R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(41
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+ ,12
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+ ,43
+ ,31
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+ ,8
+ ,9
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+ ,62
+ ,40
+ ,37
+ ,34
+ ,11
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+ ,15
+ ,67
+ ,41
+ ,35
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+ ,3
+ ,10
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+ ,52
+ ,27
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+ ,11
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+ ,12
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+ ,7
+ ,7
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+ ,62
+ ,39
+ ,29
+ ,32
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(7
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(7,264),dimnames=list(c('Connected','Separate','Software','Happiness','Depression','Sport1','Sport2'),1:264))
> 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'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, 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
Connected Separate Software Happiness Depression Sport1 Sport2
1 41 38 12 14 12.0 53 32
2 39 32 11 18 11.0 83 51
3 30 35 15 11 14.0 66 42
4 31 33 6 12 12.0 67 41
5 34 37 13 16 21.0 76 46
6 35 29 10 18 12.0 78 47
7 39 31 12 14 22.0 53 37
8 34 36 14 14 11.0 80 49
9 36 35 12 15 10.0 74 45
10 37 38 9 15 13.0 76 47
11 38 31 10 17 10.0 79 49
12 36 34 12 19 8.0 54 33
13 38 35 12 10 15.0 67 42
14 39 38 11 16 14.0 54 33
15 33 37 15 18 10.0 87 53
16 32 33 12 14 14.0 58 36
17 36 32 10 14 14.0 75 45
18 38 38 12 17 11.0 88 54
19 39 38 11 14 10.0 64 41
20 32 32 12 16 13.0 57 36
21 32 33 11 18 9.5 66 41
22 31 31 12 11 14.0 68 44
23 39 38 13 14 12.0 54 33
24 37 39 11 12 14.0 56 37
25 39 32 12 17 11.0 86 52
26 41 32 13 9 9.0 80 47
27 36 35 10 16 11.0 76 43
28 33 37 14 14 15.0 69 44
29 33 33 12 15 14.0 78 45
30 34 33 10 11 13.0 67 44
31 31 31 12 16 9.0 80 49
32 27 32 8 13 15.0 54 33
33 37 31 10 17 10.0 71 43
34 34 37 12 15 11.0 84 54
35 34 30 12 14 13.0 74 42
36 32 33 7 16 8.0 71 44
37 29 31 9 9 20.0 63 37
38 36 33 12 15 12.0 71 43
39 29 31 10 17 10.0 76 46
40 35 33 10 13 10.0 69 42
41 37 32 10 15 9.0 74 45
42 34 33 12 16 14.0 75 44
43 38 32 15 16 8.0 54 33
44 35 33 10 12 14.0 52 31
45 38 28 10 15 11.0 69 42
46 37 35 12 11 13.0 68 40
47 38 39 13 15 9.0 65 43
48 33 34 11 15 11.0 75 46
49 36 38 11 17 15.0 74 42
50 38 32 12 13 11.0 75 45
51 32 38 14 16 10.0 72 44
52 32 30 10 14 14.0 67 40
53 32 33 12 11 18.0 63 37
54 34 38 13 12 14.0 62 46
55 32 32 5 12 11.0 63 36
56 37 35 6 15 14.5 76 47
57 39 34 12 16 13.0 74 45
58 29 34 12 15 9.0 67 42
59 37 36 11 12 10.0 73 43
60 35 34 10 12 15.0 70 43
61 30 28 7 8 20.0 53 32
62 38 34 12 13 12.0 77 45
63 34 35 14 11 12.0 80 48
64 31 35 11 14 14.0 52 31
65 34 31 12 15 13.0 54 33
66 35 37 13 10 11.0 80 49
67 36 35 14 11 17.0 66 42
68 30 27 11 12 12.0 73 41
69 39 40 12 15 13.0 63 38
70 35 37 12 15 14.0 69 42
71 38 36 8 14 13.0 67 44
72 31 38 11 16 15.0 54 33
73 34 39 14 15 13.0 81 48
74 38 41 14 15 10.0 69 40
75 34 27 12 13 11.0 84 50
76 39 30 9 12 19.0 80 49
77 37 37 13 17 13.0 70 43
78 34 31 11 13 17.0 69 44
79 28 31 12 15 13.0 77 47
80 37 27 12 13 9.0 54 33
81 33 36 12 15 11.0 79 46
82 35 37 12 15 9.0 71 45
83 37 33 12 16 12.0 73 43
84 32 34 11 15 12.0 72 44
85 33 31 10 14 13.0 77 47
86 38 39 9 15 13.0 75 45
87 33 34 12 14 12.0 69 42
88 29 32 12 13 15.0 54 33
89 33 33 12 7 22.0 70 43
90 31 36 9 17 13.0 73 46
91 36 32 15 13 15.0 54 33
92 35 41 12 15 13.0 77 46
93 32 28 12 14 15.0 82 48
94 29 30 12 13 12.5 80 47
95 39 36 10 16 11.0 80 47
96 37 35 13 12 16.0 69 43
97 35 31 9 14 11.0 78 46
98 37 34 12 17 11.0 81 48
99 32 36 10 15 10.0 76 46
100 38 36 14 17 10.0 76 45
101 37 35 11 12 16.0 73 45
102 36 37 15 16 12.0 85 52
103 32 28 11 11 11.0 66 42
104 33 39 11 15 16.0 79 47
105 40 32 12 9 19.0 68 41
106 38 35 12 16 11.0 76 47
107 41 39 12 15 16.0 71 43
108 36 35 11 10 15.0 54 33
109 43 42 7 10 24.0 46 30
110 30 34 12 15 14.0 85 52
111 31 33 14 11 15.0 74 44
112 32 41 11 13 11.0 88 55
113 32 33 11 14 15.0 38 11
114 37 34 10 18 12.0 76 47
115 37 32 13 16 10.0 86 53
116 33 40 13 14 14.0 54 33
117 34 40 8 14 13.0 67 44
118 33 35 11 14 9.0 69 42
119 38 36 12 14 15.0 90 55
120 33 37 11 12 15.0 54 33
121 31 27 13 14 14.0 76 46
122 38 39 12 15 11.0 89 54
123 37 38 14 15 8.0 76 47
124 36 31 13 15 11.0 73 45
125 31 33 15 13 11.0 79 47
126 39 32 10 17 8.0 90 55
127 44 39 11 17 10.0 74 44
128 33 36 9 19 11.0 81 53
129 35 33 11 15 13.0 72 44
130 32 33 10 13 11.0 71 42
131 28 32 11 9 20.0 66 40
132 40 37 8 15 10.0 77 46
133 27 30 11 15 15.0 65 40
134 37 38 12 15 12.0 74 46
135 32 29 12 16 14.0 85 53
136 28 22 9 11 23.0 54 33
137 34 35 11 14 14.0 63 42
138 30 35 10 11 16.0 54 35
139 35 34 8 15 11.0 64 40
140 31 35 9 13 12.0 69 41
141 32 34 8 15 10.0 54 33
142 30 37 9 16 14.0 84 51
143 30 35 15 14 12.0 86 53
144 31 23 11 15 12.0 77 46
145 40 31 8 16 11.0 89 55
146 32 27 13 16 12.0 76 47
147 36 36 12 11 13.0 60 38
148 32 31 12 12 11.0 75 46
149 35 32 9 9 19.0 73 46
150 38 39 7 16 12.0 85 53
151 42 37 13 13 17.0 79 47
152 34 38 9 16 9.0 71 41
153 35 39 6 12 12.0 72 44
154 38 34 8 9 19.0 69 43
155 33 31 8 13 18.0 78 51
156 36 32 15 13 15.0 54 33
157 32 37 6 14 14.0 69 43
158 33 36 9 19 11.0 81 53
159 34 32 11 13 9.0 84 51
160 32 38 8 12 18.0 84 50
161 34 36 8 13 16.0 69 46
162 27 26 10 10 24.0 66 43
163 31 26 8 14 14.0 81 47
164 38 33 14 16 20.0 82 50
165 34 39 10 10 18.0 72 43
166 24 30 8 11 23.0 54 33
167 30 33 11 14 12.0 78 48
168 26 25 12 12 14.0 74 44
169 34 38 12 9 16.0 82 50
170 27 37 12 9 18.0 73 41
171 37 31 5 11 20.0 55 34
172 36 37 12 16 12.0 72 44
173 41 35 10 9 12.0 78 47
174 29 25 7 13 17.0 59 35
175 36 28 12 16 13.0 72 44
176 32 35 11 13 9.0 78 44
177 37 33 8 9 16.0 68 43
178 30 30 9 12 18.0 69 41
179 31 31 10 16 10.0 67 41
180 38 37 9 11 14.0 74 42
181 36 36 12 14 11.0 54 33
182 35 30 6 13 9.0 67 41
183 31 36 15 15 11.0 70 44
184 38 32 12 14 10.0 80 48
