R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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(1990
+ ,1
+ ,353.63
+ ,1990
+ ,2
+ ,354.72
+ ,1990
+ ,3
+ ,355.49
+ ,1990
+ ,4
+ ,356.1
+ ,1990
+ ,5
+ ,357.08
+ ,1990
+ ,6
+ ,356.11
+ ,1990
+ ,7
+ ,354.67
+ ,1990
+ ,8
+ ,352.67
+ ,1990
+ ,9
+ ,351.05
+ ,1990
+ ,10
+ ,351.36
+ ,1990
+ ,11
+ ,352.81
+ ,1990
+ ,12
+ ,354.21
+ ,1991
+ ,1
+ ,354.87
+ ,1991
+ ,2
+ ,355.67
+ ,1991
+ ,3
+ ,357
+ ,1991
+ ,4
+ ,358.4
+ ,1991
+ ,5
+ ,359
+ ,1991
+ ,6
+ ,357.99
+ ,1991
+ ,7
+ ,355.96
+ ,1991
+ ,8
+ ,353.78
+ ,1991
+ ,9
+ ,352.2
+ ,1991
+ ,10
+ ,352.22
+ ,1991
+ ,11
+ ,353.7
+ ,1991
+ ,12
+ ,354.98
+ ,1992
+ ,1
+ ,356.08
+ ,1992
+ ,2
+ ,356.84
+ ,1992
+ ,3
+ ,357.73
+ ,1992
+ ,4
+ ,358.91
+ ,1992
+ ,5
+ ,359.45
+ ,1992
+ ,6
+ ,359.19
+ ,1992
+ ,7
+ ,356.72
+ ,1992
+ ,8
+ ,354.77
+ ,1992
+ ,9
+ ,352.8
+ ,1992
+ ,10
+ ,353.21
+ ,1992
+ ,11
+ ,354.15
+ ,1992
+ ,12
+ ,355.39
+ ,1993
+ ,1
+ ,356.76
+ ,1993
+ ,2
+ ,357.17
+ ,1993
+ ,3
+ ,358.26
+ ,1993
+ ,4
+ ,359.17
+ ,1993
+ ,5
+ ,360.07
+ ,1993
+ ,6
+ ,359.41
+ ,1993
+ ,7
+ ,357.36
+ ,1993
+ ,8
+ ,355.29
+ ,1993
+ ,9
+ ,353.96
+ ,1993
+ ,10
+ ,354.03
+ ,1993
+ ,11
+ ,355.27
+ ,1993
+ ,12
+ ,356.7
+ ,1994
+ ,1
+ ,358.05
+ ,1994
+ ,2
+ ,358.8
+ ,1994
+ ,3
+ ,359.67
+ ,1994
+ ,4
+ ,361.13
+ ,1994
+ ,5
+ ,361.48
+ ,1994
+ ,6
+ ,360.6
+ ,1994
+ ,7
+ ,359.2
+ ,1994
+ ,8
+ ,357.23
+ ,1994
+ ,9
+ ,355.42
+ ,1994
+ ,10
+ ,355.89
+ ,1994
+ ,11
+ ,357.41
+ ,1994
+ ,12
+ ,358.74
+ ,1995
+ ,1
+ ,359.73
+ ,1995
+ ,2
+ ,360.61
+ ,1995
+ ,3
+ ,361.6
+ ,1995
+ ,4
+ ,363.05
+ ,1995
+ ,5
+ ,363.62
+ ,1995
+ ,6
+ ,363.03
+ ,1995
+ ,7
+ ,361.55
+ ,1995
+ ,8
+ ,358.94
+ ,1995
+ ,9
+ ,357.93
+ ,1995
+ ,10
+ ,357.8
+ ,1995
+ ,11
+ ,359.22
+ ,1995
+ ,12
+ ,360.42
+ ,1996
+ ,1
+ ,361.83
+ ,1996
+ ,2
+ ,362.94
+ ,1996
+ ,3
+ ,363.91
+ ,1996
+ ,4
+ ,364.28
+ ,1996
+ ,5
+ ,364.93
+ ,1996
+ ,6
+ ,364.7
+ ,1996
+ ,7
+ ,363.31
+ ,1996
+ ,8
+ ,361.15
+ ,1996
+ ,9
+ ,359.41
+ ,1996
+ ,10
+ ,359.34
+ ,1996
+ ,11
+ ,360.62
+ ,1996
+ ,12
+ ,361.96
+ ,1997
+ ,1
+ ,362.81
+ ,1997
+ ,2
+ ,363.87
+ ,1997
+ ,3
+ ,364.25
+ ,1997
+ ,4
+ ,366.02
+ ,1997
+ ,5
+ ,366.47
+ ,1997
+ ,6
+ ,365.37
+ ,1997
+ ,7
+ ,364.1
+ ,1997
+ ,8
+ ,361.89
+ ,1997
+ ,9
+ ,360.05
+ ,1997
+ ,10
+ ,360.49
+ ,1997
+ ,11
+ ,362.21
+ ,1997
+ ,12
+ ,364.12
+ ,1998
+ ,1
+ ,365
+ ,1998
+ ,2
+ ,365.82
+ ,1998
+ ,3
+ ,366.95
+ ,1998
+ ,4
+ ,368.42
+ ,1998
+ ,5
+ ,369.33
+ ,1998
+ ,6
+ ,368.78
+ ,1998
+ ,7
+ ,367.59
+ ,1998
+ ,8
+ ,365.81
+ ,1998
+ ,9
+ ,363.83
+ ,1998
+ ,10
+ ,364.18
+ ,1998
+ ,11
+ ,365.36
+ ,1998
+ ,12
+ ,366.88
+ ,1999
+ ,1
+ ,367.97
+ ,1999
+ ,2
+ ,368.83
+ ,1999
+ ,3
+ ,369.46
+ ,1999
+ ,4
+ ,370.77
+ ,1999
+ ,5
+ ,370.66
+ ,1999
+ ,6
+ ,370.1
+ ,1999
+ ,7
+ ,369.1
+ ,1999
+ ,8
+ ,366.7
+ ,1999
+ ,9
+ ,364.61
+ ,1999
+ ,10
+ ,365.17
+ ,1999
+ ,11
+ ,366.51
+ ,1999
+ ,12
+ ,367.86
+ ,2000
+ ,1
+ ,369.07
+ ,2000
+ ,2
+ ,369.32
+ ,2000
+ ,3
+ ,370.38
+ ,2000
+ ,4
+ ,371.63
+ ,2000
+ ,5
+ ,371.32
+ ,2000
+ ,6
+ ,371.51
+ ,2000
+ ,7
+ ,369.69
+ ,2000
+ ,8
+ ,368.18
+ ,2000
+ ,9
+ ,366.87
+ ,2000
+ ,10
+ ,366.94
+ ,2000
+ ,11
+ ,368.27
+ ,2000
+ ,12
+ ,369.62
+ ,2001
+ ,1
+ ,370.47
+ ,2001
+ ,2
+ ,371.44
+ ,2001
+ ,3
+ ,372.39
+ ,2001
+ ,4
+ ,373.32
+ ,2001
+ ,5
+ ,373.77
+ ,2001
+ ,6
+ ,373.13
+ ,2001
+ ,7
+ ,371.51
+ ,2001
+ ,8
+ ,369.59
+ ,2001
+ ,9
+ ,368.12
+ ,2001
+ ,10
+ ,368.38
+ ,2001
+ ,11
+ ,369.64
+ ,2001
+ ,12
+ ,371.11
+ ,2002
+ ,1
+ ,372.38
+ ,2002
+ ,2
+ ,373.08
+ ,2002
+ ,3
+ ,373.87
+ ,2002
+ ,4
+ ,374.93
+ ,2002
+ ,5
+ ,375.58
+ ,2002
+ ,6
+ ,375.44
+ ,2002
+ ,7
+ ,373.91
+ ,2002
+ ,8
+ ,371.77
+ ,2002
+ ,9
+ ,370.72
+ ,2002
+ ,10
+ ,370.5
+ ,2002
+ ,11
+ ,372.19
+ ,2002
+ ,12
+ ,373.71
+ ,2003
+ ,1
+ ,374.92
+ ,2003
+ ,2
+ ,375.63
+ ,2003
+ ,3
+ ,376.51
+ ,2003
+ ,4
+ ,377.75
+ ,2003
+ ,5
+ ,378.54
+ ,2003
+ ,6
+ ,378.21
+ ,2003
+ ,7
+ ,376.65
+ ,2003
+ ,8
+ ,374.28
+ ,2003
+ ,9
+ ,373.12
+ ,2003
+ ,10
+ ,373.1
+ ,2003
+ ,11
+ ,374.67
+ ,2003
+ ,12
+ ,375.97
+ ,2004
+ ,1
+ ,377.03
+ ,2004
+ ,2
+ ,377.87
+ ,2004
+ ,3
+ ,378.88
+ ,2004
+ ,4
+ ,380.42
+ ,2004
+ ,5
+ ,380.62
+ ,2004
+ ,6
+ ,379.66
+ ,2004
+ ,7
+ ,377.48
+ ,2004
+ ,8
+ ,376.07
+ ,2004
+ ,9
+ ,374.1
+ ,2004
+ ,10
+ ,374.47
+ ,2004
+ ,11
+ ,376.15
+ ,2004
+ ,12
+ ,377.51
+ ,2005
+ ,1
+ ,378.43
+ ,2005
+ ,2
+ ,379.7
+ ,2005
+ ,3
+ ,380.91
+ ,2005
+ ,4
+ ,382.2
+ ,2005
+ ,5
+ ,382.45
+ ,2005
+ ,6
+ ,382.14
+ ,2005
+ ,7
+ ,380.6
+ ,2005
+ ,8
+ ,378.6
+ ,2005
+ ,9
+ ,376.72
+ ,2005
+ ,10
+ ,376.98
+ ,2005
+ ,11
+ ,378.29
+ ,2005
+ ,12
+ ,380.07
+ ,2006
+ ,1
+ ,381.36
+ ,2006
+ ,2
+ ,382.19
+ ,2006
+ ,3
+ ,382.65
+ ,2006
+ ,4
+ ,384.65
+ ,2006
+ ,5
+ ,384.94
+ ,2006
+ ,6
+ ,384.01
+ ,2006
+ ,7
+ ,382.15
+ ,2006
+ ,8
+ ,380.33
+ ,2006
+ ,9
+ ,378.81
+ ,2006
+ ,10
+ ,379.06
+ ,2006
+ ,11
+ ,380.17
+ ,2006
+ ,12
+ ,381.85
+ ,2007
+ ,1
+ ,382.88
+ ,2007
+ ,2
+ ,383.77
+ ,2007
+ ,3
+ ,384.42
+ ,2007
+ ,4
+ ,386.36
+ ,2007
+ ,5
+ ,386.53
+ ,2007
+ ,6
+ ,386.01
+ ,2007
+ ,7
+ ,384.45
+ ,2007
+ ,8
+ ,381.96
+ ,2007
+ ,9
+ ,380.81
+ ,2007
+ ,10
+ ,381.09
+ ,2007
+ ,11
+ ,382.37
+ ,2007
+ ,12
+ ,383.84
+ ,2008
+ ,1
+ ,385.42
+ ,2008
+ ,2
+ ,385.72
+ ,2008
+ ,3
+ ,385.96
+ ,2008
+ ,4
+ ,387.18
+ ,2008
+ ,5
+ ,388.5
+ ,2008
+ ,6
+ ,387.88
+ ,2008
+ ,7
+ ,386.38
+ ,2008
+ ,8
+ ,384.15
+ ,2008
+ ,9
+ ,383.07
+ ,2008
+ ,10
+ ,382.98
+ ,2008
+ ,11
+ ,384.11
+ ,2008
+ ,12
+ ,385.54
+ ,2009
+ ,1
+ ,386.92
+ ,2009
+ ,2
+ ,387.41
+ ,2009
+ ,3
+ ,388.77
+ ,2009
+ ,4
+ ,389.46
+ ,2009
+ ,5
+ ,390.18
+ ,2009
+ ,6
+ ,389.43
+ ,2009
+ ,7
+ ,387.74
+ ,2009
+ ,8
+ ,385.91
+ ,2009
+ ,9
+ ,384.77
+ ,2009
+ ,10
+ ,384.38
+ ,2009
+ ,11
+ ,385.99
+ ,2009
+ ,12
+ ,387.26)
+ ,dim=c(3
