R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(14
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+ ,0
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+ ,15
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+ ,0
+ ,12
+ ,68
+ ,41
+ ,7
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+ ,11
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+ ,14
+ ,9
+ ,0
+ ,12
+ ,72
+ ,43)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Happy'
+ ,'Connected'
+ ,'Separate'
+ ,'Open'
+ ,'Selfassurance'
+ ,'Stress'
+ ,'Depression'
+ ,'Sport'
+ ,'SportII')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Happy','Connected','Separate','Open','Selfassurance','Stress','Depression','Sport','SportII'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> 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
Happy Connected Separate Open Selfassurance Stress Depression Sport SportII
1 14 41 38 13 12 0 12.0 53 32
2 18 39 32 16 11 0 11.0 83 51
3 11 30 35 19 15 1 14.0 66 42
4 12 31 33 15 6 0 12.0 67 41
5 16 34 37 14 13 8 21.0 76 46
6 18 35 29 13 10 0 12.0 78 47
7 14 39 31 19 12 9 22.0 53 37
8 14 34 36 15 14 0 11.0 80 49
9 15 36 35 14 12 0 10.0 74 45
10 15 37 38 15 9 0 13.0 76 47
11 17 38 31 16 10 0 10.0 79 49
12 19 36 34 16 12 0 8.0 54 33
13 10 38 35 16 12 2 15.0 67 42
14 16 39 38 16 11 1 14.0 54 33
15 18 33 37 17 15 0 10.0 87 53
16 14 32 33 15 12 1 14.0 58 36
17 14 36 32 15 10 1 14.0 75 45
18 17 38 38 20 12 0 11.0 88 54
19 14 39 38 18 11 0 10.0 64 41
20 16 32 32 16 12 0 13.0 57 36
21 18 32 33 16 11 0 9.5 66 41
22 11 31 31 16 12 1 14.0 68 44
23 14 39 38 19 13 0 12.0 54 33
24 12 37 39 16 11 1 14.0 56 37
25 17 39 32 17 12 0 11.0 86 52
26 9 41 32 17 13 0 9.0 80 47
27 16 36 35 16 10 0 11.0 76 43
28 14 33 37 15 14 2 15.0 69 44
29 15 33 33 16 12 1 14.0 78 45
30 11 34 33 14 10 0 13.0 67 44
31 16 31 31 15 12 0 9.0 80 49
32 13 27 32 12 8 2 15.0 54 33
33 17 37 31 14 10 0 10.0 71 43
34 15 34 37 16 12 0 11.0 84 54
35 14 34 30 14 12 0 13.0 74 42
36 16 32 33 10 7 0 8.0 71 44
37 9 29 31 10 9 7 20.0 63 37
38 15 36 33 14 12 0 12.0 71 43
39 17 29 31 16 10 0 10.0 76 46
40 13 35 33 16 10 0 10.0 69 42
41 15 37 32 16 10 0 9.0 74 45
42 16 34 33 14 12 1 14.0 75 44
43 16 38 32 20 15 0 8.0 54 33
44 12 35 33 14 10 1 14.0 52 31
45 15 38 28 14 10 0 11.0 69 42
46 11 37 35 11 12 0 13.0 68 40
47 15 38 39 14 13 0 9.0 65 43
48 15 33 34 15 11 0 11.0 75 46
49 17 36 38 16 11 2 15.0 74 42
50 13 38 32 14 12 0 11.0 75 45
51 16 32 38 16 14 0 10.0 72 44
52 14 32 30 14 10 1 14.0 67 40
53 11 32 33 12 12 5 18.0 63 37
54 12 34 38 16 13 1 14.0 62 46
55 12 32 32 9 5 0 11.0 63 36
56 15 37 35 14 6 1 14.5 76 47
57 16 39 34 16 12 0 13.0 74 45
58 15 29 34 16 12 0 9.0 67 42
59 12 37 36 15 11 0 10.0 73 43
60 12 35 34 16 10 2 15.0 70 43
61 8 30 28 12 7 7 20.0 53 32
62 13 38 34 16 12 0 12.0 77 45
63 11 34 35 16 14 0 12.0 80 48
64 14 31 35 14 11 1 14.0 52 31
65 15 34 31 16 12 0 13.0 54 33
66 10 35 37 17 13 0 11.0 80 49
67 11 36 35 18 14 4 17.0 66 42
68 12 30 27 18 11 0 12.0 73 41
69 15 39 40 12 12 0 13.0 63 38
70 15 35 37 16 12 1 14.0 69 42
71 14 38 36 10 8 0 13.0 67 44
72 16 31 38 14 11 2 15.0 54 33
73 15 34 39 18 14 0 13.0 81 48
74 15 38 41 18 14 0 10.0 69 40
75 13 34 27 16 12 0 11.0 84 50
76 12 39 30 17 9 6 19.0 80 49
77 17 37 37 16 13 0 13.0 70 43
78 13 34 31 16 11 4 17.0 69 44
79 15 28 31 13 12 0 13.0 77 47
80 13 37 27 16 12 0 9.0 54 33
81 15 33 36 16 12 0 11.0 79 46
82 15 35 37 16 12 0 9.0 71 45
83 16 37 33 15 12 0 12.0 73 43
84 15 32 34 15 11 0 12.0 72 44
85 14 33 31 16 10 0 13.0 77 47
86 15 38 39 14 9 0 13.0 75 45
87 14 33 34 16 12 0 12.0 69 42
88 13 29 32 16 12 2 15.0 54 33
89 7 33 33 15 12 9 22.0 70 43
90 17 31 36 12 9 0 13.0 73 46
91 13 36 32 17 15 2 15.0 54 33
92 15 35 41 16 12 0 13.0 77 46
93 14 32 28 15 12 2 15.0 82 48
94 13 29 30 13 12 0 12.5 80 47
95 16 39 36 16 10 0 11.0 80 47
96 12 37 35 16 13 3 16.0 69 43
97 14 35 31 16 9 0 11.0 78 46
98 17 37 34 16 12 0 11.0 81 48
99 15 32 36 14 10 0 10.0 76 46
100 17 38 36 16 14 0 10.0 76 45
101 12 37 35 16 11 3 16.0 73 45
102 16 36 37 20 15 0 12.0 85 52
103 11 32 28 15 11 0 11.0 66 42
104 15 33 39 16 11 3 16.0 79 47
105 9 40 32 13 12 6 19.0 68 41
106 16 38 35 17 12 0 11.0 76 47
107 15 41 39 16 12 3 16.0 71 43
108 10 36 35 16 11 2 15.0 54 33
109 10 43 42 12 7 11 24.0 46 30
110 15 30 34 16 12 1 14.0 85 52
111 11 31 33 16 14 2 15.0 74 44
112 13 32 41 17 11 0 11.0 88 55
113 14 32 33 13 11 2 15.0 38 11
114 18 37 34 12 10 0 12.0 76 47
115 16 37 32 18 13 0 10.0 86 53
116 14 33 40 14 13 1 14.0 54 33
117 14 34 40 14 8 0 13.0 67 44
118 14 33 35 13 11 0 9.0 69 42
119 14 38 36 16 12 2 15.0 90 55
120 12 33 37 13 11 2 15.0 54 33
121 14 31 27 16 13 1 14.0 76 46
122 15 38 39 13 12 0 11.0 89 54
123 15 37 38 16 14 0 8.0 76 47
124 15 36 31 15 13 0 11.0 73 45
125 13 31 33 16 15 0 11.0 79 47
126 17 39 32 15 10 0 8.0 90 55
