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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+ ,4
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+ ,15
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+ ,11
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+ ,41
+ ,11
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+ ,11
+ ,7
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+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43
+ ,11)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),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 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '3'
> #'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
Learning Connected Separate Software Happiness Depression Sport1 Sport2
1 13 41 38 12 14 12.0 53 32
2 16 39 32 11 18 11.0 83 51
3 19 30 35 15 11 14.0 66 42
4 15 31 33 6 12 12.0 67 41
5 14 34 37 13 16 21.0 76 46
6 13 35 29 10 18 12.0 78 47
7 19 39 31 12 14 22.0 53 37
8 15 34 36 14 14 11.0 80 49
9 14 36 35 12 15 10.0 74 45
10 15 37 38 9 15 13.0 76 47
11 16 38 31 10 17 10.0 79 49
12 16 36 34 12 19 8.0 54 33
13 16 38 35 12 10 15.0 67 42
14 16 39 38 11 16 14.0 54 33
15 17 33 37 15 18 10.0 87 53
16 15 32 33 12 14 14.0 58 36
17 15 36 32 10 14 14.0 75 45
18 20 38 38 12 17 11.0 88 54
19 18 39 38 11 14 10.0 64 41
20 16 32 32 12 16 13.0 57 36
21 16 32 33 11 18 9.5 66 41
22 16 31 31 12 11 14.0 68 44
23 19 39 38 13 14 12.0 54 33
24 16 37 39 11 12 14.0 56 37
25 17 39 32 12 17 11.0 86 52
26 17 41 32 13 9 9.0 80 47
27 16 36 35 10 16 11.0 76 43
28 15 33 37 14 14 15.0 69 44
29 16 33 33 12 15 14.0 78 45
30 14 34 33 10 11 13.0 67 44
31 15 31 31 12 16 9.0 80 49
32 12 27 32 8 13 15.0 54 33
33 14 37 31 10 17 10.0 71 43
34 16 34 37 12 15 11.0 84 54
35 14 34 30 12 14 13.0 74 42
36 10 32 33 7 16 8.0 71 44
37 10 29 31 9 9 20.0 63 37
38 14 36 33 12 15 12.0 71 43
39 16 29 31 10 17 10.0 76 46
40 16 35 33 10 13 10.0 69 42
41 16 37 32 10 15 9.0 74 45
42 14 34 33 12 16 14.0 75 44
43 20 38 32 15 16 8.0 54 33
44 14 35 33 10 12 14.0 52 31
45 14 38 28 10 15 11.0 69 42
46 11 37 35 12 11 13.0 68 40
47 14 38 39 13 15 9.0 65 43
48 15 33 34 11 15 11.0 75 46
49 16 36 38 11 17 15.0 74 42
50 14 38 32 12 13 11.0 75 45
51 16 32 38 14 16 10.0 72 44
52 14 32 30 10 14 14.0 67 40
53 12 32 33 12 11 18.0 63 37
54 16 34 38 13 12 14.0 62 46
55 9 32 32 5 12 11.0 63 36
56 14 37 35 6 15 14.5 76 47
57 16 39 34 12 16 13.0 74 45
58 16 29 34 12 15 9.0 67 42
59 15 37 36 11 12 10.0 73 43
60 16 35 34 10 12 15.0 70 43
61 12 30 28 7 8 20.0 53 32
62 16 38 34 12 13 12.0 77 45
63 16 34 35 14 11 12.0 80 48
64 14 31 35 11 14 14.0 52 31
65 16 34 31 12 15 13.0 54 33
66 17 35 37 13 10 11.0 80 49
67 18 36 35 14 11 17.0 66 42
68 18 30 27 11 12 12.0 73 41
69 12 39 40 12 15 13.0 63 38
70 16 35 37 12 15 14.0 69 42
71 10 38 36 8 14 13.0 67 44
72 14 31 38 11 16 15.0 54 33
73 18 34 39 14 15 13.0 81 48
74 18 38 41 14 15 10.0 69 40
75 16 34 27 12 13 11.0 84 50
76 17 39 30 9 12 19.0 80 49
77 16 37 37 13 17 13.0 70 43
78 16 34 31 11 13 17.0 69 44
79 13 28 31 12 15 13.0 77 47
80 16 37 27 12 13 9.0 54 33
81 16 33 36 12 15 11.0 79 46
82 16 35 37 12 15 9.0 71 45
83 15 37 33 12 16 12.0 73 43
84 15 32 34 11 15 12.0 72 44
85 16 33 31 10 14 13.0 77 47
86 14 38 39 9 15 13.0 75 45
87 16 33 34 12 14 12.0 69 42
88 16 29 32 12 13 15.0 54 33
89 15 33 33 12 7 22.0 70 43
90 12 31 36 9 17 13.0 73 46
91 17 36 32 15 13 15.0 54 33
92 16 35 41 12 15 13.0 77 46
93 15 32 28 12 14 15.0 82 48
94 13 29 30 12 13 12.5 80 47
95 16 39 36 10 16 11.0 80 47
96 16 37 35 13 12 16.0 69 43
97 16 35 31 9 14 11.0 78 46
98 16 37 34 12 17 11.0 81 48
99 14 32 36 10 15 10.0 76 46
100 16 38 36 14 17 10.0 76 45
101 16 37 35 11 12 16.0 73 45
102 20 36 37 15 16 12.0 85 52
103 15 32 28 11 11 11.0 66 42
104 16 33 39 11 15 16.0 79 47
105 13 40 32 12 9 19.0 68 41
106 17 38 35 12 16 11.0 76 47
107 16 41 39 12 15 16.0 71 43
108 16 36 35 11 10 15.0 54 33
109 12 43 42 7 10 24.0 46 30
110 16 30 34 12 15 14.0 85 52
111 16 31 33 14 11 15.0 74 44
112 17 32 41 11 13 11.0 88 55
113 13 32 33 11 14 15.0 38 11
114 12 37 34 10 18 12.0 76 47
115 18 37 32 13 16 10.0 86 53
116 14 33 40 13 14 14.0 54 33
117 14 34 40 8 14 13.0 67 44
118 13 33 35 11 14 9.0 69 42
119 16 38 36 12 14 15.0 90 55
120 13 33 37 11 12 15.0 54 33
121 16 31 27 13 14 14.0 76 46
122 13 38 39 12 15 11.0 89 54
123 16 37 38 14 15 8.0 76 47
124 15 36 31 13 15 11.0 73 45
125 16 31 33 15 13 11.0 79 47
126 15 39 32 10 17 8.0 90 55
127 17 44 39 11 17 10.0 74 44
128 15 33 36 9 19 11.0 81 53
129 12 35 33 11 15 13.0 72 44
130 16 32 33 10 13 11.0 71 42
131 10 28 32 11 9 20.0 66 40
132 16 40 37 8 15 10.0 77 46
133 12 27 30 11 15 15.0 65 40
134 14 37 38 12 15 12.0 74 46
135 15 32 29 12 16 14.0 85 53
136 13 28 22 9 11 23.0 54 33
137 15 34 35 11 14 14.0 63 42
138 11 30 35 10 11 16.0 54 35
139 12 35 34 8 15 11.0 64 40
140 11 31 35 9 13 12.0 69 41
141 16 32 34 8 15 10.0 54 33
142 15 30 37 9 16 14.0 84 51
143 17 30 35 15 14 12.0 86 53
144 16 31 23 11 15 12.0 77 46
145 10 40 31 8 16 11.0 89 55
146 18 32 27 13 16 12.0 76 47
147 13 36 36 12 11 13.0 60 38
148 16 32 31 12 12 11.0 75 46
149 13 35 32 9 9 19.0 73 46
150 10 38 39 7 16 12.0 85 53
151 15 42 37 13 13 17.0 79 47
152 16 34 38 9 16 9.0 71 41
153 16 35 39 6 12 12.0 72 44
154 14 38 34 8 9 19.0 69 43
155 10 33 31 8 13 18.0 78 51
156 17 36 32 15 13 15.0 54 33
157 13 32 37 6 14 14.0 69 43
158 15 33 36 9 19 11.0 81 53
159 16 34 32 11 13 9.0 84 51
160 12 32 38 8 12 18.0 84 50
161 13 34 36 8 13 16.0 69 46
162 13 27 26 10 10 24.0 66 43
163 12 31 26 8 14 14.0 81 47
164 17 38 33 14 16 20.0 82 50
165 15 34 39 10 10 18.0 72 43
166 10 24 30 8 11 23.0 54 33
167 14 30 33 11 14 12.0 78 48
168 11 26 25 12 12 14.0 74 44
169 13 34 38 12 9 16.0 82 50
170 16 27 37 12 9 18.0 73 41
171 12 37 31 5 11 20.0 55 34
172 16 36 37 12 16 12.0 72 44
173 12 41 35 10 9 12.0 78 47
174 9 29 25 7 13 17.0 59 35
175 12 36 28 12 16 13.0 72 44
176 15 32 35 11 13 9.0 78 44
177 12 37 33 8 9 16.0 68 43
178 12 30 30 9 12 18.0 69 41
179 14 31 31 10 16 10.0 67 41
180 12 38 37 9 11 14.0 74 42
181 16 36 36 12 14 11.0 54 33
182 11 35 30 6 13 9.0 67 41
183 19 31 36 15 15 11.0 70 44
184 15 38 32 12 14 10.0 80 48
