14.0655745 20.1959640
[58,] 32.4113486 -14.0655745
[59,] 1.5113486 32.4113486
[60,] -16.2995134 1.5113486
[61,] 26.8504866 -16.2995134
[62,] 29.9076295 26.8504866
[63,] 60.4894395 29.9076295
[64,] 39.8048242 60.4894395
[65,] -1.1259451 39.8048242
[66,] 12.1202088 -1.1259451
[67,] 17.7125165 12.1202088
[68,] 28.3078202 17.7125165
[69,] 5.4462817 28.3078202
[70,] 46.6232048 5.4462817
[71,] -18.6767952 46.6232048
[72,] -8.5876572 -18.6767952
[73,] -0.5376572 -8.5876572
[74,] -40.1805143 -0.5376572
[75,] 10.3012957 -40.1805143
[76,] 17.9166803 10.3012957
[77,] 0.2859111 17.9166803
[78,] 7.2320650 0.2859111
[79,] 16.2243727 7.2320650
[80,] 4.4196764 16.2243727
[81,] -9.1418621 4.4196764
[82,] 10.0350610 -9.1418621
[83,] -16.8649390 10.0350610
[84,] -23.8758010 -16.8649390
[85,] -9.2258010 -23.8758010
[86,] -27.7686581 -9.2258010
[87,] 33.9131519 -27.7686581
[88,] 5.7285365 33.9131519
[89,] -15.6022327 5.7285365
[90,] 8.1439211 -15.6022327
[91,] -9.0637712 8.1439211
[92,] -35.4684675 -9.0637712
[93,] -13.2300059 -35.4684675
[94,] -31.8530828 -13.2300059
[95,] -64.2530828 -31.8530828
[96,] -25.8639448 -64.2530828
[97,] -30.1139448 -25.8639448
[98,] -36.1568020 -30.1139448
[99,] 9.8250081 -36.1568020
[100,] -41.5596073 9.8250081
[101,] -14.0903765 -41.5596073
[102,] -28.1442227 -14.0903765
[103,] -25.6519150 -28.1442227
[104,] -8.8566113 -25.6519150
[105,] -9.9181497 -8.8566113
[106,] -32.3412267 -9.9181497
[107,] 21.9587733 -32.3412267
[108,] -34.7520886 21.9587733
[109,] -20.6020886 -34.7520886
[110,] 24.7550542 -20.6020886
[111,] 9.2368643 24.7550542
[112,] 9.4522489 9.2368643
[113,] 40.9214797 9.4522489
[114,] -26.7323665 40.9214797
[115,] -18.0400588 -26.7323665
[116,] -5.4837033 -18.0400588
[117,] -57.7452417 -5.4837033
[118,] -55.1683186 -57.7452417
[119,] -48.7683186 -55.1683186
[120,] -60.7791806 -48.7683186
[121,] -42.2291806 -60.7791806
[122,] -25.2720378 -42.2291806
[123,] -82.9902277 -25.2720378
[124,] -44.6748431 -82.9902277
[125,] -52.8056123 -44.6748431
[126,] -50.2594585 -52.8056123
[127,] -35.5671508 -50.2594585
[128,] -23.9718471 -35.5671508
[129,] -72.3333855 -23.9718471
[130,] 10.5435375 -72.3333855
[131,] 16.0435375 10.5435375
[132,] -10.1673244 16.0435375
[133,] 27.4826756 -10.1673244
[134,] -55.9601816 27.4826756
[135,] -57.4783715 -55.9601816
[136,] -11.0629869 -57.4783715
[137,] -4.4937561 -11.0629869
[138,] 10.8523977 -4.4937561
[139,] -1.3552946 10.8523977
[140,] 50.9400091 -1.3552946
[141,] 31.8784706 50.9400091
[142,] -1.3446063 31.8784706
[143,] 37.8553937 -1.3446063
[144,] 1.5445317 37.8553937
[145,] 8.5945317 1.5445317
[146,] -27.5483254 8.5945317
[147,] -9.9665153 -27.5483254
[148,] -31.5511307 -9.9665153
[149,] -46.3819000 -31.5511307
[150,] 28.6642539 -46.3819000
[151,] 19.2565616 28.6642539
[152,] -23.5481347 19.2565616
[153,] 86.7903268 -23.5481347
[154,] 17.3672499 86.7903268
