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(12
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+ ,14
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+ ,13
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+ ,41
+ ,8
+ ,35
+ ,30
+ ,4
+ ,3
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+ ,52
+ ,12
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+ ,30
+ ,15
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+ ,12
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+ ,11
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+ ,7
+ ,7
+ ,62
+ ,39
+ ,12
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,72
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Depression'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Sport1'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Depression','Connected','Separate','Learning','Software','Happiness','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
Depression Connected Separate Learning Software Happiness Sport1 Sport2
1 12.0 41 38 13 12 14 53 32
2 11.0 39 32 16 11 18 83 51
3 14.0 30 35 19 15 11 66 42
4 12.0 31 33 15 6 12 67 41
5 21.0 34 37 14 13 16 76 46
6 12.0 35 29 13 10 18 78 47
7 22.0 39 31 19 12 14 53 37
8 11.0 34 36 15 14 14 80 49
9 10.0 36 35 14 12 15 74 45
10 13.0 37 38 15 9 15 76 47
11 10.0 38 31 16 10 17 79 49
12 8.0 36 34 16 12 19 54 33
13 15.0 38 35 16 12 10 67 42
14 14.0 39 38 16 11 16 54 33
15 10.0 33 37 17 15 18 87 53
16 14.0 32 33 15 12 14 58 36
17 14.0 36 32 15 10 14 75 45
18 11.0 38 38 20 12 17 88 54
19 10.0 39 38 18 11 14 64 41
20 13.0 32 32 16 12 16 57 36
21 9.5 32 33 16 11 18 66 41
22 14.0 31 31 16 12 11 68 44
23 12.0 39 38 19 13 14 54 33
24 14.0 37 39 16 11 12 56 37
25 11.0 39 32 17 12 17 86 52
26 9.0 41 32 17 13 9 80 47
27 11.0 36 35 16 10 16 76 43
28 15.0 33 37 15 14 14 69 44
29 14.0 33 33 16 12 15 78 45
30 13.0 34 33 14 10 11 67 44
31 9.0 31 31 15 12 16 80 49
32 15.0 27 32 12 8 13 54 33
33 10.0 37 31 14 10 17 71 43
34 11.0 34 37 16 12 15 84 54
35 13.0 34 30 14 12 14 74 42
36 8.0 32 33 10 7 16 71 44
37 20.0 29 31 10 9 9 63 37
38 12.0 36 33 14 12 15 71 43
39 10.0 29 31 16 10 17 76 46
40 10.0 35 33 16 10 13 69 42
41 9.0 37 32 16 10 15 74 45
42 14.0 34 33 14 12 16 75 44
43 8.0 38 32 20 15 16 54 33
44 14.0 35 33 14 10 12 52 31
45 11.0 38 28 14 10 15 69 42
46 13.0 37 35 11 12 11 68 40
47 9.0 38 39 14 13 15 65 43
48 11.0 33 34 15 11 15 75 46
49 15.0 36 38 16 11 17 74 42
50 11.0 38 32 14 12 13 75 45
51 10.0 32 38 16 14 16 72 44
52 14.0 32 30 14 10 14 67 40
53 18.0 32 33 12 12 11 63 37
54 14.0 34 38 16 13 12 62 46
55 11.0 32 32 9 5 12 63 36
56 14.5 37 35 14 6 15 76 47
57 13.0 39 34 16 12 16 74 45
58 9.0 29 34 16 12 15 67 42
59 10.0 37 36 15 11 12 73 43
60 15.0 35 34 16 10 12 70 43
61 20.0 30 28 12 7 8 53 32
62 12.0 38 34 16 12 13 77 45
63 12.0 34 35 16 14 11 80 48
64 14.0 31 35 14 11 14 52 31
65 13.0 34 31 16 12 15 54 33
66 11.0 35 37 17 13 10 80 49
67 17.0 36 35 18 14 11 66 42
68 12.0 30 27 18 11 12 73 41
69 13.0 39 40 12 12 15 63 38
70 14.0 35 37 16 12 15 69 42
71 13.0 38 36 10 8 14 67 44
72 15.0 31 38 14 11 16 54 33
73 13.0 34 39 18 14 15 81 48
74 10.0 38 41 18 14 15 69 40
75 11.0 34 27 16 12 13 84 50
76 19.0 39 30 17 9 12 80 49
77 13.0 37 37 16 13 17 70 43
78 17.0 34 31 16 11 13 69 44
79 13.0 28 31 13 12 15 77 47
80 9.0 37 27 16 12 13 54 33
81 11.0 33 36 16 12 15 79 46
82 9.0 35 37 16 12 15 71 45
83 12.0 37 33 15 12 16 73 43
84 12.0 32 34 15 11 15 72 44
85 13.0 33 31 16 10 14 77 47
86 13.0 38 39 14 9 15 75 45
87 12.0 33 34 16 12 14 69 42
88 15.0 29 32 16 12 13 54 33
89 22.0 33 33 15 12 7 70 43
90 13.0 31 36 12 9 17 73 46
91 15.0 36 32 17 15 13 54 33
92 13.0 35 41 16 12 15 77 46
93 15.0 32 28 15 12 14 82 48
94 12.5 29 30 13 12 13 80 47
95 11.0 39 36 16 10 16 80 47
96 16.0 37 35 16 13 12 69 43
97 11.0 35 31 16 9 14 78 46
98 11.0 37 34 16 12 17 81 48
99 10.0 32 36 14 10 15 76 46
100 10.0 38 36 16 14 17 76 45
101 16.0 37 35 16 11 12 73 45
102 12.0 36 37 20 15 16 85 52
103 11.0 32 28 15 11 11 66 42
104 16.0 33 39 16 11 15 79 47
105 19.0 40 32 13 12 9 68 41
106 11.0 38 35 17 12 16 76 47
107 16.0 41 39 16 12 15 71 43
108 15.0 36 35 16 11 10 54 33
109 24.0 43 42 12 7 10 46 30
110 14.0 30 34 16 12 15 85 52
111 15.0 31 33 16 14 11 74 44
112 11.0 32 41 17 11 13 88 55
113 15.0 32 33 13 11 14 38 11
114 12.0 37 34 12 10 18 76 47
115 10.0 37 32 18 13 16 86 53
116 14.0 33 40 14 13 14 54 33
117 13.0 34 40 14 8 14 67 44
118 9.0 33 35 13 11 14 69 42
119 15.0 38 36 16 12 14 90 55
120 15.0 33 37 13 11 12 54 33
121 14.0 31 27 16 13 14 76 46
122 11.0 38 39 13 12 15 89 54
123 8.0 37 38 16 14 15 76 47
124 11.0 36 31 15 13 15 73 45
125 11.0 31 33 16 15 13 79 47
126 8.0 39 32 15 10 17 90 55
127 10.0 44 39 17 11 17 74 44
128 11.0 33 36 15 9 19 81 53
129 13.0 35 33 12 11 15 72 44
130 11.0 32 33 16 10 13 71 42
131 20.0 28 32 10 11 9 66 40
132 10.0 40 37 16 8 15 77 46
133 15.0 27 30 12 11 15 65 40
134 12.0 37 38 14 12 15 74 46
135 14.0 32 29 15 12 16 85 53
136 23.0 28 22 13 9 11 54 33
137 14.0 34 35 15 11 14 63 42
138 16.0 30 35 11 10 11 54 35
139 11.0 35 34 12 8 15 64 40
140 12.0 31 35 11 9 13 69 41
141 10.0 32 34 16 8 15 54 33
142 14.0 30 37 15 9 16 84 51
143 12.0 30 35 17 15 14 86 53
144 12.0 31 23 16 11 15 77 46
145 11.0 40 31 10 8 16 89 55
146 12.0 32 27 18 13 16 76 47
147 13.0 36 36 13 12 11 60 38
148 11.0 32 31 16 12 12 75 46
149 19.0 35 32 13 9 9 73 46
150 12.0 38 39 10 7 16 85 53
151 17.0 42 37 15 13 13 79 47
152 9.0 34 38 16 9 16 71 41
153 12.0 35 39 16 6 12 72 44
154 19.0 38 34 14 8 9 69 43
155 18.0 33 31 10 8 13 78 51
156 15.0 36 32 17 15 13 54 33
157 14.0 32 37 13 6 14 69 43
158 11.0 33 36 15 9 19 81 53
159 9.0 34 32 16 11 13 84 51
160 18.0 32 38 12 8 12 84 50
161 16.0 34 36 13 8 13 69 46
162 24.0 27 26 13 10 10 66 43
163 14.0 31 26 12 8 14 81 47
164 20.0 38 33 17 14 16 82 50
165 18.0 34 39 15 10 10 72 43
166 23.0 24 30 10 8 11 54 33
167 12.0 30 33 14 11 14 78 48
168 14.0 26 25 11 12 12 74 44