185 8 22 28 12 16 11.0 89 55
186 16 32 36 12 13 19.0 76 44
187 17 36 34 11 14 14.0 74 43
188 12 39 31 7 15 12.0 87 52
189 11 28 28 7 13 14.0 54 30
190 11 32 36 5 11 21.0 61 39
191 14 32 36 12 11 13.0 38 11
192 16 38 40 12 14 10.0 75 44
193 12 32 33 3 15 15.0 69 42
194 16 35 37 11 11 16.0 62 41
195 13 32 32 10 15 14.0 72 44
196 15 37 38 12 12 12.0 70 44
197 16 34 31 9 14 19.0 79 48
198 16 33 37 12 14 15.0 87 53
199 14 33 33 9 8 19.0 62 37
200 16 26 32 12 13 13.0 77 44
201 16 30 30 12 9 17.0 69 44
202 14 24 30 10 15 12.0 69 40
203 11 34 31 9 17 11.0 75 42
204 12 34 32 12 13 14.0 54 35
205 15 33 34 8 15 11.0 72 43
206 15 34 36 11 15 13.0 74 45
207 16 35 37 11 14 12.0 85 55
208 16 35 36 12 16 15.0 52 31
209 11 36 33 10 13 14.0 70 44
210 15 34 33 10 16 12.0 84 50
211 12 34 33 12 9 17.0 64 40
212 12 41 44 12 16 11.0 84 53
213 15 32 39 11 11 18.0 87 54
214 15 30 32 8 10 13.0 79 49
215 16 35 35 12 11 17.0 67 40
216 14 28 25 10 15 13.0 65 41
217 17 33 35 11 17 11.0 85 52
218 14 39 34 10 14 12.0 83 52
219 13 36 35 8 8 22.0 61 36
220 15 36 39 12 15 14.0 82 52
221 13 35 33 12 11 12.0 76 46
222 14 38 36 10 16 12.0 58 31
223 15 33 32 12 10 17.0 72 44
224 12 31 32 9 15 9.0 72 44
225 13 34 36 9 9 21.0 38 11
226 8 32 36 6 16 10.0 78 46
227 14 31 32 10 19 11.0 54 33
228 14 33 34 9 12 12.0 63 34
229 11 34 33 9 8 23.0 66 42
230 12 34 35 9 11 13.0 70 43
231 13 34 30 6 14 12.0 71 43
232 10 33 38 10 9 16.0 67 44
233 16 32 34 6 15 9.0 58 36
234 18 41 33 14 13 17.0 72 46
235 13 34 32 10 16 9.0 72 44
236 11 36 31 10 11 14.0 70 43
237 4 37 30 6 12 17.0 76 50
238 13 36 27 12 13 13.0 50 33
239 16 29 31 12 10 11.0 72 43
240 10 37 30 7 11 12.0 72 44
241 12 27 32 8 12 10.0 88 53
242 12 35 35 11 8 19.0 53 34
243 10 28 28 3 12 16.0 58 35
244 13 35 33 6 12 16.0 66 40
245 15 37 31 10 15 14.0 82 53
246 12 29 35 8 11 20.0 69 42
247 14 32 35 9 13 15.0 68 43
248 10 36 32 9 14 23.0 44 29
249 12 19 21 8 10 20.0 56 36
250 12 21 20 9 12 16.0 53 30
251 11 31 34 7 15 14.0 70 42
252 10 33 32 7 13 17.0 78 47
253 12 36 34 6 13 11.0 71 44
254 16 33 32 9 13 13.0 72 45
255 12 37 33 10 12 17.0 68 44
256 14 34 33 11 12 15.0 67 43
257 16 35 37 12 9 21.0 75 43
258 14 31 32 8 9 18.0 62 40
259 13 37 34 11 15 15.0 67 41
260 4 35 30 3 10 8.0 83 52
261 15 27 30 11 14 12.0 64 38
262 11 34 38 12 15 12.0 68 41
263 11 40 36 7 7 22.0 62 39
264 14 29 32 9 14 12.0 72 43
Month
1 9
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264 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Happiness Depression
8.35047 0.03478 0.04334 0.57606 0.07962 -0.02610
Sport1 Sport2 Month
0.02829 -0.03265 -0.40460
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9727 -1.1695 0.2867 1.1592 4.6323
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.35047 2.61698 3.191 0.0016 **
Connected 0.03478 0.03476 1.001 0.3179
Separate 0.04334 0.03528 1.228 0.2204
Software 0.57606 0.05277 10.916 <2e-16 ***
Happiness 0.07962 0.05784 1.376 0.1699
Depression -0.02610 0.04244 -0.615 0.5390
Sport1 0.02829 0.03777 0.749 0.4545
Sport2 -0.03265 0.05610 -0.582 0.5611
Month -0.40460 0.15952 -2.536 0.0118 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.865 on 255 degrees of freedom
Multiple R-squared: 0.441, Adjusted R-squared: 0.4235
F-statistic: 25.15 on 8 and 255 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,] 0.249272273 0.498544547 0.7507277
[2,] 0.168954109 0.337908219 0.8310459
[3,] 0.188652474 0.377304949 0.8113475
[4,] 0.113822200 0.227644399 0.8861778
[5,] 0.077358179 0.154716357 0.9226418
[6,] 0.111089922 0.222179843 0.8889101
[7,] 0.350297244 0.700594488 0.6497028
[8,] 0.273076148 0.546152296 0.7269239
[9,] 0.200364780 0.400729560 0.7996352
[10,] 0.147329038 0.294658076 0.8526710
[11,] 0.142429822 0.284859644 0.8575702
[12,] 0.329613611 0.659227221 0.6703864
[13,] 0.396108111 0.792216222 0.6038919
[14,] 0.364643783 0.729287565 0.6353562
[15,] 0.343013785 0.686027571 0.6569862
[16,] 0.419522217 0.839044434 0.5804778
[17,] 0.431039270 0.862078539 0.5689607
[18,] 0.411178258 0.822356515 0.5888217
[19,] 0.444562864 0.889125729 0.5554371
[20,] 0.389182053 0.778364107 0.6108179
[21,] 0.346620753 0.693241507 0.6533792
[22,] 0.316677131 0.633354262 0.6833229
[23,] 0.275674898 0.551349797 0.7243251
[24,] 0.233139525 0.466279050 0.7668605
[25,] 0.363623900 0.727247799 0.6363761
[26,] 0.400480018 0.800960036 0.5995200
[27,] 0.392518457 0.785036914 0.6074815
[28,] 0.420646898 0.841293796 0.5793531
[29,] 0.394036669 0.788073338 0.6059633
[30,] 0.352608511 0.705217021 0.6473915
[31,] 0.317815561 0.635631122 0.6821844
[32,] 0.330602998 0.661205997 0.6693970
[33,] 0.284193040 0.568386080 0.7158070
[34,] 0.259307354 0.518614708 0.7406926
[35,] 0.457373137 0.914746273 0.5426269
[36,] 0.613015043 0.773969915 0.3869850
[37,] 0.564790965 0.870418070 0.4352090
[38,] 0.550115831 0.899768338 0.4498842
[39,] 0.538886872 0.922226256 0.4611131
[40,] 0.496029188 0.992058376 0.5039708
[41,] 0.448976552 0.897953103 0.5510234
[42,] 0.469409971 0.938819942 0.5305900
[43,] 0.449833836 0.899667672 0.5501662
[44,] 0.452066183 0.904132367 0.5479338
[45,] 0.420708590 0.841417180 0.5792914
[46,] 0.377122147 0.754244294 0.6228779
[47,] 0.343504288 0.687008576 0.6564957
[48,] 0.305381737 0.610763474 0.6946183
[49,] 0.304715665 0.609431330 0.6952843
[50,] 0.274885364 0.549770729 0.7251146
[51,] 0.243391513 0.486783027 0.7566085
[52,] 0.213205556 0.426411111 0.7867944
[53,] 0.184850255 0.369700511 0.8151497
[54,] 0.159612090 0.319224181 0.8403879
[55,] 0.135701683 0.271403365 0.8642983
[56,] 0.118078555 0.236157110 0.8819214
[57,] 0.147491712 0.294983424 0.8525083
[58,] 0.347255539 0.694511077 0.6527445
[59,] 0.309383521 0.618767042 0.6906165
[60,] 0.466018474 0.932036948 0.5339815
[61,] 0.427694883 0.855389767 0.5723051
[62,] 0.415756314 0.831512629 0.5842437
[63,] 0.394545849 0.789091698 0.6054542
[64,] 0.359444377 0.718888754 0.6405556
[65,] 0.407073803 0.814147605 0.5929262
[66,] 0.374300585 0.748601169 0.6256994
[67,] 0.346591345 0.693182689 0.6534087
[68,] 0.375124097 0.750248194 0.6248759
[69,] 0.344523837 0.689047674 0.6554762
[70,] 0.311892770 0.623785539 0.6881072
[71,] 0.277800972 0.555601945 0.7221990
