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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+ ,12
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+ ,11)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '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
Software Connected Separate Learning Happiness Depression Sport1 Sport2
1 12 41 38 13 14 12.0 53 32
2 11 39 32 16 18 11.0 83 51
3 15 30 35 19 11 14.0 66 42
4 6 31 33 15 12 12.0 67 41
5 13 34 37 14 16 21.0 76 46
6 10 35 29 13 18 12.0 78 47
7 12 39 31 19 14 22.0 53 37
8 14 34 36 15 14 11.0 80 49
9 12 36 35 14 15 10.0 74 45
10 9 37 38 15 15 13.0 76 47
11 10 38 31 16 17 10.0 79 49
12 12 36 34 16 19 8.0 54 33
13 12 38 35 16 10 15.0 67 42
14 11 39 38 16 16 14.0 54 33
15 15 33 37 17 18 10.0 87 53
16 12 32 33 15 14 14.0 58 36
17 10 36 32 15 14 14.0 75 45
18 12 38 38 20 17 11.0 88 54
19 11 39 38 18 14 10.0 64 41
20 12 32 32 16 16 13.0 57 36
21 11 32 33 16 18 9.5 66 41
22 12 31 31 16 11 14.0 68 44
23 13 39 38 19 14 12.0 54 33
24 11 37 39 16 12 14.0 56 37
25 12 39 32 17 17 11.0 86 52
26 13 41 32 17 9 9.0 80 47
27 10 36 35 16 16 11.0 76 43
28 14 33 37 15 14 15.0 69 44
29 12 33 33 16 15 14.0 78 45
30 10 34 33 14 11 13.0 67 44
31 12 31 31 15 16 9.0 80 49
32 8 27 32 12 13 15.0 54 33
33 10 37 31 14 17 10.0 71 43
34 12 34 37 16 15 11.0 84 54
35 12 34 30 14 14 13.0 74 42
36 7 32 33 10 16 8.0 71 44
37 9 29 31 10 9 20.0 63 37
38 12 36 33 14 15 12.0 71 43
39 10 29 31 16 17 10.0 76 46
40 10 35 33 16 13 10.0 69 42
41 10 37 32 16 15 9.0 74 45
42 12 34 33 14 16 14.0 75 44
43 15 38 32 20 16 8.0 54 33
44 10 35 33 14 12 14.0 52 31
45 10 38 28 14 15 11.0 69 42
46 12 37 35 11 11 13.0 68 40
47 13 38 39 14 15 9.0 65 43
48 11 33 34 15 15 11.0 75 46
49 11 36 38 16 17 15.0 74 42
50 12 38 32 14 13 11.0 75 45
51 14 32 38 16 16 10.0 72 44
52 10 32 30 14 14 14.0 67 40
53 12 32 33 12 11 18.0 63 37
54 13 34 38 16 12 14.0 62 46
55 5 32 32 9 12 11.0 63 36
56 6 37 35 14 15 14.5 76 47
57 12 39 34 16 16 13.0 74 45
58 12 29 34 16 15 9.0 67 42
59 11 37 36 15 12 10.0 73 43
60 10 35 34 16 12 15.0 70 43
61 7 30 28 12 8 20.0 53 32
62 12 38 34 16 13 12.0 77 45
63 14 34 35 16 11 12.0 80 48
64 11 31 35 14 14 14.0 52 31
65 12 34 31 16 15 13.0 54 33
66 13 35 37 17 10 11.0 80 49
67 14 36 35 18 11 17.0 66 42
68 11 30 27 18 12 12.0 73 41
69 12 39 40 12 15 13.0 63 38
70 12 35 37 16 15 14.0 69 42
71 8 38 36 10 14 13.0 67 44
72 11 31 38 14 16 15.0 54 33
73 14 34 39 18 15 13.0 81 48
74 14 38 41 18 15 10.0 69 40
75 12 34 27 16 13 11.0 84 50
76 9 39 30 17 12 19.0 80 49
77 13 37 37 16 17 13.0 70 43
78 11 34 31 16 13 17.0 69 44
79 12 28 31 13 15 13.0 77 47
80 12 37 27 16 13 9.0 54 33
81 12 33 36 16 15 11.0 79 46
82 12 35 37 16 15 9.0 71 45
83 12 37 33 15 16 12.0 73 43
84 11 32 34 15 15 12.0 72 44
85 10 33 31 16 14 13.0 77 47
86 9 38 39 14 15 13.0 75 45
87 12 33 34 16 14 12.0 69 42
88 12 29 32 16 13 15.0 54 33
89 12 33 33 15 7 22.0 70 43
90 9 31 36 12 17 13.0 73 46
91 15 36 32 17 13 15.0 54 33
92 12 35 41 16 15 13.0 77 46
93 12 32 28 15 14 15.0 82 48
94 12 29 30 13 13 12.5 80 47
95 10 39 36 16 16 11.0 80 47
96 13 37 35 16 12 16.0 69 43
97 9 35 31 16 14 11.0 78 46
98 12 37 34 16 17 11.0 81 48
99 10 32 36 14 15 10.0 76 46
100 14 38 36 16 17 10.0 76 45
101 11 37 35 16 12 16.0 73 45
102 15 36 37 20 16 12.0 85 52
103 11 32 28 15 11 11.0 66 42
104 11 33 39 16 15 16.0 79 47
105 12 40 32 13 9 19.0 68 41
106 12 38 35 17 16 11.0 76 47
107 12 41 39 16 15 16.0 71 43
108 11 36 35 16 10 15.0 54 33
109 7 43 42 12 10 24.0 46 30
110 12 30 34 16 15 14.0 85 52
111 14 31 33 16 11 15.0 74 44
112 11 32 41 17 13 11.0 88 55
113 11 32 33 13 14 15.0 38 11
114 10 37 34 12 18 12.0 76 47
115 13 37 32 18 16 10.0 86 53
116 13 33 40 14 14 14.0 54 33
117 8 34 40 14 14 13.0 67 44
118 11 33 35 13 14 9.0 69 42
119 12 38 36 16 14 15.0 90 55
120 11 33 37 13 12 15.0 54 33
121 13 31 27 16 14 14.0 76 46
122 12 38 39 13 15 11.0 89 54
123 14 37 38 16 15 8.0 76 47
124 13 36 31 15 15 11.0 73 45
125 15 31 33 16 13 11.0 79 47
126 10 39 32 15 17 8.0 90 55
127 11 44 39 17 17 10.0 74 44
128 9 33 36 15 19 11.0 81 53
129 11 35 33 12 15 13.0 72 44
130 10 32 33 16 13 11.0 71 42
131 11 28 32 10 9 20.0 66 40
132 8 40 37 16 15 10.0 77 46
133 11 27 30 12 15 15.0 65 40
134 12 37 38 14 15 12.0 74 46
135 12 32 29 15 16 14.0 85 53
136 9 28 22 13 11 23.0 54 33
137 11 34 35 15 14 14.0 63 42
138 10 30 35 11 11 16.0 54 35
139 8 35 34 12 15 11.0 64 40
140 9 31 35 11 13 12.0 69 41
141 8 32 34 16 15 10.0 54 33
142 9 30 37 15 16 14.0 84 51
143 15 30 35 17 14 12.0 86 53
144 11 31 23 16 15 12.0 77 46
145 8 40 31 10 16 11.0 89 55
146 13 32 27 18 16 12.0 76 47
147 12 36 36 13 11 13.0 60 38
148 12 32 31 16 12 11.0 75 46
149 9 35 32 13 9 19.0 73 46
150 7 38 39 10 16 12.0 85 53
151 13 42 37 15 13 17.0 79 47
152 9 34 38 16 16 9.0 71 41
153 6 35 39 16 12 12.0 72 44
154 8 38 34 14 9 19.0 69 43
155 8 33 31 10 13 18.0 78 51
156 15 36 32 17 13 15.0 54 33
157 6 32 37 13 14 14.0 69 43
158 9 33 36 15 19 11.0 81 53
159 11 34 32 16 13 9.0 84 51
160 8 32 38 12 12 18.0 84 50
161 8 34 36 13 13 16.0 69 46
162 10 27 26 13 10 24.0 66 43
163 8 31 26 12 14 14.0 81 47
164 14 38 33 17 16 20.0 82 50
165 10 34 39 15 10 18.0 72 43
166 8 24 30 10 11 23.0 54 33
167 11 30 33 14 14 12.0 78 48
168 12 26 25 11 12 14.0 74 44
169 12 34 38 13 9 16.0 82 50
170 12 27 37 16 9 18.0 73 41
171 5 37 31 12 11 20.0 55 34
172 12 36 37 16 16 12.0 72 44
173 10 41 35 12 9 12.0 78 47
174 7 29 25 9 13 17.0 59 35
175 12 36 28 12 16 13.0 72 44
176 11 32 35 15 13 9.0 78 44
177 8 37 33 12 9 16.0 68 43
178 9 30 30 12 12 18.0 69 41
179 10 31 31 14 16 10.0 67 41
180 9 38 37 12 11 14.0 74 42
181 12 36 36 16 14 11.0 54 33
182 6 35 30 11 13 9.0 67 41
183 15 31 36 19 15 11.0 70 44
184 12 38 32 15 14 10.0 80 48
185 12 22 28 8 16 11.0 89 55
186 12 32 36 16 13 19.0 76 44
187 11 36 34 17 14 14.0 74 43
188 7 39 31 12 15 12.0 87 52
189 7 28 28 11 13 14.0 54 30
190 5 32 36 11 11 21.0 61 39
191 12 32 36 14 11 13.0 38 11
192 12 38 40 16 14 10.0 75 44
