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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+ ,14
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+ ,13
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+ ,10
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+ ,4
+ ,3
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+ ,52
+ ,14
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+ ,15
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+ ,7
+ ,22
+ ,62
+ ,39
+ ,14
+ ,29
+ ,32
+ ,14
+ ,9
+ ,12
+ ,72
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Happiness'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Happiness','Connected','Separate','Learning','Software','Depression','Sport1','Sport2'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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 Happiness Connected Separate Software Depression Sport1 Sport2
1 13 14 41 38 12 12.0 53 32
2 16 18 39 32 11 11.0 83 51
3 19 11 30 35 15 14.0 66 42
4 15 12 31 33 6 12.0 67 41
5 14 16 34 37 13 21.0 76 46
6 13 18 35 29 10 12.0 78 47
7 19 14 39 31 12 22.0 53 37
8 15 14 34 36 14 11.0 80 49
9 14 15 36 35 12 10.0 74 45
10 15 15 37 38 9 13.0 76 47
11 16 17 38 31 10 10.0 79 49
12 16 19 36 34 12 8.0 54 33
13 16 10 38 35 12 15.0 67 42
14 16 16 39 38 11 14.0 54 33
15 17 18 33 37 15 10.0 87 53
16 15 14 32 33 12 14.0 58 36
17 15 14 36 32 10 14.0 75 45
18 20 17 38 38 12 11.0 88 54
19 18 14 39 38 11 10.0 64 41
20 16 16 32 32 12 13.0 57 36
21 16 18 32 33 11 9.5 66 41
22 16 11 31 31 12 14.0 68 44
23 19 14 39 38 13 12.0 54 33
24 16 12 37 39 11 14.0 56 37
25 17 17 39 32 12 11.0 86 52
26 17 9 41 32 13 9.0 80 47
27 16 16 36 35 10 11.0 76 43
28 15 14 33 37 14 15.0 69 44
29 16 15 33 33 12 14.0 78 45
30 14 11 34 33 10 13.0 67 44
31 15 16 31 31 12 9.0 80 49
32 12 13 27 32 8 15.0 54 33
33 14 17 37 31 10 10.0 71 43
34 16 15 34 37 12 11.0 84 54
35 14 14 34 30 12 13.0 74 42
36 10 16 32 33 7 8.0 71 44
37 10 9 29 31 9 20.0 63 37
38 14 15 36 33 12 12.0 71 43
39 16 17 29 31 10 10.0 76 46
40 16 13 35 33 10 10.0 69 42
41 16 15 37 32 10 9.0 74 45
42 14 16 34 33 12 14.0 75 44
43 20 16 38 32 15 8.0 54 33
44 14 12 35 33 10 14.0 52 31
45 14 15 38 28 10 11.0 69 42
46 11 11 37 35 12 13.0 68 40
47 14 15 38 39 13 9.0 65 43
48 15 15 33 34 11 11.0 75 46
49 16 17 36 38 11 15.0 74 42
50 14 13 38 32 12 11.0 75 45
51 16 16 32 38 14 10.0 72 44
52 14 14 32 30 10 14.0 67 40
53 12 11 32 33 12 18.0 63 37
54 16 12 34 38 13 14.0 62 46
55 9 12 32 32 5 11.0 63 36
56 14 15 37 35 6 14.5 76 47
57 16 16 39 34 12 13.0 74 45
58 16 15 29 34 12 9.0 67 42
59 15 12 37 36 11 10.0 73 43
60 16 12 35 34 10 15.0 70 43
61 12 8 30 28 7 20.0 53 32
62 16 13 38 34 12 12.0 77 45
63 16 11 34 35 14 12.0 80 48
64 14 14 31 35 11 14.0 52 31
65 16 15 34 31 12 13.0 54 33
66 17 10 35 37 13 11.0 80 49
67 18 11 36 35 14 17.0 66 42
68 18 12 30 27 11 12.0 73 41
69 12 15 39 40 12 13.0 63 38
70 16 15 35 37 12 14.0 69 42
71 10 14 38 36 8 13.0 67 44
72 14 16 31 38 11 15.0 54 33
73 18 15 34 39 14 13.0 81 48
74 18 15 38 41 14 10.0 69 40
75 16 13 34 27 12 11.0 84 50
76 17 12 39 30 9 19.0 80 49
77 16 17 37 37 13 13.0 70 43
78 16 13 34 31 11 17.0 69 44
79 13 15 28 31 12 13.0 77 47
80 16 13 37 27 12 9.0 54 33
81 16 15 33 36 12 11.0 79 46
82 16 15 35 37 12 9.0 71 45
83 15 16 37 33 12 12.0 73 43
84 15 15 32 34 11 12.0 72 44
85 16 14 33 31 10 13.0 77 47
86 14 15 38 39 9 13.0 75 45
87 16 14 33 34 12 12.0 69 42
88 16 13 29 32 12 15.0 54 33
89 15 7 33 33 12 22.0 70 43
90 12 17 31 36 9 13.0 73 46
91 17 13 36 32 15 15.0 54 33
92 16 15 35 41 12 13.0 77 46
93 15 14 32 28 12 15.0 82 48
94 13 13 29 30 12 12.5 80 47
95 16 16 39 36 10 11.0 80 47
96 16 12 37 35 13 16.0 69 43
97 16 14 35 31 9 11.0 78 46
98 16 17 37 34 12 11.0 81 48
99 14 15 32 36 10 10.0 76 46
100 16 17 38 36 14 10.0 76 45
101 16 12 37 35 11 16.0 73 45
102 20 16 36 37 15 12.0 85 52
103 15 11 32 28 11 11.0 66 42
104 16 15 33 39 11 16.0 79 47
105 13 9 40 32 12 19.0 68 41
106 17 16 38 35 12 11.0 76 47
107 16 15 41 39 12 16.0 71 43
108 16 10 36 35 11 15.0 54 33
109 12 10 43 42 7 24.0 46 30
110 16 15 30 34 12 14.0 85 52
111 16 11 31 33 14 15.0 74 44
112 17 13 32 41 11 11.0 88 55
113 13 14 32 33 11 15.0 38 11
114 12 18 37 34 10 12.0 76 47
115 18 16 37 32 13 10.0 86 53
116 14 14 33 40 13 14.0 54 33
117 14 14 34 40 8 13.0 67 44
118 13 14 33 35 11 9.0 69 42
119 16 14 38 36 12 15.0 90 55
120 13 12 33 37 11 15.0 54 33
121 16 14 31 27 13 14.0 76 46
122 13 15 38 39 12 11.0 89 54
123 16 15 37 38 14 8.0 76 47
124 15 15 36 31 13 11.0 73 45
125 16 13 31 33 15 11.0 79 47
126 15 17 39 32 10 8.0 90 55
127 17 17 44 39 11 10.0 74 44
128 15 19 33 36 9 11.0 81 53
129 12 15 35 33 11 13.0 72 44
130 16 13 32 33 10 11.0 71 42
131 10 9 28 32 11 20.0 66 40
132 16 15 40 37 8 10.0 77 46
133 12 15 27 30 11 15.0 65 40
134 14 15 37 38 12 12.0 74 46
135 15 16 32 29 12 14.0 85 53
136 13 11 28 22 9 23.0 54 33
137 15 14 34 35 11 14.0 63 42
138 11 11 30 35 10 16.0 54 35
139 12 15 35 34 8 11.0 64 40
140 11 13 31 35 9 12.0 69 41
141 16 15 32 34 8 10.0 54 33
142 15 16 30 37 9 14.0 84 51
143 17 14 30 35 15 12.0 86 53
144 16 15 31 23 11 12.0 77 46
145 10 16 40 31 8 11.0 89 55
146 18 16 32 27 13 12.0 76 47
147 13 11 36 36 12 13.0 60 38
148 16 12 32 31 12 11.0 75 46
149 13 9 35 32 9 19.0 73 46
150 10 16 38 39 7 12.0 85 53
151 15 13 42 37 13 17.0 79 47
152 16 16 34 38 9 9.0 71 41
153 16 12 35 39 6 12.0 72 44
154 14 9 38 34 8 19.0 69 43
155 10 13 33 31 8 18.0 78 51
156 17 13 36 32 15 15.0 54 33
157 13 14 32 37 6 14.0 69 43
158 15 19 33 36 9 11.0 81 53
159 16 13 34 32 11 9.0 84 51
160 12 12 32 38 8 18.0 84 50
161 13 13 34 36 8 16.0 69 46
162 13 10 27 26 10 24.0 66 43
163 12 14 31 26 8 14.0 81 47
164 17 16 38 33 14 20.0 82 50
165 15 10 34 39 10 18.0 72 43
166 10 11 24 30 8 23.0 54 33
167 14 14 30 33 11 12.0 78 48
168 11 12 26 25 12 14.0 74 44
169 13 9 34 38 12 16.0 82 50
170 16 9 27 37 12 18.0 73 41
171 12 11 37 31 5 20.0 55 34
172 16 16 36 37 12 12.0 72 44
173 12 9 41 35 10 12.0 78 47
174 9 13 29 25 7 17.0 59 35
175 12 16 36 28 12 13.0 72 44
176 15 13 32 35 11 9.0 78 44
177 12 9 37 33 8 16.0 68 43
178 12 12 30 30 9 18.0 69 41
179 14 16 31 31 10 10.0 67 41
180 12 11 38 37 9 14.0 74 42
181 16 14 36 36 12 11.0 54 33
