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(41
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+ ,35
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+ ,16
+ ,12
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+ ,75
+ ,31
+ ,32
+ ,14
+ ,8
+ ,9
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+ ,62
+ ,37
+ ,34
+ ,13
+ ,11
+ ,15
+ ,15
+ ,67
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
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+ ,83
+ ,27
+ ,30
+ ,15
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+ ,14
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+ ,34
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+ ,11
+ ,12
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+ ,12
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+ ,11
+ ,7
+ ,7
+ ,22
+ ,62
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72)
+ ,dim=c(7
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1')
+ ,1:264))
> y <- array(NA,dim=c(7,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1'),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 = '6'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '6'
> #'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
Depression Connected Separate Learning Software Happiness Sport1
1 12.0 41 38 13 12 14 53
2 11.0 39 32 16 11 18 83
3 14.0 30 35 19 15 11 66
4 12.0 31 33 15 6 12 67
5 21.0 34 37 14 13 16 76
6 12.0 35 29 13 10 18 78
7 22.0 39 31 19 12 14 53
8 11.0 34 36 15 14 14 80
9 10.0 36 35 14 12 15 74
10 13.0 37 38 15 9 15 76
11 10.0 38 31 16 10 17 79
12 8.0 36 34 16 12 19 54
13 15.0 38 35 16 12 10 67
14 14.0 39 38 16 11 16 54
15 10.0 33 37 17 15 18 87
16 14.0 32 33 15 12 14 58
17 14.0 36 32 15 10 14 75
18 11.0 38 38 20 12 17 88
19 10.0 39 38 18 11 14 64
20 13.0 32 32 16 12 16 57
21 9.5 32 33 16 11 18 66
22 14.0 31 31 16 12 11 68
23 12.0 39 38 19 13 14 54
24 14.0 37 39 16 11 12 56
25 11.0 39 32 17 12 17 86
26 9.0 41 32 17 13 9 80
27 11.0 36 35 16 10 16 76
28 15.0 33 37 15 14 14 69
29 14.0 33 33 16 12 15 78
30 13.0 34 33 14 10 11 67
31 9.0 31 31 15 12 16 80
32 15.0 27 32 12 8 13 54
33 10.0 37 31 14 10 17 71
34 11.0 34 37 16 12 15 84
35 13.0 34 30 14 12 14 74
36 8.0 32 33 10 7 16 71
37 20.0 29 31 10 9 9 63
38 12.0 36 33 14 12 15 71
39 10.0 29 31 16 10 17 76
40 10.0 35 33 16 10 13 69
41 9.0 37 32 16 10 15 74
42 14.0 34 33 14 12 16 75
43 8.0 38 32 20 15 16 54
44 14.0 35 33 14 10 12 52
45 11.0 38 28 14 10 15 69
46 13.0 37 35 11 12 11 68
47 9.0 38 39 14 13 15 65
48 11.0 33 34 15 11 15 75
49 15.0 36 38 16 11 17 74
50 11.0 38 32 14 12 13 75
51 10.0 32 38 16 14 16 72
52 14.0 32 30 14 10 14 67
53 18.0 32 33 12 12 11 63
54 14.0 34 38 16 13 12 62
55 11.0 32 32 9 5 12 63
56 14.5 37 35 14 6 15 76
57 13.0 39 34 16 12 16 74
58 9.0 29 34 16 12 15 67
59 10.0 37 36 15 11 12 73
60 15.0 35 34 16 10 12 70
61 20.0 30 28 12 7 8 53
62 12.0 38 34 16 12 13 77
63 12.0 34 35 16 14 11 80
64 14.0 31 35 14 11 14 52
65 13.0 34 31 16 12 15 54
66 11.0 35 37 17 13 10 80
67 17.0 36 35 18 14 11 66
68 12.0 30 27 18 11 12 73
69 13.0 39 40 12 12 15 63
70 14.0 35 37 16 12 15 69
71 13.0 38 36 10 8 14 67
72 15.0 31 38 14 11 16 54
73 13.0 34 39 18 14 15 81
74 10.0 38 41 18 14 15 69
75 11.0 34 27 16 12 13 84
76 19.0 39 30 17 9 12 80
77 13.0 37 37 16 13 17 70
78 17.0 34 31 16 11 13 69
79 13.0 28 31 13 12 15 77
80 9.0 37 27 16 12 13 54
81 11.0 33 36 16 12 15 79
82 9.0 35 37 16 12 15 71
83 12.0 37 33 15 12 16 73
84 12.0 32 34 15 11 15 72
85 13.0 33 31 16 10 14 77
86 13.0 38 39 14 9 15 75
87 12.0 33 34 16 12 14 69
88 15.0 29 32 16 12 13 54
89 22.0 33 33 15 12 7 70
90 13.0 31 36 12 9 17 73
91 15.0 36 32 17 15 13 54
92 13.0 35 41 16 12 15 77
93 15.0 32 28 15 12 14 82
94 12.5 29 30 13 12 13 80
95 11.0 39 36 16 10 16 80
96 16.0 37 35 16 13 12 69
97 11.0 35 31 16 9 14 78
98 11.0 37 34 16 12 17 81
99 10.0 32 36 14 10 15 76
100 10.0 38 36 16 14 17 76
101 16.0 37 35 16 11 12 73
102 12.0 36 37 20 15 16 85
103 11.0 32 28 15 11 11 66
104 16.0 33 39 16 11 15 79
105 19.0 40 32 13 12 9 68
106 11.0 38 35 17 12 16 76
107 16.0 41 39 16 12 15 71
108 15.0 36 35 16 11 10 54
109 24.0 43 42 12 7 10 46
110 14.0 30 34 16 12 15 85
111 15.0 31 33 16 14 11 74
112 11.0 32 41 17 11 13 88
113 15.0 32 33 13 11 14 38
114 12.0 37 34 12 10 18 76
115 10.0 37 32 18 13 16 86
116 14.0 33 40 14 13 14 54
117 13.0 34 40 14 8 14 67
118 9.0 33 35 13 11 14 69
119 15.0 38 36 16 12 14 90
120 15.0 33 37 13 11 12 54
121 14.0 31 27 16 13 14 76
122 11.0 38 39 13 12 15 89
123 8.0 37 38 16 14 15 76
124 11.0 36 31 15 13 15 73
125 11.0 31 33 16 15 13 79
126 8.0 39 32 15 10 17 90
127 10.0 44 39 17 11 17 74
128 11.0 33 36 15 9 19 81
129 13.0 35 33 12 11 15 72
130 11.0 32 33 16 10 13 71
131 20.0 28 32 10 11 9 66
132 10.0 40 37 16 8 15 77
133 15.0 27 30 12 11 15 65
134 12.0 37 38 14 12 15 74
135 14.0 32 29 15 12 16 85
136 23.0 28 22 13 9 11 54
137 14.0 34 35 15 11 14 63
138 16.0 30 35 11 10 11 54
139 11.0 35 34 12 8 15 64
140 12.0 31 35 11 9 13 69
141 10.0 32 34 16 8 15 54
142 14.0 30 37 15 9 16 84
143 12.0 30 35 17 15 14 86
144 12.0 31 23 16 11 15 77
145 11.0 40 31 10 8 16 89
146 12.0 32 27 18 13 16 76
147 13.0 36 36 13 12 11 60
148 11.0 32 31 16 12 12 75
149 19.0 35 32 13 9 9 73
150 12.0 38 39 10 7 16 85
151 17.0 42 37 15 13 13 79
152 9.0 34 38 16 9 16 71
153 12.0 35 39 16 6 12 72
154 19.0 38 34 14 8 9 69
155 18.0 33 31 10 8 13 78
156 15.0 36 32 17 15 13 54
157 14.0 32 37 13 6 14 69
158 11.0 33 36 15 9 19 81
159 9.0 34 32 16 11 13 84
160 18.0 32 38 12 8 12 84
161 16.0 34 36 13 8 13 69
162 24.0 27 26 13 10 10 66
163 14.0 31 26 12 8 14 81
164 20.0 38 33 17 14 16 82
165 18.0 34 39 15 10 10 72
166 23.0 24 30 10 8 11 54
167 12.0 30 33 14 11 14 78
168 14.0 26 25 11 12 12 74
169 16.0 34 38 13 12 9 82
170 18.0 27 37 16 12 9 73
171 20.0 37 31 12 5 11 55
172 12.0 36 37 16 12 16 72
173 12.0 41 35 12 10 9 78
174 17.0 29 25 9 7 13 59
175 13.0 36 28 12 12 16 72
176 9.0 32 35 15 11 13 78
177 16.0 37 33 12 8 9 68
178 18.0 30 30 12 9 12 69
179 10.0 31 31 14 10 16 67