193 3 32 33 12 15 15.0 69 42
194 11 35 37 16 11 16.0 62 41
195 10 32 32 13 15 14.0 72 44
196 12 37 38 15 12 12.0 70 44
197 9 34 31 16 14 19.0 79 48
198 12 33 37 16 14 15.0 87 53
199 9 33 33 14 8 19.0 62 37
200 12 26 32 16 13 13.0 77 44
201 12 30 30 16 9 17.0 69 44
202 10 24 30 14 15 12.0 69 40
203 9 34 31 11 17 11.0 75 42
204 12 34 32 12 13 14.0 54 35
205 8 33 34 15 15 11.0 72 43
206 11 34 36 15 15 13.0 74 45
207 11 35 37 16 14 12.0 85 55
208 12 35 36 16 16 15.0 52 31
209 10 36 33 11 13 14.0 70 44
210 10 34 33 15 16 12.0 84 50
211 12 34 33 12 9 17.0 64 40
212 12 41 44 12 16 11.0 84 53
213 11 32 39 15 11 18.0 87 54
214 8 30 32 15 10 13.0 79 49
215 12 35 35 16 11 17.0 67 40
216 10 28 25 14 15 13.0 65 41
217 11 33 35 17 17 11.0 85 52
218 10 39 34 14 14 12.0 83 52
219 8 36 35 13 8 22.0 61 36
220 12 36 39 15 15 14.0 82 52
221 12 35 33 13 11 12.0 76 46
222 10 38 36 14 16 12.0 58 31
223 12 33 32 15 10 17.0 72 44
224 9 31 32 12 15 9.0 72 44
225 9 34 36 13 9 21.0 38 11
226 6 32 36 8 16 10.0 78 46
227 10 31 32 14 19 11.0 54 33
228 9 33 34 14 12 12.0 63 34
229 9 34 33 11 8 23.0 66 42
230 9 34 35 12 11 13.0 70 43
231 6 34 30 13 14 12.0 71 43
232 10 33 38 10 9 16.0 67 44
233 6 32 34 16 15 9.0 58 36
234 14 41 33 18 13 17.0 72 46
235 10 34 32 13 16 9.0 72 44
236 10 36 31 11 11 14.0 70 43
237 6 37 30 4 12 17.0 76 50
238 12 36 27 13 13 13.0 50 33
239 12 29 31 16 10 11.0 72 43
240 7 37 30 10 11 12.0 72 44
241 8 27 32 12 12 10.0 88 53
242 11 35 35 12 8 19.0 53 34
243 3 28 28 10 12 16.0 58 35
244 6 35 33 13 12 16.0 66 40
245 10 37 31 15 15 14.0 82 53
246 8 29 35 12 11 20.0 69 42
247 9 32 35 14 13 15.0 68 43
248 9 36 32 10 14 23.0 44 29
249 8 19 21 12 10 20.0 56 36
250 9 21 20 12 12 16.0 53 30
251 7 31 34 11 15 14.0 70 42
252 7 33 32 10 13 17.0 78 47
253 6 36 34 12 13 11.0 71 44
254 9 33 32 16 13 13.0 72 45
255 10 37 33 12 12 17.0 68 44
256 11 34 33 14 12 15.0 67 43
257 12 35 37 16 9 21.0 75 43
258 8 31 32 14 9 18.0 62 40
259 11 37 34 13 15 15.0 67 41
260 3 35 30 4 10 8.0 83 52
261 11 27 30 15 14 12.0 64 38
262 12 34 38 11 15 12.0 68 41
263 7 40 36 11 7 22.0 62 39
264 9 29 32 14 14 12.0 72 43
Month
1 9
2 9
3 9
4 9
5 9
6 9
7 9
8 9
9 9
10 9
11 9
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13 9
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255 11
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257 11
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259 11
260 11
261 11
262 11
263 11
264 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Happiness Depression
4.293692 -0.017572 0.038646 0.552864 -0.016433 -0.002358
Sport1 Sport2 Month
0.021424 -0.022855 -0.258721
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0316 -1.0536 0.1702 1.1913 5.0730
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.293692 2.600565 1.651 0.100 .
Connected -0.017572 0.034098 -0.515 0.607
Separate 0.038646 0.034580 1.118 0.265
Learning 0.552864 0.050646 10.916 <2e-16 ***
Happiness -0.016433 0.056869 -0.289 0.773
Depression -0.002358 0.041604 -0.057 0.955
Sport1 0.021424 0.037015 0.579 0.563
Sport2 -0.022855 0.054974 -0.416 0.678
Month -0.258721 0.157399 -1.644 0.101
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.827 on 255 degrees of freedom
Multiple R-squared: 0.3988, Adjusted R-squared: 0.38
F-statistic: 21.15 on 8 and 255 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.995288119 0.009423763 0.004711881
[2,] 0.988628935 0.022742131 0.011371065
[3,] 0.987443776 0.025112447 0.012556224
[4,] 0.979137119 0.041725761 0.020862881
[5,] 0.969913689 0.060172623 0.030086311
[6,] 0.950353739 0.099292523 0.049646261
[7,] 0.947033187 0.105933626 0.052966813
[8,] 0.934055949 0.131888101 0.065944051
[9,] 0.902607665 0.194784669 0.097392335
[10,] 0.874512147 0.250975706 0.125487853
[11,] 0.833787476 0.332425048 0.166212524
[12,] 0.793805486 0.412389029 0.206194514
[13,] 0.758427285 0.483145430 0.241572715
[14,] 0.710404303 0.579191395 0.289595697
[15,] 0.707345393 0.585309213 0.292654607
[16,] 0.723698455 0.552603090 0.276301545
[17,] 0.729344371 0.541311258 0.270655629
[18,] 0.672418123 0.655163753 0.327581877
[19,] 0.626112961 0.747774079 0.373887039
[20,] 0.575335392 0.849329216 0.424664608
[21,] 0.591577054 0.816845891 0.408422946
[22,] 0.531400269 0.937199463 0.468599731
[23,] 0.474741501 0.949483002 0.525258499
[24,] 0.454007174 0.908014348 0.545992826
[25,] 0.441280654 0.882561309 0.558719346
[26,] 0.386553532 0.773107064 0.613446468
[27,] 0.367179839 0.734359678 0.632820161
[28,] 0.347779026 0.695558052 0.652220974
[29,] 0.322160539 0.644321079 0.677839461
[30,] 0.291593621 0.583187243 0.708406379
[31,] 0.259288938 0.518577876 0.740711062
[32,] 0.300275970 0.600551940 0.699724030
[33,] 0.258930341 0.517860682 0.741069659
[34,] 0.219371119 0.438742239 0.780628881
[35,] 0.256113420 0.512226840 0.743886580
[36,] 0.273908789 0.547817577 0.726091211
[37,] 0.233786041 0.467572082 0.766213959
[38,] 0.222178124 0.444356248 0.777821876
[39,] 0.204410331 0.408820662 0.795589669
[40,] 0.210020643 0.420041286 0.789979357
[41,] 0.177626832 0.355253665 0.822373168
[42,] 0.181403283 0.362806567 0.818596717
[43,] 0.159659132 0.319318265 0.840340868
[44,] 0.238787585 0.477575169 0.761212415
[45,] 0.519353261 0.961293477 0.480646739
[46,] 0.475544607 0.951089214 0.524455393
[47,] 0.431681150 0.863362299 0.568318850
[48,] 0.390406716 0.780813431 0.609593284
[49,] 0.390023868 0.780047736 0.609976132
[50,] 0.402732744 0.805465489 0.597267256
[51,] 0.361823983 0.723647966 0.638176017
[52,] 0.365330933 0.730661866 0.634669067
[53,] 0.325825421 0.651650841 0.674174579
[54,] 0.297906168 0.595812335 0.702093832
[55,] 0.262263968 0.524527936 0.737736032
[56,] 0.236243320 0.472486640 0.763756680
[57,] 0.220218747 0.440437494 0.779781253
[58,] 0.210157155 0.420314310 0.789842845
[59,] 0.184168063 0.368336126 0.815831937
[60,] 0.166639911 0.333279822 0.833360089
[61,] 0.143212184 0.286424369 0.856787816
[62,] 0.122692289 0.245384578 0.877307711