127 17 44 39 17 11 0 10.0 74 44
128 19 33 36 15 9 0 11.0 81 53
129 15 35 33 12 11 0 13.0 72 44
130 13 32 33 16 10 0 11.0 71 42
131 9 28 32 10 11 7 20.0 66 40
132 15 40 37 16 8 0 10.0 77 46
133 15 27 30 12 11 2 15.0 65 40
134 15 37 38 14 12 0 12.0 74 46
135 16 32 29 15 12 1 14.0 85 53
136 11 28 22 13 9 10 23.0 54 33
137 14 34 35 15 11 1 14.0 63 42
138 11 30 35 11 10 3 16.0 54 35
139 15 35 34 12 8 0 11.0 64 40
140 13 31 35 11 9 0 12.0 69 41
141 15 32 34 16 8 0 10.0 54 33
142 16 30 37 15 9 1 14.0 84 51
143 14 30 35 17 15 0 12.0 86 53
144 15 31 23 16 11 0 12.0 77 46
145 16 40 31 10 8 0 11.0 89 55
146 16 32 27 18 13 0 12.0 76 47
147 11 36 36 13 12 0 13.0 60 38
148 12 32 31 16 12 0 11.0 75 46
149 9 35 32 13 9 6 19.0 73 46
150 16 38 39 10 7 0 12.0 85 53
151 13 42 37 15 13 4 17.0 79 47
152 16 34 38 16 9 0 9.0 71 41
153 12 35 39 16 6 0 12.0 72 44
154 9 38 34 14 8 6 19.0 69 43
155 13 33 31 10 8 5 18.0 78 51
156 13 36 32 17 15 2 15.0 54 33
157 14 32 37 13 6 1 14.0 69 43
158 19 33 36 15 9 0 11.0 81 53
159 13 34 32 16 11 0 9.0 84 51
160 12 32 38 12 8 5 18.0 84 50
161 13 34 36 13 8 3 16.0 69 46
162 10 27 26 13 10 11 24.0 66 43
163 14 31 26 12 8 1 14.0 81 47
164 16 38 33 17 14 7 20.0 82 50
165 10 34 39 15 10 5 18.0 72 43
166 11 24 30 10 8 10 23.0 54 33
167 14 30 33 14 11 0 12.0 78 48
168 12 26 25 11 12 1 14.0 74 44
169 9 34 38 13 12 3 16.0 82 50
170 9 27 37 16 12 5 18.0 73 41
171 11 37 31 12 5 7 20.0 55 34
172 16 36 37 16 12 0 12.0 72 44
173 9 41 35 12 10 0 12.0 78 47
174 13 29 25 9 7 4 17.0 59 35
175 16 36 28 12 12 0 13.0 72 44
176 13 32 35 15 11 0 9.0 78 44
177 9 37 33 12 8 3 16.0 68 43
178 12 30 30 12 9 5 18.0 69 41
179 16 31 31 14 10 0 10.0 67 41
180 11 38 37 12 9 1 14.0 74 42
181 14 36 36 16 12 0 11.0 54 33
182 13 35 30 11 6 0 9.0 67 41
183 15 31 36 19 15 0 11.0 70 44
184 14 38 32 15 12 0 10.0 80 48
185 16 22 28 8 12 0 11.0 89 55
186 13 32 36 16 12 6 19.0 76 44
187 14 36 34 17 11 1 14.0 74 43
188 15 39 31 12 7 0 12.0 87 52
189 13 28 28 11 7 1 14.0 54 30
190 11 32 36 11 5 8 21.0 61 39
191 11 32 36 14 12 0 13.0 38 11
192 14 38 40 16 12 0 10.0 75 44
193 15 32 33 12 3 2 15.0 69 42
194 11 35 37 16 11 3 16.0 62 41
195 15 32 32 13 10 1 14.0 72 44
196 12 37 38 15 12 0 12.0 70 44
197 14 34 31 16 9 6 19.0 79 48
198 14 33 37 16 12 2 15.0 87 53
199 8 33 33 14 9 6 19.0 62 37
200 13 26 32 16 12 0 13.0 77 44
201 9 30 30 16 12 4 17.0 69 44
202 15 24 30 14 10 0 12.0 69 40
203 17 34 31 11 9 0 11.0 75 42
204 13 34 32 12 12 1 14.0 54 35
205 15 33 34 15 8 0 11.0 72 43
206 15 34 36 15 11 0 13.0 74 45
207 14 35 37 16 11 0 12.0 85 55
208 16 35 36 16 12 2 15.0 52 31
209 13 36 33 11 10 1 14.0 70 44
210 16 34 33 15 10 0 12.0 84 50
211 9 34 33 12 12 4 17.0 64 40
212 16 41 44 12 12 0 11.0 84 53
213 11 32 39 15 11 5 18.0 87 54
214 10 30 32 15 8 0 13.0 79 49
215 11 35 35 16 12 4 17.0 67 40
216 15 28 25 14 10 0 13.0 65 41
217 17 33 35 17 11 0 11.0 85 52
218 14 39 34 14 10 0 12.0 83 52
219 8 36 35 13 8 9 22.0 61 36
220 15 36 39 15 12 1 14.0 82 52
221 11 35 33 13 12 0 12.0 76 46
222 16 38 36 14 10 0 12.0 58 31
223 10 33 32 15 12 4 17.0 72 44
224 15 31 32 12 9 0 9.0 72 44
225 9 34 36 13 9 8 21.0 38 11
226 16 32 36 8 6 0 10.0 78 46
227 19 31 32 14 10 0 11.0 54 33
228 12 33 34 14 9 0 12.0 63 34
229 8 34 33 11 9 10 23.0 66 42
230 11 34 35 12 9 0 13.0 70 43
231 14 34 30 13 6 0 12.0 71 43
232 9 33 38 10 10 3 16.0 67 44
233 15 32 34 16 6 0 9.0 58 36
234 13 41 33 18 14 4 17.0 72 46
235 16 34 32 13 10 0 9.0 72 44
236 11 36 31 11 10 1 14.0 70 43
237 12 37 30 4 6 4 17.0 76 50
238 13 36 27 13 12 0 13.0 50 33
239 10 29 31 16 12 0 11.0 72 43
240 11 37 30 10 7 0 12.0 72 44
241 12 27 32 12 8 0 10.0 88 53
242 8 35 35 12 11 6 19.0 53 34
243 12 28 28 10 3 3 16.0 58 35
244 12 35 33 13 6 3 16.0 66 40
245 15 37 31 15 10 1 14.0 82 53
246 11 29 35 12 8 7 20.0 69 42
247 13 32 35 14 9 2 15.0 68 43
248 14 36 32 10 9 10 23.0 44 29
249 10 19 21 12 8 7 20.0 56 36
250 12 21 20 12 9 3 16.0 53 30
251 15 31 34 11 7 1 14.0 70 42
252 13 33 32 10 7 4 17.0 78 47
253 13 36 34 12 6 0 11.0 71 44
254 13 33 32 16 9 0 13.0 72 45
255 12 37 33 12 10 4 17.0 68 44
256 12 34 33 14 11 2 15.0 67 43
257 9 35 37 16 12 8 21.0 75 43
258 9 31 32 14 8 5 18.0 62 40
259 15 37 34 13 11 2 15.0 67 41
260 10 35 30 4 3 0 8.0 83 52
261 14 27 30 15 11 0 12.0 64 38
262 15 34 38 11 12 0 12.0 68 41
263 7 40 36 11 7 9 22.0 62 39
264 14 29 32 14 9 0 12.0 72 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Open Selfassurance
13.297595 0.017824 0.006308 0.117288 -0.018021
Stress Depression Sport SportII
-0.191679 -0.244289 0.005288 0.027413
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.5028 -1.3372 0.2149 1.2500 5.4558
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.297595 2.128504 6.247 1.74e-09 ***
Connected 0.017824 0.037526 0.475 0.6352
Separate 0.006308 0.038346 0.164 0.8695
Open 0.117288 0.066572 1.762 0.0793 .