+ ,240)
+ ,dimnames=list(c('JAARTAL'
+ ,'MAAND'
+ ,'CO2')
+ ,1:240))
> y <- array(NA,dim=c(3,240),dimnames=list(c('JAARTAL','MAAND','CO2'),1:240))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '3'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
CO2 JAARTAL MAAND
1 353.63 1990 1
2 354.72 1990 2
3 355.49 1990 3
4 356.10 1990 4
5 357.08 1990 5
6 356.11 1990 6
7 354.67 1990 7
8 352.67 1990 8
9 351.05 1990 9
10 351.36 1990 10
11 352.81 1990 11
12 354.21 1990 12
13 354.87 1991 1
14 355.67 1991 2
15 357.00 1991 3
16 358.40 1991 4
17 359.00 1991 5
18 357.99 1991 6
19 355.96 1991 7
20 353.78 1991 8
21 352.20 1991 9
22 352.22 1991 10
23 353.70 1991 11
24 354.98 1991 12
25 356.08 1992 1
26 356.84 1992 2
27 357.73 1992 3
28 358.91 1992 4
29 359.45 1992 5
30 359.19 1992 6
31 356.72 1992 7
32 354.77 1992 8
33 352.80 1992 9
34 353.21 1992 10
35 354.15 1992 11
36 355.39 1992 12
37 356.76 1993 1
38 357.17 1993 2
39 358.26 1993 3
40 359.17 1993 4
41 360.07 1993 5
42 359.41 1993 6
43 357.36 1993 7
44 355.29 1993 8
45 353.96 1993 9
46 354.03 1993 10
47 355.27 1993 11
48 356.70 1993 12
49 358.05 1994 1
50 358.80 1994 2
51 359.67 1994 3
52 361.13 1994 4
53 361.48 1994 5
54 360.60 1994 6
55 359.20 1994 7
56 357.23 1994 8
57 355.42 1994 9
58 355.89 1994 10
59 357.41 1994 11
60 358.74 1994 12
61 359.73 1995 1
62 360.61 1995 2
63 361.60 1995 3
64 363.05 1995 4
65 363.62 1995 5
66 363.03 1995 6
67 361.55 1995 7
68 358.94 1995 8
69 357.93 1995 9
70 357.80 1995 10
71 359.22 1995 11
72 360.42 1995 12
73 361.83 1996 1
74 362.94 1996 2
75 363.91 1996 3
76 364.28 1996 4
77 364.93 1996 5
78 364.70 1996 6
79 363.31 1996 7
80 361.15 1996 8
81 359.41 1996 9
82 359.34 1996 10
83 360.62 1996 11
84 361.96 1996 12
85 362.81 1997 1
86 363.87 1997 2
87 364.25 1997 3
88 366.02 1997 4
89 366.47 1997 5
90 365.37 1997 6
91 364.10 1997 7
92 361.89 1997 8
93 360.05 1997 9
94 360.49 1997 10
95 362.21 1997 11
96 364.12 1997 12
97 365.00 1998 1
98 365.82 1998 2
99 366.95 1998 3
100 368.42 1998 4
101 369.33 1998 5
102 368.78 1998 6
103 367.59 1998 7
104 365.81 1998 8
105 363.83 1998 9
106 364.18 1998 10
107 365.36 1998 11
108 366.88 1998 12
109 367.97 1999 1
110 368.83 1999 2
111 369.46 1999 3
112 370.77 1999 4
113 370.66 1999 5
114 370.10 1999 6
115 369.10 1999 7
116 366.70 1999 8
117 364.61 1999 9
118 365.17 1999 10
119 366.51 1999 11
120 367.86 1999 12
121 369.07 2000 1
122 369.32 2000 2
123 370.38 2000 3
124 371.63 2000 4
125 371.32 2000 5
126 371.51 2000 6
127 369.69 2000 7
128 368.18 2000 8
129 366.87 2000 9
130 366.94 2000 10
131 368.27 2000 11
132 369.62 2000 12
133 370.47 2001 1
134 371.44 2001 2
135 372.39 2001 3
136 373.32 2001 4
137 373.77 2001 5
138 373.13 2001 6
139 371.51 2001 7
140 369.59 2001 8
141 368.12 2001 9
142 368.38 2001 10
143 369.64 2001 11
144 371.11 2001 12
145 372.38 2002 1
146 373.08 2002 2
147 373.87 2002 3
148 374.93 2002 4
149 375.58 2002 5
150 375.44 2002 6
151 373.91 2002 7
152 371.77 2002 8
153 370.72 2002 9
154 370.50 2002 10
155 372.19 2002 11
156 373.71 2002 12
157 374.92 2003 1
158 375.63 2003 2
159 376.51 2003 3
160 377.75 2003 4
161 378.54 2003 5
162 378.21 2003 6
163 376.65 2003 7
164 374.28 2003 8
165 373.12 2003 9
166 373.10 2003 10
167 374.67 2003 11
168 375.97 2003 12
169 377.03 2004 1
170 377.87 2004 2
171 378.88 2004 3
172 380.42 2004 4
173 380.62 2004 5
174 379.66 2004 6
175 377.48 2004 7
176 376.07 2004 8
177 374.10 2004 9
178 374.47 2004 10
179 376.15 2004 11
180 377.51 2004 12
181 378.43 2005 1
182 379.70 2005 2
183 380.91 2005 3
184 382.20 2005 4
185 382.45 2005 5
186 382.14 2005 6
187 380.60 2005 7
188 378.60 2005 8
189 376.72 2005 9
190 376.98 2005 10
191 378.29 2005 11
192 380.07 2005 12
193 381.36 2006 1
194 382.19 2006 2
195 382.65 2006 3
196 384.65 2006 4
197 384.94 2006 5
198 384.01 2006 6
199 382.15 2006 7
200 380.33 2006 8
201 378.81 2006 9
202 379.06 2006 10
203 380.17 2006 11
204 381.85 2006 12
205 382.88 2007 1
206 383.77 2007 2
207 384.42 2007 3
208 386.36 2007 4
209 386.53 2007 5
210 386.01 2007 6
211 384.45 2007 7
212 381.96 2007 8
213 380.81 2007 9
214 381.09 2007 10
215 382.37 2007 11
216 383.84 2007 12
217 385.42 2008 1
218 385.72 2008 2
219 385.96 2008 3
220 387.18 2008 4
221 388.50 2008 5
222 387.88 2008 6
223 386.38 2008 7
224 384.15 2008 8
225 383.07 2008 9
226 382.98 2008 10
227 384.11 2008 11
228 385.54 2008 12
229 386.92 2009 1
230 387.41 2009 2
231 388.77 2009 3
232 389.46 2009 4
233 390.18 2009 5
234 389.43 2009 6
235 387.74 2009 7
236 385.91 2009 8
237 384.77 2009 9
238 384.38 2009 10
239 385.99 2009 11
240 387.26 2009 12
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) JAARTAL MAAND
-3265.9468 1.8190 -0.2653
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-4.1286 -1.4774 -0.0467 1.3469 4.6742
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.266e+03 4.181e+01 -78.122 < 2e-16 ***
JAARTAL 1.819e+00 2.091e-02 87.001 < 2e-16 ***
MAAND -2.653e-01 3.492e-02 -7.596 7.03e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.868 on 237 degrees of freedom
Multiple R-squared: 0.9699, Adjusted R-squared: 0.9696
F-statistic: 3813 on 2 and 237 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,] 0.07498598 0.1499720 0.92501402
[2,] 0.22854603 0.4570921 0.77145397
[3,] 0.49815244 0.9963049 0.50184756
[4,] 0.67695245 0.6460951 0.32304755
[5,] 0.62597264 0.7480547 0.37402736
[6,] 0.53475880 0.9304824 0.46524120
[7,] 0.55244929 0.8951014 0.44755071
[8,] 0.45549440 0.9109888 0.54450560
[9,] 0.38127005 0.7625401 0.61872995
[10,] 0.38431289 0.7686258 0.61568711
[11,] 0.50096146 0.9980771 0.49903854
[12,] 0.63006085 0.7398783 0.36993915
[13,] 0.63014435 0.7397113 0.36985565
[14,] 0.57822792 0.8435442 0.42177208
