R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(3.88
+ ,153.3
+ ,3.98
+ ,154.5
+ ,3.29
+ ,155.2
+ ,2.88
+ ,156.9
+ ,3.22
+ ,157
+ ,3.62
+ ,157.4
+ ,3.82
+ ,157.2
+ ,3.54
+ ,157.5
+ ,2.53
+ ,158
+ ,2.22
+ ,158.5
+ ,2.85
+ ,159
+ ,2.78
+ ,159.3
+ ,2.28
+ ,160
+ ,2.26
+ ,160.8
+ ,2.71
+ ,161.9
+ ,2.77
+ ,162.5
+ ,2.77
+ ,162.7
+ ,2.64
+ ,162.8
+ ,2.56
+ ,162.9
+ ,2.07
+ ,163
+ ,2.32
+ ,164
+ ,2.16
+ ,164.7
+ ,2.23
+ ,164.8
+ ,2.4
+ ,164.9
+ ,2.84
+ ,165
+ ,2.77
+ ,165.8
+ ,2.93
+ ,166.1
+ ,2.91
+ ,167.2
+ ,2.69
+ ,167.7
+ ,2.38
+ ,168.3
+ ,2.58
+ ,168.6
+ ,3.19
+ ,168.9
+ ,2.82
+ ,169.1
+ ,2.72
+ ,169.5
+ ,2.53
+ ,169.6
+ ,2.7
+ ,169.7
+ ,2.42
+ ,169.8
+ ,2.5
+ ,170.4
+ ,2.31
+ ,170.9
+ ,2.41
+ ,171.9
+ ,2.56
+ ,171.9
+ ,2.76
+ ,172
+ ,2.71
+ ,172
+ ,2.44
+ ,172.4
+ ,2.46
+ ,173
+ ,2.12
+ ,173.7
+ ,1.99
+ ,173.8
+ ,1.86
+ ,173.8
+ ,1.88
+ ,173.9
+ ,1.82
+ ,174.6
+ ,1.74
+ ,175
+ ,1.71
+ ,175.9
+ ,1.38
+ ,176
+ ,1.27
+ ,175.1
+ ,1.19
+ ,175.6
+ ,1.28
+ ,175.9
+ ,1.19
+ ,176.7
+ ,1.22
+ ,176.1
+ ,1.47
+ ,176.1
+ ,1.46
+ ,176.2
+ ,1.96
+ ,176.3
+ ,1.88
+ ,177.8
+ ,2.03
+ ,178.5
+ ,2.04
+ ,179.4
+ ,1.9
+ ,179.5
+ ,1.8
+ ,179.6
+ ,1.92
+ ,179.7
+ ,1.92
+ ,179.7
+ ,1.97
+ ,179.8
+ ,2.46
+ ,179.9
+ ,2.36
+ ,180.2
+ ,2.53
+ ,180.4
+ ,2.31
+ ,180.4
+ ,1.98
+ ,181.3
+ ,1.46
+ ,181.9
+ ,1.26
+ ,182.5
+ ,1.58
+ ,182.7
+ ,1.74
+ ,183.1
+ ,1.89
+ ,183.6
+ ,1.85
+ ,183.7
+ ,1.62
+ ,183.8
+ ,1.3
+ ,183.9
+ ,1.42
+ ,184.1
+ ,1.15
+ ,184.4
+ ,0.42
+ ,184.5
+ ,0.74
+ ,185.9
+ ,1.02
+ ,186.6
+ ,1.51
+ ,187.6
+ ,1.86
+ ,187.8
+ ,1.59
+ ,187.9
+ ,1.03
+ ,188
+ ,0.44
+ ,188.3
+ ,0.82
+ ,188.4
+ ,0.86
+ ,188.5
+ ,0.58
+ ,188.5
+ ,0.59
+ ,188.6
+ ,0.95
+ ,188.6
+ ,0.98
+ ,189.4
+ ,1.23
+ ,190
+ ,1.17
+ ,191.9
+ ,0.84
+ ,192.5
+ ,0.74
+ ,193
+ ,0.65
+ ,193.5
+ ,0.91
+ ,193.9
+ ,1.19
+ ,194.2
+ ,1.3
+ ,194.9
+ ,1.53
+ ,194.9
+ ,1.94
+ ,194.9
+ ,1.79
+ ,194.9
+ ,1.95
+ ,195.5
+ ,2.26
+ ,196
+ ,2.04
+ ,196.2
+ ,2.16
+ ,196.2
+ ,2.75
+ ,196.2
+ ,2.79
+ ,196.2
+ ,2.88
+ ,197
+ ,3.36
+ ,197.7
+ ,2.97
+ ,198
+ ,3.1
+ ,198.2
+ ,2.49
+ ,198.5
+ ,2.2
+ ,198.6
+ ,2.25
+ ,199.5
+ ,2.09
+ ,200
+ ,2.79
+ ,201.3
+ ,3.14
+ ,202.2
+ ,2.93
+ ,202.9
+ ,2.65
+ ,203.5
+ ,2.67
+ ,203.5
+ ,2.26
+ ,204
+ ,2.35
+ ,204.1
+ ,2.13
+ ,204.3
+ ,2.18
+ ,204.5
+ ,2.9
+ ,204.8
+ ,2.63
+ ,205.1
+ ,2.67
+ ,205.7
+ ,1.81
+ ,206.5
+ ,1.33
+ ,206.9
+ ,0.88
+ ,207.1
+ ,1.28
+ ,207.8
+ ,1.26
+ ,208
+ ,1.26
+ ,208.5
+ ,1.29
+ ,208.6
+ ,1.1
+ ,209
+ ,1.37
+ ,209.1
+ ,1.21
+ ,209.7
+ ,1.74
+ ,209.8
+ ,1.76
+ ,209.9
+ ,1.48
+ ,210
+ ,1.04
+ ,210.8
+ ,1.62
+ ,211.4
+ ,1.49
+ ,211.7
+ ,1.79
+ ,212
+ ,1.8
+ ,212.2
+ ,1.58
+ ,212.4
+ ,1.86
+ ,212.9
+ ,1.74
+ ,213.4
+ ,1.59
+ ,213.7
+ ,1.26
+ ,214
+ ,1.13
+ ,214.3
+ ,1.92
+ ,214.8
+ ,2.61
+ ,215
+ ,2.26
+ ,215.9
+ ,2.41
+ ,216.4
+ ,2.26
+ ,216.9
+ ,2.03
+ ,217.2
+ ,2.86
+ ,217.5
+ ,2.55
+ ,217.9
+ ,2.27
+ ,218.1
+ ,2.26
+ ,218.6
+ ,2.57
+ ,218.9
+ ,3.07
+ ,219.3
+ ,2.76
+ ,220.4
+ ,2.51
+ ,220.9
+ ,2.87
+ ,221
+ ,3.14
+ ,221.8
+ ,3.11
+ ,222
+ ,3.16
+ ,222.2
+ ,2.47
+ ,222.5
+ ,2.57
+ ,222.9
+ ,2.89
+ ,223.1
+ ,2.63
+ ,223.4
+ ,2.38
+ ,224
+ ,1.69
+ ,225.1
+ ,1.96
+ ,225.5
+ ,2.19
+ ,225.9
+ ,1.87
+ ,226.3
+ ,1.6
+ ,226.5
+ ,1.63
+ ,227
+ ,1.22
+ ,227.3
+ ,1.21
+ ,227.8
+ ,1.49
+ ,228.1
+ ,1.64
+ ,228.4
+ ,1.66
+ ,228.5
+ ,1.77
+ ,228.8
+ ,1.82
+ ,229
+ ,1.78
+ ,229.1
+ ,1.28
+ ,229.3
+ ,1.29
+ ,229.6
+ ,1.37
+ ,229.9
+ ,1.12
+ ,230
+ ,1.51
+ ,230.2
+ ,2.24
+ ,230.8
+ ,2.94
+ ,231
+ ,3.09
+ ,231.7
+ ,3.46
+ ,231.9
+ ,3.64
+ ,233
+ ,4.39
+ ,235.1
+ ,4.15
+ ,236
+ ,5.21
+ ,236.9
+ ,5.8
+ ,237.1
+ ,5.91
+ ,237.5
+ ,5.39
+ ,238.2
+ ,5.46
+ ,238.9
+ ,4.72
+ ,239.1
+ ,3.14
+ ,240
+ ,2.63
+ ,240.2
+ ,2.32
+ ,240.5
+ ,1.93
+ ,240.7
+ ,0.62
+ ,241.1
+ ,0.6
+ ,241.4
+ ,-0.37
+ ,242.2
+ ,-1.1
+ ,242.9
+ ,-1.68
+ ,243.2
+ ,-0.78
+ ,243.9)
+ ,dim=c(2
+ ,224)
+ ,dimnames=list(c('Y'
+ ,'X')
+ ,1:224))
> y <- array(NA,dim=c(2,224),dimnames=list(c('Y','X'),1:224))
> 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 = '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
Y X
1 3.88 153.3
2 3.98 154.5
3 3.29 155.2
4 2.88 156.9
5 3.22 157.0
6 3.62 157.4
7 3.82 157.2
8 3.54 157.5
9 2.53 158.0
10 2.22 158.5
11 2.85 159.0
12 2.78 159.3
13 2.28 160.0
14 2.26 160.8
15 2.71 161.9
16 2.77 162.5
17 2.77 162.7
18 2.64 162.8
19 2.56 162.9
20 2.07 163.0
21 2.32 164.0
22 2.16 164.7
23 2.23 164.8
24 2.40 164.9
25 2.84 165.0
26 2.77 165.8
27 2.93 166.1
28 2.91 167.2
29 2.69 167.7
30 2.38 168.3
31 2.58 168.6
32 3.19 168.9
33 2.82 169.1
34 2.72 169.5
35 2.53 169.6
36 2.70 169.7
37 2.42 169.8
38 2.50 170.4
39 2.31 170.9
40 2.41 171.9
41 2.56 171.9
42 2.76 172.0
43 2.71 172.0
44 2.44 172.4
45 2.46 173.0
46 2.12 173.7
47 1.99 173.8
48 1.86 173.8
49 1.88 173.9
50 1.82 174.6
51 1.74 175.0
52 1.71 175.9
53 1.38 176.0
54 1.27 175.1
55 1.19 175.6
56 1.28 175.9
57 1.19 176.7
58 1.22 176.1
59 1.47 176.1
60 1.46 176.2
61 1.96 176.3
62 1.88 177.8
63 2.03 178.5
64 2.04 179.4
65 1.90 179.5
66 1.80 179.6
67 1.92 179.7
68 1.92 179.7
69 1.97 179.8
70 2.46 179.9
71 2.36 180.2
72 2.53 180.4
73 2.31 180.4
74 1.98 181.3
75 1.46 181.9
76 1.26 182.5
77 1.58 182.7
78 1.74 183.1
79 1.89 183.6
80 1.85 183.7
81 1.62 183.8
82 1.30 183.9
83 1.42 184.1
84 1.15 184.4
85 0.42 184.5
86 0.74 185.9
87 1.02 186.6
88 1.51 187.6
89 1.86 187.8
90 1.59 187.9
91 1.03 188.0
92 0.44 188.3
93 0.82 188.4
94 0.86 188.5
95 0.58 188.5
