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(12
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+ ,14
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+ ,13
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+ ,3
+ ,35
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+ ,4
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+ ,15
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+ ,12
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+ ,11
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+ ,12
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+ ,7
+ ,22
+ ,62
+ ,39
+ ,9
+ ,29
+ ,32
+ ,14
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Software'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Software','Connected','Separate','Learning','Happiness','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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'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
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Happiness Depression
1.325248 -0.010647 0.037520 0.574624 -0.005892 -0.010337
Sport1 Sport2
0.013370 -0.014235
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.1994 -1.1455 0.2219 1.1663 5.0624
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.325248 1.877460 0.706 0.481
Connected -0.010647 0.033949 -0.314 0.754
Separate 0.037520 0.034688 1.082 0.280
Learning 0.574624 0.049047 11.716 <2e-16 ***
Happiness -0.005892 0.056694 -0.104 0.917
Depression -0.010337 0.041457 -0.249 0.803
Sport1 0.013370 0.036811 0.363 0.717
Sport2 -0.014235 0.054905 -0.259 0.796
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.833 on 256 degrees of freedom
Multiple R-squared: 0.3925, Adjusted R-squared: 0.3758
F-statistic: 23.62 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,] 0.991740052 0.016519896 0.008259948
[2,] 0.988295110 0.023409781 0.011704890
[3,] 0.975972898 0.048054205 0.024027102
[4,] 0.975240817 0.049518366 0.024759183
[5,] 0.962499577 0.075000845 0.037500423
[6,] 0.949395926 0.101208148 0.050604074
[7,] 0.921620976 0.156758047 0.078379024
[8,] 0.918237620 0.163524761 0.081762380
[9,] 0.901424814 0.197150373 0.098575186
[10,] 0.861371065 0.277257871 0.138628935
[11,] 0.827180185 0.345639630 0.172819815
[12,] 0.779401485 0.441197030 0.220598515
[13,] 0.733998733 0.532002534 0.266001267
[14,] 0.694852496 0.610295008 0.305147504
[15,] 0.642995263 0.714009475 0.357004737
[16,] 0.641898228 0.716203543 0.358101772
[17,] 0.659873479 0.680253043 0.340126521
[18,] 0.670256866 0.659486267 0.329743134
[19,] 0.610175790 0.779648420 0.389824210
[20,] 0.562002339 0.875995322 0.437997661
[21,] 0.511644553 0.976710895 0.488355447
[22,] 0.527814153 0.944371694 0.472185847
[23,] 0.467225852 0.934451705 0.532774148
[24,] 0.412190174 0.824380348 0.587809826
[25,] 0.394828368 0.789656735 0.605171632
[26,] 0.381648786 0.763297572 0.618351214
[27,] 0.331445547 0.662891094 0.668554453
[28,] 0.315466896 0.630933792 0.684533104
[29,] 0.296056078 0.592112156 0.703943922
[30,] 0.270908544 0.541817089 0.729091456
[31,] 0.241490099 0.482980198 0.758509901
[32,] 0.214600706 0.429201412 0.785399294
[33,] 0.253167404 0.506334808 0.746832596
[34,] 0.215192428 0.430384857 0.784807572
[35,] 0.179609267 0.359218533 0.820390733
[36,] 0.217321757 0.434643515 0.782678243
[37,] 0.236309195 0.472618389 0.763690805
[38,] 0.199587346 0.399174693 0.800412654
[39,] 0.188700587 0.377401175 0.811299413
[40,] 0.174402906 0.348805812 0.825597094
[41,] 0.181626713 0.363253426 0.818373287
[42,] 0.152043162 0.304086324 0.847956838
[43,] 0.158246670 0.316493341 0.841753330
[44,] 0.139249524 0.278499047 0.860750476
[45,] 0.210538418 0.421076835 0.789461582
[46,] 0.475260882 0.950521763 0.524739118
[47,] 0.432603812 0.865207623 0.567396188
[48,] 0.390375563 0.780751127 0.609624437
[49,] 0.350019728 0.700039457 0.649980272
[50,] 0.346128015 0.692256029 0.653871985
[51,] 0.349946435 0.699892870 0.650053565
[52,] 0.311506111 0.623012221 0.688493889
[53,] 0.321419593 0.642839186 0.678580407
[54,] 0.285365082 0.570730163 0.714634918
[55,] 0.260246508 0.520493015 0.739753492
[56,] 0.227791263 0.455582526 0.772208737
[57,] 0.209294114 0.418588228 0.790705886
[58,] 0.189153120 0.378306239 0.810846880
[59,] 0.188430961 0.376861922 0.811569039
[60,] 0.162031013 0.324062025 0.837968987
[61,] 0.142906395 0.285812791 0.857093605
[62,] 0.122029080 0.244058160 0.877970920
[63,] 0.104174872 0.208349743 0.895825128
[64,] 0.088586882 0.177173764 0.911413118
[65,] 0.079536858 0.159073716 0.920463142
[66,] 0.100638059 0.201276118 0.899361941
[67,] 0.090053232 0.180106465 0.909946768
[68,] 0.074689418 0.149378836 0.925310582
[69,] 0.079930737 0.159861474 0.920069263
[70,] 0.076712207 0.153424415 0.923287793
[71,] 0.063674065 0.127348130 0.936325935
[72,] 0.052633599 0.105267198 0.947366401
[73,] 0.045783234 0.091566468 0.954216766
[74,] 0.037225461 0.074450923 0.962774539
[75,] 0.034106656 0.068213312 0.965893344
[76,] 0.038800215 0.077600430 0.961199785
[77,] 0.031425994 0.062851988 0.968574006
[78,] 0.025612976 0.051225951 0.974387024
[79,] 0.021731185 0.043462370 0.978268815
[80,] 0.018192118 0.036384237 0.981807882
[81,] 0.030001384 0.060002768 0.969998616
[82,] 0.024872577 0.049745154 0.975127423