[15,] 0.61529275 0.7694145 0.38470725
[16,] 0.71841286 0.5631743 0.28158714
[17,] 0.73542216 0.5291557 0.26457784
[18,] 0.68382013 0.6323597 0.31617987
[19,] 0.67458834 0.6508233 0.32541166
[20,] 0.67972788 0.6405442 0.32027212
[21,] 0.62688329 0.7462334 0.37311671
[22,] 0.58256568 0.8348686 0.41743432
[23,] 0.60883813 0.7823237 0.39116187
[24,] 0.68130378 0.6373924 0.31869622
[25,] 0.73159777 0.5368045 0.26840223
[26,] 0.69448581 0.6110284 0.30551419
[27,] 0.69180036 0.6163993 0.30819964
[28,] 0.77485428 0.4502914 0.22514572
[29,] 0.78194454 0.4361109 0.21805546
[30,] 0.74571601 0.5085680 0.25428399
[31,] 0.72359237 0.5528153 0.27640763
[32,] 0.74158069 0.5168386 0.25841931
[33,] 0.71470327 0.5705935 0.28529673
[34,] 0.67277027 0.6544595 0.32722973
[35,] 0.66306527 0.6738695 0.33693473
[36,] 0.71661165 0.5667767 0.28338835
[37,] 0.73479264 0.5304147 0.26520736
[38,] 0.69676302 0.6064740 0.30323698
[39,] 0.68782610 0.6243478 0.31217390
[40,] 0.72368833 0.5526233 0.27631167
[41,] 0.72654086 0.5469183 0.27345914
[42,] 0.68623762 0.6275248 0.31376238
[43,] 0.67803579 0.6439284 0.32196421
[44,] 0.67386895 0.6522621 0.32613105
[45,] 0.63458919 0.7308216 0.36541081
[46,] 0.59796559 0.8040688 0.40203441
[47,] 0.63536147 0.7292771 0.36463853
[48,] 0.70489020 0.5902196 0.29510980
[49,] 0.72453648 0.5509270 0.27546352
[50,] 0.69845503 0.6030899 0.30154497
[51,] 0.66590319 0.6681936 0.33409681
[52,] 0.68734790 0.6253042 0.31265210
[53,] 0.66978283 0.6604343 0.33021717
[54,] 0.63456398 0.7308720 0.36543602
[55,] 0.65574368 0.6885126 0.34425632
[56,] 0.64554764 0.7089047 0.35445236
[57,] 0.60700674 0.7859865 0.39299326
[58,] 0.58110421 0.8377916 0.41889579
[59,] 0.63775844 0.7244831 0.36224156
[60,] 0.74158228 0.5168354 0.25841772
[61,] 0.80090616 0.3981877 0.19909384
[62,] 0.79911065 0.4017787 0.20088935
[63,] 0.77342719 0.4531456 0.22657281
[64,] 0.76073147 0.4785371 0.23926853
[65,] 0.74241932 0.5151614 0.25758068
[66,] 0.71312597 0.5737481 0.28687403
[67,] 0.72600781 0.5479844 0.27399219
[68,] 0.70901785 0.5819643 0.29098215
[69,] 0.67493343 0.6501331 0.32506657
[70,] 0.66382491 0.6723502 0.33617509
[71,] 0.67693874 0.6461225 0.32306126
[72,] 0.73658380 0.5268324 0.26341620
[73,] 0.78798807 0.4240239 0.21201193
[74,] 0.78617931 0.4276414 0.21382069
[75,] 0.75695779 0.4860844 0.24304221
[76,] 0.75413131 0.4917374 0.24586869
[77,] 0.74364964 0.5127007 0.25635036
[78,] 0.71230853 0.5753829 0.28769147
[79,] 0.71442591 0.5711482 0.28557409
[80,] 0.72908117 0.5418377 0.27091883
[81,] 0.70275738 0.5944852 0.29724262
[82,] 0.67003451 0.6599310 0.32996549
[83,] 0.68216009 0.6356798 0.31783991
[84,] 0.72740427 0.5451915 0.27259573
[85,] 0.72945437 0.5410913 0.27054563
[86,] 0.70365875 0.5926825 0.29634125
[87,] 0.68659277 0.6268145 0.31340723
[88,] 0.73653871 0.5269226 0.26346129
[89,] 0.74916344 0.5016731 0.25083656
[90,] 0.72127654 0.5574469 0.27872346
[91,] 0.74139318 0.5172136 0.25860682
[92,] 0.74168553 0.5166289 0.25831447
[93,] 0.71746590 0.5650682 0.28253410
[94,] 0.69619645 0.6076071 0.30380355
[95,] 0.73533443 0.5293311 0.26466557
[96,] 0.83046555 0.3390689 0.16953445
[97,] 0.88588712 0.2282258 0.11411288
[98,] 0.90047729 0.1990454 0.09952271
[99,] 0.88759973 0.2248005 0.11240027
[100,] 0.87461988 0.2507602 0.12538012
[101,] 0.85583891 0.2883222 0.14416109
[102,] 0.84447352 0.3110530 0.15552648
[103,] 0.88132761 0.2373448 0.11867239
[104,] 0.86582936 0.2683413 0.13417064
[105,] 0.84676013 0.3064797 0.15323987
[106,] 0.83737136 0.3252573 0.16262864
[107,] 0.87300945 0.2539811 0.12699055
[108,] 0.90686193 0.1862761 0.09313807
[109,] 0.92648535 0.1470293 0.07351465
[110,] 0.92978110 0.1404378 0.07021890
[111,] 0.91656835 0.1668633 0.08343165
[112,] 0.92128059 0.1574388 0.07871941
[113,] 0.91404135 0.1719173 0.08595865
[114,] 0.89963956 0.2007209 0.10036044
[115,] 0.90508836 0.1898233 0.09491164
[116,] 0.90127723 0.1974455 0.09872277
[117,] 0.89052390 0.2189522 0.10947610
[118,] 0.87321270 0.2535746 0.12678730
[119,] 0.87532817 0.2493437 0.12467183
[120,] 0.87625076 0.2474985 0.12374924
[121,] 0.88815585 0.2236883 0.11184415
[122,] 0.87245805 0.2550839 0.12754195
[123,] 0.85445070 0.2910986 0.14554930
[124,] 0.85239191 0.2952162 0.14760809
[125,] 0.84399496 0.3120101 0.15600504
[126,] 0.82187478 0.3562504 0.17812522
[127,] 0.82563462 0.3487308 0.17436538
[128,] 0.83549846 0.3290031 0.16450154
[129,] 0.81862733 0.3627453 0.18137267
[130,] 0.79558085 0.4088383 0.20441915
[131,] 0.79122788 0.4175442 0.20877212
[132,] 0.81033308 0.3793338 0.18966692
[133,] 0.81572578 0.3685484 0.18427422
[134,] 0.79275891 0.4144822 0.20724109
[135,] 0.77730818 0.4453836 0.22269182
[136,] 0.80190647 0.3961871 0.19809353
[137,] 0.80930975 0.3813805 0.19069025
[138,] 0.78671507 0.4265699 0.21328493
[139,] 0.77612876 0.4477425 0.22387124
[140,] 0.79548005 0.4090399 0.20451995
[141,] 0.78735736 0.4252853 0.21264264
[142,] 0.76386262 0.4722748 0.23613738
[143,] 0.74852858 0.5029428 0.25147142
[144,] 0.75976153 0.4804769 0.24023847
[145,] 0.77526825 0.4494635 0.22473175
[146,] 0.75460284 0.4907943 0.24539716
[147,] 0.73798988 0.5240202 0.26201012
[148,] 0.74812848 0.5037430 0.25187152
[149,] 0.76113623 0.4777275 0.23886377
[150,] 0.73534516 0.5293097 0.26465484
[151,] 0.73880504 0.5223899 0.26119496
[152,] 0.74845850 0.5030830 0.25154150
[153,] 0.73308578 0.5338284 0.26691422
[154,] 0.70736237 0.5852753 0.29263763
[155,] 0.71072141 0.5785572 0.28927859
[156,] 0.76334969 0.4733006 0.23665031
[157,] 0.80818870 0.3836226 0.19181130
[158,] 0.80216043 0.3956791 0.19783957
[159,] 0.77952372 0.4409526 0.22047628
[160,] 0.77778829 0.4444234 0.22221171
[161,] 0.77223106 0.4555379 0.22776894
[162,] 0.74587450 0.5082510 0.25412550
