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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+ ,12
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+ ,75
+ ,31
+ ,14
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+ ,8
+ ,9
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+ ,62
+ ,37
+ ,13
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+ ,11
+ ,15
+ ,15
+ ,67
+ ,35
+ ,4
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+ ,27
+ ,15
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+ ,11
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+ ,12
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+ ,34
+ ,11
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+ ,12
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+ ,12
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+ ,7
+ ,7
+ ,22
+ ,62
+ ,29
+ ,14
+ ,32
+ ,9
+ ,14
+ ,12
+ ,72)
+ ,dim=c(7
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Learning'
+ ,'Separate'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1')
+ ,1:264))
> y <- array(NA,dim=c(7,264),dimnames=list(c('Connected','Learning','Separate','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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Connected Learning Separate Software Happiness Depression Sport1
1 41 13 38 12 14 12.0 53
2 39 16 32 11 18 11.0 83
3 30 19 35 15 11 14.0 66
4 31 15 33 6 12 12.0 67
5 34 14 37 13 16 21.0 76
6 35 13 29 10 18 12.0 78
7 39 19 31 12 14 22.0 53
8 34 15 36 14 14 11.0 80
9 36 14 35 12 15 10.0 74
10 37 15 38 9 15 13.0 76
11 38 16 31 10 17 10.0 79
12 36 16 34 12 19 8.0 54
13 38 16 35 12 10 15.0 67
14 39 16 38 11 16 14.0 54
15 33 17 37 15 18 10.0 87
16 32 15 33 12 14 14.0 58
17 36 15 32 10 14 14.0 75
18 38 20 38 12 17 11.0 88
19 39 18 38 11 14 10.0 64
20 32 16 32 12 16 13.0 57
21 32 16 33 11 18 9.5 66
22 31 16 31 12 11 14.0 68
23 39 19 38 13 14 12.0 54
24 37 16 39 11 12 14.0 56
25 39 17 32 12 17 11.0 86
26 41 17 32 13 9 9.0 80
27 36 16 35 10 16 11.0 76
28 33 15 37 14 14 15.0 69
29 33 16 33 12 15 14.0 78
30 34 14 33 10 11 13.0 67
31 31 15 31 12 16 9.0 80
32 27 12 32 8 13 15.0 54
33 37 14 31 10 17 10.0 71
34 34 16 37 12 15 11.0 84
35 34 14 30 12 14 13.0 74
36 32 10 33 7 16 8.0 71
37 29 10 31 9 9 20.0 63
38 36 14 33 12 15 12.0 71
39 29 16 31 10 17 10.0 76
40 35 16 33 10 13 10.0 69
41 37 16 32 10 15 9.0 74
42 34 14 33 12 16 14.0 75
43 38 20 32 15 16 8.0 54
44 35 14 33 10 12 14.0 52
45 38 14 28 10 15 11.0 69
46 37 11 35 12 11 13.0 68
47 38 14 39 13 15 9.0 65
48 33 15 34 11 15 11.0 75
49 36 16 38 11 17 15.0 74
50 38 14 32 12 13 11.0 75
51 32 16 38 14 16 10.0 72
52 32 14 30 10 14 14.0 67
53 32 12 33 12 11 18.0 63
54 34 16 38 13 12 14.0 62
55 32 9 32 5 12 11.0 63
56 37 14 35 6 15 14.5 76
57 39 16 34 12 16 13.0 74
58 29 16 34 12 15 9.0 67
59 37 15 36 11 12 10.0 73
60 35 16 34 10 12 15.0 70
61 30 12 28 7 8 20.0 53
62 38 16 34 12 13 12.0 77
63 34 16 35 14 11 12.0 80
64 31 14 35 11 14 14.0 52
65 34 16 31 12 15 13.0 54
66 35 17 37 13 10 11.0 80