[72,] 0.254136867 0.508273734 0.7458631
[73,] 0.223381796 0.446763591 0.7766182
[74,] 0.213624304 0.427248607 0.7863757
[75,] 0.186095184 0.372190367 0.8139048
[76,] 0.162784483 0.325568967 0.8372155
[77,] 0.147969314 0.295938628 0.8520307
[78,] 0.127536796 0.255073591 0.8724632
[79,] 0.129326333 0.258652666 0.8706737
[80,] 0.111535332 0.223070664 0.8884647
[81,] 0.096128398 0.192256796 0.9038716
[82,] 0.082550296 0.165100591 0.9174497
[83,] 0.087742539 0.175485078 0.9122575
[84,] 0.078087986 0.156175972 0.9219120
[85,] 0.065829406 0.131658811 0.9341706
[86,] 0.068906807 0.137813615 0.9310932
[87,] 0.057357952 0.114715903 0.9426420
[88,] 0.047590636 0.095181272 0.9524094
[89,] 0.042543858 0.085087717 0.9574561
[90,] 0.036985931 0.073971862 0.9630141
[91,] 0.042000243 0.084000487 0.9579998
[92,] 0.035665390 0.071330780 0.9643346
[93,] 0.031666520 0.063333040 0.9683335
[94,] 0.038809498 0.077618995 0.9611905
[95,] 0.033580103 0.067160206 0.9664199
[96,] 0.027290386 0.054580772 0.9727096
[97,] 0.026013415 0.052026830 0.9739866
[98,] 0.021222130 0.042444260 0.9787779
[99,] 0.017203818 0.034407637 0.9827962
[100,] 0.013632186 0.027264372 0.9863678
[101,] 0.013622757 0.027245513 0.9863772
[102,] 0.012487296 0.024974592 0.9875127
[103,] 0.019146176 0.038292352 0.9808538
[104,] 0.017761714 0.035523429 0.9822383
[105,] 0.017603646 0.035207292 0.9823964
[106,] 0.014365082 0.028730163 0.9856349
[107,] 0.014933316 0.029866632 0.9850667
[108,] 0.012069116 0.024138232 0.9879309
[109,] 0.011017215 0.022034430 0.9889828
[110,] 0.008849280 0.017698560 0.9911507
[111,] 0.014052017 0.028104034 0.9859480
[112,] 0.012043124 0.024086249 0.9879569
[113,] 0.010823044 0.021646088 0.9891770
[114,] 0.009037311 0.018074622 0.9909627
[115,] 0.007284510 0.014569021 0.9927155
[116,] 0.006399142 0.012798283 0.9936009
[117,] 0.005106591 0.010213182 0.9948934
[118,] 0.007833751 0.015667501 0.9921662
[119,] 0.008079630 0.016159261 0.9919204
[120,] 0.018094933 0.036189866 0.9819051
[121,] 0.020368964 0.040737929 0.9796310
[122,] 0.023261761 0.046523522 0.9767382
[123,] 0.023256224 0.046512448 0.9767438
[124,] 0.019036038 0.038072076 0.9809640
[125,] 0.015637249 0.031274498 0.9843628
[126,] 0.012443014 0.024886029 0.9875570
[127,] 0.016577269 0.033154538 0.9834227
[128,] 0.014924063 0.029848125 0.9850759
[129,] 0.019031155 0.038062311 0.9809688
[130,] 0.026112930 0.052225859 0.9738871
[131,] 0.023296615 0.046593229 0.9767034
[132,] 0.019013388 0.038026776 0.9809866
[133,] 0.018077456 0.036154912 0.9819225
[134,] 0.035502616 0.071005232 0.9644974
[135,] 0.038422197 0.076844393 0.9615778
[136,] 0.043198752 0.086397504 0.9568012
[137,] 0.036964585 0.073929170 0.9630354
[138,] 0.030326830 0.060653660 0.9696732
[139,] 0.045729337 0.091458674 0.9542707
[140,] 0.041381104 0.082762209 0.9586189
[141,] 0.041087092 0.082174183 0.9589129
[142,] 0.071708180 0.143416360 0.9282918
[143,] 0.063159712 0.126319425 0.9368403
[144,] 0.077112812 0.154225624 0.9228872
[145,] 0.064814840 0.129629680 0.9351852
[146,] 0.056421290 0.112842581 0.9435787
[147,] 0.047850295 0.095700591 0.9521497
[148,] 0.045457012 0.090914024 0.9545430
[149,] 0.039035534 0.078071068 0.9609645
[150,] 0.031868811 0.063737623 0.9681312
[151,] 0.026187933 0.052375865 0.9738121
[152,] 0.021581342 0.043162684 0.9784187
[153,] 0.018252422 0.036504845 0.9817476
[154,] 0.016138330 0.032276661 0.9838617
[155,] 0.016251666 0.032503332 0.9837483
[156,] 0.012956801 0.025913601 0.9870432
[157,] 0.018479417 0.036958835 0.9815206
[158,] 0.017894260 0.035788519 0.9821057
[159,] 0.017018918 0.034037836 0.9829811
[160,] 0.016561667 0.033123335 0.9834383
[161,] 0.013436048 0.026872095 0.9865640
[162,] 0.012958356 0.025916711 0.9870416
[163,] 0.013877447 0.027754894 0.9861226
[164,] 0.017240763 0.034481526 0.9827592
[165,] 0.013895174 0.027790348 0.9861048
[166,] 0.011051999 0.022103998 0.9889480
[167,] 0.008870743 0.017741487 0.9911293
[168,] 0.006883216 0.013766432 0.9931168
[169,] 0.005774436 0.011548871 0.9942256
[170,] 0.004853936 0.009707871 0.9951461
[171,] 0.003788799 0.007577598 0.9962112
[172,] 0.004241499 0.008482999 0.9957585
[173,] 0.003270547 0.006541094 0.9967295
[174,] 0.072840711 0.145681421 0.9271593
[175,] 0.063836939 0.127673878 0.9361631
[176,] 0.075551377 0.151102755 0.9244486
[177,] 0.064037139 0.128074277 0.9359629
[178,] 0.053654156 0.107308313 0.9463458
[179,] 0.044308331 0.088616662 0.9556917
[180,] 0.038733534 0.077467069 0.9612665
[181,] 0.032480446 0.064960891 0.9675196
[182,] 0.038777019 0.077554038 0.9612230
[183,] 0.042580372 0.085160744 0.9574196
[184,] 0.035541480 0.071082959 0.9644585
[185,] 0.029143015 0.058286030 0.9708570
[186,] 0.039343221 0.078686442 0.9606568
[187,] 0.032175175 0.064350349 0.9678248
[188,] 0.029969169 0.059938339 0.9700308
[189,] 0.025468536 0.050937072 0.9745315
[190,] 0.024209926 0.048419852 0.9757901
[191,] 0.020183007 0.040366014 0.9798170
[192,] 0.025527130 0.051054260 0.9744729
[193,] 0.028059869 0.056119738 0.9719401
[194,] 0.031222434 0.062444868 0.9687776
[195,] 0.024571621 0.049143242 0.9754284
[196,] 0.024269913 0.048539826 0.9757301
[197,] 0.019969288 0.039938576 0.9800307
[198,] 0.021672832 0.043345664 0.9783272
[199,] 0.017456347 0.034912694 0.9825437
[200,] 0.018929280 0.037858560 0.9810707
[201,] 0.029270319 0.058540638 0.9707297
[202,] 0.022869354 0.045738709 0.9771306
[203,] 0.033024021 0.066048042 0.9669760
[204,] 0.028456855 0.056913710 0.9715431
[205,] 0.022084984 0.044169968 0.9779150
[206,] 0.024359112 0.048718225 0.9756409
[207,] 0.020489589 0.040979178 0.9795104
[208,] 0.018320551 0.036641101 0.9816794
[209,] 0.013754798 0.027509597 0.9862452
[210,] 0.011927236 0.023854471 0.9880728
[211,] 0.008581221 0.017162442 0.9914188
[212,] 0.006346574 0.012693148 0.9936534
[213,] 0.004964717 0.009929434 0.9950353
[214,] 0.004294646 0.008589292 0.9957054
[215,] 0.009946292 0.019892583 0.9900537
[216,] 0.007534384 0.015068767 0.9924656
[217,] 0.005373386 0.010746773 0.9946266
[218,] 0.003807178 0.007614357 0.9961928
[219,] 0.002664712 0.005329424 0.9973353
[220,] 0.002671582 0.005343163 0.9973284
[221,] 0.004943364 0.009886728 0.9950566