169 16.0 34 38 13 12 9 82 50
170 18.0 27 37 16 12 9 73 41
171 20.0 37 31 12 5 11 55 34
172 12.0 36 37 16 12 16 72 44
173 12.0 41 35 12 10 9 78 47
174 17.0 29 25 9 7 13 59 35
175 13.0 36 28 12 12 16 72 44
176 9.0 32 35 15 11 13 78 44
177 16.0 37 33 12 8 9 68 43
178 18.0 30 30 12 9 12 69 41
179 10.0 31 31 14 10 16 67 41
180 14.0 38 37 12 9 11 74 42
181 11.0 36 36 16 12 14 54 33
182 9.0 35 30 11 6 13 67 41
183 11.0 31 36 19 15 15 70 44
184 10.0 38 32 15 12 14 80 48
185 11.0 22 28 8 12 16 89 55
186 19.0 32 36 16 12 13 76 44
187 14.0 36 34 17 11 14 74 43
188 12.0 39 31 12 7 15 87 52
189 14.0 28 28 11 7 13 54 30
190 21.0 32 36 11 5 11 61 39
191 13.0 32 36 14 12 11 38 11
192 10.0 38 40 16 12 14 75 44
193 15.0 32 33 12 3 15 69 42
194 16.0 35 37 16 11 11 62 41
195 14.0 32 32 13 10 15 72 44
196 12.0 37 38 15 12 12 70 44
197 19.0 34 31 16 9 14 79 48
198 15.0 33 37 16 12 14 87 53
199 19.0 33 33 14 9 8 62 37
200 13.0 26 32 16 12 13 77 44
201 17.0 30 30 16 12 9 69 44
202 12.0 24 30 14 10 15 69 40
203 11.0 34 31 11 9 17 75 42
204 14.0 34 32 12 12 13 54 35
205 11.0 33 34 15 8 15 72 43
206 13.0 34 36 15 11 15 74 45
207 12.0 35 37 16 11 14 85 55
208 15.0 35 36 16 12 16 52 31
209 14.0 36 33 11 10 13 70 44
210 12.0 34 33 15 10 16 84 50
211 17.0 34 33 12 12 9 64 40
212 11.0 41 44 12 12 16 84 53
213 18.0 32 39 15 11 11 87 54
214 13.0 30 32 15 8 10 79 49
215 17.0 35 35 16 12 11 67 40
216 13.0 28 25 14 10 15 65 41
217 11.0 33 35 17 11 17 85 52
218 12.0 39 34 14 10 14 83 52
219 22.0 36 35 13 8 8 61 36
220 14.0 36 39 15 12 15 82 52
221 12.0 35 33 13 12 11 76 46
222 12.0 38 36 14 10 16 58 31
223 17.0 33 32 15 12 10 72 44
224 9.0 31 32 12 9 15 72 44
225 21.0 34 36 13 9 9 38 11
226 10.0 32 36 8 6 16 78 46
227 11.0 31 32 14 10 19 54 33
228 12.0 33 34 14 9 12 63 34
229 23.0 34 33 11 9 8 66 42
230 13.0 34 35 12 9 11 70 43
231 12.0 34 30 13 6 14 71 43
232 16.0 33 38 10 10 9 67 44
233 9.0 32 34 16 6 15 58 36
234 17.0 41 33 18 14 13 72 46
235 9.0 34 32 13 10 16 72 44
236 14.0 36 31 11 10 11 70 43
237 17.0 37 30 4 6 12 76 50
238 13.0 36 27 13 12 13 50 33
239 11.0 29 31 16 12 10 72 43
240 12.0 37 30 10 7 11 72 44
241 10.0 27 32 12 8 12 88 53
242 19.0 35 35 12 11 8 53 34
243 16.0 28 28 10 3 12 58 35
244 16.0 35 33 13 6 12 66 40
245 14.0 37 31 15 10 15 82 53
246 20.0 29 35 12 8 11 69 42
247 15.0 32 35 14 9 13 68 43
248 23.0 36 32 10 9 14 44 29
249 20.0 19 21 12 8 10 56 36
250 16.0 21 20 12 9 12 53 30
251 14.0 31 34 11 7 15 70 42
252 17.0 33 32 10 7 13 78 47
253 11.0 36 34 12 6 13 71 44
254 13.0 33 32 16 9 13 72 45
255 17.0 37 33 12 10 12 68 44
256 15.0 34 33 14 11 12 67 43
257 21.0 35 37 16 12 9 75 43
258 18.0 31 32 14 8 9 62 40
259 15.0 37 34 13 11 15 67 41
260 8.0 35 30 4 3 10 83 52
261 12.0 27 30 15 11 14 64 38
262 12.0 34 38 11 12 15 68 41
263 22.0 40 36 11 7 7 62 39
264 12.0 29 32 14 9 14 72 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Happiness
29.948599 -0.040303 0.006017 -0.084651 -0.023489 -0.706983
Sport1 Sport2
-0.146226 0.142997
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.5387 -1.7907 -0.1608 1.6155 9.5366
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.948599 2.126358 14.084 < 2e-16 ***
Connected -0.040303 0.051123 -0.788 0.43122
Separate 0.006017 0.052407 0.115 0.90869
Learning -0.084651 0.091482 -0.925 0.35567
Software -0.023489 0.094201 -0.249 0.80329
Happiness -0.706983 0.073153 -9.664 < 2e-16 ***
Sport1 -0.146226 0.054745 -2.671 0.00805 **
Sport2 0.142997 0.082291 1.738 0.08347 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.763 on 256 degrees of freedom
Multiple R-squared: 0.3827, Adjusted R-squared: 0.3659
F-statistic: 22.68 on 7 and 256 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.44632790 0.89265581 0.55367210
[2,] 0.95223356 0.09553288 0.04776644
[3,] 0.94582073 0.10835855 0.05417927
[4,] 0.95977236 0.08045528 0.04022764
[5,] 0.93603921 0.12792157 0.06396079
[6,] 0.90125859 0.19748282 0.09874141
[7,] 0.95018537 0.09962927 0.04981463
[8,] 0.93221885 0.13556231 0.06778115
[9,] 0.95710822 0.08578356 0.04289178
[10,] 0.93760454 0.12479092 0.06239546
[11,] 0.92654164 0.14691671 0.07345836
[12,] 0.92181529 0.15636941 0.07818471
[13,] 0.89443652 0.21112696 0.10556348
[14,] 0.87507009 0.24985983 0.12492991
[15,] 0.83664619 0.32670761 0.16335381
[16,] 0.83772329 0.32455343 0.16227671
[17,] 0.84880106 0.30239788 0.15119894
[18,] 0.80896335 0.38207329 0.19103665
[19,] 0.84025738 0.31948524 0.15974262
[20,] 0.83076784 0.33846433 0.16923216
[21,] 0.83656834 0.32686331 0.16343166
[22,] 0.80529459 0.38941082 0.19470541
[23,] 0.77427168 0.45145664 0.22572832
[24,] 0.74978732 0.50042537 0.25021268
[25,] 0.71631048 0.56737904 0.28368952
[26,] 0.75068928 0.49862144 0.24931072
[27,] 0.83214166 0.33571667 0.16785834
[28,] 0.79701776 0.40596448 0.20298224
[29,] 0.76320415 0.47359170 0.23679585
[30,] 0.76282618 0.47434764 0.23717382
[31,] 0.75109268 0.49781465 0.24890732
[32,] 0.73753373 0.52493253 0.26246627
[33,] 0.79027893 0.41944215 0.20972107
[34,] 0.75505144 0.48989712 0.24494856
[35,] 0.71870577 0.56258846 0.28129423
[36,] 0.69286356 0.61427287 0.30713644
[37,] 0.74870277 0.50259446 0.25129723
[38,] 0.71485957 0.57028086 0.28514043
[39,] 0.76831800 0.46336399 0.23168200
[40,] 0.74267652 0.51464696 0.25732348
[41,] 0.73068881 0.53862239 0.26931119
[42,] 0.69635120 0.60729760 0.30364880
[43,] 0.69855567 0.60288866 0.30144433
[44,] 0.66610885 0.66778230 0.33389115
[45,] 0.67815763 0.64368474 0.32184237
[46,] 0.69398251 0.61203497 0.30601749
[47,] 0.67161923 0.65676154 0.32838077
[48,] 0.70833088 0.58333825 0.29166912
[49,] 0.72332954 0.55334091 0.27667046
[50,] 0.70014693 0.59970613 0.29985307
[51,] 0.71224935 0.57550129 0.28775065
[52,] 0.67576929 0.64846143 0.32423071
[53,] 0.65208988 0.69582025 0.34791012
[54,] 0.61208360 0.77583280 0.38791640
[55,] 0.57117044 0.85765912 0.42882956
[56,] 0.58136845 0.83726309 0.41863155
[57,] 0.58411186 0.83177627 0.41588814
[58,] 0.55426364 0.89147273 0.44573636