Selfassurance -0.018021 0.069205 -0.260 0.7948
Stress -0.191679 0.136837 -1.401 0.1625
Depression -0.244289 0.103208 -2.367 0.0187 *
Sport 0.005288 0.040522 0.131 0.8963
SportII 0.027413 0.060411 0.454 0.6504
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.017 on 255 degrees of freedom
Multiple R-squared: 0.3683, Adjusted R-squared: 0.3485
F-statistic: 18.59 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,] 0.6315596 0.736880751 0.368440375
[2,] 0.8751562 0.249687583 0.124843791
[3,] 0.8457590 0.308482092 0.154241046
[4,] 0.8029678 0.394064380 0.197032190
[5,] 0.7959081 0.408183823 0.204091911
[6,] 0.8066628 0.386674355 0.193337177
[7,] 0.7357642 0.528471501 0.264235751
[8,] 0.6694386 0.661122888 0.330561444
[9,] 0.7901611 0.419677851 0.209838926
[10,] 0.7760770 0.447845938 0.223922969
[11,] 0.7295476 0.540904866 0.270452433
[12,] 0.6952955 0.609408904 0.304704452
[13,] 0.6599464 0.680107265 0.340053633
[14,] 0.6012503 0.797499461 0.398749730
[15,] 0.9979344 0.004131282 0.002065641
[16,] 0.9967273 0.006545460 0.003272730
[17,] 0.9948855 0.010229082 0.005114541
[18,] 0.9924793 0.015041410 0.007520705
[19,] 0.9932808 0.013438409 0.006719204
[20,] 0.9905719 0.018856160 0.009428080
[21,] 0.9879521 0.024095867 0.012047933
[22,] 0.9849232 0.030153668 0.015076834
[23,] 0.9786796 0.042640820 0.021320410
[24,] 0.9706907 0.058618660 0.029309330
[25,] 0.9654817 0.069036637 0.034518319
[26,] 0.9863848 0.027230362 0.013615181
[27,] 0.9816240 0.036751952 0.018375976
[28,] 0.9781035 0.043792991 0.021896496
[29,] 0.9802318 0.039536463 0.019768232
[30,] 0.9753671 0.049265702 0.024632851
[31,] 0.9743369 0.051326214 0.025663107
[32,] 0.9664672 0.067065680 0.033532840
[33,] 0.9591435 0.081713051 0.040856526
[34,] 0.9476228 0.104754456 0.052377228
[35,] 0.9526637 0.094672586 0.047336293
[36,] 0.9396512 0.120697571 0.060348786
[37,] 0.9241064 0.151787198 0.075893599
[38,] 0.9333017 0.133396652 0.066698326
[39,] 0.9277563 0.144487497 0.072243749
[40,] 0.9124766 0.175046722 0.087523361
[41,] 0.8936872 0.212625536 0.106312768
[42,] 0.8860861 0.227827823 0.113913911
[43,] 0.8674624 0.265075148 0.132537574
[44,] 0.8626394 0.274721206 0.137360603
[45,] 0.8446132 0.310773534 0.155386767
[46,] 0.8370662 0.325867640 0.162933820
[47,] 0.8097329 0.380534295 0.190267148
[48,] 0.8575882 0.284823669 0.142411834
[49,] 0.8531366 0.293726843 0.146863422
[50,] 0.8823629 0.235274249 0.117637125
[51,] 0.8796262 0.240747629 0.120373814
[52,] 0.9219485 0.156103036 0.078051518
[53,] 0.9117485 0.176503092 0.088251546
[54,] 0.9053290 0.189342077 0.094671039
[55,] 0.9673971 0.065205759 0.032602880
[56,] 0.9666167 0.066766515 0.033383257
[57,] 0.9702585 0.059483095 0.029741548
[58,] 0.9662448 0.067510346 0.033755173
[59,] 0.9609083 0.078183416 0.039091708
[60,] 0.9521166 0.095766810 0.047883405
[61,] 0.9628825 0.074235031 0.037117515
[62,] 0.9541786 0.091642869 0.045821434
[63,] 0.9451796 0.109640809 0.054820404
[64,] 0.9402333 0.119533492 0.059766746
[65,] 0.9289580 0.142084084 0.071042042
[66,] 0.9394020 0.121195967 0.060597984
[67,] 0.9272734 0.145453137 0.072726568
[68,] 0.9198581 0.160283705 0.080141852
[69,] 0.9119268 0.176146426 0.088073213
[70,] 0.8954908 0.209018350 0.104509175
[71,] 0.8776537 0.244692602 0.122346301
[72,] 0.8704740 0.259052039 0.129526019
[73,] 0.8514577 0.297084564 0.148542282
[74,] 0.8281146 0.343770758 0.171885379
[75,] 0.8045974 0.390805143 0.195402572
[76,] 0.7774474 0.445105108 0.222552554
[77,] 0.7480018 0.503996346 0.251998173
[78,] 0.8129832 0.374033672 0.187016836
[79,] 0.8406017 0.318796550 0.159398275
[80,] 0.8167176 0.366564866 0.183282433
[81,] 0.7929482 0.414103510 0.207051755
[82,] 0.7690785 0.461843093 0.230921547
[83,] 0.7456925 0.508615001 0.254307500
[84,] 0.7198978 0.560204352 0.280102176
[85,] 0.6947188 0.610562367 0.305281183
[86,] 0.6671286 0.665742772 0.332871386
[87,] 0.6663604 0.667279223 0.333639612
[88,] 0.6326804 0.734639133 0.367319566
[89,] 0.6269301 0.746139835 0.373069918
[90,] 0.6029545 0.794090982 0.397045491
[91,] 0.5728167 0.854366585 0.427183293
[92,] 0.6196366 0.760726852 0.380363426
[93,] 0.6077735 0.784452999 0.392226500
[94,] 0.6191040 0.761791973 0.380895987
[95,] 0.5921456 0.815708856 0.407854428
[96,] 0.5834104 0.833179134 0.416589567
[97,] 0.6265079 0.746984116 0.373492058
[98,] 0.6014030 0.797193951 0.398596976
[99,] 0.5740349 0.851930143 0.425965072
[100,] 0.5803249 0.839350264 0.419675132
[101,] 0.6059042 0.788191586 0.394095793
[102,] 0.6013544 0.797291165 0.398645583
[103,] 0.6847245 0.630550905 0.315275453
[104,] 0.6546931 0.690613779 0.345306889
[105,] 0.6287785 0.742442981 0.371221490
[106,] 0.5957424 0.808515115 0.404257558
[107,] 0.5677951 0.864409765 0.432204883
[108,] 0.5333117 0.933376554 0.466688277
[109,] 0.5024180 0.995163985 0.497581992
[110,] 0.4703585 0.940716902 0.529641549
[111,] 0.4394817 0.878963387 0.560518306
[112,] 0.4080953 0.816190567 0.591904717
[113,] 0.3757209 0.751441851 0.624279074
[114,] 0.3624322 0.724864342 0.637567829
[115,] 0.3381238 0.676247548 0.661876226
[116,] 0.3290401 0.658080178 0.670959911
[117,] 0.4350949 0.870189862 0.564905069
[118,] 0.4138095 0.827618951 0.586190524
[119,] 0.4011732 0.802346353 0.598826824
[120,] 0.3798600 0.759720059 0.620139970
[121,] 0.3519562 0.703912336 0.648043832
[122,] 0.3700827 0.740165316 0.629917342
[123,] 0.3428917 0.685783490 0.657108255
[124,] 0.3491834 0.698366850 0.650816575
[125,] 0.3492061 0.698412186 0.650793907
[126,] 0.3188251 0.637650186 0.681174907
[127,] 0.2951055 0.590211031 0.704894484
[128,] 0.2700660 0.540131900 0.729934050
[129,] 0.2475715 0.495142987 0.752428507
[130,] 0.2216849 0.443369777 0.778315112
[131,] 0.2237628 0.447525511 0.776237245
[132,] 0.1996856 0.399371162 0.800314419
[133,] 0.1784270 0.356854088 0.821572956
[134,] 0.1671306 0.334261252 0.832869374
[135,] 0.1582222 0.316444354 0.841777823
[136,] 0.1715640 0.343128025 0.828435987
[137,] 0.1858016 0.371603100 0.814198450
[138,] 0.1985086 0.397017189 0.801491405
[139,] 0.1972221 0.394444111 0.802777945
[140,] 0.1774443 0.354888663 0.822555669
[141,] 0.1610837 0.322167340 0.838916330
[142,] 0.1772757 0.354551452 0.822724274
[143,] 0.1894538 0.378907675 0.810546163
[144,] 0.1760678 0.352135572 0.823932214
[145,] 0.1536009 0.307201780 0.846399110
[146,] 0.1352833 0.270566612 0.864716694
[147,] 0.2161251 0.432250161 0.783874919
[148,] 0.2246865 0.449373013 0.775313494
[149,] 0.2037398 0.407479622 0.796260189
[150,] 0.1815149 0.363029865 0.818485068
[151,] 0.1625338 0.325067572 0.837466214
[152,] 0.1425204 0.285040859 0.857479571
[153,] 0.2552457 0.510491307 0.744754347
[154,] 0.2498834 0.499766845 0.750116577
[155,] 0.2543660 0.508731940 0.745634030