[83,] 0.022694737 0.045389473 0.977305263
[84,] 0.024407901 0.048815803 0.975592099
[85,] 0.024415043 0.048830086 0.975584957
[86,] 0.022155976 0.044311953 0.977844024
[87,] 0.027257487 0.054514973 0.972742513
[88,] 0.022091549 0.044183099 0.977908451
[89,] 0.018695172 0.037390345 0.981304828
[90,] 0.021866828 0.043733655 0.978133172
[91,] 0.018052955 0.036105910 0.981947045
[92,] 0.015322927 0.030645855 0.984677073
[93,] 0.012163898 0.024327796 0.987836102
[94,] 0.010754526 0.021509051 0.989245474
[95,] 0.012059461 0.024118922 0.987940539
[96,] 0.009432811 0.018865622 0.990567189
[97,] 0.007411171 0.014822342 0.992588829
[98,] 0.006076599 0.012153199 0.993923401
[99,] 0.008539394 0.017078787 0.991460606
[100,] 0.006654245 0.013308490 0.993345755
[101,] 0.007673857 0.015347714 0.992326143
[102,] 0.008027644 0.016055288 0.991972356
[103,] 0.006654806 0.013309612 0.993345194
[104,] 0.005377913 0.010755825 0.994622087
[105,] 0.004186606 0.008373213 0.995813394
[106,] 0.004925603 0.009851207 0.995074397
[107,] 0.007096662 0.014193324 0.992903338
[108,] 0.005933367 0.011866734 0.994066633
[109,] 0.004602445 0.009204891 0.995397555
[110,] 0.003862100 0.007724200 0.996137900
[111,] 0.003714533 0.007429066 0.996285467
[112,] 0.003740026 0.007480053 0.996259974
[113,] 0.004433006 0.008866011 0.995566994
[114,] 0.005072841 0.010145682 0.994927159
[115,] 0.009115853 0.018231706 0.990884147
[116,] 0.007583795 0.015167590 0.992416205
[117,] 0.006505672 0.013011345 0.993494328
[118,] 0.006762549 0.013525099 0.993237451
[119,] 0.006667205 0.013334410 0.993332795
[120,] 0.006666249 0.013332499 0.993333751
[121,] 0.008469752 0.016939503 0.991530248
[122,] 0.017188815 0.034377630 0.982811185
[123,] 0.016775534 0.033551069 0.983224466
[124,] 0.015706893 0.031413786 0.984293107
[125,] 0.013653384 0.027306767 0.986346616
[126,] 0.011110068 0.022220136 0.988889932
[127,] 0.008812592 0.017625185 0.991187408
[128,] 0.007903894 0.015807789 0.992096106
[129,] 0.007044316 0.014088633 0.992955684
[130,] 0.005720236 0.011440472 0.994279764
[131,] 0.010877223 0.021754446 0.989122777
[132,] 0.012736091 0.025472181 0.987263909
[133,] 0.016980089 0.033960178 0.983019911
[134,] 0.013588167 0.027176335 0.986411833
[135,] 0.010761912 0.021523825 0.989238088
[136,] 0.008721014 0.017442029 0.991278986
[137,] 0.009740067 0.019480133 0.990259933
[138,] 0.007848224 0.015696448 0.992151776
[139,] 0.006593308 0.013186615 0.993406692
[140,] 0.005954085 0.011908171 0.994045915
[141,] 0.006176191 0.012352383 0.993823809
[142,] 0.008288420 0.016576839 0.991711580
[143,] 0.053269162 0.106538323 0.946730838
[144,] 0.060522279 0.121044558 0.939477721
[145,] 0.050641887 0.101283775 0.949358113
[146,] 0.072891701 0.145783402 0.927108299
[147,] 0.128092228 0.256184456 0.871907772
[148,] 0.129447319 0.258894638 0.870552681
[149,] 0.112546696 0.225093392 0.887453304
[150,] 0.108798589 0.217597178 0.891201411
[151,] 0.107918123 0.215836247 0.892081877
[152,] 0.093690915 0.187381830 0.906309085
[153,] 0.084362567 0.168725133 0.915637433
[154,] 0.089561768 0.179123537 0.910438232
[155,] 0.081124880 0.162249761 0.918875120
[156,] 0.069024685 0.138049369 0.930975315
[157,] 0.058712372 0.117424745 0.941287628
[158,] 0.097610940 0.195221881 0.902389060
[159,] 0.099120313 0.198240626 0.900879687
[160,] 0.086225929 0.172451859 0.913774071
[161,] 0.149743824 0.299487647 0.850256176
[162,] 0.129993852 0.259987703 0.870006148
[163,] 0.114476635 0.228953269 0.885523365
[164,] 0.098445382 0.196890765 0.901554618
[165,] 0.135883157 0.271766315 0.864116843
[166,] 0.117787768 0.235575537 0.882212232
[167,] 0.105876909 0.211753818 0.894123091
[168,] 0.090805456 0.181610912 0.909194544
[169,] 0.076708464 0.153416928 0.923291536
[170,] 0.064362002 0.128724003 0.935637998
[171,] 0.053788053 0.107576105 0.946211947
[172,] 0.061511137 0.123022275 0.938488863
[173,] 0.061561405 0.123122811 0.938438595
[174,] 0.057055925 0.114111850 0.942944075
[175,] 0.227133238 0.454266476 0.772866762
[176,] 0.209259891 0.418519781 0.790740109
[177,] 0.187460839 0.374921678 0.812539161
[178,] 0.187041454 0.374082908 0.812958546
[179,] 0.173830390 0.347660781 0.826169610
[180,] 0.257356290 0.514712580 0.742643710
[181,] 0.262498465 0.524996929 0.737501535
[182,] 0.232167987 0.464335974 0.767832013
[183,] 0.607905903 0.784188193 0.392094097
[184,] 0.577320338 0.845359323 0.422679662
[185,] 0.540629461 0.918741078 0.459370539
[186,] 0.507889690 0.984220621 0.492110310
[187,] 0.522529010 0.954941980 0.477470990
[188,] 0.486520175 0.973040350 0.513479825
[189,] 0.460349817 0.920699635 0.539650183
[190,] 0.462256444 0.924512888 0.537743556
[191,] 0.436178789 0.872357578 0.563821211
[192,] 0.415646341 0.831292682 0.584353659
[193,] 0.393879417 0.787758835 0.606120583
[194,] 0.444968977 0.889937954 0.555031023
[195,] 0.479081170 0.958162339 0.520918830
[196,] 0.436301053 0.872602106 0.563698947
[197,] 0.396196778 0.792393556 0.603803222
[198,] 0.356234653 0.712469306 0.643765347
[199,] 0.337357929 0.674715857 0.662642071
[200,] 0.299762949 0.599525898 0.700237051
[201,] 0.365591850 0.731183699 0.634408150
[202,] 0.382512540 0.765025079 0.617487460
[203,] 0.340410676 0.680821352 0.659589324
[204,] 0.361302824 0.722605647 0.638697176
[205,] 0.325336700 0.650673400 0.674663300
