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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+ ,16
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+ ,14
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+ ,13
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+ ,11
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+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
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+ ,52
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+ ,11
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+ ,11
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+ ,7
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+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,11
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Month'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Month','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 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'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
Happiness Connected Separate Learning Software Depression Month Sport2
1 14 41 38 13 12 12.0 9 32
2 18 39 32 16 11 11.0 9 51
3 11 30 35 19 15 14.0 9 42
4 12 31 33 15 6 12.0 9 41
5 16 34 37 14 13 21.0 9 46
6 18 35 29 13 10 12.0 9 47
7 14 39 31 19 12 22.0 9 37
8 14 34 36 15 14 11.0 9 49
9 15 36 35 14 12 10.0 9 45
10 15 37 38 15 9 13.0 9 47
11 17 38 31 16 10 10.0 9 49
12 19 36 34 16 12 8.0 9 33
13 10 38 35 16 12 15.0 9 42
14 16 39 38 16 11 14.0 9 33
15 18 33 37 17 15 10.0 9 53
16 14 32 33 15 12 14.0 9 36
17 14 36 32 15 10 14.0 9 45
18 17 38 38 20 12 11.0 9 54
19 14 39 38 18 11 10.0 9 41
20 16 32 32 16 12 13.0 9 36
21 18 32 33 16 11 9.5 9 41
22 11 31 31 16 12 14.0 9 44
23 14 39 38 19 13 12.0 9 33
24 12 37 39 16 11 14.0 9 37
25 17 39 32 17 12 11.0 9 52
26 9 41 32 17 13 9.0 9 47
27 16 36 35 16 10 11.0 9 43
28 14 33 37 15 14 15.0 9 44
29 15 33 33 16 12 14.0 9 45
30 11 34 33 14 10 13.0 9 44
31 16 31 31 15 12 9.0 9 49
32 13 27 32 12 8 15.0 9 33
33 17 37 31 14 10 10.0 9 43
34 15 34 37 16 12 11.0 9 54
35 14 34 30 14 12 13.0 9 42
36 16 32 33 10 7 8.0 9 44
37 9 29 31 10 9 20.0 9 37
38 15 36 33 14 12 12.0 9 43
39 17 29 31 16 10 10.0 9 46
40 13 35 33 16 10 10.0 9 42
41 15 37 32 16 10 9.0 9 45
42 16 34 33 14 12 14.0 9 44
43 16 38 32 20 15 8.0 9 33
44 12 35 33 14 10 14.0 9 31
45 15 38 28 14 10 11.0 9 42
46 11 37 35 11 12 13.0 9 40
47 15 38 39 14 13 9.0 9 43
48 15 33 34 15 11 11.0 9 46
49 17 36 38 16 11 15.0 9 42
50 13 38 32 14 12 11.0 9 45
51 16 32 38 16 14 10.0 9 44
52 14 32 30 14 10 14.0 9 40
53 11 32 33 12 12 18.0 9 37
54 12 34 38 16 13 14.0 9 46
55 12 32 32 9 5 11.0 9 36
56 15 37 35 14 6 14.5 9 47
57 16 39 34 16 12 13.0 9 45
58 15 29 34 16 12 9.0 9 42
59 12 37 36 15 11 10.0 9 43
60 12 35 34 16 10 15.0 9 43
61 8 30 28 12 7 20.0 9 32
62 13 38 34 16 12 12.0 9 45
63 11 34 35 16 14 12.0 9 48
64 14 31 35 14 11 14.0 9 31
65 15 34 31 16 12 13.0 9 33
66 10 35 37 17 13 11.0 10 49
67 11 36 35 18 14 17.0 10 42
68 12 30 27 18 11 12.0 10 41
69 15 39 40 12 12 13.0 10 38
70 15 35 37 16 12 14.0 10 42
71 14 38 36 10 8 13.0 10 44
72 16 31 38 14 11 15.0 10 33
73 15 34 39 18 14 13.0 10 48
74 15 38 41 18 14 10.0 10 40
75 13 34 27 16 12 11.0 10 50
76 12 39 30 17 9 19.0 10 49
77 17 37 37 16 13 13.0 10 43
78 13 34 31 16 11 17.0 10 44
79 15 28 31 13 12 13.0 10 47
80 13 37 27 16 12 9.0 10 33
81 15 33 36 16 12 11.0 10 46
82 15 35 37 16 12 9.0 10 45
83 16 37 33 15 12 12.0 10 43
84 15 32 34 15 11 12.0 10 44
85 14 33 31 16 10 13.0 10 47
86 15 38 39 14 9 13.0 10 45
87 14 33 34 16 12 12.0 10 42
88 13 29 32 16 12 15.0 10 33
89 7 33 33 15 12 22.0 10 43
90 17 31 36 12 9 13.0 10 46
91 13 36 32 17 15 15.0 10 33
92 15 35 41 16 12 13.0 10 46
93 14 32 28 15 12 15.0 10 48
94 13 29 30 13 12 12.5 10 47
95 16 39 36 16 10 11.0 10 47
96 12 37 35 16 13 16.0 10 43
97 14 35 31 16 9 11.0 10 46
98 17 37 34 16 12 11.0 10 48
99 15 32 36 14 10 10.0 10 46
100 17 38 36 16 14 10.0 10 45
101 12 37 35 16 11 16.0 10 45
102 16 36 37 20 15 12.0 10 52
103 11 32 28 15 11 11.0 10 42
104 15 33 39 16 11 16.0 10 47
105 9 40 32 13 12 19.0 10 41
106 16 38 35 17 12 11.0 10 47
107 15 41 39 16 12 16.0 10 43
108 10 36 35 16 11 15.0 10 33
109 10 43 42 12 7 24.0 10 30
110 15 30 34 16 12 14.0 10 52
111 11 31 33 16 14 15.0 10 44
112 13 32 41 17 11 11.0 10 55
113 14 32 33 13 11 15.0 10 11
114 18 37 34 12 10 12.0 10 47
115 16 37 32 18 13 10.0 10 53
116 14 33 40 14 13 14.0 10 33
117 14 34 40 14 8 13.0 10 44
118 14 33 35 13 11 9.0 10 42
119 14 38 36 16 12 15.0 10 55
120 12 33 37 13 11 15.0 10 33
121 14 31 27 16 13 14.0 10 46
122 15 38 39 13 12 11.0 10 54
123 15 37 38 16 14 8.0 10 47
124 15 36 31 15 13 11.0 10 45
125 13 31 33 16 15 11.0 10 47