[222,] 0.025946735 0.051893471 0.9740533
[223,] 0.038457941 0.076915881 0.9615421
[224,] 0.027718788 0.055437577 0.9722812
[225,] 0.023961814 0.047923628 0.9760382
[226,] 0.187771175 0.375542351 0.8122288
[227,] 0.147044861 0.294089722 0.8529551
[228,] 0.130216166 0.260432332 0.8697838
[229,] 0.098868434 0.197736868 0.9011316
[230,] 0.076600142 0.153200283 0.9233999
[231,] 0.061463418 0.122926836 0.9385366
[232,] 0.056209776 0.112419552 0.9437902
[233,] 0.103562676 0.207125352 0.8964373
[234,] 0.090783773 0.181567545 0.9092162
[235,] 0.069638293 0.139276586 0.9303617
[236,] 0.046529644 0.093059287 0.9534704
[237,] 0.037806000 0.075612000 0.9621940
[238,] 0.034074745 0.068149490 0.9659253
[239,] 0.041448869 0.082897738 0.9585511
[240,] 0.021822448 0.043644896 0.9781776
[241,] 0.054742676 0.109485353 0.9452573
> postscript(file="/var/wessaorg/rcomp/tmp/1kgj21383487669.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2icqc1383487669.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3jjxk1383487669.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/49qyf1383487669.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/56nyo1383487669.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
-2.950927427 0.381817133 2.083335628 3.127025193 -2.358051988 -1.736016272
7 8 9 10 11 12
3.846299991 -2.007756051 -1.948445497 0.701963529 1.137382762 -0.101807701
13 14 15 16 17 18
0.610689034 0.592021392 -1.004395063 -0.379815924 0.489441182 3.616619183
19 20 21 22 23 24
2.625165134 0.506467834 0.697230535 0.958823175 2.546938147 1.010762105
25 26 27 28 29 30
0.833157851 0.778791226 1.028275393 -1.763968014 0.233833016 -0.077895767
31 32 33 34 35 36
-0.746027284 -0.737377122 -0.797418619 0.071499880 -1.602200306 -2.921926401
37 38 39 40 41 42
-3.014672990 -1.789991690 1.437350999 1.527886673 1.272815400 -1.828353335
43 44 45 46 47 48
2.425987887 -0.166310878 -0.492903544 -4.579946776 -2.604228986 -0.194229434
49 50 51 52 53 54
0.370906087 -1.730933528 -1.147151290 -0.221677409 -3.145340373 0.130447599
55 56 57 58 59 60
-2.364552178 1.599325081 -0.010771712 0.412355641 -0.248651715 1.699039550
61 62 63 64 65 66
0.431959429 0.151906615 -0.732096822 -0.849165795 0.546781501 1.213271705
67 68 69 70 71 72
1.933602545 3.776399158 -3.703958865 0.552161863 -3.029195508 -0.699009795
73 74 75 76 77 78
1.178461283 0.952614163 0.938146133 3.731342200 -0.274453484 1.725904951
79 80 81 82 83 84
-2.033478554 1.075230233 0.434465248 0.463024793 -0.556378965 0.290823812
85 86 87 88 89 90
2.024351240 -0.008573324 0.779169251 1.293406531 0.645248973 -1.705080737
91 92 93 94 95 96
0.321735886 0.256979711 -0.019563985 -1.963598029 1.302627336 0.316937043
97 98 99 100 101 102
2.417701080 0.231489430 -0.319858082 -1.024703834 1.421202664 2.531232510
103 104 105 106 107 108
0.947695374 1.043666722 -1.801167422 1.341788163 0.285054577 1.734830472
109 110 111 112 113 114
-0.144502540 0.729968270 -0.019022897 2.027100876 -1.623526260 -2.561106515
115 116 117 118 119 120
1.817433595 -1.874239203 0.936575639 -1.766415230 0.427246196 -1.406745685
121 122 123 124 125 126
0.560835235 -2.891167271 -0.904258187 -0.892153489 -0.902236171 0.296367937
127 128 129 130 131 132
1.388689572 1.016139203 -2.744083838 2.006357737 -3.757672592 2.482367886
133 134 135 136 137 138
-2.216163118 -1.623796371 -0.169866756 0.857844295 0.499053080 -2.468624925
139 140 141 142 143 144
-1.015724374 -2.419446534 3.116868495 1.244149429 -0.009779507 1.726205342
145 146 147 148 149 150
-3.356731563 2.347253794 -2.022884772 1.037980818 0.122740838 -2.983866911
151 152 153 154 155 156
-1.149480124 1.972426862 4.088948985 1.118379703 -2.511077298 0.321735886
157 158 159 160 161 162
1.225159115 1.016139203 1.277952243 -0.902448709 0.276588644 0.640555249
163 164 165 166 167 168
-0.219714058 0.844074307 1.507288989 -1.369085649 -0.151183747 -3.047381403
169 170 171 172 173 174
-1.628420757 1.671359890 1.789630485 0.770582649 -1.678958916 -2.142243663
175 176 177 178 179 180
-2.813249493 0.563279913 -0.044313330 -0.527125898 0.347961979 -1.242363629
181 182 183 184 185 186
1.097128568 -0.230816591 2.369755033 -0.070957371 -6.500257228 1.261477725
187 188 189 190 191 192
2.598884232 -0.276935001 -0.337592691 0.766460632 -0.738347363 0.593164124
193 194 195 196 197 198
2.445135861 2.068898527 -0.589629361 0.067529462 3.102911981 0.981990529
199 200 201 202 203 204
1.650521446 1.458638605 2.055406796 0.677381539 -2.427517795 -2.436710987
205 206 207 208 209 210
2.330073087 0.541347575 1.532062754 1.068353080 -2.556278024 1.022060198
211 212 213 214 215 216
-2.202902206 -3.778443865 0.855979821 2.969296732 1.431517041 0.926865366
217 218 219 220 221 222
2.325387625 0.057640177 1.109497163 -0.205965436 -1.671017171 -0.131920566
223 224 225 226 227 228
0.699881753 -1.109293774 0.288333593 -3.747213134 0.198419826 0.979732572
229 230 231 232 233 234
-1.229759183 -0.896838278 1.754791518 -3.213065762 4.632282599 2.052583830
235 236 237 238 239 240
-0.869328864 -2.343002739 -6.972696730 -1.267817848 1.693092913 -1.682392341
241 242 243 244 245 246
-0.287911980 -1.501192112 1.148577467 1.897137210 1.290751900 0.031494268
247 248 249 250 251 252
1.122268014 -2.535800023 1.237586914 0.260609056 -0.922062430 -1.730463173
253 254 255 256 257 258
0.598033801 3.117443737 -1.376552283 0.095170915 1.480120220 2.331720115
259 260 261 262 263 264
-1.356690157 -5.383345806 1.152740672 -4.108360954 -0.347629194 1.085550529
> postscript(file="/var/wessaorg/rcomp/tmp/6eoi31383487669.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 -2.950927427 NA
1 0.381817133 -2.950927427
2 2.083335628 0.381817133
3 3.127025193 2.083335628
4 -2.358051988 3.127025193
5 -1.736016272 -2.358051988
6 3.846299991 -1.736016272
7 -2.007756051 3.846299991
8 -1.948445497 -2.007756051
9 0.701963529 -1.948445497
10 1.137382762 0.701963529
11 -0.101807701 1.137382762
12 0.610689034 -0.101807701
13 0.592021392 0.610689034
14 -1.004395063 0.592021392
15 -0.379815924 -1.004395063
16 0.489441182 -0.379815924
17 3.616619183 0.489441182
18 2.625165134 3.616619183
19 0.506467834 2.625165134
20 0.697230535 0.506467834