[163,] 0.75553224 0.4889355 0.24446776
[164,] 0.76338865 0.4732227 0.23661135
[165,] 0.74376420 0.5124716 0.25623580
[166,] 0.71730053 0.5653989 0.28269947
[167,] 0.74444678 0.5111064 0.25555322
[168,] 0.79622001 0.4075600 0.20377999
[169,] 0.81377830 0.3724434 0.18622170
[170,] 0.78648086 0.4270383 0.21351914
[171,] 0.76408115 0.4718377 0.23591885
[172,] 0.80800305 0.3839939 0.19199695
[173,] 0.82896351 0.3420730 0.17103649
[174,] 0.80585312 0.3882938 0.19414688
[175,] 0.79388306 0.4122339 0.20611694
[176,] 0.83534996 0.3293001 0.16465004
[177,] 0.82736778 0.3452644 0.17263222
[178,] 0.80349645 0.3930071 0.19650355
[179,] 0.80880226 0.3823955 0.19119774
[180,] 0.83672864 0.3265427 0.16327136
[181,] 0.86377504 0.2724499 0.13622496
[182,] 0.85239231 0.2952154 0.14760769
[183,] 0.82790344 0.3441931 0.17209656
[184,] 0.84717896 0.3056421 0.15282104
[185,] 0.85450386 0.2909923 0.14549614
[186,] 0.83140580 0.3371884 0.16859420
[187,] 0.83035579 0.3392884 0.16964421
[188,] 0.83569802 0.3286040 0.16430198
[189,] 0.81595408 0.3680918 0.18404592
[190,] 0.78657634 0.4268473 0.21342366
[191,] 0.80744633 0.3851073 0.19255367
[192,] 0.86242816 0.2751437 0.13757184
[193,] 0.88701046 0.2259791 0.11298954
[194,] 0.86861044 0.2627791 0.13138956
[195,] 0.84365772 0.3126846 0.15634228
[196,] 0.85634045 0.2873191 0.14365955
[197,] 0.85959597 0.2808081 0.14040403
[198,] 0.83409283 0.3318143 0.16590717
[199,] 0.82241407 0.3551719 0.17758593
[200,] 0.84708572 0.3058286 0.15291428
[201,] 0.83618346 0.3276331 0.16381654
[202,] 0.80590345 0.3881931 0.19409655
[203,] 0.80835540 0.3832892 0.19164460
[204,] 0.84461047 0.3107791 0.15538953
[205,] 0.87781971 0.2443606 0.12218029
[206,] 0.86552955 0.2689409 0.13447045
[207,] 0.83856408 0.3228718 0.16143592
[208,] 0.85062802 0.2987440 0.14937198
[209,] 0.85497478 0.2900504 0.14502522
[210,] 0.82046957 0.3590609 0.17953043
[211,] 0.80130344 0.3973931 0.19869656
[212,] 0.79799898 0.4040020 0.20200102
[213,] 0.78160863 0.4367827 0.21839137
[214,] 0.75109248 0.4978150 0.24890752
[215,] 0.69515703 0.6096859 0.30484297
[216,] 0.73619089 0.5276182 0.26380911
[217,] 0.78815652 0.4236870 0.21184348
[218,] 0.77556203 0.4488759 0.22443797
[219,] 0.70943767 0.5811247 0.29056233
[220,] 0.67947443 0.6410511 0.32052557
[221,] 0.68050383 0.6389923 0.31949617
[222,] 0.63146885 0.7370623 0.36853115
[223,] 0.54048327 0.9190335 0.45951673
[224,] 0.59454557 0.8109089 0.40545443
[225,] 0.65437517 0.6912497 0.34562483
[226,] 0.58088927 0.8382215 0.41911073
[227,] 0.45451732 0.9090346 0.54548268
[228,] 0.41027059 0.8205412 0.58972941
[229,] 0.48583203 0.9716641 0.51416797
> postscript(file="/var/wessaorg/rcomp/tmp/1n8ib1322003067.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2gyel1322003067.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3cq8e1322003067.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4cacd1322003067.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5t3201322003067.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 = 240
Frequency = 1
1 2 3 4 5 6
0.06203022 1.41731693 2.45260365 3.32789036 4.57317707 3.86846379
7 8 9 10 11 12
2.69375050 0.95903721 -0.39567607 0.17961064 1.89489735 3.56018407
13 14 15 16 17 18
-0.51695474 0.54833197 2.14361868 3.80890540 4.67419211 3.92947882
19 20 21 22 23 24
2.16476554 0.25005225 -1.06466104 -0.77937432 0.96591239 2.51119910
25 26 27 28 29 30
-1.12593971 -0.10065299 1.05463372 2.49992043 3.30520715 3.31049386
31 32 33 34 35 36
1.10578057 -0.57893271 -2.28364600 -1.60835929 -0.40307257 1.10221414
37 38 39 40 41 42
-2.26492467 -1.58963795 -0.23435124 0.94093547 2.10622219 1.71150890
43 44 45 46 47 48
-0.07320439 -1.87791767 -2.94263096 -2.60734425 -1.10205753 0.59322918
49 50 51 52 53 54
-2.79390963 -1.77862292 -0.64333620 1.08195051 1.69723722 1.08252394
55 56 57 58 59 60
-0.05218935 -1.75690264 -3.30161592 -2.56632921 -0.78104250 0.81424422
61 62 63 64 65 66
-2.93289459 -1.78760788 -0.53232117 1.18296555 2.01825226 1.69353897
67 68 69 70 71 72
0.47882569 -1.86588760 -2.61060089 -2.47531417 -0.79002746 0.67525925
73 74 75 76 77 78
-2.65187955 -1.27659284 -0.04130613 0.59398059 1.50926730 1.54455401
79 80 81 82 83 84
0.41984073 -1.47487256 -2.94958585 -2.75429914 -1.20901242 0.39627429
85 86 87 88 89 90
-3.49086452 -2.16557780 -1.52029109 0.51499562 1.23028234 0.39556905
91 92 93 94 95 96
-0.60914424 -2.55385752 -4.12857081 -3.42328410 -1.43799738 0.73728933
97 98 99 100 101 102
-3.11984948 -2.03456277 -0.63927605 1.09601066 2.27129737 1.98658409
103 104 105 106 107 108
1.06187080 -0.45284249 -2.16755577 -1.55226906 -0.10698235 1.67830437
109 110 111 112 113 114
-1.96883444 -0.84354773 0.05173898 1.62702570 1.78231241 1.48759912
115 116 117 118 119 120
0.75288584 -1.38182745 -3.20654074 -2.38125402 -0.77596731 0.83931940
121 122 123 124 125 126
-2.68781940 -2.17253269 -0.84724598 0.66804074 0.62332745 1.07861416
127 128 129 130 131 132
-0.47609912 -1.72081241 -2.76552570 -2.43023898 -0.83495227 0.78033444
133 134 135 136 137 138
-3.10680437 -1.87151765 -0.65623094 0.53905577 1.25434249 0.87962920
139 140 141 142 143 144
-0.47508409 -2.12979737 -3.33451066 -2.80922395 -1.28393723 0.45134948
145 146 147 148 149 150
-3.01578933 -2.05050262 -0.99521590 0.33007081 1.24535752 1.37064424
151 152 153 154 155 156
0.10593095 -1.76878234 -2.55349562 -2.50820891 -0.55292220 1.23236452
157 158 159 160 161 162
-2.29477429 -1.31948758 -0.17420086 1.33108585 2.38637256 2.32165927
163 164 165 166 167 168
1.02694599 -1.07776730 -1.97248059 -1.72719387 0.10809284 1.67337955
169 170 171 172 173 174
-2.00375925 -0.89847254 0.37681417 2.18210089 2.64738760 1.95267431
175 176 177 178 179 180
