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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+ ,31
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+ ,14
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+ ,62
+ ,37
+ ,34
+ ,13
+ ,11
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+ ,15
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+ ,4
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+ ,27
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+ ,34
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+ ,11
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+ ,7
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+ ,62
+ ,29
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+ ,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 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'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
Happiness Connected Separate Learning Software Depression Sport1
1 14 41 38 13 12 12.0 53
2 18 39 32 16 11 11.0 83
3 11 30 35 19 15 14.0 66
4 12 31 33 15 6 12.0 67
5 16 34 37 14 13 21.0 76
6 18 35 29 13 10 12.0 78
7 14 39 31 19 12 22.0 53
8 14 34 36 15 14 11.0 80
9 15 36 35 14 12 10.0 74
10 15 37 38 15 9 13.0 76
11 17 38 31 16 10 10.0 79
12 19 36 34 16 12 8.0 54
13 10 38 35 16 12 15.0 67
14 16 39 38 16 11 14.0 54
15 18 33 37 17 15 10.0 87
16 14 32 33 15 12 14.0 58
17 14 36 32 15 10 14.0 75
18 17 38 38 20 12 11.0 88
19 14 39 38 18 11 10.0 64
20 16 32 32 16 12 13.0 57
21 18 32 33 16 11 9.5 66
22 11 31 31 16 12 14.0 68
23 14 39 38 19 13 12.0 54
24 12 37 39 16 11 14.0 56
25 17 39 32 17 12 11.0 86
26 9 41 32 17 13 9.0 80
27 16 36 35 16 10 11.0 76
28 14 33 37 15 14 15.0 69
29 15 33 33 16 12 14.0 78
30 11 34 33 14 10 13.0 67
31 16 31 31 15 12 9.0 80
32 13 27 32 12 8 15.0 54
33 17 37 31 14 10 10.0 71
34 15 34 37 16 12 11.0 84
35 14 34 30 14 12 13.0 74
36 16 32 33 10 7 8.0 71
37 9 29 31 10 9 20.0 63
38 15 36 33 14 12 12.0 71
39 17 29 31 16 10 10.0 76
40 13 35 33 16 10 10.0 69
41 15 37 32 16 10 9.0 74
42 16 34 33 14 12 14.0 75
43 16 38 32 20 15 8.0 54
44 12 35 33 14 10 14.0 52
45 15 38 28 14 10 11.0 69
46 11 37 35 11 12 13.0 68
47 15 38 39 14 13 9.0 65
48 15 33 34 15 11 11.0 75
49 17 36 38 16 11 15.0 74
50 13 38 32 14 12 11.0 75
51 16 32 38 16 14 10.0 72
52 14 32 30 14 10 14.0 67
53 11 32 33 12 12 18.0 63
54 12 34 38 16 13 14.0 62
55 12 32 32 9 5 11.0 63
56 15 37 35 14 6 14.5 76
57 16 39 34 16 12 13.0 74
58 15 29 34 16 12 9.0 67
59 12 37 36 15 11 10.0 73
60 12 35 34 16 10 15.0 70
61 8 30 28 12 7 20.0 53
62 13 38 34 16 12 12.0 77
63 11 34 35 16 14 12.0 80
64 14 31 35 14 11 14.0 52
65 15 34 31 16 12 13.0 54
66 10 35 37 17 13 11.0 80
67 11 36 35 18 14 17.0 66
68 12 30 27 18 11 12.0 73
69 15 39 40 12 12 13.0 63
70 15 35 37 16 12 14.0 69
71 14 38 36 10 8 13.0 67
72 16 31 38 14 11 15.0 54
73 15 34 39 18 14 13.0 81
74 15 38 41 18 14 10.0 69
75 13 34 27 16 12 11.0 84
76 12 39 30 17 9 19.0 80
77 17 37 37 16 13 13.0 70
78 13 34 31 16 11 17.0 69
79 15 28 31 13 12 13.0 77
80 13 37 27 16 12 9.0 54
81 15 33 36 16 12 11.0 79
82 15 35 37 16 12 9.0 71
83 16 37 33 15 12 12.0 73
84 15 32 34 15 11 12.0 72
85 14 33 31 16 10 13.0 77
86 15 38 39 14 9 13.0 75
87 14 33 34 16 12 12.0 69
88 13 29 32 16 12 15.0 54
89 7 33 33 15 12 22.0 70
90 17 31 36 12 9 13.0 73
91 13 36 32 17 15 15.0 54
92 15 35 41 16 12 13.0 77
93 14 32 28 15 12 15.0 82
94 13 29 30 13 12 12.5 80
95 16 39 36 16 10 11.0 80
96 12 37 35 16 13 16.0 69
97 14 35 31 16 9 11.0 78
98 17 37 34 16 12 11.0 81
99 15 32 36 14 10 10.0 76
100 17 38 36 16 14 10.0 76
101 12 37 35 16 11 16.0 73
102 16 36 37 20 15 12.0 85
103 11 32 28 15 11 11.0 66
104 15 33 39 16 11 16.0 79
105 9 40 32 13 12 19.0 68
106 16 38 35 17 12 11.0 76
107 15 41 39 16 12 16.0 71
108 10 36 35 16 11 15.0 54
109 10 43 42 12 7 24.0 46
110 15 30 34 16 12 14.0 85
111 11 31 33 16 14 15.0 74
112 13 32 41 17 11 11.0 88
113 14 32 33 13 11 15.0 38
114 18 37 34 12 10 12.0 76
115 16 37 32 18 13 10.0 86
116 14 33 40 14 13 14.0 54
117 14 34 40 14 8 13.0 67
118 14 33 35 13 11 9.0 69
119 14 38 36 16 12 15.0 90
120 12 33 37 13 11 15.0 54
121 14 31 27 16 13 14.0 76
122 15 38 39 13 12 11.0 89
123 15 37 38 16 14 8.0 76
124 15 36 31 15 13 11.0 73
125 13 31 33 16 15 11.0 79
126 17 39 32 15 10 8.0 90
127 17 44 39 17 11 10.0 74
128 19 33 36 15 9 11.0 81
129 15 35 33 12 11 13.0 72
130 13 32 33 16 10 11.0 71
131 9 28 32 10 11 20.0 66
132 15 40 37 16 8 10.0 77
133 15 27 30 12 11 15.0 65
134 15 37 38 14 12 12.0 74
135 16 32 29 15 12 14.0 85
136 11 28 22 13 9 23.0 54
137 14 34 35 15 11 14.0 63
138 11 30 35 11 10 16.0 54
139 15 35 34 12 8 11.0 64
140 13 31 35 11 9 12.0 69
141 15 32 34 16 8 10.0 54
142 16 30 37 15 9 14.0 84
143 14 30 35 17 15 12.0 86
144 15 31 23 16 11 12.0 77
145 16 40 31 10 8 11.0 89
146 16 32 27 18 13 12.0 76
147 11 36 36 13 12 13.0 60
148 12 32 31 16 12 11.0 75
149 9 35 32 13 9 19.0 73
150 16 38 39 10 7 12.0 85
151 13 42 37 15 13 17.0 79
152 16 34 38 16 9 9.0 71
153 12 35 39 16 6 12.0 72
154 9 38 34 14 8 19.0 69
155 13 33 31 10 8 18.0 78
156 13 36 32 17 15 15.0 54
157 14 32 37 13 6 14.0 69
158 19 33 36 15 9 11.0 81
159 13 34 32 16 11 9.0 84
160 12 32 38 12 8 18.0 84
161 13 34 36 13 8 16.0 69
162 10 27 26 13 10 24.0 66
163 14 31 26 12 8 14.0 81
164 16 38 33 17 14 20.0 82
165 10 34 39 15 10 18.0 72
166 11 24 30 10 8 23.0 54
167 14 30 33 14 11 12.0 78
168 12 26 25 11 12 14.0 74
169 9 34 38 13 12 16.0 82
170 9 27 37 16 12 18.0 73
171 11 37 31 12 5 20.0 55
172 16 36 37 16 12 12.0 72
173 9 41 35 12 10 12.0 78
174 13 29 25 9 7 17.0 59
175 16 36 28 12 12 13.0 72
176 13 32 35 15 11 9.0 78
177 9 37 33 12 8 16.0 68
178 12 30 30 12 9 18.0 69
179 16 31 31 14 10 10.0 67
180 11 38 37 12 9 14.0 74
181 14 36 36 16 12 11.0 54
182 13 35 30 11 6 9.0 67
183 15 31 36 19 15 11.0 70
184 14 38 32 15 12 10.0 80
185 16 22 28 8 12 11.0 89
186 13 32 36 16 12 19.0 76
187 14 36 34 17 11 14.0 74