[59,] 0.51356445 0.97287110 0.48643555
[60,] 0.48953708 0.97907417 0.51046292
[61,] 0.45075822 0.90151643 0.54924178
[62,] 0.42699529 0.85399057 0.57300471
[63,] 0.40027731 0.80055462 0.59972269
[64,] 0.37938830 0.75877660 0.62061170
[65,] 0.34952548 0.69905096 0.65047452
[66,] 0.51138071 0.97723858 0.48861929
[67,] 0.49016616 0.98033233 0.50983384
[68,] 0.50643155 0.98713690 0.49356845
[69,] 0.46817990 0.93635981 0.53182010
[70,] 0.56619541 0.86760919 0.43380459
[71,] 0.52934378 0.94131245 0.47065622
[72,] 0.55342826 0.89314347 0.44657174
[73,] 0.51704146 0.96591708 0.48295854
[74,] 0.47905792 0.95811584 0.52094208
[75,] 0.44118587 0.88237173 0.55881413
[76,] 0.40762871 0.81525742 0.59237129
[77,] 0.37571077 0.75142154 0.62428923
[78,] 0.34267184 0.68534368 0.65732816
[79,] 0.43303122 0.86606244 0.56696878
[80,] 0.40351115 0.80702229 0.59648885
[81,] 0.37495608 0.74991216 0.62504392
[82,] 0.34468943 0.68937885 0.65531057
[83,] 0.34440145 0.68880290 0.65559855
[84,] 0.31318976 0.62637952 0.68681024
[85,] 0.28048108 0.56096215 0.71951892
[86,] 0.26458773 0.52917546 0.73541227
[87,] 0.24191519 0.48383037 0.75808481
[88,] 0.21545614 0.43091228 0.78454386
[89,] 0.20747880 0.41495759 0.79252120
[90,] 0.18232788 0.36465576 0.81767212
[91,] 0.17184991 0.34369982 0.82815009
[92,] 0.15371336 0.30742671 0.84628664
[93,] 0.19406135 0.38812269 0.80593865
[94,] 0.22491359 0.44982718 0.77508641
[95,] 0.23372942 0.46745884 0.76627058
[96,] 0.20672418 0.41344835 0.79327582
[97,] 0.23188686 0.46377373 0.76811314
[98,] 0.21544420 0.43088840 0.78455580
[99,] 0.34403307 0.68806614 0.65596693
[100,] 0.33538641 0.67077282 0.66461359
[101,] 0.30462717 0.60925435 0.69537283
[102,] 0.29355752 0.58711503 0.70644248
[103,] 0.26938106 0.53876211 0.73061894
[104,] 0.25051027 0.50102054 0.74948973
[105,] 0.22357212 0.44714425 0.77642788
[106,] 0.19759026 0.39518052 0.80240974
[107,] 0.17678845 0.35357691 0.82321155
[108,] 0.21542260 0.43084521 0.78457740
[109,] 0.22135707 0.44271413 0.77864293
[110,] 0.19743025 0.39486051 0.80256975
[111,] 0.18232320 0.36464639 0.81767680
[112,] 0.16145186 0.32290372 0.83854814
[113,] 0.19064331 0.38128661 0.80935669
[114,] 0.17164048 0.34328096 0.82835952
[115,] 0.16194943 0.32389885 0.83805057
[116,] 0.15076100 0.30152199 0.84923900
[117,] 0.13257452 0.26514904 0.86742548
[118,] 0.11822610 0.23645220 0.88177390
[119,] 0.10185915 0.20371831 0.89814085
[120,] 0.10088980 0.20177961 0.89911020
[121,] 0.10256171 0.20512341 0.89743829
[122,] 0.09454795 0.18909590 0.90545205
[123,] 0.08634472 0.17268944 0.91365528
[124,] 0.07337994 0.14675988 0.92662006
[125,] 0.07485015 0.14970029 0.92514985
[126,] 0.14922367 0.29844735 0.85077633
[127,] 0.13002898 0.26005795 0.86997102
[128,] 0.11520806 0.23041611 0.88479194
[129,] 0.11039814 0.22079628 0.88960186
[130,] 0.10514878 0.21029757 0.89485122
[131,] 0.11777541 0.23555082 0.88222459
[132,] 0.11853270 0.23706540 0.88146730
[133,] 0.10194328 0.20388656 0.89805672
[134,] 0.08679033 0.17358065 0.91320967
[135,] 0.07390059 0.14780117 0.92609941
[136,] 0.06261175 0.12522350 0.93738825
[137,] 0.06863007 0.13726015 0.93136993
[138,] 0.07478567 0.14957135 0.92521433
[139,] 0.07027420 0.14054840 0.92972580
[140,] 0.05970818 0.11941635 0.94029182
[141,] 0.07280106 0.14560213 0.92719894
[142,] 0.06989170 0.13978339 0.93010830
[143,] 0.06854252 0.13708504 0.93145748
[144,] 0.06373037 0.12746075 0.93626963
[145,] 0.07091898 0.14183797 0.92908102
[146,] 0.06046152 0.12092303 0.93953848
[147,] 0.05050121 0.10100241 0.94949879
[148,] 0.04306939 0.08613878 0.95693061
[149,] 0.05236391 0.10472783 0.94763609
[150,] 0.06526640 0.13053281 0.93473360
[151,] 0.05582610 0.11165221 0.94417390
[152,] 0.12302379 0.24604758 0.87697621
[153,] 0.11247702 0.22495404 0.88752298
[154,] 0.39597413 0.79194827 0.60402587
[155,] 0.37888595 0.75777191 0.62111405
[156,] 0.48708999 0.97417998 0.51291001
[157,] 0.45500288 0.91000576 0.54499712
[158,] 0.42325206 0.84650411 0.57674794
[159,] 0.38701569 0.77403139 0.61298431
[160,] 0.36511256 0.73022511 0.63488744
[161,] 0.37491066 0.74982131 0.62508934
[162,] 0.34016382 0.68032764 0.65983618
[163,] 0.38350191 0.76700381 0.61649809
[164,] 0.37075009 0.74150017 0.62924991
[165,] 0.34850765 0.69701530 0.65149235
[166,] 0.38509321 0.77018642 0.61490679
[167,] 0.35915365 0.71830731 0.64084635
[168,] 0.37169377 0.74338754 0.62830623
[169,] 0.35683292 0.71366583 0.64316708
[170,] 0.32452107 0.64904214 0.67547893
[171,] 0.35193277 0.70386554 0.64806723
[172,] 0.45707500 0.91414999 0.54292500
[173,] 0.44210907 0.88421813 0.55789093
[174,] 0.43062300 0.86124600 0.56937700
[175,] 0.41406174 0.82812348 0.58593826
[176,] 0.54122949 0.91754102 0.45877051
[177,] 0.50924311 0.98151377 0.49075689
[178,] 0.47651671 0.95303343 0.52348329
[179,] 0.44028046 0.88056092 0.55971954
[180,] 0.47903504 0.95807008 0.52096496
[181,] 0.50845262 0.98309476 0.49154738
[182,] 0.52541287 0.94917426 0.47458713
[183,] 0.51616144 0.96767711 0.48383856
[184,] 0.48535197 0.97070393 0.51464803
[185,] 0.45754744 0.91509488 0.54245256
[186,] 0.49281533 0.98563066 0.50718467
[187,] 0.69739414 0.60521172 0.30260586
[188,] 0.70550909 0.58898182 0.29449091
[189,] 0.66887204 0.66225591 0.33112796
[190,] 0.62935202 0.74129596 0.37064798
[191,] 0.58902648 0.82194704 0.41097352
[192,] 0.54822874 0.90354252 0.45177126
[193,] 0.52354645 0.95290710 0.47645355
[194,] 0.51932028 0.96135943 0.48067972
[195,] 0.48206810 0.96413621 0.51793190
[196,] 0.43944590 0.87889181 0.56055410
[197,] 0.39825272 0.79650545 0.60174728
[198,] 0.36484318 0.72968635 0.63515682
[199,] 0.32376612 0.64753223 0.67623388
[200,] 0.31643261 0.63286522 0.68356739
[201,] 0.29091114 0.58182227 0.70908886
[202,] 0.25657932 0.51315864 0.74342068
[203,] 0.28370745 0.56741489 0.71629255
[204,] 0.26594218 0.53188437 0.73405782
[205,] 0.23297216 0.46594431 0.76702784
[206,] 0.20026315 0.40052631 0.79973685