96 0.59 188.6
97 0.95 188.6
98 0.98 189.4
99 1.23 190.0
100 1.17 191.9
101 0.84 192.5
102 0.74 193.0
103 0.65 193.5
104 0.91 193.9
105 1.19 194.2
106 1.30 194.9
107 1.53 194.9
108 1.94 194.9
109 1.79 194.9
110 1.95 195.5
111 2.26 196.0
112 2.04 196.2
113 2.16 196.2
114 2.75 196.2
115 2.79 196.2
116 2.88 197.0
117 3.36 197.7
118 2.97 198.0
119 3.10 198.2
120 2.49 198.5
121 2.20 198.6
122 2.25 199.5
123 2.09 200.0
124 2.79 201.3
125 3.14 202.2
126 2.93 202.9
127 2.65 203.5
128 2.67 203.5
129 2.26 204.0
130 2.35 204.1
131 2.13 204.3
132 2.18 204.5
133 2.90 204.8
134 2.63 205.1
135 2.67 205.7
136 1.81 206.5
137 1.33 206.9
138 0.88 207.1
139 1.28 207.8
140 1.26 208.0
141 1.26 208.5
142 1.29 208.6
143 1.10 209.0
144 1.37 209.1
145 1.21 209.7
146 1.74 209.8
147 1.76 209.9
148 1.48 210.0
149 1.04 210.8
150 1.62 211.4
151 1.49 211.7
152 1.79 212.0
153 1.80 212.2
154 1.58 212.4
155 1.86 212.9
156 1.74 213.4
157 1.59 213.7
158 1.26 214.0
159 1.13 214.3
160 1.92 214.8
161 2.61 215.0
162 2.26 215.9
163 2.41 216.4
164 2.26 216.9
165 2.03 217.2
166 2.86 217.5
167 2.55 217.9
168 2.27 218.1
169 2.26 218.6
170 2.57 218.9
171 3.07 219.3
172 2.76 220.4
173 2.51 220.9
174 2.87 221.0
175 3.14 221.8
176 3.11 222.0
177 3.16 222.2
178 2.47 222.5
179 2.57 222.9
180 2.89 223.1
181 2.63 223.4
182 2.38 224.0
183 1.69 225.1
184 1.96 225.5
185 2.19 225.9
186 1.87 226.3
187 1.60 226.5
188 1.63 227.0
189 1.22 227.3
190 1.21 227.8
191 1.49 228.1
192 1.64 228.4
193 1.66 228.5
194 1.77 228.8
195 1.82 229.0
196 1.78 229.1
197 1.28 229.3
198 1.29 229.6
199 1.37 229.9
200 1.12 230.0
201 1.51 230.2
202 2.24 230.8
203 2.94 231.0
204 3.09 231.7
205 3.46 231.9
206 3.64 233.0
207 4.39 235.1
208 4.15 236.0
209 5.21 236.9
210 5.80 237.1
211 5.91 237.5
212 5.39 238.2
213 5.46 238.9
214 4.72 239.1
215 3.14 240.0
216 2.63 240.2
217 2.32 240.5
218 1.93 240.7
219 0.62 241.1
220 0.60 241.4
221 -0.37 242.2
222 -1.10 242.9
223 -1.68 243.2
224 -0.78 243.9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
2.465736 -0.001801
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.70769 -0.63290 -0.05843 0.55162 3.87204
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.465736 0.547683 4.502 1.09e-05 ***
X -0.001801 0.002751 -0.655 0.513
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.022 on 222 degrees of freedom
Multiple R-squared: 0.001927, Adjusted R-squared: -0.002569
F-statistic: 0.4287 on 1 and 222 DF, p-value: 0.5133
> 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,] 2.369546e-02 4.739091e-02 0.97630454
[2,] 2.634216e-02 5.268431e-02 0.97365784
[3,] 2.098955e-02 4.197910e-02 0.97901045
[4,] 8.104533e-03 1.620907e-02 0.99189547
[5,] 1.136908e-02 2.273815e-02 0.98863092
[6,] 1.357051e-02 2.714101e-02 0.98642949
[7,] 5.982230e-03 1.196446e-02 0.99401777
[8,] 2.498619e-03 4.997239e-03 0.99750138
[9,] 1.126591e-03 2.253183e-03 0.99887341
[10,] 4.289565e-04 8.579129e-04 0.99957104
[11,] 3.945488e-04 7.890975e-04 0.99960545
[12,] 3.448888e-04 6.897777e-04 0.99965511
[13,] 2.299083e-04 4.598165e-04 0.99977009
[14,] 1.096666e-04 2.193332e-04 0.99989033
[15,] 4.624968e-05 9.249936e-05 0.99995375
[16,] 2.278168e-05 4.556336e-05 0.99997722
[17,] 8.890413e-06 1.778083e-05 0.99999111
[18,] 3.287057e-06 6.574114e-06 0.99999671
[19,] 1.223224e-06 2.446447e-06 0.99999878
[20,] 5.386371e-07 1.077274e-06 0.99999946
[21,] 7.272829e-07 1.454566e-06 0.99999927
[22,] 7.592421e-07 1.518484e-06 0.99999924
[23,] 1.104494e-06 2.208988e-06 0.99999890
[24,] 1.466163e-06 2.932327e-06 0.99999853
[25,] 9.626240e-07 1.925248e-06 0.99999904
[26,] 4.151370e-07 8.302739e-07 0.99999958
[27,] 2.228235e-07 4.456471e-07 0.99999978
[28,] 5.811080e-07 1.162216e-06 0.99999942
[29,] 4.005278e-07 8.010556e-07 0.99999960
[30,] 2.258013e-07 4.516027e-07 0.99999977
[31,] 1.018972e-07 2.037944e-07 0.99999990
[32,] 5.352122e-08 1.070424e-07 0.99999995
[33,] 2.277717e-08 4.555434e-08 0.99999998
[34,] 9.894409e-09 1.978882e-08 0.99999999
[35,] 4.110552e-09 8.221103e-09 1.00000000
[36,] 1.717440e-09 3.434879e-09 1.00000000
[37,] 8.077974e-10 1.615595e-09 1.00000000
[38,] 5.229065e-10 1.045813e-09 1.00000000
[39,] 2.928901e-10 5.857802e-10 1.00000000
[40,] 1.211007e-10 2.422014e-10 1.00000000
[41,] 5.068017e-11 1.013603e-10 1.00000000
[42,] 2.210400e-11 4.420801e-11 1.00000000
[43,] 1.103817e-11 2.207634e-11 1.00000000
[44,] 6.721927e-12 1.344385e-11 1.00000000
[45,] 3.655710e-12 7.311421e-12 1.00000000
[46,] 1.972359e-12 3.944718e-12 1.00000000
[47,] 1.132069e-12 2.264139e-12 1.00000000
[48,] 5.875841e-13 1.175168e-12 1.00000000
[49,] 6.668702e-13 1.333740e-12 1.00000000
[50,] 1.133730e-12 2.267460e-12 1.00000000
[51,] 1.847319e-12 3.694639e-12 1.00000000
[52,] 1.762039e-12 3.524078e-12 1.00000000
[53,] 1.662310e-12 3.324620e-12 1.00000000
[54,] 1.458062e-12 2.916124e-12 1.00000000
[55,] 7.218944e-13 1.443789e-12 1.00000000
[56,] 3.494945e-13 6.989889e-13 1.00000000
[57,] 1.517543e-13 3.035086e-13 1.00000000
[58,] 6.626021e-14 1.325204e-13 1.00000000
[59,] 3.830028e-14 7.660056e-14 1.00000000
[60,] 2.446297e-14 4.892595e-14 1.00000000
[61,] 1.168704e-14 2.337408e-14 1.00000000
[62,] 4.864387e-15 9.728774e-15 1.00000000
[63,] 2.331472e-15 4.662944e-15 1.00000000
[64,] 1.089727e-15 2.179455e-15 1.00000000
[65,] 5.442107e-16 1.088421e-15 1.00000000
[66,] 1.071178e-15 2.142356e-15 1.00000000
[67,] 1.335047e-15 2.670094e-15 1.00000000