180 14.0 38 37 12 9 11 74
181 11.0 36 36 16 12 14 54
182 9.0 35 30 11 6 13 67
183 11.0 31 36 19 15 15 70
184 10.0 38 32 15 12 14 80
185 11.0 22 28 8 12 16 89
186 19.0 32 36 16 12 13 76
187 14.0 36 34 17 11 14 74
188 12.0 39 31 12 7 15 87
189 14.0 28 28 11 7 13 54
190 21.0 32 36 11 5 11 61
191 13.0 32 36 14 12 11 38
192 10.0 38 40 16 12 14 75
193 15.0 32 33 12 3 15 69
194 16.0 35 37 16 11 11 62
195 14.0 32 32 13 10 15 72
196 12.0 37 38 15 12 12 70
197 19.0 34 31 16 9 14 79
198 15.0 33 37 16 12 14 87
199 19.0 33 33 14 9 8 62
200 13.0 26 32 16 12 13 77
201 17.0 30 30 16 12 9 69
202 12.0 24 30 14 10 15 69
203 11.0 34 31 11 9 17 75
204 14.0 34 32 12 12 13 54
205 11.0 33 34 15 8 15 72
206 13.0 34 36 15 11 15 74
207 12.0 35 37 16 11 14 85
208 15.0 35 36 16 12 16 52
209 14.0 36 33 11 10 13 70
210 12.0 34 33 15 10 16 84
211 17.0 34 33 12 12 9 64
212 11.0 41 44 12 12 16 84
213 18.0 32 39 15 11 11 87
214 13.0 30 32 15 8 10 79
215 17.0 35 35 16 12 11 67
216 13.0 28 25 14 10 15 65
217 11.0 33 35 17 11 17 85
218 12.0 39 34 14 10 14 83
219 22.0 36 35 13 8 8 61
220 14.0 36 39 15 12 15 82
221 12.0 35 33 13 12 11 76
222 12.0 38 36 14 10 16 58
223 17.0 33 32 15 12 10 72
224 9.0 31 32 12 9 15 72
225 21.0 34 36 13 9 9 38
226 10.0 32 36 8 6 16 78
227 11.0 31 32 14 10 19 54
228 12.0 33 34 14 9 12 63
229 23.0 34 33 11 9 8 66
230 13.0 34 35 12 9 11 70
231 12.0 34 30 13 6 14 71
232 16.0 33 38 10 10 9 67
233 9.0 32 34 16 6 15 58
234 17.0 41 33 18 14 13 72
235 9.0 34 32 13 10 16 72
236 14.0 36 31 11 10 11 70
237 17.0 37 30 4 6 12 76
238 13.0 36 27 13 12 13 50
239 11.0 29 31 16 12 10 72
240 12.0 37 30 10 7 11 72
241 10.0 27 32 12 8 12 88
242 19.0 35 35 12 11 8 53
243 16.0 28 28 10 3 12 58
244 16.0 35 33 13 6 12 66
245 14.0 37 31 15 10 15 82
246 20.0 29 35 12 8 11 69
247 15.0 32 35 14 9 13 68
248 23.0 36 32 10 9 14 44
249 20.0 19 21 12 8 10 56
250 16.0 21 20 12 9 12 53
251 14.0 31 34 11 7 15 70
252 17.0 33 32 10 7 13 78
253 11.0 36 34 12 6 13 71
254 13.0 33 32 16 9 13 72
255 17.0 37 33 12 10 12 68
256 15.0 34 33 14 11 12 67
257 21.0 35 37 16 12 9 75
258 18.0 31 32 14 8 9 62
259 15.0 37 34 13 11 15 67
260 8.0 35 30 4 3 10 83
261 12.0 27 30 15 11 14 64
262 12.0 34 38 11 12 15 68
263 22.0 40 36 11 7 7 62
264 12.0 29 32 14 9 14 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Happiness
29.595294 -0.032936 0.007032 -0.089092 -0.026434 -0.711344
Sport1
-0.055927
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.4624 -1.7652 -0.1499 1.7132 9.4873
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 29.595294 2.124916 13.928 < 2e-16 ***
Connected -0.032936 0.051147 -0.644 0.52018
Separate 0.007032 0.052609 0.134 0.89377
Learning -0.089092 0.091805 -0.970 0.33274
Software -0.026434 0.094555 -0.280 0.78004
Happiness -0.711344 0.073397 -9.692 < 2e-16 ***
Sport1 -0.055927 0.017293 -3.234 0.00138 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.774 on 257 degrees of freedom
Multiple R-squared: 0.3755, Adjusted R-squared: 0.3609
F-statistic: 25.75 on 6 and 257 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,] 0.83775137 0.324497253 0.1622486264
[2,] 0.74521554 0.509568923 0.2547844616
[3,] 0.99941784 0.001164324 0.0005821621
[4,] 0.99874677 0.002506451 0.0012532253
[5,] 0.99740785 0.005184308 0.0025921541
[6,] 0.99555495 0.008890095 0.0044450473
[7,] 0.99189059 0.016218826 0.0081094130
[8,] 0.98634292 0.027314160 0.0136570800
[9,] 0.97851848 0.042963034 0.0214815169
[10,] 0.97954385 0.040912304 0.0204561522
[11,] 0.96845597 0.063088064 0.0315440322
[12,] 0.95690673 0.086186547 0.0430932734
[13,] 0.94140648 0.117187036 0.0585935179
[14,] 0.92704589 0.145908219 0.0729541097
[15,] 0.90121319 0.197573620 0.0987868102
[16,] 0.87445140 0.251097190 0.1255485952
[17,] 0.94500467 0.109990664 0.0549953322
[18,] 0.92556915 0.148861705 0.0744308523
[19,] 0.90981422 0.180371552 0.0901857761
[20,] 0.89168782 0.216624363 0.1083121815
[21,] 0.86607366 0.267852679 0.1339263396
[22,] 0.86721626 0.265567486 0.1327837430
[23,] 0.83530579 0.329388410 0.1646942051
[24,] 0.80779165 0.384416696 0.1922083481
[25,] 0.76779353 0.464412942 0.2322064712
[26,] 0.72312145 0.553757102 0.2768785508
[27,] 0.73796221 0.524075583 0.2620377917
[28,] 0.80562378 0.388752430 0.1943762150
[29,] 0.76786080 0.464278390 0.2321391950
[30,] 0.73383664 0.532326715 0.2661633576
[31,] 0.73748997 0.525020061 0.2625100305
[32,] 0.72646963 0.547060744 0.2735303719
[33,] 0.70540739 0.589185227 0.2945926134
[34,] 0.77252185 0.454956299 0.2274781496
[35,] 0.73677557 0.526448855 0.2632244275
[36,] 0.69846298 0.603074038 0.3015370192
[37,] 0.67651772 0.646964556 0.3234822780
[38,] 0.70639848 0.587203042 0.2936015210
[39,] 0.67001432 0.659971369 0.3299856847
[40,] 0.71488513 0.570229736 0.2851148679
[41,] 0.68766954 0.624660914 0.3123304570
[42,] 0.67451837 0.650963267 0.3254816337
[43,] 0.63752534 0.724949317 0.3624746583
[44,] 0.63990895 0.720182102 0.3600910510
[45,] 0.59775216 0.804495680 0.4022478400
[46,] 0.61503504 0.769929911 0.3849649556
[47,] 0.63247579 0.735048425 0.3675242125
[48,] 0.60863414 0.782731715 0.3913658574
[49,] 0.64414189 0.711716217 0.3558581087
[50,] 0.66473188 0.670536238 0.3352681190
[51,] 0.63961787 0.720764270 0.3603821348
[52,] 0.65250523 0.694989549 0.3474947743
[53,] 0.61500542 0.769989156 0.3849945782
[54,] 0.59155737 0.816885263 0.4084426314
[55,] 0.54990114 0.900197727 0.4500988634
[56,] 0.50782333 0.984353332 0.4921766662
[57,] 0.51789223 0.964215532 0.4821077659
[58,] 0.52283168 0.954336633 0.4771683167
[59,] 0.49886149 0.997722974 0.5011385132
[60,] 0.45795645 0.915912892 0.5420435541
[61,] 0.43355922 0.867118436 0.5664407821
[62,] 0.39418210 0.788364200 0.6058179000
[63,] 0.37163417 0.743268336 0.6283658320
[64,] 0.34418815 0.688376303 0.6558118486
[65,] 0.32847804 0.656956090 0.6715219550
[66,] 0.30206365 0.604127305 0.6979363475
[67,] 0.45987362 0.919747244 0.5401263782
[68,] 0.43970118 0.879402368 0.5602988160
[69,] 0.46441015 0.928820297 0.5355898516