188 15 39 31 12 7 12.0 87
189 13 28 28 11 7 14.0 54
190 11 32 36 11 5 21.0 61
191 11 32 36 14 12 13.0 38
192 14 38 40 16 12 10.0 75
193 15 32 33 12 3 15.0 69
194 11 35 37 16 11 16.0 62
195 15 32 32 13 10 14.0 72
196 12 37 38 15 12 12.0 70
197 14 34 31 16 9 19.0 79
198 14 33 37 16 12 15.0 87
199 8 33 33 14 9 19.0 62
200 13 26 32 16 12 13.0 77
201 9 30 30 16 12 17.0 69
202 15 24 30 14 10 12.0 69
203 17 34 31 11 9 11.0 75
204 13 34 32 12 12 14.0 54
205 15 33 34 15 8 11.0 72
206 15 34 36 15 11 13.0 74
207 14 35 37 16 11 12.0 85
208 16 35 36 16 12 15.0 52
209 13 36 33 11 10 14.0 70
210 16 34 33 15 10 12.0 84
211 9 34 33 12 12 17.0 64
212 16 41 44 12 12 11.0 84
213 11 32 39 15 11 18.0 87
214 10 30 32 15 8 13.0 79
215 11 35 35 16 12 17.0 67
216 15 28 25 14 10 13.0 65
217 17 33 35 17 11 11.0 85
218 14 39 34 14 10 12.0 83
219 8 36 35 13 8 22.0 61
220 15 36 39 15 12 14.0 82
221 11 35 33 13 12 12.0 76
222 16 38 36 14 10 12.0 58
223 10 33 32 15 12 17.0 72
224 15 31 32 12 9 9.0 72
225 9 34 36 13 9 21.0 38
226 16 32 36 8 6 10.0 78
227 19 31 32 14 10 11.0 54
228 12 33 34 14 9 12.0 63
229 8 34 33 11 9 23.0 66
230 11 34 35 12 9 13.0 70
231 14 34 30 13 6 12.0 71
232 9 33 38 10 10 16.0 67
233 15 32 34 16 6 9.0 58
234 13 41 33 18 14 17.0 72
235 16 34 32 13 10 9.0 72
236 11 36 31 11 10 14.0 70
237 12 37 30 4 6 17.0 76
238 13 36 27 13 12 13.0 50
239 10 29 31 16 12 11.0 72
240 11 37 30 10 7 12.0 72
241 12 27 32 12 8 10.0 88
242 8 35 35 12 11 19.0 53
243 12 28 28 10 3 16.0 58
244 12 35 33 13 6 16.0 66
245 15 37 31 15 10 14.0 82
246 11 29 35 12 8 20.0 69
247 13 32 35 14 9 15.0 68
248 14 36 32 10 9 23.0 44
249 10 19 21 12 8 20.0 56
250 12 21 20 12 9 16.0 53
251 15 31 34 11 7 14.0 70
252 13 33 32 10 7 17.0 78
253 13 36 34 12 6 11.0 71
254 13 33 32 16 9 13.0 72
255 12 37 33 12 10 17.0 68
256 12 34 33 14 11 15.0 67
257 9 35 37 16 12 21.0 75
258 9 31 32 14 8 18.0 62
259 15 37 34 13 11 15.0 67
260 10 35 30 4 3 8.0 83
261 14 27 30 15 11 12.0 64
262 15 34 38 11 12 12.0 68
263 7 40 36 11 7 22.0 62
264 14 29 32 14 9 12.0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Depression
14.653495 0.014201 0.010280 0.112860 -0.007641 -0.376277
Sport1
0.023006
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.8380 -1.3748 0.2488 1.2801 5.1560
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.653495 1.831853 7.999 4.29e-14 ***
Connected 0.014201 0.037218 0.382 0.7031
Separate 0.010280 0.038258 0.269 0.7884
Learning 0.112860 0.066521 1.697 0.0910 .
Software -0.007641 0.068779 -0.111 0.9116
Depression -0.376277 0.038824 -9.692 < 2e-16 ***
Sport1 0.023006 0.012750 1.804 0.0724 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.017 on 257 degrees of freedom
Multiple R-squared: 0.363, Adjusted R-squared: 0.3481
F-statistic: 24.41 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,] 0.05059810 0.101196198 0.949401901
[2,] 0.01419615 0.028392305 0.985803848
[3,] 0.82769286 0.344614279 0.172307139
[4,] 0.96570703 0.068585939 0.034292970
[5,] 0.96753486 0.064930273 0.032465137
[6,] 0.97634527 0.047309460 0.023654730
[7,] 0.96089885 0.078202296 0.039101148
[8,] 0.94807015 0.103859700 0.051929850
[9,] 0.92927222 0.141455552 0.070727776
[10,] 0.90677095 0.186458099 0.093229050
[11,] 0.90111651 0.197766975 0.098883488
[12,] 0.91710950 0.165781003 0.082890501
[13,] 0.95086311 0.098273778 0.049136889
[14,] 0.93176933 0.136461342 0.068230671
[15,] 0.91520952 0.169580959 0.084790480
[16,] 0.89089232 0.218215351 0.109107675
[17,] 0.99832589 0.003348225 0.001674113
[18,] 0.99744868 0.005102642 0.002551321
[19,] 0.99608885 0.007822309 0.003911155
[20,] 0.99418376 0.011632476 0.005816238
[21,] 0.99682040 0.006359202 0.003179601
[22,] 0.99521991 0.009560189 0.004780094
[23,] 0.99306200 0.013876003 0.006938001
[24,] 0.99170013 0.016599730 0.008299865
[25,] 0.98819120 0.023617610 0.011808805
[26,] 0.98431053 0.031378947 0.015689473
[27,] 0.97845706 0.043085887 0.021542944
[28,] 0.98420422 0.031591555 0.015795777
[29,] 0.97861620 0.042767591 0.021383796
[30,] 0.97602425 0.047951491 0.023975746
[31,] 0.97632442 0.047351152 0.023675576
[32,] 0.96939837 0.061203259 0.030601630
[33,] 0.96716936 0.065661285 0.032830642
[34,] 0.95753940 0.084921191 0.042460596
[35,] 0.94974357 0.100512860 0.050256430
[36,] 0.93628599 0.127428025 0.063714012
[37,] 0.94676654 0.106466914 0.053233457
[38,] 0.93269691 0.134606178 0.067303089
[39,] 0.91610753 0.167784938 0.083892469
[40,] 0.93356011 0.132879777 0.066439889
[41,] 0.92947223 0.141055531 0.070527765
[42,] 0.91499250 0.170015002 0.085007501
[43,] 0.89676139 0.206477213 0.103238607
[44,] 0.88066334 0.238673320 0.119336660
[45,] 0.87257572 0.254848550 0.127424275
[46,] 0.86295547 0.274089068 0.137044534
[47,] 0.84354765 0.312904695 0.156452348
[48,] 0.83035902 0.339281962 0.169640981
[49,] 0.80140032 0.397199355 0.198599677
[50,] 0.83802479 0.323950410 0.161975205
[51,] 0.83305385 0.333892297 0.166946149
[52,] 0.85161983 0.296760346 0.148380173
[53,] 0.84775864 0.304482723 0.152241362
[54,] 0.90052711 0.198945772 0.099472886
[55,] 0.88813929 0.223721424 0.111860712
[56,] 0.87812110 0.243757797 0.121878898
[57,] 0.95478400 0.090432004 0.045216002
[58,] 0.95324437 0.093511257 0.046755629
[59,] 0.95806449 0.083871030 0.041935515
[60,] 0.95274450 0.094510997 0.047255499
[61,] 0.94608360 0.107832804 0.053916402
[62,] 0.93480399 0.130392016 0.065196008
[63,] 0.95023326 0.099533473 0.049766737
[64,] 0.93959841 0.120803181 0.060401590
[65,] 0.92743464 0.145130721 0.072565360
[66,] 0.92259802 0.154803959 0.077401980
[67,] 0.90827681 0.183446381 0.091723190
[68,] 0.92135268 0.157294636 0.078647318
[69,] 0.90683418 0.186331631 0.093165816
[70,] 0.89745915 0.205081703 0.102540851
[71,] 0.89012595 0.219748090 0.109874045
[72,] 0.87086372 0.258272557 0.129136278
[73,] 0.85060499 0.298790021 0.149395011
[74,] 0.84304945 0.313901105 0.156950552