[156,] 0.2260624 0.452124872 0.773937564
[157,] 0.2053149 0.410629864 0.794685068
[158,] 0.2642310 0.528461987 0.735769006
[159,] 0.2857832 0.571566411 0.714216795
[160,] 0.2590350 0.518070099 0.740964951
[161,] 0.2522509 0.504501869 0.747749066
[162,] 0.4261887 0.852377477 0.573811262
[163,] 0.4111935 0.822386904 0.588806548
[164,] 0.4249671 0.849934124 0.575032938
[165,] 0.4201273 0.840254680 0.579872660
[166,] 0.4834068 0.966813604 0.516593198
[167,] 0.4501712 0.900342335 0.549828832
[168,] 0.4337292 0.867458399 0.566270800
[169,] 0.4421092 0.884218334 0.557890833
[170,] 0.4039746 0.807949137 0.596025431
[171,] 0.3858893 0.771778572 0.614110714
[172,] 0.3499125 0.699824936 0.650087532
[173,] 0.3189499 0.637899869 0.681050066
[174,] 0.3386648 0.677329617 0.661335191
[175,] 0.3359689 0.671937717 0.664031142
[176,] 0.3009441 0.601888201 0.699055899
[177,] 0.2722394 0.544478899 0.727760551
[178,] 0.2402834 0.480566765 0.759716618
[179,] 0.2187089 0.437417701 0.781291150
[180,] 0.2341457 0.468291443 0.765854279
[181,] 0.2099381 0.419876183 0.790061909
[182,] 0.2161713 0.432342643 0.783828679
[183,] 0.2054354 0.410870870 0.794564565
[184,] 0.2002454 0.400490857 0.799754572
[185,] 0.2121363 0.424272553 0.787863724
[186,] 0.2639135 0.527826906 0.736086547
[187,] 0.2442990 0.488597934 0.755701033
[188,] 0.2733195 0.546638967 0.726680516
[189,] 0.2426555 0.485311038 0.757344481
[190,] 0.2757956 0.551591236 0.724204382
[191,] 0.2531685 0.506337075 0.746831463
[192,] 0.3162002 0.632400339 0.683799831
[193,] 0.2820317 0.564063398 0.717968301
[194,] 0.2491955 0.498390903 0.750804549
[195,] 0.2240451 0.448090101 0.775954950
[196,] 0.1941871 0.388374224 0.805812888
[197,] 0.2090530 0.418105955 0.790947022
[198,] 0.1784746 0.356949107 0.821525446
[199,] 0.1926754 0.385350849 0.807324576
[200,] 0.2185996 0.437199291 0.781400354
[201,] 0.2043608 0.408721548 0.795639226
[202,] 0.1778894 0.355778825 0.822110587
[203,] 0.2386209 0.477241748 0.761379126
[204,] 0.2111784 0.422356789 0.788821606
[205,] 0.1929721 0.385944242 0.807027879
[206,] 0.2328867 0.465773333 0.767113333
[207,] 0.2023709 0.404741781 0.797629109
[208,] 0.1828298 0.365659512 0.817170244
[209,] 0.1837837 0.367567317 0.816216341
[210,] 0.1929968 0.385993552 0.807003224
[211,] 0.1900654 0.380130751 0.809934625
[212,] 0.1740500 0.348099914 0.825950043
[213,] 0.1477723 0.295544614 0.852227693
[214,] 0.1314474 0.262894705 0.868552647
[215,] 0.1636285 0.327257074 0.836371463
[216,] 0.3681780 0.736356021 0.631821990
[217,] 0.3503746 0.700749210 0.649625395
[218,] 0.3095314 0.619062801 0.690468600
[219,] 0.3174713 0.634942695 0.682528653
[220,] 0.2691397 0.538279302 0.730860349
[221,] 0.3355373 0.671074613 0.664462694
[222,] 0.3233356 0.646671251 0.676664375
[223,] 0.2884958 0.576991518 0.711504241
[224,] 0.5195209 0.960958127 0.480479063
[225,] 0.5584359 0.883128247 0.441564123
[226,] 0.5312226 0.937554812 0.468777406
[227,] 0.4751606 0.950321146 0.524839427
[228,] 0.4841123 0.968224598 0.515887701
[229,] 0.5652374 0.869525221 0.434762611
[230,] 0.5078991 0.984201850 0.492100925
[231,] 0.7690070 0.461985970 0.230992985
[232,] 0.6978361 0.604327809 0.302163905
[233,] 0.6150253 0.769949470 0.384974735
[234,] 0.7265112 0.546977630 0.273488815
[235,] 0.6461868 0.707626349 0.353813174
[236,] 0.5455436 0.908912836 0.454456418
[237,] 0.9615415 0.076917002 0.038458501
[238,] 0.9900531 0.019893849 0.009946924
[239,] 0.9897965 0.020406918 0.010203459
[240,] 0.9675748 0.064850425 0.032425212
[241,] 0.9087326 0.182534791 0.091267396
> postscript(file="/var/fisher/rcomp/tmp/1p97z1384604191.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/fisher/rcomp/tmp/28e231384604191.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3w5fa1384604191.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/45k3m1384604191.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5usjb1384604191.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.197371622 2.977187494 -2.899929319 -2.256304924 5.455794021 3.781636716
7 8 9 10 11 12
3.604377176 -0.716879208 0.232120858 0.691486109 1.814989792 3.950001619
13 14 15 16 17 18
-3.314042819 2.510689900 2.687120600 0.698915036 0.261261179 1.397351014
19 20 21 22 23 24
-1.164912494 2.157255227 3.093252358 -2.660123930 -0.485389884 -1.580199585
25 26 27 28 29 30
1.834641749 -6.502767281 1.250042011 0.850389853 1.211315036 -2.958357378
31 32 33 34 35 36
0.843512861 0.613487120 2.274177823 -0.034737512 0.114416459 1.249782489
37 38 39 40 41 42
-1.140666496 0.804006657 2.073514348 -1.899375451 -0.281687453 2.471345535
43 44 45 46 47 48
0.511878286 -1.104514480 0.557556153 -2.532174214 0.047394384 0.368180047
49 50 51 52 53 54
3.647643304 -1.545603585 1.123950276 0.641836269 -0.259204448 -1.762827774
55 56 57 58 59 60
-1.668181679 1.331747416 1.683246611 0.003592182 -2.867205241 -1.333582101
61 62 63 64 65 66
-2.220235224 -1.559083158 -3.556157021 0.972188233 1.226019598 -4.993608732
67 68 69 70 71 72
-1.599704997 -2.494123837 1.364618245 1.280270270 0.384532848 3.323828986
73 74 75 76 77 78
0.423036098 -0.110977089 -1.862005604 0.011549621 2.793972950 0.570996933
79 80 81 82 83 84
1.179410895 -1.779378872 0.235143783 -0.225671054 1.658317418 0.700985417
85 86 87 88 89 90
-0.297617385 0.844757354 -0.345414438 0.180769045 -3.086521492 3.206191013
91 92 93 94 95 96
-0.007227057 0.667110945 0.710540823 -0.970115204 1.059454060 -0.880219927
97 98 99 100 101 102
-0.817740211 2.111058439 0.223078632 1.981052993 -0.992241877 0.808351234
103 104 105 106 107 108
-3.418900031 1.967267497 -2.212907413 1.023493088 1.994653816 -2.980945839
109 110 111 112 113 114
1.295454655 1.029567985 -2.232460001 -2.208195898 2.142540726 3.842313784
115 116 117 118 119 120
0.499320542 0.875638685 -0.038553870 -0.750745945 0.201643236 -0.588223698
121 122 123 124 125 126
0.285994760 0.206772464 -0.557143471 0.407662269 -1.683634808 1.196915334
127 128 129 130 131 132
1.721822550 4.095898632 1.249973984 -1.612189847 -1.191213451 -0.201730652
133 134 135 136 137 138
2.430099802 0.656537931 2.115333128 2.047215360 0.441707545 -0.924438563
139 140 141 142 143 144
0.852985515 -0.756281624 0.437793834 2.106571854 -0.752924031 0.589639037
145 146 147 148 149 150
1.473953948 1.325923622 -2.658100656 -2.694339478 -2.341357070 1.761388587
151 152 153 154 155 156
0.408761586 0.841436571 -2.591419432 -2.439361304 1.434966737 -0.007227057
157 158 159 160 161 162
0.550069264 4.095898632 -2.427557332 0.169743429 0.346465267 1.156202816
163 164 165 166 167 168
0.617495978 4.498532626 -1.932682070 2.401894078 -0.281153503 -0.978444022
169 170 171 172 173 174
-3.772468762 -2.827003788 0.534625480 1.511497178 -5.245868135 1.746503848