[70,] 0.42727952 0.854559032 0.5727204842
[71,] 0.52204261 0.955914781 0.4779573907
[72,] 0.48760355 0.975207091 0.5123964545
[73,] 0.50319544 0.993609119 0.4968045595
[74,] 0.46580038 0.931600752 0.5341996242
[75,] 0.42808866 0.856177315 0.5719113427
[76,] 0.39133424 0.782668485 0.6086657573
[77,] 0.35793139 0.715862776 0.6420686119
[78,] 0.32724986 0.654499716 0.6727501422
[79,] 0.29538408 0.590768170 0.7046159150
[80,] 0.38203852 0.764077036 0.6179614822
[81,] 0.35684005 0.713680097 0.6431599513
[82,] 0.32872829 0.657456586 0.6712717069
[83,] 0.29879854 0.597597079 0.7012014605
[84,] 0.29391508 0.587830169 0.7060849157
[85,] 0.26634528 0.532690565 0.7336547176
[86,] 0.23619709 0.472394184 0.7638029081
[87,] 0.22301893 0.446037864 0.7769810680
[88,] 0.20469951 0.409399011 0.7953004947
[89,] 0.18005371 0.360107414 0.8199462928
[90,] 0.17325153 0.346503056 0.8267484722
[91,] 0.15144123 0.302882463 0.8485587687
[92,] 0.14268281 0.285365630 0.8573171852
[93,] 0.12691364 0.253827270 0.8730863648
[94,] 0.15488305 0.309766104 0.8451169478
[95,] 0.17774644 0.355492885 0.8222535573
[96,] 0.18450234 0.369004690 0.8154976551
[97,] 0.16111716 0.322234310 0.8388828449
[98,] 0.18139392 0.362787831 0.8186060843
[99,] 0.16589211 0.331784224 0.8341078880
[100,] 0.28978516 0.579570316 0.7102148419
[101,] 0.28213720 0.564274399 0.7178628004
[102,] 0.25389139 0.507782789 0.7461086057
[103,] 0.24198604 0.483972079 0.7580139607
[104,] 0.21488132 0.429762632 0.7851186839
[105,] 0.19839299 0.396785971 0.8016070143
[106,] 0.17498326 0.349966523 0.8250167384
[107,] 0.15275985 0.305519697 0.8472401513
[108,] 0.13400074 0.268001474 0.8659992632
[109,] 0.16575038 0.331500754 0.8342496228
[110,] 0.16936640 0.338732790 0.8306336050
[111,] 0.14862539 0.297250788 0.8513746060
[112,] 0.13601319 0.272026380 0.8639868098
[113,] 0.11897894 0.237957883 0.8810210584
[114,] 0.14088958 0.281779160 0.8591104198
[115,] 0.12520735 0.250414707 0.8747926463
[116,] 0.11886100 0.237721991 0.8811390043
[117,] 0.11068811 0.221376213 0.8893118936
[118,] 0.09633358 0.192667169 0.9036664154
[119,] 0.08766195 0.175323898 0.9123380510
[120,] 0.07463878 0.149277568 0.9253612161
[121,] 0.07492852 0.149857037 0.9250714816
[122,] 0.07633430 0.152668602 0.9236656988
[123,] 0.07089090 0.141781800 0.9291090998
[124,] 0.06493184 0.129863671 0.9350681646
[125,] 0.05449800 0.108995997 0.9455020014
[126,] 0.05646293 0.112925869 0.9435370656
[127,] 0.11979611 0.239592215 0.8802038924
[128,] 0.10313070 0.206261396 0.8968693020
[129,] 0.08884830 0.177696606 0.9111516971
[130,] 0.08338715 0.166774302 0.9166128489
[131,] 0.08000818 0.160016360 0.9199918201
[132,] 0.08768890 0.175377804 0.9123110981
[133,] 0.08796242 0.175924844 0.9120375781
[134,] 0.07462243 0.149244860 0.9253775701
[135,] 0.06284389 0.125687771 0.9371561144
[136,] 0.05260723 0.105214453 0.9473927736
[137,] 0.04416113 0.088322265 0.9558388676
[138,] 0.04594057 0.091881149 0.9540594253
[139,] 0.04993715 0.099874309 0.9500628453
[140,] 0.04741523 0.094830464 0.9525847681
[141,] 0.03983981 0.079679617 0.9601601914
[142,] 0.04637449 0.092748988 0.9536255058
[143,] 0.04602504 0.092050077 0.9539749615
[144,] 0.04441290 0.088825791 0.9555871044
[145,] 0.04135338 0.082706766 0.9586466168
[146,] 0.05094161 0.101883219 0.9490583904
[147,] 0.04281110 0.085622205 0.9571888975
[148,] 0.03533902 0.070678039 0.9646609804
[149,] 0.03153027 0.063060536 0.9684697318
[150,] 0.03967829 0.079356584 0.9603217078
[151,] 0.04731513 0.094630252 0.9526848742
[152,] 0.04212517 0.084250340 0.9578748301
[153,] 0.11167233 0.223344653 0.8883276735
[154,] 0.09796187 0.195923747 0.9020381266
[155,] 0.34785631 0.695712626 0.6521436869
[156,] 0.32908960 0.658179207 0.6709103966
[157,] 0.44642312 0.892846244 0.5535768782
[158,] 0.41400744 0.828014879 0.5859925605
[159,] 0.38316726 0.766334526 0.6168327371
[160,] 0.34832311 0.696646229 0.6516768856
[161,] 0.32061908 0.641238156 0.6793809218
[162,] 0.33302851 0.666057022 0.6669714891
[163,] 0.30022358 0.600447159 0.6997764206
[164,] 0.35380299 0.707605970 0.6461970149
[165,] 0.33792724 0.675854485 0.6620727577
[166,] 0.31342641 0.626852820 0.6865735901
[167,] 0.37354601 0.747092017 0.6264539917
[168,] 0.34557040 0.691140790 0.6544296050
[169,] 0.35150311 0.703006222 0.6484968888
[170,] 0.33496941 0.669938810 0.6650305949
[171,] 0.31052926 0.621058518 0.6894707412
[172,] 0.32803597 0.656071948 0.6719640262
[173,] 0.43059815 0.861196306 0.5694018468
[174,] 0.40596411 0.811928213 0.5940358933
[175,] 0.40440413 0.808808266 0.5955958672
[176,] 0.38472753 0.769455066 0.6152724669
[177,] 0.48443516 0.968870325 0.5155648377
[178,] 0.44842707 0.896854142 0.5515729291
[179,] 0.40971785 0.819435705 0.5902821474
[180,] 0.37873220 0.757464406 0.6212677969
[181,] 0.44405573 0.888111466 0.5559442670
[182,] 0.54539268 0.909214633 0.4546073165
[183,] 0.56956260 0.860874792 0.4304373961
[184,] 0.56264396 0.874712083 0.4373560416
[185,] 0.52857250 0.942854995 0.4714274974
[186,] 0.50078174 0.998436510 0.4992182552
[187,] 0.52952338 0.940953234 0.4704766171
[188,] 0.72025789 0.559484226 0.2797421129
[189,] 0.72578842 0.548423160 0.2742115798
[190,] 0.69015021 0.619699576 0.3098497878
[191,] 0.65244220 0.695115601 0.3475578006
[192,] 0.61237940 0.775241194 0.3876205971
[193,] 0.57301636 0.853967281 0.4269836404
[194,] 0.53622431 0.927551385 0.4637756923
[195,] 0.52022021 0.959559576 0.4797797878
[196,] 0.48481349 0.969626988 0.5151865059
[197,] 0.44228732 0.884574645 0.5577126775
[198,] 0.39890013 0.797800266 0.6010998672
[199,] 0.36547198 0.730943955 0.6345280226
[200,] 0.32436576 0.648731526 0.6756342369
[201,] 0.30391663 0.607833264 0.6960833678
[202,] 0.27706415 0.554128310 0.7229358450
[203,] 0.24179532 0.483590641 0.7582046795
[204,] 0.27560031 0.551200615 0.7243996926
[205,] 0.25531053 0.510621057 0.7446894714
[206,] 0.22254987 0.445099749 0.7774501253
[207,] 0.19128853 0.382577055 0.8087114726
[208,] 0.17964435 0.359288702 0.8203556492