182 11 13 35 30 6 9.0 67 41
183 19 15 31 36 15 11.0 70 44
184 15 14 38 32 12 10.0 80 48
185 8 16 22 28 12 11.0 89 55
186 16 13 32 36 12 19.0 76 44
187 17 14 36 34 11 14.0 74 43
188 12 15 39 31 7 12.0 87 52
189 11 13 28 28 7 14.0 54 30
190 11 11 32 36 5 21.0 61 39
191 14 11 32 36 12 13.0 38 11
192 16 14 38 40 12 10.0 75 44
193 12 15 32 33 3 15.0 69 42
194 16 11 35 37 11 16.0 62 41
195 13 15 32 32 10 14.0 72 44
196 15 12 37 38 12 12.0 70 44
197 16 14 34 31 9 19.0 79 48
198 16 14 33 37 12 15.0 87 53
199 14 8 33 33 9 19.0 62 37
200 16 13 26 32 12 13.0 77 44
201 16 9 30 30 12 17.0 69 44
202 14 15 24 30 10 12.0 69 40
203 11 17 34 31 9 11.0 75 42
204 12 13 34 32 12 14.0 54 35
205 15 15 33 34 8 11.0 72 43
206 15 15 34 36 11 13.0 74 45
207 16 14 35 37 11 12.0 85 55
208 16 16 35 36 12 15.0 52 31
209 11 13 36 33 10 14.0 70 44
210 15 16 34 33 10 12.0 84 50
211 12 9 34 33 12 17.0 64 40
212 12 16 41 44 12 11.0 84 53
213 15 11 32 39 11 18.0 87 54
214 15 10 30 32 8 13.0 79 49
215 16 11 35 35 12 17.0 67 40
216 14 15 28 25 10 13.0 65 41
217 17 17 33 35 11 11.0 85 52
218 14 14 39 34 10 12.0 83 52
219 13 8 36 35 8 22.0 61 36
220 15 15 36 39 12 14.0 82 52
221 13 11 35 33 12 12.0 76 46
222 14 16 38 36 10 12.0 58 31
223 15 10 33 32 12 17.0 72 44
224 12 15 31 32 9 9.0 72 44
225 13 9 34 36 9 21.0 38 11
226 8 16 32 36 6 10.0 78 46
227 14 19 31 32 10 11.0 54 33
228 14 12 33 34 9 12.0 63 34
229 11 8 34 33 9 23.0 66 42
230 12 11 34 35 9 13.0 70 43
231 13 14 34 30 6 12.0 71 43
232 10 9 33 38 10 16.0 67 44
233 16 15 32 34 6 9.0 58 36
234 18 13 41 33 14 17.0 72 46
235 13 16 34 32 10 9.0 72 44
236 11 11 36 31 10 14.0 70 43
237 4 12 37 30 6 17.0 76 50
238 13 13 36 27 12 13.0 50 33
239 16 10 29 31 12 11.0 72 43
240 10 11 37 30 7 12.0 72 44
241 12 12 27 32 8 10.0 88 53
242 12 8 35 35 11 19.0 53 34
243 10 12 28 28 3 16.0 58 35
244 13 12 35 33 6 16.0 66 40
245 15 15 37 31 10 14.0 82 53
246 12 11 29 35 8 20.0 69 42
247 14 13 32 35 9 15.0 68 43
248 10 14 36 32 9 23.0 44 29
249 12 10 19 21 8 20.0 56 36
250 12 12 21 20 9 16.0 53 30
251 11 15 31 34 7 14.0 70 42
252 10 13 33 32 7 17.0 78 47
253 12 13 36 34 6 11.0 71 44
254 16 13 33 32 9 13.0 72 45
255 12 12 37 33 10 17.0 68 44
256 14 12 34 33 11 15.0 67 43
257 16 9 35 37 12 21.0 75 43
258 14 9 31 32 8 18.0 62 40
259 13 15 37 34 11 15.0 67 41
260 4 10 35 30 3 8.0 83 52
261 15 14 27 30 11 12.0 64 38
262 11 15 34 38 12 12.0 68 41
263 11 7 40 36 7 22.0 62 39
264 14 14 29 32 9 12.0 72 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Happiness Connected Separate Software Depression
3.77957 0.09863 0.04694 0.04200 0.60741 -0.03938
Sport1 Sport2
0.01587 -0.01942
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.1700 -1.3147 0.2823 1.1739 4.2995
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.77957 1.91765 1.971 0.0498 *
Happiness 0.09863 0.05796 1.702 0.0900 .
Connected 0.04694 0.03479 1.349 0.1784
Separate 0.04200 0.03565 1.178 0.2398
Software 0.60741 0.05185 11.716 <2e-16 ***
Depression -0.03938 0.04256 -0.925 0.3557
Sport1 0.01587 0.03784 0.419 0.6754
Sport2 -0.01942 0.05644 -0.344 0.7311
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.884 on 256 degrees of freedom
Multiple R-squared: 0.4269, Adjusted R-squared: 0.4113
F-statistic: 27.24 on 7 and 256 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.234449524 0.468899048 0.7655505
[2,] 0.120293518 0.240587036 0.8797065
[3,] 0.081554157 0.163108315 0.9184458
[4,] 0.102874776 0.205749553 0.8971252
[5,] 0.058751976 0.117503951 0.9412480
[6,] 0.039168668 0.078337335 0.9608313
[7,] 0.063019118 0.126038237 0.9369809
[8,] 0.255864328 0.511728656 0.7441357
[9,] 0.194216633 0.388433266 0.8057834
[10,] 0.137764883 0.275529767 0.8622351
[11,] 0.098606262 0.197212523 0.9013937
[12,] 0.096853399 0.193706798 0.9031466
[13,] 0.257054378 0.514108756 0.7429456
[14,] 0.321495418 0.642990837 0.6785046
[15,] 0.294869539 0.589739077 0.7051305
[16,] 0.277447858 0.554895715 0.7225521
[17,] 0.352098889 0.704197778 0.6479011
[18,] 0.363849649 0.727699298 0.6361504
[19,] 0.346785592 0.693571184 0.6532144
[20,] 0.380898386 0.761796773 0.6191016
[21,] 0.328675803 0.657351606 0.6713242
[22,] 0.289492868 0.578985736 0.7105071
[23,] 0.262490777 0.524981555 0.7375092
[24,] 0.226304417 0.452608834 0.7736956
[25,] 0.187880542 0.375761084 0.8121195
[26,] 0.304929815 0.609859630 0.6950702
[27,] 0.334971074 0.669942148 0.6650289
[28,] 0.325963623 0.651927245 0.6740364
[29,] 0.356022740 0.712045480 0.6439773
[30,] 0.334209449 0.668418898 0.6657906
[31,] 0.298001365 0.596002731 0.7019986
[32,] 0.263835729 0.527671458 0.7361643
[33,] 0.278274265 0.556548530 0.7217257
[34,] 0.236536521 0.473073043 0.7634635
[35,] 0.214442280 0.428884560 0.7855577
[36,] 0.392689718 0.785379436 0.6073103
[37,] 0.545519033 0.908961933 0.4544810
[38,] 0.496548776 0.993097553 0.5034512
[39,] 0.483216161 0.966432321 0.5167838
[40,] 0.468390231 0.936780461 0.5316098
[41,] 0.423671720 0.847343441 0.5763283
[42,] 0.378169198 0.756338395 0.6218308
[43,] 0.386603610 0.773207219 0.6133964
[44,] 0.369709551 0.739419103 0.6302904
[45,] 0.360937557 0.721875114 0.6390624
[46,] 0.339385239 0.678770477 0.6606148
[47,] 0.299777754 0.599555509 0.7002222
[48,] 0.271157720 0.542315440 0.7288423
[49,] 0.236694270 0.473388540 0.7633057
[50,] 0.242458615 0.484917230 0.7575414
[51,] 0.218650159 0.437300318 0.7813498
[52,] 0.191735671 0.383471342 0.8082643
[53,] 0.163732058 0.327464116 0.8362679
[54,] 0.137978889 0.275957778 0.8620211
[55,] 0.119073866 0.238147733 0.8809261
[56,] 0.110228851 0.220457703 0.8897711
[57,] 0.105986150 0.211972300 0.8940138
[58,] 0.223058679 0.446117359 0.7769413
[59,] 0.313038853 0.626077705 0.6869611
[60,] 0.282142510 0.564285020 0.7178575
[61,] 0.397902377 0.795804755 0.6020976
[62,] 0.359897835 0.719795670 0.6401022
[63,] 0.351564646 0.703129293 0.6484354
[64,] 0.336197027 0.672394054 0.6638030
[65,] 0.304024155 0.608048310 0.6959758
[66,] 0.369544726 0.739089451 0.6304553
[67,] 0.333817781 0.667635562 0.6661822
[68,] 0.312977053 0.625954105 0.6870229
[69,] 0.326056569 0.652113138 0.6739434
[70,] 0.298478347 0.596956695 0.7015217
[71,] 0.270341907 0.540683814 0.7296581
[72,] 0.239446043 0.478892085 0.7605540