126 17 39 32 15 10 8.0 10 55
127 17 44 39 17 11 10.0 10 44
128 19 33 36 15 9 11.0 10 53
129 15 35 33 12 11 13.0 10 44
130 13 32 33 16 10 11.0 10 42
131 9 28 32 10 11 20.0 10 40
132 15 40 37 16 8 10.0 10 46
133 15 27 30 12 11 15.0 10 40
134 15 37 38 14 12 12.0 10 46
135 16 32 29 15 12 14.0 10 53
136 11 28 22 13 9 23.0 10 33
137 14 34 35 15 11 14.0 10 42
138 11 30 35 11 10 16.0 10 35
139 15 35 34 12 8 11.0 10 40
140 13 31 35 11 9 12.0 10 41
141 15 32 34 16 8 10.0 10 33
142 16 30 37 15 9 14.0 10 51
143 14 30 35 17 15 12.0 10 53
144 15 31 23 16 11 12.0 10 46
145 16 40 31 10 8 11.0 10 55
146 16 32 27 18 13 12.0 10 47
147 11 36 36 13 12 13.0 10 38
148 12 32 31 16 12 11.0 10 46
149 9 35 32 13 9 19.0 10 46
150 16 38 39 10 7 12.0 10 53
151 13 42 37 15 13 17.0 10 47
152 16 34 38 16 9 9.0 10 41
153 12 35 39 16 6 12.0 10 44
154 9 38 34 14 8 19.0 9 43
155 13 33 31 10 8 18.0 10 51
156 13 36 32 17 15 15.0 10 33
157 14 32 37 13 6 14.0 10 43
158 19 33 36 15 9 11.0 10 53
159 13 34 32 16 11 9.0 10 51
160 12 32 38 12 8 18.0 10 50
161 13 34 36 13 8 16.0 10 46
162 10 27 26 13 10 24.0 11 43
163 14 31 26 12 8 14.0 11 47
164 16 38 33 17 14 20.0 11 50
165 10 34 39 15 10 18.0 11 43
166 11 24 30 10 8 23.0 11 33
167 14 30 33 14 11 12.0 11 48
168 12 26 25 11 12 14.0 11 44
169 9 34 38 13 12 16.0 11 50
170 9 27 37 16 12 18.0 11 41
171 11 37 31 12 5 20.0 11 34
172 16 36 37 16 12 12.0 11 44
173 9 41 35 12 10 12.0 11 47
174 13 29 25 9 7 17.0 11 35
175 16 36 28 12 12 13.0 11 44
176 13 32 35 15 11 9.0 11 44
177 9 37 33 12 8 16.0 11 43
178 12 30 30 12 9 18.0 11 41
179 16 31 31 14 10 10.0 11 41
180 11 38 37 12 9 14.0 11 42
181 14 36 36 16 12 11.0 11 33
182 13 35 30 11 6 9.0 11 41
183 15 31 36 19 15 11.0 11 44
184 14 38 32 15 12 10.0 11 48
185 16 22 28 8 12 11.0 11 55
186 13 32 36 16 12 19.0 11 44
187 14 36 34 17 11 14.0 11 43
188 15 39 31 12 7 12.0 11 52
189 13 28 28 11 7 14.0 11 30
190 11 32 36 11 5 21.0 11 39
191 11 32 36 14 12 13.0 11 11
192 14 38 40 16 12 10.0 11 44
193 15 32 33 12 3 15.0 11 42
194 11 35 37 16 11 16.0 11 41
195 15 32 32 13 10 14.0 11 44
196 12 37 38 15 12 12.0 11 44
197 14 34 31 16 9 19.0 11 48
198 14 33 37 16 12 15.0 11 53
199 8 33 33 14 9 19.0 11 37
200 13 26 32 16 12 13.0 11 44
201 9 30 30 16 12 17.0 11 44
202 15 24 30 14 10 12.0 11 40
203 17 34 31 11 9 11.0 11 42
204 13 34 32 12 12 14.0 11 35
205 15 33 34 15 8 11.0 11 43
206 15 34 36 15 11 13.0 11 45
207 14 35 37 16 11 12.0 11 55
208 16 35 36 16 12 15.0 11 31
209 13 36 33 11 10 14.0 11 44
210 16 34 33 15 10 12.0 11 50
211 9 34 33 12 12 17.0 11 40
212 16 41 44 12 12 11.0 11 53
213 11 32 39 15 11 18.0 11 54
214 10 30 32 15 8 13.0 11 49
215 11 35 35 16 12 17.0 11 40
216 15 28 25 14 10 13.0 11 41
217 17 33 35 17 11 11.0 11 52
218 14 39 34 14 10 12.0 11 52
219 8 36 35 13 8 22.0 11 36
220 15 36 39 15 12 14.0 11 52
221 11 35 33 13 12 12.0 11 46
222 16 38 36 14 10 12.0 11 31
223 10 33 32 15 12 17.0 11 44
224 15 31 32 12 9 9.0 11 44
225 9 34 36 13 9 21.0 11 11
226 16 32 36 8 6 10.0 11 46
227 19 31 32 14 10 11.0 11 33
228 12 33 34 14 9 12.0 11 34
229 8 34 33 11 9 23.0 11 42
230 11 34 35 12 9 13.0 11 43
231 14 34 30 13 6 12.0 11 43
232 9 33 38 10 10 16.0 11 44
233 15 32 34 16 6 9.0 11 36
234 13 41 33 18 14 17.0 11 46
235 16 34 32 13 10 9.0 11 44
236 11 36 31 11 10 14.0 11 43
237 12 37 30 4 6 17.0 11 50
238 13 36 27 13 12 13.0 11 33
239 10 29 31 16 12 11.0 11 43
240 11 37 30 10 7 12.0 11 44
241 12 27 32 12 8 10.0 11 53
242 8 35 35 12 11 19.0 11 34
243 12 28 28 10 3 16.0 11 35
244 12 35 33 13 6 16.0 11 40
245 15 37 31 15 10 14.0 11 53
246 11 29 35 12 8 20.0 11 42
247 13 32 35 14 9 15.0 11 43
248 14 36 32 10 9 23.0 11 29
249 10 19 21 12 8 20.0 11 36
250 12 21 20 12 9 16.0 11 30
251 15 31 34 11 7 14.0 11 42
252 13 33 32 10 7 17.0 11 47
253 13 36 34 12 6 11.0 11 44
254 13 33 32 16 9 13.0 11 45
255 12 37 33 12 10 17.0 11 44
256 12 34 33 14 11 15.0 11 43
257 9 35 37 16 12 21.0 11 43
258 9 31 32 14 8 18.0 11 40
259 15 37 34 13 11 15.0 11 41
260 10 35 30 4 3 8.0 11 52
261 14 27 30 15 11 12.0 11 38
262 15 34 38 11 12 12.0 11 41
263 7 40 36 11 7 22.0 11 39
264 14 29 32 14 9 12.0 11 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Depression
18.348299 0.003257 0.011763 0.093974 -0.018919 -0.367088
Month Sport2
-0.307801 0.038033
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9234 -1.5085 0.2344 1.3628 5.2166
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.348299 2.622730 6.996 2.3e-11 ***
Connected 0.003257 0.037455 0.087 0.9308
Separate 0.011763 0.038095 0.309 0.7577
Learning 0.093974 0.067103 1.400 0.1626
Software -0.018919 0.068779 -0.275 0.7835
Depression -0.367088 0.038716 -9.482 < 2e-16 ***
Month -0.307801 0.171249 -1.797 0.0735 .