[209,] 0.15071946 0.301438916 0.8492805420
[210,] 0.15088638 0.301772754 0.8491136232
[211,] 0.15087314 0.301746290 0.8491268551
[212,] 0.15704800 0.314095999 0.8429520004
[213,] 0.13512377 0.270247533 0.8648762337
[214,] 0.11130441 0.222608824 0.8886955880
[215,] 0.11289336 0.225786719 0.8871066404
[216,] 0.09658404 0.193168082 0.9034159589
[217,] 0.07894962 0.157899231 0.9210503847
[218,] 0.06165636 0.123312719 0.9383436404
[219,] 0.07161082 0.143221644 0.9283891781
[220,] 0.09816561 0.196331218 0.9018343911
[221,] 0.09438282 0.188765646 0.9056171770
[222,] 0.07409028 0.148180569 0.9259097153
[223,] 0.06946470 0.138929404 0.9305352978
[224,] 0.13287527 0.265750543 0.8671247284
[225,] 0.14536468 0.290729353 0.8546353235
[226,] 0.14177026 0.283540529 0.8582297355
[227,] 0.11367043 0.227340863 0.8863295686
[228,] 0.15942881 0.318857625 0.8405711876
[229,] 0.21071861 0.421437213 0.7892813937
[230,] 0.33226682 0.664533641 0.6677331793
[231,] 0.33070488 0.661409763 0.6692951184
[232,] 0.28161164 0.563223282 0.7183883590
[233,] 0.33949685 0.678993706 0.6605031468
[234,] 0.27376685 0.547533697 0.7262331514
[235,] 0.21314268 0.426285367 0.7868573163
[236,] 0.28732058 0.574641157 0.7126794216
[237,] 0.31082530 0.621650605 0.6891746974
[238,] 0.23741399 0.474827975 0.7625860127
[239,] 0.29852571 0.597051424 0.7014742878
[240,] 0.29923477 0.598469550 0.7007652252
[241,] 0.28312919 0.566258383 0.7168708084
[242,] 0.32719788 0.654395758 0.6728021208
[243,] 0.94691882 0.106162368 0.0530811841
[244,] 0.90626377 0.187472458 0.0937362290
[245,] 0.94759893 0.104802147 0.0524010736
> postscript(file="/var/wessaorg/rcomp/tmp/1lcbx1384461577.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/23xpt1384461577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ow7p1384461577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4xipu1384461577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5opbp1384461577.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
-2.11376765 1.62659417 -1.24809279 -3.02809150 9.48725530 1.94259976
7 8 9 10 11 12
8.40413632 -1.58916463 -2.28243994 0.85104459 -0.36079724 -2.37039823
13 14 15 16 17 18
-0.98659519 1.53981612 0.81235139 0.08278802 1.11946484 1.50255795
19 20 21 22 23 24
-3.14541346 0.54567221 -1.06175915 -1.42174870 -1.56272879 -1.26660912
25 26 27 28 29 30
1.19855825 -6.73545057 -0.33392540 1.75566464 2.03470908 -2.62398292
31 32 33 34 35 36
-2.28299183 -0.38292510 -1.01933665 -0.62491920 -0.02449467 -4.34509484
37 38 39 40 41 42
2.19620218 -0.43615765 -0.82500637 -3.87832068 -3.10309081 2.43302339
43 44 45 46 47 48
-3.98882417 -1.71861475 -1.49984587 -2.69771874 -3.72161005 -1.25563114
49 50 51 52 53 54
4.27090026 -2.56223047 -1.60474230 0.46527290 1.96111881 -0.96995333
55 56 57 58 59 60
-4.77281671 2.20374852 1.71292852 -3.71927056 -4.38383695 0.45923064
61 62 63 64 65 66
1.10493288 -1.28625682 -2.62707200 -0.41530323 -0.26054904 -4.25688627
67 68 69 70 71 72
1.83399954 -2.28382492 -0.01217821 1.56910517 -0.78853770 2.09814229
73 74 75 76 77 78
1.42428473 -2.12916428 -1.97728343 5.24103774 2.14002665 3.12924149
79 80 81 82 83 84
0.56088966 -5.55629839 -0.93046048 -3.31903990 0.50906908 -0.45634984
85 86 87 88 89 90
0.22863490 0.73192939 -1.18701423 0.14504949 3.80744723 1.65512156
91 92 93 94 95 96
0.54399670 0.98839552 2.46020880 -1.15404692 -0.01843897 1.54144483
97 98 99 100 101 102
-1.67599883 0.74989195 -2.36223016 -0.45800614 1.71228709 1.64389287
103 104 105 106 107 108
-4.59509580 4.02200872 2.17768298 -0.12609350 3.86451324 -1.80595860
109 110 111 112 113 114
6.46584747 2.32036006 -0.04738101 -1.85524101 -0.24037843 1.77236405
115 116 117 118 119 120
-0.46313267 -0.21986980 -0.59204546 -4.48775547 3.13807932 -0.76341969
121 122 123 124 125 126
1.21426556 -0.49487645 -3.92769473 -1.19471256 -2.31862230 -1.80878290
127 128 129 130 131 132
-0.38354994 1.85837659 0.38221631 -2.86527466 2.37688351 -1.92452879
133 134 135 136 137 138
1.74833065 -0.27060065 3.04364646 6.41317247 0.38779947 -0.76412493
139 140 141 142 143 144
-2.15153711 -2.49602287 -3.45325384 2.78628631 -0.17369553 -0.04320141
145 146 147 148 149 150
-0.03441162 0.84807288 -3.00692368 -3.28597635 2.21333739 0.59331343
151 152 153 154 155 156
3.87358840 -2.72696612 -2.56981130 2.13702968 3.98580075 0.54399670
157 158 159 160 161 162
0.33307458 1.85837659 -4.03887891 3.70604246 1.74750331 7.33832635
163 164 165 166 167 168
1.01239943 9.27640354 1.99120826 5.93145965 -0.98006084 -0.94278612
169 170 171 172 173 174
-0.27914440 1.26126176 3.50740874 0.48116767 -4.39316240 1.71810228
175 176 177 178 179 180
1.18809190 -4.55050501 -1.12298515 2.88395041 -2.15200813 -1.33349202
181 182 183 184 185 186
-2.94118193 -5.52027227 -1.15310375 -2.48215765 -0.67861630 5.44613322
187 188 189 190 191 192
1.25408990 0.26120296 -1.43738494 4.55403861 -4.27998136 -2.72896196
193 194 195 196 197 198
1.90415466 0.30580392 1.35309764 -2.53925021 6.34699233 2.79858308
199 200 201 202 203 204
0.90297826 -0.66742778 0.18558719 -0.97501880 -0.18814461 -1.04663580
205 206 207 208 209 210
-1.50271491 0.70731302 -0.27383303 2.33671445 -0.23491542 0.97259467
211 212 213 214 215 216
-0.33976873 -0.08861444 3.50202519 -2.75268594 1.62593970 -0.03182174
217 218 219 220 221 222
0.89748216 -0.43746293 3.81626953 2.22594211 -3.12392357 -0.45996285
223 224 225 226 227 228
1.06036591 -3.79536416 2.19481055 -2.17931682 0.24793394 -3.20275140
229 230 231 232 233 234
4.89234930 -2.67488241 -1.43997152 -1.47113533 -4.28241158 3.77099811
235 236 237 238 239 240
-2.86968595 -1.64353836 1.71396177 -2.08021965 -4.97525526 -3.66010787
241 242 243 244 245 246
-4.19273519 0.32612321 -0.11984578 0.86954285 2.26226953 4.07807482
247 248 249 250 251 252
0.74826108 7.91382121 2.40876370 -0.23699234 0.93675695 2.95233462
253 254 255 256 257 258
-3.20266377 -0.79581235 2.06391386 0.11379468 4.63660711 0.52904800
259 260 261 262 263 264