[75,] 0.82184264 0.356314728 0.178157364
[76,] 0.79572833 0.408543331 0.204271665
[77,] 0.77147667 0.457046650 0.228523325
[78,] 0.74214416 0.515711681 0.257855841
[79,] 0.71100309 0.577993824 0.288996912
[80,] 0.78669010 0.426619794 0.213309897
[81,] 0.82135578 0.357288432 0.178644216
[82,] 0.79632211 0.407355777 0.203677888
[83,] 0.77215039 0.455699228 0.227849614
[84,] 0.74807373 0.503852532 0.251926266
[85,] 0.72285155 0.554296905 0.277148453
[86,] 0.69589428 0.608211435 0.304105717
[87,] 0.67004542 0.659909164 0.329954582
[88,] 0.64207451 0.715850987 0.357925494
[89,] 0.64067019 0.718659614 0.359329807
[90,] 0.60650403 0.786991946 0.393495973
[91,] 0.59701342 0.805973169 0.402986585
[92,] 0.57272732 0.854545361 0.427272680
[93,] 0.54313173 0.913736550 0.456868275
[94,] 0.59333641 0.813327170 0.406663585
[95,] 0.58206185 0.835876300 0.417938150
[96,] 0.59820291 0.803594176 0.401797088
[97,] 0.57055374 0.858892528 0.429446264
[98,] 0.56320293 0.873594138 0.436797069
[99,] 0.60034277 0.799314457 0.399657229
[100,] 0.57403577 0.851928450 0.425964225
[101,] 0.54799056 0.904018883 0.452009442
[102,] 0.55260346 0.894793083 0.447396542
[103,] 0.58366975 0.832660509 0.416330254
[104,] 0.58346627 0.833067457 0.416533728
[105,] 0.66876824 0.662463529 0.331231765
[106,] 0.63713973 0.725720534 0.362860267
[107,] 0.61402342 0.771953157 0.385976578
[108,] 0.58404540 0.831909200 0.415954600
[109,] 0.56042843 0.879143144 0.439571572
[110,] 0.52605198 0.947896046 0.473948023
[111,] 0.49495425 0.989908505 0.505045748
[112,] 0.46388099 0.927761975 0.536119013
[113,] 0.43288261 0.865765221 0.567117389
[114,] 0.40672448 0.813448951 0.593275525
[115,] 0.37406572 0.748131447 0.625934277
[116,] 0.36324334 0.726486681 0.636756659
[117,] 0.33375508 0.667510166 0.666244917
[118,] 0.32071743 0.641434859 0.679282571
[119,] 0.42436794 0.848735882 0.575632059
[120,] 0.40719393 0.814387854 0.592806073
[121,] 0.39550360 0.791007191 0.604496405
[122,] 0.38094434 0.761888681 0.619055659
[123,] 0.35290836 0.705816715 0.647091643
[124,] 0.37356253 0.747125063 0.626437468
[125,] 0.34759670 0.695193399 0.652403301
[126,] 0.35811186 0.716223720 0.641888140
[127,] 0.34724991 0.694499821 0.652750089
[128,] 0.31999703 0.639994050 0.680002975
[129,] 0.29611088 0.592221765 0.703889118
[130,] 0.27165290 0.543305808 0.728347096
[131,] 0.24850684 0.497013681 0.751493160
[132,] 0.22234287 0.444685732 0.777657134
[133,] 0.22827862 0.456557238 0.771721381
[134,] 0.20401885 0.408037699 0.795981151
[135,] 0.18345875 0.366917503 0.816541248
[136,] 0.17071072 0.341421432 0.829289284
[137,] 0.16300292 0.326005831 0.836997085
[138,] 0.17013356 0.340267129 0.829866436
[139,] 0.18597210 0.371944201 0.814027900
[140,] 0.20280648 0.405612965 0.797193518
[141,] 0.20410170 0.408203405 0.795898298
[142,] 0.18264977 0.365299539 0.817350230
[143,] 0.16351714 0.327034282 0.836482859
[144,] 0.17569120 0.351382401 0.824308800
[145,] 0.19093141 0.381862825 0.809068588
[146,] 0.17834900 0.356698009 0.821650995
[147,] 0.15608477 0.312169535 0.843915233
[148,] 0.13991024 0.279820473 0.860089764
[149,] 0.22711410 0.454228205 0.772885897
[150,] 0.24584076 0.491681521 0.754159240
[151,] 0.22459989 0.449199784 0.775400108
[152,] 0.20301175 0.406023502 0.796988249
[153,] 0.17988890 0.359777791 0.820111105
[154,] 0.15871814 0.317436282 0.841281859
[155,] 0.26151873 0.523037456 0.738481272
[156,] 0.25837033 0.516740668 0.741629666
[157,] 0.25593215 0.511864303 0.744067849
[158,] 0.22782402 0.455648039 0.772175981
[159,] 0.20658528 0.413170568 0.793414716
[160,] 0.26459632 0.529192638 0.735403681
[161,] 0.29339153 0.586783067 0.706608466
[162,] 0.26578497 0.531569932 0.734215034
[163,] 0.26142331 0.522846625 0.738576688
[164,] 0.43174889 0.863497789 0.568251106
[165,] 0.41736411 0.834728220 0.582635890
[166,] 0.43949633 0.878992665 0.560503667
[167,] 0.45274319 0.905486371 0.547256814
[168,] 0.50960713 0.980785750 0.490392875
[169,] 0.47572330 0.951446594 0.524276703
[170,] 0.45372858 0.907457166 0.546271417
[171,] 0.45687228 0.913744566 0.543127717
[172,] 0.41936786 0.838735726 0.580632137
[173,] 0.41216183 0.824323669 0.587838165
[174,] 0.37489428 0.749788555 0.625105722
[175,] 0.34825204 0.696504083 0.651747958
[176,] 0.36481377 0.729627540 0.635186230
[177,] 0.35553346 0.711066926 0.644466537
[178,] 0.32045820 0.640916409 0.679541795
[179,] 0.29133486 0.582669710 0.708665145
[180,] 0.25941273 0.518825460 0.740587270
[181,] 0.23787676 0.475753526 0.762123237
[182,] 0.24688332 0.493766642 0.753116679
[183,] 0.22806557 0.456131148 0.771934426
[184,] 0.24561305 0.491226099 0.754386950
[185,] 0.23435724 0.468714471 0.765642764
[186,] 0.23432042 0.468640845 0.765679578
[187,] 0.24656004 0.493120085 0.753439958
[188,] 0.29599579 0.591991579 0.704004210
[189,] 0.27708633 0.554172662 0.722913669
[190,] 0.31358541 0.627170824 0.686414588
[191,] 0.28035475 0.560709494 0.719645253
[192,] 0.31753299 0.635065983 0.682467008
[193,] 0.29450492 0.589009850 0.705495075
[194,] 0.34222291 0.684445824 0.657777088
[195,] 0.30462173 0.609243452 0.695378274
[196,] 0.27005619 0.540112390 0.729943805
[197,] 0.24836167 0.496723333 0.751638333
[198,] 0.21633353 0.432667059 0.783666470
[199,] 0.23990985 0.479819708 0.760090146
[200,] 0.20747290 0.414945808 0.792527096
[201,] 0.21570494 0.431409871 0.784295064
[202,] 0.24176578 0.483531563 0.758234219
[203,] 0.23061381 0.461227623 0.769386188
[204,] 0.20356022 0.407120430 0.796439785
[205,] 0.25094869 0.501897376 0.749051312
[206,] 0.22496248 0.449924950 0.775037525
[207,] 0.21330843 0.426616860 0.786691570
[208,] 0.24493602 0.489872041 0.755063979
[209,] 0.21347418 0.426948354 0.786525823
[210,] 0.20232572 0.404651433 0.797674283
[211,] 0.21685812 0.433716245 0.783141878
[212,] 0.23012202 0.460244034 0.769877983
[213,] 0.22192161 0.443843210 0.778078395
[214,] 0.20804703 0.416094052 0.791952974