[63,] 0.104733317 0.209466634 0.895266683
[64,] 0.092537735 0.185075470 0.907462265
[65,] 0.119420080 0.238840159 0.880579920
[66,] 0.106752911 0.213505822 0.893247089
[67,] 0.089410454 0.178820908 0.910589546
[68,] 0.092063392 0.184126784 0.907936608
[69,] 0.084459856 0.168919713 0.915540144
[70,] 0.070945887 0.141891773 0.929054113
[71,] 0.059324096 0.118648192 0.940675904
[72,] 0.050398676 0.100797352 0.949601324
[73,] 0.041589040 0.083178080 0.958410960
[74,] 0.039570071 0.079140142 0.960429929
[75,] 0.046391709 0.092783418 0.953608291
[76,] 0.037641260 0.075282521 0.962358740
[77,] 0.030583660 0.061167320 0.969416340
[78,] 0.025663328 0.051326656 0.974336672
[79,] 0.021807731 0.043615461 0.978192269
[80,] 0.033580137 0.067160274 0.966419863
[81,] 0.028109619 0.056219238 0.971890381
[82,] 0.025008914 0.050017828 0.974991086
[83,] 0.025560481 0.051120961 0.974439519
[84,] 0.026612606 0.053225212 0.973387394
[85,] 0.023541395 0.047082791 0.976458605
[86,] 0.030355771 0.060711541 0.969644229
[87,] 0.024511605 0.049023211 0.975488395
[88,] 0.021098579 0.042197158 0.978901421
[89,] 0.023701688 0.047403375 0.976298312
[90,] 0.019847651 0.039695303 0.980152349
[91,] 0.016695317 0.033390634 0.983304683
[92,] 0.013173468 0.026346936 0.986826532
[93,] 0.011810987 0.023621975 0.988189013
[94,] 0.012620181 0.025240361 0.987379819
[95,] 0.009919131 0.019838261 0.990080869
[96,] 0.007793085 0.015586170 0.992206915
[97,] 0.006504457 0.013008915 0.993495543
[98,] 0.009465834 0.018931667 0.990534166
[99,] 0.007367106 0.014734212 0.992632894
[100,] 0.008197941 0.016395882 0.991802059
[101,] 0.008699172 0.017398343 0.991300828
[102,] 0.007100268 0.014200535 0.992899732
[103,] 0.005670647 0.011341294 0.994329353
[104,] 0.004388911 0.008777821 0.995611089
[105,] 0.004986207 0.009972413 0.995013793
[106,] 0.007449119 0.014898238 0.992550881
[107,] 0.006154547 0.012309094 0.993845453
[108,] 0.004762102 0.009524204 0.995237898
[109,] 0.003956612 0.007913223 0.996043388
[110,] 0.003725207 0.007450414 0.996274793
[111,] 0.003700956 0.007401912 0.996299044
[112,] 0.004291389 0.008582779 0.995708611
[113,] 0.004770365 0.009540731 0.995229635
[114,] 0.008568541 0.017137083 0.991431459
[115,] 0.007237095 0.014474189 0.992762905
[116,] 0.006294190 0.012588380 0.993705810
[117,] 0.006697998 0.013395997 0.993302002
[118,] 0.006612235 0.013224470 0.993387765
[119,] 0.006704054 0.013408107 0.993295946
[120,] 0.008853805 0.017707610 0.991146195
[121,] 0.017741414 0.035482829 0.982258586
[122,] 0.017739206 0.035478413 0.982260794
[123,] 0.016999057 0.033998113 0.983000943
[124,] 0.015050948 0.030101897 0.984949052
[125,] 0.012361422 0.024722843 0.987638578
[126,] 0.009989885 0.019979770 0.990010115
[127,] 0.009595934 0.019191867 0.990404066
[128,] 0.008605344 0.017210687 0.991394656
[129,] 0.007371290 0.014742580 0.992628710
[130,] 0.013035202 0.026070404 0.986964798
[131,] 0.014705970 0.029411940 0.985294030
[132,] 0.021288632 0.042577265 0.978711368
[133,] 0.017174690 0.034349379 0.982825310
[134,] 0.013753256 0.027506512 0.986246744
[135,] 0.011643513 0.023287025 0.988356487
[136,] 0.014874539 0.029749077 0.985125461
[137,] 0.013210557 0.026421114 0.986789443
[138,] 0.011189736 0.022379473 0.988810264
[139,] 0.009866584 0.019733169 0.990133416
[140,] 0.012208032 0.024416065 0.987791968
[141,] 0.014430356 0.028860711 0.985569644
[142,] 0.064967118 0.129934237 0.935032882
[143,] 0.065110366 0.130220731 0.934889634
[144,] 0.055983468 0.111966936 0.944016532
[145,] 0.106070044 0.212140089 0.893929956
[146,] 0.147699772 0.295399543 0.852300228
[147,] 0.138783287 0.277566574 0.861216713
[148,] 0.124655039 0.249310078 0.875344961
[149,] 0.114722413 0.229444825 0.885277587
[150,] 0.104785642 0.209571285 0.895214358
[151,] 0.090790423 0.181580846 0.909209577
[152,] 0.081633509 0.163267019 0.918366491
[153,] 0.086501494 0.173002988 0.913498506
[154,] 0.078275682 0.156551365 0.921724318
[155,] 0.066428788 0.132857576 0.933571212
[156,] 0.056390556 0.112781113 0.943609444
[157,] 0.093537063 0.187074127 0.906462937
[158,] 0.094392226 0.188784452 0.905607774
[159,] 0.082005263 0.164010526 0.917994737
[160,] 0.143740430 0.287480860 0.856259570
[161,] 0.124507842 0.249015685 0.875492158
[162,] 0.109399888 0.218799775 0.890600112
[163,] 0.093782679 0.187565359 0.906217321
[164,] 0.129414737 0.258829474 0.870585263
[165,] 0.111975807 0.223951614 0.888024193
[166,] 0.100498109 0.200996219 0.899501891
[167,] 0.085930270 0.171860540 0.914069730
[168,] 0.072329895 0.144659791 0.927670105
[169,] 0.060450041 0.120900083 0.939549959
[170,] 0.050370531 0.100741061 0.949629469
[171,] 0.057231259 0.114462518 0.942768741
[172,] 0.057436152 0.114872305 0.942563848
[173,] 0.053156215 0.106312431 0.946843785
[174,] 0.215285124 0.430570249 0.784714876
[175,] 0.197781733 0.395563466 0.802218267
[176,] 0.176640089 0.353280177 0.823359911
[177,] 0.175793968 0.351587937 0.824206032
[178,] 0.162464375 0.324928750 0.837535625
[179,] 0.240843875 0.481687750 0.759156125
[180,] 0.245853260 0.491706521 0.754146740
[181,] 0.216573598 0.433147197 0.783426402
[182,] 0.580722911 0.838554178 0.419277089
[183,] 0.548982205 0.902035590 0.451017795
[184,] 0.511899023 0.976201953 0.488100977
[185,] 0.480157965 0.960315930 0.519842035
[186,] 0.492110529 0.984221059 0.507889471
[187,] 0.456077706 0.912155412 0.543922294
[188,] 0.428647641 0.857295282 0.571352359
[189,] 0.429815637 0.859631273 0.570184363
[190,] 0.404188183 0.808376365 0.595811817
[191,] 0.382750820 0.765501640 0.617249180
[192,] 0.361352390 0.722704781 0.638647610
[193,] 0.414483041 0.828966081 0.585516959
[194,] 0.444379548 0.888759096 0.555620452
[195,] 0.401835197 0.803670394 0.598164803
[196,] 0.361709479 0.723418959 0.638290521
[197,] 0.323588293 0.647176586 0.676411707
[198,] 0.306288631 0.612577263 0.693711369
[199,] 0.269288550 0.538577099 0.730711450
[200,] 0.333280824 0.666561648 0.666719176
[201,] 0.350849700 0.701699400 0.649150300