[73,] 0.215139663 0.430279326 0.7848603
[74,] 0.187913723 0.375827445 0.8120863
[75,] 0.184607192 0.369214383 0.8153928
[76,] 0.159572512 0.319145024 0.8404275
[77,] 0.140322537 0.280645075 0.8596775
[78,] 0.130738802 0.261477604 0.8692612
[79,] 0.112976438 0.225952876 0.8870236
[80,] 0.110203114 0.220406228 0.8897969
[81,] 0.094291937 0.188583874 0.9057081
[82,] 0.081424091 0.162848181 0.9185759
[83,] 0.069162205 0.138324409 0.9308378
[84,] 0.071050562 0.142101124 0.9289494
[85,] 0.064204775 0.128409550 0.9357952
[86,] 0.053797587 0.107595173 0.9462024
[87,] 0.059431515 0.118863029 0.9405685
[88,] 0.049227051 0.098454102 0.9507729
[89,] 0.040363797 0.080727594 0.9596362
[90,] 0.035314977 0.070629954 0.9646850
[91,] 0.031330014 0.062660028 0.9686700
[92,] 0.036882071 0.073764142 0.9631179
[93,] 0.031651390 0.063302781 0.9683486
[94,] 0.028585914 0.057171828 0.9714141
[95,] 0.033817136 0.067634272 0.9661829
[96,] 0.029717088 0.059434176 0.9702829
[97,] 0.024133079 0.048266157 0.9758669
[98,] 0.024007911 0.048015823 0.9759921
[99,] 0.019451505 0.038903010 0.9805485
[100,] 0.015913666 0.031827332 0.9840863
[101,] 0.012614652 0.025229305 0.9873853
[102,] 0.013038128 0.026076256 0.9869619
[103,] 0.011645215 0.023290429 0.9883548
[104,] 0.016720656 0.033441312 0.9832793
[105,] 0.015971023 0.031942047 0.9840290
[106,] 0.015281198 0.030562396 0.9847188
[107,] 0.012639030 0.025278060 0.9873610
[108,] 0.012621956 0.025243912 0.9873780
[109,] 0.010261202 0.020522403 0.9897388
[110,] 0.009038026 0.018076051 0.9909620
[111,] 0.007353568 0.014707136 0.9926464
[112,] 0.011319551 0.022639102 0.9886804
[113,] 0.009538496 0.019076992 0.9904615
[114,] 0.008473116 0.016946233 0.9915269
[115,] 0.006935560 0.013871119 0.9930644
[116,] 0.005650421 0.011300842 0.9943496
[117,] 0.005087164 0.010174328 0.9949128
[118,] 0.004098794 0.008197587 0.9959012
[119,] 0.006048870 0.012097740 0.9939511
[120,] 0.006553238 0.013106476 0.9934468
[121,] 0.013540294 0.027080589 0.9864597
[122,] 0.016298728 0.032597455 0.9837013
[123,] 0.017582365 0.035164730 0.9824176
[124,] 0.016983345 0.033966690 0.9830167
[125,] 0.013880374 0.027760749 0.9861196
[126,] 0.011712339 0.023424677 0.9882877
[127,] 0.009374643 0.018749287 0.9906254
[128,] 0.011416902 0.022833804 0.9885831
[129,] 0.009937140 0.019874280 0.9900629
[130,] 0.011292815 0.022585631 0.9887072
[131,] 0.017516565 0.035033130 0.9824834
[132,] 0.015928958 0.031857916 0.9840710
[133,] 0.012683164 0.025366327 0.9873168
[134,] 0.013445833 0.026891667 0.9865542
[135,] 0.025640313 0.051280627 0.9743597
[136,] 0.031418411 0.062836822 0.9685816
[137,] 0.032979578 0.065959156 0.9670204
[138,] 0.029773423 0.059546845 0.9702266
[139,] 0.024812729 0.049625457 0.9751873
[140,] 0.034436232 0.068872464 0.9655638
[141,] 0.029712419 0.059424838 0.9702876
[142,] 0.031280658 0.062561316 0.9687193
[143,] 0.063880433 0.127760866 0.9361196
[144,] 0.062761436 0.125522871 0.9372386
[145,] 0.072174777 0.144349554 0.9278252
[146,] 0.061747857 0.123495715 0.9382521
[147,] 0.055162879 0.110325759 0.9448371
[148,] 0.048270412 0.096540825 0.9517296
[149,] 0.047226109 0.094452217 0.9527739
[150,] 0.040493028 0.080986056 0.9595070
[151,] 0.033412431 0.066824861 0.9665876
[152,] 0.027466795 0.054933591 0.9725332
[153,] 0.022875653 0.045751307 0.9771243
[154,] 0.019296413 0.038592827 0.9807036
[155,] 0.016935356 0.033870713 0.9830646
[156,] 0.017184305 0.034368610 0.9828157
[157,] 0.013807624 0.027615249 0.9861924
[158,] 0.020373586 0.040747172 0.9796264
[159,] 0.020192886 0.040385772 0.9798071
[160,] 0.018858017 0.037716034 0.9811420
[161,] 0.018134965 0.036269931 0.9818650
[162,] 0.014694197 0.029388393 0.9853058
[163,] 0.014742291 0.029484581 0.9852577
[164,] 0.016422616 0.032845232 0.9835774
[165,] 0.021949460 0.043898919 0.9780505
[166,] 0.017653152 0.035306305 0.9823468
[167,] 0.014382692 0.028765385 0.9856173
[168,] 0.011742743 0.023485486 0.9882573
[169,] 0.009192160 0.018384320 0.9908078
[170,] 0.008005362 0.016010723 0.9919946
[171,] 0.006603604 0.013207208 0.9933964
[172,] 0.005345217 0.010690434 0.9946548
[173,] 0.005609366 0.011218732 0.9943906
[174,] 0.004436473 0.008872946 0.9955635
[175,] 0.094086540 0.188173080 0.9059135
[176,] 0.081684581 0.163369162 0.9183154
[177,] 0.092843980 0.185687961 0.9071560
[178,] 0.080212714 0.160425427 0.9197873
[179,] 0.067959634 0.135919268 0.9320404
[180,] 0.056356144 0.112712287 0.9436439
[181,] 0.049711162 0.099422324 0.9502888
[182,] 0.041961193 0.083922387 0.9580388
[183,] 0.048715528 0.097431056 0.9512845
[184,] 0.052289067 0.104578133 0.9477109
[185,] 0.044381174 0.088762348 0.9556188
[186,] 0.037026328 0.074052656 0.9629737
[187,] 0.048315706 0.096631412 0.9516843
[188,] 0.039586295 0.079172589 0.9604137
[189,] 0.036701156 0.073402313 0.9632988
[190,] 0.031039868 0.062079735 0.9689601
[191,] 0.029221972 0.058443945 0.9707780
[192,] 0.024380571 0.048761141 0.9756194
[193,] 0.031305369 0.062610738 0.9686946
[194,] 0.035680318 0.071360636 0.9643197
[195,] 0.038906336 0.077812673 0.9610937
[196,] 0.030879155 0.061758309 0.9691208
[197,] 0.030342223 0.060684446 0.9696578
[198,] 0.024949873 0.049899745 0.9750501
[199,] 0.027868141 0.055736282 0.9721319
[200,] 0.022541743 0.045083485 0.9774583
[201,] 0.024977533 0.049955067 0.9750225
[202,] 0.039973509 0.079947017 0.9600265
[203,] 0.031587106 0.063174212 0.9684129
[204,] 0.044158943 0.088317885 0.9558411
[205,] 0.038059309 0.076118619 0.9619407
[206,] 0.029750826 0.059501652 0.9702492
[207,] 0.032195531 0.064391063 0.9678045
[208,] 0.027529491 0.055058982 0.9724705
[209,] 0.024741807 0.049483614 0.9752582
[210,] 0.018963377 0.037926755 0.9810366
[211,] 0.016894734 0.033789468 0.9831053
[212,] 0.012420649 0.024841297 0.9875794
[213,] 0.009311742 0.018623484 0.9906883
[214,] 0.007502119 0.015004237 0.9924979
[215,] 0.006547056 0.013094112 0.9934529
[216,] 0.014995190 0.029990381 0.9850048
[217,] 0.011541618 0.023083237 0.9884584
[218,] 0.008350319 0.016700638 0.9916497
[219,] 0.006096834 0.012193668 0.9939032
[220,] 0.004410363 0.008820725 0.9955896
[221,] 0.004394615 0.008789229 0.9956054
[222,] 0.008144980 0.016289959 0.9918550