[215,] 0.17534095 0.350681906 0.824659047
[216,] 0.17327734 0.346554676 0.826722662
[217,] 0.20631558 0.412631154 0.793684423
[218,] 0.41309999 0.826199985 0.586900007
[219,] 0.38442413 0.768848270 0.615575865
[220,] 0.35802500 0.716049991 0.641975004
[221,] 0.34212627 0.684252533 0.657873733
[222,] 0.29923637 0.598472734 0.700763633
[223,] 0.34257143 0.685142867 0.657428567
[224,] 0.29686119 0.593722374 0.703138813
[225,] 0.25261030 0.505220610 0.747389695
[226,] 0.25218057 0.504361136 0.747819432
[227,] 0.22796201 0.455924027 0.772037986
[228,] 0.19471389 0.389427785 0.805286107
[229,] 0.15510065 0.310201308 0.844899346
[230,] 0.28809508 0.576190166 0.711904917
[231,] 0.27750225 0.555004490 0.722497755
[232,] 0.24586253 0.491725062 0.754137469
[233,] 0.47215141 0.944302812 0.527848594
[234,] 0.42924765 0.858495290 0.570752355
[235,] 0.36594598 0.731891960 0.634054020
[236,] 0.49285762 0.985715237 0.507142381
[237,] 0.40757103 0.815142057 0.592428972
[238,] 0.32408191 0.648163822 0.675918089
[239,] 0.49086026 0.981720522 0.509139739
[240,] 0.39061565 0.781231299 0.609384350
[241,] 0.29418335 0.588366707 0.705816647
[242,] 0.44419938 0.888398768 0.555800616
[243,] 0.92160195 0.156796091 0.078398045
[244,] 0.84056285 0.318874297 0.159437149
[245,] 0.79058870 0.418822607 0.209411304
> postscript(file="/var/wessaorg/rcomp/tmp/1pq5d1383549487.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2hhtn1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mlzb1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/43hb11383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5s3bu1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
0.29415296 2.97156405 -2.71955366 -2.10608395 5.15597970 3.88145267
7 8 9 10 11 12
3.48011849 -0.79374834 0.04746467 0.94945878 1.70415011 3.53958524
13 14 15 16 17 18
-3.16423507 2.70588100 2.45477720 0.88516657 0.43226211 1.36525834
19 20 21 22 23 24
-1.25500295 2.42931528 2.88737479 -2.42299009 -0.36997043 -1.32200788
25 26 27 28 29 30
1.79732787 -6.83795216 1.13672708 0.96834232 1.29799083 -2.62898598
31 32 33 34 35 36
0.53241853 0.74276756 2.12811742 -0.02419354 0.25609567 0.85452779
37 38 39 40 41 42
-1.36766194 0.88959456 1.90097779 -2.04374875 -0.55317650 2.57852721
43 44 45 46 47 48
0.10322519 -0.92182478 0.56704362 -2.36129183 -0.18364074 0.33311746
49 50 51 52 53 54
3.66464672 -1.59682746 0.90900340 0.80653229 -0.38617506 -1.39187656
55 56 57 58 59 60
-1.72473905 1.63464968 1.91825032 -0.28380433 -3.07451193 -1.19565129
61 62 63 64 65 66
-2.36196933 -1.51284226 -3.52005217 1.12206152 1.48020988 -5.05159086
67 68 69 70 71 72
-1.57071188 -2.46861314 1.56107534 1.43552085 0.71952845 3.42148734
73 74 75 76 77 78
0.56637919 -0.36374619 -1.92139575 -0.05678904 3.01547737 0.63258929
79 80 81 82 83 84
1.37486595 -2.02638088 0.11531600 -0.49187355 1.71652176 0.79261237
85 86 87 88 89 90
-0.05000283 1.06084375 -0.25779066 0.29348913 -3.39489049 3.46282310
91 92 93 94 95 96
0.10414427 0.83407939 0.76070481 -0.88621089 0.99182092 -0.81212758
97 98 99 100 101 102
-0.86160525 2.03305944 0.03269578 1.75233306 -0.91943280 0.87215773
103 104 105 106 107 108
-3.38395123 1.95821845 -2.34111684 1.01074689 2.03629602 -2.84439957
109 110 111 112 113 114
0.97564497 1.16927453 -2.19002502 -2.24943456 1.93963676 3.96052307
115 116 117 118 119 120
0.34423460 1.01153119 0.28377322 -1.06596080 0.29635362 -0.48377503
121 122 123 124 125 126
0.44172445 0.12199422 -1.00657851 0.38264707 -1.80251880 0.78691327
127 128 129 130 131 132
1.54651443 4.15924124 1.47514583 -1.67088009 -1.41747536 -0.35520181
133 134 135 136 137 138
2.53318773 0.75497734 2.30513128 1.73635789 0.71353491 -0.82625615
139 140 141 142 143 144
0.87343509 -0.69829064 0.31837841 2.25137759 -0.70650083 0.69200232
145 146 147 148 149 150
1.48384601 1.44924971 -2.39904508 -2.72706106 -2.40806310 1.89066841
151 152 153 154 155 156
0.35538662 0.48912432 -2.45245609 -2.49970467 1.47025324 0.10414427
157 158 159 160 161 162
0.77085790 4.15924124 -2.73298900 0.04874126 0.52057087 0.81728909
163 164 165 166 167 168
0.75021056 4.29504381 -2.03717048 2.04186375 -0.19387963 -0.86403890
169 170 171 172 173 174
-3.76849833 -3.03778631 0.44648937 1.59974934 -5.15257282 1.75478768
175 176 177 178 179 180
2.51998472 -2.48453149 -3.35532738 0.51210878 1.30534731 -2.29359399
181 182 183 184 185 186
-0.35214454 -1.80943828 0.03511274 -1.20099284 2.02659148 1.20874706
187 188 189 190 191 192
0.21662905 0.68697364 0.49862794 0.81719880 -1.94897479 -1.28106268
193 194 195 196 197 198
2.27819029 -1.65852711 1.78380433 -2.26585998 2.13980294 0.42609684
199 200 201 202 203 204
-3.24973784 -0.94559208 -3.29268508 1.12157702 2.78591406 0.29764748
205 206 207 208 209 210
0.37921109 1.07391563 -0.69276501 3.21317443 -0.01154829 1.49078002
211 212 213 214 215 216
-2.81385971 1.25588061 -1.34621275 -3.96611269 -1.36907838 1.58447071
217 218 219 220 221 222
1.86706010 -0.45463969 -2.05584638 1.21454597 -3.09837266 2.11414488
223 224 225 226 227 228
-2.31200510 0.02184157 -0.87722771 1.63328083 4.97041847 -1.91695948
229 230 231 232 233 234
-1.51227492 -2.50048347 0.01584939 -3.08591314 -0.16520341 0.24080752
235 236 237 238 239 240
0.87401904 -1.99098873 0.75534202 -0.07646988 -4.61544040 -2.70353807
241 242 243 244 245 246
-2.92080984 -2.85064552 0.24145375 -0.40905615 1.26730069 0.21982306
247 248 249 250 251 252
0.10076309 5.08858853 -0.19517400 0.35825540 2.02625426 1.07605570
253 254 255 256 257 258
-1.31708845 -0.95289519 0.03623156 -0.86879162 -2.06857732 -2.59497348
259 260 261 262 263 264
2.19118529 -4.78670852 0.08878308 1.27419575 -2.92785734 -0.04664671
> postscript(file="/var/wessaorg/rcomp/tmp/6t0e91383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 0.29415296 NA
1 2.97156405 0.29415296
2 -2.71955366 2.97156405
3 -2.10608395 -2.71955366