67 36 18 35 14 11 17.0 66
68 30 18 27 11 12 12.0 73
69 39 12 40 12 15 13.0 63
70 35 16 37 12 15 14.0 69
71 38 10 36 8 14 13.0 67
72 31 14 38 11 16 15.0 54
73 34 18 39 14 15 13.0 81
74 38 18 41 14 15 10.0 69
75 34 16 27 12 13 11.0 84
76 39 17 30 9 12 19.0 80
77 37 16 37 13 17 13.0 70
78 34 16 31 11 13 17.0 69
79 28 13 31 12 15 13.0 77
80 37 16 27 12 13 9.0 54
81 33 16 36 12 15 11.0 79
82 35 16 37 12 15 9.0 71
83 37 15 33 12 16 12.0 73
84 32 15 34 11 15 12.0 72
85 33 16 31 10 14 13.0 77
86 38 14 39 9 15 13.0 75
87 33 16 34 12 14 12.0 69
88 29 16 32 12 13 15.0 54
89 33 15 33 12 7 22.0 70
90 31 12 36 9 17 13.0 73
91 36 17 32 15 13 15.0 54
92 35 16 41 12 15 13.0 77
93 32 15 28 12 14 15.0 82
94 29 13 30 12 13 12.5 80
95 39 16 36 10 16 11.0 80
96 37 16 35 13 12 16.0 69
97 35 16 31 9 14 11.0 78
98 37 16 34 12 17 11.0 81
99 32 14 36 10 15 10.0 76
100 38 16 36 14 17 10.0 76
101 37 16 35 11 12 16.0 73
102 36 20 37 15 16 12.0 85
103 32 15 28 11 11 11.0 66
104 33 16 39 11 15 16.0 79
105 40 13 32 12 9 19.0 68
106 38 17 35 12 16 11.0 76
107 41 16 39 12 15 16.0 71
108 36 16 35 11 10 15.0 54
109 43 12 42 7 10 24.0 46
110 30 16 34 12 15 14.0 85
111 31 16 33 14 11 15.0 74
112 32 17 41 11 13 11.0 88
113 32 13 33 11 14 15.0 38
114 37 12 34 10 18 12.0 76
115 37 18 32 13 16 10.0 86
116 33 14 40 13 14 14.0 54
117 34 14 40 8 14 13.0 67
118 33 13 35 11 14 9.0 69
119 38 16 36 12 14 15.0 90
120 33 13 37 11 12 15.0 54
121 31 16 27 13 14 14.0 76
122 38 13 39 12 15 11.0 89
123 37 16 38 14 15 8.0 76
124 36 15 31 13 15 11.0 73
125 31 16 33 15 13 11.0 79
126 39 15 32 10 17 8.0 90
127 44 17 39 11 17 10.0 74
128 33 15 36 9 19 11.0 81
129 35 12 33 11 15 13.0 72
130 32 16 33 10 13 11.0 71
131 28 10 32 11 9 20.0 66
132 40 16 37 8 15 10.0 77
133 27 12 30 11 15 15.0 65
134 37 14 38 12 15 12.0 74
135 32 15 29 12 16 14.0 85
136 28 13 22 9 11 23.0 54
137 34 15 35 11 14 14.0 63
138 30 11 35 10 11 16.0 54
139 35 12 34 8 15 11.0 64
140 31 11 35 9 13 12.0 69
141 32 16 34 8 15 10.0 54
142 30 15 37 9 16 14.0 84
143 30 17 35 15 14 12.0 86
144 31 16 23 11 15 12.0 77
145 40 10 31 8 16 11.0 89
146 32 18 27 13 16 12.0 76
147 36 13 36 12 11 13.0 60
148 32 16 31 12 12 11.0 75
149 35 13 32 9 9 19.0 73
150 38 10 39 7 16 12.0 85
151 42 15 37 13 13 17.0 79
152 34 16 38 9 16 9.0 71
153 35 16 39 6 12 12.0 72
154 38 14 34 8 9 19.0 69
155 33 10 31 8 13 18.0 78
156 36 17 32 15 13 15.0 54
157 32 13 37 6 14 14.0 69
158 33 15 36 9 19 11.0 81
159 34 16 32 11 13 9.0 84
160 32 12 38 8 12 18.0 84
161 34 13 36 8 13 16.0 69
162 27 13 26 10 10 24.0 66
163 31 12 26 8 14 14.0 81
164 38 17 33 14 16 20.0 82
165 34 15 39 10 10 18.0 72
166 24 10 30 8 11 23.0 54
167 30 14 33 11 14 12.0 78
168 26 11 25 12 12 14.0 74
169 34 13 38 12 9 16.0 82
170 27 16 37 12 9 18.0 73
171 37 12 31 5 11 20.0 55
172 36 16 37 12 16 12.0 72
173 41 12 35 10 9 12.0 78
174 29 9 25 7 13 17.0 59
175 36 12 28 12 16 13.0 72
176 32 15 35 11 13 9.0 78
177 37 12 33 8 9 16.0 68
178 30 12 30 9 12 18.0 69
179 31 14 31 10 16 10.0 67