[223,] 0.037756544 0.075513088 0.9622435
[224,] 0.054294220 0.108588440 0.9457058
[225,] 0.040506586 0.081013172 0.9594934
[226,] 0.036110113 0.072220225 0.9638899
[227,] 0.246505701 0.493011401 0.7534943
[228,] 0.200782131 0.401564262 0.7992179
[229,] 0.180044405 0.360088811 0.8199556
[230,] 0.142128535 0.284257071 0.8578715
[231,] 0.113707343 0.227414686 0.8862927
[232,] 0.094530103 0.189060206 0.9054699
[233,] 0.087902319 0.175804637 0.9120977
[234,] 0.153550434 0.307100868 0.8464496
[235,] 0.137630340 0.275260679 0.8623697
[236,] 0.110109331 0.220218661 0.8898907
[237,] 0.077770601 0.155541202 0.9222294
[238,] 0.066694847 0.133389694 0.9333052
[239,] 0.062488827 0.124977654 0.9375112
[240,] 0.077380993 0.154761986 0.9226190
[241,] 0.046054976 0.092109953 0.9539450
[242,] 0.112285664 0.224571328 0.8877143
[243,] 0.234998511 0.469997022 0.7650015
> postscript(file="/var/fisher/rcomp/tmp/1qjm51383501554.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2b0dl1383501554.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35e721383501554.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4wwkh1383501554.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5lf8z1383501554.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
-2.716932667 0.695354701 2.465773394 3.756800929 -1.889687332 -1.342423946
7 8 9 10 11 12
4.161807991 -1.656841747 -1.614396605 1.160117737 1.475586780 0.038654809
13 14 15 16 17 18
1.034583103 0.869405423 -0.726605178 -0.007360227 0.966688731 3.960453337
19 20 21 22 23 24
2.905813838 0.813862378 0.998463167 1.416135174 2.773099012 1.361748946
25 26 27 28 29 30
1.158397864 1.165514867 1.470598801 -1.416929777 0.704482544 0.382605544
31 32 33 34 35 36
-0.367231470 -0.157794884 -0.467033523 0.450952519 -1.151997771 -2.454806771
37 38 39 40 41 42
-2.290802595 -1.442875350 1.887432350 1.949699309 1.640084339 -1.412909516
43 44 45 46 47 48
2.502440756 0.262000289 -0.139025784 -4.150559388 -2.419095997 0.218766088
49 50 51 52 53 54
0.808402277 -1.361515364 -0.853739839 0.268310726 -2.613865360 0.409310097
55 56 57 58 59 60
-1.713731807 2.167395395 0.306276310 0.769644429 0.176995441 2.206777551
61 62 63 64 65 66
1.163272051 0.562135982 -0.298986970 -0.438887734 0.849955730 1.256146159
67 68 69 70 71 72
1.909653498 3.923509508 -3.808460881 0.527152017 -3.012245827 -0.715665538
73 74 75 76 77 78
1.162010456 0.807171814 0.990531311 3.909747507 -0.407232544 1.787718277
79 80 81 82 83 84
-1.961485328 0.916861489 0.463901031 0.356770523 -0.620182287 0.313856292
85 86 87 88 89 90
2.117237372 0.048211388 0.766905814 1.318697005 0.896661826 -1.643303567
91 92 93 94 95 96
0.167875359 0.270514769 0.094207665 -1.836456819 1.301967194 0.303921953
97 98 99 100 101 102
2.516715321 0.169958299 -0.266125863 -1.194086507 1.494098871 2.420900716
103 104 105 106 107 108
0.977353986 1.161623748 -1.712444774 1.239562685 0.227935475 1.767395736
109 110 111 112 113 114
-0.002480266 0.828161450 0.061511967 2.137467172 -1.528640241 -2.614568461
115 116 117 118 119 120
1.723547556 -1.950475963 1.007537805 -1.785824239 0.485542044 -1.373030868
121 122 123 124 125 126
0.592730467 -2.900148395 -1.073803686 -0.998565253 -0.921755895 0.249852821
127 128 129 130 131 132
1.232789740 0.995768005 -2.745597967 2.098177534 -3.490018923 2.515270651
133 134 135 136 137 138
-2.131895571 -1.689160164 -0.134952532 1.120129701 0.459324011 -2.363957787
139 140 141 142 143 144
-0.994871780 -2.279773405 3.129332967 1.422202992 -0.012629669 1.782276715
145 146 147 148 149 150
-3.307648252 2.289181735 -2.057515845 1.080191400 0.362236196 -2.878325739
151 152 153 154 155 156
-1.155043271 2.007639148 4.295954949 1.750032454 -2.310629981 0.167875359
157 158 159 160 161 162
1.430460681 0.995768005 1.328604480 -0.573651755 0.399396308 0.533448646
163 164 165 166 167 168
-0.388160789 0.426210073 1.327399554 -1.420672833 -0.469161997 -3.290986580
169 170 171 172 173 174
-1.848292762 1.569125254 1.634688184 0.294051606 -1.988393653 -2.312033591
175 176 177 178 179 180
-3.288588788 0.255786884 -0.263297297 -0.687934672 -0.062106612 -1.476269705
181 182 183 184 185 186
0.565951923 -0.521748993 1.839536729 -0.520609608 -6.766288090 1.031907280
187 188 189 190 191 192
2.252323131 -0.541803289 -0.526967526 0.700677591 -1.044903908 0.145076363
193 194 195 196 197 198
2.342000107 1.699486741 -0.915984634 -0.368633528 2.901656852 0.687030247
199 200 201 202 203 204
1.512544799 1.229420219 1.804610223 0.434739187 -2.762297678 -2.916569977
205 206 207 208 209 210
2.030333482 0.163035580 1.152973504 0.566051290 -2.916763144 0.696840530
211 212 213 214 215 216
-2.507489607 -4.289684969 0.690829519 2.832504112 1.116710656 0.579212169
217 218 219 220 221 222
1.937338022 -0.327916940 1.009711098 -0.615891961 -2.022491333 -0.573303347
223 224 225 226 227 228
0.433554087 -1.458531370 0.195719260 -3.966868429 -0.309683138 0.726256447
229 230 231 232 233 234
-1.343269573 -1.160998557 1.520067648 -3.465040334 4.299546178 1.544133629
235 236 237 238 239 240
-1.305398824 -2.654920743 -7.170028137 -1.815214028 1.407636695 -1.928726129
241 242 243 244 245 246
-0.407197066 -1.795600382 1.113655299 1.722993120 0.907372684 -0.046770178
247 248 249 250 251 252
0.846117134 -2.890292844 1.199034180 0.116066371 -1.137919803 -1.862263458
253 254 255 256 257 258
0.336856218 2.821775236 -1.715206305 -0.264087860 1.318811443 2.176071319
259 260 261 262 263 264
-1.781640984 -5.483306743 0.825633349 -4.550209689 -0.471645252 0.832709147
> postscript(file="/var/fisher/rcomp/tmp/6rm3q1383501554.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 -2.716932667 NA
1 0.695354701 -2.716932667
2 2.465773394 0.695354701
3 3.756800929 2.465773394
4 -1.889687332 3.756800929
5 -1.342423946 -1.889687332
6 4.161807991 -1.342423946
7 -1.656841747 4.161807991
8 -1.614396605 -1.656841747
9 1.160117737 -1.614396605
10 1.475586780 1.160117737
11 0.038654809 1.475586780
12 1.034583103 0.038654809
13 0.869405423 1.034583103
14 -0.726605178 0.869405423
15 -0.007360227 -0.726605178
16 0.966688731 -0.007360227
17 3.960453337 0.966688731
18 2.905813838 3.960453337
19 0.813862378 2.905813838
20 0.998463167 0.813862378