4 5.15597970 -2.10608395
5 3.88145267 5.15597970
6 3.48011849 3.88145267
7 -0.79374834 3.48011849
8 0.04746467 -0.79374834
9 0.94945878 0.04746467
10 1.70415011 0.94945878
11 3.53958524 1.70415011
12 -3.16423507 3.53958524
13 2.70588100 -3.16423507
14 2.45477720 2.70588100
15 0.88516657 2.45477720
16 0.43226211 0.88516657
17 1.36525834 0.43226211
18 -1.25500295 1.36525834
19 2.42931528 -1.25500295
20 2.88737479 2.42931528
21 -2.42299009 2.88737479
22 -0.36997043 -2.42299009
23 -1.32200788 -0.36997043
24 1.79732787 -1.32200788
25 -6.83795216 1.79732787
26 1.13672708 -6.83795216
27 0.96834232 1.13672708
28 1.29799083 0.96834232
29 -2.62898598 1.29799083
30 0.53241853 -2.62898598
31 0.74276756 0.53241853
32 2.12811742 0.74276756
33 -0.02419354 2.12811742
34 0.25609567 -0.02419354
35 0.85452779 0.25609567
36 -1.36766194 0.85452779
37 0.88959456 -1.36766194
38 1.90097779 0.88959456
39 -2.04374875 1.90097779
40 -0.55317650 -2.04374875
41 2.57852721 -0.55317650
42 0.10322519 2.57852721
43 -0.92182478 0.10322519
44 0.56704362 -0.92182478
45 -2.36129183 0.56704362
46 -0.18364074 -2.36129183
47 0.33311746 -0.18364074
48 3.66464672 0.33311746
49 -1.59682746 3.66464672
50 0.90900340 -1.59682746
51 0.80653229 0.90900340
52 -0.38617506 0.80653229
53 -1.39187656 -0.38617506
54 -1.72473905 -1.39187656
55 1.63464968 -1.72473905
56 1.91825032 1.63464968
57 -0.28380433 1.91825032
58 -3.07451193 -0.28380433
59 -1.19565129 -3.07451193
60 -2.36196933 -1.19565129
61 -1.51284226 -2.36196933
62 -3.52005217 -1.51284226
63 1.12206152 -3.52005217
64 1.48020988 1.12206152
65 -5.05159086 1.48020988
66 -1.57071188 -5.05159086
67 -2.46861314 -1.57071188
68 1.56107534 -2.46861314
69 1.43552085 1.56107534
70 0.71952845 1.43552085
71 3.42148734 0.71952845
72 0.56637919 3.42148734
73 -0.36374619 0.56637919
74 -1.92139575 -0.36374619
75 -0.05678904 -1.92139575
76 3.01547737 -0.05678904
77 0.63258929 3.01547737
78 1.37486595 0.63258929
79 -2.02638088 1.37486595
80 0.11531600 -2.02638088
81 -0.49187355 0.11531600
82 1.71652176 -0.49187355
83 0.79261237 1.71652176
84 -0.05000283 0.79261237
85 1.06084375 -0.05000283
86 -0.25779066 1.06084375
87 0.29348913 -0.25779066
88 -3.39489049 0.29348913
89 3.46282310 -3.39489049
90 0.10414427 3.46282310
91 0.83407939 0.10414427
92 0.76070481 0.83407939
93 -0.88621089 0.76070481
94 0.99182092 -0.88621089
95 -0.81212758 0.99182092
96 -0.86160525 -0.81212758
97 2.03305944 -0.86160525
98 0.03269578 2.03305944
99 1.75233306 0.03269578
100 -0.91943280 1.75233306
101 0.87215773 -0.91943280
102 -3.38395123 0.87215773
103 1.95821845 -3.38395123
104 -2.34111684 1.95821845
105 1.01074689 -2.34111684
106 2.03629602 1.01074689
107 -2.84439957 2.03629602
108 0.97564497 -2.84439957
109 1.16927453 0.97564497
110 -2.19002502 1.16927453
111 -2.24943456 -2.19002502
112 1.93963676 -2.24943456
113 3.96052307 1.93963676
114 0.34423460 3.96052307
115 1.01153119 0.34423460
116 0.28377322 1.01153119
117 -1.06596080 0.28377322
118 0.29635362 -1.06596080
119 -0.48377503 0.29635362
120 0.44172445 -0.48377503
121 0.12199422 0.44172445
122 -1.00657851 0.12199422
123 0.38264707 -1.00657851
124 -1.80251880 0.38264707
125 0.78691327 -1.80251880
126 1.54651443 0.78691327
127 4.15924124 1.54651443
128 1.47514583 4.15924124
129 -1.67088009 1.47514583
130 -1.41747536 -1.67088009
131 -0.35520181 -1.41747536
132 2.53318773 -0.35520181
133 0.75497734 2.53318773
134 2.30513128 0.75497734
135 1.73635789 2.30513128
136 0.71353491 1.73635789
137 -0.82625615 0.71353491
138 0.87343509 -0.82625615
139 -0.69829064 0.87343509
140 0.31837841 -0.69829064
141 2.25137759 0.31837841
142 -0.70650083 2.25137759
143 0.69200232 -0.70650083
144 1.48384601 0.69200232
145 1.44924971 1.48384601
146 -2.39904508 1.44924971
147 -2.72706106 -2.39904508
148 -2.40806310 -2.72706106
149 1.89066841 -2.40806310
150 0.35538662 1.89066841
151 0.48912432 0.35538662
152 -2.45245609 0.48912432
153 -2.49970467 -2.45245609
154 1.47025324 -2.49970467
155 0.10414427 1.47025324
156 0.77085790 0.10414427
157 4.15924124 0.77085790
158 -2.73298900 4.15924124
159 0.04874126 -2.73298900
160 0.52057087 0.04874126
161 0.81728909 0.52057087
162 0.75021056 0.81728909
163 4.29504381 0.75021056
164 -2.03717048 4.29504381
165 2.04186375 -2.03717048
166 -0.19387963 2.04186375
167 -0.86403890 -0.19387963
168 -3.76849833 -0.86403890
169 -3.03778631 -3.76849833
170 0.44648937 -3.03778631
171 1.59974934 0.44648937
172 -5.15257282 1.59974934
173 1.75478768 -5.15257282
174 2.51998472 1.75478768
175 -2.48453149 2.51998472
176 -3.35532738 -2.48453149
177 0.51210878 -3.35532738
178 1.30534731 0.51210878
179 -2.29359399 1.30534731
180 -0.35214454 -2.29359399
181 -1.80943828 -0.35214454
182 0.03511274 -1.80943828
183 -1.20099284 0.03511274
184 2.02659148 -1.20099284
185 1.20874706 2.02659148
186 0.21662905 1.20874706
187 0.68697364 0.21662905
188 0.49862794 0.68697364
189 0.81719880 0.49862794
190 -1.94897479 0.81719880
191 -1.28106268 -1.94897479
192 2.27819029 -1.28106268
193 -1.65852711 2.27819029
194 1.78380433 -1.65852711
195 -2.26585998 1.78380433
196 2.13980294 -2.26585998
197 0.42609684 2.13980294
198 -3.24973784 0.42609684
199 -0.94559208 -3.24973784
200 -3.29268508 -0.94559208
201 1.12157702 -3.29268508
202 2.78591406 1.12157702
203 0.29764748 2.78591406
204 0.37921109 0.29764748
205 1.07391563 0.37921109
206 -0.69276501 1.07391563
207 3.21317443 -0.69276501
208 -0.01154829 3.21317443
209 1.49078002 -0.01154829
210 -2.81385971 1.49078002
211 1.25588061 -2.81385971
212 -1.34621275 1.25588061
213 -3.96611269 -1.34621275
214 -1.36907838 -3.96611269
215 1.58447071 -1.36907838
216 1.86706010 1.58447071
217 -0.45463969 1.86706010
218 -2.05584638 -0.45463969
219 1.21454597 -2.05584638
220 -3.09837266 1.21454597
221 2.11414488 -3.09837266
222 -2.31200510 2.11414488
223 0.02184157 -2.31200510
224 -0.87722771 0.02184157