[206,] 0.288896710 0.577793421 0.711103290
[207,] 0.253151123 0.506302247 0.746848877
[208,] 0.216334226 0.432668452 0.783665774
[209,] 0.214751636 0.429503271 0.785248364
[210,] 0.190569056 0.381138111 0.809430944
[211,] 0.233358077 0.466716154 0.766641923
[212,] 0.197358307 0.394716614 0.802641693
[213,] 0.189416419 0.378832838 0.810583581
[214,] 0.164175213 0.328350426 0.835824787
[215,] 0.135709946 0.271419891 0.864290054
[216,] 0.111113531 0.222227061 0.888886469
[217,] 0.089433969 0.178867938 0.910566031
[218,] 0.071801020 0.143602039 0.928198980
[219,] 0.054971681 0.109943363 0.945028319
[220,] 0.041992688 0.083985377 0.958007312
[221,] 0.061438395 0.122876790 0.938561605
[222,] 0.075087201 0.150174402 0.924912799
[223,] 0.275054101 0.550108203 0.724945899
[224,] 0.244076316 0.488152631 0.755923684
[225,] 0.198723531 0.397447063 0.801276469
[226,] 0.197642247 0.395284494 0.802357753
[227,] 0.235553842 0.471107685 0.764446158
[228,] 0.252636412 0.505272825 0.747363588
[229,] 0.243944441 0.487888882 0.756055559
[230,] 0.199514907 0.399029815 0.800485093
[231,] 0.161942737 0.323885474 0.838057263
[232,] 0.202204673 0.404409346 0.797795327
[233,] 0.486233668 0.972467336 0.513766332
[234,] 0.680244484 0.639511031 0.319755516
[235,] 0.604163232 0.791673536 0.395836768
[236,] 0.533902085 0.932195830 0.466097915
[237,] 0.458896976 0.917793952 0.541103024
[238,] 0.393357509 0.786715019 0.606642491
[239,] 0.301859057 0.603718115 0.698140943
[240,] 0.244789407 0.489578814 0.755210593
[241,] 0.357006688 0.714013377 0.642993312
[242,] 0.580107019 0.839785962 0.419892981
[243,] 0.521626342 0.956747315 0.478373658
> postscript(file="/var/wessaorg/rcomp/tmp/18wqk1384774461.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2lw5e1384774461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3djdx1384774461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4buwq1384774461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5zbzs1384774461.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
2.168854133 -0.468580317 1.688101313 -4.970101696 2.553833157 0.345510511
7 8 9 10 11 12
-0.862997342 2.890801419 1.543070573 -2.100724837 -1.432927867 0.530804063
13 14 15 16 17 18
0.488223603 -0.542988831 2.669969609 1.122152966 -0.896906170 -2.032879023
19 20 21 22 23 24
-1.765183818 0.599864692 -0.511202079 0.574432593 -0.299319523 -0.595167886
25 26 27 28 29 30
-0.074969874 0.887556819 -1.645158525 2.959872417 0.424792453 -0.316386534
31 32 33 34 35 36
1.037571188 -1.154469293 -0.272780401 0.302246244 1.691792008 -1.114887717
37 38 39 40 41 42
1.018326110 1.650423423 -1.531344857 -1.529425698 -1.493308132 1.616453012
43 44 45 46 47 48
1.310965179 -0.274024572 -0.138515782 3.294076154 2.495801970 -0.014770973
49 50 51 52 53 54
-0.697974667 1.662108388 2.257070537 -0.254047715 2.817086573 1.458311116
55 56 57 58 59 60
-2.502223649 -4.398034480 0.500185274 0.397359503 -0.091203149 -1.520286340
61 62 63 64 65 66
-1.951087465 0.421417572 2.332123963 0.620131934 0.650189664 0.691112361
67 68 69 70 71 72
1.357617149 -1.559717868 2.615089086 0.373625843 -0.070187625 0.531423395
73 74 75 76 77 78
1.053329461 1.036418041 0.608723383 -2.909179528 1.397231193 -0.364201992
79 80 81 82 83 84
2.201974855 0.779078194 0.282086309 0.337906525 1.065598469 -0.003442177
85 86 87 88 89 90
-1.474556378 -1.568074680 0.438326809 0.568326843 1.113465839 -0.328034812
91 92 93 94 95 96
3.068229366 0.163192439 1.170043272 2.193080967 -1.647276204 1.487193593
97 98 99 100 101 102
-2.501541929 0.413227995 -0.549540258 2.362639632 -0.537814309 0.999431448
103 104 105 106 107 108
0.239521383 -0.764553274 2.353802351 -0.141548917 0.370636053 -0.487381934
109 110 111 112 113 114
-2.219713598 0.361391406 2.429512227 -1.494778461 1.193251205 0.780566698
115 116 117 118 119 120
0.327118689 2.455555908 -2.561352104 1.093668243 0.351827759 1.141294196
121 122 123 124 125 126
1.663702249 1.926817990 2.272965216 2.142233404 3.375804912 -0.967505401
127 128 129 130 131 132
-1.248160939 -2.046815515 1.800228074 -1.577767728 2.972756077 -3.664505017
133 134 135 136 137 138
1.884935419 1.476066298 1.165036576 -0.272332218 0.086969204 1.366558628
139 140 141 142 143 144
-1.207950495 0.232507979 -3.514675453 -2.171518476 2.723546562 -0.214369324
145 146 147 148 149 150
-0.007727864 0.530444701 2.175146796 0.494843123 -0.695103142 -1.293837161
151 152 153 154 155 156
1.979485111 -2.761309070 -5.751402263 -2.302054851 0.062554369 3.068229366
157 158 159 160 161 162
-3.926097913 -2.046815515 -0.585316328 -1.460325914 -1.809796507 0.553302827
163 164 165 166 167 168
-1.052892968 1.989013613 -1.151420518 0.108793008 0.558240401 3.545115900
169 170 171 172 173 174
1.974732730 0.226737009 -3.969714546 0.357851672 0.705868487 -0.164365342
175 176 177 178 179 180
3.004367107 -0.163974438 -1.143574640 -0.109031420 -0.317544394 -0.386351563
181 182 183 184 185 186
0.457316448 -2.541576964 1.628775610 1.058896392 5.062440683 0.353991491
187 188 189 190 191 192
-1.136295755 -2.179144120 -1.472160350 -3.634628734 1.467717029 0.194018311
193 194 195 196 197 198
-6.199399384 -0.549915246 0.241520936 0.908774632 -2.414390875 0.272712548
199 200 201 202 203 204
-1.315483946 0.364799514 0.607164355 -0.380742828 0.361776283 2.938190683