175 176 177 178 179 180
2.281709591 -2.069919823 -3.483267514 0.599921366 1.457104175 -2.384554202
181 182 183 184 185 186
-0.329746531 -1.572393504 0.075412288 -1.015862903 2.120375957 1.428044649
187 188 189 190 191 192
0.092205181 0.576287086 0.366433946 0.976667040 -1.847585503 -1.047518290
193 194 195 196 197 198
2.101907244 -1.801383472 1.610413497 -2.384770127 2.244353611 0.355149084
199 200 201 202 203 204
-3.124411890 -1.060872564 -3.333376906 1.093597647 2.912044008 0.070005539
205 206 207 208 209 210
0.412222659 0.859020134 -0.858995366 3.113995121 -0.222038672 1.425683108
211 212 213 214 215 216
-2.818349533 1.292903576 -1.259885117 -4.234609185 -1.333807409 1.291868291
217 218 219 220 221 222
1.909931917 -0.601996850 -1.750628348 1.024235921 -3.169562683 2.111102155
223 224 225 226 227 228
-2.298042373 0.314380832 -0.332266894 1.844147786 4.983141225 -1.913863215
229 230 231 232 233 234
-1.204721163 -2.742868980 -0.132258282 -3.195016018 0.054069287 0.182405126
235 236 237 238 239 240
1.161640404 -2.182009603 0.639687043 -0.411379889 -4.542761104 -2.848547562
241 242 243 244 245 246
-2.719387557 -2.772222790 0.125353577 -0.508131835 0.993418868 0.412707767
247 248 249 250 251 252
-0.059284614 5.355943958 -0.087510341 0.337644342 1.861539534 1.084324057
253 254 255 256 257 258
-1.347552644 -1.240677598 -0.038671448 -1.040987605 -1.727099052 -2.618684271
259 260 261 262 263 264
2.071346589 -4.435888734 0.022125713 1.230672831 -2.699206690 -0.124265976
> postscript(file="/var/fisher/rcomp/tmp/6ltln1384604191.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 0.197371622 NA
1 2.977187494 0.197371622
2 -2.899929319 2.977187494
3 -2.256304924 -2.899929319
4 5.455794021 -2.256304924
5 3.781636716 5.455794021
6 3.604377176 3.781636716
7 -0.716879208 3.604377176
8 0.232120858 -0.716879208
9 0.691486109 0.232120858
10 1.814989792 0.691486109
11 3.950001619 1.814989792
12 -3.314042819 3.950001619
13 2.510689900 -3.314042819
14 2.687120600 2.510689900
15 0.698915036 2.687120600
16 0.261261179 0.698915036
17 1.397351014 0.261261179
18 -1.164912494 1.397351014
19 2.157255227 -1.164912494
20 3.093252358 2.157255227
21 -2.660123930 3.093252358
22 -0.485389884 -2.660123930
23 -1.580199585 -0.485389884
24 1.834641749 -1.580199585
25 -6.502767281 1.834641749
26 1.250042011 -6.502767281
27 0.850389853 1.250042011
28 1.211315036 0.850389853
29 -2.958357378 1.211315036
30 0.843512861 -2.958357378
31 0.613487120 0.843512861
32 2.274177823 0.613487120
33 -0.034737512 2.274177823
34 0.114416459 -0.034737512
35 1.249782489 0.114416459
36 -1.140666496 1.249782489
37 0.804006657 -1.140666496
38 2.073514348 0.804006657
39 -1.899375451 2.073514348
40 -0.281687453 -1.899375451
41 2.471345535 -0.281687453
42 0.511878286 2.471345535
43 -1.104514480 0.511878286
44 0.557556153 -1.104514480
45 -2.532174214 0.557556153
46 0.047394384 -2.532174214
47 0.368180047 0.047394384
48 3.647643304 0.368180047
49 -1.545603585 3.647643304
50 1.123950276 -1.545603585
51 0.641836269 1.123950276
52 -0.259204448 0.641836269
53 -1.762827774 -0.259204448
54 -1.668181679 -1.762827774
55 1.331747416 -1.668181679
56 1.683246611 1.331747416
57 0.003592182 1.683246611
58 -2.867205241 0.003592182
59 -1.333582101 -2.867205241
60 -2.220235224 -1.333582101
61 -1.559083158 -2.220235224
62 -3.556157021 -1.559083158
63 0.972188233 -3.556157021
64 1.226019598 0.972188233
65 -4.993608732 1.226019598
66 -1.599704997 -4.993608732
67 -2.494123837 -1.599704997
68 1.364618245 -2.494123837
69 1.280270270 1.364618245
70 0.384532848 1.280270270
71 3.323828986 0.384532848
72 0.423036098 3.323828986
73 -0.110977089 0.423036098
74 -1.862005604 -0.110977089
75 0.011549621 -1.862005604
76 2.793972950 0.011549621
77 0.570996933 2.793972950
78 1.179410895 0.570996933
79 -1.779378872 1.179410895
80 0.235143783 -1.779378872
81 -0.225671054 0.235143783
82 1.658317418 -0.225671054
83 0.700985417 1.658317418
84 -0.297617385 0.700985417
85 0.844757354 -0.297617385
86 -0.345414438 0.844757354
87 0.180769045 -0.345414438
88 -3.086521492 0.180769045
89 3.206191013 -3.086521492
90 -0.007227057 3.206191013
91 0.667110945 -0.007227057
92 0.710540823 0.667110945
93 -0.970115204 0.710540823
94 1.059454060 -0.970115204
95 -0.880219927 1.059454060
96 -0.817740211 -0.880219927
97 2.111058439 -0.817740211
98 0.223078632 2.111058439
99 1.981052993 0.223078632
100 -0.992241877 1.981052993
101 0.808351234 -0.992241877
102 -3.418900031 0.808351234
103 1.967267497 -3.418900031
104 -2.212907413 1.967267497
105 1.023493088 -2.212907413
106 1.994653816 1.023493088
107 -2.980945839 1.994653816
108 1.295454655 -2.980945839
109 1.029567985 1.295454655
110 -2.232460001 1.029567985
111 -2.208195898 -2.232460001
112 2.142540726 -2.208195898
113 3.842313784 2.142540726
114 0.499320542 3.842313784
115 0.875638685 0.499320542
116 -0.038553870 0.875638685
117 -0.750745945 -0.038553870
118 0.201643236 -0.750745945
119 -0.588223698 0.201643236
120 0.285994760 -0.588223698
121 0.206772464 0.285994760
122 -0.557143471 0.206772464
123 0.407662269 -0.557143471
124 -1.683634808 0.407662269
125 1.196915334 -1.683634808
126 1.721822550 1.196915334
127 4.095898632 1.721822550
128 1.249973984 4.095898632
129 -1.612189847 1.249973984
130 -1.191213451 -1.612189847
131 -0.201730652 -1.191213451
132 2.430099802 -0.201730652
133 0.656537931 2.430099802
134 2.115333128 0.656537931
135 2.047215360 2.115333128
136 0.441707545 2.047215360
137 -0.924438563 0.441707545
138 0.852985515 -0.924438563
139 -0.756281624 0.852985515
140 0.437793834 -0.756281624
141 2.106571854 0.437793834
142 -0.752924031 2.106571854
143 0.589639037 -0.752924031
144 1.473953948 0.589639037
145 1.325923622 1.473953948
146 -2.658100656 1.325923622
147 -2.694339478 -2.658100656
148 -2.341357070 -2.694339478
149 1.761388587 -2.341357070
150 0.408761586 1.761388587
151 0.841436571 0.408761586
152 -2.591419432 0.841436571
153 -2.439361304 -2.591419432
154 1.434966737 -2.439361304
155 -0.007227057 1.434966737
156 0.550069264 -0.007227057
157 4.095898632 0.550069264
158 -2.427557332 4.095898632
159 0.169743429 -2.427557332
160 0.346465267 0.169743429
161 1.156202816 0.346465267
162 0.617495978 1.156202816
163 4.498532626 0.617495978
164 -1.932682070 4.498532626
165 2.401894078 -1.932682070
166 -0.281153503 2.401894078
167 -0.978444022 -0.281153503
168 -3.772468762 -0.978444022
169 -2.827003788 -3.772468762
170 0.534625480 -2.827003788
171 1.511497178 0.534625480
172 -5.245868135 1.511497178
173 1.746503848 -5.245868135
174 2.281709591 1.746503848
175 -2.069919823 2.281709591
176 -3.483267514 -2.069919823
177 0.599921366 -3.483267514
178 1.457104175 0.599921366
179 -2.384554202 1.457104175
180 -0.329746531 -2.384554202