[68,] 2.800251e-15 5.600503e-15 1.00000000
[69,] 2.519654e-15 5.039307e-15 1.00000000
[70,] 1.221698e-15 2.443396e-15 1.00000000
[71,] 5.119884e-16 1.023977e-15 1.00000000
[72,] 2.564716e-16 5.129433e-16 1.00000000
[73,] 9.769530e-17 1.953906e-16 1.00000000
[74,] 3.970216e-17 7.940432e-17 1.00000000
[75,] 1.971841e-17 3.943683e-17 1.00000000
[76,] 9.074710e-18 1.814942e-17 1.00000000
[77,] 3.387779e-18 6.775558e-18 1.00000000
[78,] 1.440515e-18 2.881030e-18 1.00000000
[79,] 5.384003e-19 1.076801e-18 1.00000000
[80,] 2.660580e-19 5.321160e-19 1.00000000
[81,] 1.457863e-18 2.915727e-18 1.00000000
[82,] 1.367875e-18 2.735750e-18 1.00000000
[83,] 6.278184e-19 1.255637e-18 1.00000000
[84,] 2.751415e-19 5.502831e-19 1.00000000
[85,] 2.367153e-19 4.734306e-19 1.00000000
[86,] 1.109057e-19 2.218115e-19 1.00000000
[87,] 4.792406e-20 9.584811e-20 1.00000000
[88,] 8.287301e-20 1.657460e-19 1.00000000
[89,] 4.629228e-20 9.258456e-20 1.00000000
[90,] 2.366987e-20 4.733975e-20 1.00000000
[91,] 2.227600e-20 4.455199e-20 1.00000000
[92,] 1.955721e-20 3.911442e-20 1.00000000
[93,] 8.787749e-21 1.757550e-20 1.00000000
[94,] 3.789166e-21 7.578332e-21 1.00000000
[95,] 1.660085e-21 3.320170e-21 1.00000000
[96,] 7.912766e-22 1.582553e-21 1.00000000
[97,] 3.736761e-22 7.473522e-22 1.00000000
[98,] 1.936912e-22 3.873824e-22 1.00000000
[99,] 1.125292e-22 2.250585e-22 1.00000000
[100,] 5.754415e-23 1.150883e-22 1.00000000
[101,] 3.772448e-23 7.544895e-23 1.00000000
[102,] 3.217708e-23 6.435416e-23 1.00000000
[103,] 4.562521e-23 9.125042e-23 1.00000000
[104,] 2.497056e-22 4.994113e-22 1.00000000
[105,] 5.610376e-22 1.122075e-21 1.00000000
[106,] 2.091856e-21 4.183712e-21 1.00000000
[107,] 2.356669e-20 4.713337e-20 1.00000000
[108,] 7.497401e-20 1.499480e-19 1.00000000
[109,] 2.823375e-19 5.646750e-19 1.00000000
[110,] 8.695873e-18 1.739175e-17 1.00000000
[111,] 1.584217e-16 3.168434e-16 1.00000000
[112,] 2.664784e-15 5.329567e-15 1.00000000
[113,] 1.901231e-13 3.802463e-13 1.00000000
[114,] 1.500294e-12 3.000589e-12 1.00000000
[115,] 1.233151e-11 2.466301e-11 1.00000000
[116,] 1.834897e-11 3.669794e-11 1.00000000
[117,] 1.636868e-11 3.273736e-11 1.00000000
[118,] 1.551123e-11 3.102247e-11 1.00000000
[119,] 1.188980e-11 2.377959e-11 1.00000000
[120,] 2.835086e-11 5.670171e-11 1.00000000
[121,] 1.359178e-10 2.718357e-10 1.00000000
[122,] 3.381183e-10 6.762365e-10 1.00000000
[123,] 4.620195e-10 9.240389e-10 1.00000000
[124,] 6.115776e-10 1.223155e-09 1.00000000
[125,] 4.707271e-10 9.414542e-10 1.00000000
[126,] 3.882044e-10 7.764087e-10 1.00000000
[127,] 2.583802e-10 5.167604e-10 1.00000000
[128,] 1.763802e-10 3.527605e-10 1.00000000
[129,] 2.999628e-10 5.999256e-10 1.00000000
[130,] 3.216856e-10 6.433711e-10 1.00000000
[131,] 3.584384e-10 7.168767e-10 1.00000000
[132,] 1.974650e-10 3.949299e-10 1.00000000
[133,] 1.138052e-10 2.276105e-10 1.00000000
[134,] 9.188975e-11 1.837795e-10 1.00000000
[135,] 5.381829e-11 1.076366e-10 1.00000000
[136,] 3.174233e-11 6.348465e-11 1.00000000
[137,] 1.868050e-11 3.736099e-11 1.00000000
[138,] 1.080741e-11 2.161483e-11 1.00000000
[139,] 7.102653e-12 1.420531e-11 1.00000000
[140,] 3.972713e-12 7.945426e-12 1.00000000
[141,] 2.431308e-12 4.862616e-12 1.00000000
[142,] 1.290988e-12 2.581977e-12 1.00000000
[143,] 6.819349e-13 1.363870e-12 1.00000000
[144,] 3.665571e-13 7.331142e-13 1.00000000
[145,] 2.658495e-13 5.316989e-13 1.00000000
[146,] 1.416653e-13 2.833305e-13 1.00000000
[147,] 7.783820e-14 1.556764e-13 1.00000000
[148,] 4.175463e-14 8.350926e-14 1.00000000
[149,] 2.233482e-14 4.466965e-14 1.00000000
[150,] 1.208150e-14 2.416301e-14 1.00000000
[151,] 6.537746e-15 1.307549e-14 1.00000000
[152,] 3.495744e-15 6.991488e-15 1.00000000
[153,] 1.924734e-15 3.849467e-15 1.00000000
[154,] 1.320974e-15 2.641948e-15 1.00000000
[155,] 1.091813e-15 2.183626e-15 1.00000000
[156,] 6.376430e-16 1.275286e-15 1.00000000
[157,] 6.307284e-16 1.261457e-15 1.00000000
[158,] 4.205298e-16 8.410596e-16 1.00000000
[159,] 3.127653e-16 6.255307e-16 1.00000000
[160,] 2.006602e-16 4.013203e-16 1.00000000
[161,] 1.138947e-16 2.277894e-16 1.00000000
[162,] 1.415747e-16 2.831494e-16 1.00000000
[163,] 1.088475e-16 2.176950e-16 1.00000000
[164,] 6.421958e-17 1.284392e-16 1.00000000
[165,] 3.715828e-17 7.431656e-17 1.00000000
[166,] 2.702195e-17 5.404390e-17 1.00000000
[167,] 3.928346e-17 7.856691e-17 1.00000000
[168,] 3.334988e-17 6.669976e-17 1.00000000
[169,] 2.074113e-17 4.148226e-17 1.00000000
[170,] 1.911182e-17 3.822364e-17 1.00000000
[171,] 2.677285e-17 5.354570e-17 1.00000000
[172,] 3.366140e-17 6.732281e-17 1.00000000
[173,] 4.451611e-17 8.903222e-17 1.00000000
[174,] 2.323633e-17 4.647266e-17 1.00000000
[175,] 1.295872e-17 2.591744e-17 1.00000000
[176,] 1.035043e-17 2.070087e-17 1.00000000
[177,] 5.924449e-18 1.184890e-17 1.00000000
[178,] 2.716386e-18 5.432772e-18 1.00000000
[179,] 1.166281e-18 2.332563e-18 1.00000000
[180,] 4.623036e-19 9.246072e-19 1.00000000
[181,] 1.856072e-19 3.712144e-19 1.00000000
[182,] 7.284230e-20 1.456846e-19 1.00000000
[183,] 3.265533e-20 6.531067e-20 1.00000000
[184,] 1.452788e-20 2.905576e-20 1.00000000
[185,] 1.053209e-20 2.106418e-20 1.00000000
[186,] 8.366479e-21 1.673296e-20 1.00000000
[187,] 4.930723e-21 9.861446e-21 1.00000000
[188,] 2.641789e-21 5.283577e-21 1.00000000
[189,] 1.494506e-21 2.989012e-21 1.00000000