205 206 207 208 209 210
-3.017367748 -0.059355856 -0.681796098 0.498070587 1.410791784 -1.013760631
211 212 213 214 215 216
2.845595183 2.404286113 -0.210778808 -2.991228081 0.454379269 -0.072505042
217 218 219 220 221 222
-1.238040829 -0.393366678 -1.753814009 0.852403898 2.166691725 -0.431969358
223 224 225 226 227 228
1.104471626 -0.246187080 -0.865451765 -1.122641725 -0.267129149 -1.457871893
229 230 231 232 233 234
0.478081690 -0.296521911 -3.689577045 1.803091307 -5.535785348 1.474397624
235 236 237 238 239 240
0.217020433 1.459813381 1.586682188 2.587131118 0.448522772 -0.950573705
241 242 243 244 245 246
-1.381908444 1.857638303 -4.865056034 -3.737783394 -0.822553331 -1.278261064
247 248 249 250 251 252
-1.407867070 1.255943204 -0.776944820 0.207001495 -1.696648552 -1.042243415
253 254 255 256 257 258
-3.245733570 -2.479590804 0.898672879 0.695944338 0.344653099 -2.260997180
259 260 261 262 263 264
1.254193590 -1.604545374 0.109058625 3.177040744 -1.576054634 -1.405844588
> postscript(file="/var/wessaorg/rcomp/tmp/6thel1384774461.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 2.168854133 NA
1 -0.468580317 2.168854133
2 1.688101313 -0.468580317
3 -4.970101696 1.688101313
4 2.553833157 -4.970101696
5 0.345510511 2.553833157
6 -0.862997342 0.345510511
7 2.890801419 -0.862997342
8 1.543070573 2.890801419
9 -2.100724837 1.543070573
10 -1.432927867 -2.100724837
11 0.530804063 -1.432927867
12 0.488223603 0.530804063
13 -0.542988831 0.488223603
14 2.669969609 -0.542988831
15 1.122152966 2.669969609
16 -0.896906170 1.122152966
17 -2.032879023 -0.896906170
18 -1.765183818 -2.032879023
19 0.599864692 -1.765183818
20 -0.511202079 0.599864692
21 0.574432593 -0.511202079
22 -0.299319523 0.574432593
23 -0.595167886 -0.299319523
24 -0.074969874 -0.595167886
25 0.887556819 -0.074969874
26 -1.645158525 0.887556819
27 2.959872417 -1.645158525
28 0.424792453 2.959872417
29 -0.316386534 0.424792453
30 1.037571188 -0.316386534
31 -1.154469293 1.037571188
32 -0.272780401 -1.154469293
33 0.302246244 -0.272780401
34 1.691792008 0.302246244
35 -1.114887717 1.691792008
36 1.018326110 -1.114887717
37 1.650423423 1.018326110
38 -1.531344857 1.650423423
39 -1.529425698 -1.531344857
40 -1.493308132 -1.529425698
41 1.616453012 -1.493308132
42 1.310965179 1.616453012
43 -0.274024572 1.310965179
44 -0.138515782 -0.274024572
45 3.294076154 -0.138515782
46 2.495801970 3.294076154
47 -0.014770973 2.495801970
48 -0.697974667 -0.014770973
49 1.662108388 -0.697974667
50 2.257070537 1.662108388
51 -0.254047715 2.257070537
52 2.817086573 -0.254047715
53 1.458311116 2.817086573
54 -2.502223649 1.458311116
55 -4.398034480 -2.502223649
56 0.500185274 -4.398034480
57 0.397359503 0.500185274
58 -0.091203149 0.397359503
59 -1.520286340 -0.091203149
60 -1.951087465 -1.520286340
61 0.421417572 -1.951087465
62 2.332123963 0.421417572
63 0.620131934 2.332123963
64 0.650189664 0.620131934
65 0.691112361 0.650189664
66 1.357617149 0.691112361
67 -1.559717868 1.357617149
68 2.615089086 -1.559717868
69 0.373625843 2.615089086
70 -0.070187625 0.373625843
71 0.531423395 -0.070187625
72 1.053329461 0.531423395
73 1.036418041 1.053329461
74 0.608723383 1.036418041
75 -2.909179528 0.608723383
76 1.397231193 -2.909179528
77 -0.364201992 1.397231193
78 2.201974855 -0.364201992
79 0.779078194 2.201974855
80 0.282086309 0.779078194
81 0.337906525 0.282086309
82 1.065598469 0.337906525
83 -0.003442177 1.065598469
84 -1.474556378 -0.003442177
85 -1.568074680 -1.474556378
86 0.438326809 -1.568074680
87 0.568326843 0.438326809
88 1.113465839 0.568326843
89 -0.328034812 1.113465839
90 3.068229366 -0.328034812
91 0.163192439 3.068229366
92 1.170043272 0.163192439
93 2.193080967 1.170043272
94 -1.647276204 2.193080967
95 1.487193593 -1.647276204
96 -2.501541929 1.487193593
97 0.413227995 -2.501541929
98 -0.549540258 0.413227995
99 2.362639632 -0.549540258
100 -0.537814309 2.362639632
101 0.999431448 -0.537814309
102 0.239521383 0.999431448
103 -0.764553274 0.239521383
104 2.353802351 -0.764553274
105 -0.141548917 2.353802351
106 0.370636053 -0.141548917
107 -0.487381934 0.370636053
108 -2.219713598 -0.487381934
109 0.361391406 -2.219713598
110 2.429512227 0.361391406
111 -1.494778461 2.429512227
112 1.193251205 -1.494778461
113 0.780566698 1.193251205
114 0.327118689 0.780566698
115 2.455555908 0.327118689
116 -2.561352104 2.455555908
117 1.093668243 -2.561352104
118 0.351827759 1.093668243
119 1.141294196 0.351827759
120 1.663702249 1.141294196
121 1.926817990 1.663702249
122 2.272965216 1.926817990
123 2.142233404 2.272965216
124 3.375804912 2.142233404
125 -0.967505401 3.375804912
126 -1.248160939 -0.967505401
127 -2.046815515 -1.248160939
128 1.800228074 -2.046815515
129 -1.577767728 1.800228074
130 2.972756077 -1.577767728
131 -3.664505017 2.972756077
132 1.884935419 -3.664505017
133 1.476066298 1.884935419
134 1.165036576 1.476066298
135 -0.272332218 1.165036576
136 0.086969204 -0.272332218
137 1.366558628 0.086969204
138 -1.207950495 1.366558628
139 0.232507979 -1.207950495
140 -3.514675453 0.232507979
141 -2.171518476 -3.514675453
142 2.723546562 -2.171518476
143 -0.214369324 2.723546562
144 -0.007727864 -0.214369324
145 0.530444701 -0.007727864