0.03796103 -1.10675226 -2.81146555 -2.17617883 -0.23089212 1.39439459
181 182 183 184 185 186
-2.42274422 -0.88745750 0.58782921 2.14311592 2.65840264 2.61368935
187 188 189 190 191 192
1.33897606 -0.39573722 -2.01045051 -1.48516380 0.09012292 2.13540963
193 194 195 196 197 198
-1.31172918 -0.21644247 0.50884425 2.77413096 3.32941767 2.66470439
199 200 201 202 203 204
1.06999110 -0.48472219 -1.73943547 -1.22414876 0.15113795 2.09642467
205 206 207 208 209 210
-1.61071414 -0.45542743 0.45985929 2.66514600 3.10043271 2.84571943
211 212 213 214 215 216
1.55100614 -0.67370715 -1.55842043 -1.01313372 0.53215299 2.26743971
217 218 219 220 221 222
-0.88969910 -0.32441239 0.18087432 1.66616104 3.25144775 2.89673446
223 224 225 226 227 228
1.66202118 -0.30269211 -1.11740540 -0.94211868 0.45316803 2.14845474
229 230 231 232 233 234
-1.20868407 -0.45339735 1.17188936 2.12717607 3.11246279 2.62774950
235 236 237 238 239 240
1.20303621 -0.36167707 -1.23639036 -1.36110365 0.51418307 2.04946978
> postscript(file="/var/wessaorg/rcomp/tmp/64bmb1322003067.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 = 240
Frequency = 1
lag(myerror, k = 1) myerror
0 0.06203022 NA
1 1.41731693 0.06203022
2 2.45260365 1.41731693
3 3.32789036 2.45260365
4 4.57317707 3.32789036
5 3.86846379 4.57317707
6 2.69375050 3.86846379
7 0.95903721 2.69375050
8 -0.39567607 0.95903721
9 0.17961064 -0.39567607
10 1.89489735 0.17961064
11 3.56018407 1.89489735
12 -0.51695474 3.56018407
13 0.54833197 -0.51695474
14 2.14361868 0.54833197
15 3.80890540 2.14361868
16 4.67419211 3.80890540
17 3.92947882 4.67419211
18 2.16476554 3.92947882
19 0.25005225 2.16476554
20 -1.06466104 0.25005225
21 -0.77937432 -1.06466104
22 0.96591239 -0.77937432
23 2.51119910 0.96591239
24 -1.12593971 2.51119910
25 -0.10065299 -1.12593971
26 1.05463372 -0.10065299
27 2.49992043 1.05463372
28 3.30520715 2.49992043
29 3.31049386 3.30520715
30 1.10578057 3.31049386
31 -0.57893271 1.10578057
32 -2.28364600 -0.57893271
33 -1.60835929 -2.28364600
34 -0.40307257 -1.60835929
35 1.10221414 -0.40307257
36 -2.26492467 1.10221414
37 -1.58963795 -2.26492467
38 -0.23435124 -1.58963795
39 0.94093547 -0.23435124
40 2.10622219 0.94093547
41 1.71150890 2.10622219
42 -0.07320439 1.71150890
43 -1.87791767 -0.07320439
44 -2.94263096 -1.87791767
45 -2.60734425 -2.94263096
46 -1.10205753 -2.60734425
47 0.59322918 -1.10205753
48 -2.79390963 0.59322918
49 -1.77862292 -2.79390963
50 -0.64333620 -1.77862292
51 1.08195051 -0.64333620
52 1.69723722 1.08195051
53 1.08252394 1.69723722
54 -0.05218935 1.08252394
55 -1.75690264 -0.05218935
56 -3.30161592 -1.75690264
57 -2.56632921 -3.30161592
58 -0.78104250 -2.56632921
59 0.81424422 -0.78104250
60 -2.93289459 0.81424422
61 -1.78760788 -2.93289459
62 -0.53232117 -1.78760788
63 1.18296555 -0.53232117
64 2.01825226 1.18296555
65 1.69353897 2.01825226
66 0.47882569 1.69353897
67 -1.86588760 0.47882569
68 -2.61060089 -1.86588760
69 -2.47531417 -2.61060089
70 -0.79002746 -2.47531417
71 0.67525925 -0.79002746
72 -2.65187955 0.67525925
73 -1.27659284 -2.65187955
74 -0.04130613 -1.27659284
75 0.59398059 -0.04130613
76 1.50926730 0.59398059
77 1.54455401 1.50926730
78 0.41984073 1.54455401
79 -1.47487256 0.41984073
80 -2.94958585 -1.47487256
81 -2.75429914 -2.94958585
82 -1.20901242 -2.75429914
83 0.39627429 -1.20901242
84 -3.49086452 0.39627429
85 -2.16557780 -3.49086452
86 -1.52029109 -2.16557780
87 0.51499562 -1.52029109
88 1.23028234 0.51499562
89 0.39556905 1.23028234
90 -0.60914424 0.39556905
91 -2.55385752 -0.60914424
92 -4.12857081 -2.55385752
93 -3.42328410 -4.12857081
94 -1.43799738 -3.42328410
95 0.73728933 -1.43799738
96 -3.11984948 0.73728933
97 -2.03456277 -3.11984948
98 -0.63927605 -2.03456277
99 1.09601066 -0.63927605
100 2.27129737 1.09601066
101 1.98658409 2.27129737
102 1.06187080 1.98658409
103 -0.45284249 1.06187080
104 -2.16755577 -0.45284249
105 -1.55226906 -2.16755577
106 -0.10698235 -1.55226906
107 1.67830437 -0.10698235
108 -1.96883444 1.67830437
109 -0.84354773 -1.96883444
110 0.05173898 -0.84354773
111 1.62702570 0.05173898
112 1.78231241 1.62702570
113 1.48759912 1.78231241
114 0.75288584 1.48759912
115 -1.38182745 0.75288584
116 -3.20654074 -1.38182745
117 -2.38125402 -3.20654074
118 -0.77596731 -2.38125402
119 0.83931940 -0.77596731
120 -2.68781940 0.83931940
121 -2.17253269 -2.68781940
122 -0.84724598 -2.17253269
123 0.66804074 -0.84724598
124 0.62332745 0.66804074
125 1.07861416 0.62332745
126 -0.47609912 1.07861416
127 -1.72081241 -0.47609912
128 -2.76552570 -1.72081241
129 -2.43023898 -2.76552570
130 -0.83495227 -2.43023898
131 0.78033444 -0.83495227
132 -3.10680437 0.78033444
133 -1.87151765 -3.10680437
134 -0.65623094 -1.87151765
135 0.53905577 -0.65623094
136 1.25434249 0.53905577
137 0.87962920 1.25434249
138 -0.47508409 0.87962920
139 -2.12979737 -0.47508409
140 -3.33451066 -2.12979737
141 -2.80922395 -3.33451066
142 -1.28393723 -2.80922395
143 0.45134948 -1.28393723
144 -3.01578933 0.45134948
145 -2.05050262 -3.01578933
146 -0.99521590 -2.05050262
147 0.33007081 -0.99521590
148 1.24535752 0.33007081
149 1.37064424 1.24535752
150 0.10593095 1.37064424
151 -1.76878234 0.10593095
152 -2.55349562 -1.76878234
153 -2.50820891 -2.55349562
154 -0.55292220 -2.50820891
155 1.23236452 -0.55292220
156 -2.29477429 1.23236452
157 -1.31948758 -2.29477429
158 -0.17420086 -1.31948758
159 1.33108585 -0.17420086
160 2.38637256 1.33108585
161 2.32165927 2.38637256
162 1.02694599 2.32165927
163 -1.07776730 1.02694599
164 -1.97248059 -1.07776730
165 -1.72719387 -1.97248059
166 0.10809284 -1.72719387
167 1.67337955 0.10809284
168 -2.00375925 1.67337955
169 -0.89847254 -2.00375925
170 0.37681417 -0.89847254