181 -1.572393504 -0.329746531
182 0.075412288 -1.572393504
183 -1.015862903 0.075412288
184 2.120375957 -1.015862903
185 1.428044649 2.120375957
186 0.092205181 1.428044649
187 0.576287086 0.092205181
188 0.366433946 0.576287086
189 0.976667040 0.366433946
190 -1.847585503 0.976667040
191 -1.047518290 -1.847585503
192 2.101907244 -1.047518290
193 -1.801383472 2.101907244
194 1.610413497 -1.801383472
195 -2.384770127 1.610413497
196 2.244353611 -2.384770127
197 0.355149084 2.244353611
198 -3.124411890 0.355149084
199 -1.060872564 -3.124411890
200 -3.333376906 -1.060872564
201 1.093597647 -3.333376906
202 2.912044008 1.093597647
203 0.070005539 2.912044008
204 0.412222659 0.070005539
205 0.859020134 0.412222659
206 -0.858995366 0.859020134
207 3.113995121 -0.858995366
208 -0.222038672 3.113995121
209 1.425683108 -0.222038672
210 -2.818349533 1.425683108
211 1.292903576 -2.818349533
212 -1.259885117 1.292903576
213 -4.234609185 -1.259885117
214 -1.333807409 -4.234609185
215 1.291868291 -1.333807409
216 1.909931917 1.291868291
217 -0.601996850 1.909931917
218 -1.750628348 -0.601996850
219 1.024235921 -1.750628348
220 -3.169562683 1.024235921
221 2.111102155 -3.169562683
222 -2.298042373 2.111102155
223 0.314380832 -2.298042373
224 -0.332266894 0.314380832
225 1.844147786 -0.332266894
226 4.983141225 1.844147786
227 -1.913863215 4.983141225
228 -1.204721163 -1.913863215
229 -2.742868980 -1.204721163
230 -0.132258282 -2.742868980
231 -3.195016018 -0.132258282
232 0.054069287 -3.195016018
233 0.182405126 0.054069287
234 1.161640404 0.182405126
235 -2.182009603 1.161640404
236 0.639687043 -2.182009603
237 -0.411379889 0.639687043
238 -4.542761104 -0.411379889
239 -2.848547562 -4.542761104
240 -2.719387557 -2.848547562
241 -2.772222790 -2.719387557
242 0.125353577 -2.772222790
243 -0.508131835 0.125353577
244 0.993418868 -0.508131835
245 0.412707767 0.993418868
246 -0.059284614 0.412707767
247 5.355943958 -0.059284614
248 -0.087510341 5.355943958
249 0.337644342 -0.087510341
250 1.861539534 0.337644342
251 1.084324057 1.861539534
252 -1.347552644 1.084324057
253 -1.240677598 -1.347552644
254 -0.038671448 -1.240677598
255 -1.040987605 -0.038671448
256 -1.727099052 -1.040987605
257 -2.618684271 -1.727099052
258 2.071346589 -2.618684271
259 -4.435888734 2.071346589
260 0.022125713 -4.435888734
261 1.230672831 0.022125713
262 -2.699206690 1.230672831
263 -0.124265976 -2.699206690
264 NA -0.124265976
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.977187494 0.197371622
[2,] -2.899929319 2.977187494
[3,] -2.256304924 -2.899929319
[4,] 5.455794021 -2.256304924
[5,] 3.781636716 5.455794021
[6,] 3.604377176 3.781636716
[7,] -0.716879208 3.604377176
[8,] 0.232120858 -0.716879208
[9,] 0.691486109 0.232120858
[10,] 1.814989792 0.691486109
[11,] 3.950001619 1.814989792
[12,] -3.314042819 3.950001619
[13,] 2.510689900 -3.314042819
[14,] 2.687120600 2.510689900
[15,] 0.698915036 2.687120600
[16,] 0.261261179 0.698915036
[17,] 1.397351014 0.261261179
[18,] -1.164912494 1.397351014
[19,] 2.157255227 -1.164912494
[20,] 3.093252358 2.157255227
[21,] -2.660123930 3.093252358
[22,] -0.485389884 -2.660123930
[23,] -1.580199585 -0.485389884
[24,] 1.834641749 -1.580199585
[25,] -6.502767281 1.834641749
[26,] 1.250042011 -6.502767281
[27,] 0.850389853 1.250042011
[28,] 1.211315036 0.850389853
[29,] -2.958357378 1.211315036
[30,] 0.843512861 -2.958357378
[31,] 0.613487120 0.843512861
[32,] 2.274177823 0.613487120
[33,] -0.034737512 2.274177823
[34,] 0.114416459 -0.034737512
[35,] 1.249782489 0.114416459
[36,] -1.140666496 1.249782489
[37,] 0.804006657 -1.140666496
[38,] 2.073514348 0.804006657
[39,] -1.899375451 2.073514348
[40,] -0.281687453 -1.899375451
[41,] 2.471345535 -0.281687453
[42,] 0.511878286 2.471345535
[43,] -1.104514480 0.511878286
[44,] 0.557556153 -1.104514480
[45,] -2.532174214 0.557556153
[46,] 0.047394384 -2.532174214
[47,] 0.368180047 0.047394384
[48,] 3.647643304 0.368180047
[49,] -1.545603585 3.647643304
[50,] 1.123950276 -1.545603585
[51,] 0.641836269 1.123950276
[52,] -0.259204448 0.641836269
[53,] -1.762827774 -0.259204448
[54,] -1.668181679 -1.762827774
[55,] 1.331747416 -1.668181679
[56,] 1.683246611 1.331747416
[57,] 0.003592182 1.683246611
[58,] -2.867205241 0.003592182
[59,] -1.333582101 -2.867205241
[60,] -2.220235224 -1.333582101
[61,] -1.559083158 -2.220235224
[62,] -3.556157021 -1.559083158
[63,] 0.972188233 -3.556157021
[64,] 1.226019598 0.972188233
[65,] -4.993608732 1.226019598
[66,] -1.599704997 -4.993608732
[67,] -2.494123837 -1.599704997
[68,] 1.364618245 -2.494123837
[69,] 1.280270270 1.364618245
[70,] 0.384532848 1.280270270
[71,] 3.323828986 0.384532848
[72,] 0.423036098 3.323828986
[73,] -0.110977089 0.423036098
[74,] -1.862005604 -0.110977089
[75,] 0.011549621 -1.862005604
[76,] 2.793972950 0.011549621
[77,] 0.570996933 2.793972950
[78,] 1.179410895 0.570996933
[79,] -1.779378872 1.179410895
[80,] 0.235143783 -1.779378872
[81,] -0.225671054 0.235143783
[82,] 1.658317418 -0.225671054
[83,] 0.700985417 1.658317418
[84,] -0.297617385 0.700985417
[85,] 0.844757354 -0.297617385
[86,] -0.345414438 0.844757354
[87,] 0.180769045 -0.345414438
[88,] -3.086521492 0.180769045
[89,] 3.206191013 -3.086521492
[90,] -0.007227057 3.206191013
[91,] 0.667110945 -0.007227057
[92,] 0.710540823 0.667110945
[93,] -0.970115204 0.710540823
[94,] 1.059454060 -0.970115204
[95,] -0.880219927 1.059454060
[96,] -0.817740211 -0.880219927
[97,] 2.111058439 -0.817740211
[98,] 0.223078632 2.111058439
[99,] 1.981052993 0.223078632
[100,] -0.992241877 1.981052993
[101,] 0.808351234 -0.992241877
[102,] -3.418900031 0.808351234
[103,] 1.967267497 -3.418900031
[104,] -2.212907413 1.967267497
[105,] 1.023493088 -2.212907413
[106,] 1.994653816 1.023493088
[107,] -2.980945839 1.994653816
[108,] 1.295454655 -2.980945839
[109,] 1.029567985 1.295454655
[110,] -2.232460001 1.029567985
[111,] -2.208195898 -2.232460001
[112,] 2.142540726 -2.208195898
[113,] 3.842313784 2.142540726
[114,] 0.499320542 3.842313784
[115,] 0.875638685 0.499320542
[116,] -0.038553870 0.875638685
[117,] -0.750745945 -0.038553870
[118,] 0.201643236 -0.750745945
[119,] -0.588223698 0.201643236
[120,] 0.285994760 -0.588223698
[121,] 0.206772464 0.285994760
[122,] -0.557143471 0.206772464
[123,] 0.407662269 -0.557143471
[124,] -1.683634808 0.407662269
[125,] 1.196915334 -1.683634808
[126,] 1.721822550 1.196915334
[127,] 4.095898632 1.721822550
[128,] 1.249973984 4.095898632
[129,] -1.612189847 1.249973984
[130,] -1.191213451 -1.612189847
[131,] -0.201730652 -1.191213451
[132,] 2.430099802 -0.201730652
[133,] 0.656537931 2.430099802
[134,] 2.115333128 0.656537931
[135,] 2.047215360 2.115333128