21 1.416135174 0.998463167
22 2.773099012 1.416135174
23 1.361748946 2.773099012
24 1.158397864 1.361748946
25 1.165514867 1.158397864
26 1.470598801 1.165514867
27 -1.416929777 1.470598801
28 0.704482544 -1.416929777
29 0.382605544 0.704482544
30 -0.367231470 0.382605544
31 -0.157794884 -0.367231470
32 -0.467033523 -0.157794884
33 0.450952519 -0.467033523
34 -1.151997771 0.450952519
35 -2.454806771 -1.151997771
36 -2.290802595 -2.454806771
37 -1.442875350 -2.290802595
38 1.887432350 -1.442875350
39 1.949699309 1.887432350
40 1.640084339 1.949699309
41 -1.412909516 1.640084339
42 2.502440756 -1.412909516
43 0.262000289 2.502440756
44 -0.139025784 0.262000289
45 -4.150559388 -0.139025784
46 -2.419095997 -4.150559388
47 0.218766088 -2.419095997
48 0.808402277 0.218766088
49 -1.361515364 0.808402277
50 -0.853739839 -1.361515364
51 0.268310726 -0.853739839
52 -2.613865360 0.268310726
53 0.409310097 -2.613865360
54 -1.713731807 0.409310097
55 2.167395395 -1.713731807
56 0.306276310 2.167395395
57 0.769644429 0.306276310
58 0.176995441 0.769644429
59 2.206777551 0.176995441
60 1.163272051 2.206777551
61 0.562135982 1.163272051
62 -0.298986970 0.562135982
63 -0.438887734 -0.298986970
64 0.849955730 -0.438887734
65 1.256146159 0.849955730
66 1.909653498 1.256146159
67 3.923509508 1.909653498
68 -3.808460881 3.923509508
69 0.527152017 -3.808460881
70 -3.012245827 0.527152017
71 -0.715665538 -3.012245827
72 1.162010456 -0.715665538
73 0.807171814 1.162010456
74 0.990531311 0.807171814
75 3.909747507 0.990531311
76 -0.407232544 3.909747507
77 1.787718277 -0.407232544
78 -1.961485328 1.787718277
79 0.916861489 -1.961485328
80 0.463901031 0.916861489
81 0.356770523 0.463901031
82 -0.620182287 0.356770523
83 0.313856292 -0.620182287
84 2.117237372 0.313856292
85 0.048211388 2.117237372
86 0.766905814 0.048211388
87 1.318697005 0.766905814
88 0.896661826 1.318697005
89 -1.643303567 0.896661826
90 0.167875359 -1.643303567
91 0.270514769 0.167875359
92 0.094207665 0.270514769
93 -1.836456819 0.094207665
94 1.301967194 -1.836456819
95 0.303921953 1.301967194
96 2.516715321 0.303921953
97 0.169958299 2.516715321
98 -0.266125863 0.169958299
99 -1.194086507 -0.266125863
100 1.494098871 -1.194086507
101 2.420900716 1.494098871
102 0.977353986 2.420900716
103 1.161623748 0.977353986
104 -1.712444774 1.161623748
105 1.239562685 -1.712444774
106 0.227935475 1.239562685
107 1.767395736 0.227935475
108 -0.002480266 1.767395736
109 0.828161450 -0.002480266
110 0.061511967 0.828161450
111 2.137467172 0.061511967
112 -1.528640241 2.137467172
113 -2.614568461 -1.528640241
114 1.723547556 -2.614568461
115 -1.950475963 1.723547556
116 1.007537805 -1.950475963
117 -1.785824239 1.007537805
118 0.485542044 -1.785824239
119 -1.373030868 0.485542044
120 0.592730467 -1.373030868
121 -2.900148395 0.592730467
122 -1.073803686 -2.900148395
123 -0.998565253 -1.073803686
124 -0.921755895 -0.998565253
125 0.249852821 -0.921755895
126 1.232789740 0.249852821
127 0.995768005 1.232789740
128 -2.745597967 0.995768005
129 2.098177534 -2.745597967
130 -3.490018923 2.098177534
131 2.515270651 -3.490018923
132 -2.131895571 2.515270651
133 -1.689160164 -2.131895571
134 -0.134952532 -1.689160164
135 1.120129701 -0.134952532
136 0.459324011 1.120129701
137 -2.363957787 0.459324011
138 -0.994871780 -2.363957787
139 -2.279773405 -0.994871780
140 3.129332967 -2.279773405
141 1.422202992 3.129332967
142 -0.012629669 1.422202992
143 1.782276715 -0.012629669
144 -3.307648252 1.782276715
145 2.289181735 -3.307648252
146 -2.057515845 2.289181735
147 1.080191400 -2.057515845
148 0.362236196 1.080191400
149 -2.878325739 0.362236196
150 -1.155043271 -2.878325739
151 2.007639148 -1.155043271
152 4.295954949 2.007639148
153 1.750032454 4.295954949
154 -2.310629981 1.750032454
155 0.167875359 -2.310629981
156 1.430460681 0.167875359
157 0.995768005 1.430460681
158 1.328604480 0.995768005
159 -0.573651755 1.328604480
160 0.399396308 -0.573651755
161 0.533448646 0.399396308
162 -0.388160789 0.533448646
163 0.426210073 -0.388160789
164 1.327399554 0.426210073
165 -1.420672833 1.327399554
166 -0.469161997 -1.420672833
167 -3.290986580 -0.469161997
168 -1.848292762 -3.290986580
169 1.569125254 -1.848292762
170 1.634688184 1.569125254
171 0.294051606 1.634688184
172 -1.988393653 0.294051606
173 -2.312033591 -1.988393653
174 -3.288588788 -2.312033591
175 0.255786884 -3.288588788
176 -0.263297297 0.255786884
177 -0.687934672 -0.263297297
178 -0.062106612 -0.687934672
179 -1.476269705 -0.062106612
180 0.565951923 -1.476269705
181 -0.521748993 0.565951923
182 1.839536729 -0.521748993
183 -0.520609608 1.839536729
184 -6.766288090 -0.520609608
185 1.031907280 -6.766288090
186 2.252323131 1.031907280
187 -0.541803289 2.252323131
188 -0.526967526 -0.541803289
189 0.700677591 -0.526967526
190 -1.044903908 0.700677591
191 0.145076363 -1.044903908
192 2.342000107 0.145076363
193 1.699486741 2.342000107
194 -0.915984634 1.699486741
195 -0.368633528 -0.915984634
196 2.901656852 -0.368633528
197 0.687030247 2.901656852
198 1.512544799 0.687030247
199 1.229420219 1.512544799
200 1.804610223 1.229420219
201 0.434739187 1.804610223
202 -2.762297678 0.434739187
203 -2.916569977 -2.762297678
204 2.030333482 -2.916569977
205 0.163035580 2.030333482
206 1.152973504 0.163035580
207 0.566051290 1.152973504
208 -2.916763144 0.566051290
209 0.696840530 -2.916763144
210 -2.507489607 0.696840530
211 -4.289684969 -2.507489607
212 0.690829519 -4.289684969
213 2.832504112 0.690829519
214 1.116710656 2.832504112
215 0.579212169 1.116710656
216 1.937338022 0.579212169
217 -0.327916940 1.937338022
218 1.009711098 -0.327916940
219 -0.615891961 1.009711098
220 -2.022491333 -0.615891961
221 -0.573303347 -2.022491333
222 0.433554087 -0.573303347
223 -1.458531370 0.433554087
224 0.195719260 -1.458531370
225 -3.966868429 0.195719260
226 -0.309683138 -3.966868429
227 0.726256447 -0.309683138
228 -1.343269573 0.726256447
229 -1.160998557 -1.343269573
230 1.520067648 -1.160998557
231 -3.465040334 1.520067648
232 4.299546178 -3.465040334
233 1.544133629 4.299546178
234 -1.305398824 1.544133629
235 -2.654920743 -1.305398824
236 -7.170028137 -2.654920743