2.25051097 -8.46240759 -1.75166551 -0.97224944 3.08094887 -1.39439717
> postscript(file="/var/wessaorg/rcomp/tmp/693fk1384461577.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 -2.11376765 NA
1 1.62659417 -2.11376765
2 -1.24809279 1.62659417
3 -3.02809150 -1.24809279
4 9.48725530 -3.02809150
5 1.94259976 9.48725530
6 8.40413632 1.94259976
7 -1.58916463 8.40413632
8 -2.28243994 -1.58916463
9 0.85104459 -2.28243994
10 -0.36079724 0.85104459
11 -2.37039823 -0.36079724
12 -0.98659519 -2.37039823
13 1.53981612 -0.98659519
14 0.81235139 1.53981612
15 0.08278802 0.81235139
16 1.11946484 0.08278802
17 1.50255795 1.11946484
18 -3.14541346 1.50255795
19 0.54567221 -3.14541346
20 -1.06175915 0.54567221
21 -1.42174870 -1.06175915
22 -1.56272879 -1.42174870
23 -1.26660912 -1.56272879
24 1.19855825 -1.26660912
25 -6.73545057 1.19855825
26 -0.33392540 -6.73545057
27 1.75566464 -0.33392540
28 2.03470908 1.75566464
29 -2.62398292 2.03470908
30 -2.28299183 -2.62398292
31 -0.38292510 -2.28299183
32 -1.01933665 -0.38292510
33 -0.62491920 -1.01933665
34 -0.02449467 -0.62491920
35 -4.34509484 -0.02449467
36 2.19620218 -4.34509484
37 -0.43615765 2.19620218
38 -0.82500637 -0.43615765
39 -3.87832068 -0.82500637
40 -3.10309081 -3.87832068
41 2.43302339 -3.10309081
42 -3.98882417 2.43302339
43 -1.71861475 -3.98882417
44 -1.49984587 -1.71861475
45 -2.69771874 -1.49984587
46 -3.72161005 -2.69771874
47 -1.25563114 -3.72161005
48 4.27090026 -1.25563114
49 -2.56223047 4.27090026
50 -1.60474230 -2.56223047
51 0.46527290 -1.60474230
52 1.96111881 0.46527290
53 -0.96995333 1.96111881
54 -4.77281671 -0.96995333
55 2.20374852 -4.77281671
56 1.71292852 2.20374852
57 -3.71927056 1.71292852
58 -4.38383695 -3.71927056
59 0.45923064 -4.38383695
60 1.10493288 0.45923064
61 -1.28625682 1.10493288
62 -2.62707200 -1.28625682
63 -0.41530323 -2.62707200
64 -0.26054904 -0.41530323
65 -4.25688627 -0.26054904
66 1.83399954 -4.25688627
67 -2.28382492 1.83399954
68 -0.01217821 -2.28382492
69 1.56910517 -0.01217821
70 -0.78853770 1.56910517
71 2.09814229 -0.78853770
72 1.42428473 2.09814229
73 -2.12916428 1.42428473
74 -1.97728343 -2.12916428
75 5.24103774 -1.97728343
76 2.14002665 5.24103774
77 3.12924149 2.14002665
78 0.56088966 3.12924149
79 -5.55629839 0.56088966
80 -0.93046048 -5.55629839
81 -3.31903990 -0.93046048
82 0.50906908 -3.31903990
83 -0.45634984 0.50906908
84 0.22863490 -0.45634984
85 0.73192939 0.22863490
86 -1.18701423 0.73192939
87 0.14504949 -1.18701423
88 3.80744723 0.14504949
89 1.65512156 3.80744723
90 0.54399670 1.65512156
91 0.98839552 0.54399670
92 2.46020880 0.98839552
93 -1.15404692 2.46020880
94 -0.01843897 -1.15404692
95 1.54144483 -0.01843897
96 -1.67599883 1.54144483
97 0.74989195 -1.67599883
98 -2.36223016 0.74989195
99 -0.45800614 -2.36223016
100 1.71228709 -0.45800614
101 1.64389287 1.71228709
102 -4.59509580 1.64389287
103 4.02200872 -4.59509580
104 2.17768298 4.02200872
105 -0.12609350 2.17768298
106 3.86451324 -0.12609350
107 -1.80595860 3.86451324
108 6.46584747 -1.80595860
109 2.32036006 6.46584747
110 -0.04738101 2.32036006
111 -1.85524101 -0.04738101
112 -0.24037843 -1.85524101
113 1.77236405 -0.24037843
114 -0.46313267 1.77236405
115 -0.21986980 -0.46313267
116 -0.59204546 -0.21986980
117 -4.48775547 -0.59204546
118 3.13807932 -4.48775547
119 -0.76341969 3.13807932
120 1.21426556 -0.76341969
121 -0.49487645 1.21426556
122 -3.92769473 -0.49487645
123 -1.19471256 -3.92769473
124 -2.31862230 -1.19471256
125 -1.80878290 -2.31862230
126 -0.38354994 -1.80878290
127 1.85837659 -0.38354994
128 0.38221631 1.85837659
129 -2.86527466 0.38221631
130 2.37688351 -2.86527466
131 -1.92452879 2.37688351
132 1.74833065 -1.92452879
133 -0.27060065 1.74833065
134 3.04364646 -0.27060065
135 6.41317247 3.04364646
136 0.38779947 6.41317247
137 -0.76412493 0.38779947
138 -2.15153711 -0.76412493
139 -2.49602287 -2.15153711
140 -3.45325384 -2.49602287
141 2.78628631 -3.45325384
142 -0.17369553 2.78628631
143 -0.04320141 -0.17369553
144 -0.03441162 -0.04320141
145 0.84807288 -0.03441162
146 -3.00692368 0.84807288
147 -3.28597635 -3.00692368
148 2.21333739 -3.28597635
149 0.59331343 2.21333739
150 3.87358840 0.59331343
151 -2.72696612 3.87358840
152 -2.56981130 -2.72696612
153 2.13702968 -2.56981130
154 3.98580075 2.13702968
155 0.54399670 3.98580075
156 0.33307458 0.54399670
157 1.85837659 0.33307458
158 -4.03887891 1.85837659
159 3.70604246 -4.03887891
160 1.74750331 3.70604246
161 7.33832635 1.74750331
162 1.01239943 7.33832635
163 9.27640354 1.01239943
164 1.99120826 9.27640354
165 5.93145965 1.99120826
166 -0.98006084 5.93145965
167 -0.94278612 -0.98006084
168 -0.27914440 -0.94278612
169 1.26126176 -0.27914440
170 3.50740874 1.26126176
171 0.48116767 3.50740874
172 -4.39316240 0.48116767
173 1.71810228 -4.39316240
174 1.18809190 1.71810228
175 -4.55050501 1.18809190
176 -1.12298515 -4.55050501
177 2.88395041 -1.12298515
178 -2.15200813 2.88395041
179 -1.33349202 -2.15200813
180 -2.94118193 -1.33349202
181 -5.52027227 -2.94118193
182 -1.15310375 -5.52027227
183 -2.48215765 -1.15310375
184 -0.67861630 -2.48215765
185 5.44613322 -0.67861630
186 1.25408990 5.44613322
187 0.26120296 1.25408990
188 -1.43738494 0.26120296
189 4.55403861 -1.43738494
190 -4.27998136 4.55403861
191 -2.72896196 -4.27998136
192 1.90415466 -2.72896196
193 0.30580392 1.90415466
194 1.35309764 0.30580392
195 -2.53925021 1.35309764
196 6.34699233 -2.53925021
197 2.79858308 6.34699233
198 0.90297826 2.79858308
199 -0.66742778 0.90297826
200 0.18558719 -0.66742778
201 -0.97501880 0.18558719
202 -0.18814461 -0.97501880
203 -1.04663580 -0.18814461
204 -1.50271491 -1.04663580
205 0.70731302 -1.50271491
206 -0.27383303 0.70731302
207 2.33671445 -0.27383303
208 -0.23491542 2.33671445
209 0.97259467 -0.23491542
210 -0.33976873 0.97259467
211 -0.08861444 -0.33976873
212 3.50202519 -0.08861444
213 -2.75268594 3.50202519
214 1.62593970 -2.75268594
215 -0.03182174 1.62593970
216 0.89748216 -0.03182174
217 -0.43746293 0.89748216