[190,] 8.212458e-22 1.642492e-21 1.00000000
[191,] 4.711948e-22 9.423895e-22 1.00000000
[192,] 3.200050e-22 6.400100e-22 1.00000000
[193,] 6.379805e-22 1.275961e-21 1.00000000
[194,] 2.004561e-21 4.009123e-21 1.00000000
[195,] 9.951214e-21 1.990243e-20 1.00000000
[196,] 3.409463e-19 6.818927e-19 1.00000000
[197,] 1.948621e-17 3.897242e-17 1.00000000
[198,] 6.464157e-16 1.292831e-15 1.00000000
[199,] 2.785934e-14 5.571867e-14 1.00000000
[200,] 4.203821e-12 8.407643e-12 1.00000000
[201,] 9.240599e-09 1.848120e-08 0.99999999
[202,] 2.224142e-04 4.448284e-04 0.99977759
[203,] 1.707567e-02 3.415135e-02 0.98292433
[204,] 4.835833e-01 9.671667e-01 0.51641666
[205,] 7.467971e-01 5.064058e-01 0.25320290
[206,] 8.357694e-01 3.284612e-01 0.16423060
[207,] 8.459095e-01 3.081810e-01 0.15409048
[208,] 8.155029e-01 3.689942e-01 0.18449708
[209,] 8.790699e-01 2.418602e-01 0.12093008
[210,] 9.004571e-01 1.990859e-01 0.09954293
[211,] 8.788657e-01 2.422687e-01 0.12113434
[212,] 8.334501e-01 3.330999e-01 0.16654995
[213,] 8.163118e-01 3.673763e-01 0.18368815
[214,] 8.453716e-01 3.092568e-01 0.15462838
[215,] 7.155401e-01 5.689197e-01 0.28445985
> postscript(file="/var/www/html/rcomp/tmp/15u301258639019.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2plht1258639019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33pot1258639019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wcya1258639019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/50e6n1258639019.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 = 224
Frequency = 1
1 2 3 4 5 6
1.690383699 1.792545108 1.103805929 0.696867925 1.037048043 1.437768512
7 8 9 10 11 12
1.637408278 1.357948630 0.348849217 0.039749804 0.670650391 0.601190743
13 14 15 16 17 18
0.102451565 0.083892504 0.535873796 0.596954500 0.597314735 0.467494852
19 20 21 22 23 24
0.387674970 -0.102144913 0.149656261 -0.009082917 0.061097201 0.231277318
25 26 27 28 29 30
0.671457435 0.602898375 0.763438727 0.745420018 0.526320605 0.217401310
31 32 33 34 35 36
0.417941662 1.028482014 0.658842249 0.559562719 0.369742836 0.539922954
37 38 39 40 41 42
0.260103071 0.341183775 0.152084363 0.253885537 0.403885537 0.604065654
43 44 45 46 47 48
0.554065654 0.284786124 0.305866828 -0.032872350 -0.162692233 -0.292692233
49 50 51 52 53 54
-0.272512115 -0.331251293 -0.410530824 -0.438909767 -0.768729650 -0.880350706
55 56 57 58 59 60
-0.959450119 -0.868909767 -0.957468828 -0.928549532 -0.678549532 -0.688369415
61 62 63 64 65 66
-0.188189297 -0.265487536 -0.114226714 -0.102605658 -0.242425540 -0.342245423
67 68 69 70 71 72
-0.222065306 -0.222065306 -0.171885188 0.318294929 0.218835281 0.389195516
73 74 75 76 77 78
0.169195516 -0.159183427 -0.678102723 -0.877022018 -0.556661783 -0.395941314
79 80 81 82 83 84
-0.245040727 -0.284860609 -0.514680492 -0.834500374 -0.714140140 -0.983599787
85 86 87 88 89 90
-1.713419670 -1.390898026 -1.109637204 -0.617836030 -0.267475796 -0.537295678
91 92 93 94 95 96
-1.097115561 -1.686575209 -1.306395091 -1.266214974 -1.546214974 -1.536034856
97 98 99 100 101 102
-1.176034856 -1.144593917 -0.893513213 -0.950090982 -1.279010277 -1.378109690
103 104 105 106 107 108
-1.467209103 -1.206488634 -0.925948281 -0.814687460 -0.584687460 -0.174687460
109 110 111 112 113 114
-0.324687460 -0.163606755 0.147293832 -0.072345933 0.047654067 0.637654067
115 116 117 118 119 120
0.677654067 0.769095006 1.250355828 0.860896180 0.991256415 0.381796767
121 122 123 124 125 126
0.091976885 0.143597941 -0.015501472 0.686840055 1.038461111 0.829721933
127 128 129 130 131 132
0.550802638 0.570802638 0.161703225 0.251883342 0.032243577 0.082603812
133 134 135 136 137 138
0.803144164 0.533684516 0.574765220 -0.283793840 -0.763073371 -1.212713136
139 140 141 142 143 144
-0.811452314 -0.831092079 -0.830191492 -0.800011375 -0.989290905 -0.719110788
145 146 147 148 149 150
-0.878030083 -0.347849966 -0.327669848 -0.607489731 -1.046048792 -0.464968087
151 152 153 154 155 156
-0.594427735 -0.293887383 -0.283527148 -0.503166913 -0.222266326 -0.341365739
157 158 159 160 161 162
-0.490825387 -0.820285035 -0.949744682 -0.158844095 0.531516139 0.183137196
163 164 165 166 167 168
0.334037783 0.184938370 -0.044521278 0.786019075 0.476739544 0.197099779
169 170 171 172 173 174
0.188000366 0.498540718 0.999261188 0.691242479 0.442143066 0.802323184
175 176 177 178 179 180
1.073764123 1.044124358 1.094484593 0.405024945 0.505745415 0.826105649
181 182 183 184 185 186
0.566646002 0.317726706 -0.370292002 -0.099571533 0.131148937 -0.188130593
187 188 189 190 191 192
-0.457770359 -0.426869772 -0.836329419 -0.845428832 -0.564888480 -0.414348128
193 194 195 196 197 198
-0.394168010 -0.283627658 -0.233267423 -0.273087306 -0.772727071 -0.762186719
199 200 201 202 203 204
-0.681646367 -0.931466249 -0.541106015 0.189974690 0.890334925 1.041595747
205 206 207 208 209 210
1.411955981 1.593937273 2.347719738 2.109340795 3.170961852 3.761322087
211 212 213 214 215 216
3.872042556 3.353303378 3.424564200 2.684924435 1.106545491 0.596905726
217 218 219 220 221 222
0.287446078 -0.102193687 -1.411473217 -1.430932865 -2.399491926 -3.128231104
223 224
-3.707690752 -2.806429930
> postscript(file="/var/www/html/rcomp/tmp/6uqfo1258639019.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 = 224
Frequency = 1
lag(myerror, k = 1) myerror
0 1.690383699 NA