[202,] 0.309713533 0.619427066 0.690286467
[203,] 0.327624922 0.655249844 0.672375078
[204,] 0.293004219 0.586008439 0.706995781
[205,] 0.257811753 0.515623505 0.742188247
[206,] 0.223142737 0.446285473 0.776857263
[207,] 0.188551105 0.377102210 0.811448895
[208,] 0.186130097 0.372260195 0.813869903
[209,] 0.164180990 0.328361981 0.835819010
[210,] 0.203466465 0.406932930 0.796533535
[211,] 0.169860604 0.339721208 0.830139396
[212,] 0.162483953 0.324967907 0.837516047
[213,] 0.139149791 0.278299582 0.860850209
[214,] 0.113435176 0.226870351 0.886564824
[215,] 0.091434032 0.182868065 0.908565968
[216,] 0.072441373 0.144882747 0.927558627
[217,] 0.057051659 0.114103318 0.942948341
[218,] 0.042970878 0.085941757 0.957029122
[219,] 0.032217735 0.064435470 0.967782265
[220,] 0.047009874 0.094019748 0.952990126
[221,] 0.057923991 0.115847982 0.942076009
[222,] 0.225139617 0.450279235 0.774860383
[223,] 0.198299432 0.396598863 0.801700568
[224,] 0.158049872 0.316099744 0.841950128
[225,] 0.156670805 0.313341609 0.843329195
[226,] 0.188508404 0.377016808 0.811491596
[227,] 0.203717564 0.407435128 0.796282436
[228,] 0.194767188 0.389534377 0.805232812
[229,] 0.154996906 0.309993812 0.845003094
[230,] 0.122273697 0.244547395 0.877726303
[231,] 0.154417277 0.308834555 0.845582723
[232,] 0.405514984 0.811029968 0.594485016
[233,] 0.596306813 0.807386373 0.403693187
[234,] 0.512311836 0.975376328 0.487688164
[235,] 0.437211638 0.874423275 0.562788362
[236,] 0.359998505 0.719997009 0.640001495
[237,] 0.295332148 0.590664296 0.704667852
[238,] 0.211514200 0.423028399 0.788485800
[239,] 0.160220574 0.320441148 0.839779426
[240,] 0.237560797 0.475121595 0.762439203
[241,] 0.410921366 0.821842732 0.589078634
> postscript(file="/var/wessaorg/rcomp/tmp/1he6e1383490493.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2wg7d1383490493.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3j46a1383490493.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4kjqk1383490493.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5kjon1383490493.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
1.95373453 -0.65321729 1.46465336 -5.26158782 2.19782671 0.06908159
7 8 9 10 11 12
-0.99021228 2.61002967 1.28788139 -2.35341159 -1.61094980 0.43602844
13 14 15 16 17 18
0.22832568 -0.70098850 2.45291039 0.87210533 -1.17746714 -2.16911010
19 20 21 22 23 24
-1.88041299 0.40981872 -0.68275419 0.29826569 -0.39716344 -0.79193847
25 26 27 28 29 30
-0.26393045 0.64930106 -1.88761298 2.68463021 0.13046745 -0.60151642
31 32 33 34 35 36
0.77869335 -1.51546225 -0.48853427 0.06353245 1.36804528 -1.44052638
37 38 39 40 41 42
0.50872353 1.38845173 -1.77339425 -1.75243897 -1.68669410 1.31161701
43 44 45 46 47 48
1.28771025 -0.54091241 -0.36553830 2.91965650 2.31318583 -0.27526364
49 50 51 52 53 54
-0.95769304 1.38703354 2.03235055 -0.56048503 2.40656877 1.27114382
55 56 57 58 59 60
-2.91912314 -4.68107113 0.29702427 0.17683600 -0.35964185 -1.79429512
61 62 63 64 65 66
-2.37996456 0.16352370 2.02601634 0.34437180 0.46288270 0.67621668
67 68 69 70 71 72
1.38874723 -1.57569605 2.59456685 0.39399623 -0.12768981 0.52524083
73 74 75 76 77 78
1.07109043 1.13125632 0.58443025 -2.93123759 1.46107963 -0.37177923
79 80 81 82 83 84
2.10198731 0.88660634 0.26760588 0.40792209 1.08546643 -0.01319548
85 86 87 88 89 90
-1.48517658 -1.58718678 0.45364084 0.56694684 0.95513548 -0.38995740
91 92 93 94 95 96
3.13708870 0.15708320 1.08650727 2.05988969 -1.60909618 1.48470623
97 98 99 100 101 102
-2.49902743 0.45091605 -0.58232541 2.42739092 -0.55527856 1.09759911
103 104 105 106 107 108
0.23342424 -0.81368616 2.24544417 -0.05519149 0.40686169 -0.47528482
109 110 111 112 113 114
-2.28729650 0.30784447 2.35351028 -1.51604601 1.09601177 0.76542732
115 116 117 118 119 120
0.41084559 2.44786862 -2.56402050 1.06651186 0.31850211 1.08616402
121 122 123 124 125 126
1.63519099 1.86672387 2.34065353 2.17210542 3.33838926 -0.92368605
127 128 129 130 131 132
-1.11598592 -1.99666058 1.73911835 -1.58692387 2.71551989 -3.60754536
133 134 135 136 137 138
1.77774150 1.47580757 1.12837246 -0.41956943 0.11868989 1.24810339
139 140 141 142 143 144
-1.22427504 0.10488264 -3.43655474 -2.24022859 2.69661574 -0.21993139
145 146 147 148 149 150
-0.09108109 0.59803922 2.14211032 0.47966095 -0.83526071 -1.39305268
151 152 153 154 155 156
1.94411351 -2.72328356 -5.75587293 -2.65429174 -0.10263671 3.13708870
157 158 159 160 161 162
-3.99370619 -1.99666058 -0.59066281 -1.66429130 -1.86306656 0.62438526
163 164 165 166 167 168
-0.94024877 2.24207003 -1.00343087 0.11828463 0.74833622 3.61193523
169 170 171 172 173 174
2.06554203 0.31442492 -3.80329397 0.66344466 0.86504731 -0.09045122
175 176 177 178 179 180
3.22507593 0.03839609 -0.99569887 -0.01588422 -0.05297410 -0.25595973
181 182 183 184 185 186
0.80108826 -2.33710352 1.97969328 1.32713252 5.07300589 0.51331663
187 188 189 190 191 192
-0.86733224 -1.99545281 -1.34392214 -3.54343144 1.63191431 0.48079671
193 194 195 196 197 198
-6.03159970 -0.28118691 0.43326301 1.17235043 -2.21472558 0.46927652
199 200 201 202 203 204
-1.18966195 0.52689421 0.78956716 -0.21475246 0.52859177 3.16833739
205 206 207 208 209 210
-2.76211556 0.18574350 -0.41409655 0.82295266 1.58061563 -0.78420509
211 212 213 214 215 216
2.97107243 2.63849093 -0.09209294 -2.82782373 0.66849046 0.15967621
217 218 219 220 221 222
-0.94643725 -0.14785799 -1.65572977 1.09590303 2.33690032 -0.15421954
223 224 225 226 227 228
1.27001741 -0.04323692 -0.79407607 -1.03283416 0.05570487 -1.26907366
229 230 231 232 233 234
0.52451633 -0.14276484 -3.47688084 1.89078760 -5.19732188 1.80836588
235 236 237 238 239 240
0.47304875 1.60218728 1.58340551 2.88147564 0.64850531 -0.81343915
241 242 243 244 245 246
-1.29755069 1.99816661 -4.77419403 -3.56012635 -0.55449950 -1.21554909
247 248 249 250 251 252
-1.20320765 1.42397529 -0.72527896 0.29908541 -1.55873619 -0.97634037
253 254 255 256 257 258