21 0.958823175 0.697230535
22 2.546938147 0.958823175
23 1.010762105 2.546938147
24 0.833157851 1.010762105
25 0.778791226 0.833157851
26 1.028275393 0.778791226
27 -1.763968014 1.028275393
28 0.233833016 -1.763968014
29 -0.077895767 0.233833016
30 -0.746027284 -0.077895767
31 -0.737377122 -0.746027284
32 -0.797418619 -0.737377122
33 0.071499880 -0.797418619
34 -1.602200306 0.071499880
35 -2.921926401 -1.602200306
36 -3.014672990 -2.921926401
37 -1.789991690 -3.014672990
38 1.437350999 -1.789991690
39 1.527886673 1.437350999
40 1.272815400 1.527886673
41 -1.828353335 1.272815400
42 2.425987887 -1.828353335
43 -0.166310878 2.425987887
44 -0.492903544 -0.166310878
45 -4.579946776 -0.492903544
46 -2.604228986 -4.579946776
47 -0.194229434 -2.604228986
48 0.370906087 -0.194229434
49 -1.730933528 0.370906087
50 -1.147151290 -1.730933528
51 -0.221677409 -1.147151290
52 -3.145340373 -0.221677409
53 0.130447599 -3.145340373
54 -2.364552178 0.130447599
55 1.599325081 -2.364552178
56 -0.010771712 1.599325081
57 0.412355641 -0.010771712
58 -0.248651715 0.412355641
59 1.699039550 -0.248651715
60 0.431959429 1.699039550
61 0.151906615 0.431959429
62 -0.732096822 0.151906615
63 -0.849165795 -0.732096822
64 0.546781501 -0.849165795
65 1.213271705 0.546781501
66 1.933602545 1.213271705
67 3.776399158 1.933602545
68 -3.703958865 3.776399158
69 0.552161863 -3.703958865
70 -3.029195508 0.552161863
71 -0.699009795 -3.029195508
72 1.178461283 -0.699009795
73 0.952614163 1.178461283
74 0.938146133 0.952614163
75 3.731342200 0.938146133
76 -0.274453484 3.731342200
77 1.725904951 -0.274453484
78 -2.033478554 1.725904951
79 1.075230233 -2.033478554
80 0.434465248 1.075230233
81 0.463024793 0.434465248
82 -0.556378965 0.463024793
83 0.290823812 -0.556378965
84 2.024351240 0.290823812
85 -0.008573324 2.024351240
86 0.779169251 -0.008573324
87 1.293406531 0.779169251
88 0.645248973 1.293406531
89 -1.705080737 0.645248973
90 0.321735886 -1.705080737
91 0.256979711 0.321735886
92 -0.019563985 0.256979711
93 -1.963598029 -0.019563985
94 1.302627336 -1.963598029
95 0.316937043 1.302627336
96 2.417701080 0.316937043
97 0.231489430 2.417701080
98 -0.319858082 0.231489430
99 -1.024703834 -0.319858082
100 1.421202664 -1.024703834
101 2.531232510 1.421202664
102 0.947695374 2.531232510
103 1.043666722 0.947695374
104 -1.801167422 1.043666722
105 1.341788163 -1.801167422
106 0.285054577 1.341788163
107 1.734830472 0.285054577
108 -0.144502540 1.734830472
109 0.729968270 -0.144502540
110 -0.019022897 0.729968270
111 2.027100876 -0.019022897
112 -1.623526260 2.027100876
113 -2.561106515 -1.623526260
114 1.817433595 -2.561106515
115 -1.874239203 1.817433595
116 0.936575639 -1.874239203
117 -1.766415230 0.936575639
118 0.427246196 -1.766415230
119 -1.406745685 0.427246196
120 0.560835235 -1.406745685
121 -2.891167271 0.560835235
122 -0.904258187 -2.891167271
123 -0.892153489 -0.904258187
124 -0.902236171 -0.892153489
125 0.296367937 -0.902236171
126 1.388689572 0.296367937
127 1.016139203 1.388689572
128 -2.744083838 1.016139203
129 2.006357737 -2.744083838
130 -3.757672592 2.006357737
131 2.482367886 -3.757672592
132 -2.216163118 2.482367886
133 -1.623796371 -2.216163118
134 -0.169866756 -1.623796371
135 0.857844295 -0.169866756
136 0.499053080 0.857844295
137 -2.468624925 0.499053080
138 -1.015724374 -2.468624925
139 -2.419446534 -1.015724374
140 3.116868495 -2.419446534
141 1.244149429 3.116868495
142 -0.009779507 1.244149429
143 1.726205342 -0.009779507
144 -3.356731563 1.726205342
145 2.347253794 -3.356731563
146 -2.022884772 2.347253794
147 1.037980818 -2.022884772
148 0.122740838 1.037980818
149 -2.983866911 0.122740838
150 -1.149480124 -2.983866911
151 1.972426862 -1.149480124
152 4.088948985 1.972426862
153 1.118379703 4.088948985
154 -2.511077298 1.118379703
155 0.321735886 -2.511077298
156 1.225159115 0.321735886
157 1.016139203 1.225159115
158 1.277952243 1.016139203
159 -0.902448709 1.277952243
160 0.276588644 -0.902448709
161 0.640555249 0.276588644
162 -0.219714058 0.640555249
163 0.844074307 -0.219714058
164 1.507288989 0.844074307
165 -1.369085649 1.507288989
166 -0.151183747 -1.369085649
167 -3.047381403 -0.151183747
168 -1.628420757 -3.047381403
169 1.671359890 -1.628420757
170 1.789630485 1.671359890
171 0.770582649 1.789630485
172 -1.678958916 0.770582649
173 -2.142243663 -1.678958916
174 -2.813249493 -2.142243663
175 0.563279913 -2.813249493
176 -0.044313330 0.563279913
177 -0.527125898 -0.044313330
178 0.347961979 -0.527125898
179 -1.242363629 0.347961979
180 1.097128568 -1.242363629
181 -0.230816591 1.097128568
182 2.369755033 -0.230816591
183 -0.070957371 2.369755033
184 -6.500257228 -0.070957371
185 1.261477725 -6.500257228
186 2.598884232 1.261477725
187 -0.276935001 2.598884232
188 -0.337592691 -0.276935001
189 0.766460632 -0.337592691
190 -0.738347363 0.766460632
191 0.593164124 -0.738347363
192 2.445135861 0.593164124
193 2.068898527 2.445135861
194 -0.589629361 2.068898527
195 0.067529462 -0.589629361
196 3.102911981 0.067529462
197 0.981990529 3.102911981
198 1.650521446 0.981990529
199 1.458638605 1.650521446
200 2.055406796 1.458638605
201 0.677381539 2.055406796
202 -2.427517795 0.677381539
203 -2.436710987 -2.427517795
204 2.330073087 -2.436710987
205 0.541347575 2.330073087
206 1.532062754 0.541347575
207 1.068353080 1.532062754
208 -2.556278024 1.068353080
209 1.022060198 -2.556278024
210 -2.202902206 1.022060198
211 -3.778443865 -2.202902206
212 0.855979821 -3.778443865
213 2.969296732 0.855979821
214 1.431517041 2.969296732
215 0.926865366 1.431517041
216 2.325387625 0.926865366
217 0.057640177 2.325387625
218 1.109497163 0.057640177
219 -0.205965436 1.109497163
220 -1.671017171 -0.205965436
221 -0.131920566 -1.671017171
222 0.699881753 -0.131920566
223 -1.109293774 0.699881753
224 0.288333593 -1.109293774
225 -3.747213134 0.288333593
226 0.198419826 -3.747213134
227 0.979732572 0.198419826
228 -1.229759183 0.979732572
229 -0.896838278 -1.229759183
230 1.754791518 -0.896838278
231 -3.213065762 1.754791518
232 4.632282599 -3.213065762
233 2.052583830 4.632282599
234 -0.869328864 2.052583830