185 22 28 12 16 11.0 89 55
186 32 36 12 13 19.0 76 44
187 36 34 11 14 14.0 74 43
188 39 31 7 15 12.0 87 52
189 28 28 7 13 14.0 54 30
190 32 36 5 11 21.0 61 39
191 32 36 12 11 13.0 38 11
192 38 40 12 14 10.0 75 44
193 32 33 3 15 15.0 69 42
194 35 37 11 11 16.0 62 41
195 32 32 10 15 14.0 72 44
196 37 38 12 12 12.0 70 44
197 34 31 9 14 19.0 79 48
198 33 37 12 14 15.0 87 53
199 33 33 9 8 19.0 62 37
200 26 32 12 13 13.0 77 44
201 30 30 12 9 17.0 69 44
202 24 30 10 15 12.0 69 40
203 34 31 9 17 11.0 75 42
204 34 32 12 13 14.0 54 35
205 33 34 8 15 11.0 72 43
206 34 36 11 15 13.0 74 45
207 35 37 11 14 12.0 85 55
208 35 36 12 16 15.0 52 31
209 36 33 10 13 14.0 70 44
210 34 33 10 16 12.0 84 50
211 34 33 12 9 17.0 64 40
212 41 44 12 16 11.0 84 53
213 32 39 11 11 18.0 87 54
214 30 32 8 10 13.0 79 49
215 35 35 12 11 17.0 67 40
216 28 25 10 15 13.0 65 41
217 33 35 11 17 11.0 85 52
218 39 34 10 14 12.0 83 52
219 36 35 8 8 22.0 61 36
220 36 39 12 15 14.0 82 52
221 35 33 12 11 12.0 76 46
222 38 36 10 16 12.0 58 31
223 33 32 12 10 17.0 72 44
224 31 32 9 15 9.0 72 44
225 34 36 9 9 21.0 38 11
226 32 36 6 16 10.0 78 46
227 31 32 10 19 11.0 54 33
228 33 34 9 12 12.0 63 34
229 34 33 9 8 23.0 66 42
230 34 35 9 11 13.0 70 43
231 34 30 6 14 12.0 71 43
232 33 38 10 9 16.0 67 44
233 32 34 6 15 9.0 58 36
234 41 33 14 13 17.0 72 46
235 34 32 10 16 9.0 72 44
236 36 31 10 11 14.0 70 43
237 37 30 6 12 17.0 76 50
238 36 27 12 13 13.0 50 33
239 29 31 12 10 11.0 72 43
240 37 30 7 11 12.0 72 44
241 27 32 8 12 10.0 88 53
242 35 35 11 8 19.0 53 34
243 28 28 3 12 16.0 58 35
244 35 33 6 12 16.0 66 40
245 37 31 10 15 14.0 82 53
246 29 35 8 11 20.0 69 42
247 32 35 9 13 15.0 68 43
248 36 32 9 14 23.0 44 29
249 19 21 8 10 20.0 56 36
250 21 20 9 12 16.0 53 30
251 31 34 7 15 14.0 70 42
252 33 32 7 13 17.0 78 47
253 36 34 6 13 11.0 71 44
254 33 32 9 13 13.0 72 45
255 37 33 10 12 17.0 68 44
256 34 33 11 12 15.0 67 43
257 35 37 12 9 21.0 75 43
258 31 32 8 9 18.0 62 40
259 37 34 11 15 15.0 67 41
260 35 30 3 10 8.0 83 52
261 27 30 11 14 12.0 64 38
262 34 38 12 15 12.0 68 41
263 40 36 7 7 22.0 62 39
264 29 32 9 14 12.0 72 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Software Happiness Depression Sport1
17.77596 0.44189 0.05571 0.05086 -0.06649 -0.07086
Sport2
0.13982
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-10.2829 -2.4298 0.1666 2.4313 7.5977
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.77596 3.25492 5.461 1.11e-07 ***
Separate 0.44189 0.05767 7.662 3.75e-13 ***
Software 0.05571 0.09290 0.600 0.549
Happiness 0.05086 0.10389 0.490 0.625
Depression -0.06649 0.07620 -0.873 0.384
Sport1 -0.07086 0.06771 -1.047 0.296
Sport2 0.13982 0.10084 1.387 0.167
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.379 on 257 degrees of freedom
Multiple R-squared: 0.2259, Adjusted R-squared: 0.2078
F-statistic: 12.5 on 6 and 257 DF, p-value: 2.346e-12
> 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,] 0.04565971 0.09131942 0.9543403
[2,] 0.01248782 0.02497563 0.9875122
[3,] 0.63358908 0.73282184 0.3664109
[4,] 0.76430967 0.47138065 0.2356903
[5,] 0.67499369 0.65001262 0.3250063
[6,] 0.64612042 0.70775916 0.3538796
[7,] 0.64074331 0.71851338 0.3592567
[8,] 0.59257034 0.81485932 0.4074297
[9,] 0.51398840 0.97202319 0.4860116
[10,] 0.43235500 0.86471001 0.5676450
[11,] 0.46794118 0.93588236 0.5320588
[12,] 0.52582340 0.94835321 0.4741766
[13,] 0.47973902 0.95947805 0.5202610
[14,] 0.45588608 0.91177216 0.5441139
[15,] 0.39514337 0.79028675 0.6048566
[16,] 0.46699336 0.93398672 0.5330066
[17,] 0.70356535 0.59286930 0.2964346
[18,] 0.65468779 0.69062443 0.3453122
[19,] 0.63126776 0.73746447 0.3687322
[20,] 0.60546014 0.78907972 0.3945399
[21,] 0.55200305 0.89599391 0.4479970
[22,] 0.55916019 0.88167961 0.4408398
[23,] 0.75723468 0.48553063 0.2427653
[24,] 0.73638502 0.52722995 0.2636150
[25,] 0.71121381 0.57757239 0.2887862
[26,] 0.66317671 0.67364659 0.3368233
[27,] 0.64784528 0.70430944 0.3521547
[28,] 0.64637349 0.70725301 0.3536265
[29,] 0.60122012 0.79755977 0.3987799
[30,] 0.66154094 0.67691813 0.3384591
[31,] 0.61369747 0.77260507 0.3863025
[32,] 0.59342430 0.81315141 0.4065757
[33,] 0.54428433 0.91143134 0.4557157
[34,] 0.51929521 0.96140958 0.4807048
[35,] 0.47321817 0.94643634 0.5267818
[36,] 0.53349807 0.93300386 0.4665019
[37,] 0.49895907 0.99791813 0.5010409
[38,] 0.45136211 0.90272421 0.5486379
[39,] 0.42240912 0.84481825 0.5775909
[40,] 0.37548917 0.75097834 0.6245108
[41,] 0.37614950 0.75229900 0.6238505
[42,] 0.43594860 0.87189720 0.5640514
[43,] 0.39966534 0.79933069 0.6003347
[44,] 0.36735506 0.73471013 0.6326449
[45,] 0.33608896 0.67217792 0.6639110
[46,] 0.30233602 0.60467204 0.6976640
[47,] 0.30033766 0.60067531 0.6996623
[48,] 0.31906857 0.63813714 0.6809314
[49,] 0.43252774 0.86505548 0.5674723
[50,] 0.39769216 0.79538432 0.6023078
[51,] 0.35786398 0.71572796 0.6421360
[52,] 0.32163408 0.64326816 0.6783659
[53,] 0.31449047 0.62898094 0.6855095
[54,] 0.28426784 0.56853568 0.7157322
[55,] 0.29362024 0.58724047 0.7063798
[56,] 0.25908960 0.51817920 0.7409104
[57,] 0.22773858 0.45547717 0.7722614
[58,] 0.20241068 0.40482136 0.7975893
[59,] 0.19231011 0.38462021 0.8076899
[60,] 0.17651605 0.35303211 0.8234839
[61,] 0.15198777 0.30397554 0.8480122
[62,] 0.14673053 0.29346107 0.8532695
[63,] 0.17242291 0.34484583 0.8275771
[64,] 0.16339878 0.32679757 0.8366012
[65,] 0.14013370 0.28026741 0.8598663
[66,] 0.12381507 0.24763013 0.8761849
[67,] 0.18525059 0.37050118 0.8147494
[68,] 0.16186007 0.32372014 0.8381399
[69,] 0.13916957 0.27833914 0.8608304
[70,] 0.19806307 0.39612614 0.8019369
[71,] 0.22901538 0.45803077 0.7709846
[72,] 0.21620923 0.43241847 0.7837908
[73,] 0.19152549 0.38305097 0.8084745
[74,] 0.18162525 0.36325050 0.8183748
[75,] 0.17424650 0.34849301 0.8257535
[76,] 0.15222497 0.30444993 0.8477750
[77,] 0.13766434 0.27532868 0.8623357
[78,] 0.12268295 0.24536590 0.8773170
[79,] 0.13904998 0.27809996 0.8609500
[80,] 0.11866413 0.23732826 0.8813359
[81,] 0.13400468 0.26800935 0.8659953
[82,] 0.12479989 0.24959979 0.8752001