1 1.792545108 1.690383699
2 1.103805929 1.792545108
3 0.696867925 1.103805929
4 1.037048043 0.696867925
5 1.437768512 1.037048043
6 1.637408278 1.437768512
7 1.357948630 1.637408278
8 0.348849217 1.357948630
9 0.039749804 0.348849217
10 0.670650391 0.039749804
11 0.601190743 0.670650391
12 0.102451565 0.601190743
13 0.083892504 0.102451565
14 0.535873796 0.083892504
15 0.596954500 0.535873796
16 0.597314735 0.596954500
17 0.467494852 0.597314735
18 0.387674970 0.467494852
19 -0.102144913 0.387674970
20 0.149656261 -0.102144913
21 -0.009082917 0.149656261
22 0.061097201 -0.009082917
23 0.231277318 0.061097201
24 0.671457435 0.231277318
25 0.602898375 0.671457435
26 0.763438727 0.602898375
27 0.745420018 0.763438727
28 0.526320605 0.745420018
29 0.217401310 0.526320605
30 0.417941662 0.217401310
31 1.028482014 0.417941662
32 0.658842249 1.028482014
33 0.559562719 0.658842249
34 0.369742836 0.559562719
35 0.539922954 0.369742836
36 0.260103071 0.539922954
37 0.341183775 0.260103071
38 0.152084363 0.341183775
39 0.253885537 0.152084363
40 0.403885537 0.253885537
41 0.604065654 0.403885537
42 0.554065654 0.604065654
43 0.284786124 0.554065654
44 0.305866828 0.284786124
45 -0.032872350 0.305866828
46 -0.162692233 -0.032872350
47 -0.292692233 -0.162692233
48 -0.272512115 -0.292692233
49 -0.331251293 -0.272512115
50 -0.410530824 -0.331251293
51 -0.438909767 -0.410530824
52 -0.768729650 -0.438909767
53 -0.880350706 -0.768729650
54 -0.959450119 -0.880350706
55 -0.868909767 -0.959450119
56 -0.957468828 -0.868909767
57 -0.928549532 -0.957468828
58 -0.678549532 -0.928549532
59 -0.688369415 -0.678549532
60 -0.188189297 -0.688369415
61 -0.265487536 -0.188189297
62 -0.114226714 -0.265487536
63 -0.102605658 -0.114226714
64 -0.242425540 -0.102605658
65 -0.342245423 -0.242425540
66 -0.222065306 -0.342245423
67 -0.222065306 -0.222065306
68 -0.171885188 -0.222065306
69 0.318294929 -0.171885188
70 0.218835281 0.318294929
71 0.389195516 0.218835281
72 0.169195516 0.389195516
73 -0.159183427 0.169195516
74 -0.678102723 -0.159183427
75 -0.877022018 -0.678102723
76 -0.556661783 -0.877022018
77 -0.395941314 -0.556661783
78 -0.245040727 -0.395941314
79 -0.284860609 -0.245040727
80 -0.514680492 -0.284860609
81 -0.834500374 -0.514680492
82 -0.714140140 -0.834500374
83 -0.983599787 -0.714140140
84 -1.713419670 -0.983599787
85 -1.390898026 -1.713419670
86 -1.109637204 -1.390898026
87 -0.617836030 -1.109637204
88 -0.267475796 -0.617836030
89 -0.537295678 -0.267475796
90 -1.097115561 -0.537295678
91 -1.686575209 -1.097115561
92 -1.306395091 -1.686575209
93 -1.266214974 -1.306395091
94 -1.546214974 -1.266214974
95 -1.536034856 -1.546214974
96 -1.176034856 -1.536034856
97 -1.144593917 -1.176034856
98 -0.893513213 -1.144593917
99 -0.950090982 -0.893513213
100 -1.279010277 -0.950090982
101 -1.378109690 -1.279010277
102 -1.467209103 -1.378109690
103 -1.206488634 -1.467209103
104 -0.925948281 -1.206488634
105 -0.814687460 -0.925948281
106 -0.584687460 -0.814687460
107 -0.174687460 -0.584687460
108 -0.324687460 -0.174687460
109 -0.163606755 -0.324687460
110 0.147293832 -0.163606755
111 -0.072345933 0.147293832
112 0.047654067 -0.072345933
113 0.637654067 0.047654067
114 0.677654067 0.637654067
115 0.769095006 0.677654067
116 1.250355828 0.769095006
117 0.860896180 1.250355828
118 0.991256415 0.860896180
119 0.381796767 0.991256415
120 0.091976885 0.381796767
121 0.143597941 0.091976885
122 -0.015501472 0.143597941
123 0.686840055 -0.015501472
124 1.038461111 0.686840055
125 0.829721933 1.038461111
126 0.550802638 0.829721933
127 0.570802638 0.550802638
128 0.161703225 0.570802638
129 0.251883342 0.161703225
130 0.032243577 0.251883342
131 0.082603812 0.032243577
132 0.803144164 0.082603812
133 0.533684516 0.803144164
134 0.574765220 0.533684516
135 -0.283793840 0.574765220
136 -0.763073371 -0.283793840
137 -1.212713136 -0.763073371
138 -0.811452314 -1.212713136
139 -0.831092079 -0.811452314
140 -0.830191492 -0.831092079
141 -0.800011375 -0.830191492
142 -0.989290905 -0.800011375
143 -0.719110788 -0.989290905
144 -0.878030083 -0.719110788
145 -0.347849966 -0.878030083
146 -0.327669848 -0.347849966
147 -0.607489731 -0.327669848
148 -1.046048792 -0.607489731
149 -0.464968087 -1.046048792
150 -0.594427735 -0.464968087
151 -0.293887383 -0.594427735
152 -0.283527148 -0.293887383
153 -0.503166913 -0.283527148
154 -0.222266326 -0.503166913
155 -0.341365739 -0.222266326
156 -0.490825387 -0.341365739
157 -0.820285035 -0.490825387
158 -0.949744682 -0.820285035
159 -0.158844095 -0.949744682
160 0.531516139 -0.158844095
161 0.183137196 0.531516139
162 0.334037783 0.183137196
163 0.184938370 0.334037783
164 -0.044521278 0.184938370
165 0.786019075 -0.044521278
166 0.476739544 0.786019075
167 0.197099779 0.476739544
168 0.188000366 0.197099779
169 0.498540718 0.188000366
170 0.999261188 0.498540718
171 0.691242479 0.999261188
172 0.442143066 0.691242479
173 0.802323184 0.442143066
174 1.073764123 0.802323184
175 1.044124358 1.073764123
176 1.094484593 1.044124358
177 0.405024945 1.094484593
178 0.505745415 0.405024945
179 0.826105649 0.505745415
180 0.566646002 0.826105649
181 0.317726706 0.566646002
182 -0.370292002 0.317726706
183 -0.099571533 -0.370292002
184 0.131148937 -0.099571533
185 -0.188130593 0.131148937
186 -0.457770359 -0.188130593
187 -0.426869772 -0.457770359