235 -2.343002739 -0.869328864
236 -6.972696730 -2.343002739
237 -1.267817848 -6.972696730
238 1.693092913 -1.267817848
239 -1.682392341 1.693092913
240 -0.287911980 -1.682392341
241 -1.501192112 -0.287911980
242 1.148577467 -1.501192112
243 1.897137210 1.148577467
244 1.290751900 1.897137210
245 0.031494268 1.290751900
246 1.122268014 0.031494268
247 -2.535800023 1.122268014
248 1.237586914 -2.535800023
249 0.260609056 1.237586914
250 -0.922062430 0.260609056
251 -1.730463173 -0.922062430
252 0.598033801 -1.730463173
253 3.117443737 0.598033801
254 -1.376552283 3.117443737
255 0.095170915 -1.376552283
256 1.480120220 0.095170915
257 2.331720115 1.480120220
258 -1.356690157 2.331720115
259 -5.383345806 -1.356690157
260 1.152740672 -5.383345806
261 -4.108360954 1.152740672
262 -0.347629194 -4.108360954
263 1.085550529 -0.347629194
264 NA 1.085550529
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.381817133 -2.950927427
[2,] 2.083335628 0.381817133
[3,] 3.127025193 2.083335628
[4,] -2.358051988 3.127025193
[5,] -1.736016272 -2.358051988
[6,] 3.846299991 -1.736016272
[7,] -2.007756051 3.846299991
[8,] -1.948445497 -2.007756051
[9,] 0.701963529 -1.948445497
[10,] 1.137382762 0.701963529
[11,] -0.101807701 1.137382762
[12,] 0.610689034 -0.101807701
[13,] 0.592021392 0.610689034
[14,] -1.004395063 0.592021392
[15,] -0.379815924 -1.004395063
[16,] 0.489441182 -0.379815924
[17,] 3.616619183 0.489441182
[18,] 2.625165134 3.616619183
[19,] 0.506467834 2.625165134
[20,] 0.697230535 0.506467834
[21,] 0.958823175 0.697230535
[22,] 2.546938147 0.958823175
[23,] 1.010762105 2.546938147
[24,] 0.833157851 1.010762105
[25,] 0.778791226 0.833157851
[26,] 1.028275393 0.778791226
[27,] -1.763968014 1.028275393
[28,] 0.233833016 -1.763968014
[29,] -0.077895767 0.233833016
[30,] -0.746027284 -0.077895767
[31,] -0.737377122 -0.746027284
[32,] -0.797418619 -0.737377122
[33,] 0.071499880 -0.797418619
[34,] -1.602200306 0.071499880
[35,] -2.921926401 -1.602200306
[36,] -3.014672990 -2.921926401
[37,] -1.789991690 -3.014672990
[38,] 1.437350999 -1.789991690
[39,] 1.527886673 1.437350999
[40,] 1.272815400 1.527886673
[41,] -1.828353335 1.272815400
[42,] 2.425987887 -1.828353335
[43,] -0.166310878 2.425987887
[44,] -0.492903544 -0.166310878
[45,] -4.579946776 -0.492903544
[46,] -2.604228986 -4.579946776
[47,] -0.194229434 -2.604228986
[48,] 0.370906087 -0.194229434
[49,] -1.730933528 0.370906087
[50,] -1.147151290 -1.730933528
[51,] -0.221677409 -1.147151290
[52,] -3.145340373 -0.221677409
[53,] 0.130447599 -3.145340373
[54,] -2.364552178 0.130447599
[55,] 1.599325081 -2.364552178
[56,] -0.010771712 1.599325081
[57,] 0.412355641 -0.010771712
[58,] -0.248651715 0.412355641
[59,] 1.699039550 -0.248651715
[60,] 0.431959429 1.699039550
[61,] 0.151906615 0.431959429
[62,] -0.732096822 0.151906615
[63,] -0.849165795 -0.732096822
[64,] 0.546781501 -0.849165795
[65,] 1.213271705 0.546781501
[66,] 1.933602545 1.213271705
[67,] 3.776399158 1.933602545
[68,] -3.703958865 3.776399158
[69,] 0.552161863 -3.703958865
[70,] -3.029195508 0.552161863
[71,] -0.699009795 -3.029195508
[72,] 1.178461283 -0.699009795
[73,] 0.952614163 1.178461283
[74,] 0.938146133 0.952614163
[75,] 3.731342200 0.938146133
[76,] -0.274453484 3.731342200
[77,] 1.725904951 -0.274453484
[78,] -2.033478554 1.725904951
[79,] 1.075230233 -2.033478554
[80,] 0.434465248 1.075230233
[81,] 0.463024793 0.434465248
[82,] -0.556378965 0.463024793
[83,] 0.290823812 -0.556378965
[84,] 2.024351240 0.290823812
[85,] -0.008573324 2.024351240
[86,] 0.779169251 -0.008573324
[87,] 1.293406531 0.779169251
[88,] 0.645248973 1.293406531
[89,] -1.705080737 0.645248973
[90,] 0.321735886 -1.705080737
[91,] 0.256979711 0.321735886
[92,] -0.019563985 0.256979711
[93,] -1.963598029 -0.019563985
[94,] 1.302627336 -1.963598029
[95,] 0.316937043 1.302627336
[96,] 2.417701080 0.316937043
[97,] 0.231489430 2.417701080
[98,] -0.319858082 0.231489430
[99,] -1.024703834 -0.319858082
[100,] 1.421202664 -1.024703834
[101,] 2.531232510 1.421202664
[102,] 0.947695374 2.531232510
[103,] 1.043666722 0.947695374
[104,] -1.801167422 1.043666722
[105,] 1.341788163 -1.801167422
[106,] 0.285054577 1.341788163
[107,] 1.734830472 0.285054577
[108,] -0.144502540 1.734830472
[109,] 0.729968270 -0.144502540
[110,] -0.019022897 0.729968270
[111,] 2.027100876 -0.019022897
[112,] -1.623526260 2.027100876
[113,] -2.561106515 -1.623526260
[114,] 1.817433595 -2.561106515
[115,] -1.874239203 1.817433595
[116,] 0.936575639 -1.874239203
[117,] -1.766415230 0.936575639
[118,] 0.427246196 -1.766415230
[119,] -1.406745685 0.427246196
[120,] 0.560835235 -1.406745685
[121,] -2.891167271 0.560835235
[122,] -0.904258187 -2.891167271
[123,] -0.892153489 -0.904258187
[124,] -0.902236171 -0.892153489
[125,] 0.296367937 -0.902236171
[126,] 1.388689572 0.296367937
[127,] 1.016139203 1.388689572
[128,] -2.744083838 1.016139203
[129,] 2.006357737 -2.744083838
[130,] -3.757672592 2.006357737
[131,] 2.482367886 -3.757672592
[132,] -2.216163118 2.482367886
[133,] -1.623796371 -2.216163118
[134,] -0.169866756 -1.623796371
[135,] 0.857844295 -0.169866756
[136,] 0.499053080 0.857844295
[137,] -2.468624925 0.499053080
[138,] -1.015724374 -2.468624925
[139,] -2.419446534 -1.015724374
[140,] 3.116868495 -2.419446534
[141,] 1.244149429 3.116868495
[142,] -0.009779507 1.244149429
[143,] 1.726205342 -0.009779507
[144,] -3.356731563 1.726205342
[145,] 2.347253794 -3.356731563
[146,] -2.022884772 2.347253794
[147,] 1.037980818 -2.022884772
[148,] 0.122740838 1.037980818
[149,] -2.983866911 0.122740838
[150,] -1.149480124 -2.983866911
[151,] 1.972426862 -1.149480124
[152,] 4.088948985 1.972426862
[153,] 1.118379703 4.088948985
[154,] -2.511077298 1.118379703
[155,] 0.321735886 -2.511077298
[156,] 1.225159115 0.321735886
[157,] 1.016139203 1.225159115
[158,] 1.277952243 1.016139203
[159,] -0.902448709 1.277952243
[160,] 0.276588644 -0.902448709
[161,] 0.640555249 0.276588644
[162,] -0.219714058 0.640555249
[163,] 0.844074307 -0.219714058
[164,] 1.507288989 0.844074307
[165,] -1.369085649 1.507288989
[166,] -0.151183747 -1.369085649
[167,] -3.047381403 -0.151183747