[207,] 0.19013946 0.38027892 0.80986054
[208,] 0.16049201 0.32098402 0.83950799
[209,] 0.16349653 0.32699305 0.83650347
[210,] 0.15515528 0.31031055 0.84484472
[211,] 0.15411998 0.30823996 0.84588002
[212,] 0.12753784 0.25507568 0.87246216
[213,] 0.10492168 0.20984335 0.89507832
[214,] 0.10583941 0.21167882 0.89416059
[215,] 0.09576818 0.19153636 0.90423182
[216,] 0.07798028 0.15596056 0.92201972
[217,] 0.06071951 0.12143901 0.93928049
[218,] 0.05700005 0.11400011 0.94299995
[219,] 0.07715873 0.15431746 0.92284127
[220,] 0.07343509 0.14687017 0.92656491
[221,] 0.05652088 0.11304176 0.94347912
[222,] 0.05802216 0.11604432 0.94197784
[223,] 0.12077490 0.24154981 0.87922510
[224,] 0.12851749 0.25703499 0.87148251
[225,] 0.12218704 0.24437407 0.87781296
[226,] 0.09518633 0.19037266 0.90481367
[227,] 0.12383116 0.24766232 0.87616884
[228,] 0.16843732 0.33687464 0.83156268
[229,] 0.27078106 0.54156212 0.72921894
[230,] 0.26914962 0.53829924 0.73085038
[231,] 0.22318435 0.44636870 0.77681565
[232,] 0.27116527 0.54233054 0.72883473
[233,] 0.21146720 0.42293440 0.78853280
[234,] 0.15856757 0.31713514 0.84143243
[235,] 0.23503972 0.47007945 0.76496028
[236,] 0.25658381 0.51316762 0.74341619
[237,] 0.18968722 0.37937444 0.81031278
[238,] 0.23454993 0.46909986 0.76545007
[239,] 0.34415530 0.68831061 0.65584470
[240,] 0.24757472 0.49514944 0.75242528
[241,] 0.27867754 0.55735508 0.72132246
[242,] 0.90927734 0.18144532 0.09072266
[243,] 0.84307645 0.31384711 0.15692355
> postscript(file="/var/wessaorg/rcomp/tmp/1k32o1384860135.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2nrmr1384860135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3k5mp1384860135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4a9gk1384860135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5dtn11384860135.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 6
-2.07067879 1.61303356 -1.56758106 -3.06903891 9.53655110 2.03329236
7 8 9 10 11 12
7.68375049 -1.60734906 -2.25073559 0.79215851 -0.45063268 -2.45602890
13 14 15 16 17 18
-1.13033324 1.49637384 0.81864529 -0.07488120 1.24423003 1.49387406
19 20 21 22 23 24
-3.43001359 0.28352757 -1.23096670 -1.82117217 -1.61666392 -1.69772024
25 26 27 28 29 30
1.30986888 -6.40427189 0.15701722 1.45283592 2.39459253 -3.07480095
31 32 33 34 35 36
-2.33118454 -0.48117995 -0.97205836 -0.99879263 0.42075028 -4.44465573
37 38 39 40 41 42
2.37573760 -0.39138443 -0.82304442 -3.87278439 -3.07005848 2.67689821
43 44 45 46 47 48
-4.07527144 -1.66193479 -1.47712605 -2.45468593 -4.20074371 -1.30123734
49 50 51 52 53 54
4.71998515 -2.41982082 -1.65618955 0.55558247 2.13834714 -2.17525610
55 56 57 58 59 60
-4.42402800 2.15509214 1.75247473 -3.95212566 -4.13646910 0.41744378
61 62 63 64 65 66
1.10216147 -0.97010198 -2.49463496 -0.39772448 -0.34651844 -4.25518420
67 68 69 70 71 72
1.56609735 -1.82449281 0.06328726 1.56409289 -1.19626879 2.00464901
73 74 75 76 77 78
1.62475872 -1.83679238 -1.78060342 5.26815715 2.08538281 2.83644052
79 80 81 82 83 84
0.51893955 -5.61550953 -0.62022934 -3.57244748 0.73300373 -0.49422264
85 86 87 88 89 90
0.22044535 0.88156285 -1.20544623 0.03198310 3.77026398 1.42670990
91 92 93 94 95 96
0.46922038 1.13784169 2.74864999 -0.91003103 0.28482290 1.41627338
97 98 99 100 101 102
-1.43321471 0.97343972 -2.31548753 -0.25344980 1.66820395 1.63008509
103 104 105 106 107 108
-4.87741504 4.19523458 2.29661030 -0.20273756 3.94333104 -1.84838560
109 110 111 112 113 114
6.21824766 2.29026570 0.09112500 -2.01436548 1.38274677 1.70671276
115 116 117 118 119 120
-0.51257837 -0.29376821 -1.04294513 -4.48890350 3.19580862 -0.82131286
121 122 123 124 125 126
1.23114407 -0.37243841 -4.00574753 -1.26475485 -2.16928308 -1.75049895
127 128 129 130 131 132
-0.16495170 1.34405894 0.37875111 -2.70124212 2.38608043 -1.73053122
133 134 135 136 137 138
1.62278752 -0.37148006 2.88029106 6.31346287 -0.15665265 -1.11595590
139 140 141 142 143 144
-2.29554746 -2.34977429 -3.53912928 2.82085390 -0.26438951 0.06144277
145 146 147 148 149 150
-0.02761870 0.71171804 -3.21551453 -3.33630117 2.04077153 0.52124480
151 152 153 154 155 156
4.11835440 -2.41026107 -2.55714070 2.05489808 3.53281774 0.46922038
157 158 159 160 161 162
0.19831954 1.34405894 -3.97717301 3.93306578 1.19594458 6.89033318
163 164 165 166 167 168
1.36924562 9.30463513 2.14089061 5.82667652 -1.05508118 -0.82550268
169 170 171 172 173 174
-0.22112457 1.72766782 3.44666175 0.46406167 -4.20849236 1.80923616
175 176 177 178 179 180
1.17960997 -4.03685301 -1.29491491 3.01767367 -2.21976744 -0.82087286
181 182 183 184 185 186
-3.00297978 -5.52139584 -1.40645306 -2.32605043 -0.81036811 5.77281841
187 188 189 190 191 192
1.56475519 0.50744781 -1.09595779 4.29278015 -2.64811463 -2.44867298
193 194 195 196 197 198
1.91725057 -0.16791141 1.32502096 -2.70668777 6.38671358 2.83559477
199 200 201 202 203 204
1.01030934 -0.29870672 -0.12319976 -0.76740654 0.35749165 -1.39109894
205 206 207 208 209 210
-1.38138853 0.72380371 -0.68573343 2.36422742 -0.39550329 1.17261854
211 212 213 214 215 216
-0.47777983 -0.24741109 3.41117144 -2.85958532 1.74173570 -0.30401036
217 218 219 220 221 222
0.88028670 -0.56272091 4.00781700 1.97867235 -3.04213560 0.14621104
223 224 225 226 227 228
1.04668511 -3.82342127 3.86340769 -1.91791132 0.13821128 -2.59255648
229 230 231 232 233 234
4.66657686 -2.69795054 -1.38650618 -1.89775709 -4.43019587 3.49897726
235 236 237 238 239 240
-2.88738966 -1.65443957 1.28872782 -2.49466656 -4.88086216 -3.61378254
241 242 243 244 245 246
-4.07643771 0.06951931 -0.11163013 0.91964326 1.87713483 4.07381765
247 248 249 250 251 252
0.51226064 7.55245059 2.00508994 -0.05152550 1.02646047 3.07530096
253 254 255 256 257 258
-3.26459854 -0.96117701 1.73001569 -0.20133165 5.05654821 0.19022352
259 260 261 262 263 264
2.23585458 -8.53872646 -1.69047534 -0.90870830 2.98047274 -1.29871225
> postscript(file="/var/wessaorg/rcomp/tmp/6jf4p1384860135.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 -2.07067879 NA