180 38 12 37 9 11 14.0 74
181 36 16 36 12 14 11.0 54
182 35 11 30 6 13 9.0 67
183 31 19 36 15 15 11.0 70
184 38 15 32 12 14 10.0 80
185 22 8 28 12 16 11.0 89
186 32 16 36 12 13 19.0 76
187 36 17 34 11 14 14.0 74
188 39 12 31 7 15 12.0 87
189 28 11 28 7 13 14.0 54
190 32 11 36 5 11 21.0 61
191 32 14 36 12 11 13.0 38
192 38 16 40 12 14 10.0 75
193 32 12 33 3 15 15.0 69
194 35 16 37 11 11 16.0 62
195 32 13 32 10 15 14.0 72
196 37 15 38 12 12 12.0 70
197 34 16 31 9 14 19.0 79
198 33 16 37 12 14 15.0 87
199 33 14 33 9 8 19.0 62
200 26 16 32 12 13 13.0 77
201 30 16 30 12 9 17.0 69
202 24 14 30 10 15 12.0 69
203 34 11 31 9 17 11.0 75
204 34 12 32 12 13 14.0 54
205 33 15 34 8 15 11.0 72
206 34 15 36 11 15 13.0 74
207 35 16 37 11 14 12.0 85
208 35 16 36 12 16 15.0 52
209 36 11 33 10 13 14.0 70
210 34 15 33 10 16 12.0 84
211 34 12 33 12 9 17.0 64
212 41 12 44 12 16 11.0 84
213 32 15 39 11 11 18.0 87
214 30 15 32 8 10 13.0 79
215 35 16 35 12 11 17.0 67
216 28 14 25 10 15 13.0 65
217 33 17 35 11 17 11.0 85
218 39 14 34 10 14 12.0 83
219 36 13 35 8 8 22.0 61
220 36 15 39 12 15 14.0 82
221 35 13 33 12 11 12.0 76
222 38 14 36 10 16 12.0 58
223 33 15 32 12 10 17.0 72
224 31 12 32 9 15 9.0 72
225 34 13 36 9 9 21.0 38
226 32 8 36 6 16 10.0 78
227 31 14 32 10 19 11.0 54
228 33 14 34 9 12 12.0 63
229 34 11 33 9 8 23.0 66
230 34 12 35 9 11 13.0 70
231 34 13 30 6 14 12.0 71
232 33 10 38 10 9 16.0 67
233 32 16 34 6 15 9.0 58
234 41 18 33 14 13 17.0 72
235 34 13 32 10 16 9.0 72
236 36 11 31 10 11 14.0 70
237 37 4 30 6 12 17.0 76
238 36 13 27 12 13 13.0 50
239 29 16 31 12 10 11.0 72
240 37 10 30 7 11 12.0 72
241 27 12 32 8 12 10.0 88
242 35 12 35 11 8 19.0 53
243 28 10 28 3 12 16.0 58
244 35 13 33 6 12 16.0 66
245 37 15 31 10 15 14.0 82
246 29 12 35 8 11 20.0 69
247 32 14 35 9 13 15.0 68
248 36 10 32 9 14 23.0 44
249 19 12 21 8 10 20.0 56
250 21 12 20 9 12 16.0 53
251 31 11 34 7 15 14.0 70
252 33 10 32 7 13 17.0 78
253 36 12 34 6 13 11.0 71
254 33 16 32 9 13 13.0 72
255 37 12 33 10 12 17.0 68
256 34 14 33 11 12 15.0 67
257 35 16 37 12 9 21.0 75
258 31 14 32 8 9 18.0 62
259 37 13 34 11 15 15.0 67
260 35 4 30 3 10 8.0 83
261 27 15 30 11 14 12.0 64
262 34 11 38 12 15 12.0 68
263 40 11 36 7 7 22.0 62
264 29 14 32 9 14 12.0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Learning Separate Software Happiness Depression
16.51640 0.14823 0.43673 -0.03898 0.03987 -0.04891
Sport1
0.01754
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.1239 -2.4171 0.0263 2.4171 7.4268
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.51640 3.27184 5.048 8.46e-07 ***
Learning 0.14823 0.11170 1.327 0.186
Separate 0.43673 0.05804 7.525 8.88e-13 ***
Software -0.03898 0.11522 -0.338 0.735
Happiness 0.03987 0.10449 0.382 0.703
Depression -0.04891 0.07595 -0.644 0.520
Sport1 0.01754 0.02147 0.817 0.415
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.38 on 257 degrees of freedom
Multiple R-squared: 0.2254, Adjusted R-squared: 0.2073
F-statistic: 12.46 on 6 and 257 DF, p-value: 2.532e-12