[83,] 0.11213853 0.22427706 0.8878615
[84,] 0.09736154 0.19472308 0.9026385
[85,] 0.10954222 0.21908444 0.8904578
[86,] 0.11376304 0.22752608 0.8862370
[87,] 0.10545017 0.21090034 0.8945498
[88,] 0.09332819 0.18665639 0.9066718
[89,] 0.08472284 0.16944568 0.9152772
[90,] 0.08618471 0.17236941 0.9138153
[91,] 0.07897696 0.15795391 0.9210230
[92,] 0.07320461 0.14640922 0.9267954
[93,] 0.06113542 0.12227084 0.9388646
[94,] 0.05170319 0.10340638 0.9482968
[95,] 0.05005941 0.10011881 0.9499406
[96,] 0.09248334 0.18496667 0.9075167
[97,] 0.08830156 0.17660312 0.9116984
[98,] 0.10709036 0.21418072 0.8929096
[99,] 0.09522567 0.19045134 0.9047743
[100,] 0.14506777 0.29013554 0.8549322
[101,] 0.16620018 0.33240037 0.8337998
[102,] 0.16180967 0.32361934 0.8381903
[103,] 0.19905469 0.39810937 0.8009453
[104,] 0.18582111 0.37164222 0.8141789
[105,] 0.17279145 0.34558289 0.8272086
[106,] 0.16741661 0.33483322 0.8325834
[107,] 0.16930578 0.33861155 0.8306942
[108,] 0.16510955 0.33021910 0.8348905
[109,] 0.15009580 0.30019161 0.8499042
[110,] 0.14231905 0.28463810 0.8576810
[111,] 0.13089650 0.26179301 0.8691035
[112,] 0.11717448 0.23434896 0.8828255
[113,] 0.10320724 0.20641447 0.8967928
[114,] 0.08856312 0.17712625 0.9114369
[115,] 0.08333889 0.16667779 0.9166611
[116,] 0.08109029 0.16218059 0.9189097
[117,] 0.09727837 0.19455673 0.9027216
[118,] 0.17642530 0.35285060 0.8235747
[119,] 0.17359146 0.34718292 0.8264085
[120,] 0.15433982 0.30867964 0.8456602
[121,] 0.14040551 0.28081102 0.8595945
[122,] 0.16443997 0.32887994 0.8355600
[123,] 0.17992151 0.35984301 0.8200785
[124,] 0.22516551 0.45033101 0.7748345
[125,] 0.20069332 0.40138664 0.7993067
[126,] 0.17875668 0.35751336 0.8212433
[127,] 0.15877436 0.31754872 0.8412256
[128,] 0.13973645 0.27947290 0.8602636
[129,] 0.15591687 0.31183374 0.8440831
[130,] 0.13605409 0.27210818 0.8639459
[131,] 0.13856514 0.27713027 0.8614349
[132,] 0.12839327 0.25678655 0.8716067
[133,] 0.16478738 0.32957476 0.8352126
[134,] 0.19290779 0.38581557 0.8070922
[135,] 0.18074085 0.36148170 0.8192591
[136,] 0.26208274 0.52416549 0.7379173
[137,] 0.23950912 0.47901825 0.7604909
[138,] 0.21425332 0.42850664 0.7857467
[139,] 0.19175335 0.38350670 0.8082467
[140,] 0.17627420 0.35254839 0.8237258
[141,] 0.15611729 0.31223458 0.8438827
[142,] 0.23607096 0.47214191 0.7639290
[143,] 0.21849982 0.43699964 0.7815002
[144,] 0.19898018 0.39796035 0.8010198
[145,] 0.21172386 0.42344772 0.7882761
[146,] 0.18721674 0.37443348 0.8127833
[147,] 0.18303712 0.36607424 0.8169629
[148,] 0.18975033 0.37950066 0.8102497
[149,] 0.18564308 0.37128615 0.8143569
[150,] 0.16462289 0.32924577 0.8353771
[151,] 0.16774226 0.33548452 0.8322577
[152,] 0.15263429 0.30526857 0.8473657
[153,] 0.14746899 0.29493798 0.8525310
[154,] 0.13685561 0.27371121 0.8631444
[155,] 0.17044927 0.34089854 0.8295507
[156,] 0.15616741 0.31233482 0.8438326
[157,] 0.25541228 0.51082455 0.7445877
[158,] 0.26065044 0.52130088 0.7393496
[159,] 0.26061797 0.52123593 0.7393820
[160,] 0.23828708 0.47657416 0.7617129
[161,] 0.36044007 0.72088015 0.6395599
[162,] 0.39345586 0.78691171 0.6065441
[163,] 0.35797696 0.71595391 0.6420230
[164,] 0.45740226 0.91480451 0.5425977
[165,] 0.42410746 0.84821492 0.5758925
[166,] 0.49299138 0.98598276 0.5070086
[167,] 0.46948376 0.93896752 0.5305162
[168,] 0.46725849 0.93451698 0.5327415
[169,] 0.43566039 0.87132078 0.5643396
[170,] 0.40852284 0.81704568 0.5914772
[171,] 0.40192313 0.80384627 0.5980769
[172,] 0.36625893 0.73251786 0.6337411
[173,] 0.35677068 0.71354137 0.6432293
[174,] 0.38287162 0.76574324 0.6171284
[175,] 0.44950455 0.89900910 0.5504955
[176,] 0.67033980 0.65932041 0.3296602
[177,] 0.65233566 0.69532869 0.3476643
[178,] 0.63698962 0.72602077 0.3630104
[179,] 0.76984384 0.46031233 0.2301562
[180,] 0.75129514 0.49740972 0.2487049
[181,] 0.76096288 0.47807423 0.2390371
[182,] 0.73157447 0.53685105 0.2684255
[183,] 0.70124421 0.59751158 0.2987558
[184,] 0.67421203 0.65157593 0.3257880
[185,] 0.65157735 0.69684530 0.3484227
[186,] 0.61504955 0.76990090 0.3849505
[187,] 0.57529148 0.84941703 0.4247085
[188,] 0.55431771 0.89136457 0.4456823
[189,] 0.52976782 0.94046437 0.4702322
[190,] 0.48742687 0.97485375 0.5125731
[191,] 0.56156936 0.87686128 0.4384306
[192,] 0.53399078 0.93201845 0.4660092
[193,] 0.68054954 0.63890091 0.3194505
[194,] 0.67513030 0.64973940 0.3248697
[195,] 0.63474966 0.73050068 0.3652503
[196,] 0.59466808 0.81066385 0.4053319
[197,] 0.55641544 0.88716913 0.4435846
[198,] 0.52383720 0.95232560 0.4761628
[199,] 0.48151653 0.96303305 0.5184835
[200,] 0.45349009 0.90698018 0.5465099
[201,] 0.41711224 0.83422449 0.5828878
[202,] 0.37327788 0.74655577 0.6267221
[203,] 0.33339572 0.66679144 0.6666043
[204,] 0.39618008 0.79236017 0.6038199
[205,] 0.38499064 0.76998128 0.6150094
[206,] 0.34127986 0.68255972 0.6587201
[207,] 0.30433466 0.60866933 0.6956653
[208,] 0.27685651 0.55371301 0.7231435
[209,] 0.30187098 0.60374196 0.6981290
[210,] 0.27027111 0.54054222 0.7297289
[211,] 0.24985686 0.49971372 0.7501431
[212,] 0.21956998 0.43913996 0.7804300
[213,] 0.23546424 0.47092847 0.7645358
[214,] 0.19798928 0.39597855 0.8020107
[215,] 0.17200824 0.34401648 0.8279918
[216,] 0.26241466 0.52482932 0.7375853
[217,] 0.23815333 0.47630666 0.7618467
[218,] 0.21156169 0.42312338 0.7884383
[219,] 0.22684463 0.45368926 0.7731554
[220,] 0.18846992 0.37693983 0.8115301
[221,] 0.15330834 0.30661668 0.8466917
[222,] 0.14614819 0.29229638 0.8538518
[223,] 0.26550005 0.53100010 0.7344999
[224,] 0.23981800 0.47963599 0.7601820
[225,] 0.31705643 0.63411286 0.6829436
[226,] 0.27051917 0.54103834 0.7294808
[227,] 0.30029862 0.60059723 0.6997014
[228,] 0.27337348 0.54674696 0.7266265
[229,] 0.37568310 0.75136621 0.6243169
[230,] 0.32019821 0.64039642 0.6798018
[231,] 0.51743182 0.96513636 0.4825682
[232,] 0.58404786 0.83190428 0.4159521
[233,] 0.50991760 0.98016480 0.4900824
[234,] 0.43934701 0.87869402 0.5606530
[235,] 0.38993142 0.77986284 0.6100686
[236,] 0.39180224 0.78360448 0.6081978
[237,] 0.61041011 0.77917977 0.3895899