-3.03939352 -2.22012629 1.07881332 0.91422022 0.46494085 -2.10352063
259 260 261 262 263 264
1.48474372 -1.61008602 0.33007583 3.35467126 -1.48765030 -1.21632261
> postscript(file="/var/wessaorg/rcomp/tmp/6ru8k1383490493.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 1.95373453 NA
1 -0.65321729 1.95373453
2 1.46465336 -0.65321729
3 -5.26158782 1.46465336
4 2.19782671 -5.26158782
5 0.06908159 2.19782671
6 -0.99021228 0.06908159
7 2.61002967 -0.99021228
8 1.28788139 2.61002967
9 -2.35341159 1.28788139
10 -1.61094980 -2.35341159
11 0.43602844 -1.61094980
12 0.22832568 0.43602844
13 -0.70098850 0.22832568
14 2.45291039 -0.70098850
15 0.87210533 2.45291039
16 -1.17746714 0.87210533
17 -2.16911010 -1.17746714
18 -1.88041299 -2.16911010
19 0.40981872 -1.88041299
20 -0.68275419 0.40981872
21 0.29826569 -0.68275419
22 -0.39716344 0.29826569
23 -0.79193847 -0.39716344
24 -0.26393045 -0.79193847
25 0.64930106 -0.26393045
26 -1.88761298 0.64930106
27 2.68463021 -1.88761298
28 0.13046745 2.68463021
29 -0.60151642 0.13046745
30 0.77869335 -0.60151642
31 -1.51546225 0.77869335
32 -0.48853427 -1.51546225
33 0.06353245 -0.48853427
34 1.36804528 0.06353245
35 -1.44052638 1.36804528
36 0.50872353 -1.44052638
37 1.38845173 0.50872353
38 -1.77339425 1.38845173
39 -1.75243897 -1.77339425
40 -1.68669410 -1.75243897
41 1.31161701 -1.68669410
42 1.28771025 1.31161701
43 -0.54091241 1.28771025
44 -0.36553830 -0.54091241
45 2.91965650 -0.36553830
46 2.31318583 2.91965650
47 -0.27526364 2.31318583
48 -0.95769304 -0.27526364
49 1.38703354 -0.95769304
50 2.03235055 1.38703354
51 -0.56048503 2.03235055
52 2.40656877 -0.56048503
53 1.27114382 2.40656877
54 -2.91912314 1.27114382
55 -4.68107113 -2.91912314
56 0.29702427 -4.68107113
57 0.17683600 0.29702427
58 -0.35964185 0.17683600
59 -1.79429512 -0.35964185
60 -2.37996456 -1.79429512
61 0.16352370 -2.37996456
62 2.02601634 0.16352370
63 0.34437180 2.02601634
64 0.46288270 0.34437180
65 0.67621668 0.46288270
66 1.38874723 0.67621668
67 -1.57569605 1.38874723
68 2.59456685 -1.57569605
69 0.39399623 2.59456685
70 -0.12768981 0.39399623
71 0.52524083 -0.12768981
72 1.07109043 0.52524083
73 1.13125632 1.07109043
74 0.58443025 1.13125632
75 -2.93123759 0.58443025
76 1.46107963 -2.93123759
77 -0.37177923 1.46107963
78 2.10198731 -0.37177923
79 0.88660634 2.10198731
80 0.26760588 0.88660634
81 0.40792209 0.26760588
82 1.08546643 0.40792209
83 -0.01319548 1.08546643
84 -1.48517658 -0.01319548
85 -1.58718678 -1.48517658
86 0.45364084 -1.58718678
87 0.56694684 0.45364084
88 0.95513548 0.56694684
89 -0.38995740 0.95513548
90 3.13708870 -0.38995740
91 0.15708320 3.13708870
92 1.08650727 0.15708320
93 2.05988969 1.08650727
94 -1.60909618 2.05988969
95 1.48470623 -1.60909618
96 -2.49902743 1.48470623
97 0.45091605 -2.49902743
98 -0.58232541 0.45091605
99 2.42739092 -0.58232541
100 -0.55527856 2.42739092
101 1.09759911 -0.55527856
102 0.23342424 1.09759911
103 -0.81368616 0.23342424
104 2.24544417 -0.81368616
105 -0.05519149 2.24544417
106 0.40686169 -0.05519149
107 -0.47528482 0.40686169
108 -2.28729650 -0.47528482
109 0.30784447 -2.28729650
110 2.35351028 0.30784447
111 -1.51604601 2.35351028
112 1.09601177 -1.51604601
113 0.76542732 1.09601177
114 0.41084559 0.76542732
115 2.44786862 0.41084559
116 -2.56402050 2.44786862
117 1.06651186 -2.56402050
118 0.31850211 1.06651186
119 1.08616402 0.31850211
120 1.63519099 1.08616402
121 1.86672387 1.63519099
122 2.34065353 1.86672387
123 2.17210542 2.34065353
124 3.33838926 2.17210542
125 -0.92368605 3.33838926
126 -1.11598592 -0.92368605
127 -1.99666058 -1.11598592
128 1.73911835 -1.99666058
129 -1.58692387 1.73911835
130 2.71551989 -1.58692387
131 -3.60754536 2.71551989
132 1.77774150 -3.60754536
133 1.47580757 1.77774150
134 1.12837246 1.47580757
135 -0.41956943 1.12837246
136 0.11868989 -0.41956943
137 1.24810339 0.11868989
138 -1.22427504 1.24810339
139 0.10488264 -1.22427504
140 -3.43655474 0.10488264
141 -2.24022859 -3.43655474
142 2.69661574 -2.24022859
143 -0.21993139 2.69661574
144 -0.09108109 -0.21993139
145 0.59803922 -0.09108109
146 2.14211032 0.59803922
147 0.47966095 2.14211032
148 -0.83526071 0.47966095
149 -1.39305268 -0.83526071
150 1.94411351 -1.39305268
151 -2.72328356 1.94411351
152 -5.75587293 -2.72328356
153 -2.65429174 -5.75587293
154 -0.10263671 -2.65429174
155 3.13708870 -0.10263671
156 -3.99370619 3.13708870
157 -1.99666058 -3.99370619
158 -0.59066281 -1.99666058
159 -1.66429130 -0.59066281
160 -1.86306656 -1.66429130
161 0.62438526 -1.86306656
162 -0.94024877 0.62438526
163 2.24207003 -0.94024877
164 -1.00343087 2.24207003
165 0.11828463 -1.00343087
166 0.74833622 0.11828463
167 3.61193523 0.74833622
168 2.06554203 3.61193523
169 0.31442492 2.06554203
170 -3.80329397 0.31442492
171 0.66344466 -3.80329397
172 0.86504731 0.66344466
173 -0.09045122 0.86504731
174 3.22507593 -0.09045122
175 0.03839609 3.22507593
176 -0.99569887 0.03839609
177 -0.01588422 -0.99569887
178 -0.05297410 -0.01588422
179 -0.25595973 -0.05297410
180 0.80108826 -0.25595973
181 -2.33710352 0.80108826
182 1.97969328 -2.33710352
183 1.32713252 1.97969328
184 5.07300589 1.32713252
185 0.51331663 5.07300589
186 -0.86733224 0.51331663
187 -1.99545281 -0.86733224
188 -1.34392214 -1.99545281
189 -3.54343144 -1.34392214
190 1.63191431 -3.54343144
191 0.48079671 1.63191431
192 -6.03159970 0.48079671
193 -0.28118691 -6.03159970
194 0.43326301 -0.28118691
195 1.17235043 0.43326301
196 -2.21472558 1.17235043
197 0.46927652 -2.21472558
198 -1.18966195 0.46927652
199 0.52689421 -1.18966195
200 0.78956716 0.52689421
201 -0.21475246 0.78956716
202 0.52859177 -0.21475246
203 3.16833739 0.52859177
204 -2.76211556 3.16833739
205 0.18574350 -2.76211556
206 -0.41409655 0.18574350
207 0.82295266 -0.41409655
208 1.58061563 0.82295266
209 -0.78420509 1.58061563
210 2.97107243 -0.78420509
211 2.63849093 2.97107243
212 -0.09209294 2.63849093
213 -2.82782373 -0.09209294
214 0.66849046 -2.82782373