[155,] 100.1672499 17.3672499
[156,] 266.6563879 100.1672499
[157,] 90.2063879 266.6563879
[158,] 134.0635308 90.2063879
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -12.9087943 -28.4587943
2 2.5483486 -12.9087943
3 7.9301586 2.5483486
4 18.7455433 7.9301586
5 -11.7852260 18.7455433
6 34.1609279 -11.7852260
7 -8.6467644 34.1609279
8 -4.3514607 -8.6467644
9 -5.5129992 -4.3514607
10 -4.1360761 -5.5129992
11 -7.3360761 -4.1360761
12 -26.5469381 -7.3360761
13 -12.3969381 -26.5469381
14 -18.4397952 -12.3969381
15 -2.3579852 -18.4397952
16 24.2573994 -2.3579852
17 41.9266302 24.2573994
18 29.1727841 41.9266302
19 25.8650917 29.1727841
20 -8.8396046 25.8650917
21 28.0988570 -8.8396046
22 -12.0242199 28.0988570
23 -1.0242199 -12.0242199
24 17.0649181 -1.0242199
25 -0.1850819 17.0649181
26 38.1720609 -0.1850819
27 33.2538710 38.1720609
28 23.2692556 33.2538710
29 41.8384864 23.2692556
30 24.2846402 41.8384864
31 25.3769479 24.2846402
32 22.3722516 25.3769479
33 47.8107132 22.3722516
34 36.8876362 47.8107132
35 -3.4123638 36.8876362
36 -21.9232257 -3.4123638
37 -17.7732257 -21.9232257
38 10.8839171 -17.7732257
39 -4.6342728 10.8839171
40 -12.5188882 -4.6342728
41 24.8503426 -12.5188882
42 -27.6035036 24.8503426
43 -13.7111959 -27.6035036
44 -15.7158922 -13.7111959
45 -18.0774306 -15.7158922
46 -17.0005076 -18.0774306
47 -17.2005076 -17.0005076
48 -28.0113696 -17.2005076
49 -7.1613696 -28.0113696
50 -9.0042267 -7.1613696
51 -7.5224166 -9.0042267
52 2.1929680 -7.5224166
53 -3.5378012 2.1929680
54 -21.8916474 -3.5378012
55 7.6006603 -21.8916474
56 20.1959640 7.6006603
57 -14.0655745 20.1959640
58 32.4113486 -14.0655745
59 1.5113486 32.4113486
60 -16.2995134 1.5113486
61 26.8504866 -16.2995134
62 29.9076295 26.8504866
63 60.4894395 29.9076295
64 39.8048242 60.4894395
65 -1.1259451 39.8048242
66 12.1202088 -1.1259451
67 17.7125165 12.1202088
68 28.3078202 17.7125165
69 5.4462817 28.3078202
70 46.6232048 5.4462817
71 -18.6767952 46.6232048
72 -8.5876572 -18.6767952
73 -0.5376572 -8.5876572
74 -40.1805143 -0.5376572
75 10.3012957 -40.1805143
76 17.9166803 10.3012957
77 0.2859111 17.9166803
78 7.2320650 0.2859111
79 16.2243727 7.2320650
80 4.4196764 16.2243727
81 -9.1418621 4.4196764
82 10.0350610 -9.1418621
83 -16.8649390 10.0350610
84 -23.8758010 -16.8649390
85 -9.2258010 -23.8758010
86 -27.7686581 -9.2258010
87 33.9131519 -27.7686581
88 5.7285365 33.9131519
89 -15.6022327 5.7285365
90 8.1439211 -15.6022327
91 -9.0637712 8.1439211
92 -35.4684675 -9.0637712
93 -13.2300059 -35.4684675
94 -31.8530828 -13.2300059
95 -64.2530828 -31.8530828
96 -25.8639448 -64.2530828
97 -30.1139448 -25.8639448
98 -36.1568020 -30.1139448
99 9.8250081 -36.1568020
100 -41.5596073 9.8250081
101 -14.0903765 -41.5596073
102 -28.1442227 -14.0903765
103 -25.6519150 -28.1442227
104 -8.8566113 -25.6519150
105 -9.9181497 -8.8566113