146 2.175146796 0.530444701
147 0.494843123 2.175146796
148 -0.695103142 0.494843123
149 -1.293837161 -0.695103142
150 1.979485111 -1.293837161
151 -2.761309070 1.979485111
152 -5.751402263 -2.761309070
153 -2.302054851 -5.751402263
154 0.062554369 -2.302054851
155 3.068229366 0.062554369
156 -3.926097913 3.068229366
157 -2.046815515 -3.926097913
158 -0.585316328 -2.046815515
159 -1.460325914 -0.585316328
160 -1.809796507 -1.460325914
161 0.553302827 -1.809796507
162 -1.052892968 0.553302827
163 1.989013613 -1.052892968
164 -1.151420518 1.989013613
165 0.108793008 -1.151420518
166 0.558240401 0.108793008
167 3.545115900 0.558240401
168 1.974732730 3.545115900
169 0.226737009 1.974732730
170 -3.969714546 0.226737009
171 0.357851672 -3.969714546
172 0.705868487 0.357851672
173 -0.164365342 0.705868487
174 3.004367107 -0.164365342
175 -0.163974438 3.004367107
176 -1.143574640 -0.163974438
177 -0.109031420 -1.143574640
178 -0.317544394 -0.109031420
179 -0.386351563 -0.317544394
180 0.457316448 -0.386351563
181 -2.541576964 0.457316448
182 1.628775610 -2.541576964
183 1.058896392 1.628775610
184 5.062440683 1.058896392
185 0.353991491 5.062440683
186 -1.136295755 0.353991491
187 -2.179144120 -1.136295755
188 -1.472160350 -2.179144120
189 -3.634628734 -1.472160350
190 1.467717029 -3.634628734
191 0.194018311 1.467717029
192 -6.199399384 0.194018311
193 -0.549915246 -6.199399384
194 0.241520936 -0.549915246
195 0.908774632 0.241520936
196 -2.414390875 0.908774632
197 0.272712548 -2.414390875
198 -1.315483946 0.272712548
199 0.364799514 -1.315483946
200 0.607164355 0.364799514
201 -0.380742828 0.607164355
202 0.361776283 -0.380742828
203 2.938190683 0.361776283
204 -3.017367748 2.938190683
205 -0.059355856 -3.017367748
206 -0.681796098 -0.059355856
207 0.498070587 -0.681796098
208 1.410791784 0.498070587
209 -1.013760631 1.410791784
210 2.845595183 -1.013760631
211 2.404286113 2.845595183
212 -0.210778808 2.404286113
213 -2.991228081 -0.210778808
214 0.454379269 -2.991228081
215 -0.072505042 0.454379269
216 -1.238040829 -0.072505042
217 -0.393366678 -1.238040829
218 -1.753814009 -0.393366678
219 0.852403898 -1.753814009
220 2.166691725 0.852403898
221 -0.431969358 2.166691725
222 1.104471626 -0.431969358
223 -0.246187080 1.104471626
224 -0.865451765 -0.246187080
225 -1.122641725 -0.865451765
226 -0.267129149 -1.122641725
227 -1.457871893 -0.267129149
228 0.478081690 -1.457871893
229 -0.296521911 0.478081690
230 -3.689577045 -0.296521911
231 1.803091307 -3.689577045
232 -5.535785348 1.803091307
233 1.474397624 -5.535785348
234 0.217020433 1.474397624
235 1.459813381 0.217020433
236 1.586682188 1.459813381
237 2.587131118 1.586682188
238 0.448522772 2.587131118
239 -0.950573705 0.448522772
240 -1.381908444 -0.950573705
241 1.857638303 -1.381908444
242 -4.865056034 1.857638303
243 -3.737783394 -4.865056034
244 -0.822553331 -3.737783394
245 -1.278261064 -0.822553331
246 -1.407867070 -1.278261064
247 1.255943204 -1.407867070
248 -0.776944820 1.255943204
249 0.207001495 -0.776944820
250 -1.696648552 0.207001495
251 -1.042243415 -1.696648552
252 -3.245733570 -1.042243415
253 -2.479590804 -3.245733570
254 0.898672879 -2.479590804
255 0.695944338 0.898672879
256 0.344653099 0.695944338
257 -2.260997180 0.344653099
258 1.254193590 -2.260997180
259 -1.604545374 1.254193590
260 0.109058625 -1.604545374
261 3.177040744 0.109058625
262 -1.576054634 3.177040744
263 -1.405844588 -1.576054634
264 NA -1.405844588
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.468580317 2.168854133
[2,] 1.688101313 -0.468580317
[3,] -4.970101696 1.688101313
[4,] 2.553833157 -4.970101696
[5,] 0.345510511 2.553833157
[6,] -0.862997342 0.345510511
[7,] 2.890801419 -0.862997342
[8,] 1.543070573 2.890801419
[9,] -2.100724837 1.543070573
[10,] -1.432927867 -2.100724837
[11,] 0.530804063 -1.432927867
[12,] 0.488223603 0.530804063
[13,] -0.542988831 0.488223603
[14,] 2.669969609 -0.542988831
[15,] 1.122152966 2.669969609
[16,] -0.896906170 1.122152966
[17,] -2.032879023 -0.896906170
[18,] -1.765183818 -2.032879023
[19,] 0.599864692 -1.765183818
[20,] -0.511202079 0.599864692
[21,] 0.574432593 -0.511202079
[22,] -0.299319523 0.574432593
[23,] -0.595167886 -0.299319523
[24,] -0.074969874 -0.595167886
[25,] 0.887556819 -0.074969874
[26,] -1.645158525 0.887556819
[27,] 2.959872417 -1.645158525
[28,] 0.424792453 2.959872417
[29,] -0.316386534 0.424792453
[30,] 1.037571188 -0.316386534
[31,] -1.154469293 1.037571188
[32,] -0.272780401 -1.154469293
[33,] 0.302246244 -0.272780401
[34,] 1.691792008 0.302246244
[35,] -1.114887717 1.691792008
[36,] 1.018326110 -1.114887717
[37,] 1.650423423 1.018326110
[38,] -1.531344857 1.650423423
[39,] -1.529425698 -1.531344857
[40,] -1.493308132 -1.529425698
[41,] 1.616453012 -1.493308132
[42,] 1.310965179 1.616453012
[43,] -0.274024572 1.310965179
[44,] -0.138515782 -0.274024572
[45,] 3.294076154 -0.138515782
[46,] 2.495801970 3.294076154
[47,] -0.014770973 2.495801970
[48,] -0.697974667 -0.014770973
[49,] 1.662108388 -0.697974667
[50,] 2.257070537 1.662108388
[51,] -0.254047715 2.257070537
[52,] 2.817086573 -0.254047715
[53,] 1.458311116 2.817086573
[54,] -2.502223649 1.458311116
[55,] -4.398034480 -2.502223649
[56,] 0.500185274 -4.398034480
[57,] 0.397359503 0.500185274