1 1.61303356 -2.07067879
2 -1.56758106 1.61303356
3 -3.06903891 -1.56758106
4 9.53655110 -3.06903891
5 2.03329236 9.53655110
6 7.68375049 2.03329236
7 -1.60734906 7.68375049
8 -2.25073559 -1.60734906
9 0.79215851 -2.25073559
10 -0.45063268 0.79215851
11 -2.45602890 -0.45063268
12 -1.13033324 -2.45602890
13 1.49637384 -1.13033324
14 0.81864529 1.49637384
15 -0.07488120 0.81864529
16 1.24423003 -0.07488120
17 1.49387406 1.24423003
18 -3.43001359 1.49387406
19 0.28352757 -3.43001359
20 -1.23096670 0.28352757
21 -1.82117217 -1.23096670
22 -1.61666392 -1.82117217
23 -1.69772024 -1.61666392
24 1.30986888 -1.69772024
25 -6.40427189 1.30986888
26 0.15701722 -6.40427189
27 1.45283592 0.15701722
28 2.39459253 1.45283592
29 -3.07480095 2.39459253
30 -2.33118454 -3.07480095
31 -0.48117995 -2.33118454
32 -0.97205836 -0.48117995
33 -0.99879263 -0.97205836
34 0.42075028 -0.99879263
35 -4.44465573 0.42075028
36 2.37573760 -4.44465573
37 -0.39138443 2.37573760
38 -0.82304442 -0.39138443
39 -3.87278439 -0.82304442
40 -3.07005848 -3.87278439
41 2.67689821 -3.07005848
42 -4.07527144 2.67689821
43 -1.66193479 -4.07527144
44 -1.47712605 -1.66193479
45 -2.45468593 -1.47712605
46 -4.20074371 -2.45468593
47 -1.30123734 -4.20074371
48 4.71998515 -1.30123734
49 -2.41982082 4.71998515
50 -1.65618955 -2.41982082
51 0.55558247 -1.65618955
52 2.13834714 0.55558247
53 -2.17525610 2.13834714
54 -4.42402800 -2.17525610
55 2.15509214 -4.42402800
56 1.75247473 2.15509214
57 -3.95212566 1.75247473
58 -4.13646910 -3.95212566
59 0.41744378 -4.13646910
60 1.10216147 0.41744378
61 -0.97010198 1.10216147
62 -2.49463496 -0.97010198
63 -0.39772448 -2.49463496
64 -0.34651844 -0.39772448
65 -4.25518420 -0.34651844
66 1.56609735 -4.25518420
67 -1.82449281 1.56609735
68 0.06328726 -1.82449281
69 1.56409289 0.06328726
70 -1.19626879 1.56409289
71 2.00464901 -1.19626879
72 1.62475872 2.00464901
73 -1.83679238 1.62475872
74 -1.78060342 -1.83679238
75 5.26815715 -1.78060342
76 2.08538281 5.26815715
77 2.83644052 2.08538281
78 0.51893955 2.83644052
79 -5.61550953 0.51893955
80 -0.62022934 -5.61550953
81 -3.57244748 -0.62022934
82 0.73300373 -3.57244748
83 -0.49422264 0.73300373
84 0.22044535 -0.49422264
85 0.88156285 0.22044535
86 -1.20544623 0.88156285
87 0.03198310 -1.20544623
88 3.77026398 0.03198310
89 1.42670990 3.77026398
90 0.46922038 1.42670990
91 1.13784169 0.46922038
92 2.74864999 1.13784169
93 -0.91003103 2.74864999
94 0.28482290 -0.91003103
95 1.41627338 0.28482290
96 -1.43321471 1.41627338
97 0.97343972 -1.43321471
98 -2.31548753 0.97343972
99 -0.25344980 -2.31548753
100 1.66820395 -0.25344980
101 1.63008509 1.66820395
102 -4.87741504 1.63008509
103 4.19523458 -4.87741504
104 2.29661030 4.19523458
105 -0.20273756 2.29661030
106 3.94333104 -0.20273756
107 -1.84838560 3.94333104
108 6.21824766 -1.84838560
109 2.29026570 6.21824766
110 0.09112500 2.29026570
111 -2.01436548 0.09112500
112 1.38274677 -2.01436548
113 1.70671276 1.38274677
114 -0.51257837 1.70671276
115 -0.29376821 -0.51257837
116 -1.04294513 -0.29376821
117 -4.48890350 -1.04294513
118 3.19580862 -4.48890350
119 -0.82131286 3.19580862
120 1.23114407 -0.82131286
121 -0.37243841 1.23114407
122 -4.00574753 -0.37243841
123 -1.26475485 -4.00574753
124 -2.16928308 -1.26475485
125 -1.75049895 -2.16928308
126 -0.16495170 -1.75049895
127 1.34405894 -0.16495170
128 0.37875111 1.34405894
129 -2.70124212 0.37875111
130 2.38608043 -2.70124212
131 -1.73053122 2.38608043
132 1.62278752 -1.73053122
133 -0.37148006 1.62278752
134 2.88029106 -0.37148006
135 6.31346287 2.88029106
136 -0.15665265 6.31346287
137 -1.11595590 -0.15665265
138 -2.29554746 -1.11595590
139 -2.34977429 -2.29554746
140 -3.53912928 -2.34977429
141 2.82085390 -3.53912928
142 -0.26438951 2.82085390
143 0.06144277 -0.26438951
144 -0.02761870 0.06144277
145 0.71171804 -0.02761870
146 -3.21551453 0.71171804
147 -3.33630117 -3.21551453
148 2.04077153 -3.33630117
149 0.52124480 2.04077153
150 4.11835440 0.52124480
151 -2.41026107 4.11835440
152 -2.55714070 -2.41026107
153 2.05489808 -2.55714070
154 3.53281774 2.05489808
155 0.46922038 3.53281774
156 0.19831954 0.46922038
157 1.34405894 0.19831954
158 -3.97717301 1.34405894
159 3.93306578 -3.97717301
160 1.19594458 3.93306578
161 6.89033318 1.19594458
162 1.36924562 6.89033318
163 9.30463513 1.36924562
164 2.14089061 9.30463513
165 5.82667652 2.14089061
166 -1.05508118 5.82667652
167 -0.82550268 -1.05508118
168 -0.22112457 -0.82550268
169 1.72766782 -0.22112457
170 3.44666175 1.72766782
171 0.46406167 3.44666175
172 -4.20849236 0.46406167
173 1.80923616 -4.20849236
174 1.17960997 1.80923616
175 -4.03685301 1.17960997
176 -1.29491491 -4.03685301
177 3.01767367 -1.29491491
178 -2.21976744 3.01767367
179 -0.82087286 -2.21976744
180 -3.00297978 -0.82087286
181 -5.52139584 -3.00297978
182 -1.40645306 -5.52139584
183 -2.32605043 -1.40645306
184 -0.81036811 -2.32605043
185 5.77281841 -0.81036811
186 1.56475519 5.77281841
187 0.50744781 1.56475519
188 -1.09595779 0.50744781
189 4.29278015 -1.09595779
190 -2.64811463 4.29278015
191 -2.44867298 -2.64811463
192 1.91725057 -2.44867298
193 -0.16791141 1.91725057
194 1.32502096 -0.16791141
195 -2.70668777 1.32502096
196 6.38671358 -2.70668777
197 2.83559477 6.38671358
198 1.01030934 2.83559477
199 -0.29870672 1.01030934
200 -0.12319976 -0.29870672
201 -0.76740654 -0.12319976
202 0.35749165 -0.76740654
203 -1.39109894 0.35749165
204 -1.38138853 -1.39109894
205 0.72380371 -1.38138853
206 -0.68573343 0.72380371
207 2.36422742 -0.68573343
208 -0.39550329 2.36422742
209 1.17261854 -0.39550329
210 -0.47777983 1.17261854
211 -0.24741109 -0.47777983
212 3.41117144 -0.24741109
213 -2.85958532 3.41117144
214 1.74173570 -2.85958532
215 -0.30401036 1.74173570
216 0.88028670 -0.30401036
217 -0.56272091 0.88028670
218 4.00781700 -0.56272091
219 1.97867235 4.00781700
220 -3.04213560 1.97867235