237 -1.815214028 -7.170028137
238 1.407636695 -1.815214028
239 -1.928726129 1.407636695
240 -0.407197066 -1.928726129
241 -1.795600382 -0.407197066
242 1.113655299 -1.795600382
243 1.722993120 1.113655299
244 0.907372684 1.722993120
245 -0.046770178 0.907372684
246 0.846117134 -0.046770178
247 -2.890292844 0.846117134
248 1.199034180 -2.890292844
249 0.116066371 1.199034180
250 -1.137919803 0.116066371
251 -1.862263458 -1.137919803
252 0.336856218 -1.862263458
253 2.821775236 0.336856218
254 -1.715206305 2.821775236
255 -0.264087860 -1.715206305
256 1.318811443 -0.264087860
257 2.176071319 1.318811443
258 -1.781640984 2.176071319
259 -5.483306743 -1.781640984
260 0.825633349 -5.483306743
261 -4.550209689 0.825633349
262 -0.471645252 -4.550209689
263 0.832709147 -0.471645252
264 NA 0.832709147
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.695354701 -2.716932667
[2,] 2.465773394 0.695354701
[3,] 3.756800929 2.465773394
[4,] -1.889687332 3.756800929
[5,] -1.342423946 -1.889687332
[6,] 4.161807991 -1.342423946
[7,] -1.656841747 4.161807991
[8,] -1.614396605 -1.656841747
[9,] 1.160117737 -1.614396605
[10,] 1.475586780 1.160117737
[11,] 0.038654809 1.475586780
[12,] 1.034583103 0.038654809
[13,] 0.869405423 1.034583103
[14,] -0.726605178 0.869405423
[15,] -0.007360227 -0.726605178
[16,] 0.966688731 -0.007360227
[17,] 3.960453337 0.966688731
[18,] 2.905813838 3.960453337
[19,] 0.813862378 2.905813838
[20,] 0.998463167 0.813862378
[21,] 1.416135174 0.998463167
[22,] 2.773099012 1.416135174
[23,] 1.361748946 2.773099012
[24,] 1.158397864 1.361748946
[25,] 1.165514867 1.158397864
[26,] 1.470598801 1.165514867
[27,] -1.416929777 1.470598801
[28,] 0.704482544 -1.416929777
[29,] 0.382605544 0.704482544
[30,] -0.367231470 0.382605544
[31,] -0.157794884 -0.367231470
[32,] -0.467033523 -0.157794884
[33,] 0.450952519 -0.467033523
[34,] -1.151997771 0.450952519
[35,] -2.454806771 -1.151997771
[36,] -2.290802595 -2.454806771
[37,] -1.442875350 -2.290802595
[38,] 1.887432350 -1.442875350
[39,] 1.949699309 1.887432350
[40,] 1.640084339 1.949699309
[41,] -1.412909516 1.640084339
[42,] 2.502440756 -1.412909516
[43,] 0.262000289 2.502440756
[44,] -0.139025784 0.262000289
[45,] -4.150559388 -0.139025784
[46,] -2.419095997 -4.150559388
[47,] 0.218766088 -2.419095997
[48,] 0.808402277 0.218766088
[49,] -1.361515364 0.808402277
[50,] -0.853739839 -1.361515364
[51,] 0.268310726 -0.853739839
[52,] -2.613865360 0.268310726
[53,] 0.409310097 -2.613865360
[54,] -1.713731807 0.409310097
[55,] 2.167395395 -1.713731807
[56,] 0.306276310 2.167395395
[57,] 0.769644429 0.306276310
[58,] 0.176995441 0.769644429
[59,] 2.206777551 0.176995441
[60,] 1.163272051 2.206777551
[61,] 0.562135982 1.163272051
[62,] -0.298986970 0.562135982
[63,] -0.438887734 -0.298986970
[64,] 0.849955730 -0.438887734
[65,] 1.256146159 0.849955730
[66,] 1.909653498 1.256146159
[67,] 3.923509508 1.909653498
[68,] -3.808460881 3.923509508
[69,] 0.527152017 -3.808460881
[70,] -3.012245827 0.527152017
[71,] -0.715665538 -3.012245827
[72,] 1.162010456 -0.715665538
[73,] 0.807171814 1.162010456
[74,] 0.990531311 0.807171814
[75,] 3.909747507 0.990531311
[76,] -0.407232544 3.909747507
[77,] 1.787718277 -0.407232544
[78,] -1.961485328 1.787718277
[79,] 0.916861489 -1.961485328
[80,] 0.463901031 0.916861489
[81,] 0.356770523 0.463901031
[82,] -0.620182287 0.356770523
[83,] 0.313856292 -0.620182287
[84,] 2.117237372 0.313856292
[85,] 0.048211388 2.117237372
[86,] 0.766905814 0.048211388
[87,] 1.318697005 0.766905814
[88,] 0.896661826 1.318697005
[89,] -1.643303567 0.896661826
[90,] 0.167875359 -1.643303567
[91,] 0.270514769 0.167875359
[92,] 0.094207665 0.270514769
[93,] -1.836456819 0.094207665
[94,] 1.301967194 -1.836456819
[95,] 0.303921953 1.301967194
[96,] 2.516715321 0.303921953
[97,] 0.169958299 2.516715321
[98,] -0.266125863 0.169958299
[99,] -1.194086507 -0.266125863
[100,] 1.494098871 -1.194086507
[101,] 2.420900716 1.494098871
[102,] 0.977353986 2.420900716
[103,] 1.161623748 0.977353986
[104,] -1.712444774 1.161623748
[105,] 1.239562685 -1.712444774
[106,] 0.227935475 1.239562685
[107,] 1.767395736 0.227935475
[108,] -0.002480266 1.767395736
[109,] 0.828161450 -0.002480266
[110,] 0.061511967 0.828161450
[111,] 2.137467172 0.061511967
[112,] -1.528640241 2.137467172
[113,] -2.614568461 -1.528640241
[114,] 1.723547556 -2.614568461
[115,] -1.950475963 1.723547556
[116,] 1.007537805 -1.950475963
[117,] -1.785824239 1.007537805
[118,] 0.485542044 -1.785824239
[119,] -1.373030868 0.485542044
[120,] 0.592730467 -1.373030868
[121,] -2.900148395 0.592730467
[122,] -1.073803686 -2.900148395
[123,] -0.998565253 -1.073803686
[124,] -0.921755895 -0.998565253
[125,] 0.249852821 -0.921755895
[126,] 1.232789740 0.249852821
[127,] 0.995768005 1.232789740
[128,] -2.745597967 0.995768005
[129,] 2.098177534 -2.745597967
[130,] -3.490018923 2.098177534
[131,] 2.515270651 -3.490018923
[132,] -2.131895571 2.515270651
[133,] -1.689160164 -2.131895571
[134,] -0.134952532 -1.689160164
[135,] 1.120129701 -0.134952532
[136,] 0.459324011 1.120129701
[137,] -2.363957787 0.459324011
[138,] -0.994871780 -2.363957787
[139,] -2.279773405 -0.994871780
[140,] 3.129332967 -2.279773405
[141,] 1.422202992 3.129332967
[142,] -0.012629669 1.422202992
[143,] 1.782276715 -0.012629669
[144,] -3.307648252 1.782276715
[145,] 2.289181735 -3.307648252
[146,] -2.057515845 2.289181735
[147,] 1.080191400 -2.057515845
[148,] 0.362236196 1.080191400
[149,] -2.878325739 0.362236196
[150,] -1.155043271 -2.878325739
[151,] 2.007639148 -1.155043271
[152,] 4.295954949 2.007639148
[153,] 1.750032454 4.295954949
[154,] -2.310629981 1.750032454
[155,] 0.167875359 -2.310629981
[156,] 1.430460681 0.167875359
[157,] 0.995768005 1.430460681
[158,] 1.328604480 0.995768005
[159,] -0.573651755 1.328604480
[160,] 0.399396308 -0.573651755
[161,] 0.533448646 0.399396308
[162,] -0.388160789 0.533448646
[163,] 0.426210073 -0.388160789
[164,] 1.327399554 0.426210073
[165,] -1.420672833 1.327399554
[166,] -0.469161997 -1.420672833
[167,] -3.290986580 -0.469161997
[168,] -1.848292762 -3.290986580
[169,] 1.569125254 -1.848292762
[170,] 1.634688184 1.569125254