Sport2 0.038033 0.018992 2.003 0.0463 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.008 on 256 degrees of freedom
Multiple R-squared: 0.3713, Adjusted R-squared: 0.3541
F-statistic: 21.6 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,] 0.08015115 0.160302307 0.919848847
[2,] 0.90233897 0.195322069 0.097661035
[3,] 0.98240959 0.035180829 0.017590415
[4,] 0.98221326 0.035573473 0.017786737
[5,] 0.98785756 0.024284880 0.012142440
[6,] 0.97827223 0.043455540 0.021727770
[7,] 0.96862679 0.062746411 0.031373205
[8,] 0.95666258 0.086674840 0.043337420
[9,] 0.94188149 0.116237010 0.058118505
[10,] 0.93464696 0.130706082 0.065353041
[11,] 0.94341359 0.113172818 0.056586409
[12,] 0.96894069 0.062118614 0.031059307
[13,] 0.95525012 0.089499757 0.044749878
[14,] 0.94531278 0.109374434 0.054687217
[15,] 0.92625694 0.147486116 0.073743058
[16,] 0.99894494 0.002110111 0.001055056
[17,] 0.99838916 0.003221673 0.001610836
[18,] 0.99745561 0.005088789 0.002544394
[19,] 0.99614026 0.007719477 0.003859739
[20,] 0.99816950 0.003660995 0.001830498
[21,] 0.99716170 0.005676608 0.002838304
[22,] 0.99575754 0.008484911 0.004242456
[23,] 0.99475259 0.010494816 0.005247408
[24,] 0.99238149 0.015237021 0.007618511
[25,] 0.98945290 0.021094208 0.010547104
[26,] 0.98513129 0.029737423 0.014868712
[27,] 0.98878461 0.022430770 0.011215385
[28,] 0.98446348 0.031073041 0.015536520
[29,] 0.98239036 0.035219279 0.017609640
[30,] 0.98267138 0.034657242 0.017328621
[31,] 0.97731257 0.045374859 0.022687430
[32,] 0.97571181 0.048576385 0.024288192
[33,] 0.96795419 0.064091628 0.032045814
[34,] 0.96177163 0.076456737 0.038228368
[35,] 0.95068408 0.098631841 0.049315921
[36,] 0.95915659 0.081686824 0.040843412
[37,] 0.94771407 0.104571857 0.052285929
[38,] 0.93377224 0.132455512 0.066227756
[39,] 0.95167294 0.096654124 0.048327062
[40,] 0.94831777 0.103364453 0.051682226
[41,] 0.93659317 0.126813659 0.063406829
[42,] 0.92154780 0.156904406 0.078452203
[43,] 0.90781416 0.184371675 0.092185838
[44,] 0.90997384 0.180052330 0.090026165
[45,] 0.90327334 0.193453329 0.096726665
[46,] 0.88700346 0.225993086 0.112996543
[47,] 0.87578982 0.248420354 0.124210177
[48,] 0.85247747 0.295045063 0.147522531
[49,] 0.87979521 0.240409574 0.120204787
[50,] 0.87578736 0.248425274 0.124212637
[51,] 0.89603872 0.207922554 0.103961277
[52,] 0.89156593 0.216868140 0.108434070
[53,] 0.93295198 0.134096049 0.067048025
[54,] 0.92260966 0.154780671 0.077390336
[55,] 0.91228832 0.175423369 0.087711684
[56,] 0.92341743 0.153165137 0.076582568
[57,] 0.92127275 0.157454499 0.078727250
[58,] 0.91400103 0.171997944 0.085998972
[59,] 0.94277091 0.114458179 0.057229090
[60,] 0.94722444 0.105551126 0.052775563
[61,] 0.93925323 0.121493534 0.060746767
[62,] 0.96022471 0.079550574 0.039775287
[63,] 0.95224835 0.095503300 0.047751650
[64,] 0.94151536 0.116969283 0.058484642
[65,] 0.93473229 0.130535415 0.065267707
[66,] 0.92117531 0.157649381 0.078824690
[67,] 0.93674578 0.126508434 0.063254217
[68,] 0.92448072 0.151038554 0.075519277
[69,] 0.91752519 0.164949619 0.082474810
[70,] 0.91184827 0.176303463 0.088151732
[71,] 0.89546655 0.209066903 0.104533452
[72,] 0.87781039 0.244379224 0.122189612
[73,] 0.87361458 0.252770840 0.126385420
[74,] 0.85525224 0.289495512 0.144747756
[75,] 0.83238101 0.335237989 0.167618995
[76,] 0.81039801 0.379203988 0.189601994
[77,] 0.78388333 0.432233349 0.216116674
[78,] 0.75507924 0.489841526 0.244920763
[79,] 0.82262961 0.354740787 0.177370393
[80,] 0.85061264 0.298774723 0.149387361
[81,] 0.82832558 0.343348850 0.171674425
[82,] 0.80568757 0.388624857 0.194312429
[83,] 0.78463280 0.430734409 0.215367204
[84,] 0.76153817 0.476923662 0.238461831
[85,] 0.73703700 0.525925997 0.262962999
[86,] 0.71339281 0.573214373 0.286607187
[87,] 0.68675929 0.626481430 0.313240715
[88,] 0.68674684 0.626506316 0.313253158
[89,] 0.65353537 0.692929259 0.346464629
[90,] 0.64462434 0.710751322 0.355375661
[91,] 0.62173682 0.756526352 0.378263176
[92,] 0.59258020 0.814839593 0.407419797
[93,] 0.65283594 0.694328115 0.347164057
[94,] 0.64094380 0.718112404 0.359056202
[95,] 0.65811358 0.683772847 0.341886424
[96,] 0.63073078 0.738538441 0.369269221
[97,] 0.62193742 0.756125155 0.378062578
[98,] 0.66199418 0.676011640 0.338005820
[99,] 0.63260584 0.734788322 0.367394161
[100,] 0.60570194 0.788596119 0.394298059
[101,] 0.61102898 0.777942040 0.388971020
[102,] 0.64128853 0.717422948 0.358711474
[103,] 0.65111830 0.697763397 0.348881699
[104,] 0.72631034 0.547379330 0.273689665
[105,] 0.69742307 0.605153860 0.302576930
[106,] 0.67221141 0.655577172 0.327788586
[107,] 0.64180348 0.716393036 0.358196518
[108,] 0.62030803 0.759383935 0.379691967
[109,] 0.58576252 0.828474967 0.414237484
[110,] 0.55555720 0.888885598 0.444442799
[111,] 0.52552567 0.948948656 0.474474328
[112,] 0.49159682 0.983193637 0.508403182
[113,] 0.46567622 0.931352446 0.534323777
[114,] 0.43168353 0.863367062 0.568316469
[115,] 0.42286100 0.845721997 0.577139001
[116,] 0.39197542 0.783950848 0.608024576
[117,] 0.37628854 0.752577088 0.623711456
[118,] 0.46822532 0.936450636 0.531774682
[119,] 0.44743071 0.894861426 0.552569287
[120,] 0.43675838 0.873516767 0.563241617
[121,] 0.42544341 0.850886815 0.574556593
[122,] 0.39459708 0.789194157 0.605402922
[123,] 0.40836770 0.816735405 0.591632298
[124,] 0.37858191 0.757163826 0.621418087
[125,] 0.38525145 0.770502901 0.614748549
[126,] 0.37120368 0.742407362 0.628796319
[127,] 0.34034025 0.680680509 0.659659746
[128,] 0.31858047 0.637160941 0.681419530
[129,] 0.29119604 0.582392083 0.708803958
[130,] 0.26687118 0.533742352 0.733128824
[131,] 0.23875621 0.477512412 0.761243794
[132,] 0.24389302 0.487786035 0.756106983
[133,] 0.21863598 0.437271964 0.781364018
[134,] 0.19726181 0.394523621 0.802738189
[135,] 0.18354400 0.367088005 0.816455997
[136,] 0.17468487 0.349369736 0.825315132
[137,] 0.18403447 0.368068946 0.815965527
[138,] 0.20331560 0.406631195 0.796684402
[139,] 0.22568902 0.451378048 0.774310976
[140,] 0.22170798 0.443415955 0.778292022
[141,] 0.19791659 0.395833179 0.802083410
[142,] 0.17719209 0.354384178 0.822807911
[143,] 0.18983348 0.379666960 0.810166520
[144,] 0.22909850 0.458197006 0.770901497
[145,] 0.20562679 0.411253575 0.794373212
[146,] 0.18423378 0.368467563 0.815766219
[147,] 0.16129290 0.322585802 0.838707099
[148,] 0.23215405 0.464308108 0.767845946
[149,] 0.25936687 0.518733749 0.740633126
[150,] 0.23207101 0.464142025 0.767928988
[151,] 0.20502187 0.410043740 0.794978130
[152,] 0.18132283 0.362645662 0.818677169
[153,] 0.16056818 0.321136356 0.839431822
[154,] 0.26261778 0.525235560 0.737382220
[155,] 0.25823675 0.516473504 0.741763248
[156,] 0.25547983 0.510959668 0.744520166