215 0.15967621 0.66849046
216 -0.94643725 0.15967621
217 -0.14785799 -0.94643725
218 -1.65572977 -0.14785799
219 1.09590303 -1.65572977
220 2.33690032 1.09590303
221 -0.15421954 2.33690032
222 1.27001741 -0.15421954
223 -0.04323692 1.27001741
224 -0.79407607 -0.04323692
225 -1.03283416 -0.79407607
226 0.05570487 -1.03283416
227 -1.26907366 0.05570487
228 0.52451633 -1.26907366
229 -0.14276484 0.52451633
230 -3.47688084 -0.14276484
231 1.89078760 -3.47688084
232 -5.19732188 1.89078760
233 1.80836588 -5.19732188
234 0.47304875 1.80836588
235 1.60218728 0.47304875
236 1.58340551 1.60218728
237 2.88147564 1.58340551
238 0.64850531 2.88147564
239 -0.81343915 0.64850531
240 -1.29755069 -0.81343915
241 1.99816661 -1.29755069
242 -4.77419403 1.99816661
243 -3.56012635 -4.77419403
244 -0.55449950 -3.56012635
245 -1.21554909 -0.55449950
246 -1.20320765 -1.21554909
247 1.42397529 -1.20320765
248 -0.72527896 1.42397529
249 0.29908541 -0.72527896
250 -1.55873619 0.29908541
251 -0.97634037 -1.55873619
252 -3.03939352 -0.97634037
253 -2.22012629 -3.03939352
254 1.07881332 -2.22012629
255 0.91422022 1.07881332
256 0.46494085 0.91422022
257 -2.10352063 0.46494085
258 1.48474372 -2.10352063
259 -1.61008602 1.48474372
260 0.33007583 -1.61008602
261 3.35467126 0.33007583
262 -1.48765030 3.35467126
263 -1.21632261 -1.48765030
264 NA -1.21632261
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.65321729 1.95373453
[2,] 1.46465336 -0.65321729
[3,] -5.26158782 1.46465336
[4,] 2.19782671 -5.26158782
[5,] 0.06908159 2.19782671
[6,] -0.99021228 0.06908159
[7,] 2.61002967 -0.99021228
[8,] 1.28788139 2.61002967
[9,] -2.35341159 1.28788139
[10,] -1.61094980 -2.35341159
[11,] 0.43602844 -1.61094980
[12,] 0.22832568 0.43602844
[13,] -0.70098850 0.22832568
[14,] 2.45291039 -0.70098850
[15,] 0.87210533 2.45291039
[16,] -1.17746714 0.87210533
[17,] -2.16911010 -1.17746714
[18,] -1.88041299 -2.16911010
[19,] 0.40981872 -1.88041299
[20,] -0.68275419 0.40981872
[21,] 0.29826569 -0.68275419
[22,] -0.39716344 0.29826569
[23,] -0.79193847 -0.39716344
[24,] -0.26393045 -0.79193847
[25,] 0.64930106 -0.26393045
[26,] -1.88761298 0.64930106
[27,] 2.68463021 -1.88761298
[28,] 0.13046745 2.68463021
[29,] -0.60151642 0.13046745
[30,] 0.77869335 -0.60151642
[31,] -1.51546225 0.77869335
[32,] -0.48853427 -1.51546225
[33,] 0.06353245 -0.48853427
[34,] 1.36804528 0.06353245
[35,] -1.44052638 1.36804528
[36,] 0.50872353 -1.44052638
[37,] 1.38845173 0.50872353
[38,] -1.77339425 1.38845173
[39,] -1.75243897 -1.77339425
[40,] -1.68669410 -1.75243897
[41,] 1.31161701 -1.68669410
[42,] 1.28771025 1.31161701
[43,] -0.54091241 1.28771025
[44,] -0.36553830 -0.54091241
[45,] 2.91965650 -0.36553830
[46,] 2.31318583 2.91965650
[47,] -0.27526364 2.31318583
[48,] -0.95769304 -0.27526364
[49,] 1.38703354 -0.95769304
[50,] 2.03235055 1.38703354
[51,] -0.56048503 2.03235055
[52,] 2.40656877 -0.56048503
[53,] 1.27114382 2.40656877
[54,] -2.91912314 1.27114382
[55,] -4.68107113 -2.91912314
[56,] 0.29702427 -4.68107113
[57,] 0.17683600 0.29702427
[58,] -0.35964185 0.17683600
[59,] -1.79429512 -0.35964185
[60,] -2.37996456 -1.79429512
[61,] 0.16352370 -2.37996456
[62,] 2.02601634 0.16352370
[63,] 0.34437180 2.02601634
[64,] 0.46288270 0.34437180
[65,] 0.67621668 0.46288270
[66,] 1.38874723 0.67621668
[67,] -1.57569605 1.38874723
[68,] 2.59456685 -1.57569605
[69,] 0.39399623 2.59456685
[70,] -0.12768981 0.39399623
[71,] 0.52524083 -0.12768981
[72,] 1.07109043 0.52524083
[73,] 1.13125632 1.07109043
[74,] 0.58443025 1.13125632
[75,] -2.93123759 0.58443025
[76,] 1.46107963 -2.93123759
[77,] -0.37177923 1.46107963
[78,] 2.10198731 -0.37177923
[79,] 0.88660634 2.10198731
[80,] 0.26760588 0.88660634
[81,] 0.40792209 0.26760588
[82,] 1.08546643 0.40792209
[83,] -0.01319548 1.08546643
[84,] -1.48517658 -0.01319548
[85,] -1.58718678 -1.48517658
[86,] 0.45364084 -1.58718678
[87,] 0.56694684 0.45364084
[88,] 0.95513548 0.56694684
[89,] -0.38995740 0.95513548
[90,] 3.13708870 -0.38995740
[91,] 0.15708320 3.13708870
[92,] 1.08650727 0.15708320
[93,] 2.05988969 1.08650727
[94,] -1.60909618 2.05988969
[95,] 1.48470623 -1.60909618
[96,] -2.49902743 1.48470623
[97,] 0.45091605 -2.49902743
[98,] -0.58232541 0.45091605
[99,] 2.42739092 -0.58232541
[100,] -0.55527856 2.42739092
[101,] 1.09759911 -0.55527856
[102,] 0.23342424 1.09759911
[103,] -0.81368616 0.23342424
[104,] 2.24544417 -0.81368616
[105,] -0.05519149 2.24544417
[106,] 0.40686169 -0.05519149
[107,] -0.47528482 0.40686169
[108,] -2.28729650 -0.47528482
[109,] 0.30784447 -2.28729650
[110,] 2.35351028 0.30784447
[111,] -1.51604601 2.35351028
[112,] 1.09601177 -1.51604601
[113,] 0.76542732 1.09601177
[114,] 0.41084559 0.76542732
[115,] 2.44786862 0.41084559
[116,] -2.56402050 2.44786862
[117,] 1.06651186 -2.56402050
[118,] 0.31850211 1.06651186
[119,] 1.08616402 0.31850211
[120,] 1.63519099 1.08616402
[121,] 1.86672387 1.63519099
[122,] 2.34065353 1.86672387
[123,] 2.17210542 2.34065353
[124,] 3.33838926 2.17210542
[125,] -0.92368605 3.33838926
[126,] -1.11598592 -0.92368605
[127,] -1.99666058 -1.11598592
[128,] 1.73911835 -1.99666058
[129,] -1.58692387 1.73911835
[130,] 2.71551989 -1.58692387
[131,] -3.60754536 2.71551989
[132,] 1.77774150 -3.60754536
[133,] 1.47580757 1.77774150
[134,] 1.12837246 1.47580757
[135,] -0.41956943 1.12837246
[136,] 0.11868989 -0.41956943
[137,] 1.24810339 0.11868989
[138,] -1.22427504 1.24810339
[139,] 0.10488264 -1.22427504
[140,] -3.43655474 0.10488264
[141,] -2.24022859 -3.43655474
[142,] 2.69661574 -2.24022859
[143,] -0.21993139 2.69661574
[144,] -0.09108109 -0.21993139
[145,] 0.59803922 -0.09108109
[146,] 2.14211032 0.59803922
[147,] 0.47966095 2.14211032
[148,] -0.83526071 0.47966095
[149,] -1.39305268 -0.83526071
[150,] 1.94411351 -1.39305268
[151,] -2.72328356 1.94411351
[152,] -5.75587293 -2.72328356
[153,] -2.65429174 -5.75587293
[154,] -0.10263671 -2.65429174
[155,] 3.13708870 -0.10263671
[156,] -3.99370619 3.13708870