[171,] 0.294051606 1.634688184
[172,] -1.988393653 0.294051606
[173,] -2.312033591 -1.988393653
[174,] -3.288588788 -2.312033591
[175,] 0.255786884 -3.288588788
[176,] -0.263297297 0.255786884
[177,] -0.687934672 -0.263297297
[178,] -0.062106612 -0.687934672
[179,] -1.476269705 -0.062106612
[180,] 0.565951923 -1.476269705
[181,] -0.521748993 0.565951923
[182,] 1.839536729 -0.521748993
[183,] -0.520609608 1.839536729
[184,] -6.766288090 -0.520609608
[185,] 1.031907280 -6.766288090
[186,] 2.252323131 1.031907280
[187,] -0.541803289 2.252323131
[188,] -0.526967526 -0.541803289
[189,] 0.700677591 -0.526967526
[190,] -1.044903908 0.700677591
[191,] 0.145076363 -1.044903908
[192,] 2.342000107 0.145076363
[193,] 1.699486741 2.342000107
[194,] -0.915984634 1.699486741
[195,] -0.368633528 -0.915984634
[196,] 2.901656852 -0.368633528
[197,] 0.687030247 2.901656852
[198,] 1.512544799 0.687030247
[199,] 1.229420219 1.512544799
[200,] 1.804610223 1.229420219
[201,] 0.434739187 1.804610223
[202,] -2.762297678 0.434739187
[203,] -2.916569977 -2.762297678
[204,] 2.030333482 -2.916569977
[205,] 0.163035580 2.030333482
[206,] 1.152973504 0.163035580
[207,] 0.566051290 1.152973504
[208,] -2.916763144 0.566051290
[209,] 0.696840530 -2.916763144
[210,] -2.507489607 0.696840530
[211,] -4.289684969 -2.507489607
[212,] 0.690829519 -4.289684969
[213,] 2.832504112 0.690829519
[214,] 1.116710656 2.832504112
[215,] 0.579212169 1.116710656
[216,] 1.937338022 0.579212169
[217,] -0.327916940 1.937338022
[218,] 1.009711098 -0.327916940
[219,] -0.615891961 1.009711098
[220,] -2.022491333 -0.615891961
[221,] -0.573303347 -2.022491333
[222,] 0.433554087 -0.573303347
[223,] -1.458531370 0.433554087
[224,] 0.195719260 -1.458531370
[225,] -3.966868429 0.195719260
[226,] -0.309683138 -3.966868429
[227,] 0.726256447 -0.309683138
[228,] -1.343269573 0.726256447
[229,] -1.160998557 -1.343269573
[230,] 1.520067648 -1.160998557
[231,] -3.465040334 1.520067648
[232,] 4.299546178 -3.465040334
[233,] 1.544133629 4.299546178
[234,] -1.305398824 1.544133629
[235,] -2.654920743 -1.305398824
[236,] -7.170028137 -2.654920743
[237,] -1.815214028 -7.170028137
[238,] 1.407636695 -1.815214028
[239,] -1.928726129 1.407636695
[240,] -0.407197066 -1.928726129
[241,] -1.795600382 -0.407197066
[242,] 1.113655299 -1.795600382
[243,] 1.722993120 1.113655299
[244,] 0.907372684 1.722993120
[245,] -0.046770178 0.907372684
[246,] 0.846117134 -0.046770178
[247,] -2.890292844 0.846117134
[248,] 1.199034180 -2.890292844
[249,] 0.116066371 1.199034180
[250,] -1.137919803 0.116066371
[251,] -1.862263458 -1.137919803
[252,] 0.336856218 -1.862263458
[253,] 2.821775236 0.336856218
[254,] -1.715206305 2.821775236
[255,] -0.264087860 -1.715206305
[256,] 1.318811443 -0.264087860
[257,] 2.176071319 1.318811443
[258,] -1.781640984 2.176071319
[259,] -5.483306743 -1.781640984
[260,] 0.825633349 -5.483306743
[261,] -4.550209689 0.825633349
[262,] -0.471645252 -4.550209689
[263,] 0.832709147 -0.471645252
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.695354701 -2.716932667
2 2.465773394 0.695354701
3 3.756800929 2.465773394
4 -1.889687332 3.756800929
5 -1.342423946 -1.889687332
6 4.161807991 -1.342423946
7 -1.656841747 4.161807991
8 -1.614396605 -1.656841747
9 1.160117737 -1.614396605
10 1.475586780 1.160117737
11 0.038654809 1.475586780
12 1.034583103 0.038654809
13 0.869405423 1.034583103
14 -0.726605178 0.869405423
15 -0.007360227 -0.726605178
16 0.966688731 -0.007360227
17 3.960453337 0.966688731
18 2.905813838 3.960453337
19 0.813862378 2.905813838
20 0.998463167 0.813862378
21 1.416135174 0.998463167
22 2.773099012 1.416135174
23 1.361748946 2.773099012
24 1.158397864 1.361748946
25 1.165514867 1.158397864
26 1.470598801 1.165514867
27 -1.416929777 1.470598801
28 0.704482544 -1.416929777
29 0.382605544 0.704482544
30 -0.367231470 0.382605544
31 -0.157794884 -0.367231470
32 -0.467033523 -0.157794884
33 0.450952519 -0.467033523
34 -1.151997771 0.450952519
35 -2.454806771 -1.151997771
36 -2.290802595 -2.454806771
37 -1.442875350 -2.290802595
38 1.887432350 -1.442875350
39 1.949699309 1.887432350
40 1.640084339 1.949699309
41 -1.412909516 1.640084339
42 2.502440756 -1.412909516
43 0.262000289 2.502440756
44 -0.139025784 0.262000289
45 -4.150559388 -0.139025784
46 -2.419095997 -4.150559388
47 0.218766088 -2.419095997
48 0.808402277 0.218766088
49 -1.361515364 0.808402277
50 -0.853739839 -1.361515364
51 0.268310726 -0.853739839
52 -2.613865360 0.268310726
53 0.409310097 -2.613865360
54 -1.713731807 0.409310097
55 2.167395395 -1.713731807
56 0.306276310 2.167395395
57 0.769644429 0.306276310
58 0.176995441 0.769644429
59 2.206777551 0.176995441
60 1.163272051 2.206777551
61 0.562135982 1.163272051
62 -0.298986970 0.562135982
63 -0.438887734 -0.298986970
64 0.849955730 -0.438887734
65 1.256146159 0.849955730
66 1.909653498 1.256146159
67 3.923509508 1.909653498
68 -3.808460881 3.923509508
69 0.527152017 -3.808460881
70 -3.012245827 0.527152017
71 -0.715665538 -3.012245827
72 1.162010456 -0.715665538
73 0.807171814 1.162010456
74 0.990531311 0.807171814
75 3.909747507 0.990531311
76 -0.407232544 3.909747507
77 1.787718277 -0.407232544
78 -1.961485328 1.787718277
79 0.916861489 -1.961485328
80 0.463901031 0.916861489
81 0.356770523 0.463901031
82 -0.620182287 0.356770523
83 0.313856292 -0.620182287
84 2.117237372 0.313856292
85 0.048211388 2.117237372
86 0.766905814 0.048211388
87 1.318697005 0.766905814
88 0.896661826 1.318697005
89 -1.643303567 0.896661826
90 0.167875359 -1.643303567
91 0.270514769 0.167875359
92 0.094207665 0.270514769
93 -1.836456819 0.094207665
94 1.301967194 -1.836456819
95 0.303921953 1.301967194
96 2.516715321 0.303921953
97 0.169958299 2.516715321
98 -0.266125863 0.169958299
99 -1.194086507 -0.266125863
100 1.494098871 -1.194086507
101 2.420900716 1.494098871
102 0.977353986 2.420900716
103 1.161623748 0.977353986
104 -1.712444774 1.161623748
105 1.239562685 -1.712444774
106 0.227935475 1.239562685
107 1.767395736 0.227935475
108 -0.002480266 1.767395736
109 0.828161450 -0.002480266
110 0.061511967 0.828161450
111 2.137467172 0.061511967
112 -1.528640241 2.137467172
113 -2.614568461 -1.528640241