[157,] 0.22738936 0.454778721 0.772610639
[158,] 0.20611795 0.412235903 0.793882049
[159,] 0.26183450 0.523668995 0.738165502
[160,] 0.28395251 0.567905020 0.716047490
[161,] 0.25662781 0.513255627 0.743372186
[162,] 0.25327341 0.506546817 0.746726592
[163,] 0.41090099 0.821801990 0.589099005
[164,] 0.39937223 0.798744458 0.600627771
[165,] 0.42427899 0.848557988 0.575721006
[166,] 0.43021778 0.860435557 0.569782221
[167,] 0.48282009 0.965640178 0.517179911
[168,] 0.44969814 0.899396282 0.550301859
[169,] 0.42991260 0.859825207 0.570087397
[170,] 0.42526275 0.850525495 0.574737253
[171,] 0.38816510 0.776330190 0.611834905
[172,] 0.37790373 0.755807461 0.622096270
[173,] 0.34194124 0.683882477 0.658058761
[174,] 0.31358136 0.627162723 0.686418639
[175,] 0.32995027 0.659900532 0.670049734
[176,] 0.32297353 0.645947057 0.677026471
[177,] 0.28994697 0.579893935 0.710053033
[178,] 0.26250848 0.525016958 0.737491521
[179,] 0.23327463 0.466549266 0.766725367
[180,] 0.21304721 0.426094427 0.786952786
[181,] 0.21730066 0.434601320 0.782699340
[182,] 0.19823553 0.396471061 0.801764470
[183,] 0.21687922 0.433758449 0.783120776
[184,] 0.20395936 0.407918724 0.796040638
[185,] 0.20503742 0.410074848 0.794962576
[186,] 0.21288027 0.425760536 0.787119732
[187,] 0.25658704 0.513174084 0.743412958
[188,] 0.23717195 0.474343909 0.762828046
[189,] 0.26697125 0.533942504 0.733028748
[190,] 0.23531987 0.470639736 0.764680132
[191,] 0.26617923 0.532358454 0.733820773
[192,] 0.24568660 0.491373202 0.754313399
[193,] 0.28544359 0.570887181 0.714556410
[194,] 0.25014908 0.500298157 0.749850921
[195,] 0.21934862 0.438697242 0.780651379
[196,] 0.20062237 0.401244744 0.799377628
[197,] 0.17205922 0.344118440 0.827940780
[198,] 0.20184719 0.403694372 0.798152814
[199,] 0.17243232 0.344864637 0.827567682
[200,] 0.17522395 0.350447901 0.824776050
[201,] 0.19524111 0.390482215 0.804758893
[202,] 0.18630507 0.372610143 0.813694928
[203,] 0.16082778 0.321655562 0.839172219
[204,] 0.20336148 0.406722957 0.796638522
[205,] 0.17947766 0.358955320 0.820522340
[206,] 0.16959302 0.339186036 0.830406982
[207,] 0.19100148 0.382002960 0.808998520
[208,] 0.16198217 0.323964337 0.838017832
[209,] 0.15181887 0.303637746 0.848181127
[210,] 0.16101230 0.322024600 0.838987700
[211,] 0.17394800 0.347896004 0.826051998
[212,] 0.16996948 0.339938952 0.830030524
[213,] 0.15982438 0.319648761 0.840175620
[214,] 0.13218020 0.264360392 0.867819804
[215,] 0.14083284 0.281665679 0.859167160
[216,] 0.16472302 0.329446037 0.835276981
[217,] 0.36208982 0.724179632 0.637910184
[218,] 0.33826468 0.676529352 0.661735324
[219,] 0.31136432 0.622728638 0.688635681
[220,] 0.29503473 0.590069452 0.704965274
[221,] 0.25354851 0.507097025 0.746451488
[222,] 0.28361905 0.567238100 0.716380950
[223,] 0.24348169 0.486963385 0.756518308
[224,] 0.20298668 0.405973350 0.797013325
[225,] 0.20211407 0.404228132 0.797885934
[226,] 0.18086567 0.361731332 0.819134334
[227,] 0.15200460 0.304009206 0.847995397
[228,] 0.11767837 0.235356749 0.882321625
[229,] 0.23120020 0.462400393 0.768799803
[230,] 0.22451561 0.449031227 0.775484386
[231,] 0.19235326 0.384706515 0.807646742
[232,] 0.38383338 0.767666753 0.616166623
[233,] 0.33875307 0.677506149 0.661246925
[234,] 0.27620265 0.552405304 0.723797348
[235,] 0.41581168 0.831623361 0.584188320
[236,] 0.33235639 0.664712773 0.667643613
[237,] 0.25809726 0.516194513 0.741902744
[238,] 0.39264248 0.785284958 0.607357521
[239,] 0.29507052 0.590141044 0.704929478
[240,] 0.20639884 0.412797671 0.793601165
[241,] 0.31798072 0.635961433 0.682019284
[242,] 0.86923652 0.261526970 0.130763485
[243,] 0.74493562 0.510128761 0.255064381
> postscript(file="/var/wessaorg/rcomp/tmp/15bdh1384699267.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ret61384699267.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3sxzh1384699267.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4sxt61384699267.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5gv6x1384699267.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
0.034736322 2.721281062 -3.047383383 -2.517630388 4.765584429 3.551822597
7 8 9 10 11 12
3.040469164 -1.082690147 -0.236262696 0.599659689 1.426359369 3.309765812
13 14 15 16 17 18
-3.481185755 2.436553114 2.220555115 0.516965198 0.135568944 1.182882268
19 20 21 22 23 24
-1.524010031 2.067665651 2.562011073 -2.854486021 -0.541709228 -1.720826096
25 26 27 28 29 30
1.608192958 -6.923416623 0.981104529 0.567320462 1.077440503 -3.104761500
31 32 33 34 35 36
0.213883175 0.232448302 1.845760706 -0.416428305 0.044431126 0.385451508
37 38 39 40 41 42
-1.872123221 0.597505916 1.569771690 -2.321167633 -0.797104895 2.300164619
43 44 45 46 47 48
0.007637050 -1.246506770 0.282914381 -2.666168548 -0.561934630 0.001434808
49 50 51 52 53 54
3.471119951 -1.840398772 0.629397707 0.456261428 -0.770790453 -2.003746785
55 56 57 58 59 60
-2.141122554 1.222800070 1.679045702 -0.642638639 -3.288111192 -1.535521180
61 62 63 64 65 66
-2.875714095 -1.684785580 -3.759780201 0.761914225 1.187012288 -4.996777499
67 68 69 70 71 72
-1.582804891 -2.323321851 1.558392163 1.445771422 0.492780399 3.325448368
73 74 75 76 77 78
0.680107094 -0.153452961 -1.838864629 -0.066430937 3.053054863 0.525889645
79 80 81 82 83 84
1.263823065 -1.936259342 0.210653380 -0.503768597 1.808075027 0.755646241
85 86 87 88 89 90
-0.072223566 1.062479394 -0.246601322 0.233512473 -3.508011661 3.270485608
91 92 93 94 95 96
0.173494357 0.879499576 0.794279800 -0.911215069 1.115240079 -0.822153210
97 98 99 100 101 102
-0.793801099 2.145086069 -0.003066841 1.903149263 -0.936056016 1.008870642
103 104 105 106 107 108
-3.464797432 1.953854456 -2.356300146 1.074123830 2.098846154 -2.843496423
109 110 111 112 113 114
0.769476398 1.117021403 -2.165285799 -2.300092157 2.311698523 3.888266884
115 116 117 118 119 120
0.442331400 0.966156665 0.082858575 -1.096625723 0.320428173 -0.575328354
121 122 123 124 125 126
0.443221407 0.136740167 -0.927362172 0.410623976 -1.728818527 0.850741570
127 128 129 130 131 132
1.735618097 3.981643240 1.406649522 -1.636506990 -1.649088726 -0.266673919
133 134 135 136 137 138
2.354303978 0.729135302 2.225265202 1.516276658 0.547610677 -1.081977068
139 140 141 142 143 144
0.756083131 -0.800702353 0.289096773 2.156982026 -0.704169235 0.718259921
145 146 147 148 149 150
1.392547188 1.479806015 -2.478757569 -2.727273075 -2.586933611 1.729190132
151 152 153 154 155 156
0.446966882 0.583099110 -2.501510170 -2.926827813 1.137096946 0.173494357
157 158 159 160 161 162
0.585920485 3.981643240 -2.688809319 -0.091904939 0.249086263 0.785962952
163 164 165 166 167 168
1.006056049 4.632987107 -1.780239330 2.006004083 0.023568589 -0.682147388