225 1.63328083 -0.87722771
226 4.97041847 1.63328083
227 -1.91695948 4.97041847
228 -1.51227492 -1.91695948
229 -2.50048347 -1.51227492
230 0.01584939 -2.50048347
231 -3.08591314 0.01584939
232 -0.16520341 -3.08591314
233 0.24080752 -0.16520341
234 0.87401904 0.24080752
235 -1.99098873 0.87401904
236 0.75534202 -1.99098873
237 -0.07646988 0.75534202
238 -4.61544040 -0.07646988
239 -2.70353807 -4.61544040
240 -2.92080984 -2.70353807
241 -2.85064552 -2.92080984
242 0.24145375 -2.85064552
243 -0.40905615 0.24145375
244 1.26730069 -0.40905615
245 0.21982306 1.26730069
246 0.10076309 0.21982306
247 5.08858853 0.10076309
248 -0.19517400 5.08858853
249 0.35825540 -0.19517400
250 2.02625426 0.35825540
251 1.07605570 2.02625426
252 -1.31708845 1.07605570
253 -0.95289519 -1.31708845
254 0.03623156 -0.95289519
255 -0.86879162 0.03623156
256 -2.06857732 -0.86879162
257 -2.59497348 -2.06857732
258 2.19118529 -2.59497348
259 -4.78670852 2.19118529
260 0.08878308 -4.78670852
261 1.27419575 0.08878308
262 -2.92785734 1.27419575
263 -0.04664671 -2.92785734
264 NA -0.04664671
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.97156405 0.29415296
[2,] -2.71955366 2.97156405
[3,] -2.10608395 -2.71955366
[4,] 5.15597970 -2.10608395
[5,] 3.88145267 5.15597970
[6,] 3.48011849 3.88145267
[7,] -0.79374834 3.48011849
[8,] 0.04746467 -0.79374834
[9,] 0.94945878 0.04746467
[10,] 1.70415011 0.94945878
[11,] 3.53958524 1.70415011
[12,] -3.16423507 3.53958524
[13,] 2.70588100 -3.16423507
[14,] 2.45477720 2.70588100
[15,] 0.88516657 2.45477720
[16,] 0.43226211 0.88516657
[17,] 1.36525834 0.43226211
[18,] -1.25500295 1.36525834
[19,] 2.42931528 -1.25500295
[20,] 2.88737479 2.42931528
[21,] -2.42299009 2.88737479
[22,] -0.36997043 -2.42299009
[23,] -1.32200788 -0.36997043
[24,] 1.79732787 -1.32200788
[25,] -6.83795216 1.79732787
[26,] 1.13672708 -6.83795216
[27,] 0.96834232 1.13672708
[28,] 1.29799083 0.96834232
[29,] -2.62898598 1.29799083
[30,] 0.53241853 -2.62898598
[31,] 0.74276756 0.53241853
[32,] 2.12811742 0.74276756
[33,] -0.02419354 2.12811742
[34,] 0.25609567 -0.02419354
[35,] 0.85452779 0.25609567
[36,] -1.36766194 0.85452779
[37,] 0.88959456 -1.36766194
[38,] 1.90097779 0.88959456
[39,] -2.04374875 1.90097779
[40,] -0.55317650 -2.04374875
[41,] 2.57852721 -0.55317650
[42,] 0.10322519 2.57852721
[43,] -0.92182478 0.10322519
[44,] 0.56704362 -0.92182478
[45,] -2.36129183 0.56704362
[46,] -0.18364074 -2.36129183
[47,] 0.33311746 -0.18364074
[48,] 3.66464672 0.33311746
[49,] -1.59682746 3.66464672
[50,] 0.90900340 -1.59682746
[51,] 0.80653229 0.90900340
[52,] -0.38617506 0.80653229
[53,] -1.39187656 -0.38617506
[54,] -1.72473905 -1.39187656
[55,] 1.63464968 -1.72473905
[56,] 1.91825032 1.63464968
[57,] -0.28380433 1.91825032
[58,] -3.07451193 -0.28380433
[59,] -1.19565129 -3.07451193
[60,] -2.36196933 -1.19565129
[61,] -1.51284226 -2.36196933
[62,] -3.52005217 -1.51284226
[63,] 1.12206152 -3.52005217
[64,] 1.48020988 1.12206152
[65,] -5.05159086 1.48020988
[66,] -1.57071188 -5.05159086
[67,] -2.46861314 -1.57071188
[68,] 1.56107534 -2.46861314
[69,] 1.43552085 1.56107534
[70,] 0.71952845 1.43552085
[71,] 3.42148734 0.71952845
[72,] 0.56637919 3.42148734
[73,] -0.36374619 0.56637919
[74,] -1.92139575 -0.36374619
[75,] -0.05678904 -1.92139575
[76,] 3.01547737 -0.05678904
[77,] 0.63258929 3.01547737
[78,] 1.37486595 0.63258929
[79,] -2.02638088 1.37486595
[80,] 0.11531600 -2.02638088
[81,] -0.49187355 0.11531600
[82,] 1.71652176 -0.49187355
[83,] 0.79261237 1.71652176
[84,] -0.05000283 0.79261237
[85,] 1.06084375 -0.05000283
[86,] -0.25779066 1.06084375
[87,] 0.29348913 -0.25779066
[88,] -3.39489049 0.29348913
[89,] 3.46282310 -3.39489049
[90,] 0.10414427 3.46282310
[91,] 0.83407939 0.10414427
[92,] 0.76070481 0.83407939
[93,] -0.88621089 0.76070481
[94,] 0.99182092 -0.88621089
[95,] -0.81212758 0.99182092
[96,] -0.86160525 -0.81212758
[97,] 2.03305944 -0.86160525
[98,] 0.03269578 2.03305944
[99,] 1.75233306 0.03269578
[100,] -0.91943280 1.75233306
[101,] 0.87215773 -0.91943280
[102,] -3.38395123 0.87215773
[103,] 1.95821845 -3.38395123
[104,] -2.34111684 1.95821845
[105,] 1.01074689 -2.34111684
[106,] 2.03629602 1.01074689
[107,] -2.84439957 2.03629602
[108,] 0.97564497 -2.84439957
[109,] 1.16927453 0.97564497
[110,] -2.19002502 1.16927453
[111,] -2.24943456 -2.19002502
[112,] 1.93963676 -2.24943456
[113,] 3.96052307 1.93963676
[114,] 0.34423460 3.96052307
[115,] 1.01153119 0.34423460
[116,] 0.28377322 1.01153119
[117,] -1.06596080 0.28377322
[118,] 0.29635362 -1.06596080
[119,] -0.48377503 0.29635362
[120,] 0.44172445 -0.48377503
[121,] 0.12199422 0.44172445
[122,] -1.00657851 0.12199422
[123,] 0.38264707 -1.00657851
[124,] -1.80251880 0.38264707
[125,] 0.78691327 -1.80251880
[126,] 1.54651443 0.78691327
[127,] 4.15924124 1.54651443
[128,] 1.47514583 4.15924124
[129,] -1.67088009 1.47514583
[130,] -1.41747536 -1.67088009
[131,] -0.35520181 -1.41747536
[132,] 2.53318773 -0.35520181
[133,] 0.75497734 2.53318773
[134,] 2.30513128 0.75497734
[135,] 1.73635789 2.30513128
[136,] 0.71353491 1.73635789
[137,] -0.82625615 0.71353491
[138,] 0.87343509 -0.82625615
[139,] -0.69829064 0.87343509
[140,] 0.31837841 -0.69829064
[141,] 2.25137759 0.31837841
[142,] -0.70650083 2.25137759
[143,] 0.69200232 -0.70650083
[144,] 1.48384601 0.69200232
[145,] 1.44924971 1.48384601
[146,] -2.39904508 1.44924971
[147,] -2.72706106 -2.39904508
[148,] -2.40806310 -2.72706106
[149,] 1.89066841 -2.40806310
[150,] 0.35538662 1.89066841
[151,] 0.48912432 0.35538662
[152,] -2.45245609 0.48912432
[153,] -2.49970467 -2.45245609
[154,] 1.47025324 -2.49970467
[155,] 0.10414427 1.47025324
[156,] 0.77085790 0.10414427
[157,] 4.15924124 0.77085790
[158,] -2.73298900 4.15924124
[159,] 0.04874126 -2.73298900
[160,] 0.52057087 0.04874126
[161,] 0.81728909 0.52057087
[162,] 0.75021056 0.81728909