[238,] 0.65459335 0.69081329 0.3454066
[239,] 0.62906770 0.74186460 0.3709323
[240,] 0.79946503 0.40106994 0.2005350
[241,] 0.80688890 0.38622219 0.1931111
[242,] 0.79695271 0.40609458 0.2030473
[243,] 0.82404174 0.35191652 0.1759583
[244,] 0.70330722 0.59338557 0.2966928
[245,] 0.55219914 0.89560173 0.4478009
> postscript(file="/var/wessaorg/rcomp/tmp/14p2s1384463353.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2br6k1384463353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32xyx1384463353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4xw9t1384463353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5g2ci1384463353.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 = 264
Frequency = 1
1 2 3 4 5
5.131123526 5.037620859 -4.901728293 -2.489689371 -1.313619836
6 7 8 9 10
2.690433855 6.190113707 -1.626574908 0.943474200 0.846499598
11 12 13 14 15
4.515777108 1.309461621 3.453624312 3.149128494 -3.457336006
16 17 18 19 20
-1.731411888 2.768237260 1.316310446 2.575012401 -1.528601034
21 22 23 24 25
-2.310513933 -2.104943391 3.006457265 0.493151272 5.105546940
26 27 28 29 30
7.597671514 1.491887355 -2.882938377 -0.623346732 -0.014644862
31 32 33 34 35
-2.540406896 -5.813326878 3.787768576 -2.423532349 1.822691092
36 37 38 39 40
-2.150796193 -2.812758325 2.027265409 -4.277362688 1.105534548
41 42 43 44 45
3.314078607 0.253014328 4.178689811 1.755643169 6.279735311
46 47 48 49 50
2.620288810 0.695577571 -1.561391904 0.323679806 4.508216474
51 52 53 54 55
-4.546390834 -0.215812580 -1.098372581 -3.009560185 -0.642942910
56 57 58 59 60
2.439032255 4.533957549 -5.757721350 1.918660253 0.977987048
61 62 63 64 65
-0.334359703 3.832655006 -0.825792395 -3.285573056 1.171010853
66 67 68 69 70
-0.809296726 1.353443035 -0.691737156 2.132707189 -0.609226475
71 72 73 74 75
2.618525807 -4.784385350 -2.659448501 0.525485797 2.656348756
76 77 78 79 80
6.936935131 1.097894306 1.119365605 -5.156556537 5.774346157
81 82 83 84 85
-2.217423448 -1.219381930 3.118130947 -2.427863415 0.005732583
86 87 88 89 90
1.613382588 -1.365670540 -4.036178314 0.028169711 -4.444226179
91 92 93 94 95
2.796683109 -2.435621484 0.567448501 -3.433591121 3.774185468
96 97 98 99 100
2.364581875 2.139155470 2.426719511 -3.385077295 2.430162337
101 102 103 104 105
2.479829730 -0.224547959 0.214882648 -3.294762524 7.306779524
106 107 108 109 110
2.821190152 4.641880343 1.846459903 6.427006627 -4.547908823
111 112 113 114 115
-2.608472697 -5.890005807 0.468944508 2.339263506 2.894393454
116 117 118 119 120
-3.744346986 -3.149027321 -1.951304432 2.620532907 -2.139043463
121 122 123 124 125
-0.258413309 1.047013436 0.235504521 2.650936155 -3.096988248
126 127 128 129 130
4.881518455 7.269725000 -3.090734633 1.080511023 -1.686250356
131 132 133 134 135
-4.572932529 4.355324611 -5.397631623 0.610963796 -0.529148686
136 137 138 139 140
0.183459134 -1.044060469 -4.361840225 0.665144731 -3.449738973
141 142 143 144 145
-2.131261026 -5.688345091 -5.307997994 1.507594292 6.614289855
146 147 148 149 150
0.367069705 0.891120512 -1.138849714 2.129151095 1.197560478
151 152 153 154 155
6.645801280 -1.985723551 -1.206150326 4.437083125 0.012045469
156 157 158 159 160
2.796683109 -3.363903790 -3.090734633 0.229830022 -3.465304413
161 162 163 164 165
-1.269059126 -3.070262367 0.676543746 4.197649610 -1.788540151
166 167 168 169 170
-7.295934262 -4.069196398 -4.079290282 -1.810266898 -7.614825692
171 172 173 174 175
5.160904164 0.139895479 6.496874775 -0.456737126 4.183376172
176 177 178 179 180
-2.542296545 3.608648468 -1.790517225 -2.165190160 3.115688056
181 182 183 184 185
0.879460532 2.585653605 -4.742706511 4.325735652 -10.282896735
186 187 188 189 190
-2.516765062 2.037519109 6.065076945 -2.637093435 -2.255953626
191 192 193 194 195
-0.892815306 0.995578532 -1.273771459 -0.573321116 -1.355401680
196 197 198 199 200
0.759731862 1.462273448 -2.754313042 0.216977886 -7.077264403
201 202 203 204 205
-2.291005368 -8.257919354 1.333243341 0.617699787 -1.187393123
206 207 208 209 210
-1.243242754 -1.319423334 0.181585157 2.162707901 0.030340736
211 212 213 214 215
0.588278922 1.572204148 -4.370145946 -3.259183179 0.815368965
216 217 218 219 220
-2.405267539 -2.235269517 4.339679044 2.657326052 -0.969943641
221 222 223 224 225
1.165587749 3.518739225 -0.013051923 -2.632119602 1.907939324
226 227 228 229 230
-3.071360321 -2.495877628 -0.403449011 1.067291036 -0.490297287
231 232 233 234 235
1.738070810 -2.922901000 -2.222317198 7.001408131 0.261304137
236 237 238 239 240
3.288029138 4.547825645 4.756832903 -3.830262708 4.765994753
241 242 243 244 245
-6.481859706 1.003448989 -2.646037761 1.845208656 3.536770461
246 247 248 249 250
-4.900227932 -2.600780716 3.462612898 -8.745259466 -6.100441654
251 252 253 254 255
-2.934132864 0.118655374 1.815077056 -0.404266046 3.271300831
256 257 258 259 260
0.151569061 0.446725974 -1.822224143 2.836726318 2.434731165
261 262 263 264
-5.337455419 -2.115133668 5.973424887 -4.241979861
> postscript(file="/var/wessaorg/rcomp/tmp/6njll1384463353.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 5.131123526 NA
1 5.037620859 5.131123526
2 -4.901728293 5.037620859
3 -2.489689371 -4.901728293
4 -1.313619836 -2.489689371
5 2.690433855 -1.313619836
6 6.190113707 2.690433855
7 -1.626574908 6.190113707
8 0.943474200 -1.626574908
9 0.846499598 0.943474200
10 4.515777108 0.846499598
11 1.309461621 4.515777108
12 3.453624312 1.309461621
13 3.149128494 3.453624312
14 -3.457336006 3.149128494
15 -1.731411888 -3.457336006
16 2.768237260 -1.731411888
17 1.316310446 2.768237260
18 2.575012401 1.316310446
19 -1.528601034 2.575012401
20 -2.310513933 -1.528601034
21 -2.104943391 -2.310513933
22 3.006457265 -2.104943391
23 0.493151272 3.006457265
24 5.105546940 0.493151272
25 7.597671514 5.105546940
26 1.491887355 7.597671514
27 -2.882938377 1.491887355
28 -0.623346732 -2.882938377
29 -0.014644862 -0.623346732
30 -2.540406896 -0.014644862
31 -5.813326878 -2.540406896
32 3.787768576 -5.813326878
33 -2.423532349 3.787768576
34 1.822691092 -2.423532349