[168,] -1.628420757 -3.047381403
[169,] 1.671359890 -1.628420757
[170,] 1.789630485 1.671359890
[171,] 0.770582649 1.789630485
[172,] -1.678958916 0.770582649
[173,] -2.142243663 -1.678958916
[174,] -2.813249493 -2.142243663
[175,] 0.563279913 -2.813249493
[176,] -0.044313330 0.563279913
[177,] -0.527125898 -0.044313330
[178,] 0.347961979 -0.527125898
[179,] -1.242363629 0.347961979
[180,] 1.097128568 -1.242363629
[181,] -0.230816591 1.097128568
[182,] 2.369755033 -0.230816591
[183,] -0.070957371 2.369755033
[184,] -6.500257228 -0.070957371
[185,] 1.261477725 -6.500257228
[186,] 2.598884232 1.261477725
[187,] -0.276935001 2.598884232
[188,] -0.337592691 -0.276935001
[189,] 0.766460632 -0.337592691
[190,] -0.738347363 0.766460632
[191,] 0.593164124 -0.738347363
[192,] 2.445135861 0.593164124
[193,] 2.068898527 2.445135861
[194,] -0.589629361 2.068898527
[195,] 0.067529462 -0.589629361
[196,] 3.102911981 0.067529462
[197,] 0.981990529 3.102911981
[198,] 1.650521446 0.981990529
[199,] 1.458638605 1.650521446
[200,] 2.055406796 1.458638605
[201,] 0.677381539 2.055406796
[202,] -2.427517795 0.677381539
[203,] -2.436710987 -2.427517795
[204,] 2.330073087 -2.436710987
[205,] 0.541347575 2.330073087
[206,] 1.532062754 0.541347575
[207,] 1.068353080 1.532062754
[208,] -2.556278024 1.068353080
[209,] 1.022060198 -2.556278024
[210,] -2.202902206 1.022060198
[211,] -3.778443865 -2.202902206
[212,] 0.855979821 -3.778443865
[213,] 2.969296732 0.855979821
[214,] 1.431517041 2.969296732
[215,] 0.926865366 1.431517041
[216,] 2.325387625 0.926865366
[217,] 0.057640177 2.325387625
[218,] 1.109497163 0.057640177
[219,] -0.205965436 1.109497163
[220,] -1.671017171 -0.205965436
[221,] -0.131920566 -1.671017171
[222,] 0.699881753 -0.131920566
[223,] -1.109293774 0.699881753
[224,] 0.288333593 -1.109293774
[225,] -3.747213134 0.288333593
[226,] 0.198419826 -3.747213134
[227,] 0.979732572 0.198419826
[228,] -1.229759183 0.979732572
[229,] -0.896838278 -1.229759183
[230,] 1.754791518 -0.896838278
[231,] -3.213065762 1.754791518
[232,] 4.632282599 -3.213065762
[233,] 2.052583830 4.632282599
[234,] -0.869328864 2.052583830
[235,] -2.343002739 -0.869328864
[236,] -6.972696730 -2.343002739
[237,] -1.267817848 -6.972696730
[238,] 1.693092913 -1.267817848
[239,] -1.682392341 1.693092913
[240,] -0.287911980 -1.682392341
[241,] -1.501192112 -0.287911980
[242,] 1.148577467 -1.501192112
[243,] 1.897137210 1.148577467
[244,] 1.290751900 1.897137210
[245,] 0.031494268 1.290751900
[246,] 1.122268014 0.031494268
[247,] -2.535800023 1.122268014
[248,] 1.237586914 -2.535800023
[249,] 0.260609056 1.237586914
[250,] -0.922062430 0.260609056
[251,] -1.730463173 -0.922062430
[252,] 0.598033801 -1.730463173
[253,] 3.117443737 0.598033801
[254,] -1.376552283 3.117443737
[255,] 0.095170915 -1.376552283
[256,] 1.480120220 0.095170915
[257,] 2.331720115 1.480120220
[258,] -1.356690157 2.331720115
[259,] -5.383345806 -1.356690157
[260,] 1.152740672 -5.383345806
[261,] -4.108360954 1.152740672
[262,] -0.347629194 -4.108360954
[263,] 1.085550529 -0.347629194
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.381817133 -2.950927427
2 2.083335628 0.381817133
3 3.127025193 2.083335628
4 -2.358051988 3.127025193
5 -1.736016272 -2.358051988
6 3.846299991 -1.736016272
7 -2.007756051 3.846299991
8 -1.948445497 -2.007756051
9 0.701963529 -1.948445497
10 1.137382762 0.701963529
11 -0.101807701 1.137382762
12 0.610689034 -0.101807701
13 0.592021392 0.610689034
14 -1.004395063 0.592021392
15 -0.379815924 -1.004395063
16 0.489441182 -0.379815924
17 3.616619183 0.489441182
18 2.625165134 3.616619183
19 0.506467834 2.625165134
20 0.697230535 0.506467834
21 0.958823175 0.697230535
22 2.546938147 0.958823175
23 1.010762105 2.546938147
24 0.833157851 1.010762105
25 0.778791226 0.833157851
26 1.028275393 0.778791226
27 -1.763968014 1.028275393
28 0.233833016 -1.763968014
29 -0.077895767 0.233833016
30 -0.746027284 -0.077895767
31 -0.737377122 -0.746027284
32 -0.797418619 -0.737377122
33 0.071499880 -0.797418619
34 -1.602200306 0.071499880
35 -2.921926401 -1.602200306
36 -3.014672990 -2.921926401
37 -1.789991690 -3.014672990
38 1.437350999 -1.789991690
39 1.527886673 1.437350999
40 1.272815400 1.527886673
41 -1.828353335 1.272815400
42 2.425987887 -1.828353335
43 -0.166310878 2.425987887
44 -0.492903544 -0.166310878
45 -4.579946776 -0.492903544
46 -2.604228986 -4.579946776
47 -0.194229434 -2.604228986
48 0.370906087 -0.194229434
49 -1.730933528 0.370906087
50 -1.147151290 -1.730933528
51 -0.221677409 -1.147151290
52 -3.145340373 -0.221677409
53 0.130447599 -3.145340373
54 -2.364552178 0.130447599
55 1.599325081 -2.364552178
56 -0.010771712 1.599325081
57 0.412355641 -0.010771712
58 -0.248651715 0.412355641
59 1.699039550 -0.248651715
60 0.431959429 1.699039550
61 0.151906615 0.431959429
62 -0.732096822 0.151906615
63 -0.849165795 -0.732096822
64 0.546781501 -0.849165795
65 1.213271705 0.546781501
66 1.933602545 1.213271705
67 3.776399158 1.933602545
68 -3.703958865 3.776399158
69 0.552161863 -3.703958865
70 -3.029195508 0.552161863
71 -0.699009795 -3.029195508
72 1.178461283 -0.699009795
73 0.952614163 1.178461283
74 0.938146133 0.952614163
75 3.731342200 0.938146133
76 -0.274453484 3.731342200
77 1.725904951 -0.274453484
78 -2.033478554 1.725904951
79 1.075230233 -2.033478554
80 0.434465248 1.075230233
81 0.463024793 0.434465248
82 -0.556378965 0.463024793
83 0.290823812 -0.556378965
84 2.024351240 0.290823812
85 -0.008573324 2.024351240
86 0.779169251 -0.008573324
87 1.293406531 0.779169251
88 0.645248973 1.293406531
89 -1.705080737 0.645248973
90 0.321735886 -1.705080737
91 0.256979711 0.321735886
92 -0.019563985 0.256979711
93 -1.963598029 -0.019563985
94 1.302627336 -1.963598029
95 0.316937043 1.302627336
96 2.417701080 0.316937043
97 0.231489430 2.417701080
98 -0.319858082 0.231489430
99 -1.024703834 -0.319858082
100 1.421202664 -1.024703834
101 2.531232510 1.421202664
102 0.947695374 2.531232510
103 1.043666722 0.947695374
104 -1.801167422 1.043666722
105 1.341788163 -1.801167422
106 0.285054577 1.341788163
107 1.734830472 0.285054577
108 -0.144502540 1.734830472