169 170 171 172 173 174
-3.543094606 -2.713984092 0.567894333 1.940072828 -4.828724452 1.844994186
175 176 177 178 179 180
2.788928191 -2.049581763 -3.209522721 0.677728241 1.556970219 -1.937058510
181 182 183 184 185 186
0.003105944 -1.605135918 0.375866939 -0.799958012 2.057890902 1.534484033
187 188 189 190 191 192
0.634678951 0.977923644 0.713910307 0.796263811 -1.225023099 -0.835908269
193 194 195 196 197 198
2.383113132 -1.493137266 1.990180249 -1.980973263 2.377899263 0.708816585
199 200 201 202 203 204
-3.036063083 -0.601450565 -3.122598890 1.363743231 3.139258823 0.457771069
205 206 207 208 209 210
0.674377336 1.362461875 -0.493947127 3.550782107 0.153336984 1.821581541
211 212 213 214 215 216
-2.642889689 1.507959849 -1.173164447 -3.786343101 -1.045570856 1.738586773
217 218 219 220 221 222
2.189128675 -0.188558408 -1.855007680 1.440436977 -2.803758965 2.589856255
223 224 225 226 227 228
-2.061922611 0.233050657 -0.257611904 1.792904652 5.216555989 -1.503347857
229 230 231 232 233 234
-1.479206204 -2.305624284 0.235372773 -3.067556569 0.077873584 0.580106124
235 236 237 238 239 240
1.148223593 -1.785103896 0.640584773 0.125075406 -4.295603173 -2.511589422
241 242 243 244 245 246
-2.748044157 -2.726219886 0.276223403 -0.220722667 1.455415755 0.299394568
247 248 249 250 251 252
0.247118271 5.114440522 -0.275152386 0.508856960 2.177168257 1.199257324
253 254 255 256 257 258
-1.129341349 -0.719039874 0.157370826 -0.698031879 -1.714841282 -2.517890462
259 260 261 262 263 264
2.450472820 -4.789518646 0.354981312 1.518793431 -2.824867493 0.190914245
> postscript(file="/var/wessaorg/rcomp/tmp/66gq01384699267.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 0.034736322 NA
1 2.721281062 0.034736322
2 -3.047383383 2.721281062
3 -2.517630388 -3.047383383
4 4.765584429 -2.517630388
5 3.551822597 4.765584429
6 3.040469164 3.551822597
7 -1.082690147 3.040469164
8 -0.236262696 -1.082690147
9 0.599659689 -0.236262696
10 1.426359369 0.599659689
11 3.309765812 1.426359369
12 -3.481185755 3.309765812
13 2.436553114 -3.481185755
14 2.220555115 2.436553114
15 0.516965198 2.220555115
16 0.135568944 0.516965198
17 1.182882268 0.135568944
18 -1.524010031 1.182882268
19 2.067665651 -1.524010031
20 2.562011073 2.067665651
21 -2.854486021 2.562011073
22 -0.541709228 -2.854486021
23 -1.720826096 -0.541709228
24 1.608192958 -1.720826096
25 -6.923416623 1.608192958
26 0.981104529 -6.923416623
27 0.567320462 0.981104529
28 1.077440503 0.567320462
29 -3.104761500 1.077440503
30 0.213883175 -3.104761500
31 0.232448302 0.213883175
32 1.845760706 0.232448302
33 -0.416428305 1.845760706
34 0.044431126 -0.416428305
35 0.385451508 0.044431126
36 -1.872123221 0.385451508
37 0.597505916 -1.872123221
38 1.569771690 0.597505916
39 -2.321167633 1.569771690
40 -0.797104895 -2.321167633
41 2.300164619 -0.797104895
42 0.007637050 2.300164619
43 -1.246506770 0.007637050
44 0.282914381 -1.246506770
45 -2.666168548 0.282914381
46 -0.561934630 -2.666168548
47 0.001434808 -0.561934630
48 3.471119951 0.001434808
49 -1.840398772 3.471119951
50 0.629397707 -1.840398772
51 0.456261428 0.629397707
52 -0.770790453 0.456261428
53 -2.003746785 -0.770790453
54 -2.141122554 -2.003746785
55 1.222800070 -2.141122554
56 1.679045702 1.222800070
57 -0.642638639 1.679045702
58 -3.288111192 -0.642638639
59 -1.535521180 -3.288111192
60 -2.875714095 -1.535521180
61 -1.684785580 -2.875714095
62 -3.759780201 -1.684785580
63 0.761914225 -3.759780201
64 1.187012288 0.761914225
65 -4.996777499 1.187012288
66 -1.582804891 -4.996777499
67 -2.323321851 -1.582804891
68 1.558392163 -2.323321851
69 1.445771422 1.558392163
70 0.492780399 1.445771422
71 3.325448368 0.492780399
72 0.680107094 3.325448368
73 -0.153452961 0.680107094
74 -1.838864629 -0.153452961
75 -0.066430937 -1.838864629
76 3.053054863 -0.066430937
77 0.525889645 3.053054863
78 1.263823065 0.525889645
79 -1.936259342 1.263823065
80 0.210653380 -1.936259342
81 -0.503768597 0.210653380
82 1.808075027 -0.503768597
83 0.755646241 1.808075027
84 -0.072223566 0.755646241
85 1.062479394 -0.072223566
86 -0.246601322 1.062479394
87 0.233512473 -0.246601322
88 -3.508011661 0.233512473
89 3.270485608 -3.508011661
90 0.173494357 3.270485608
91 0.879499576 0.173494357
92 0.794279800 0.879499576
93 -0.911215069 0.794279800
94 1.115240079 -0.911215069
95 -0.822153210 1.115240079
96 -0.793801099 -0.822153210
97 2.145086069 -0.793801099
98 -0.003066841 2.145086069
99 1.903149263 -0.003066841
100 -0.936056016 1.903149263
101 1.008870642 -0.936056016
102 -3.464797432 1.008870642
103 1.953854456 -3.464797432
104 -2.356300146 1.953854456
105 1.074123830 -2.356300146
106 2.098846154 1.074123830
107 -2.843496423 2.098846154
108 0.769476398 -2.843496423
109 1.117021403 0.769476398
110 -2.165285799 1.117021403
111 -2.300092157 -2.165285799
112 2.311698523 -2.300092157
113 3.888266884 2.311698523
114 0.442331400 3.888266884
115 0.966156665 0.442331400
116 0.082858575 0.966156665
117 -1.096625723 0.082858575
118 0.320428173 -1.096625723
119 -0.575328354 0.320428173
120 0.443221407 -0.575328354
121 0.136740167 0.443221407
122 -0.927362172 0.136740167
123 0.410623976 -0.927362172
124 -1.728818527 0.410623976
125 0.850741570 -1.728818527
126 1.735618097 0.850741570
127 3.981643240 1.735618097
128 1.406649522 3.981643240
129 -1.636506990 1.406649522
130 -1.649088726 -1.636506990
131 -0.266673919 -1.649088726
132 2.354303978 -0.266673919
133 0.729135302 2.354303978
134 2.225265202 0.729135302
135 1.516276658 2.225265202
136 0.547610677 1.516276658
137 -1.081977068 0.547610677
138 0.756083131 -1.081977068
139 -0.800702353 0.756083131
140 0.289096773 -0.800702353
141 2.156982026 0.289096773
142 -0.704169235 2.156982026
143 0.718259921 -0.704169235
144 1.392547188 0.718259921
145 1.479806015 1.392547188
146 -2.478757569 1.479806015
147 -2.727273075 -2.478757569
148 -2.586933611 -2.727273075
149 1.729190132 -2.586933611
150 0.446966882 1.729190132
151 0.583099110 0.446966882
152 -2.501510170 0.583099110
153 -2.926827813 -2.501510170
154 1.137096946 -2.926827813
155 0.173494357 1.137096946
156 0.585920485 0.173494357
157 3.981643240 0.585920485
158 -2.688809319 3.981643240
159 -0.091904939 -2.688809319
160 0.249086263 -0.091904939
161 0.785962952 0.249086263
162 1.006056049 0.785962952
163 4.632987107 1.006056049
164 -1.780239330 4.632987107
165 2.006004083 -1.780239330
166 0.023568589 2.006004083
167 -0.682147388 0.023568589
168 -3.543094606 -0.682147388
169 -2.713984092 -3.543094606
170 0.567894333 -2.713984092
171 1.940072828 0.567894333
172 -4.828724452 1.940072828
173 1.844994186 -4.828724452
174 2.788928191 1.844994186
175 -2.049581763 2.788928191