188 -0.836329419 -0.426869772
189 -0.845428832 -0.836329419
190 -0.564888480 -0.845428832
191 -0.414348128 -0.564888480
192 -0.394168010 -0.414348128
193 -0.283627658 -0.394168010
194 -0.233267423 -0.283627658
195 -0.273087306 -0.233267423
196 -0.772727071 -0.273087306
197 -0.762186719 -0.772727071
198 -0.681646367 -0.762186719
199 -0.931466249 -0.681646367
200 -0.541106015 -0.931466249
201 0.189974690 -0.541106015
202 0.890334925 0.189974690
203 1.041595747 0.890334925
204 1.411955981 1.041595747
205 1.593937273 1.411955981
206 2.347719738 1.593937273
207 2.109340795 2.347719738
208 3.170961852 2.109340795
209 3.761322087 3.170961852
210 3.872042556 3.761322087
211 3.353303378 3.872042556
212 3.424564200 3.353303378
213 2.684924435 3.424564200
214 1.106545491 2.684924435
215 0.596905726 1.106545491
216 0.287446078 0.596905726
217 -0.102193687 0.287446078
218 -1.411473217 -0.102193687
219 -1.430932865 -1.411473217
220 -2.399491926 -1.430932865
221 -3.128231104 -2.399491926
222 -3.707690752 -3.128231104
223 -2.806429930 -3.707690752
224 NA -2.806429930
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.792545108 1.690383699
[2,] 1.103805929 1.792545108
[3,] 0.696867925 1.103805929
[4,] 1.037048043 0.696867925
[5,] 1.437768512 1.037048043
[6,] 1.637408278 1.437768512
[7,] 1.357948630 1.637408278
[8,] 0.348849217 1.357948630
[9,] 0.039749804 0.348849217
[10,] 0.670650391 0.039749804
[11,] 0.601190743 0.670650391
[12,] 0.102451565 0.601190743
[13,] 0.083892504 0.102451565
[14,] 0.535873796 0.083892504
[15,] 0.596954500 0.535873796
[16,] 0.597314735 0.596954500
[17,] 0.467494852 0.597314735
[18,] 0.387674970 0.467494852
[19,] -0.102144913 0.387674970
[20,] 0.149656261 -0.102144913
[21,] -0.009082917 0.149656261
[22,] 0.061097201 -0.009082917
[23,] 0.231277318 0.061097201
[24,] 0.671457435 0.231277318
[25,] 0.602898375 0.671457435
[26,] 0.763438727 0.602898375
[27,] 0.745420018 0.763438727
[28,] 0.526320605 0.745420018
[29,] 0.217401310 0.526320605
[30,] 0.417941662 0.217401310
[31,] 1.028482014 0.417941662
[32,] 0.658842249 1.028482014
[33,] 0.559562719 0.658842249
[34,] 0.369742836 0.559562719
[35,] 0.539922954 0.369742836
[36,] 0.260103071 0.539922954
[37,] 0.341183775 0.260103071
[38,] 0.152084363 0.341183775
[39,] 0.253885537 0.152084363
[40,] 0.403885537 0.253885537
[41,] 0.604065654 0.403885537
[42,] 0.554065654 0.604065654
[43,] 0.284786124 0.554065654
[44,] 0.305866828 0.284786124
[45,] -0.032872350 0.305866828
[46,] -0.162692233 -0.032872350
[47,] -0.292692233 -0.162692233
[48,] -0.272512115 -0.292692233
[49,] -0.331251293 -0.272512115
[50,] -0.410530824 -0.331251293
[51,] -0.438909767 -0.410530824
[52,] -0.768729650 -0.438909767
[53,] -0.880350706 -0.768729650
[54,] -0.959450119 -0.880350706
[55,] -0.868909767 -0.959450119
[56,] -0.957468828 -0.868909767
[57,] -0.928549532 -0.957468828
[58,] -0.678549532 -0.928549532
[59,] -0.688369415 -0.678549532
[60,] -0.188189297 -0.688369415
[61,] -0.265487536 -0.188189297
[62,] -0.114226714 -0.265487536
[63,] -0.102605658 -0.114226714
[64,] -0.242425540 -0.102605658
[65,] -0.342245423 -0.242425540
[66,] -0.222065306 -0.342245423
[67,] -0.222065306 -0.222065306
[68,] -0.171885188 -0.222065306
[69,] 0.318294929 -0.171885188
[70,] 0.218835281 0.318294929
[71,] 0.389195516 0.218835281
[72,] 0.169195516 0.389195516
[73,] -0.159183427 0.169195516
[74,] -0.678102723 -0.159183427
[75,] -0.877022018 -0.678102723
[76,] -0.556661783 -0.877022018
[77,] -0.395941314 -0.556661783
[78,] -0.245040727 -0.395941314
[79,] -0.284860609 -0.245040727
[80,] -0.514680492 -0.284860609
[81,] -0.834500374 -0.514680492
[82,] -0.714140140 -0.834500374
[83,] -0.983599787 -0.714140140
[84,] -1.713419670 -0.983599787
[85,] -1.390898026 -1.713419670
[86,] -1.109637204 -1.390898026
[87,] -0.617836030 -1.109637204
[88,] -0.267475796 -0.617836030
[89,] -0.537295678 -0.267475796
[90,] -1.097115561 -0.537295678
[91,] -1.686575209 -1.097115561
[92,] -1.306395091 -1.686575209
[93,] -1.266214974 -1.306395091
[94,] -1.546214974 -1.266214974
[95,] -1.536034856 -1.546214974
[96,] -1.176034856 -1.536034856
[97,] -1.144593917 -1.176034856
[98,] -0.893513213 -1.144593917
[99,] -0.950090982 -0.893513213
[100,] -1.279010277 -0.950090982
[101,] -1.378109690 -1.279010277
[102,] -1.467209103 -1.378109690
[103,] -1.206488634 -1.467209103
[104,] -0.925948281 -1.206488634
[105,] -0.814687460 -0.925948281
[106,] -0.584687460 -0.814687460
[107,] -0.174687460 -0.584687460
[108,] -0.324687460 -0.174687460
[109,] -0.163606755 -0.324687460
[110,] 0.147293832 -0.163606755
[111,] -0.072345933 0.147293832
[112,] 0.047654067 -0.072345933
[113,] 0.637654067 0.047654067
[114,] 0.677654067 0.637654067
[115,] 0.769095006 0.677654067
[116,] 1.250355828 0.769095006
[117,] 0.860896180 1.250355828
[118,] 0.991256415 0.860896180
[119,] 0.381796767 0.991256415
[120,] 0.091976885 0.381796767
[121,] 0.143597941 0.091976885
[122,] -0.015501472 0.143597941
[123,] 0.686840055 -0.015501472
[124,] 1.038461111 0.686840055
[125,] 0.829721933 1.038461111
[126,] 0.550802638 0.829721933
[127,] 0.570802638 0.550802638
[128,] 0.161703225 0.570802638
[129,] 0.251883342 0.161703225
[130,] 0.032243577 0.251883342
[131,] 0.082603812 0.032243577
[132,] 0.803144164 0.082603812
[133,] 0.533684516 0.803144164
[134,] 0.574765220 0.533684516
[135,] -0.283793840 0.574765220
[136,] -0.763073371 -0.283793840