35 -2.150796193 1.822691092
36 -2.812758325 -2.150796193
37 2.027265409 -2.812758325
38 -4.277362688 2.027265409
39 1.105534548 -4.277362688
40 3.314078607 1.105534548
41 0.253014328 3.314078607
42 4.178689811 0.253014328
43 1.755643169 4.178689811
44 6.279735311 1.755643169
45 2.620288810 6.279735311
46 0.695577571 2.620288810
47 -1.561391904 0.695577571
48 0.323679806 -1.561391904
49 4.508216474 0.323679806
50 -4.546390834 4.508216474
51 -0.215812580 -4.546390834
52 -1.098372581 -0.215812580
53 -3.009560185 -1.098372581
54 -0.642942910 -3.009560185
55 2.439032255 -0.642942910
56 4.533957549 2.439032255
57 -5.757721350 4.533957549
58 1.918660253 -5.757721350
59 0.977987048 1.918660253
60 -0.334359703 0.977987048
61 3.832655006 -0.334359703
62 -0.825792395 3.832655006
63 -3.285573056 -0.825792395
64 1.171010853 -3.285573056
65 -0.809296726 1.171010853
66 1.353443035 -0.809296726
67 -0.691737156 1.353443035
68 2.132707189 -0.691737156
69 -0.609226475 2.132707189
70 2.618525807 -0.609226475
71 -4.784385350 2.618525807
72 -2.659448501 -4.784385350
73 0.525485797 -2.659448501
74 2.656348756 0.525485797
75 6.936935131 2.656348756
76 1.097894306 6.936935131
77 1.119365605 1.097894306
78 -5.156556537 1.119365605
79 5.774346157 -5.156556537
80 -2.217423448 5.774346157
81 -1.219381930 -2.217423448
82 3.118130947 -1.219381930
83 -2.427863415 3.118130947
84 0.005732583 -2.427863415
85 1.613382588 0.005732583
86 -1.365670540 1.613382588
87 -4.036178314 -1.365670540
88 0.028169711 -4.036178314
89 -4.444226179 0.028169711
90 2.796683109 -4.444226179
91 -2.435621484 2.796683109
92 0.567448501 -2.435621484
93 -3.433591121 0.567448501
94 3.774185468 -3.433591121
95 2.364581875 3.774185468
96 2.139155470 2.364581875
97 2.426719511 2.139155470
98 -3.385077295 2.426719511
99 2.430162337 -3.385077295
100 2.479829730 2.430162337
101 -0.224547959 2.479829730
102 0.214882648 -0.224547959
103 -3.294762524 0.214882648
104 7.306779524 -3.294762524
105 2.821190152 7.306779524
106 4.641880343 2.821190152
107 1.846459903 4.641880343
108 6.427006627 1.846459903
109 -4.547908823 6.427006627
110 -2.608472697 -4.547908823
111 -5.890005807 -2.608472697
112 0.468944508 -5.890005807
113 2.339263506 0.468944508
114 2.894393454 2.339263506
115 -3.744346986 2.894393454
116 -3.149027321 -3.744346986
117 -1.951304432 -3.149027321
118 2.620532907 -1.951304432
119 -2.139043463 2.620532907
120 -0.258413309 -2.139043463
121 1.047013436 -0.258413309
122 0.235504521 1.047013436
123 2.650936155 0.235504521
124 -3.096988248 2.650936155
125 4.881518455 -3.096988248
126 7.269725000 4.881518455
127 -3.090734633 7.269725000
128 1.080511023 -3.090734633
129 -1.686250356 1.080511023
130 -4.572932529 -1.686250356
131 4.355324611 -4.572932529
132 -5.397631623 4.355324611
133 0.610963796 -5.397631623
134 -0.529148686 0.610963796
135 0.183459134 -0.529148686
136 -1.044060469 0.183459134
137 -4.361840225 -1.044060469
138 0.665144731 -4.361840225
139 -3.449738973 0.665144731
140 -2.131261026 -3.449738973
141 -5.688345091 -2.131261026
142 -5.307997994 -5.688345091
143 1.507594292 -5.307997994
144 6.614289855 1.507594292
145 0.367069705 6.614289855
146 0.891120512 0.367069705
147 -1.138849714 0.891120512
148 2.129151095 -1.138849714
149 1.197560478 2.129151095
150 6.645801280 1.197560478
151 -1.985723551 6.645801280
152 -1.206150326 -1.985723551
153 4.437083125 -1.206150326
154 0.012045469 4.437083125
155 2.796683109 0.012045469
156 -3.363903790 2.796683109
157 -3.090734633 -3.363903790
158 0.229830022 -3.090734633
159 -3.465304413 0.229830022
160 -1.269059126 -3.465304413
161 -3.070262367 -1.269059126
162 0.676543746 -3.070262367
163 4.197649610 0.676543746
164 -1.788540151 4.197649610
165 -7.295934262 -1.788540151
166 -4.069196398 -7.295934262
167 -4.079290282 -4.069196398
168 -1.810266898 -4.079290282
169 -7.614825692 -1.810266898
170 5.160904164 -7.614825692
171 0.139895479 5.160904164
172 6.496874775 0.139895479
173 -0.456737126 6.496874775
174 4.183376172 -0.456737126
175 -2.542296545 4.183376172
176 3.608648468 -2.542296545
177 -1.790517225 3.608648468
178 -2.165190160 -1.790517225
179 3.115688056 -2.165190160
180 0.879460532 3.115688056
181 2.585653605 0.879460532
182 -4.742706511 2.585653605
183 4.325735652 -4.742706511
184 -10.282896735 4.325735652
185 -2.516765062 -10.282896735
186 2.037519109 -2.516765062
187 6.065076945 2.037519109
188 -2.637093435 6.065076945
189 -2.255953626 -2.637093435
190 -0.892815306 -2.255953626
191 0.995578532 -0.892815306
192 -1.273771459 0.995578532
193 -0.573321116 -1.273771459
194 -1.355401680 -0.573321116
195 0.759731862 -1.355401680
196 1.462273448 0.759731862
197 -2.754313042 1.462273448
198 0.216977886 -2.754313042
199 -7.077264403 0.216977886
200 -2.291005368 -7.077264403
201 -8.257919354 -2.291005368
202 1.333243341 -8.257919354
203 0.617699787 1.333243341
204 -1.187393123 0.617699787
205 -1.243242754 -1.187393123
206 -1.319423334 -1.243242754
207 0.181585157 -1.319423334
208 2.162707901 0.181585157
209 0.030340736 2.162707901
210 0.588278922 0.030340736
211 1.572204148 0.588278922
212 -4.370145946 1.572204148
213 -3.259183179 -4.370145946
214 0.815368965 -3.259183179
215 -2.405267539 0.815368965
216 -2.235269517 -2.405267539
217 4.339679044 -2.235269517
218 2.657326052 4.339679044
219 -0.969943641 2.657326052
220 1.165587749 -0.969943641
221 3.518739225 1.165587749
222 -0.013051923 3.518739225
223 -2.632119602 -0.013051923
224 1.907939324 -2.632119602
225 -3.071360321 1.907939324
226 -2.495877628 -3.071360321
227 -0.403449011 -2.495877628
228 1.067291036 -0.403449011
229 -0.490297287 1.067291036
230 1.738070810 -0.490297287
231 -2.922901000 1.738070810
232 -2.222317198 -2.922901000
233 7.001408131 -2.222317198
234 0.261304137 7.001408131
235 3.288029138 0.261304137
236 4.547825645 3.288029138
237 4.756832903 4.547825645
238 -3.830262708 4.756832903
239 4.765994753 -3.830262708
240 -6.481859706 4.765994753
241 1.003448989 -6.481859706
242 -2.646037761 1.003448989
243 1.845208656 -2.646037761
244 3.536770461 1.845208656