176 -3.209522721 -2.049581763
177 0.677728241 -3.209522721
178 1.556970219 0.677728241
179 -1.937058510 1.556970219
180 0.003105944 -1.937058510
181 -1.605135918 0.003105944
182 0.375866939 -1.605135918
183 -0.799958012 0.375866939
184 2.057890902 -0.799958012
185 1.534484033 2.057890902
186 0.634678951 1.534484033
187 0.977923644 0.634678951
188 0.713910307 0.977923644
189 0.796263811 0.713910307
190 -1.225023099 0.796263811
191 -0.835908269 -1.225023099
192 2.383113132 -0.835908269
193 -1.493137266 2.383113132
194 1.990180249 -1.493137266
195 -1.980973263 1.990180249
196 2.377899263 -1.980973263
197 0.708816585 2.377899263
198 -3.036063083 0.708816585
199 -0.601450565 -3.036063083
200 -3.122598890 -0.601450565
201 1.363743231 -3.122598890
202 3.139258823 1.363743231
203 0.457771069 3.139258823
204 0.674377336 0.457771069
205 1.362461875 0.674377336
206 -0.493947127 1.362461875
207 3.550782107 -0.493947127
208 0.153336984 3.550782107
209 1.821581541 0.153336984
210 -2.642889689 1.821581541
211 1.507959849 -2.642889689
212 -1.173164447 1.507959849
213 -3.786343101 -1.173164447
214 -1.045570856 -3.786343101
215 1.738586773 -1.045570856
216 2.189128675 1.738586773
217 -0.188558408 2.189128675
218 -1.855007680 -0.188558408
219 1.440436977 -1.855007680
220 -2.803758965 1.440436977
221 2.589856255 -2.803758965
222 -2.061922611 2.589856255
223 0.233050657 -2.061922611
224 -0.257611904 0.233050657
225 1.792904652 -0.257611904
226 5.216555989 1.792904652
227 -1.503347857 5.216555989
228 -1.479206204 -1.503347857
229 -2.305624284 -1.479206204
230 0.235372773 -2.305624284
231 -3.067556569 0.235372773
232 0.077873584 -3.067556569
233 0.580106124 0.077873584
234 1.148223593 0.580106124
235 -1.785103896 1.148223593
236 0.640584773 -1.785103896
237 0.125075406 0.640584773
238 -4.295603173 0.125075406
239 -2.511589422 -4.295603173
240 -2.748044157 -2.511589422
241 -2.726219886 -2.748044157
242 0.276223403 -2.726219886
243 -0.220722667 0.276223403
244 1.455415755 -0.220722667
245 0.299394568 1.455415755
246 0.247118271 0.299394568
247 5.114440522 0.247118271
248 -0.275152386 5.114440522
249 0.508856960 -0.275152386
250 2.177168257 0.508856960
251 1.199257324 2.177168257
252 -1.129341349 1.199257324
253 -0.719039874 -1.129341349
254 0.157370826 -0.719039874
255 -0.698031879 0.157370826
256 -1.714841282 -0.698031879
257 -2.517890462 -1.714841282
258 2.450472820 -2.517890462
259 -4.789518646 2.450472820
260 0.354981312 -4.789518646
261 1.518793431 0.354981312
262 -2.824867493 1.518793431
263 0.190914245 -2.824867493
264 NA 0.190914245
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.721281062 0.034736322
[2,] -3.047383383 2.721281062
[3,] -2.517630388 -3.047383383
[4,] 4.765584429 -2.517630388
[5,] 3.551822597 4.765584429
[6,] 3.040469164 3.551822597
[7,] -1.082690147 3.040469164
[8,] -0.236262696 -1.082690147
[9,] 0.599659689 -0.236262696
[10,] 1.426359369 0.599659689
[11,] 3.309765812 1.426359369
[12,] -3.481185755 3.309765812
[13,] 2.436553114 -3.481185755
[14,] 2.220555115 2.436553114
[15,] 0.516965198 2.220555115
[16,] 0.135568944 0.516965198
[17,] 1.182882268 0.135568944
[18,] -1.524010031 1.182882268
[19,] 2.067665651 -1.524010031
[20,] 2.562011073 2.067665651
[21,] -2.854486021 2.562011073
[22,] -0.541709228 -2.854486021
[23,] -1.720826096 -0.541709228
[24,] 1.608192958 -1.720826096
[25,] -6.923416623 1.608192958
[26,] 0.981104529 -6.923416623
[27,] 0.567320462 0.981104529
[28,] 1.077440503 0.567320462
[29,] -3.104761500 1.077440503
[30,] 0.213883175 -3.104761500
[31,] 0.232448302 0.213883175
[32,] 1.845760706 0.232448302
[33,] -0.416428305 1.845760706
[34,] 0.044431126 -0.416428305
[35,] 0.385451508 0.044431126
[36,] -1.872123221 0.385451508
[37,] 0.597505916 -1.872123221
[38,] 1.569771690 0.597505916
[39,] -2.321167633 1.569771690
[40,] -0.797104895 -2.321167633
[41,] 2.300164619 -0.797104895
[42,] 0.007637050 2.300164619
[43,] -1.246506770 0.007637050
[44,] 0.282914381 -1.246506770
[45,] -2.666168548 0.282914381
[46,] -0.561934630 -2.666168548
[47,] 0.001434808 -0.561934630
[48,] 3.471119951 0.001434808
[49,] -1.840398772 3.471119951
[50,] 0.629397707 -1.840398772
[51,] 0.456261428 0.629397707
[52,] -0.770790453 0.456261428
[53,] -2.003746785 -0.770790453
[54,] -2.141122554 -2.003746785
[55,] 1.222800070 -2.141122554
[56,] 1.679045702 1.222800070
[57,] -0.642638639 1.679045702
[58,] -3.288111192 -0.642638639
[59,] -1.535521180 -3.288111192
[60,] -2.875714095 -1.535521180
[61,] -1.684785580 -2.875714095
[62,] -3.759780201 -1.684785580
[63,] 0.761914225 -3.759780201
[64,] 1.187012288 0.761914225
[65,] -4.996777499 1.187012288
[66,] -1.582804891 -4.996777499
[67,] -2.323321851 -1.582804891
[68,] 1.558392163 -2.323321851
[69,] 1.445771422 1.558392163
[70,] 0.492780399 1.445771422
[71,] 3.325448368 0.492780399
[72,] 0.680107094 3.325448368
[73,] -0.153452961 0.680107094
[74,] -1.838864629 -0.153452961
[75,] -0.066430937 -1.838864629
[76,] 3.053054863 -0.066430937
[77,] 0.525889645 3.053054863
[78,] 1.263823065 0.525889645
[79,] -1.936259342 1.263823065
[80,] 0.210653380 -1.936259342
[81,] -0.503768597 0.210653380
[82,] 1.808075027 -0.503768597
[83,] 0.755646241 1.808075027
[84,] -0.072223566 0.755646241
[85,] 1.062479394 -0.072223566
[86,] -0.246601322 1.062479394
[87,] 0.233512473 -0.246601322
[88,] -3.508011661 0.233512473
[89,] 3.270485608 -3.508011661
[90,] 0.173494357 3.270485608
[91,] 0.879499576 0.173494357
[92,] 0.794279800 0.879499576
[93,] -0.911215069 0.794279800
[94,] 1.115240079 -0.911215069
[95,] -0.822153210 1.115240079
[96,] -0.793801099 -0.822153210
[97,] 2.145086069 -0.793801099
[98,] -0.003066841 2.145086069
[99,] 1.903149263 -0.003066841
[100,] -0.936056016 1.903149263
[101,] 1.008870642 -0.936056016
[102,] -3.464797432 1.008870642
[103,] 1.953854456 -3.464797432
[104,] -2.356300146 1.953854456
[105,] 1.074123830 -2.356300146
[106,] 2.098846154 1.074123830
[107,] -2.843496423 2.098846154
[108,] 0.769476398 -2.843496423
[109,] 1.117021403 0.769476398
[110,] -2.165285799 1.117021403
[111,] -2.300092157 -2.165285799
[112,] 2.311698523 -2.300092157
[113,] 3.888266884 2.311698523
[114,] 0.442331400 3.888266884
[115,] 0.966156665 0.442331400
[116,] 0.082858575 0.966156665
[117,] -1.096625723 0.082858575
[118,] 0.320428173 -1.096625723
[119,] -0.575328354 0.320428173
[120,] 0.443221407 -0.575328354
[121,] 0.136740167 0.443221407
[122,] -0.927362172 0.136740167
[123,] 0.410623976 -0.927362172
[124,] -1.728818527 0.410623976
[125,] 0.850741570 -1.728818527
[126,] 1.735618097 0.850741570
[127,] 3.981643240 1.735618097
[128,] 1.406649522 3.981643240
[129,] -1.636506990 1.406649522
[130,] -1.649088726 -1.636506990
[131,] -0.266673919 -1.649088726