[58,] -0.091203149 0.397359503
[59,] -1.520286340 -0.091203149
[60,] -1.951087465 -1.520286340
[61,] 0.421417572 -1.951087465
[62,] 2.332123963 0.421417572
[63,] 0.620131934 2.332123963
[64,] 0.650189664 0.620131934
[65,] 0.691112361 0.650189664
[66,] 1.357617149 0.691112361
[67,] -1.559717868 1.357617149
[68,] 2.615089086 -1.559717868
[69,] 0.373625843 2.615089086
[70,] -0.070187625 0.373625843
[71,] 0.531423395 -0.070187625
[72,] 1.053329461 0.531423395
[73,] 1.036418041 1.053329461
[74,] 0.608723383 1.036418041
[75,] -2.909179528 0.608723383
[76,] 1.397231193 -2.909179528
[77,] -0.364201992 1.397231193
[78,] 2.201974855 -0.364201992
[79,] 0.779078194 2.201974855
[80,] 0.282086309 0.779078194
[81,] 0.337906525 0.282086309
[82,] 1.065598469 0.337906525
[83,] -0.003442177 1.065598469
[84,] -1.474556378 -0.003442177
[85,] -1.568074680 -1.474556378
[86,] 0.438326809 -1.568074680
[87,] 0.568326843 0.438326809
[88,] 1.113465839 0.568326843
[89,] -0.328034812 1.113465839
[90,] 3.068229366 -0.328034812
[91,] 0.163192439 3.068229366
[92,] 1.170043272 0.163192439
[93,] 2.193080967 1.170043272
[94,] -1.647276204 2.193080967
[95,] 1.487193593 -1.647276204
[96,] -2.501541929 1.487193593
[97,] 0.413227995 -2.501541929
[98,] -0.549540258 0.413227995
[99,] 2.362639632 -0.549540258
[100,] -0.537814309 2.362639632
[101,] 0.999431448 -0.537814309
[102,] 0.239521383 0.999431448
[103,] -0.764553274 0.239521383
[104,] 2.353802351 -0.764553274
[105,] -0.141548917 2.353802351
[106,] 0.370636053 -0.141548917
[107,] -0.487381934 0.370636053
[108,] -2.219713598 -0.487381934
[109,] 0.361391406 -2.219713598
[110,] 2.429512227 0.361391406
[111,] -1.494778461 2.429512227
[112,] 1.193251205 -1.494778461
[113,] 0.780566698 1.193251205
[114,] 0.327118689 0.780566698
[115,] 2.455555908 0.327118689
[116,] -2.561352104 2.455555908
[117,] 1.093668243 -2.561352104
[118,] 0.351827759 1.093668243
[119,] 1.141294196 0.351827759
[120,] 1.663702249 1.141294196
[121,] 1.926817990 1.663702249
[122,] 2.272965216 1.926817990
[123,] 2.142233404 2.272965216
[124,] 3.375804912 2.142233404
[125,] -0.967505401 3.375804912
[126,] -1.248160939 -0.967505401
[127,] -2.046815515 -1.248160939
[128,] 1.800228074 -2.046815515
[129,] -1.577767728 1.800228074
[130,] 2.972756077 -1.577767728
[131,] -3.664505017 2.972756077
[132,] 1.884935419 -3.664505017
[133,] 1.476066298 1.884935419
[134,] 1.165036576 1.476066298
[135,] -0.272332218 1.165036576
[136,] 0.086969204 -0.272332218
[137,] 1.366558628 0.086969204
[138,] -1.207950495 1.366558628
[139,] 0.232507979 -1.207950495
[140,] -3.514675453 0.232507979
[141,] -2.171518476 -3.514675453
[142,] 2.723546562 -2.171518476
[143,] -0.214369324 2.723546562
[144,] -0.007727864 -0.214369324
[145,] 0.530444701 -0.007727864
[146,] 2.175146796 0.530444701
[147,] 0.494843123 2.175146796
[148,] -0.695103142 0.494843123
[149,] -1.293837161 -0.695103142
[150,] 1.979485111 -1.293837161
[151,] -2.761309070 1.979485111
[152,] -5.751402263 -2.761309070
[153,] -2.302054851 -5.751402263
[154,] 0.062554369 -2.302054851
[155,] 3.068229366 0.062554369
[156,] -3.926097913 3.068229366
[157,] -2.046815515 -3.926097913
[158,] -0.585316328 -2.046815515
[159,] -1.460325914 -0.585316328
[160,] -1.809796507 -1.460325914
[161,] 0.553302827 -1.809796507
[162,] -1.052892968 0.553302827
[163,] 1.989013613 -1.052892968
[164,] -1.151420518 1.989013613
[165,] 0.108793008 -1.151420518
[166,] 0.558240401 0.108793008
[167,] 3.545115900 0.558240401
[168,] 1.974732730 3.545115900
[169,] 0.226737009 1.974732730
[170,] -3.969714546 0.226737009
[171,] 0.357851672 -3.969714546
[172,] 0.705868487 0.357851672
[173,] -0.164365342 0.705868487
[174,] 3.004367107 -0.164365342
[175,] -0.163974438 3.004367107
[176,] -1.143574640 -0.163974438
[177,] -0.109031420 -1.143574640
[178,] -0.317544394 -0.109031420
[179,] -0.386351563 -0.317544394
[180,] 0.457316448 -0.386351563
[181,] -2.541576964 0.457316448
[182,] 1.628775610 -2.541576964
[183,] 1.058896392 1.628775610
[184,] 5.062440683 1.058896392
[185,] 0.353991491 5.062440683
[186,] -1.136295755 0.353991491
[187,] -2.179144120 -1.136295755
[188,] -1.472160350 -2.179144120
[189,] -3.634628734 -1.472160350
[190,] 1.467717029 -3.634628734
[191,] 0.194018311 1.467717029
[192,] -6.199399384 0.194018311
[193,] -0.549915246 -6.199399384
[194,] 0.241520936 -0.549915246
[195,] 0.908774632 0.241520936
[196,] -2.414390875 0.908774632
[197,] 0.272712548 -2.414390875
[198,] -1.315483946 0.272712548
[199,] 0.364799514 -1.315483946
[200,] 0.607164355 0.364799514
[201,] -0.380742828 0.607164355
[202,] 0.361776283 -0.380742828
[203,] 2.938190683 0.361776283
[204,] -3.017367748 2.938190683
[205,] -0.059355856 -3.017367748
[206,] -0.681796098 -0.059355856
[207,] 0.498070587 -0.681796098
[208,] 1.410791784 0.498070587
[209,] -1.013760631 1.410791784
[210,] 2.845595183 -1.013760631
[211,] 2.404286113 2.845595183
[212,] -0.210778808 2.404286113
[213,] -2.991228081 -0.210778808
[214,] 0.454379269 -2.991228081
[215,] -0.072505042 0.454379269
[216,] -1.238040829 -0.072505042
[217,] -0.393366678 -1.238040829
[218,] -1.753814009 -0.393366678
[219,] 0.852403898 -1.753814009
[220,] 2.166691725 0.852403898
[221,] -0.431969358 2.166691725
[222,] 1.104471626 -0.431969358
[223,] -0.246187080 1.104471626