218 3.81626953 -0.43746293
219 2.22594211 3.81626953
220 -3.12392357 2.22594211
221 -0.45996285 -3.12392357
222 1.06036591 -0.45996285
223 -3.79536416 1.06036591
224 2.19481055 -3.79536416
225 -2.17931682 2.19481055
226 0.24793394 -2.17931682
227 -3.20275140 0.24793394
228 4.89234930 -3.20275140
229 -2.67488241 4.89234930
230 -1.43997152 -2.67488241
231 -1.47113533 -1.43997152
232 -4.28241158 -1.47113533
233 3.77099811 -4.28241158
234 -2.86968595 3.77099811
235 -1.64353836 -2.86968595
236 1.71396177 -1.64353836
237 -2.08021965 1.71396177
238 -4.97525526 -2.08021965
239 -3.66010787 -4.97525526
240 -4.19273519 -3.66010787
241 0.32612321 -4.19273519
242 -0.11984578 0.32612321
243 0.86954285 -0.11984578
244 2.26226953 0.86954285
245 4.07807482 2.26226953
246 0.74826108 4.07807482
247 7.91382121 0.74826108
248 2.40876370 7.91382121
249 -0.23699234 2.40876370
250 0.93675695 -0.23699234
251 2.95233462 0.93675695
252 -3.20266377 2.95233462
253 -0.79581235 -3.20266377
254 2.06391386 -0.79581235
255 0.11379468 2.06391386
256 4.63660711 0.11379468
257 0.52904800 4.63660711
258 2.25051097 0.52904800
259 -8.46240759 2.25051097
260 -1.75166551 -8.46240759
261 -0.97224944 -1.75166551
262 3.08094887 -0.97224944
263 -1.39439717 3.08094887
264 NA -1.39439717
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.62659417 -2.11376765
[2,] -1.24809279 1.62659417
[3,] -3.02809150 -1.24809279
[4,] 9.48725530 -3.02809150
[5,] 1.94259976 9.48725530
[6,] 8.40413632 1.94259976
[7,] -1.58916463 8.40413632
[8,] -2.28243994 -1.58916463
[9,] 0.85104459 -2.28243994
[10,] -0.36079724 0.85104459
[11,] -2.37039823 -0.36079724
[12,] -0.98659519 -2.37039823
[13,] 1.53981612 -0.98659519
[14,] 0.81235139 1.53981612
[15,] 0.08278802 0.81235139
[16,] 1.11946484 0.08278802
[17,] 1.50255795 1.11946484
[18,] -3.14541346 1.50255795
[19,] 0.54567221 -3.14541346
[20,] -1.06175915 0.54567221
[21,] -1.42174870 -1.06175915
[22,] -1.56272879 -1.42174870
[23,] -1.26660912 -1.56272879
[24,] 1.19855825 -1.26660912
[25,] -6.73545057 1.19855825
[26,] -0.33392540 -6.73545057
[27,] 1.75566464 -0.33392540
[28,] 2.03470908 1.75566464
[29,] -2.62398292 2.03470908
[30,] -2.28299183 -2.62398292
[31,] -0.38292510 -2.28299183
[32,] -1.01933665 -0.38292510
[33,] -0.62491920 -1.01933665
[34,] -0.02449467 -0.62491920
[35,] -4.34509484 -0.02449467
[36,] 2.19620218 -4.34509484
[37,] -0.43615765 2.19620218
[38,] -0.82500637 -0.43615765
[39,] -3.87832068 -0.82500637
[40,] -3.10309081 -3.87832068
[41,] 2.43302339 -3.10309081
[42,] -3.98882417 2.43302339
[43,] -1.71861475 -3.98882417
[44,] -1.49984587 -1.71861475
[45,] -2.69771874 -1.49984587
[46,] -3.72161005 -2.69771874
[47,] -1.25563114 -3.72161005
[48,] 4.27090026 -1.25563114
[49,] -2.56223047 4.27090026
[50,] -1.60474230 -2.56223047
[51,] 0.46527290 -1.60474230
[52,] 1.96111881 0.46527290
[53,] -0.96995333 1.96111881
[54,] -4.77281671 -0.96995333
[55,] 2.20374852 -4.77281671
[56,] 1.71292852 2.20374852
[57,] -3.71927056 1.71292852
[58,] -4.38383695 -3.71927056
[59,] 0.45923064 -4.38383695
[60,] 1.10493288 0.45923064
[61,] -1.28625682 1.10493288
[62,] -2.62707200 -1.28625682
[63,] -0.41530323 -2.62707200
[64,] -0.26054904 -0.41530323
[65,] -4.25688627 -0.26054904
[66,] 1.83399954 -4.25688627
[67,] -2.28382492 1.83399954
[68,] -0.01217821 -2.28382492
[69,] 1.56910517 -0.01217821
[70,] -0.78853770 1.56910517
[71,] 2.09814229 -0.78853770
[72,] 1.42428473 2.09814229
[73,] -2.12916428 1.42428473
[74,] -1.97728343 -2.12916428
[75,] 5.24103774 -1.97728343
[76,] 2.14002665 5.24103774
[77,] 3.12924149 2.14002665
[78,] 0.56088966 3.12924149
[79,] -5.55629839 0.56088966
[80,] -0.93046048 -5.55629839
[81,] -3.31903990 -0.93046048
[82,] 0.50906908 -3.31903990
[83,] -0.45634984 0.50906908
[84,] 0.22863490 -0.45634984
[85,] 0.73192939 0.22863490
[86,] -1.18701423 0.73192939
[87,] 0.14504949 -1.18701423
[88,] 3.80744723 0.14504949
[89,] 1.65512156 3.80744723
[90,] 0.54399670 1.65512156
[91,] 0.98839552 0.54399670
[92,] 2.46020880 0.98839552
[93,] -1.15404692 2.46020880
[94,] -0.01843897 -1.15404692
[95,] 1.54144483 -0.01843897
[96,] -1.67599883 1.54144483
[97,] 0.74989195 -1.67599883
[98,] -2.36223016 0.74989195
[99,] -0.45800614 -2.36223016
[100,] 1.71228709 -0.45800614
[101,] 1.64389287 1.71228709
[102,] -4.59509580 1.64389287
[103,] 4.02200872 -4.59509580
[104,] 2.17768298 4.02200872
[105,] -0.12609350 2.17768298
[106,] 3.86451324 -0.12609350
[107,] -1.80595860 3.86451324
[108,] 6.46584747 -1.80595860
[109,] 2.32036006 6.46584747
[110,] -0.04738101 2.32036006
[111,] -1.85524101 -0.04738101
[112,] -0.24037843 -1.85524101
[113,] 1.77236405 -0.24037843
[114,] -0.46313267 1.77236405
[115,] -0.21986980 -0.46313267
[116,] -0.59204546 -0.21986980
[117,] -4.48775547 -0.59204546
[118,] 3.13807932 -4.48775547
[119,] -0.76341969 3.13807932
[120,] 1.21426556 -0.76341969
[121,] -0.49487645 1.21426556
[122,] -3.92769473 -0.49487645
[123,] -1.19471256 -3.92769473
[124,] -2.31862230 -1.19471256
[125,] -1.80878290 -2.31862230
[126,] -0.38354994 -1.80878290
[127,] 1.85837659 -0.38354994
[128,] 0.38221631 1.85837659
[129,] -2.86527466 0.38221631
[130,] 2.37688351 -2.86527466
[131,] -1.92452879 2.37688351
[132,] 1.74833065 -1.92452879
[133,] -0.27060065 1.74833065
[134,] 3.04364646 -0.27060065
[135,] 6.41317247 3.04364646
[136,] 0.38779947 6.41317247
[137,] -0.76412493 0.38779947
[138,] -2.15153711 -0.76412493
[139,] -2.49602287 -2.15153711
[140,] -3.45325384 -2.49602287
[141,] 2.78628631 -3.45325384
[142,] -0.17369553 2.78628631
[143,] -0.04320141 -0.17369553
[144,] -0.03441162 -0.04320141
[145,] 0.84807288 -0.03441162
[146,] -3.00692368 0.84807288
[147,] -3.28597635 -3.00692368
[148,] 2.21333739 -3.28597635
[149,] 0.59331343 2.21333739
[150,] 3.87358840 0.59331343
[151,] -2.72696612 3.87358840
[152,] -2.56981130 -2.72696612
[153,] 2.13702968 -2.56981130
[154,] 3.98580075 2.13702968
[155,] 0.54399670 3.98580075
[156,] 0.33307458 0.54399670
[157,] 1.85837659 0.33307458