[132,] 2.354303978 -0.266673919
[133,] 0.729135302 2.354303978
[134,] 2.225265202 0.729135302
[135,] 1.516276658 2.225265202
[136,] 0.547610677 1.516276658
[137,] -1.081977068 0.547610677
[138,] 0.756083131 -1.081977068
[139,] -0.800702353 0.756083131
[140,] 0.289096773 -0.800702353
[141,] 2.156982026 0.289096773
[142,] -0.704169235 2.156982026
[143,] 0.718259921 -0.704169235
[144,] 1.392547188 0.718259921
[145,] 1.479806015 1.392547188
[146,] -2.478757569 1.479806015
[147,] -2.727273075 -2.478757569
[148,] -2.586933611 -2.727273075
[149,] 1.729190132 -2.586933611
[150,] 0.446966882 1.729190132
[151,] 0.583099110 0.446966882
[152,] -2.501510170 0.583099110
[153,] -2.926827813 -2.501510170
[154,] 1.137096946 -2.926827813
[155,] 0.173494357 1.137096946
[156,] 0.585920485 0.173494357
[157,] 3.981643240 0.585920485
[158,] -2.688809319 3.981643240
[159,] -0.091904939 -2.688809319
[160,] 0.249086263 -0.091904939
[161,] 0.785962952 0.249086263
[162,] 1.006056049 0.785962952
[163,] 4.632987107 1.006056049
[164,] -1.780239330 4.632987107
[165,] 2.006004083 -1.780239330
[166,] 0.023568589 2.006004083
[167,] -0.682147388 0.023568589
[168,] -3.543094606 -0.682147388
[169,] -2.713984092 -3.543094606
[170,] 0.567894333 -2.713984092
[171,] 1.940072828 0.567894333
[172,] -4.828724452 1.940072828
[173,] 1.844994186 -4.828724452
[174,] 2.788928191 1.844994186
[175,] -2.049581763 2.788928191
[176,] -3.209522721 -2.049581763
[177,] 0.677728241 -3.209522721
[178,] 1.556970219 0.677728241
[179,] -1.937058510 1.556970219
[180,] 0.003105944 -1.937058510
[181,] -1.605135918 0.003105944
[182,] 0.375866939 -1.605135918
[183,] -0.799958012 0.375866939
[184,] 2.057890902 -0.799958012
[185,] 1.534484033 2.057890902
[186,] 0.634678951 1.534484033
[187,] 0.977923644 0.634678951
[188,] 0.713910307 0.977923644
[189,] 0.796263811 0.713910307
[190,] -1.225023099 0.796263811
[191,] -0.835908269 -1.225023099
[192,] 2.383113132 -0.835908269
[193,] -1.493137266 2.383113132
[194,] 1.990180249 -1.493137266
[195,] -1.980973263 1.990180249
[196,] 2.377899263 -1.980973263
[197,] 0.708816585 2.377899263
[198,] -3.036063083 0.708816585
[199,] -0.601450565 -3.036063083
[200,] -3.122598890 -0.601450565
[201,] 1.363743231 -3.122598890
[202,] 3.139258823 1.363743231
[203,] 0.457771069 3.139258823
[204,] 0.674377336 0.457771069
[205,] 1.362461875 0.674377336
[206,] -0.493947127 1.362461875
[207,] 3.550782107 -0.493947127
[208,] 0.153336984 3.550782107
[209,] 1.821581541 0.153336984
[210,] -2.642889689 1.821581541
[211,] 1.507959849 -2.642889689
[212,] -1.173164447 1.507959849
[213,] -3.786343101 -1.173164447
[214,] -1.045570856 -3.786343101
[215,] 1.738586773 -1.045570856
[216,] 2.189128675 1.738586773
[217,] -0.188558408 2.189128675
[218,] -1.855007680 -0.188558408
[219,] 1.440436977 -1.855007680
[220,] -2.803758965 1.440436977
[221,] 2.589856255 -2.803758965
[222,] -2.061922611 2.589856255
[223,] 0.233050657 -2.061922611
[224,] -0.257611904 0.233050657
[225,] 1.792904652 -0.257611904
[226,] 5.216555989 1.792904652
[227,] -1.503347857 5.216555989
[228,] -1.479206204 -1.503347857
[229,] -2.305624284 -1.479206204
[230,] 0.235372773 -2.305624284
[231,] -3.067556569 0.235372773
[232,] 0.077873584 -3.067556569
[233,] 0.580106124 0.077873584
[234,] 1.148223593 0.580106124
[235,] -1.785103896 1.148223593
[236,] 0.640584773 -1.785103896
[237,] 0.125075406 0.640584773
[238,] -4.295603173 0.125075406
[239,] -2.511589422 -4.295603173
[240,] -2.748044157 -2.511589422
[241,] -2.726219886 -2.748044157
[242,] 0.276223403 -2.726219886
[243,] -0.220722667 0.276223403
[244,] 1.455415755 -0.220722667
[245,] 0.299394568 1.455415755
[246,] 0.247118271 0.299394568
[247,] 5.114440522 0.247118271
[248,] -0.275152386 5.114440522
[249,] 0.508856960 -0.275152386
[250,] 2.177168257 0.508856960
[251,] 1.199257324 2.177168257
[252,] -1.129341349 1.199257324
[253,] -0.719039874 -1.129341349
[254,] 0.157370826 -0.719039874
[255,] -0.698031879 0.157370826
[256,] -1.714841282 -0.698031879
[257,] -2.517890462 -1.714841282
[258,] 2.450472820 -2.517890462
[259,] -4.789518646 2.450472820
[260,] 0.354981312 -4.789518646
[261,] 1.518793431 0.354981312
[262,] -2.824867493 1.518793431
[263,] 0.190914245 -2.824867493
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.721281062 0.034736322
2 -3.047383383 2.721281062
3 -2.517630388 -3.047383383
4 4.765584429 -2.517630388
5 3.551822597 4.765584429
6 3.040469164 3.551822597
7 -1.082690147 3.040469164
8 -0.236262696 -1.082690147
9 0.599659689 -0.236262696
10 1.426359369 0.599659689
11 3.309765812 1.426359369
12 -3.481185755 3.309765812
13 2.436553114 -3.481185755
14 2.220555115 2.436553114
15 0.516965198 2.220555115
16 0.135568944 0.516965198
17 1.182882268 0.135568944
18 -1.524010031 1.182882268
19 2.067665651 -1.524010031
20 2.562011073 2.067665651
21 -2.854486021 2.562011073
22 -0.541709228 -2.854486021
23 -1.720826096 -0.541709228
24 1.608192958 -1.720826096
25 -6.923416623 1.608192958
26 0.981104529 -6.923416623
27 0.567320462 0.981104529
28 1.077440503 0.567320462
29 -3.104761500 1.077440503
30 0.213883175 -3.104761500
31 0.232448302 0.213883175
32 1.845760706 0.232448302
33 -0.416428305 1.845760706
34 0.044431126 -0.416428305
35 0.385451508 0.044431126
36 -1.872123221 0.385451508
37 0.597505916 -1.872123221
38 1.569771690 0.597505916
39 -2.321167633 1.569771690
40 -0.797104895 -2.321167633
41 2.300164619 -0.797104895
42 0.007637050 2.300164619
43 -1.246506770 0.007637050
44 0.282914381 -1.246506770
45 -2.666168548 0.282914381
46 -0.561934630 -2.666168548
47 0.001434808 -0.561934630
48 3.471119951 0.001434808
49 -1.840398772 3.471119951
50 0.629397707 -1.840398772
51 0.456261428 0.629397707
52 -0.770790453 0.456261428
53 -2.003746785 -0.770790453
54 -2.141122554 -2.003746785
55 1.222800070 -2.141122554
56 1.679045702 1.222800070
57 -0.642638639 1.679045702
58 -3.288111192 -0.642638639
59 -1.535521180 -3.288111192
60 -2.875714095 -1.535521180
61 -1.684785580 -2.875714095
62 -3.759780201 -1.684785580
63 0.761914225 -3.759780201
64 1.187012288 0.761914225
65 -4.996777499 1.187012288
66 -1.582804891 -4.996777499
67 -2.323321851 -1.582804891
68 1.558392163 -2.323321851
69 1.445771422 1.558392163
70 0.492780399 1.445771422
71 3.325448368 0.492780399
72 0.680107094 3.325448368
73 -0.153452961 0.680107094
74 -1.838864629 -0.153452961
75 -0.066430937 -1.838864629
76 3.053054863 -0.066430937
77 0.525889645 3.053054863
78 1.263823065 0.525889645
79 -1.936259342 1.263823065
80 0.210653380 -1.936259342
81 -0.503768597 0.210653380
82 1.808075027 -0.503768597
83 0.755646241 1.808075027
84 -0.072223566 0.755646241
85 1.062479394 -0.072223566
86 -0.246601322 1.062479394
87 0.233512473 -0.246601322