221 0.14621104 -3.04213560
222 1.04668511 0.14621104
223 -3.82342127 1.04668511
224 3.86340769 -3.82342127
225 -1.91791132 3.86340769
226 0.13821128 -1.91791132
227 -2.59255648 0.13821128
228 4.66657686 -2.59255648
229 -2.69795054 4.66657686
230 -1.38650618 -2.69795054
231 -1.89775709 -1.38650618
232 -4.43019587 -1.89775709
233 3.49897726 -4.43019587
234 -2.88738966 3.49897726
235 -1.65443957 -2.88738966
236 1.28872782 -1.65443957
237 -2.49466656 1.28872782
238 -4.88086216 -2.49466656
239 -3.61378254 -4.88086216
240 -4.07643771 -3.61378254
241 0.06951931 -4.07643771
242 -0.11163013 0.06951931
243 0.91964326 -0.11163013
244 1.87713483 0.91964326
245 4.07381765 1.87713483
246 0.51226064 4.07381765
247 7.55245059 0.51226064
248 2.00508994 7.55245059
249 -0.05152550 2.00508994
250 1.02646047 -0.05152550
251 3.07530096 1.02646047
252 -3.26459854 3.07530096
253 -0.96117701 -3.26459854
254 1.73001569 -0.96117701
255 -0.20133165 1.73001569
256 5.05654821 -0.20133165
257 0.19022352 5.05654821
258 2.23585458 0.19022352
259 -8.53872646 2.23585458
260 -1.69047534 -8.53872646
261 -0.90870830 -1.69047534
262 2.98047274 -0.90870830
263 -1.29871225 2.98047274
264 NA -1.29871225
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.61303356 -2.07067879
[2,] -1.56758106 1.61303356
[3,] -3.06903891 -1.56758106
[4,] 9.53655110 -3.06903891
[5,] 2.03329236 9.53655110
[6,] 7.68375049 2.03329236
[7,] -1.60734906 7.68375049
[8,] -2.25073559 -1.60734906
[9,] 0.79215851 -2.25073559
[10,] -0.45063268 0.79215851
[11,] -2.45602890 -0.45063268
[12,] -1.13033324 -2.45602890
[13,] 1.49637384 -1.13033324
[14,] 0.81864529 1.49637384
[15,] -0.07488120 0.81864529
[16,] 1.24423003 -0.07488120
[17,] 1.49387406 1.24423003
[18,] -3.43001359 1.49387406
[19,] 0.28352757 -3.43001359
[20,] -1.23096670 0.28352757
[21,] -1.82117217 -1.23096670
[22,] -1.61666392 -1.82117217
[23,] -1.69772024 -1.61666392
[24,] 1.30986888 -1.69772024
[25,] -6.40427189 1.30986888
[26,] 0.15701722 -6.40427189
[27,] 1.45283592 0.15701722
[28,] 2.39459253 1.45283592
[29,] -3.07480095 2.39459253
[30,] -2.33118454 -3.07480095
[31,] -0.48117995 -2.33118454
[32,] -0.97205836 -0.48117995
[33,] -0.99879263 -0.97205836
[34,] 0.42075028 -0.99879263
[35,] -4.44465573 0.42075028
[36,] 2.37573760 -4.44465573
[37,] -0.39138443 2.37573760
[38,] -0.82304442 -0.39138443
[39,] -3.87278439 -0.82304442
[40,] -3.07005848 -3.87278439
[41,] 2.67689821 -3.07005848
[42,] -4.07527144 2.67689821
[43,] -1.66193479 -4.07527144
[44,] -1.47712605 -1.66193479
[45,] -2.45468593 -1.47712605
[46,] -4.20074371 -2.45468593
[47,] -1.30123734 -4.20074371
[48,] 4.71998515 -1.30123734
[49,] -2.41982082 4.71998515
[50,] -1.65618955 -2.41982082
[51,] 0.55558247 -1.65618955
[52,] 2.13834714 0.55558247
[53,] -2.17525610 2.13834714
[54,] -4.42402800 -2.17525610
[55,] 2.15509214 -4.42402800
[56,] 1.75247473 2.15509214
[57,] -3.95212566 1.75247473
[58,] -4.13646910 -3.95212566
[59,] 0.41744378 -4.13646910
[60,] 1.10216147 0.41744378
[61,] -0.97010198 1.10216147
[62,] -2.49463496 -0.97010198
[63,] -0.39772448 -2.49463496
[64,] -0.34651844 -0.39772448
[65,] -4.25518420 -0.34651844
[66,] 1.56609735 -4.25518420
[67,] -1.82449281 1.56609735
[68,] 0.06328726 -1.82449281
[69,] 1.56409289 0.06328726
[70,] -1.19626879 1.56409289
[71,] 2.00464901 -1.19626879
[72,] 1.62475872 2.00464901
[73,] -1.83679238 1.62475872
[74,] -1.78060342 -1.83679238
[75,] 5.26815715 -1.78060342
[76,] 2.08538281 5.26815715
[77,] 2.83644052 2.08538281
[78,] 0.51893955 2.83644052
[79,] -5.61550953 0.51893955
[80,] -0.62022934 -5.61550953
[81,] -3.57244748 -0.62022934
[82,] 0.73300373 -3.57244748
[83,] -0.49422264 0.73300373
[84,] 0.22044535 -0.49422264
[85,] 0.88156285 0.22044535
[86,] -1.20544623 0.88156285
[87,] 0.03198310 -1.20544623
[88,] 3.77026398 0.03198310
[89,] 1.42670990 3.77026398
[90,] 0.46922038 1.42670990
[91,] 1.13784169 0.46922038
[92,] 2.74864999 1.13784169
[93,] -0.91003103 2.74864999
[94,] 0.28482290 -0.91003103
[95,] 1.41627338 0.28482290
[96,] -1.43321471 1.41627338
[97,] 0.97343972 -1.43321471
[98,] -2.31548753 0.97343972
[99,] -0.25344980 -2.31548753
[100,] 1.66820395 -0.25344980
[101,] 1.63008509 1.66820395
[102,] -4.87741504 1.63008509
[103,] 4.19523458 -4.87741504
[104,] 2.29661030 4.19523458
[105,] -0.20273756 2.29661030
[106,] 3.94333104 -0.20273756
[107,] -1.84838560 3.94333104
[108,] 6.21824766 -1.84838560
[109,] 2.29026570 6.21824766
[110,] 0.09112500 2.29026570
[111,] -2.01436548 0.09112500
[112,] 1.38274677 -2.01436548
[113,] 1.70671276 1.38274677
[114,] -0.51257837 1.70671276
[115,] -0.29376821 -0.51257837
[116,] -1.04294513 -0.29376821
[117,] -4.48890350 -1.04294513
[118,] 3.19580862 -4.48890350
[119,] -0.82131286 3.19580862
[120,] 1.23114407 -0.82131286
[121,] -0.37243841 1.23114407
[122,] -4.00574753 -0.37243841
[123,] -1.26475485 -4.00574753
[124,] -2.16928308 -1.26475485
[125,] -1.75049895 -2.16928308
[126,] -0.16495170 -1.75049895
[127,] 1.34405894 -0.16495170
[128,] 0.37875111 1.34405894
[129,] -2.70124212 0.37875111
[130,] 2.38608043 -2.70124212
[131,] -1.73053122 2.38608043
[132,] 1.62278752 -1.73053122
[133,] -0.37148006 1.62278752
[134,] 2.88029106 -0.37148006
[135,] 6.31346287 2.88029106
[136,] -0.15665265 6.31346287
[137,] -1.11595590 -0.15665265
[138,] -2.29554746 -1.11595590
[139,] -2.34977429 -2.29554746
[140,] -3.53912928 -2.34977429
[141,] 2.82085390 -3.53912928
[142,] -0.26438951 2.82085390
[143,] 0.06144277 -0.26438951
[144,] -0.02761870 0.06144277
[145,] 0.71171804 -0.02761870
[146,] -3.21551453 0.71171804
[147,] -3.33630117 -3.21551453
[148,] 2.04077153 -3.33630117
[149,] 0.52124480 2.04077153
[150,] 4.11835440 0.52124480
[151,] -2.41026107 4.11835440
[152,] -2.55714070 -2.41026107
[153,] 2.05489808 -2.55714070
[154,] 3.53281774 2.05489808
[155,] 0.46922038 3.53281774
[156,] 0.19831954 0.46922038
[157,] 1.34405894 0.19831954
[158,] -3.97717301 1.34405894
[159,] 3.93306578 -3.97717301