> 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,] 0.13693571 0.27387142 0.8630643
[2,] 0.05464006 0.10928011 0.9453599
[3,] 0.75513007 0.48973986 0.2448699
[4,] 0.80607900 0.38784200 0.1939210
[5,] 0.72677008 0.54645984 0.2732299
[6,] 0.65488884 0.69022232 0.3451112
[7,] 0.70588481 0.58823039 0.2941152
[8,] 0.62886343 0.74227313 0.3711366
[9,] 0.57308409 0.85383181 0.4269159
[10,] 0.51827829 0.96344341 0.4817217
[11,] 0.54507074 0.90985852 0.4549293
[12,] 0.57817336 0.84365329 0.4218266
[13,] 0.51746018 0.96507964 0.4825398
[14,] 0.46878919 0.93757838 0.5312108
[15,] 0.39760585 0.79521171 0.6023941
[16,] 0.44631787 0.89263574 0.5536821
[17,] 0.68918503 0.62162995 0.3108150
[18,] 0.63085459 0.73829081 0.3691454
[19,] 0.59700300 0.80599399 0.4029970
[20,] 0.55846560 0.88306879 0.4415344
[21,] 0.50045985 0.99908030 0.4995401
[22,] 0.49995408 0.99990815 0.5000459
[23,] 0.63542464 0.72915073 0.3645754
[24,] 0.62459774 0.75080452 0.3754023
[25,] 0.59575704 0.80848592 0.4042430
[26,] 0.54558633 0.90882735 0.4544137
[27,] 0.49516588 0.99033176 0.5048341
[28,] 0.44779648 0.89559295 0.5522035
[29,] 0.41492609 0.82985218 0.5850739
[30,] 0.52604850 0.94790301 0.4739515
[31,] 0.47324283 0.94648566 0.5267572
[32,] 0.44137308 0.88274617 0.5586269
[33,] 0.39025936 0.78051873 0.6097406
[34,] 0.35595161 0.71190322 0.6440484
[35,] 0.31652042 0.63304084 0.6834796
[36,] 0.38253030 0.76506060 0.6174697
[37,] 0.39746973 0.79493947 0.6025303
[38,] 0.36001984 0.72003967 0.6399802
[39,] 0.33215545 0.66431090 0.6678445
[40,] 0.28888041 0.57776082 0.7111196
[41,] 0.30246416 0.60492833 0.6975358
[42,] 0.34358862 0.68717725 0.6564114
[43,] 0.30869761 0.61739523 0.6913024
[44,] 0.27050320 0.54100640 0.7294968
[45,] 0.24492053 0.48984106 0.7550795
[46,] 0.21031925 0.42063850 0.7896808
[47,] 0.19620126 0.39240252 0.8037987
[48,] 0.21036923 0.42073846 0.7896308
[49,] 0.31455081 0.62910163 0.6854492
[50,] 0.28391447 0.56782895 0.7160855
[51,] 0.24789158 0.49578315 0.7521084
[52,] 0.21966476 0.43932951 0.7803352
[53,] 0.21174711 0.42349422 0.7882529
[54,] 0.18617666 0.37235333 0.8138233
[55,] 0.18433137 0.36866275 0.8156686
[56,] 0.15823215 0.31646430 0.8417679
[57,] 0.13739232 0.27478464 0.8626077
[58,] 0.11664860 0.23329720 0.8833514
[59,] 0.12516975 0.25033951 0.8748302
[60,] 0.12592518 0.25185035 0.8740748
[61,] 0.10678299 0.21356598 0.8932170
[62,] 0.11229735 0.22459470 0.8877026
[63,] 0.13069203 0.26138406 0.8693080
[64,] 0.12753166 0.25506333 0.8724683
[65,] 0.10729804 0.21459607 0.8927020
[66,] 0.09294437 0.18588874 0.9070556
[67,] 0.12620473 0.25240946 0.8737953
[68,] 0.10867975 0.21735949 0.8913203
[69,] 0.09185930 0.18371861 0.9081407
[70,] 0.11952900 0.23905799 0.8804710
[71,] 0.14312432 0.28624865 0.8568757
[72,] 0.13636650 0.27273301 0.8636335
[73,] 0.11790692 0.23581384 0.8820931
[74,] 0.11163275 0.22326550 0.8883673
[75,] 0.10610305 0.21220610 0.8938969
[76,] 0.09212655 0.18425310 0.9078734
[77,] 0.08144115 0.16288230 0.9185588