171 2.18210089 0.37681417
172 2.64738760 2.18210089
173 1.95267431 2.64738760
174 0.03796103 1.95267431
175 -1.10675226 0.03796103
176 -2.81146555 -1.10675226
177 -2.17617883 -2.81146555
178 -0.23089212 -2.17617883
179 1.39439459 -0.23089212
180 -2.42274422 1.39439459
181 -0.88745750 -2.42274422
182 0.58782921 -0.88745750
183 2.14311592 0.58782921
184 2.65840264 2.14311592
185 2.61368935 2.65840264
186 1.33897606 2.61368935
187 -0.39573722 1.33897606
188 -2.01045051 -0.39573722
189 -1.48516380 -2.01045051
190 0.09012292 -1.48516380
191 2.13540963 0.09012292
192 -1.31172918 2.13540963
193 -0.21644247 -1.31172918
194 0.50884425 -0.21644247
195 2.77413096 0.50884425
196 3.32941767 2.77413096
197 2.66470439 3.32941767
198 1.06999110 2.66470439
199 -0.48472219 1.06999110
200 -1.73943547 -0.48472219
201 -1.22414876 -1.73943547
202 0.15113795 -1.22414876
203 2.09642467 0.15113795
204 -1.61071414 2.09642467
205 -0.45542743 -1.61071414
206 0.45985929 -0.45542743
207 2.66514600 0.45985929
208 3.10043271 2.66514600
209 2.84571943 3.10043271
210 1.55100614 2.84571943
211 -0.67370715 1.55100614
212 -1.55842043 -0.67370715
213 -1.01313372 -1.55842043
214 0.53215299 -1.01313372
215 2.26743971 0.53215299
216 -0.88969910 2.26743971
217 -0.32441239 -0.88969910
218 0.18087432 -0.32441239
219 1.66616104 0.18087432
220 3.25144775 1.66616104
221 2.89673446 3.25144775
222 1.66202118 2.89673446
223 -0.30269211 1.66202118
224 -1.11740540 -0.30269211
225 -0.94211868 -1.11740540
226 0.45316803 -0.94211868
227 2.14845474 0.45316803
228 -1.20868407 2.14845474
229 -0.45339735 -1.20868407
230 1.17188936 -0.45339735
231 2.12717607 1.17188936
232 3.11246279 2.12717607
233 2.62774950 3.11246279
234 1.20303621 2.62774950
235 -0.36167707 1.20303621
236 -1.23639036 -0.36167707
237 -1.36110365 -1.23639036
238 0.51418307 -1.36110365
239 2.04946978 0.51418307
240 NA 2.04946978
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.41731693 0.06203022
[2,] 2.45260365 1.41731693
[3,] 3.32789036 2.45260365
[4,] 4.57317707 3.32789036
[5,] 3.86846379 4.57317707
[6,] 2.69375050 3.86846379
[7,] 0.95903721 2.69375050
[8,] -0.39567607 0.95903721
[9,] 0.17961064 -0.39567607
[10,] 1.89489735 0.17961064
[11,] 3.56018407 1.89489735
[12,] -0.51695474 3.56018407
[13,] 0.54833197 -0.51695474
[14,] 2.14361868 0.54833197
[15,] 3.80890540 2.14361868
[16,] 4.67419211 3.80890540
[17,] 3.92947882 4.67419211
[18,] 2.16476554 3.92947882
[19,] 0.25005225 2.16476554
[20,] -1.06466104 0.25005225
[21,] -0.77937432 -1.06466104
[22,] 0.96591239 -0.77937432
[23,] 2.51119910 0.96591239
[24,] -1.12593971 2.51119910
[25,] -0.10065299 -1.12593971
[26,] 1.05463372 -0.10065299
[27,] 2.49992043 1.05463372
[28,] 3.30520715 2.49992043
[29,] 3.31049386 3.30520715
[30,] 1.10578057 3.31049386
[31,] -0.57893271 1.10578057
[32,] -2.28364600 -0.57893271
[33,] -1.60835929 -2.28364600
[34,] -0.40307257 -1.60835929
[35,] 1.10221414 -0.40307257
[36,] -2.26492467 1.10221414
[37,] -1.58963795 -2.26492467
[38,] -0.23435124 -1.58963795
[39,] 0.94093547 -0.23435124
[40,] 2.10622219 0.94093547
[41,] 1.71150890 2.10622219
[42,] -0.07320439 1.71150890
[43,] -1.87791767 -0.07320439
[44,] -2.94263096 -1.87791767
[45,] -2.60734425 -2.94263096
[46,] -1.10205753 -2.60734425
[47,] 0.59322918 -1.10205753
[48,] -2.79390963 0.59322918
[49,] -1.77862292 -2.79390963
[50,] -0.64333620 -1.77862292
[51,] 1.08195051 -0.64333620
[52,] 1.69723722 1.08195051
[53,] 1.08252394 1.69723722
[54,] -0.05218935 1.08252394
[55,] -1.75690264 -0.05218935
[56,] -3.30161592 -1.75690264
[57,] -2.56632921 -3.30161592
[58,] -0.78104250 -2.56632921
[59,] 0.81424422 -0.78104250
[60,] -2.93289459 0.81424422
[61,] -1.78760788 -2.93289459
[62,] -0.53232117 -1.78760788
[63,] 1.18296555 -0.53232117
[64,] 2.01825226 1.18296555
[65,] 1.69353897 2.01825226
[66,] 0.47882569 1.69353897
[67,] -1.86588760 0.47882569
[68,] -2.61060089 -1.86588760
[69,] -2.47531417 -2.61060089
[70,] -0.79002746 -2.47531417
[71,] 0.67525925 -0.79002746
[72,] -2.65187955 0.67525925
[73,] -1.27659284 -2.65187955
[74,] -0.04130613 -1.27659284
[75,] 0.59398059 -0.04130613
[76,] 1.50926730 0.59398059
[77,] 1.54455401 1.50926730
[78,] 0.41984073 1.54455401
[79,] -1.47487256 0.41984073
[80,] -2.94958585 -1.47487256
[81,] -2.75429914 -2.94958585
[82,] -1.20901242 -2.75429914
[83,] 0.39627429 -1.20901242
[84,] -3.49086452 0.39627429
[85,] -2.16557780 -3.49086452
[86,] -1.52029109 -2.16557780
[87,] 0.51499562 -1.52029109
[88,] 1.23028234 0.51499562
[89,] 0.39556905 1.23028234
[90,] -0.60914424 0.39556905
[91,] -2.55385752 -0.60914424
[92,] -4.12857081 -2.55385752
[93,] -3.42328410 -4.12857081
[94,] -1.43799738 -3.42328410
[95,] 0.73728933 -1.43799738
[96,] -3.11984948 0.73728933
[97,] -2.03456277 -3.11984948
[98,] -0.63927605 -2.03456277
[99,] 1.09601066 -0.63927605
[100,] 2.27129737 1.09601066
[101,] 1.98658409 2.27129737
[102,] 1.06187080 1.98658409
[103,] -0.45284249 1.06187080
[104,] -2.16755577 -0.45284249
[105,] -1.55226906 -2.16755577
[106,] -0.10698235 -1.55226906
[107,] 1.67830437 -0.10698235
[108,] -1.96883444 1.67830437
[109,] -0.84354773 -1.96883444
[110,] 0.05173898 -0.84354773
[111,] 1.62702570 0.05173898
[112,] 1.78231241 1.62702570
[113,] 1.48759912 1.78231241
[114,] 0.75288584 1.48759912
[115,] -1.38182745 0.75288584
[116,] -3.20654074 -1.38182745
[117,] -2.38125402 -3.20654074
[118,] -0.77596731 -2.38125402
[119,] 0.83931940 -0.77596731
[120,] -2.68781940 0.83931940
[121,] -2.17253269 -2.68781940
[122,] -0.84724598 -2.17253269
[123,] 0.66804074 -0.84724598
[124,] 0.62332745 0.66804074
[125,] 1.07861416 0.62332745
[126,] -0.47609912 1.07861416
[127,] -1.72081241 -0.47609912
[128,] -2.76552570 -1.72081241
[129,] -2.43023898 -2.76552570