245 -4.900227932 3.536770461
246 -2.600780716 -4.900227932
247 3.462612898 -2.600780716
248 -8.745259466 3.462612898
249 -6.100441654 -8.745259466
250 -2.934132864 -6.100441654
251 0.118655374 -2.934132864
252 1.815077056 0.118655374
253 -0.404266046 1.815077056
254 3.271300831 -0.404266046
255 0.151569061 3.271300831
256 0.446725974 0.151569061
257 -1.822224143 0.446725974
258 2.836726318 -1.822224143
259 2.434731165 2.836726318
260 -5.337455419 2.434731165
261 -2.115133668 -5.337455419
262 5.973424887 -2.115133668
263 -4.241979861 5.973424887
264 NA -4.241979861
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.037620859 5.131123526
[2,] -4.901728293 5.037620859
[3,] -2.489689371 -4.901728293
[4,] -1.313619836 -2.489689371
[5,] 2.690433855 -1.313619836
[6,] 6.190113707 2.690433855
[7,] -1.626574908 6.190113707
[8,] 0.943474200 -1.626574908
[9,] 0.846499598 0.943474200
[10,] 4.515777108 0.846499598
[11,] 1.309461621 4.515777108
[12,] 3.453624312 1.309461621
[13,] 3.149128494 3.453624312
[14,] -3.457336006 3.149128494
[15,] -1.731411888 -3.457336006
[16,] 2.768237260 -1.731411888
[17,] 1.316310446 2.768237260
[18,] 2.575012401 1.316310446
[19,] -1.528601034 2.575012401
[20,] -2.310513933 -1.528601034
[21,] -2.104943391 -2.310513933
[22,] 3.006457265 -2.104943391
[23,] 0.493151272 3.006457265
[24,] 5.105546940 0.493151272
[25,] 7.597671514 5.105546940
[26,] 1.491887355 7.597671514
[27,] -2.882938377 1.491887355
[28,] -0.623346732 -2.882938377
[29,] -0.014644862 -0.623346732
[30,] -2.540406896 -0.014644862
[31,] -5.813326878 -2.540406896
[32,] 3.787768576 -5.813326878
[33,] -2.423532349 3.787768576
[34,] 1.822691092 -2.423532349
[35,] -2.150796193 1.822691092
[36,] -2.812758325 -2.150796193
[37,] 2.027265409 -2.812758325
[38,] -4.277362688 2.027265409
[39,] 1.105534548 -4.277362688
[40,] 3.314078607 1.105534548
[41,] 0.253014328 3.314078607
[42,] 4.178689811 0.253014328
[43,] 1.755643169 4.178689811
[44,] 6.279735311 1.755643169
[45,] 2.620288810 6.279735311
[46,] 0.695577571 2.620288810
[47,] -1.561391904 0.695577571
[48,] 0.323679806 -1.561391904
[49,] 4.508216474 0.323679806
[50,] -4.546390834 4.508216474
[51,] -0.215812580 -4.546390834
[52,] -1.098372581 -0.215812580
[53,] -3.009560185 -1.098372581
[54,] -0.642942910 -3.009560185
[55,] 2.439032255 -0.642942910
[56,] 4.533957549 2.439032255
[57,] -5.757721350 4.533957549
[58,] 1.918660253 -5.757721350
[59,] 0.977987048 1.918660253
[60,] -0.334359703 0.977987048
[61,] 3.832655006 -0.334359703
[62,] -0.825792395 3.832655006
[63,] -3.285573056 -0.825792395
[64,] 1.171010853 -3.285573056
[65,] -0.809296726 1.171010853
[66,] 1.353443035 -0.809296726
[67,] -0.691737156 1.353443035
[68,] 2.132707189 -0.691737156
[69,] -0.609226475 2.132707189
[70,] 2.618525807 -0.609226475
[71,] -4.784385350 2.618525807
[72,] -2.659448501 -4.784385350
[73,] 0.525485797 -2.659448501
[74,] 2.656348756 0.525485797
[75,] 6.936935131 2.656348756
[76,] 1.097894306 6.936935131
[77,] 1.119365605 1.097894306
[78,] -5.156556537 1.119365605
[79,] 5.774346157 -5.156556537
[80,] -2.217423448 5.774346157
[81,] -1.219381930 -2.217423448
[82,] 3.118130947 -1.219381930
[83,] -2.427863415 3.118130947
[84,] 0.005732583 -2.427863415
[85,] 1.613382588 0.005732583
[86,] -1.365670540 1.613382588
[87,] -4.036178314 -1.365670540
[88,] 0.028169711 -4.036178314
[89,] -4.444226179 0.028169711
[90,] 2.796683109 -4.444226179
[91,] -2.435621484 2.796683109
[92,] 0.567448501 -2.435621484
[93,] -3.433591121 0.567448501
[94,] 3.774185468 -3.433591121
[95,] 2.364581875 3.774185468
[96,] 2.139155470 2.364581875
[97,] 2.426719511 2.139155470
[98,] -3.385077295 2.426719511
[99,] 2.430162337 -3.385077295
[100,] 2.479829730 2.430162337
[101,] -0.224547959 2.479829730
[102,] 0.214882648 -0.224547959
[103,] -3.294762524 0.214882648
[104,] 7.306779524 -3.294762524
[105,] 2.821190152 7.306779524
[106,] 4.641880343 2.821190152
[107,] 1.846459903 4.641880343
[108,] 6.427006627 1.846459903
[109,] -4.547908823 6.427006627
[110,] -2.608472697 -4.547908823
[111,] -5.890005807 -2.608472697
[112,] 0.468944508 -5.890005807
[113,] 2.339263506 0.468944508
[114,] 2.894393454 2.339263506
[115,] -3.744346986 2.894393454
[116,] -3.149027321 -3.744346986
[117,] -1.951304432 -3.149027321
[118,] 2.620532907 -1.951304432
[119,] -2.139043463 2.620532907
[120,] -0.258413309 -2.139043463
[121,] 1.047013436 -0.258413309
[122,] 0.235504521 1.047013436
[123,] 2.650936155 0.235504521
[124,] -3.096988248 2.650936155
[125,] 4.881518455 -3.096988248
[126,] 7.269725000 4.881518455
[127,] -3.090734633 7.269725000
[128,] 1.080511023 -3.090734633
[129,] -1.686250356 1.080511023
[130,] -4.572932529 -1.686250356
[131,] 4.355324611 -4.572932529
[132,] -5.397631623 4.355324611
[133,] 0.610963796 -5.397631623
[134,] -0.529148686 0.610963796
[135,] 0.183459134 -0.529148686
[136,] -1.044060469 0.183459134
[137,] -4.361840225 -1.044060469
[138,] 0.665144731 -4.361840225
[139,] -3.449738973 0.665144731
[140,] -2.131261026 -3.449738973
[141,] -5.688345091 -2.131261026
[142,] -5.307997994 -5.688345091
[143,] 1.507594292 -5.307997994
[144,] 6.614289855 1.507594292
[145,] 0.367069705 6.614289855
[146,] 0.891120512 0.367069705
[147,] -1.138849714 0.891120512
[148,] 2.129151095 -1.138849714
[149,] 1.197560478 2.129151095
[150,] 6.645801280 1.197560478
[151,] -1.985723551 6.645801280
[152,] -1.206150326 -1.985723551
[153,] 4.437083125 -1.206150326
[154,] 0.012045469 4.437083125
[155,] 2.796683109 0.012045469
[156,] -3.363903790 2.796683109
[157,] -3.090734633 -3.363903790
[158,] 0.229830022 -3.090734633
[159,] -3.465304413 0.229830022
[160,] -1.269059126 -3.465304413
[161,] -3.070262367 -1.269059126
[162,] 0.676543746 -3.070262367
[163,] 4.197649610 0.676543746
[164,] -1.788540151 4.197649610
[165,] -7.295934262 -1.788540151
[166,] -4.069196398 -7.295934262
[167,] -4.079290282 -4.069196398
[168,] -1.810266898 -4.079290282
[169,] -7.614825692 -1.810266898
[170,] 5.160904164 -7.614825692
[171,] 0.139895479 5.160904164
[172,] 6.496874775 0.139895479
[173,] -0.456737126 6.496874775