[163,] 4.29504381 0.75021056
[164,] -2.03717048 4.29504381
[165,] 2.04186375 -2.03717048
[166,] -0.19387963 2.04186375
[167,] -0.86403890 -0.19387963
[168,] -3.76849833 -0.86403890
[169,] -3.03778631 -3.76849833
[170,] 0.44648937 -3.03778631
[171,] 1.59974934 0.44648937
[172,] -5.15257282 1.59974934
[173,] 1.75478768 -5.15257282
[174,] 2.51998472 1.75478768
[175,] -2.48453149 2.51998472
[176,] -3.35532738 -2.48453149
[177,] 0.51210878 -3.35532738
[178,] 1.30534731 0.51210878
[179,] -2.29359399 1.30534731
[180,] -0.35214454 -2.29359399
[181,] -1.80943828 -0.35214454
[182,] 0.03511274 -1.80943828
[183,] -1.20099284 0.03511274
[184,] 2.02659148 -1.20099284
[185,] 1.20874706 2.02659148
[186,] 0.21662905 1.20874706
[187,] 0.68697364 0.21662905
[188,] 0.49862794 0.68697364
[189,] 0.81719880 0.49862794
[190,] -1.94897479 0.81719880
[191,] -1.28106268 -1.94897479
[192,] 2.27819029 -1.28106268
[193,] -1.65852711 2.27819029
[194,] 1.78380433 -1.65852711
[195,] -2.26585998 1.78380433
[196,] 2.13980294 -2.26585998
[197,] 0.42609684 2.13980294
[198,] -3.24973784 0.42609684
[199,] -0.94559208 -3.24973784
[200,] -3.29268508 -0.94559208
[201,] 1.12157702 -3.29268508
[202,] 2.78591406 1.12157702
[203,] 0.29764748 2.78591406
[204,] 0.37921109 0.29764748
[205,] 1.07391563 0.37921109
[206,] -0.69276501 1.07391563
[207,] 3.21317443 -0.69276501
[208,] -0.01154829 3.21317443
[209,] 1.49078002 -0.01154829
[210,] -2.81385971 1.49078002
[211,] 1.25588061 -2.81385971
[212,] -1.34621275 1.25588061
[213,] -3.96611269 -1.34621275
[214,] -1.36907838 -3.96611269
[215,] 1.58447071 -1.36907838
[216,] 1.86706010 1.58447071
[217,] -0.45463969 1.86706010
[218,] -2.05584638 -0.45463969
[219,] 1.21454597 -2.05584638
[220,] -3.09837266 1.21454597
[221,] 2.11414488 -3.09837266
[222,] -2.31200510 2.11414488
[223,] 0.02184157 -2.31200510
[224,] -0.87722771 0.02184157
[225,] 1.63328083 -0.87722771
[226,] 4.97041847 1.63328083
[227,] -1.91695948 4.97041847
[228,] -1.51227492 -1.91695948
[229,] -2.50048347 -1.51227492
[230,] 0.01584939 -2.50048347
[231,] -3.08591314 0.01584939
[232,] -0.16520341 -3.08591314
[233,] 0.24080752 -0.16520341
[234,] 0.87401904 0.24080752
[235,] -1.99098873 0.87401904
[236,] 0.75534202 -1.99098873
[237,] -0.07646988 0.75534202
[238,] -4.61544040 -0.07646988
[239,] -2.70353807 -4.61544040
[240,] -2.92080984 -2.70353807
[241,] -2.85064552 -2.92080984
[242,] 0.24145375 -2.85064552
[243,] -0.40905615 0.24145375
[244,] 1.26730069 -0.40905615
[245,] 0.21982306 1.26730069
[246,] 0.10076309 0.21982306
[247,] 5.08858853 0.10076309
[248,] -0.19517400 5.08858853
[249,] 0.35825540 -0.19517400
[250,] 2.02625426 0.35825540
[251,] 1.07605570 2.02625426
[252,] -1.31708845 1.07605570
[253,] -0.95289519 -1.31708845
[254,] 0.03623156 -0.95289519
[255,] -0.86879162 0.03623156
[256,] -2.06857732 -0.86879162
[257,] -2.59497348 -2.06857732
[258,] 2.19118529 -2.59497348
[259,] -4.78670852 2.19118529
[260,] 0.08878308 -4.78670852
[261,] 1.27419575 0.08878308
[262,] -2.92785734 1.27419575
[263,] -0.04664671 -2.92785734
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.97156405 0.29415296
2 -2.71955366 2.97156405
3 -2.10608395 -2.71955366
4 5.15597970 -2.10608395
5 3.88145267 5.15597970
6 3.48011849 3.88145267
7 -0.79374834 3.48011849
8 0.04746467 -0.79374834
9 0.94945878 0.04746467
10 1.70415011 0.94945878
11 3.53958524 1.70415011
12 -3.16423507 3.53958524
13 2.70588100 -3.16423507
14 2.45477720 2.70588100
15 0.88516657 2.45477720
16 0.43226211 0.88516657
17 1.36525834 0.43226211
18 -1.25500295 1.36525834
19 2.42931528 -1.25500295
20 2.88737479 2.42931528
21 -2.42299009 2.88737479
22 -0.36997043 -2.42299009
23 -1.32200788 -0.36997043
24 1.79732787 -1.32200788
25 -6.83795216 1.79732787
26 1.13672708 -6.83795216
27 0.96834232 1.13672708
28 1.29799083 0.96834232
29 -2.62898598 1.29799083
30 0.53241853 -2.62898598
31 0.74276756 0.53241853
32 2.12811742 0.74276756
33 -0.02419354 2.12811742
34 0.25609567 -0.02419354
35 0.85452779 0.25609567
36 -1.36766194 0.85452779
37 0.88959456 -1.36766194
38 1.90097779 0.88959456
39 -2.04374875 1.90097779
40 -0.55317650 -2.04374875
41 2.57852721 -0.55317650
42 0.10322519 2.57852721
43 -0.92182478 0.10322519
44 0.56704362 -0.92182478
45 -2.36129183 0.56704362
46 -0.18364074 -2.36129183
47 0.33311746 -0.18364074
48 3.66464672 0.33311746
49 -1.59682746 3.66464672
50 0.90900340 -1.59682746
51 0.80653229 0.90900340
52 -0.38617506 0.80653229
53 -1.39187656 -0.38617506
54 -1.72473905 -1.39187656
55 1.63464968 -1.72473905
56 1.91825032 1.63464968
57 -0.28380433 1.91825032
58 -3.07451193 -0.28380433
59 -1.19565129 -3.07451193
60 -2.36196933 -1.19565129
61 -1.51284226 -2.36196933
62 -3.52005217 -1.51284226
63 1.12206152 -3.52005217
64 1.48020988 1.12206152
65 -5.05159086 1.48020988
66 -1.57071188 -5.05159086
67 -2.46861314 -1.57071188
68 1.56107534 -2.46861314
69 1.43552085 1.56107534
70 0.71952845 1.43552085
71 3.42148734 0.71952845
72 0.56637919 3.42148734
73 -0.36374619 0.56637919
74 -1.92139575 -0.36374619
75 -0.05678904 -1.92139575
76 3.01547737 -0.05678904
77 0.63258929 3.01547737
78 1.37486595 0.63258929
79 -2.02638088 1.37486595
80 0.11531600 -2.02638088
81 -0.49187355 0.11531600
82 1.71652176 -0.49187355
83 0.79261237 1.71652176
84 -0.05000283 0.79261237
85 1.06084375 -0.05000283
86 -0.25779066 1.06084375
87 0.29348913 -0.25779066
88 -3.39489049 0.29348913
89 3.46282310 -3.39489049
90 0.10414427 3.46282310
91 0.83407939 0.10414427
92 0.76070481 0.83407939
93 -0.88621089 0.76070481
94 0.99182092 -0.88621089
95 -0.81212758 0.99182092
96 -0.86160525 -0.81212758
97 2.03305944 -0.86160525
98 0.03269578 2.03305944
99 1.75233306 0.03269578
100 -0.91943280 1.75233306
101 0.87215773 -0.91943280
102 -3.38395123 0.87215773
103 1.95821845 -3.38395123
104 -2.34111684 1.95821845
105 1.01074689 -2.34111684
106 2.03629602 1.01074689