[136,] 0.441707545 2.047215360
[137,] -0.924438563 0.441707545
[138,] 0.852985515 -0.924438563
[139,] -0.756281624 0.852985515
[140,] 0.437793834 -0.756281624
[141,] 2.106571854 0.437793834
[142,] -0.752924031 2.106571854
[143,] 0.589639037 -0.752924031
[144,] 1.473953948 0.589639037
[145,] 1.325923622 1.473953948
[146,] -2.658100656 1.325923622
[147,] -2.694339478 -2.658100656
[148,] -2.341357070 -2.694339478
[149,] 1.761388587 -2.341357070
[150,] 0.408761586 1.761388587
[151,] 0.841436571 0.408761586
[152,] -2.591419432 0.841436571
[153,] -2.439361304 -2.591419432
[154,] 1.434966737 -2.439361304
[155,] -0.007227057 1.434966737
[156,] 0.550069264 -0.007227057
[157,] 4.095898632 0.550069264
[158,] -2.427557332 4.095898632
[159,] 0.169743429 -2.427557332
[160,] 0.346465267 0.169743429
[161,] 1.156202816 0.346465267
[162,] 0.617495978 1.156202816
[163,] 4.498532626 0.617495978
[164,] -1.932682070 4.498532626
[165,] 2.401894078 -1.932682070
[166,] -0.281153503 2.401894078
[167,] -0.978444022 -0.281153503
[168,] -3.772468762 -0.978444022
[169,] -2.827003788 -3.772468762
[170,] 0.534625480 -2.827003788
[171,] 1.511497178 0.534625480
[172,] -5.245868135 1.511497178
[173,] 1.746503848 -5.245868135
[174,] 2.281709591 1.746503848
[175,] -2.069919823 2.281709591
[176,] -3.483267514 -2.069919823
[177,] 0.599921366 -3.483267514
[178,] 1.457104175 0.599921366
[179,] -2.384554202 1.457104175
[180,] -0.329746531 -2.384554202
[181,] -1.572393504 -0.329746531
[182,] 0.075412288 -1.572393504
[183,] -1.015862903 0.075412288
[184,] 2.120375957 -1.015862903
[185,] 1.428044649 2.120375957
[186,] 0.092205181 1.428044649
[187,] 0.576287086 0.092205181
[188,] 0.366433946 0.576287086
[189,] 0.976667040 0.366433946
[190,] -1.847585503 0.976667040
[191,] -1.047518290 -1.847585503
[192,] 2.101907244 -1.047518290
[193,] -1.801383472 2.101907244
[194,] 1.610413497 -1.801383472
[195,] -2.384770127 1.610413497
[196,] 2.244353611 -2.384770127
[197,] 0.355149084 2.244353611
[198,] -3.124411890 0.355149084
[199,] -1.060872564 -3.124411890
[200,] -3.333376906 -1.060872564
[201,] 1.093597647 -3.333376906
[202,] 2.912044008 1.093597647
[203,] 0.070005539 2.912044008
[204,] 0.412222659 0.070005539
[205,] 0.859020134 0.412222659
[206,] -0.858995366 0.859020134
[207,] 3.113995121 -0.858995366
[208,] -0.222038672 3.113995121
[209,] 1.425683108 -0.222038672
[210,] -2.818349533 1.425683108
[211,] 1.292903576 -2.818349533
[212,] -1.259885117 1.292903576
[213,] -4.234609185 -1.259885117
[214,] -1.333807409 -4.234609185
[215,] 1.291868291 -1.333807409
[216,] 1.909931917 1.291868291
[217,] -0.601996850 1.909931917
[218,] -1.750628348 -0.601996850
[219,] 1.024235921 -1.750628348
[220,] -3.169562683 1.024235921
[221,] 2.111102155 -3.169562683
[222,] -2.298042373 2.111102155
[223,] 0.314380832 -2.298042373
[224,] -0.332266894 0.314380832
[225,] 1.844147786 -0.332266894
[226,] 4.983141225 1.844147786
[227,] -1.913863215 4.983141225
[228,] -1.204721163 -1.913863215
[229,] -2.742868980 -1.204721163
[230,] -0.132258282 -2.742868980
[231,] -3.195016018 -0.132258282
[232,] 0.054069287 -3.195016018
[233,] 0.182405126 0.054069287
[234,] 1.161640404 0.182405126
[235,] -2.182009603 1.161640404
[236,] 0.639687043 -2.182009603
[237,] -0.411379889 0.639687043
[238,] -4.542761104 -0.411379889
[239,] -2.848547562 -4.542761104
[240,] -2.719387557 -2.848547562
[241,] -2.772222790 -2.719387557
[242,] 0.125353577 -2.772222790
[243,] -0.508131835 0.125353577
[244,] 0.993418868 -0.508131835
[245,] 0.412707767 0.993418868
[246,] -0.059284614 0.412707767
[247,] 5.355943958 -0.059284614
[248,] -0.087510341 5.355943958
[249,] 0.337644342 -0.087510341
[250,] 1.861539534 0.337644342
[251,] 1.084324057 1.861539534
[252,] -1.347552644 1.084324057
[253,] -1.240677598 -1.347552644
[254,] -0.038671448 -1.240677598
[255,] -1.040987605 -0.038671448
[256,] -1.727099052 -1.040987605
[257,] -2.618684271 -1.727099052
[258,] 2.071346589 -2.618684271
[259,] -4.435888734 2.071346589
[260,] 0.022125713 -4.435888734
[261,] 1.230672831 0.022125713
[262,] -2.699206690 1.230672831
[263,] -0.124265976 -2.699206690
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.977187494 0.197371622
2 -2.899929319 2.977187494
3 -2.256304924 -2.899929319
4 5.455794021 -2.256304924
5 3.781636716 5.455794021
6 3.604377176 3.781636716
7 -0.716879208 3.604377176
8 0.232120858 -0.716879208
9 0.691486109 0.232120858
10 1.814989792 0.691486109
11 3.950001619 1.814989792
12 -3.314042819 3.950001619
13 2.510689900 -3.314042819
14 2.687120600 2.510689900
15 0.698915036 2.687120600
16 0.261261179 0.698915036
17 1.397351014 0.261261179
18 -1.164912494 1.397351014
19 2.157255227 -1.164912494
20 3.093252358 2.157255227
21 -2.660123930 3.093252358
22 -0.485389884 -2.660123930
23 -1.580199585 -0.485389884
24 1.834641749 -1.580199585
25 -6.502767281 1.834641749
26 1.250042011 -6.502767281
27 0.850389853 1.250042011
28 1.211315036 0.850389853
29 -2.958357378 1.211315036
30 0.843512861 -2.958357378
31 0.613487120 0.843512861
32 2.274177823 0.613487120
33 -0.034737512 2.274177823
34 0.114416459 -0.034737512
35 1.249782489 0.114416459
36 -1.140666496 1.249782489
37 0.804006657 -1.140666496
38 2.073514348 0.804006657
39 -1.899375451 2.073514348
40 -0.281687453 -1.899375451
41 2.471345535 -0.281687453
42 0.511878286 2.471345535
43 -1.104514480 0.511878286
44 0.557556153 -1.104514480
45 -2.532174214 0.557556153
46 0.047394384 -2.532174214
47 0.368180047 0.047394384
48 3.647643304 0.368180047
49 -1.545603585 3.647643304
50 1.123950276 -1.545603585
51 0.641836269 1.123950276
52 -0.259204448 0.641836269
53 -1.762827774 -0.259204448
54 -1.668181679 -1.762827774
55 1.331747416 -1.668181679
56 1.683246611 1.331747416
57 0.003592182 1.683246611
58 -2.867205241 0.003592182
59 -1.333582101 -2.867205241
60 -2.220235224 -1.333582101
61 -1.559083158 -2.220235224
62 -3.556157021 -1.559083158
63 0.972188233 -3.556157021
64 1.226019598 0.972188233
65 -4.993608732 1.226019598
66 -1.599704997 -4.993608732
67 -2.494123837 -1.599704997
68 1.364618245 -2.494123837
69 1.280270270 1.364618245
70 0.384532848 1.280270270
71 3.323828986 0.384532848
72 0.423036098 3.323828986
73 -0.110977089 0.423036098
74 -1.862005604 -0.110977089
75 0.011549621 -1.862005604
76 2.793972950 0.011549621
77 0.570996933 2.793972950
78 1.179410895 0.570996933
79 -1.779378872 1.179410895
80 0.235143783 -1.779378872
81 -0.225671054 0.235143783
82 1.658317418 -0.225671054
83 0.700985417 1.658317418
84 -0.297617385 0.700985417
85 0.844757354 -0.297617385
86 -0.345414438 0.844757354
87 0.180769045 -0.345414438
88 -3.086521492 0.180769045
89 3.206191013 -3.086521492
90 -0.007227057 3.206191013
91 0.667110945 -0.007227057