[174,] 4.183376172 -0.456737126
[175,] -2.542296545 4.183376172
[176,] 3.608648468 -2.542296545
[177,] -1.790517225 3.608648468
[178,] -2.165190160 -1.790517225
[179,] 3.115688056 -2.165190160
[180,] 0.879460532 3.115688056
[181,] 2.585653605 0.879460532
[182,] -4.742706511 2.585653605
[183,] 4.325735652 -4.742706511
[184,] -10.282896735 4.325735652
[185,] -2.516765062 -10.282896735
[186,] 2.037519109 -2.516765062
[187,] 6.065076945 2.037519109
[188,] -2.637093435 6.065076945
[189,] -2.255953626 -2.637093435
[190,] -0.892815306 -2.255953626
[191,] 0.995578532 -0.892815306
[192,] -1.273771459 0.995578532
[193,] -0.573321116 -1.273771459
[194,] -1.355401680 -0.573321116
[195,] 0.759731862 -1.355401680
[196,] 1.462273448 0.759731862
[197,] -2.754313042 1.462273448
[198,] 0.216977886 -2.754313042
[199,] -7.077264403 0.216977886
[200,] -2.291005368 -7.077264403
[201,] -8.257919354 -2.291005368
[202,] 1.333243341 -8.257919354
[203,] 0.617699787 1.333243341
[204,] -1.187393123 0.617699787
[205,] -1.243242754 -1.187393123
[206,] -1.319423334 -1.243242754
[207,] 0.181585157 -1.319423334
[208,] 2.162707901 0.181585157
[209,] 0.030340736 2.162707901
[210,] 0.588278922 0.030340736
[211,] 1.572204148 0.588278922
[212,] -4.370145946 1.572204148
[213,] -3.259183179 -4.370145946
[214,] 0.815368965 -3.259183179
[215,] -2.405267539 0.815368965
[216,] -2.235269517 -2.405267539
[217,] 4.339679044 -2.235269517
[218,] 2.657326052 4.339679044
[219,] -0.969943641 2.657326052
[220,] 1.165587749 -0.969943641
[221,] 3.518739225 1.165587749
[222,] -0.013051923 3.518739225
[223,] -2.632119602 -0.013051923
[224,] 1.907939324 -2.632119602
[225,] -3.071360321 1.907939324
[226,] -2.495877628 -3.071360321
[227,] -0.403449011 -2.495877628
[228,] 1.067291036 -0.403449011
[229,] -0.490297287 1.067291036
[230,] 1.738070810 -0.490297287
[231,] -2.922901000 1.738070810
[232,] -2.222317198 -2.922901000
[233,] 7.001408131 -2.222317198
[234,] 0.261304137 7.001408131
[235,] 3.288029138 0.261304137
[236,] 4.547825645 3.288029138
[237,] 4.756832903 4.547825645
[238,] -3.830262708 4.756832903
[239,] 4.765994753 -3.830262708
[240,] -6.481859706 4.765994753
[241,] 1.003448989 -6.481859706
[242,] -2.646037761 1.003448989
[243,] 1.845208656 -2.646037761
[244,] 3.536770461 1.845208656
[245,] -4.900227932 3.536770461
[246,] -2.600780716 -4.900227932
[247,] 3.462612898 -2.600780716
[248,] -8.745259466 3.462612898
[249,] -6.100441654 -8.745259466
[250,] -2.934132864 -6.100441654
[251,] 0.118655374 -2.934132864
[252,] 1.815077056 0.118655374
[253,] -0.404266046 1.815077056
[254,] 3.271300831 -0.404266046
[255,] 0.151569061 3.271300831
[256,] 0.446725974 0.151569061
[257,] -1.822224143 0.446725974
[258,] 2.836726318 -1.822224143
[259,] 2.434731165 2.836726318
[260,] -5.337455419 2.434731165
[261,] -2.115133668 -5.337455419
[262,] 5.973424887 -2.115133668
[263,] -4.241979861 5.973424887
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.037620859 5.131123526
2 -4.901728293 5.037620859
3 -2.489689371 -4.901728293
4 -1.313619836 -2.489689371
5 2.690433855 -1.313619836
6 6.190113707 2.690433855
7 -1.626574908 6.190113707
8 0.943474200 -1.626574908
9 0.846499598 0.943474200
10 4.515777108 0.846499598
11 1.309461621 4.515777108
12 3.453624312 1.309461621
13 3.149128494 3.453624312
14 -3.457336006 3.149128494
15 -1.731411888 -3.457336006
16 2.768237260 -1.731411888
17 1.316310446 2.768237260
18 2.575012401 1.316310446
19 -1.528601034 2.575012401
20 -2.310513933 -1.528601034
21 -2.104943391 -2.310513933
22 3.006457265 -2.104943391
23 0.493151272 3.006457265
24 5.105546940 0.493151272
25 7.597671514 5.105546940
26 1.491887355 7.597671514
27 -2.882938377 1.491887355
28 -0.623346732 -2.882938377
29 -0.014644862 -0.623346732
30 -2.540406896 -0.014644862
31 -5.813326878 -2.540406896
32 3.787768576 -5.813326878
33 -2.423532349 3.787768576
34 1.822691092 -2.423532349
35 -2.150796193 1.822691092
36 -2.812758325 -2.150796193
37 2.027265409 -2.812758325
38 -4.277362688 2.027265409
39 1.105534548 -4.277362688
40 3.314078607 1.105534548
41 0.253014328 3.314078607
42 4.178689811 0.253014328
43 1.755643169 4.178689811
44 6.279735311 1.755643169
45 2.620288810 6.279735311
46 0.695577571 2.620288810
47 -1.561391904 0.695577571
48 0.323679806 -1.561391904
49 4.508216474 0.323679806
50 -4.546390834 4.508216474
51 -0.215812580 -4.546390834
52 -1.098372581 -0.215812580
53 -3.009560185 -1.098372581
54 -0.642942910 -3.009560185
55 2.439032255 -0.642942910
56 4.533957549 2.439032255
57 -5.757721350 4.533957549
58 1.918660253 -5.757721350
59 0.977987048 1.918660253
60 -0.334359703 0.977987048
61 3.832655006 -0.334359703
62 -0.825792395 3.832655006
63 -3.285573056 -0.825792395
64 1.171010853 -3.285573056
65 -0.809296726 1.171010853
66 1.353443035 -0.809296726
67 -0.691737156 1.353443035
68 2.132707189 -0.691737156
69 -0.609226475 2.132707189
70 2.618525807 -0.609226475
71 -4.784385350 2.618525807
72 -2.659448501 -4.784385350
73 0.525485797 -2.659448501
74 2.656348756 0.525485797
75 6.936935131 2.656348756
76 1.097894306 6.936935131
77 1.119365605 1.097894306
78 -5.156556537 1.119365605
79 5.774346157 -5.156556537
80 -2.217423448 5.774346157
81 -1.219381930 -2.217423448
82 3.118130947 -1.219381930
83 -2.427863415 3.118130947
84 0.005732583 -2.427863415
85 1.613382588 0.005732583
86 -1.365670540 1.613382588
87 -4.036178314 -1.365670540
88 0.028169711 -4.036178314
89 -4.444226179 0.028169711
90 2.796683109 -4.444226179
91 -2.435621484 2.796683109
92 0.567448501 -2.435621484
93 -3.433591121 0.567448501
94 3.774185468 -3.433591121
95 2.364581875 3.774185468
96 2.139155470 2.364581875
97 2.426719511 2.139155470
98 -3.385077295 2.426719511
99 2.430162337 -3.385077295
100 2.479829730 2.430162337
101 -0.224547959 2.479829730
102 0.214882648 -0.224547959
103 -3.294762524 0.214882648
104 7.306779524 -3.294762524
105 2.821190152 7.306779524
106 4.641880343 2.821190152
107 1.846459903 4.641880343
108 6.427006627 1.846459903
109 -4.547908823 6.427006627
110 -2.608472697 -4.547908823
111 -5.890005807 -2.608472697