107 -2.84439957 2.03629602
108 0.97564497 -2.84439957
109 1.16927453 0.97564497
110 -2.19002502 1.16927453
111 -2.24943456 -2.19002502
112 1.93963676 -2.24943456
113 3.96052307 1.93963676
114 0.34423460 3.96052307
115 1.01153119 0.34423460
116 0.28377322 1.01153119
117 -1.06596080 0.28377322
118 0.29635362 -1.06596080
119 -0.48377503 0.29635362
120 0.44172445 -0.48377503
121 0.12199422 0.44172445
122 -1.00657851 0.12199422
123 0.38264707 -1.00657851
124 -1.80251880 0.38264707
125 0.78691327 -1.80251880
126 1.54651443 0.78691327
127 4.15924124 1.54651443
128 1.47514583 4.15924124
129 -1.67088009 1.47514583
130 -1.41747536 -1.67088009
131 -0.35520181 -1.41747536
132 2.53318773 -0.35520181
133 0.75497734 2.53318773
134 2.30513128 0.75497734
135 1.73635789 2.30513128
136 0.71353491 1.73635789
137 -0.82625615 0.71353491
138 0.87343509 -0.82625615
139 -0.69829064 0.87343509
140 0.31837841 -0.69829064
141 2.25137759 0.31837841
142 -0.70650083 2.25137759
143 0.69200232 -0.70650083
144 1.48384601 0.69200232
145 1.44924971 1.48384601
146 -2.39904508 1.44924971
147 -2.72706106 -2.39904508
148 -2.40806310 -2.72706106
149 1.89066841 -2.40806310
150 0.35538662 1.89066841
151 0.48912432 0.35538662
152 -2.45245609 0.48912432
153 -2.49970467 -2.45245609
154 1.47025324 -2.49970467
155 0.10414427 1.47025324
156 0.77085790 0.10414427
157 4.15924124 0.77085790
158 -2.73298900 4.15924124
159 0.04874126 -2.73298900
160 0.52057087 0.04874126
161 0.81728909 0.52057087
162 0.75021056 0.81728909
163 4.29504381 0.75021056
164 -2.03717048 4.29504381
165 2.04186375 -2.03717048
166 -0.19387963 2.04186375
167 -0.86403890 -0.19387963
168 -3.76849833 -0.86403890
169 -3.03778631 -3.76849833
170 0.44648937 -3.03778631
171 1.59974934 0.44648937
172 -5.15257282 1.59974934
173 1.75478768 -5.15257282
174 2.51998472 1.75478768
175 -2.48453149 2.51998472
176 -3.35532738 -2.48453149
177 0.51210878 -3.35532738
178 1.30534731 0.51210878
179 -2.29359399 1.30534731
180 -0.35214454 -2.29359399
181 -1.80943828 -0.35214454
182 0.03511274 -1.80943828
183 -1.20099284 0.03511274
184 2.02659148 -1.20099284
185 1.20874706 2.02659148
186 0.21662905 1.20874706
187 0.68697364 0.21662905
188 0.49862794 0.68697364
189 0.81719880 0.49862794
190 -1.94897479 0.81719880
191 -1.28106268 -1.94897479
192 2.27819029 -1.28106268
193 -1.65852711 2.27819029
194 1.78380433 -1.65852711
195 -2.26585998 1.78380433
196 2.13980294 -2.26585998
197 0.42609684 2.13980294
198 -3.24973784 0.42609684
199 -0.94559208 -3.24973784
200 -3.29268508 -0.94559208
201 1.12157702 -3.29268508
202 2.78591406 1.12157702
203 0.29764748 2.78591406
204 0.37921109 0.29764748
205 1.07391563 0.37921109
206 -0.69276501 1.07391563
207 3.21317443 -0.69276501
208 -0.01154829 3.21317443
209 1.49078002 -0.01154829
210 -2.81385971 1.49078002
211 1.25588061 -2.81385971
212 -1.34621275 1.25588061
213 -3.96611269 -1.34621275
214 -1.36907838 -3.96611269
215 1.58447071 -1.36907838
216 1.86706010 1.58447071
217 -0.45463969 1.86706010
218 -2.05584638 -0.45463969
219 1.21454597 -2.05584638
220 -3.09837266 1.21454597
221 2.11414488 -3.09837266
222 -2.31200510 2.11414488
223 0.02184157 -2.31200510
224 -0.87722771 0.02184157
225 1.63328083 -0.87722771
226 4.97041847 1.63328083
227 -1.91695948 4.97041847
228 -1.51227492 -1.91695948
229 -2.50048347 -1.51227492
230 0.01584939 -2.50048347
231 -3.08591314 0.01584939
232 -0.16520341 -3.08591314
233 0.24080752 -0.16520341
234 0.87401904 0.24080752
235 -1.99098873 0.87401904
236 0.75534202 -1.99098873
237 -0.07646988 0.75534202
238 -4.61544040 -0.07646988
239 -2.70353807 -4.61544040
240 -2.92080984 -2.70353807
241 -2.85064552 -2.92080984
242 0.24145375 -2.85064552
243 -0.40905615 0.24145375
244 1.26730069 -0.40905615
245 0.21982306 1.26730069
246 0.10076309 0.21982306
247 5.08858853 0.10076309
248 -0.19517400 5.08858853
249 0.35825540 -0.19517400
250 2.02625426 0.35825540
251 1.07605570 2.02625426
252 -1.31708845 1.07605570
253 -0.95289519 -1.31708845
254 0.03623156 -0.95289519
255 -0.86879162 0.03623156
256 -2.06857732 -0.86879162
257 -2.59497348 -2.06857732
258 2.19118529 -2.59497348
259 -4.78670852 2.19118529
260 0.08878308 -4.78670852
261 1.27419575 0.08878308
262 -2.92785734 1.27419575
263 -0.04664671 -2.92785734
> 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/7rpxh1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/85urj1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ybfg1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10g6cb1383549487.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11657b1383549487.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/12a4ba1383549487.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/1377i01383549488.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/145jba1383549488.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/15l8xj1383549488.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/16d8li1383549488.tab")
+ }
>
> try(system("convert tmp/1pq5d1383549487.ps tmp/1pq5d1383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hhtn1383549487.ps tmp/2hhtn1383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mlzb1383549487.ps tmp/3mlzb1383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/43hb11383549487.ps tmp/43hb11383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/5s3bu1383549487.ps tmp/5s3bu1383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/6t0e91383549487.ps tmp/6t0e91383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rpxh1383549487.ps tmp/7rpxh1383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/85urj1383549487.ps tmp/85urj1383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ybfg1383549487.ps tmp/9ybfg1383549487.png",intern=TRUE))
character(0)
> try(system("convert tmp/10g6cb1383549487.ps tmp/10g6cb1383549487.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.250 2.579 17.810