[137,] -1.212713136 -0.763073371
[138,] -0.811452314 -1.212713136
[139,] -0.831092079 -0.811452314
[140,] -0.830191492 -0.831092079
[141,] -0.800011375 -0.830191492
[142,] -0.989290905 -0.800011375
[143,] -0.719110788 -0.989290905
[144,] -0.878030083 -0.719110788
[145,] -0.347849966 -0.878030083
[146,] -0.327669848 -0.347849966
[147,] -0.607489731 -0.327669848
[148,] -1.046048792 -0.607489731
[149,] -0.464968087 -1.046048792
[150,] -0.594427735 -0.464968087
[151,] -0.293887383 -0.594427735
[152,] -0.283527148 -0.293887383
[153,] -0.503166913 -0.283527148
[154,] -0.222266326 -0.503166913
[155,] -0.341365739 -0.222266326
[156,] -0.490825387 -0.341365739
[157,] -0.820285035 -0.490825387
[158,] -0.949744682 -0.820285035
[159,] -0.158844095 -0.949744682
[160,] 0.531516139 -0.158844095
[161,] 0.183137196 0.531516139
[162,] 0.334037783 0.183137196
[163,] 0.184938370 0.334037783
[164,] -0.044521278 0.184938370
[165,] 0.786019075 -0.044521278
[166,] 0.476739544 0.786019075
[167,] 0.197099779 0.476739544
[168,] 0.188000366 0.197099779
[169,] 0.498540718 0.188000366
[170,] 0.999261188 0.498540718
[171,] 0.691242479 0.999261188
[172,] 0.442143066 0.691242479
[173,] 0.802323184 0.442143066
[174,] 1.073764123 0.802323184
[175,] 1.044124358 1.073764123
[176,] 1.094484593 1.044124358
[177,] 0.405024945 1.094484593
[178,] 0.505745415 0.405024945
[179,] 0.826105649 0.505745415
[180,] 0.566646002 0.826105649
[181,] 0.317726706 0.566646002
[182,] -0.370292002 0.317726706
[183,] -0.099571533 -0.370292002
[184,] 0.131148937 -0.099571533
[185,] -0.188130593 0.131148937
[186,] -0.457770359 -0.188130593
[187,] -0.426869772 -0.457770359
[188,] -0.836329419 -0.426869772
[189,] -0.845428832 -0.836329419
[190,] -0.564888480 -0.845428832
[191,] -0.414348128 -0.564888480
[192,] -0.394168010 -0.414348128
[193,] -0.283627658 -0.394168010
[194,] -0.233267423 -0.283627658
[195,] -0.273087306 -0.233267423
[196,] -0.772727071 -0.273087306
[197,] -0.762186719 -0.772727071
[198,] -0.681646367 -0.762186719
[199,] -0.931466249 -0.681646367
[200,] -0.541106015 -0.931466249
[201,] 0.189974690 -0.541106015
[202,] 0.890334925 0.189974690
[203,] 1.041595747 0.890334925
[204,] 1.411955981 1.041595747
[205,] 1.593937273 1.411955981
[206,] 2.347719738 1.593937273
[207,] 2.109340795 2.347719738
[208,] 3.170961852 2.109340795
[209,] 3.761322087 3.170961852
[210,] 3.872042556 3.761322087
[211,] 3.353303378 3.872042556
[212,] 3.424564200 3.353303378
[213,] 2.684924435 3.424564200
[214,] 1.106545491 2.684924435
[215,] 0.596905726 1.106545491
[216,] 0.287446078 0.596905726
[217,] -0.102193687 0.287446078
[218,] -1.411473217 -0.102193687
[219,] -1.430932865 -1.411473217
[220,] -2.399491926 -1.430932865
[221,] -3.128231104 -2.399491926
[222,] -3.707690752 -3.128231104
[223,] -2.806429930 -3.707690752
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.792545108 1.690383699
2 1.103805929 1.792545108
3 0.696867925 1.103805929
4 1.037048043 0.696867925
5 1.437768512 1.037048043
6 1.637408278 1.437768512
7 1.357948630 1.637408278
8 0.348849217 1.357948630
9 0.039749804 0.348849217
10 0.670650391 0.039749804
11 0.601190743 0.670650391
12 0.102451565 0.601190743
13 0.083892504 0.102451565
14 0.535873796 0.083892504
15 0.596954500 0.535873796
16 0.597314735 0.596954500
17 0.467494852 0.597314735
18 0.387674970 0.467494852
19 -0.102144913 0.387674970
20 0.149656261 -0.102144913
21 -0.009082917 0.149656261
22 0.061097201 -0.009082917
23 0.231277318 0.061097201
24 0.671457435 0.231277318
25 0.602898375 0.671457435
26 0.763438727 0.602898375
27 0.745420018 0.763438727
28 0.526320605 0.745420018
29 0.217401310 0.526320605
30 0.417941662 0.217401310
31 1.028482014 0.417941662
32 0.658842249 1.028482014
33 0.559562719 0.658842249
34 0.369742836 0.559562719
35 0.539922954 0.369742836
36 0.260103071 0.539922954
37 0.341183775 0.260103071
38 0.152084363 0.341183775
39 0.253885537 0.152084363
40 0.403885537 0.253885537
41 0.604065654 0.403885537
42 0.554065654 0.604065654
43 0.284786124 0.554065654
44 0.305866828 0.284786124
45 -0.032872350 0.305866828
46 -0.162692233 -0.032872350
47 -0.292692233 -0.162692233
48 -0.272512115 -0.292692233
49 -0.331251293 -0.272512115
50 -0.410530824 -0.331251293
51 -0.438909767 -0.410530824
52 -0.768729650 -0.438909767
53 -0.880350706 -0.768729650
54 -0.959450119 -0.880350706
55 -0.868909767 -0.959450119
56 -0.957468828 -0.868909767
57 -0.928549532 -0.957468828
58 -0.678549532 -0.928549532
59 -0.688369415 -0.678549532
60 -0.188189297 -0.688369415
61 -0.265487536 -0.188189297
62 -0.114226714 -0.265487536
63 -0.102605658 -0.114226714
64 -0.242425540 -0.102605658
65 -0.342245423 -0.242425540
66 -0.222065306 -0.342245423
67 -0.222065306 -0.222065306
68 -0.171885188 -0.222065306
69 0.318294929 -0.171885188
70 0.218835281 0.318294929
71 0.389195516 0.218835281
72 0.169195516 0.389195516
73 -0.159183427 0.169195516
74 -0.678102723 -0.159183427
75 -0.877022018 -0.678102723
76 -0.556661783 -0.877022018
77 -0.395941314 -0.556661783
78 -0.245040727 -0.395941314
79 -0.284860609 -0.245040727
80 -0.514680492 -0.284860609
81 -0.834500374 -0.514680492
82 -0.714140140 -0.834500374
83 -0.983599787 -0.714140140
84 -1.713419670 -0.983599787
85 -1.390898026 -1.713419670
86 -1.109637204 -1.390898026
87 -0.617836030 -1.109637204
88 -0.267475796 -0.617836030
89 -0.537295678 -0.267475796