[224,] -0.865451765 -0.246187080
[225,] -1.122641725 -0.865451765
[226,] -0.267129149 -1.122641725
[227,] -1.457871893 -0.267129149
[228,] 0.478081690 -1.457871893
[229,] -0.296521911 0.478081690
[230,] -3.689577045 -0.296521911
[231,] 1.803091307 -3.689577045
[232,] -5.535785348 1.803091307
[233,] 1.474397624 -5.535785348
[234,] 0.217020433 1.474397624
[235,] 1.459813381 0.217020433
[236,] 1.586682188 1.459813381
[237,] 2.587131118 1.586682188
[238,] 0.448522772 2.587131118
[239,] -0.950573705 0.448522772
[240,] -1.381908444 -0.950573705
[241,] 1.857638303 -1.381908444
[242,] -4.865056034 1.857638303
[243,] -3.737783394 -4.865056034
[244,] -0.822553331 -3.737783394
[245,] -1.278261064 -0.822553331
[246,] -1.407867070 -1.278261064
[247,] 1.255943204 -1.407867070
[248,] -0.776944820 1.255943204
[249,] 0.207001495 -0.776944820
[250,] -1.696648552 0.207001495
[251,] -1.042243415 -1.696648552
[252,] -3.245733570 -1.042243415
[253,] -2.479590804 -3.245733570
[254,] 0.898672879 -2.479590804
[255,] 0.695944338 0.898672879
[256,] 0.344653099 0.695944338
[257,] -2.260997180 0.344653099
[258,] 1.254193590 -2.260997180
[259,] -1.604545374 1.254193590
[260,] 0.109058625 -1.604545374
[261,] 3.177040744 0.109058625
[262,] -1.576054634 3.177040744
[263,] -1.405844588 -1.576054634
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.468580317 2.168854133
2 1.688101313 -0.468580317
3 -4.970101696 1.688101313
4 2.553833157 -4.970101696
5 0.345510511 2.553833157
6 -0.862997342 0.345510511
7 2.890801419 -0.862997342
8 1.543070573 2.890801419
9 -2.100724837 1.543070573
10 -1.432927867 -2.100724837
11 0.530804063 -1.432927867
12 0.488223603 0.530804063
13 -0.542988831 0.488223603
14 2.669969609 -0.542988831
15 1.122152966 2.669969609
16 -0.896906170 1.122152966
17 -2.032879023 -0.896906170
18 -1.765183818 -2.032879023
19 0.599864692 -1.765183818
20 -0.511202079 0.599864692
21 0.574432593 -0.511202079
22 -0.299319523 0.574432593
23 -0.595167886 -0.299319523
24 -0.074969874 -0.595167886
25 0.887556819 -0.074969874
26 -1.645158525 0.887556819
27 2.959872417 -1.645158525
28 0.424792453 2.959872417
29 -0.316386534 0.424792453
30 1.037571188 -0.316386534
31 -1.154469293 1.037571188
32 -0.272780401 -1.154469293
33 0.302246244 -0.272780401
34 1.691792008 0.302246244
35 -1.114887717 1.691792008
36 1.018326110 -1.114887717
37 1.650423423 1.018326110
38 -1.531344857 1.650423423
39 -1.529425698 -1.531344857
40 -1.493308132 -1.529425698
41 1.616453012 -1.493308132
42 1.310965179 1.616453012
43 -0.274024572 1.310965179
44 -0.138515782 -0.274024572
45 3.294076154 -0.138515782
46 2.495801970 3.294076154
47 -0.014770973 2.495801970
48 -0.697974667 -0.014770973
49 1.662108388 -0.697974667
50 2.257070537 1.662108388
51 -0.254047715 2.257070537
52 2.817086573 -0.254047715
53 1.458311116 2.817086573
54 -2.502223649 1.458311116
55 -4.398034480 -2.502223649
56 0.500185274 -4.398034480
57 0.397359503 0.500185274
58 -0.091203149 0.397359503
59 -1.520286340 -0.091203149
60 -1.951087465 -1.520286340
61 0.421417572 -1.951087465
62 2.332123963 0.421417572
63 0.620131934 2.332123963
64 0.650189664 0.620131934
65 0.691112361 0.650189664
66 1.357617149 0.691112361
67 -1.559717868 1.357617149
68 2.615089086 -1.559717868
69 0.373625843 2.615089086
70 -0.070187625 0.373625843
71 0.531423395 -0.070187625
72 1.053329461 0.531423395
73 1.036418041 1.053329461
74 0.608723383 1.036418041
75 -2.909179528 0.608723383
76 1.397231193 -2.909179528
77 -0.364201992 1.397231193
78 2.201974855 -0.364201992
79 0.779078194 2.201974855
80 0.282086309 0.779078194
81 0.337906525 0.282086309
82 1.065598469 0.337906525
83 -0.003442177 1.065598469
84 -1.474556378 -0.003442177
85 -1.568074680 -1.474556378
86 0.438326809 -1.568074680
87 0.568326843 0.438326809
88 1.113465839 0.568326843
89 -0.328034812 1.113465839
90 3.068229366 -0.328034812
91 0.163192439 3.068229366
92 1.170043272 0.163192439
93 2.193080967 1.170043272
94 -1.647276204 2.193080967
95 1.487193593 -1.647276204
96 -2.501541929 1.487193593
97 0.413227995 -2.501541929
98 -0.549540258 0.413227995
99 2.362639632 -0.549540258
100 -0.537814309 2.362639632
101 0.999431448 -0.537814309
102 0.239521383 0.999431448
103 -0.764553274 0.239521383
104 2.353802351 -0.764553274
105 -0.141548917 2.353802351
106 0.370636053 -0.141548917
107 -0.487381934 0.370636053
108 -2.219713598 -0.487381934
109 0.361391406 -2.219713598
110 2.429512227 0.361391406
111 -1.494778461 2.429512227
112 1.193251205 -1.494778461
113 0.780566698 1.193251205
114 0.327118689 0.780566698
115 2.455555908 0.327118689
116 -2.561352104 2.455555908
117 1.093668243 -2.561352104
118 0.351827759 1.093668243
119 1.141294196 0.351827759
120 1.663702249 1.141294196
121 1.926817990 1.663702249
122 2.272965216 1.926817990
123 2.142233404 2.272965216
124 3.375804912 2.142233404
125 -0.967505401 3.375804912
126 -1.248160939 -0.967505401
127 -2.046815515 -1.248160939
128 1.800228074 -2.046815515
129 -1.577767728 1.800228074
130 2.972756077 -1.577767728
131 -3.664505017 2.972756077
132 1.884935419 -3.664505017
133 1.476066298 1.884935419
134 1.165036576 1.476066298
135 -0.272332218 1.165036576
136 0.086969204 -0.272332218
137 1.366558628 0.086969204
138 -1.207950495 1.366558628
139 0.232507979 -1.207950495