[157,] -1.99666058 -3.99370619
[158,] -0.59066281 -1.99666058
[159,] -1.66429130 -0.59066281
[160,] -1.86306656 -1.66429130
[161,] 0.62438526 -1.86306656
[162,] -0.94024877 0.62438526
[163,] 2.24207003 -0.94024877
[164,] -1.00343087 2.24207003
[165,] 0.11828463 -1.00343087
[166,] 0.74833622 0.11828463
[167,] 3.61193523 0.74833622
[168,] 2.06554203 3.61193523
[169,] 0.31442492 2.06554203
[170,] -3.80329397 0.31442492
[171,] 0.66344466 -3.80329397
[172,] 0.86504731 0.66344466
[173,] -0.09045122 0.86504731
[174,] 3.22507593 -0.09045122
[175,] 0.03839609 3.22507593
[176,] -0.99569887 0.03839609
[177,] -0.01588422 -0.99569887
[178,] -0.05297410 -0.01588422
[179,] -0.25595973 -0.05297410
[180,] 0.80108826 -0.25595973
[181,] -2.33710352 0.80108826
[182,] 1.97969328 -2.33710352
[183,] 1.32713252 1.97969328
[184,] 5.07300589 1.32713252
[185,] 0.51331663 5.07300589
[186,] -0.86733224 0.51331663
[187,] -1.99545281 -0.86733224
[188,] -1.34392214 -1.99545281
[189,] -3.54343144 -1.34392214
[190,] 1.63191431 -3.54343144
[191,] 0.48079671 1.63191431
[192,] -6.03159970 0.48079671
[193,] -0.28118691 -6.03159970
[194,] 0.43326301 -0.28118691
[195,] 1.17235043 0.43326301
[196,] -2.21472558 1.17235043
[197,] 0.46927652 -2.21472558
[198,] -1.18966195 0.46927652
[199,] 0.52689421 -1.18966195
[200,] 0.78956716 0.52689421
[201,] -0.21475246 0.78956716
[202,] 0.52859177 -0.21475246
[203,] 3.16833739 0.52859177
[204,] -2.76211556 3.16833739
[205,] 0.18574350 -2.76211556
[206,] -0.41409655 0.18574350
[207,] 0.82295266 -0.41409655
[208,] 1.58061563 0.82295266
[209,] -0.78420509 1.58061563
[210,] 2.97107243 -0.78420509
[211,] 2.63849093 2.97107243
[212,] -0.09209294 2.63849093
[213,] -2.82782373 -0.09209294
[214,] 0.66849046 -2.82782373
[215,] 0.15967621 0.66849046
[216,] -0.94643725 0.15967621
[217,] -0.14785799 -0.94643725
[218,] -1.65572977 -0.14785799
[219,] 1.09590303 -1.65572977
[220,] 2.33690032 1.09590303
[221,] -0.15421954 2.33690032
[222,] 1.27001741 -0.15421954
[223,] -0.04323692 1.27001741
[224,] -0.79407607 -0.04323692
[225,] -1.03283416 -0.79407607
[226,] 0.05570487 -1.03283416
[227,] -1.26907366 0.05570487
[228,] 0.52451633 -1.26907366
[229,] -0.14276484 0.52451633
[230,] -3.47688084 -0.14276484
[231,] 1.89078760 -3.47688084
[232,] -5.19732188 1.89078760
[233,] 1.80836588 -5.19732188
[234,] 0.47304875 1.80836588
[235,] 1.60218728 0.47304875
[236,] 1.58340551 1.60218728
[237,] 2.88147564 1.58340551
[238,] 0.64850531 2.88147564
[239,] -0.81343915 0.64850531
[240,] -1.29755069 -0.81343915
[241,] 1.99816661 -1.29755069
[242,] -4.77419403 1.99816661
[243,] -3.56012635 -4.77419403
[244,] -0.55449950 -3.56012635
[245,] -1.21554909 -0.55449950
[246,] -1.20320765 -1.21554909
[247,] 1.42397529 -1.20320765
[248,] -0.72527896 1.42397529
[249,] 0.29908541 -0.72527896
[250,] -1.55873619 0.29908541
[251,] -0.97634037 -1.55873619
[252,] -3.03939352 -0.97634037
[253,] -2.22012629 -3.03939352
[254,] 1.07881332 -2.22012629
[255,] 0.91422022 1.07881332
[256,] 0.46494085 0.91422022
[257,] -2.10352063 0.46494085
[258,] 1.48474372 -2.10352063
[259,] -1.61008602 1.48474372
[260,] 0.33007583 -1.61008602
[261,] 3.35467126 0.33007583
[262,] -1.48765030 3.35467126
[263,] -1.21632261 -1.48765030
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.65321729 1.95373453
2 1.46465336 -0.65321729
3 -5.26158782 1.46465336
4 2.19782671 -5.26158782
5 0.06908159 2.19782671
6 -0.99021228 0.06908159
7 2.61002967 -0.99021228
8 1.28788139 2.61002967
9 -2.35341159 1.28788139
10 -1.61094980 -2.35341159
11 0.43602844 -1.61094980
12 0.22832568 0.43602844
13 -0.70098850 0.22832568
14 2.45291039 -0.70098850
15 0.87210533 2.45291039
16 -1.17746714 0.87210533
17 -2.16911010 -1.17746714
18 -1.88041299 -2.16911010
19 0.40981872 -1.88041299
20 -0.68275419 0.40981872
21 0.29826569 -0.68275419
22 -0.39716344 0.29826569
23 -0.79193847 -0.39716344
24 -0.26393045 -0.79193847
25 0.64930106 -0.26393045
26 -1.88761298 0.64930106
27 2.68463021 -1.88761298
28 0.13046745 2.68463021
29 -0.60151642 0.13046745
30 0.77869335 -0.60151642
31 -1.51546225 0.77869335
32 -0.48853427 -1.51546225
33 0.06353245 -0.48853427
34 1.36804528 0.06353245
35 -1.44052638 1.36804528
36 0.50872353 -1.44052638
37 1.38845173 0.50872353
38 -1.77339425 1.38845173
39 -1.75243897 -1.77339425
40 -1.68669410 -1.75243897
41 1.31161701 -1.68669410
42 1.28771025 1.31161701
43 -0.54091241 1.28771025
44 -0.36553830 -0.54091241
45 2.91965650 -0.36553830
46 2.31318583 2.91965650
47 -0.27526364 2.31318583
48 -0.95769304 -0.27526364
49 1.38703354 -0.95769304
50 2.03235055 1.38703354
51 -0.56048503 2.03235055
52 2.40656877 -0.56048503
53 1.27114382 2.40656877
54 -2.91912314 1.27114382
55 -4.68107113 -2.91912314
56 0.29702427 -4.68107113
57 0.17683600 0.29702427
58 -0.35964185 0.17683600
59 -1.79429512 -0.35964185
60 -2.37996456 -1.79429512
61 0.16352370 -2.37996456
62 2.02601634 0.16352370
63 0.34437180 2.02601634
64 0.46288270 0.34437180
65 0.67621668 0.46288270
66 1.38874723 0.67621668
67 -1.57569605 1.38874723
68 2.59456685 -1.57569605
69 0.39399623 2.59456685
70 -0.12768981 0.39399623
71 0.52524083 -0.12768981
72 1.07109043 0.52524083
73 1.13125632 1.07109043
74 0.58443025 1.13125632
75 -2.93123759 0.58443025
76 1.46107963 -2.93123759
77 -0.37177923 1.46107963
78 2.10198731 -0.37177923
79 0.88660634 2.10198731
80 0.26760588 0.88660634
81 0.40792209 0.26760588
82 1.08546643 0.40792209
83 -0.01319548 1.08546643
84 -1.48517658 -0.01319548
85 -1.58718678 -1.48517658
86 0.45364084 -1.58718678
87 0.56694684 0.45364084
88 0.95513548 0.56694684
89 -0.38995740 0.95513548
90 3.13708870 -0.38995740
91 0.15708320 3.13708870
92 1.08650727 0.15708320
93 2.05988969 1.08650727
94 -1.60909618 2.05988969
95 1.48470623 -1.60909618
96 -2.49902743 1.48470623
97 0.45091605 -2.49902743
98 -0.58232541 0.45091605
99 2.42739092 -0.58232541
100 -0.55527856 2.42739092
101 1.09759911 -0.55527856
102 0.23342424 1.09759911
103 -0.81368616 0.23342424