90 -1.097115561 -0.537295678
91 -1.686575209 -1.097115561
92 -1.306395091 -1.686575209
93 -1.266214974 -1.306395091
94 -1.546214974 -1.266214974
95 -1.536034856 -1.546214974
96 -1.176034856 -1.536034856
97 -1.144593917 -1.176034856
98 -0.893513213 -1.144593917
99 -0.950090982 -0.893513213
100 -1.279010277 -0.950090982
101 -1.378109690 -1.279010277
102 -1.467209103 -1.378109690
103 -1.206488634 -1.467209103
104 -0.925948281 -1.206488634
105 -0.814687460 -0.925948281
106 -0.584687460 -0.814687460
107 -0.174687460 -0.584687460
108 -0.324687460 -0.174687460
109 -0.163606755 -0.324687460
110 0.147293832 -0.163606755
111 -0.072345933 0.147293832
112 0.047654067 -0.072345933
113 0.637654067 0.047654067
114 0.677654067 0.637654067
115 0.769095006 0.677654067
116 1.250355828 0.769095006
117 0.860896180 1.250355828
118 0.991256415 0.860896180
119 0.381796767 0.991256415
120 0.091976885 0.381796767
121 0.143597941 0.091976885
122 -0.015501472 0.143597941
123 0.686840055 -0.015501472
124 1.038461111 0.686840055
125 0.829721933 1.038461111
126 0.550802638 0.829721933
127 0.570802638 0.550802638
128 0.161703225 0.570802638
129 0.251883342 0.161703225
130 0.032243577 0.251883342
131 0.082603812 0.032243577
132 0.803144164 0.082603812
133 0.533684516 0.803144164
134 0.574765220 0.533684516
135 -0.283793840 0.574765220
136 -0.763073371 -0.283793840
137 -1.212713136 -0.763073371
138 -0.811452314 -1.212713136
139 -0.831092079 -0.811452314
140 -0.830191492 -0.831092079
141 -0.800011375 -0.830191492
142 -0.989290905 -0.800011375
143 -0.719110788 -0.989290905
144 -0.878030083 -0.719110788
145 -0.347849966 -0.878030083
146 -0.327669848 -0.347849966
147 -0.607489731 -0.327669848
148 -1.046048792 -0.607489731
149 -0.464968087 -1.046048792
150 -0.594427735 -0.464968087
151 -0.293887383 -0.594427735
152 -0.283527148 -0.293887383
153 -0.503166913 -0.283527148
154 -0.222266326 -0.503166913
155 -0.341365739 -0.222266326
156 -0.490825387 -0.341365739
157 -0.820285035 -0.490825387
158 -0.949744682 -0.820285035
159 -0.158844095 -0.949744682
160 0.531516139 -0.158844095
161 0.183137196 0.531516139
162 0.334037783 0.183137196
163 0.184938370 0.334037783
164 -0.044521278 0.184938370
165 0.786019075 -0.044521278
166 0.476739544 0.786019075
167 0.197099779 0.476739544
168 0.188000366 0.197099779
169 0.498540718 0.188000366
170 0.999261188 0.498540718
171 0.691242479 0.999261188
172 0.442143066 0.691242479
173 0.802323184 0.442143066
174 1.073764123 0.802323184
175 1.044124358 1.073764123
176 1.094484593 1.044124358
177 0.405024945 1.094484593
178 0.505745415 0.405024945
179 0.826105649 0.505745415
180 0.566646002 0.826105649
181 0.317726706 0.566646002
182 -0.370292002 0.317726706
183 -0.099571533 -0.370292002
184 0.131148937 -0.099571533
185 -0.188130593 0.131148937
186 -0.457770359 -0.188130593
187 -0.426869772 -0.457770359
188 -0.836329419 -0.426869772
189 -0.845428832 -0.836329419
190 -0.564888480 -0.845428832
191 -0.414348128 -0.564888480
192 -0.394168010 -0.414348128
193 -0.283627658 -0.394168010
194 -0.233267423 -0.283627658
195 -0.273087306 -0.233267423
196 -0.772727071 -0.273087306
197 -0.762186719 -0.772727071
198 -0.681646367 -0.762186719
199 -0.931466249 -0.681646367
200 -0.541106015 -0.931466249
201 0.189974690 -0.541106015
202 0.890334925 0.189974690
203 1.041595747 0.890334925
204 1.411955981 1.041595747
205 1.593937273 1.411955981
206 2.347719738 1.593937273
207 2.109340795 2.347719738
208 3.170961852 2.109340795
209 3.761322087 3.170961852
210 3.872042556 3.761322087
211 3.353303378 3.872042556
212 3.424564200 3.353303378
213 2.684924435 3.424564200
214 1.106545491 2.684924435
215 0.596905726 1.106545491
216 0.287446078 0.596905726
217 -0.102193687 0.287446078
218 -1.411473217 -0.102193687
219 -1.430932865 -1.411473217
220 -2.399491926 -1.430932865
221 -3.128231104 -2.399491926
222 -3.707690752 -3.128231104
223 -2.806429930 -3.707690752
> 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/7pwxq1258639019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8wxlt1258639019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/94be11258639019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10bvcz1258639019.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114c1v1258639019.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/125krq1258639019.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/13uxq71258639019.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/144z5z1258639019.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/15r0gd1258639019.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/16rfkt1258639019.tab")
+ }
>
> system("convert tmp/15u301258639019.ps tmp/15u301258639019.png")
> system("convert tmp/2plht1258639019.ps tmp/2plht1258639019.png")
> system("convert tmp/33pot1258639019.ps tmp/33pot1258639019.png")
> system("convert tmp/4wcya1258639019.ps tmp/4wcya1258639019.png")
> system("convert tmp/50e6n1258639019.ps tmp/50e6n1258639019.png")
> system("convert tmp/6uqfo1258639019.ps tmp/6uqfo1258639019.png")
> system("convert tmp/7pwxq1258639019.ps tmp/7pwxq1258639019.png")
> system("convert tmp/8wxlt1258639019.ps tmp/8wxlt1258639019.png")
> system("convert tmp/94be11258639019.ps tmp/94be11258639019.png")
> system("convert tmp/10bvcz1258639019.ps tmp/10bvcz1258639019.png")
>
>
> proc.time()
user system elapsed
5.064 1.770 5.584