140 -3.514675453 0.232507979
141 -2.171518476 -3.514675453
142 2.723546562 -2.171518476
143 -0.214369324 2.723546562
144 -0.007727864 -0.214369324
145 0.530444701 -0.007727864
146 2.175146796 0.530444701
147 0.494843123 2.175146796
148 -0.695103142 0.494843123
149 -1.293837161 -0.695103142
150 1.979485111 -1.293837161
151 -2.761309070 1.979485111
152 -5.751402263 -2.761309070
153 -2.302054851 -5.751402263
154 0.062554369 -2.302054851
155 3.068229366 0.062554369
156 -3.926097913 3.068229366
157 -2.046815515 -3.926097913
158 -0.585316328 -2.046815515
159 -1.460325914 -0.585316328
160 -1.809796507 -1.460325914
161 0.553302827 -1.809796507
162 -1.052892968 0.553302827
163 1.989013613 -1.052892968
164 -1.151420518 1.989013613
165 0.108793008 -1.151420518
166 0.558240401 0.108793008
167 3.545115900 0.558240401
168 1.974732730 3.545115900
169 0.226737009 1.974732730
170 -3.969714546 0.226737009
171 0.357851672 -3.969714546
172 0.705868487 0.357851672
173 -0.164365342 0.705868487
174 3.004367107 -0.164365342
175 -0.163974438 3.004367107
176 -1.143574640 -0.163974438
177 -0.109031420 -1.143574640
178 -0.317544394 -0.109031420
179 -0.386351563 -0.317544394
180 0.457316448 -0.386351563
181 -2.541576964 0.457316448
182 1.628775610 -2.541576964
183 1.058896392 1.628775610
184 5.062440683 1.058896392
185 0.353991491 5.062440683
186 -1.136295755 0.353991491
187 -2.179144120 -1.136295755
188 -1.472160350 -2.179144120
189 -3.634628734 -1.472160350
190 1.467717029 -3.634628734
191 0.194018311 1.467717029
192 -6.199399384 0.194018311
193 -0.549915246 -6.199399384
194 0.241520936 -0.549915246
195 0.908774632 0.241520936
196 -2.414390875 0.908774632
197 0.272712548 -2.414390875
198 -1.315483946 0.272712548
199 0.364799514 -1.315483946
200 0.607164355 0.364799514
201 -0.380742828 0.607164355
202 0.361776283 -0.380742828
203 2.938190683 0.361776283
204 -3.017367748 2.938190683
205 -0.059355856 -3.017367748
206 -0.681796098 -0.059355856
207 0.498070587 -0.681796098
208 1.410791784 0.498070587
209 -1.013760631 1.410791784
210 2.845595183 -1.013760631
211 2.404286113 2.845595183
212 -0.210778808 2.404286113
213 -2.991228081 -0.210778808
214 0.454379269 -2.991228081
215 -0.072505042 0.454379269
216 -1.238040829 -0.072505042
217 -0.393366678 -1.238040829
218 -1.753814009 -0.393366678
219 0.852403898 -1.753814009
220 2.166691725 0.852403898
221 -0.431969358 2.166691725
222 1.104471626 -0.431969358
223 -0.246187080 1.104471626
224 -0.865451765 -0.246187080
225 -1.122641725 -0.865451765
226 -0.267129149 -1.122641725
227 -1.457871893 -0.267129149
228 0.478081690 -1.457871893
229 -0.296521911 0.478081690
230 -3.689577045 -0.296521911
231 1.803091307 -3.689577045
232 -5.535785348 1.803091307
233 1.474397624 -5.535785348
234 0.217020433 1.474397624
235 1.459813381 0.217020433
236 1.586682188 1.459813381
237 2.587131118 1.586682188
238 0.448522772 2.587131118
239 -0.950573705 0.448522772
240 -1.381908444 -0.950573705
241 1.857638303 -1.381908444
242 -4.865056034 1.857638303
243 -3.737783394 -4.865056034
244 -0.822553331 -3.737783394
245 -1.278261064 -0.822553331
246 -1.407867070 -1.278261064
247 1.255943204 -1.407867070
248 -0.776944820 1.255943204
249 0.207001495 -0.776944820
250 -1.696648552 0.207001495
251 -1.042243415 -1.696648552
252 -3.245733570 -1.042243415
253 -2.479590804 -3.245733570
254 0.898672879 -2.479590804
255 0.695944338 0.898672879
256 0.344653099 0.695944338
257 -2.260997180 0.344653099
258 1.254193590 -2.260997180
259 -1.604545374 1.254193590
260 0.109058625 -1.604545374
261 3.177040744 0.109058625
262 -1.576054634 3.177040744
263 -1.405844588 -1.576054634
> 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/7ku091384774461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/856mw1384774461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/966o11384774461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10corp1384774461.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111lqp1384774461.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/12w2us1384774462.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/13ll011384774462.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/14swip1384774462.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/150smm1384774462.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/16t2q81384774462.tab")
+ }
>
> try(system("convert tmp/18wqk1384774461.ps tmp/18wqk1384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lw5e1384774461.ps tmp/2lw5e1384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/3djdx1384774461.ps tmp/3djdx1384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/4buwq1384774461.ps tmp/4buwq1384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zbzs1384774461.ps tmp/5zbzs1384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/6thel1384774461.ps tmp/6thel1384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ku091384774461.ps tmp/7ku091384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/856mw1384774461.ps tmp/856mw1384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/966o11384774461.ps tmp/966o11384774461.png",intern=TRUE))
character(0)
> try(system("convert tmp/10corp1384774461.ps tmp/10corp1384774461.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.216 2.920 19.115