92 0.710540823 0.667110945
93 -0.970115204 0.710540823
94 1.059454060 -0.970115204
95 -0.880219927 1.059454060
96 -0.817740211 -0.880219927
97 2.111058439 -0.817740211
98 0.223078632 2.111058439
99 1.981052993 0.223078632
100 -0.992241877 1.981052993
101 0.808351234 -0.992241877
102 -3.418900031 0.808351234
103 1.967267497 -3.418900031
104 -2.212907413 1.967267497
105 1.023493088 -2.212907413
106 1.994653816 1.023493088
107 -2.980945839 1.994653816
108 1.295454655 -2.980945839
109 1.029567985 1.295454655
110 -2.232460001 1.029567985
111 -2.208195898 -2.232460001
112 2.142540726 -2.208195898
113 3.842313784 2.142540726
114 0.499320542 3.842313784
115 0.875638685 0.499320542
116 -0.038553870 0.875638685
117 -0.750745945 -0.038553870
118 0.201643236 -0.750745945
119 -0.588223698 0.201643236
120 0.285994760 -0.588223698
121 0.206772464 0.285994760
122 -0.557143471 0.206772464
123 0.407662269 -0.557143471
124 -1.683634808 0.407662269
125 1.196915334 -1.683634808
126 1.721822550 1.196915334
127 4.095898632 1.721822550
128 1.249973984 4.095898632
129 -1.612189847 1.249973984
130 -1.191213451 -1.612189847
131 -0.201730652 -1.191213451
132 2.430099802 -0.201730652
133 0.656537931 2.430099802
134 2.115333128 0.656537931
135 2.047215360 2.115333128
136 0.441707545 2.047215360
137 -0.924438563 0.441707545
138 0.852985515 -0.924438563
139 -0.756281624 0.852985515
140 0.437793834 -0.756281624
141 2.106571854 0.437793834
142 -0.752924031 2.106571854
143 0.589639037 -0.752924031
144 1.473953948 0.589639037
145 1.325923622 1.473953948
146 -2.658100656 1.325923622
147 -2.694339478 -2.658100656
148 -2.341357070 -2.694339478
149 1.761388587 -2.341357070
150 0.408761586 1.761388587
151 0.841436571 0.408761586
152 -2.591419432 0.841436571
153 -2.439361304 -2.591419432
154 1.434966737 -2.439361304
155 -0.007227057 1.434966737
156 0.550069264 -0.007227057
157 4.095898632 0.550069264
158 -2.427557332 4.095898632
159 0.169743429 -2.427557332
160 0.346465267 0.169743429
161 1.156202816 0.346465267
162 0.617495978 1.156202816
163 4.498532626 0.617495978
164 -1.932682070 4.498532626
165 2.401894078 -1.932682070
166 -0.281153503 2.401894078
167 -0.978444022 -0.281153503
168 -3.772468762 -0.978444022
169 -2.827003788 -3.772468762
170 0.534625480 -2.827003788
171 1.511497178 0.534625480
172 -5.245868135 1.511497178
173 1.746503848 -5.245868135
174 2.281709591 1.746503848
175 -2.069919823 2.281709591
176 -3.483267514 -2.069919823
177 0.599921366 -3.483267514
178 1.457104175 0.599921366
179 -2.384554202 1.457104175
180 -0.329746531 -2.384554202
181 -1.572393504 -0.329746531
182 0.075412288 -1.572393504
183 -1.015862903 0.075412288
184 2.120375957 -1.015862903
185 1.428044649 2.120375957
186 0.092205181 1.428044649
187 0.576287086 0.092205181
188 0.366433946 0.576287086
189 0.976667040 0.366433946
190 -1.847585503 0.976667040
191 -1.047518290 -1.847585503
192 2.101907244 -1.047518290
193 -1.801383472 2.101907244
194 1.610413497 -1.801383472
195 -2.384770127 1.610413497
196 2.244353611 -2.384770127
197 0.355149084 2.244353611
198 -3.124411890 0.355149084
199 -1.060872564 -3.124411890
200 -3.333376906 -1.060872564
201 1.093597647 -3.333376906
202 2.912044008 1.093597647
203 0.070005539 2.912044008
204 0.412222659 0.070005539
205 0.859020134 0.412222659
206 -0.858995366 0.859020134
207 3.113995121 -0.858995366
208 -0.222038672 3.113995121
209 1.425683108 -0.222038672
210 -2.818349533 1.425683108
211 1.292903576 -2.818349533
212 -1.259885117 1.292903576
213 -4.234609185 -1.259885117
214 -1.333807409 -4.234609185
215 1.291868291 -1.333807409
216 1.909931917 1.291868291
217 -0.601996850 1.909931917
218 -1.750628348 -0.601996850
219 1.024235921 -1.750628348
220 -3.169562683 1.024235921
221 2.111102155 -3.169562683
222 -2.298042373 2.111102155
223 0.314380832 -2.298042373
224 -0.332266894 0.314380832
225 1.844147786 -0.332266894
226 4.983141225 1.844147786
227 -1.913863215 4.983141225
228 -1.204721163 -1.913863215
229 -2.742868980 -1.204721163
230 -0.132258282 -2.742868980
231 -3.195016018 -0.132258282
232 0.054069287 -3.195016018
233 0.182405126 0.054069287
234 1.161640404 0.182405126
235 -2.182009603 1.161640404
236 0.639687043 -2.182009603
237 -0.411379889 0.639687043
238 -4.542761104 -0.411379889
239 -2.848547562 -4.542761104
240 -2.719387557 -2.848547562
241 -2.772222790 -2.719387557
242 0.125353577 -2.772222790
243 -0.508131835 0.125353577
244 0.993418868 -0.508131835
245 0.412707767 0.993418868
246 -0.059284614 0.412707767
247 5.355943958 -0.059284614
248 -0.087510341 5.355943958
249 0.337644342 -0.087510341
250 1.861539534 0.337644342
251 1.084324057 1.861539534
252 -1.347552644 1.084324057
253 -1.240677598 -1.347552644
254 -0.038671448 -1.240677598
255 -1.040987605 -0.038671448
256 -1.727099052 -1.040987605
257 -2.618684271 -1.727099052
258 2.071346589 -2.618684271
259 -4.435888734 2.071346589
260 0.022125713 -4.435888734
261 1.230672831 0.022125713
262 -2.699206690 1.230672831
263 -0.124265976 -2.699206690
> 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/fisher/rcomp/tmp/7hmev1384604191.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/fisher/rcomp/tmp/8e58c1384604191.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/fisher/rcomp/tmp/99qhj1384604191.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/fisher/rcomp/tmp/102x2z1384604191.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11elwi1384604191.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12b1ok1384604191.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13v4u61384604192.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14rslq1384604192.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15o99a1384604192.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16v2vv1384604192.tab")
+ }
>
> try(system("convert tmp/1p97z1384604191.ps tmp/1p97z1384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/28e231384604191.ps tmp/28e231384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w5fa1384604191.ps tmp/3w5fa1384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/45k3m1384604191.ps tmp/45k3m1384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/5usjb1384604191.ps tmp/5usjb1384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ltln1384604191.ps tmp/6ltln1384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hmev1384604191.ps tmp/7hmev1384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/8e58c1384604191.ps tmp/8e58c1384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/99qhj1384604191.ps tmp/99qhj1384604191.png",intern=TRUE))
character(0)
> try(system("convert tmp/102x2z1384604191.ps tmp/102x2z1384604191.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.412 1.801 14.207