[78,] 0.07183001 0.14366002 0.9281700
[79,] 0.08576281 0.17152562 0.9142372
[80,] 0.07149182 0.14298364 0.9285082
[81,] 0.07582096 0.15164192 0.9241790
[82,] 0.07074705 0.14149411 0.9292529
[83,] 0.06335901 0.12671803 0.9366410
[84,] 0.05303766 0.10607531 0.9469623
[85,] 0.05507875 0.11015749 0.9449213
[86,] 0.05416360 0.10832720 0.9458364
[87,] 0.04992215 0.09984429 0.9500779
[88,] 0.04236588 0.08473176 0.9576341
[89,] 0.03735780 0.07471560 0.9626422
[90,] 0.03745133 0.07490267 0.9625487
[91,] 0.03441681 0.06883363 0.9655832
[92,] 0.03072651 0.06145302 0.9692735
[93,] 0.02542163 0.05084325 0.9745784
[94,] 0.02106696 0.04213392 0.9789330
[95,] 0.02142047 0.04284095 0.9785795
[96,] 0.05108880 0.10217759 0.9489112
[97,] 0.04785445 0.09570890 0.9521456
[98,] 0.05890935 0.11781870 0.9410907
[99,] 0.05122405 0.10244810 0.9487760
[100,] 0.08594395 0.17188790 0.9140561
[101,] 0.10211911 0.20423821 0.8978809
[102,] 0.09800425 0.19600850 0.9019957
[103,] 0.13086482 0.26172964 0.8691352
[104,] 0.11770880 0.23541760 0.8822912
[105,] 0.11348021 0.22696042 0.8865198
[106,] 0.10828255 0.21656510 0.8917174
[107,] 0.10562331 0.21124662 0.8943767
[108,] 0.10021708 0.20043417 0.8997829
[109,] 0.08750683 0.17501367 0.9124932
[110,] 0.08203401 0.16406802 0.9179660
[111,] 0.07268402 0.14536804 0.9273160
[112,] 0.06350358 0.12700716 0.9364964
[113,] 0.05741622 0.11483244 0.9425838
[114,] 0.04866965 0.09733930 0.9513304
[115,] 0.04675913 0.09351825 0.9532409
[116,] 0.04443459 0.08886918 0.9555654
[117,] 0.05497566 0.10995131 0.9450243
[118,] 0.10021251 0.20042502 0.8997875
[119,] 0.09637184 0.19274368 0.9036282
[120,] 0.08496057 0.16992115 0.9150394
[121,] 0.07834992 0.15669985 0.9216501
[122,] 0.08606023 0.17212047 0.9139398
[123,] 0.09193394 0.18386787 0.9080661
[124,] 0.11213625 0.22427251 0.8878637
[125,] 0.09836081 0.19672163 0.9016392
[126,] 0.08459548 0.16919095 0.9154045
[127,] 0.07351916 0.14703832 0.9264808
[128,] 0.06232912 0.12465823 0.9376709
[129,] 0.06471188 0.12942375 0.9352881
[130,] 0.05500354 0.11000707 0.9449965
[131,] 0.05404520 0.10809040 0.9459548
[132,] 0.05202946 0.10405892 0.9479705
[133,] 0.07506428 0.15012856 0.9249357
[134,] 0.09040130 0.18080260 0.9095987
[135,] 0.08353958 0.16707916 0.9164604
[136,] 0.14666336 0.29332671 0.8533366
[137,] 0.13466948 0.26933896 0.8653305
[138,] 0.11987825 0.23975651 0.8801217
[139,] 0.10520138 0.21040276 0.8947986
[140,] 0.09635231 0.19270461 0.9036477
[141,] 0.08487744 0.16975487 0.9151226
[142,] 0.13475277 0.26950553 0.8652472
[143,] 0.12581595 0.25163191 0.8741840
[144,] 0.11358643 0.22717285 0.8864136
[145,] 0.12409023 0.24818045 0.8759098
[146,] 0.10710840 0.21421681 0.8928916
[147,] 0.10693161 0.21386322 0.8930684
[148,] 0.10956577 0.21913153 0.8904342
[149,] 0.10242701 0.20485401 0.8975730
[150,] 0.08926205 0.17852410 0.9107380
[151,] 0.09502138 0.19004276 0.9049786
[152,] 0.08161893 0.16323785 0.9183811
[153,] 0.07721261 0.15442523 0.9227874