114 1.723547556 -2.614568461
115 -1.950475963 1.723547556
116 1.007537805 -1.950475963
117 -1.785824239 1.007537805
118 0.485542044 -1.785824239
119 -1.373030868 0.485542044
120 0.592730467 -1.373030868
121 -2.900148395 0.592730467
122 -1.073803686 -2.900148395
123 -0.998565253 -1.073803686
124 -0.921755895 -0.998565253
125 0.249852821 -0.921755895
126 1.232789740 0.249852821
127 0.995768005 1.232789740
128 -2.745597967 0.995768005
129 2.098177534 -2.745597967
130 -3.490018923 2.098177534
131 2.515270651 -3.490018923
132 -2.131895571 2.515270651
133 -1.689160164 -2.131895571
134 -0.134952532 -1.689160164
135 1.120129701 -0.134952532
136 0.459324011 1.120129701
137 -2.363957787 0.459324011
138 -0.994871780 -2.363957787
139 -2.279773405 -0.994871780
140 3.129332967 -2.279773405
141 1.422202992 3.129332967
142 -0.012629669 1.422202992
143 1.782276715 -0.012629669
144 -3.307648252 1.782276715
145 2.289181735 -3.307648252
146 -2.057515845 2.289181735
147 1.080191400 -2.057515845
148 0.362236196 1.080191400
149 -2.878325739 0.362236196
150 -1.155043271 -2.878325739
151 2.007639148 -1.155043271
152 4.295954949 2.007639148
153 1.750032454 4.295954949
154 -2.310629981 1.750032454
155 0.167875359 -2.310629981
156 1.430460681 0.167875359
157 0.995768005 1.430460681
158 1.328604480 0.995768005
159 -0.573651755 1.328604480
160 0.399396308 -0.573651755
161 0.533448646 0.399396308
162 -0.388160789 0.533448646
163 0.426210073 -0.388160789
164 1.327399554 0.426210073
165 -1.420672833 1.327399554
166 -0.469161997 -1.420672833
167 -3.290986580 -0.469161997
168 -1.848292762 -3.290986580
169 1.569125254 -1.848292762
170 1.634688184 1.569125254
171 0.294051606 1.634688184
172 -1.988393653 0.294051606
173 -2.312033591 -1.988393653
174 -3.288588788 -2.312033591
175 0.255786884 -3.288588788
176 -0.263297297 0.255786884
177 -0.687934672 -0.263297297
178 -0.062106612 -0.687934672
179 -1.476269705 -0.062106612
180 0.565951923 -1.476269705
181 -0.521748993 0.565951923
182 1.839536729 -0.521748993
183 -0.520609608 1.839536729
184 -6.766288090 -0.520609608
185 1.031907280 -6.766288090
186 2.252323131 1.031907280
187 -0.541803289 2.252323131
188 -0.526967526 -0.541803289
189 0.700677591 -0.526967526
190 -1.044903908 0.700677591
191 0.145076363 -1.044903908
192 2.342000107 0.145076363
193 1.699486741 2.342000107
194 -0.915984634 1.699486741
195 -0.368633528 -0.915984634
196 2.901656852 -0.368633528
197 0.687030247 2.901656852
198 1.512544799 0.687030247
199 1.229420219 1.512544799
200 1.804610223 1.229420219
201 0.434739187 1.804610223
202 -2.762297678 0.434739187
203 -2.916569977 -2.762297678
204 2.030333482 -2.916569977
205 0.163035580 2.030333482
206 1.152973504 0.163035580
207 0.566051290 1.152973504
208 -2.916763144 0.566051290
209 0.696840530 -2.916763144
210 -2.507489607 0.696840530
211 -4.289684969 -2.507489607
212 0.690829519 -4.289684969
213 2.832504112 0.690829519
214 1.116710656 2.832504112
215 0.579212169 1.116710656
216 1.937338022 0.579212169
217 -0.327916940 1.937338022
218 1.009711098 -0.327916940
219 -0.615891961 1.009711098
220 -2.022491333 -0.615891961
221 -0.573303347 -2.022491333
222 0.433554087 -0.573303347
223 -1.458531370 0.433554087
224 0.195719260 -1.458531370
225 -3.966868429 0.195719260
226 -0.309683138 -3.966868429
227 0.726256447 -0.309683138
228 -1.343269573 0.726256447
229 -1.160998557 -1.343269573
230 1.520067648 -1.160998557
231 -3.465040334 1.520067648
232 4.299546178 -3.465040334
233 1.544133629 4.299546178
234 -1.305398824 1.544133629
235 -2.654920743 -1.305398824
236 -7.170028137 -2.654920743
237 -1.815214028 -7.170028137
238 1.407636695 -1.815214028
239 -1.928726129 1.407636695
240 -0.407197066 -1.928726129
241 -1.795600382 -0.407197066
242 1.113655299 -1.795600382
243 1.722993120 1.113655299
244 0.907372684 1.722993120
245 -0.046770178 0.907372684
246 0.846117134 -0.046770178
247 -2.890292844 0.846117134
248 1.199034180 -2.890292844
249 0.116066371 1.199034180
250 -1.137919803 0.116066371
251 -1.862263458 -1.137919803
252 0.336856218 -1.862263458
253 2.821775236 0.336856218
254 -1.715206305 2.821775236
255 -0.264087860 -1.715206305
256 1.318811443 -0.264087860
257 2.176071319 1.318811443
258 -1.781640984 2.176071319
259 -5.483306743 -1.781640984
260 0.825633349 -5.483306743
261 -4.550209689 0.825633349
262 -0.471645252 -4.550209689
263 0.832709147 -0.471645252
> 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/7bwwz1383501554.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8nycq1383501554.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9baq31383501554.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10gm2w1383501554.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, 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/fisher/rcomp/tmp/119c861383501554.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/fisher/rcomp/tmp/1299nh1383501554.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/fisher/rcomp/tmp/135ps71383501554.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/fisher/rcomp/tmp/14wze01383501554.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/fisher/rcomp/tmp/15p4sb1383501554.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/fisher/rcomp/tmp/16i8qi1383501554.tab")
+ }
>
> try(system("convert tmp/1qjm51383501554.ps tmp/1qjm51383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/2b0dl1383501554.ps tmp/2b0dl1383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/35e721383501554.ps tmp/35e721383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wwkh1383501554.ps tmp/4wwkh1383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/5lf8z1383501554.ps tmp/5lf8z1383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rm3q1383501554.ps tmp/6rm3q1383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/7bwwz1383501554.ps tmp/7bwwz1383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nycq1383501554.ps tmp/8nycq1383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/9baq31383501554.ps tmp/9baq31383501554.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gm2w1383501554.ps tmp/10gm2w1383501554.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.073 1.583 12.648