[158,] -4.03887891 1.85837659
[159,] 3.70604246 -4.03887891
[160,] 1.74750331 3.70604246
[161,] 7.33832635 1.74750331
[162,] 1.01239943 7.33832635
[163,] 9.27640354 1.01239943
[164,] 1.99120826 9.27640354
[165,] 5.93145965 1.99120826
[166,] -0.98006084 5.93145965
[167,] -0.94278612 -0.98006084
[168,] -0.27914440 -0.94278612
[169,] 1.26126176 -0.27914440
[170,] 3.50740874 1.26126176
[171,] 0.48116767 3.50740874
[172,] -4.39316240 0.48116767
[173,] 1.71810228 -4.39316240
[174,] 1.18809190 1.71810228
[175,] -4.55050501 1.18809190
[176,] -1.12298515 -4.55050501
[177,] 2.88395041 -1.12298515
[178,] -2.15200813 2.88395041
[179,] -1.33349202 -2.15200813
[180,] -2.94118193 -1.33349202
[181,] -5.52027227 -2.94118193
[182,] -1.15310375 -5.52027227
[183,] -2.48215765 -1.15310375
[184,] -0.67861630 -2.48215765
[185,] 5.44613322 -0.67861630
[186,] 1.25408990 5.44613322
[187,] 0.26120296 1.25408990
[188,] -1.43738494 0.26120296
[189,] 4.55403861 -1.43738494
[190,] -4.27998136 4.55403861
[191,] -2.72896196 -4.27998136
[192,] 1.90415466 -2.72896196
[193,] 0.30580392 1.90415466
[194,] 1.35309764 0.30580392
[195,] -2.53925021 1.35309764
[196,] 6.34699233 -2.53925021
[197,] 2.79858308 6.34699233
[198,] 0.90297826 2.79858308
[199,] -0.66742778 0.90297826
[200,] 0.18558719 -0.66742778
[201,] -0.97501880 0.18558719
[202,] -0.18814461 -0.97501880
[203,] -1.04663580 -0.18814461
[204,] -1.50271491 -1.04663580
[205,] 0.70731302 -1.50271491
[206,] -0.27383303 0.70731302
[207,] 2.33671445 -0.27383303
[208,] -0.23491542 2.33671445
[209,] 0.97259467 -0.23491542
[210,] -0.33976873 0.97259467
[211,] -0.08861444 -0.33976873
[212,] 3.50202519 -0.08861444
[213,] -2.75268594 3.50202519
[214,] 1.62593970 -2.75268594
[215,] -0.03182174 1.62593970
[216,] 0.89748216 -0.03182174
[217,] -0.43746293 0.89748216
[218,] 3.81626953 -0.43746293
[219,] 2.22594211 3.81626953
[220,] -3.12392357 2.22594211
[221,] -0.45996285 -3.12392357
[222,] 1.06036591 -0.45996285
[223,] -3.79536416 1.06036591
[224,] 2.19481055 -3.79536416
[225,] -2.17931682 2.19481055
[226,] 0.24793394 -2.17931682
[227,] -3.20275140 0.24793394
[228,] 4.89234930 -3.20275140
[229,] -2.67488241 4.89234930
[230,] -1.43997152 -2.67488241
[231,] -1.47113533 -1.43997152
[232,] -4.28241158 -1.47113533
[233,] 3.77099811 -4.28241158
[234,] -2.86968595 3.77099811
[235,] -1.64353836 -2.86968595
[236,] 1.71396177 -1.64353836
[237,] -2.08021965 1.71396177
[238,] -4.97525526 -2.08021965
[239,] -3.66010787 -4.97525526
[240,] -4.19273519 -3.66010787
[241,] 0.32612321 -4.19273519
[242,] -0.11984578 0.32612321
[243,] 0.86954285 -0.11984578
[244,] 2.26226953 0.86954285
[245,] 4.07807482 2.26226953
[246,] 0.74826108 4.07807482
[247,] 7.91382121 0.74826108
[248,] 2.40876370 7.91382121
[249,] -0.23699234 2.40876370
[250,] 0.93675695 -0.23699234
[251,] 2.95233462 0.93675695
[252,] -3.20266377 2.95233462
[253,] -0.79581235 -3.20266377
[254,] 2.06391386 -0.79581235
[255,] 0.11379468 2.06391386
[256,] 4.63660711 0.11379468
[257,] 0.52904800 4.63660711
[258,] 2.25051097 0.52904800
[259,] -8.46240759 2.25051097
[260,] -1.75166551 -8.46240759
[261,] -0.97224944 -1.75166551
[262,] 3.08094887 -0.97224944
[263,] -1.39439717 3.08094887
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.62659417 -2.11376765
2 -1.24809279 1.62659417
3 -3.02809150 -1.24809279
4 9.48725530 -3.02809150
5 1.94259976 9.48725530
6 8.40413632 1.94259976
7 -1.58916463 8.40413632
8 -2.28243994 -1.58916463
9 0.85104459 -2.28243994
10 -0.36079724 0.85104459
11 -2.37039823 -0.36079724
12 -0.98659519 -2.37039823
13 1.53981612 -0.98659519
14 0.81235139 1.53981612
15 0.08278802 0.81235139
16 1.11946484 0.08278802
17 1.50255795 1.11946484
18 -3.14541346 1.50255795
19 0.54567221 -3.14541346
20 -1.06175915 0.54567221
21 -1.42174870 -1.06175915
22 -1.56272879 -1.42174870
23 -1.26660912 -1.56272879
24 1.19855825 -1.26660912
25 -6.73545057 1.19855825
26 -0.33392540 -6.73545057
27 1.75566464 -0.33392540
28 2.03470908 1.75566464
29 -2.62398292 2.03470908
30 -2.28299183 -2.62398292
31 -0.38292510 -2.28299183
32 -1.01933665 -0.38292510
33 -0.62491920 -1.01933665
34 -0.02449467 -0.62491920
35 -4.34509484 -0.02449467
36 2.19620218 -4.34509484
37 -0.43615765 2.19620218
38 -0.82500637 -0.43615765
39 -3.87832068 -0.82500637
40 -3.10309081 -3.87832068
41 2.43302339 -3.10309081
42 -3.98882417 2.43302339
43 -1.71861475 -3.98882417
44 -1.49984587 -1.71861475
45 -2.69771874 -1.49984587
46 -3.72161005 -2.69771874
47 -1.25563114 -3.72161005
48 4.27090026 -1.25563114
49 -2.56223047 4.27090026
50 -1.60474230 -2.56223047
51 0.46527290 -1.60474230
52 1.96111881 0.46527290
53 -0.96995333 1.96111881
54 -4.77281671 -0.96995333
55 2.20374852 -4.77281671
56 1.71292852 2.20374852
57 -3.71927056 1.71292852
58 -4.38383695 -3.71927056
59 0.45923064 -4.38383695
60 1.10493288 0.45923064
61 -1.28625682 1.10493288
62 -2.62707200 -1.28625682
63 -0.41530323 -2.62707200
64 -0.26054904 -0.41530323
65 -4.25688627 -0.26054904
66 1.83399954 -4.25688627
67 -2.28382492 1.83399954
68 -0.01217821 -2.28382492
69 1.56910517 -0.01217821
70 -0.78853770 1.56910517
71 2.09814229 -0.78853770
72 1.42428473 2.09814229
73 -2.12916428 1.42428473
74 -1.97728343 -2.12916428
75 5.24103774 -1.97728343
76 2.14002665 5.24103774
77 3.12924149 2.14002665
78 0.56088966 3.12924149
79 -5.55629839 0.56088966
80 -0.93046048 -5.55629839
81 -3.31903990 -0.93046048
82 0.50906908 -3.31903990
83 -0.45634984 0.50906908
84 0.22863490 -0.45634984
85 0.73192939 0.22863490
86 -1.18701423 0.73192939
87 0.14504949 -1.18701423
88 3.80744723 0.14504949
89 1.65512156 3.80744723
90 0.54399670 1.65512156
91 0.98839552 0.54399670
92 2.46020880 0.98839552
93 -1.15404692 2.46020880
94 -0.01843897 -1.15404692
95 1.54144483 -0.01843897
96 -1.67599883 1.54144483
97 0.74989195 -1.67599883
98 -2.36223016 0.74989195
99 -0.45800614 -2.36223016
100 1.71228709 -0.45800614
101 1.64389287 1.71228709
102 -4.59509580 1.64389287
103 4.02200872 -4.59509580