[160,] 1.19594458 3.93306578
[161,] 6.89033318 1.19594458
[162,] 1.36924562 6.89033318
[163,] 9.30463513 1.36924562
[164,] 2.14089061 9.30463513
[165,] 5.82667652 2.14089061
[166,] -1.05508118 5.82667652
[167,] -0.82550268 -1.05508118
[168,] -0.22112457 -0.82550268
[169,] 1.72766782 -0.22112457
[170,] 3.44666175 1.72766782
[171,] 0.46406167 3.44666175
[172,] -4.20849236 0.46406167
[173,] 1.80923616 -4.20849236
[174,] 1.17960997 1.80923616
[175,] -4.03685301 1.17960997
[176,] -1.29491491 -4.03685301
[177,] 3.01767367 -1.29491491
[178,] -2.21976744 3.01767367
[179,] -0.82087286 -2.21976744
[180,] -3.00297978 -0.82087286
[181,] -5.52139584 -3.00297978
[182,] -1.40645306 -5.52139584
[183,] -2.32605043 -1.40645306
[184,] -0.81036811 -2.32605043
[185,] 5.77281841 -0.81036811
[186,] 1.56475519 5.77281841
[187,] 0.50744781 1.56475519
[188,] -1.09595779 0.50744781
[189,] 4.29278015 -1.09595779
[190,] -2.64811463 4.29278015
[191,] -2.44867298 -2.64811463
[192,] 1.91725057 -2.44867298
[193,] -0.16791141 1.91725057
[194,] 1.32502096 -0.16791141
[195,] -2.70668777 1.32502096
[196,] 6.38671358 -2.70668777
[197,] 2.83559477 6.38671358
[198,] 1.01030934 2.83559477
[199,] -0.29870672 1.01030934
[200,] -0.12319976 -0.29870672
[201,] -0.76740654 -0.12319976
[202,] 0.35749165 -0.76740654
[203,] -1.39109894 0.35749165
[204,] -1.38138853 -1.39109894
[205,] 0.72380371 -1.38138853
[206,] -0.68573343 0.72380371
[207,] 2.36422742 -0.68573343
[208,] -0.39550329 2.36422742
[209,] 1.17261854 -0.39550329
[210,] -0.47777983 1.17261854
[211,] -0.24741109 -0.47777983
[212,] 3.41117144 -0.24741109
[213,] -2.85958532 3.41117144
[214,] 1.74173570 -2.85958532
[215,] -0.30401036 1.74173570
[216,] 0.88028670 -0.30401036
[217,] -0.56272091 0.88028670
[218,] 4.00781700 -0.56272091
[219,] 1.97867235 4.00781700
[220,] -3.04213560 1.97867235
[221,] 0.14621104 -3.04213560
[222,] 1.04668511 0.14621104
[223,] -3.82342127 1.04668511
[224,] 3.86340769 -3.82342127
[225,] -1.91791132 3.86340769
[226,] 0.13821128 -1.91791132
[227,] -2.59255648 0.13821128
[228,] 4.66657686 -2.59255648
[229,] -2.69795054 4.66657686
[230,] -1.38650618 -2.69795054
[231,] -1.89775709 -1.38650618
[232,] -4.43019587 -1.89775709
[233,] 3.49897726 -4.43019587
[234,] -2.88738966 3.49897726
[235,] -1.65443957 -2.88738966
[236,] 1.28872782 -1.65443957
[237,] -2.49466656 1.28872782
[238,] -4.88086216 -2.49466656
[239,] -3.61378254 -4.88086216
[240,] -4.07643771 -3.61378254
[241,] 0.06951931 -4.07643771
[242,] -0.11163013 0.06951931
[243,] 0.91964326 -0.11163013
[244,] 1.87713483 0.91964326
[245,] 4.07381765 1.87713483
[246,] 0.51226064 4.07381765
[247,] 7.55245059 0.51226064
[248,] 2.00508994 7.55245059
[249,] -0.05152550 2.00508994
[250,] 1.02646047 -0.05152550
[251,] 3.07530096 1.02646047
[252,] -3.26459854 3.07530096
[253,] -0.96117701 -3.26459854
[254,] 1.73001569 -0.96117701
[255,] -0.20133165 1.73001569
[256,] 5.05654821 -0.20133165
[257,] 0.19022352 5.05654821
[258,] 2.23585458 0.19022352
[259,] -8.53872646 2.23585458
[260,] -1.69047534 -8.53872646
[261,] -0.90870830 -1.69047534
[262,] 2.98047274 -0.90870830
[263,] -1.29871225 2.98047274
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.61303356 -2.07067879
2 -1.56758106 1.61303356
3 -3.06903891 -1.56758106
4 9.53655110 -3.06903891
5 2.03329236 9.53655110
6 7.68375049 2.03329236
7 -1.60734906 7.68375049
8 -2.25073559 -1.60734906
9 0.79215851 -2.25073559
10 -0.45063268 0.79215851
11 -2.45602890 -0.45063268
12 -1.13033324 -2.45602890
13 1.49637384 -1.13033324
14 0.81864529 1.49637384
15 -0.07488120 0.81864529
16 1.24423003 -0.07488120
17 1.49387406 1.24423003
18 -3.43001359 1.49387406
19 0.28352757 -3.43001359
20 -1.23096670 0.28352757
21 -1.82117217 -1.23096670
22 -1.61666392 -1.82117217
23 -1.69772024 -1.61666392
24 1.30986888 -1.69772024
25 -6.40427189 1.30986888
26 0.15701722 -6.40427189
27 1.45283592 0.15701722
28 2.39459253 1.45283592
29 -3.07480095 2.39459253
30 -2.33118454 -3.07480095
31 -0.48117995 -2.33118454
32 -0.97205836 -0.48117995
33 -0.99879263 -0.97205836
34 0.42075028 -0.99879263
35 -4.44465573 0.42075028
36 2.37573760 -4.44465573
37 -0.39138443 2.37573760
38 -0.82304442 -0.39138443
39 -3.87278439 -0.82304442
40 -3.07005848 -3.87278439
41 2.67689821 -3.07005848
42 -4.07527144 2.67689821
43 -1.66193479 -4.07527144
44 -1.47712605 -1.66193479
45 -2.45468593 -1.47712605
46 -4.20074371 -2.45468593
47 -1.30123734 -4.20074371
48 4.71998515 -1.30123734
49 -2.41982082 4.71998515
50 -1.65618955 -2.41982082
51 0.55558247 -1.65618955
52 2.13834714 0.55558247
53 -2.17525610 2.13834714
54 -4.42402800 -2.17525610
55 2.15509214 -4.42402800
56 1.75247473 2.15509214
57 -3.95212566 1.75247473
58 -4.13646910 -3.95212566
59 0.41744378 -4.13646910
60 1.10216147 0.41744378
61 -0.97010198 1.10216147
62 -2.49463496 -0.97010198
63 -0.39772448 -2.49463496
64 -0.34651844 -0.39772448
65 -4.25518420 -0.34651844
66 1.56609735 -4.25518420
67 -1.82449281 1.56609735
68 0.06328726 -1.82449281
69 1.56409289 0.06328726
70 -1.19626879 1.56409289
71 2.00464901 -1.19626879
72 1.62475872 2.00464901
73 -1.83679238 1.62475872
74 -1.78060342 -1.83679238
75 5.26815715 -1.78060342
76 2.08538281 5.26815715
77 2.83644052 2.08538281
78 0.51893955 2.83644052
79 -5.61550953 0.51893955
80 -0.62022934 -5.61550953
81 -3.57244748 -0.62022934
82 0.73300373 -3.57244748
83 -0.49422264 0.73300373
84 0.22044535 -0.49422264
85 0.88156285 0.22044535
86 -1.20544623 0.88156285
87 0.03198310 -1.20544623
88 3.77026398 0.03198310
89 1.42670990 3.77026398
90 0.46922038 1.42670990
91 1.13784169 0.46922038
92 2.74864999 1.13784169
93 -0.91003103 2.74864999
94 0.28482290 -0.91003103
95 1.41627338 0.28482290
96 -1.43321471 1.41627338
97 0.97343972 -1.43321471
98 -2.31548753 0.97343972
99 -0.25344980 -2.31548753
100 1.66820395 -0.25344980
101 1.63008509 1.66820395
102 -4.87741504 1.63008509
103 4.19523458 -4.87741504
104 2.29661030 4.19523458
105 -0.20273756 2.29661030