[130,] -0.83495227 -2.43023898
[131,] 0.78033444 -0.83495227
[132,] -3.10680437 0.78033444
[133,] -1.87151765 -3.10680437
[134,] -0.65623094 -1.87151765
[135,] 0.53905577 -0.65623094
[136,] 1.25434249 0.53905577
[137,] 0.87962920 1.25434249
[138,] -0.47508409 0.87962920
[139,] -2.12979737 -0.47508409
[140,] -3.33451066 -2.12979737
[141,] -2.80922395 -3.33451066
[142,] -1.28393723 -2.80922395
[143,] 0.45134948 -1.28393723
[144,] -3.01578933 0.45134948
[145,] -2.05050262 -3.01578933
[146,] -0.99521590 -2.05050262
[147,] 0.33007081 -0.99521590
[148,] 1.24535752 0.33007081
[149,] 1.37064424 1.24535752
[150,] 0.10593095 1.37064424
[151,] -1.76878234 0.10593095
[152,] -2.55349562 -1.76878234
[153,] -2.50820891 -2.55349562
[154,] -0.55292220 -2.50820891
[155,] 1.23236452 -0.55292220
[156,] -2.29477429 1.23236452
[157,] -1.31948758 -2.29477429
[158,] -0.17420086 -1.31948758
[159,] 1.33108585 -0.17420086
[160,] 2.38637256 1.33108585
[161,] 2.32165927 2.38637256
[162,] 1.02694599 2.32165927
[163,] -1.07776730 1.02694599
[164,] -1.97248059 -1.07776730
[165,] -1.72719387 -1.97248059
[166,] 0.10809284 -1.72719387
[167,] 1.67337955 0.10809284
[168,] -2.00375925 1.67337955
[169,] -0.89847254 -2.00375925
[170,] 0.37681417 -0.89847254
[171,] 2.18210089 0.37681417
[172,] 2.64738760 2.18210089
[173,] 1.95267431 2.64738760
[174,] 0.03796103 1.95267431
[175,] -1.10675226 0.03796103
[176,] -2.81146555 -1.10675226
[177,] -2.17617883 -2.81146555
[178,] -0.23089212 -2.17617883
[179,] 1.39439459 -0.23089212
[180,] -2.42274422 1.39439459
[181,] -0.88745750 -2.42274422
[182,] 0.58782921 -0.88745750
[183,] 2.14311592 0.58782921
[184,] 2.65840264 2.14311592
[185,] 2.61368935 2.65840264
[186,] 1.33897606 2.61368935
[187,] -0.39573722 1.33897606
[188,] -2.01045051 -0.39573722
[189,] -1.48516380 -2.01045051
[190,] 0.09012292 -1.48516380
[191,] 2.13540963 0.09012292
[192,] -1.31172918 2.13540963
[193,] -0.21644247 -1.31172918
[194,] 0.50884425 -0.21644247
[195,] 2.77413096 0.50884425
[196,] 3.32941767 2.77413096
[197,] 2.66470439 3.32941767
[198,] 1.06999110 2.66470439
[199,] -0.48472219 1.06999110
[200,] -1.73943547 -0.48472219
[201,] -1.22414876 -1.73943547
[202,] 0.15113795 -1.22414876
[203,] 2.09642467 0.15113795
[204,] -1.61071414 2.09642467
[205,] -0.45542743 -1.61071414
[206,] 0.45985929 -0.45542743
[207,] 2.66514600 0.45985929
[208,] 3.10043271 2.66514600
[209,] 2.84571943 3.10043271
[210,] 1.55100614 2.84571943
[211,] -0.67370715 1.55100614
[212,] -1.55842043 -0.67370715
[213,] -1.01313372 -1.55842043
[214,] 0.53215299 -1.01313372
[215,] 2.26743971 0.53215299
[216,] -0.88969910 2.26743971
[217,] -0.32441239 -0.88969910
[218,] 0.18087432 -0.32441239
[219,] 1.66616104 0.18087432
[220,] 3.25144775 1.66616104
[221,] 2.89673446 3.25144775
[222,] 1.66202118 2.89673446
[223,] -0.30269211 1.66202118
[224,] -1.11740540 -0.30269211
[225,] -0.94211868 -1.11740540
[226,] 0.45316803 -0.94211868
[227,] 2.14845474 0.45316803
[228,] -1.20868407 2.14845474
[229,] -0.45339735 -1.20868407
[230,] 1.17188936 -0.45339735
[231,] 2.12717607 1.17188936
[232,] 3.11246279 2.12717607
[233,] 2.62774950 3.11246279
[234,] 1.20303621 2.62774950
[235,] -0.36167707 1.20303621
[236,] -1.23639036 -0.36167707
[237,] -1.36110365 -1.23639036
[238,] 0.51418307 -1.36110365
[239,] 2.04946978 0.51418307
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.41731693 0.06203022
2 2.45260365 1.41731693
3 3.32789036 2.45260365
4 4.57317707 3.32789036
5 3.86846379 4.57317707
6 2.69375050 3.86846379
7 0.95903721 2.69375050
8 -0.39567607 0.95903721
9 0.17961064 -0.39567607
10 1.89489735 0.17961064
11 3.56018407 1.89489735
12 -0.51695474 3.56018407
13 0.54833197 -0.51695474
14 2.14361868 0.54833197
15 3.80890540 2.14361868
16 4.67419211 3.80890540
17 3.92947882 4.67419211
18 2.16476554 3.92947882
19 0.25005225 2.16476554
20 -1.06466104 0.25005225
21 -0.77937432 -1.06466104
22 0.96591239 -0.77937432
23 2.51119910 0.96591239
24 -1.12593971 2.51119910
25 -0.10065299 -1.12593971
26 1.05463372 -0.10065299
27 2.49992043 1.05463372
28 3.30520715 2.49992043
29 3.31049386 3.30520715
30 1.10578057 3.31049386
31 -0.57893271 1.10578057
32 -2.28364600 -0.57893271
33 -1.60835929 -2.28364600
34 -0.40307257 -1.60835929
35 1.10221414 -0.40307257
36 -2.26492467 1.10221414
37 -1.58963795 -2.26492467
38 -0.23435124 -1.58963795
39 0.94093547 -0.23435124
40 2.10622219 0.94093547
41 1.71150890 2.10622219
42 -0.07320439 1.71150890
43 -1.87791767 -0.07320439
44 -2.94263096 -1.87791767
45 -2.60734425 -2.94263096
46 -1.10205753 -2.60734425
47 0.59322918 -1.10205753
48 -2.79390963 0.59322918
49 -1.77862292 -2.79390963
50 -0.64333620 -1.77862292
51 1.08195051 -0.64333620
52 1.69723722 1.08195051
53 1.08252394 1.69723722
54 -0.05218935 1.08252394
55 -1.75690264 -0.05218935
56 -3.30161592 -1.75690264
57 -2.56632921 -3.30161592
58 -0.78104250 -2.56632921
59 0.81424422 -0.78104250
60 -2.93289459 0.81424422
61 -1.78760788 -2.93289459
62 -0.53232117 -1.78760788
63 1.18296555 -0.53232117
64 2.01825226 1.18296555
65 1.69353897 2.01825226
66 0.47882569 1.69353897
67 -1.86588760 0.47882569
68 -2.61060089 -1.86588760
69 -2.47531417 -2.61060089
70 -0.79002746 -2.47531417
71 0.67525925 -0.79002746
72 -2.65187955 0.67525925
73 -1.27659284 -2.65187955
74 -0.04130613 -1.27659284
75 0.59398059 -0.04130613
76 1.50926730 0.59398059
77 1.54455401 1.50926730
78 0.41984073 1.54455401
79 -1.47487256 0.41984073
80 -2.94958585 -1.47487256
81 -2.75429914 -2.94958585
82 -1.20901242 -2.75429914
83 0.39627429 -1.20901242
84 -3.49086452 0.39627429
85 -2.16557780 -3.49086452
86 -1.52029109 -2.16557780
87 0.51499562 -1.52029109
88 1.23028234 0.51499562
89 0.39556905 1.23028234