109 0.729968270 -0.144502540
110 -0.019022897 0.729968270
111 2.027100876 -0.019022897
112 -1.623526260 2.027100876
113 -2.561106515 -1.623526260
114 1.817433595 -2.561106515
115 -1.874239203 1.817433595
116 0.936575639 -1.874239203
117 -1.766415230 0.936575639
118 0.427246196 -1.766415230
119 -1.406745685 0.427246196
120 0.560835235 -1.406745685
121 -2.891167271 0.560835235
122 -0.904258187 -2.891167271
123 -0.892153489 -0.904258187
124 -0.902236171 -0.892153489
125 0.296367937 -0.902236171
126 1.388689572 0.296367937
127 1.016139203 1.388689572
128 -2.744083838 1.016139203
129 2.006357737 -2.744083838
130 -3.757672592 2.006357737
131 2.482367886 -3.757672592
132 -2.216163118 2.482367886
133 -1.623796371 -2.216163118
134 -0.169866756 -1.623796371
135 0.857844295 -0.169866756
136 0.499053080 0.857844295
137 -2.468624925 0.499053080
138 -1.015724374 -2.468624925
139 -2.419446534 -1.015724374
140 3.116868495 -2.419446534
141 1.244149429 3.116868495
142 -0.009779507 1.244149429
143 1.726205342 -0.009779507
144 -3.356731563 1.726205342
145 2.347253794 -3.356731563
146 -2.022884772 2.347253794
147 1.037980818 -2.022884772
148 0.122740838 1.037980818
149 -2.983866911 0.122740838
150 -1.149480124 -2.983866911
151 1.972426862 -1.149480124
152 4.088948985 1.972426862
153 1.118379703 4.088948985
154 -2.511077298 1.118379703
155 0.321735886 -2.511077298
156 1.225159115 0.321735886
157 1.016139203 1.225159115
158 1.277952243 1.016139203
159 -0.902448709 1.277952243
160 0.276588644 -0.902448709
161 0.640555249 0.276588644
162 -0.219714058 0.640555249
163 0.844074307 -0.219714058
164 1.507288989 0.844074307
165 -1.369085649 1.507288989
166 -0.151183747 -1.369085649
167 -3.047381403 -0.151183747
168 -1.628420757 -3.047381403
169 1.671359890 -1.628420757
170 1.789630485 1.671359890
171 0.770582649 1.789630485
172 -1.678958916 0.770582649
173 -2.142243663 -1.678958916
174 -2.813249493 -2.142243663
175 0.563279913 -2.813249493
176 -0.044313330 0.563279913
177 -0.527125898 -0.044313330
178 0.347961979 -0.527125898
179 -1.242363629 0.347961979
180 1.097128568 -1.242363629
181 -0.230816591 1.097128568
182 2.369755033 -0.230816591
183 -0.070957371 2.369755033
184 -6.500257228 -0.070957371
185 1.261477725 -6.500257228
186 2.598884232 1.261477725
187 -0.276935001 2.598884232
188 -0.337592691 -0.276935001
189 0.766460632 -0.337592691
190 -0.738347363 0.766460632
191 0.593164124 -0.738347363
192 2.445135861 0.593164124
193 2.068898527 2.445135861
194 -0.589629361 2.068898527
195 0.067529462 -0.589629361
196 3.102911981 0.067529462
197 0.981990529 3.102911981
198 1.650521446 0.981990529
199 1.458638605 1.650521446
200 2.055406796 1.458638605
201 0.677381539 2.055406796
202 -2.427517795 0.677381539
203 -2.436710987 -2.427517795
204 2.330073087 -2.436710987
205 0.541347575 2.330073087
206 1.532062754 0.541347575
207 1.068353080 1.532062754
208 -2.556278024 1.068353080
209 1.022060198 -2.556278024
210 -2.202902206 1.022060198
211 -3.778443865 -2.202902206
212 0.855979821 -3.778443865
213 2.969296732 0.855979821
214 1.431517041 2.969296732
215 0.926865366 1.431517041
216 2.325387625 0.926865366
217 0.057640177 2.325387625
218 1.109497163 0.057640177
219 -0.205965436 1.109497163
220 -1.671017171 -0.205965436
221 -0.131920566 -1.671017171
222 0.699881753 -0.131920566
223 -1.109293774 0.699881753
224 0.288333593 -1.109293774
225 -3.747213134 0.288333593
226 0.198419826 -3.747213134
227 0.979732572 0.198419826
228 -1.229759183 0.979732572
229 -0.896838278 -1.229759183
230 1.754791518 -0.896838278
231 -3.213065762 1.754791518
232 4.632282599 -3.213065762
233 2.052583830 4.632282599
234 -0.869328864 2.052583830
235 -2.343002739 -0.869328864
236 -6.972696730 -2.343002739
237 -1.267817848 -6.972696730
238 1.693092913 -1.267817848
239 -1.682392341 1.693092913
240 -0.287911980 -1.682392341
241 -1.501192112 -0.287911980
242 1.148577467 -1.501192112
243 1.897137210 1.148577467
244 1.290751900 1.897137210
245 0.031494268 1.290751900
246 1.122268014 0.031494268
247 -2.535800023 1.122268014
248 1.237586914 -2.535800023
249 0.260609056 1.237586914
250 -0.922062430 0.260609056
251 -1.730463173 -0.922062430
252 0.598033801 -1.730463173
253 3.117443737 0.598033801
254 -1.376552283 3.117443737
255 0.095170915 -1.376552283
256 1.480120220 0.095170915
257 2.331720115 1.480120220
258 -1.356690157 2.331720115
259 -5.383345806 -1.356690157
260 1.152740672 -5.383345806
261 -4.108360954 1.152740672
262 -0.347629194 -4.108360954
263 1.085550529 -0.347629194
> 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/7w2sy1383487669.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8cqps1383487669.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ic191383487669.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10vcc11383487669.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ncc21383487669.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/12vktv1383487669.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/136szo1383487669.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/144eqo1383487669.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/15xypr1383487669.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/16om9e1383487669.tab")
+ }
>
> try(system("convert tmp/1kgj21383487669.ps tmp/1kgj21383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/2icqc1383487669.ps tmp/2icqc1383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jjxk1383487669.ps tmp/3jjxk1383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/49qyf1383487669.ps tmp/49qyf1383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/56nyo1383487669.ps tmp/56nyo1383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/6eoi31383487669.ps tmp/6eoi31383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/7w2sy1383487669.ps tmp/7w2sy1383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cqps1383487669.ps tmp/8cqps1383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ic191383487669.ps tmp/9ic191383487669.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vcc11383487669.ps tmp/10vcc11383487669.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
20.843 3.432 24.260