88 -3.508011661 0.233512473
89 3.270485608 -3.508011661
90 0.173494357 3.270485608
91 0.879499576 0.173494357
92 0.794279800 0.879499576
93 -0.911215069 0.794279800
94 1.115240079 -0.911215069
95 -0.822153210 1.115240079
96 -0.793801099 -0.822153210
97 2.145086069 -0.793801099
98 -0.003066841 2.145086069
99 1.903149263 -0.003066841
100 -0.936056016 1.903149263
101 1.008870642 -0.936056016
102 -3.464797432 1.008870642
103 1.953854456 -3.464797432
104 -2.356300146 1.953854456
105 1.074123830 -2.356300146
106 2.098846154 1.074123830
107 -2.843496423 2.098846154
108 0.769476398 -2.843496423
109 1.117021403 0.769476398
110 -2.165285799 1.117021403
111 -2.300092157 -2.165285799
112 2.311698523 -2.300092157
113 3.888266884 2.311698523
114 0.442331400 3.888266884
115 0.966156665 0.442331400
116 0.082858575 0.966156665
117 -1.096625723 0.082858575
118 0.320428173 -1.096625723
119 -0.575328354 0.320428173
120 0.443221407 -0.575328354
121 0.136740167 0.443221407
122 -0.927362172 0.136740167
123 0.410623976 -0.927362172
124 -1.728818527 0.410623976
125 0.850741570 -1.728818527
126 1.735618097 0.850741570
127 3.981643240 1.735618097
128 1.406649522 3.981643240
129 -1.636506990 1.406649522
130 -1.649088726 -1.636506990
131 -0.266673919 -1.649088726
132 2.354303978 -0.266673919
133 0.729135302 2.354303978
134 2.225265202 0.729135302
135 1.516276658 2.225265202
136 0.547610677 1.516276658
137 -1.081977068 0.547610677
138 0.756083131 -1.081977068
139 -0.800702353 0.756083131
140 0.289096773 -0.800702353
141 2.156982026 0.289096773
142 -0.704169235 2.156982026
143 0.718259921 -0.704169235
144 1.392547188 0.718259921
145 1.479806015 1.392547188
146 -2.478757569 1.479806015
147 -2.727273075 -2.478757569
148 -2.586933611 -2.727273075
149 1.729190132 -2.586933611
150 0.446966882 1.729190132
151 0.583099110 0.446966882
152 -2.501510170 0.583099110
153 -2.926827813 -2.501510170
154 1.137096946 -2.926827813
155 0.173494357 1.137096946
156 0.585920485 0.173494357
157 3.981643240 0.585920485
158 -2.688809319 3.981643240
159 -0.091904939 -2.688809319
160 0.249086263 -0.091904939
161 0.785962952 0.249086263
162 1.006056049 0.785962952
163 4.632987107 1.006056049
164 -1.780239330 4.632987107
165 2.006004083 -1.780239330
166 0.023568589 2.006004083
167 -0.682147388 0.023568589
168 -3.543094606 -0.682147388
169 -2.713984092 -3.543094606
170 0.567894333 -2.713984092
171 1.940072828 0.567894333
172 -4.828724452 1.940072828
173 1.844994186 -4.828724452
174 2.788928191 1.844994186
175 -2.049581763 2.788928191
176 -3.209522721 -2.049581763
177 0.677728241 -3.209522721
178 1.556970219 0.677728241
179 -1.937058510 1.556970219
180 0.003105944 -1.937058510
181 -1.605135918 0.003105944
182 0.375866939 -1.605135918
183 -0.799958012 0.375866939
184 2.057890902 -0.799958012
185 1.534484033 2.057890902
186 0.634678951 1.534484033
187 0.977923644 0.634678951
188 0.713910307 0.977923644
189 0.796263811 0.713910307
190 -1.225023099 0.796263811
191 -0.835908269 -1.225023099
192 2.383113132 -0.835908269
193 -1.493137266 2.383113132
194 1.990180249 -1.493137266
195 -1.980973263 1.990180249
196 2.377899263 -1.980973263
197 0.708816585 2.377899263
198 -3.036063083 0.708816585
199 -0.601450565 -3.036063083
200 -3.122598890 -0.601450565
201 1.363743231 -3.122598890
202 3.139258823 1.363743231
203 0.457771069 3.139258823
204 0.674377336 0.457771069
205 1.362461875 0.674377336
206 -0.493947127 1.362461875
207 3.550782107 -0.493947127
208 0.153336984 3.550782107
209 1.821581541 0.153336984
210 -2.642889689 1.821581541
211 1.507959849 -2.642889689
212 -1.173164447 1.507959849
213 -3.786343101 -1.173164447
214 -1.045570856 -3.786343101
215 1.738586773 -1.045570856
216 2.189128675 1.738586773
217 -0.188558408 2.189128675
218 -1.855007680 -0.188558408
219 1.440436977 -1.855007680
220 -2.803758965 1.440436977
221 2.589856255 -2.803758965
222 -2.061922611 2.589856255
223 0.233050657 -2.061922611
224 -0.257611904 0.233050657
225 1.792904652 -0.257611904
226 5.216555989 1.792904652
227 -1.503347857 5.216555989
228 -1.479206204 -1.503347857
229 -2.305624284 -1.479206204
230 0.235372773 -2.305624284
231 -3.067556569 0.235372773
232 0.077873584 -3.067556569
233 0.580106124 0.077873584
234 1.148223593 0.580106124
235 -1.785103896 1.148223593
236 0.640584773 -1.785103896
237 0.125075406 0.640584773
238 -4.295603173 0.125075406
239 -2.511589422 -4.295603173
240 -2.748044157 -2.511589422
241 -2.726219886 -2.748044157
242 0.276223403 -2.726219886
243 -0.220722667 0.276223403
244 1.455415755 -0.220722667
245 0.299394568 1.455415755
246 0.247118271 0.299394568
247 5.114440522 0.247118271
248 -0.275152386 5.114440522
249 0.508856960 -0.275152386
250 2.177168257 0.508856960
251 1.199257324 2.177168257
252 -1.129341349 1.199257324
253 -0.719039874 -1.129341349
254 0.157370826 -0.719039874
255 -0.698031879 0.157370826
256 -1.714841282 -0.698031879
257 -2.517890462 -1.714841282
258 2.450472820 -2.517890462
259 -4.789518646 2.450472820
260 0.354981312 -4.789518646
261 1.518793431 0.354981312
262 -2.824867493 1.518793431
263 0.190914245 -2.824867493
> 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/7rnf41384699267.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8cmqe1384699267.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9sezu1384699267.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10ftb51384699267.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11g5501384699267.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/12xqz21384699267.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/133ne81384699267.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/14ikz71384699267.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/155uk51384699267.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/16709z1384699267.tab")
+ }
>
> try(system("convert tmp/15bdh1384699267.ps tmp/15bdh1384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ret61384699267.ps tmp/2ret61384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sxzh1384699267.ps tmp/3sxzh1384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sxt61384699267.ps tmp/4sxt61384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gv6x1384699267.ps tmp/5gv6x1384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/66gq01384699267.ps tmp/66gq01384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rnf41384699267.ps tmp/7rnf41384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cmqe1384699267.ps tmp/8cmqe1384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sezu1384699267.ps tmp/9sezu1384699267.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ftb51384699267.ps tmp/10ftb51384699267.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
17.334 2.945 20.260