106 3.94333104 -0.20273756
107 -1.84838560 3.94333104
108 6.21824766 -1.84838560
109 2.29026570 6.21824766
110 0.09112500 2.29026570
111 -2.01436548 0.09112500
112 1.38274677 -2.01436548
113 1.70671276 1.38274677
114 -0.51257837 1.70671276
115 -0.29376821 -0.51257837
116 -1.04294513 -0.29376821
117 -4.48890350 -1.04294513
118 3.19580862 -4.48890350
119 -0.82131286 3.19580862
120 1.23114407 -0.82131286
121 -0.37243841 1.23114407
122 -4.00574753 -0.37243841
123 -1.26475485 -4.00574753
124 -2.16928308 -1.26475485
125 -1.75049895 -2.16928308
126 -0.16495170 -1.75049895
127 1.34405894 -0.16495170
128 0.37875111 1.34405894
129 -2.70124212 0.37875111
130 2.38608043 -2.70124212
131 -1.73053122 2.38608043
132 1.62278752 -1.73053122
133 -0.37148006 1.62278752
134 2.88029106 -0.37148006
135 6.31346287 2.88029106
136 -0.15665265 6.31346287
137 -1.11595590 -0.15665265
138 -2.29554746 -1.11595590
139 -2.34977429 -2.29554746
140 -3.53912928 -2.34977429
141 2.82085390 -3.53912928
142 -0.26438951 2.82085390
143 0.06144277 -0.26438951
144 -0.02761870 0.06144277
145 0.71171804 -0.02761870
146 -3.21551453 0.71171804
147 -3.33630117 -3.21551453
148 2.04077153 -3.33630117
149 0.52124480 2.04077153
150 4.11835440 0.52124480
151 -2.41026107 4.11835440
152 -2.55714070 -2.41026107
153 2.05489808 -2.55714070
154 3.53281774 2.05489808
155 0.46922038 3.53281774
156 0.19831954 0.46922038
157 1.34405894 0.19831954
158 -3.97717301 1.34405894
159 3.93306578 -3.97717301
160 1.19594458 3.93306578
161 6.89033318 1.19594458
162 1.36924562 6.89033318
163 9.30463513 1.36924562
164 2.14089061 9.30463513
165 5.82667652 2.14089061
166 -1.05508118 5.82667652
167 -0.82550268 -1.05508118
168 -0.22112457 -0.82550268
169 1.72766782 -0.22112457
170 3.44666175 1.72766782
171 0.46406167 3.44666175
172 -4.20849236 0.46406167
173 1.80923616 -4.20849236
174 1.17960997 1.80923616
175 -4.03685301 1.17960997
176 -1.29491491 -4.03685301
177 3.01767367 -1.29491491
178 -2.21976744 3.01767367
179 -0.82087286 -2.21976744
180 -3.00297978 -0.82087286
181 -5.52139584 -3.00297978
182 -1.40645306 -5.52139584
183 -2.32605043 -1.40645306
184 -0.81036811 -2.32605043
185 5.77281841 -0.81036811
186 1.56475519 5.77281841
187 0.50744781 1.56475519
188 -1.09595779 0.50744781
189 4.29278015 -1.09595779
190 -2.64811463 4.29278015
191 -2.44867298 -2.64811463
192 1.91725057 -2.44867298
193 -0.16791141 1.91725057
194 1.32502096 -0.16791141
195 -2.70668777 1.32502096
196 6.38671358 -2.70668777
197 2.83559477 6.38671358
198 1.01030934 2.83559477
199 -0.29870672 1.01030934
200 -0.12319976 -0.29870672
201 -0.76740654 -0.12319976
202 0.35749165 -0.76740654
203 -1.39109894 0.35749165
204 -1.38138853 -1.39109894
205 0.72380371 -1.38138853
206 -0.68573343 0.72380371
207 2.36422742 -0.68573343
208 -0.39550329 2.36422742
209 1.17261854 -0.39550329
210 -0.47777983 1.17261854
211 -0.24741109 -0.47777983
212 3.41117144 -0.24741109
213 -2.85958532 3.41117144
214 1.74173570 -2.85958532
215 -0.30401036 1.74173570
216 0.88028670 -0.30401036
217 -0.56272091 0.88028670
218 4.00781700 -0.56272091
219 1.97867235 4.00781700
220 -3.04213560 1.97867235
221 0.14621104 -3.04213560
222 1.04668511 0.14621104
223 -3.82342127 1.04668511
224 3.86340769 -3.82342127
225 -1.91791132 3.86340769
226 0.13821128 -1.91791132
227 -2.59255648 0.13821128
228 4.66657686 -2.59255648
229 -2.69795054 4.66657686
230 -1.38650618 -2.69795054
231 -1.89775709 -1.38650618
232 -4.43019587 -1.89775709
233 3.49897726 -4.43019587
234 -2.88738966 3.49897726
235 -1.65443957 -2.88738966
236 1.28872782 -1.65443957
237 -2.49466656 1.28872782
238 -4.88086216 -2.49466656
239 -3.61378254 -4.88086216
240 -4.07643771 -3.61378254
241 0.06951931 -4.07643771
242 -0.11163013 0.06951931
243 0.91964326 -0.11163013
244 1.87713483 0.91964326
245 4.07381765 1.87713483
246 0.51226064 4.07381765
247 7.55245059 0.51226064
248 2.00508994 7.55245059
249 -0.05152550 2.00508994
250 1.02646047 -0.05152550
251 3.07530096 1.02646047
252 -3.26459854 3.07530096
253 -0.96117701 -3.26459854
254 1.73001569 -0.96117701
255 -0.20133165 1.73001569
256 5.05654821 -0.20133165
257 0.19022352 5.05654821
258 2.23585458 0.19022352
259 -8.53872646 2.23585458
260 -1.69047534 -8.53872646
261 -0.90870830 -1.69047534
262 2.98047274 -0.90870830
263 -1.29871225 2.98047274
> 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/7uxn81384860135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/81ptl1384860135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/94i8j1384860135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/107l1b1384860135.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11q6bf1384860135.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/127now1384860135.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/1344d41384860135.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/149k681384860135.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/15cepx1384860135.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/16ik3d1384860135.tab")
+ }
>
> try(system("convert tmp/1k32o1384860135.ps tmp/1k32o1384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nrmr1384860135.ps tmp/2nrmr1384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/3k5mp1384860135.ps tmp/3k5mp1384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a9gk1384860135.ps tmp/4a9gk1384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/5dtn11384860135.ps tmp/5dtn11384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/6jf4p1384860135.ps tmp/6jf4p1384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/7uxn81384860135.ps tmp/7uxn81384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/81ptl1384860135.ps tmp/81ptl1384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/94i8j1384860135.ps tmp/94i8j1384860135.png",intern=TRUE))
character(0)
> try(system("convert tmp/107l1b1384860135.ps tmp/107l1b1384860135.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.420 2.675 18.070