106 -32.3412267 -9.9181497
107 21.9587733 -32.3412267
108 -34.7520886 21.9587733
109 -20.6020886 -34.7520886
110 24.7550542 -20.6020886
111 9.2368643 24.7550542
112 9.4522489 9.2368643
113 40.9214797 9.4522489
114 -26.7323665 40.9214797
115 -18.0400588 -26.7323665
116 -5.4837033 -18.0400588
117 -57.7452417 -5.4837033
118 -55.1683186 -57.7452417
119 -48.7683186 -55.1683186
120 -60.7791806 -48.7683186
121 -42.2291806 -60.7791806
122 -25.2720378 -42.2291806
123 -82.9902277 -25.2720378
124 -44.6748431 -82.9902277
125 -52.8056123 -44.6748431
126 -50.2594585 -52.8056123
127 -35.5671508 -50.2594585
128 -23.9718471 -35.5671508
129 -72.3333855 -23.9718471
130 10.5435375 -72.3333855
131 16.0435375 10.5435375
132 -10.1673244 16.0435375
133 27.4826756 -10.1673244
134 -55.9601816 27.4826756
135 -57.4783715 -55.9601816
136 -11.0629869 -57.4783715
137 -4.4937561 -11.0629869
138 10.8523977 -4.4937561
139 -1.3552946 10.8523977
140 50.9400091 -1.3552946
141 31.8784706 50.9400091
142 -1.3446063 31.8784706
143 37.8553937 -1.3446063
144 1.5445317 37.8553937
145 8.5945317 1.5445317
146 -27.5483254 8.5945317
147 -9.9665153 -27.5483254
148 -31.5511307 -9.9665153
149 -46.3819000 -31.5511307
150 28.6642539 -46.3819000
151 19.2565616 28.6642539
152 -23.5481347 19.2565616
153 86.7903268 -23.5481347
154 17.3672499 86.7903268
155 100.1672499 17.3672499
156 266.6563879 100.1672499
157 90.2063879 266.6563879
158 134.0635308 90.2063879
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7qq2t1230036616.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87ujt1230036616.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9o3hy1230036616.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106uj21230036616.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/113zax1230036616.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12gul81230036616.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13318n1230036616.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14bu5l1230036617.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15juql1230036617.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16q0uz1230036617.tab")
+ }
>
> system("convert tmp/182gr1230036616.ps tmp/182gr1230036616.png")
> system("convert tmp/2u1cm1230036616.ps tmp/2u1cm1230036616.png")
> system("convert tmp/39zv11230036616.ps tmp/39zv11230036616.png")
> system("convert tmp/4ziuq1230036616.ps tmp/4ziuq1230036616.png")
> system("convert tmp/5a4gf1230036616.ps tmp/5a4gf1230036616.png")
> system("convert tmp/6l4b91230036616.ps tmp/6l4b91230036616.png")
> system("convert tmp/7qq2t1230036616.ps tmp/7qq2t1230036616.png")
> system("convert tmp/87ujt1230036616.ps tmp/87ujt1230036616.png")
> system("convert tmp/9o3hy1230036616.ps tmp/9o3hy1230036616.png")
> system("convert tmp/106uj21230036616.ps tmp/106uj21230036616.png")
>
>
> proc.time()
user system elapsed
4.138 1.712 7.036