104 2.17768298 4.02200872
105 -0.12609350 2.17768298
106 3.86451324 -0.12609350
107 -1.80595860 3.86451324
108 6.46584747 -1.80595860
109 2.32036006 6.46584747
110 -0.04738101 2.32036006
111 -1.85524101 -0.04738101
112 -0.24037843 -1.85524101
113 1.77236405 -0.24037843
114 -0.46313267 1.77236405
115 -0.21986980 -0.46313267
116 -0.59204546 -0.21986980
117 -4.48775547 -0.59204546
118 3.13807932 -4.48775547
119 -0.76341969 3.13807932
120 1.21426556 -0.76341969
121 -0.49487645 1.21426556
122 -3.92769473 -0.49487645
123 -1.19471256 -3.92769473
124 -2.31862230 -1.19471256
125 -1.80878290 -2.31862230
126 -0.38354994 -1.80878290
127 1.85837659 -0.38354994
128 0.38221631 1.85837659
129 -2.86527466 0.38221631
130 2.37688351 -2.86527466
131 -1.92452879 2.37688351
132 1.74833065 -1.92452879
133 -0.27060065 1.74833065
134 3.04364646 -0.27060065
135 6.41317247 3.04364646
136 0.38779947 6.41317247
137 -0.76412493 0.38779947
138 -2.15153711 -0.76412493
139 -2.49602287 -2.15153711
140 -3.45325384 -2.49602287
141 2.78628631 -3.45325384
142 -0.17369553 2.78628631
143 -0.04320141 -0.17369553
144 -0.03441162 -0.04320141
145 0.84807288 -0.03441162
146 -3.00692368 0.84807288
147 -3.28597635 -3.00692368
148 2.21333739 -3.28597635
149 0.59331343 2.21333739
150 3.87358840 0.59331343
151 -2.72696612 3.87358840
152 -2.56981130 -2.72696612
153 2.13702968 -2.56981130
154 3.98580075 2.13702968
155 0.54399670 3.98580075
156 0.33307458 0.54399670
157 1.85837659 0.33307458
158 -4.03887891 1.85837659
159 3.70604246 -4.03887891
160 1.74750331 3.70604246
161 7.33832635 1.74750331
162 1.01239943 7.33832635
163 9.27640354 1.01239943
164 1.99120826 9.27640354
165 5.93145965 1.99120826
166 -0.98006084 5.93145965
167 -0.94278612 -0.98006084
168 -0.27914440 -0.94278612
169 1.26126176 -0.27914440
170 3.50740874 1.26126176
171 0.48116767 3.50740874
172 -4.39316240 0.48116767
173 1.71810228 -4.39316240
174 1.18809190 1.71810228
175 -4.55050501 1.18809190
176 -1.12298515 -4.55050501
177 2.88395041 -1.12298515
178 -2.15200813 2.88395041
179 -1.33349202 -2.15200813
180 -2.94118193 -1.33349202
181 -5.52027227 -2.94118193
182 -1.15310375 -5.52027227
183 -2.48215765 -1.15310375
184 -0.67861630 -2.48215765
185 5.44613322 -0.67861630
186 1.25408990 5.44613322
187 0.26120296 1.25408990
188 -1.43738494 0.26120296
189 4.55403861 -1.43738494
190 -4.27998136 4.55403861
191 -2.72896196 -4.27998136
192 1.90415466 -2.72896196
193 0.30580392 1.90415466
194 1.35309764 0.30580392
195 -2.53925021 1.35309764
196 6.34699233 -2.53925021
197 2.79858308 6.34699233
198 0.90297826 2.79858308
199 -0.66742778 0.90297826
200 0.18558719 -0.66742778
201 -0.97501880 0.18558719
202 -0.18814461 -0.97501880
203 -1.04663580 -0.18814461
204 -1.50271491 -1.04663580
205 0.70731302 -1.50271491
206 -0.27383303 0.70731302
207 2.33671445 -0.27383303
208 -0.23491542 2.33671445
209 0.97259467 -0.23491542
210 -0.33976873 0.97259467
211 -0.08861444 -0.33976873
212 3.50202519 -0.08861444
213 -2.75268594 3.50202519
214 1.62593970 -2.75268594
215 -0.03182174 1.62593970
216 0.89748216 -0.03182174
217 -0.43746293 0.89748216
218 3.81626953 -0.43746293
219 2.22594211 3.81626953
220 -3.12392357 2.22594211
221 -0.45996285 -3.12392357
222 1.06036591 -0.45996285
223 -3.79536416 1.06036591
224 2.19481055 -3.79536416
225 -2.17931682 2.19481055
226 0.24793394 -2.17931682
227 -3.20275140 0.24793394
228 4.89234930 -3.20275140
229 -2.67488241 4.89234930
230 -1.43997152 -2.67488241
231 -1.47113533 -1.43997152
232 -4.28241158 -1.47113533
233 3.77099811 -4.28241158
234 -2.86968595 3.77099811
235 -1.64353836 -2.86968595
236 1.71396177 -1.64353836
237 -2.08021965 1.71396177
238 -4.97525526 -2.08021965
239 -3.66010787 -4.97525526
240 -4.19273519 -3.66010787
241 0.32612321 -4.19273519
242 -0.11984578 0.32612321
243 0.86954285 -0.11984578
244 2.26226953 0.86954285
245 4.07807482 2.26226953
246 0.74826108 4.07807482
247 7.91382121 0.74826108
248 2.40876370 7.91382121
249 -0.23699234 2.40876370
250 0.93675695 -0.23699234
251 2.95233462 0.93675695
252 -3.20266377 2.95233462
253 -0.79581235 -3.20266377
254 2.06391386 -0.79581235
255 0.11379468 2.06391386
256 4.63660711 0.11379468
257 0.52904800 4.63660711
258 2.25051097 0.52904800
259 -8.46240759 2.25051097
260 -1.75166551 -8.46240759
261 -0.97224944 -1.75166551
262 3.08094887 -0.97224944
263 -1.39439717 3.08094887
> 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/7c6hn1384461577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8088y1384461577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9i70g1384461577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10p4bk1384461577.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119fo41384461577.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/12o15d1384461577.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/138x2l1384461577.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/14q3kk1384461577.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/15oxti1384461577.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/16bve91384461577.tab")
+ }
>
> try(system("convert tmp/1lcbx1384461577.ps tmp/1lcbx1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/23xpt1384461577.ps tmp/23xpt1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ow7p1384461577.ps tmp/3ow7p1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xipu1384461577.ps tmp/4xipu1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/5opbp1384461577.ps tmp/5opbp1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/693fk1384461577.ps tmp/693fk1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/7c6hn1384461577.ps tmp/7c6hn1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/8088y1384461577.ps tmp/8088y1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/9i70g1384461577.ps tmp/9i70g1384461577.png",intern=TRUE))
character(0)
> try(system("convert tmp/10p4bk1384461577.ps tmp/10p4bk1384461577.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.513 1.966 13.471