104 2.24544417 -0.81368616
105 -0.05519149 2.24544417
106 0.40686169 -0.05519149
107 -0.47528482 0.40686169
108 -2.28729650 -0.47528482
109 0.30784447 -2.28729650
110 2.35351028 0.30784447
111 -1.51604601 2.35351028
112 1.09601177 -1.51604601
113 0.76542732 1.09601177
114 0.41084559 0.76542732
115 2.44786862 0.41084559
116 -2.56402050 2.44786862
117 1.06651186 -2.56402050
118 0.31850211 1.06651186
119 1.08616402 0.31850211
120 1.63519099 1.08616402
121 1.86672387 1.63519099
122 2.34065353 1.86672387
123 2.17210542 2.34065353
124 3.33838926 2.17210542
125 -0.92368605 3.33838926
126 -1.11598592 -0.92368605
127 -1.99666058 -1.11598592
128 1.73911835 -1.99666058
129 -1.58692387 1.73911835
130 2.71551989 -1.58692387
131 -3.60754536 2.71551989
132 1.77774150 -3.60754536
133 1.47580757 1.77774150
134 1.12837246 1.47580757
135 -0.41956943 1.12837246
136 0.11868989 -0.41956943
137 1.24810339 0.11868989
138 -1.22427504 1.24810339
139 0.10488264 -1.22427504
140 -3.43655474 0.10488264
141 -2.24022859 -3.43655474
142 2.69661574 -2.24022859
143 -0.21993139 2.69661574
144 -0.09108109 -0.21993139
145 0.59803922 -0.09108109
146 2.14211032 0.59803922
147 0.47966095 2.14211032
148 -0.83526071 0.47966095
149 -1.39305268 -0.83526071
150 1.94411351 -1.39305268
151 -2.72328356 1.94411351
152 -5.75587293 -2.72328356
153 -2.65429174 -5.75587293
154 -0.10263671 -2.65429174
155 3.13708870 -0.10263671
156 -3.99370619 3.13708870
157 -1.99666058 -3.99370619
158 -0.59066281 -1.99666058
159 -1.66429130 -0.59066281
160 -1.86306656 -1.66429130
161 0.62438526 -1.86306656
162 -0.94024877 0.62438526
163 2.24207003 -0.94024877
164 -1.00343087 2.24207003
165 0.11828463 -1.00343087
166 0.74833622 0.11828463
167 3.61193523 0.74833622
168 2.06554203 3.61193523
169 0.31442492 2.06554203
170 -3.80329397 0.31442492
171 0.66344466 -3.80329397
172 0.86504731 0.66344466
173 -0.09045122 0.86504731
174 3.22507593 -0.09045122
175 0.03839609 3.22507593
176 -0.99569887 0.03839609
177 -0.01588422 -0.99569887
178 -0.05297410 -0.01588422
179 -0.25595973 -0.05297410
180 0.80108826 -0.25595973
181 -2.33710352 0.80108826
182 1.97969328 -2.33710352
183 1.32713252 1.97969328
184 5.07300589 1.32713252
185 0.51331663 5.07300589
186 -0.86733224 0.51331663
187 -1.99545281 -0.86733224
188 -1.34392214 -1.99545281
189 -3.54343144 -1.34392214
190 1.63191431 -3.54343144
191 0.48079671 1.63191431
192 -6.03159970 0.48079671
193 -0.28118691 -6.03159970
194 0.43326301 -0.28118691
195 1.17235043 0.43326301
196 -2.21472558 1.17235043
197 0.46927652 -2.21472558
198 -1.18966195 0.46927652
199 0.52689421 -1.18966195
200 0.78956716 0.52689421
201 -0.21475246 0.78956716
202 0.52859177 -0.21475246
203 3.16833739 0.52859177
204 -2.76211556 3.16833739
205 0.18574350 -2.76211556
206 -0.41409655 0.18574350
207 0.82295266 -0.41409655
208 1.58061563 0.82295266
209 -0.78420509 1.58061563
210 2.97107243 -0.78420509
211 2.63849093 2.97107243
212 -0.09209294 2.63849093
213 -2.82782373 -0.09209294
214 0.66849046 -2.82782373
215 0.15967621 0.66849046
216 -0.94643725 0.15967621
217 -0.14785799 -0.94643725
218 -1.65572977 -0.14785799
219 1.09590303 -1.65572977
220 2.33690032 1.09590303
221 -0.15421954 2.33690032
222 1.27001741 -0.15421954
223 -0.04323692 1.27001741
224 -0.79407607 -0.04323692
225 -1.03283416 -0.79407607
226 0.05570487 -1.03283416
227 -1.26907366 0.05570487
228 0.52451633 -1.26907366
229 -0.14276484 0.52451633
230 -3.47688084 -0.14276484
231 1.89078760 -3.47688084
232 -5.19732188 1.89078760
233 1.80836588 -5.19732188
234 0.47304875 1.80836588
235 1.60218728 0.47304875
236 1.58340551 1.60218728
237 2.88147564 1.58340551
238 0.64850531 2.88147564
239 -0.81343915 0.64850531
240 -1.29755069 -0.81343915
241 1.99816661 -1.29755069
242 -4.77419403 1.99816661
243 -3.56012635 -4.77419403
244 -0.55449950 -3.56012635
245 -1.21554909 -0.55449950
246 -1.20320765 -1.21554909
247 1.42397529 -1.20320765
248 -0.72527896 1.42397529
249 0.29908541 -0.72527896
250 -1.55873619 0.29908541
251 -0.97634037 -1.55873619
252 -3.03939352 -0.97634037
253 -2.22012629 -3.03939352
254 1.07881332 -2.22012629
255 0.91422022 1.07881332
256 0.46494085 0.91422022
257 -2.10352063 0.46494085
258 1.48474372 -2.10352063
259 -1.61008602 1.48474372
260 0.33007583 -1.61008602
261 3.35467126 0.33007583
262 -1.48765030 3.35467126
263 -1.21632261 -1.48765030
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7ogcf1383490493.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/87flf1383490493.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9kf2g1383490493.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10x22o1383490493.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/115a6c1383490493.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12l8vt1383490493.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13mkip1383490493.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14ioib1383490493.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/150ayh1383490493.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16lx421383490493.tab")
+ }
>
> try(system("convert tmp/1he6e1383490493.ps tmp/1he6e1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wg7d1383490493.ps tmp/2wg7d1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j46a1383490493.ps tmp/3j46a1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/4kjqk1383490493.ps tmp/4kjqk1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/5kjon1383490493.ps tmp/5kjon1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ru8k1383490493.ps tmp/6ru8k1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ogcf1383490493.ps tmp/7ogcf1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/87flf1383490493.ps tmp/87flf1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/9kf2g1383490493.ps tmp/9kf2g1383490493.png",intern=TRUE))
character(0)
> try(system("convert tmp/10x22o1383490493.ps tmp/10x22o1383490493.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
20.085 3.354 23.405