112 0.468944508 -5.890005807
113 2.339263506 0.468944508
114 2.894393454 2.339263506
115 -3.744346986 2.894393454
116 -3.149027321 -3.744346986
117 -1.951304432 -3.149027321
118 2.620532907 -1.951304432
119 -2.139043463 2.620532907
120 -0.258413309 -2.139043463
121 1.047013436 -0.258413309
122 0.235504521 1.047013436
123 2.650936155 0.235504521
124 -3.096988248 2.650936155
125 4.881518455 -3.096988248
126 7.269725000 4.881518455
127 -3.090734633 7.269725000
128 1.080511023 -3.090734633
129 -1.686250356 1.080511023
130 -4.572932529 -1.686250356
131 4.355324611 -4.572932529
132 -5.397631623 4.355324611
133 0.610963796 -5.397631623
134 -0.529148686 0.610963796
135 0.183459134 -0.529148686
136 -1.044060469 0.183459134
137 -4.361840225 -1.044060469
138 0.665144731 -4.361840225
139 -3.449738973 0.665144731
140 -2.131261026 -3.449738973
141 -5.688345091 -2.131261026
142 -5.307997994 -5.688345091
143 1.507594292 -5.307997994
144 6.614289855 1.507594292
145 0.367069705 6.614289855
146 0.891120512 0.367069705
147 -1.138849714 0.891120512
148 2.129151095 -1.138849714
149 1.197560478 2.129151095
150 6.645801280 1.197560478
151 -1.985723551 6.645801280
152 -1.206150326 -1.985723551
153 4.437083125 -1.206150326
154 0.012045469 4.437083125
155 2.796683109 0.012045469
156 -3.363903790 2.796683109
157 -3.090734633 -3.363903790
158 0.229830022 -3.090734633
159 -3.465304413 0.229830022
160 -1.269059126 -3.465304413
161 -3.070262367 -1.269059126
162 0.676543746 -3.070262367
163 4.197649610 0.676543746
164 -1.788540151 4.197649610
165 -7.295934262 -1.788540151
166 -4.069196398 -7.295934262
167 -4.079290282 -4.069196398
168 -1.810266898 -4.079290282
169 -7.614825692 -1.810266898
170 5.160904164 -7.614825692
171 0.139895479 5.160904164
172 6.496874775 0.139895479
173 -0.456737126 6.496874775
174 4.183376172 -0.456737126
175 -2.542296545 4.183376172
176 3.608648468 -2.542296545
177 -1.790517225 3.608648468
178 -2.165190160 -1.790517225
179 3.115688056 -2.165190160
180 0.879460532 3.115688056
181 2.585653605 0.879460532
182 -4.742706511 2.585653605
183 4.325735652 -4.742706511
184 -10.282896735 4.325735652
185 -2.516765062 -10.282896735
186 2.037519109 -2.516765062
187 6.065076945 2.037519109
188 -2.637093435 6.065076945
189 -2.255953626 -2.637093435
190 -0.892815306 -2.255953626
191 0.995578532 -0.892815306
192 -1.273771459 0.995578532
193 -0.573321116 -1.273771459
194 -1.355401680 -0.573321116
195 0.759731862 -1.355401680
196 1.462273448 0.759731862
197 -2.754313042 1.462273448
198 0.216977886 -2.754313042
199 -7.077264403 0.216977886
200 -2.291005368 -7.077264403
201 -8.257919354 -2.291005368
202 1.333243341 -8.257919354
203 0.617699787 1.333243341
204 -1.187393123 0.617699787
205 -1.243242754 -1.187393123
206 -1.319423334 -1.243242754
207 0.181585157 -1.319423334
208 2.162707901 0.181585157
209 0.030340736 2.162707901
210 0.588278922 0.030340736
211 1.572204148 0.588278922
212 -4.370145946 1.572204148
213 -3.259183179 -4.370145946
214 0.815368965 -3.259183179
215 -2.405267539 0.815368965
216 -2.235269517 -2.405267539
217 4.339679044 -2.235269517
218 2.657326052 4.339679044
219 -0.969943641 2.657326052
220 1.165587749 -0.969943641
221 3.518739225 1.165587749
222 -0.013051923 3.518739225
223 -2.632119602 -0.013051923
224 1.907939324 -2.632119602
225 -3.071360321 1.907939324
226 -2.495877628 -3.071360321
227 -0.403449011 -2.495877628
228 1.067291036 -0.403449011
229 -0.490297287 1.067291036
230 1.738070810 -0.490297287
231 -2.922901000 1.738070810
232 -2.222317198 -2.922901000
233 7.001408131 -2.222317198
234 0.261304137 7.001408131
235 3.288029138 0.261304137
236 4.547825645 3.288029138
237 4.756832903 4.547825645
238 -3.830262708 4.756832903
239 4.765994753 -3.830262708
240 -6.481859706 4.765994753
241 1.003448989 -6.481859706
242 -2.646037761 1.003448989
243 1.845208656 -2.646037761
244 3.536770461 1.845208656
245 -4.900227932 3.536770461
246 -2.600780716 -4.900227932
247 3.462612898 -2.600780716
248 -8.745259466 3.462612898
249 -6.100441654 -8.745259466
250 -2.934132864 -6.100441654
251 0.118655374 -2.934132864
252 1.815077056 0.118655374
253 -0.404266046 1.815077056
254 3.271300831 -0.404266046
255 0.151569061 3.271300831
256 0.446725974 0.151569061
257 -1.822224143 0.446725974
258 2.836726318 -1.822224143
259 2.434731165 2.836726318
260 -5.337455419 2.434731165
261 -2.115133668 -5.337455419
262 5.973424887 -2.115133668
263 -4.241979861 5.973424887
> 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/7w9t11384463353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/81lm71384463353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/969611384463353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10bw6h1384463353.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/11dd1d1384463353.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12q16u1384463353.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/137ftx1384463353.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14kvhb1384463354.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15khdi1384463354.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/162ou71384463354.tab")
+ }
>
> try(system("convert tmp/14p2s1384463353.ps tmp/14p2s1384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/2br6k1384463353.ps tmp/2br6k1384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/32xyx1384463353.ps tmp/32xyx1384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xw9t1384463353.ps tmp/4xw9t1384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/5g2ci1384463353.ps tmp/5g2ci1384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/6njll1384463353.ps tmp/6njll1384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/7w9t11384463353.ps tmp/7w9t11384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/81lm71384463353.ps tmp/81lm71384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/969611384463353.ps tmp/969611384463353.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bw6h1384463353.ps tmp/10bw6h1384463353.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.052 2.135 14.176