90 -0.60914424 0.39556905
91 -2.55385752 -0.60914424
92 -4.12857081 -2.55385752
93 -3.42328410 -4.12857081
94 -1.43799738 -3.42328410
95 0.73728933 -1.43799738
96 -3.11984948 0.73728933
97 -2.03456277 -3.11984948
98 -0.63927605 -2.03456277
99 1.09601066 -0.63927605
100 2.27129737 1.09601066
101 1.98658409 2.27129737
102 1.06187080 1.98658409
103 -0.45284249 1.06187080
104 -2.16755577 -0.45284249
105 -1.55226906 -2.16755577
106 -0.10698235 -1.55226906
107 1.67830437 -0.10698235
108 -1.96883444 1.67830437
109 -0.84354773 -1.96883444
110 0.05173898 -0.84354773
111 1.62702570 0.05173898
112 1.78231241 1.62702570
113 1.48759912 1.78231241
114 0.75288584 1.48759912
115 -1.38182745 0.75288584
116 -3.20654074 -1.38182745
117 -2.38125402 -3.20654074
118 -0.77596731 -2.38125402
119 0.83931940 -0.77596731
120 -2.68781940 0.83931940
121 -2.17253269 -2.68781940
122 -0.84724598 -2.17253269
123 0.66804074 -0.84724598
124 0.62332745 0.66804074
125 1.07861416 0.62332745
126 -0.47609912 1.07861416
127 -1.72081241 -0.47609912
128 -2.76552570 -1.72081241
129 -2.43023898 -2.76552570
130 -0.83495227 -2.43023898
131 0.78033444 -0.83495227
132 -3.10680437 0.78033444
133 -1.87151765 -3.10680437
134 -0.65623094 -1.87151765
135 0.53905577 -0.65623094
136 1.25434249 0.53905577
137 0.87962920 1.25434249
138 -0.47508409 0.87962920
139 -2.12979737 -0.47508409
140 -3.33451066 -2.12979737
141 -2.80922395 -3.33451066
142 -1.28393723 -2.80922395
143 0.45134948 -1.28393723
144 -3.01578933 0.45134948
145 -2.05050262 -3.01578933
146 -0.99521590 -2.05050262
147 0.33007081 -0.99521590
148 1.24535752 0.33007081
149 1.37064424 1.24535752
150 0.10593095 1.37064424
151 -1.76878234 0.10593095
152 -2.55349562 -1.76878234
153 -2.50820891 -2.55349562
154 -0.55292220 -2.50820891
155 1.23236452 -0.55292220
156 -2.29477429 1.23236452
157 -1.31948758 -2.29477429
158 -0.17420086 -1.31948758
159 1.33108585 -0.17420086
160 2.38637256 1.33108585
161 2.32165927 2.38637256
162 1.02694599 2.32165927
163 -1.07776730 1.02694599
164 -1.97248059 -1.07776730
165 -1.72719387 -1.97248059
166 0.10809284 -1.72719387
167 1.67337955 0.10809284
168 -2.00375925 1.67337955
169 -0.89847254 -2.00375925
170 0.37681417 -0.89847254
171 2.18210089 0.37681417
172 2.64738760 2.18210089
173 1.95267431 2.64738760
174 0.03796103 1.95267431
175 -1.10675226 0.03796103
176 -2.81146555 -1.10675226
177 -2.17617883 -2.81146555
178 -0.23089212 -2.17617883
179 1.39439459 -0.23089212
180 -2.42274422 1.39439459
181 -0.88745750 -2.42274422
182 0.58782921 -0.88745750
183 2.14311592 0.58782921
184 2.65840264 2.14311592
185 2.61368935 2.65840264
186 1.33897606 2.61368935
187 -0.39573722 1.33897606
188 -2.01045051 -0.39573722
189 -1.48516380 -2.01045051
190 0.09012292 -1.48516380
191 2.13540963 0.09012292
192 -1.31172918 2.13540963
193 -0.21644247 -1.31172918
194 0.50884425 -0.21644247
195 2.77413096 0.50884425
196 3.32941767 2.77413096
197 2.66470439 3.32941767
198 1.06999110 2.66470439
199 -0.48472219 1.06999110
200 -1.73943547 -0.48472219
201 -1.22414876 -1.73943547
202 0.15113795 -1.22414876
203 2.09642467 0.15113795
204 -1.61071414 2.09642467
205 -0.45542743 -1.61071414
206 0.45985929 -0.45542743
207 2.66514600 0.45985929
208 3.10043271 2.66514600
209 2.84571943 3.10043271
210 1.55100614 2.84571943
211 -0.67370715 1.55100614
212 -1.55842043 -0.67370715
213 -1.01313372 -1.55842043
214 0.53215299 -1.01313372
215 2.26743971 0.53215299
216 -0.88969910 2.26743971
217 -0.32441239 -0.88969910
218 0.18087432 -0.32441239
219 1.66616104 0.18087432
220 3.25144775 1.66616104
221 2.89673446 3.25144775
222 1.66202118 2.89673446
223 -0.30269211 1.66202118
224 -1.11740540 -0.30269211
225 -0.94211868 -1.11740540
226 0.45316803 -0.94211868
227 2.14845474 0.45316803
228 -1.20868407 2.14845474
229 -0.45339735 -1.20868407
230 1.17188936 -0.45339735
231 2.12717607 1.17188936
232 3.11246279 2.12717607
233 2.62774950 3.11246279
234 1.20303621 2.62774950
235 -0.36167707 1.20303621
236 -1.23639036 -0.36167707
237 -1.36110365 -1.23639036
238 0.51418307 -1.36110365
239 2.04946978 0.51418307
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7cwjz1322003067.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8iw151322003067.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/94khl1322003067.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10jv6v1322003067.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11meay1322003067.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12omch1322003067.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13qfiu1322003067.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14q5621322003067.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/151q2j1322003067.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16m3pq1322003067.tab")
+ }
>
> try(system("convert tmp/1n8ib1322003067.ps tmp/1n8ib1322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gyel1322003067.ps tmp/2gyel1322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cq8e1322003067.ps tmp/3cq8e1322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cacd1322003067.ps tmp/4cacd1322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/5t3201322003067.ps tmp/5t3201322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/64bmb1322003067.ps tmp/64bmb1322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/7cwjz1322003067.ps tmp/7cwjz1322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/8iw151322003067.ps tmp/8iw151322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/94khl1322003067.ps tmp/94khl1322003067.png",intern=TRUE))
character(0)
> try(system("convert tmp/10jv6v1322003067.ps tmp/10jv6v1322003067.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.121 0.638 6.803