[154,] 0.06684063 0.13368125 0.9331594
[155,] 0.08168266 0.16336532 0.9183173
[156,] 0.07402018 0.14804035 0.9259798
[157,] 0.13523314 0.27046628 0.8647669
[158,] 0.13615449 0.27230898 0.8638455
[159,] 0.13249538 0.26499076 0.8675046
[160,] 0.11915914 0.23831827 0.8808409
[161,] 0.23554617 0.47109234 0.7644538
[162,] 0.26926016 0.53852032 0.7307398
[163,] 0.24003177 0.48006353 0.7599682
[164,] 0.32782214 0.65564428 0.6721779
[165,] 0.29464750 0.58929500 0.7053525
[166,] 0.34816102 0.69632203 0.6518390
[167,] 0.33420477 0.66840953 0.6657952
[168,] 0.34139946 0.68279892 0.6586005
[169,] 0.31453999 0.62907999 0.6854600
[170,] 0.28880399 0.57760797 0.7111960
[171,] 0.27412320 0.54824640 0.7258768
[172,] 0.24720446 0.49440891 0.7527955
[173,] 0.24486535 0.48973070 0.7551347
[174,] 0.25479264 0.50958528 0.7452074
[175,] 0.30395121 0.60790241 0.6960488
[176,] 0.57183939 0.85632122 0.4281606
[177,] 0.56785505 0.86428989 0.4321449
[178,] 0.55285261 0.89429477 0.4471474
[179,] 0.66494181 0.67011637 0.3350582
[180,] 0.64822971 0.70354058 0.3517703
[181,] 0.63955088 0.72089825 0.3604491
[182,] 0.62699314 0.74601372 0.3730069
[183,] 0.59145217 0.81709566 0.4085478
[184,] 0.55759473 0.88481053 0.4424053
[185,] 0.51669546 0.96660908 0.4833045
[186,] 0.47883346 0.95766692 0.5211665
[187,] 0.44227919 0.88455838 0.5577208
[188,] 0.42688450 0.85376900 0.5731155
[189,] 0.40682008 0.81364017 0.5931799
[190,] 0.36695551 0.73391101 0.6330445
[191,] 0.48682386 0.97364772 0.5131761
[192,] 0.45259181 0.90518362 0.5474082
[193,] 0.64024006 0.71951988 0.3597599
[194,] 0.60280521 0.79438957 0.3971948
[195,] 0.56307496 0.87385008 0.4369250
[196,] 0.52334161 0.95331678 0.4766584
[197,] 0.48433554 0.96867109 0.5156645
[198,] 0.44128876 0.88257752 0.5587112
[199,] 0.39756391 0.79512781 0.6024361
[200,] 0.37110385 0.74220770 0.6288961
[201,] 0.33156254 0.66312508 0.6684375
[202,] 0.29371554 0.58743107 0.7062845
[203,] 0.26439893 0.52879786 0.7356011
[204,] 0.31646473 0.63292945 0.6835353
[205,] 0.29458146 0.58916291 0.7054185
[206,] 0.25559340 0.51118681 0.7444066
[207,] 0.22345287 0.44690574 0.7765471
[208,] 0.19995124 0.39990247 0.8000488
[209,] 0.22724504 0.45449008 0.7727550
[210,] 0.20090650 0.40181299 0.7990935
[211,] 0.17743097 0.35486194 0.8225690
[212,] 0.14962715 0.29925430 0.8503728
[213,] 0.14203144 0.28406289 0.8579686
[214,] 0.11554855 0.23109709 0.8844515
[215,] 0.09856545 0.19713090 0.9014346
[216,] 0.07826406 0.15652813 0.9217359
[217,] 0.08607598 0.17215195 0.9139240
[218,] 0.07445874 0.14891747 0.9255413
[219,] 0.05784121 0.11568242 0.9421588
[220,] 0.04443893 0.08887787 0.9555611
[221,] 0.03355058 0.06710115 0.9664494
[222,] 0.03102058 0.06204116 0.9689794
[223,] 0.04376731 0.08753461 0.9562327
[224,] 0.03414413 0.06828826 0.9658559
[225,] 0.11776189 0.23552378 0.8822381
[226,] 0.09285480 0.18570960 0.9071452
[227,] 0.08585751 0.17171502 0.9141425
[228,] 0.08104427 0.16208853 0.9189557
[229,] 0.18799565 0.37599130 0.8120044
[230,] 0.15269101 0.30538202 0.8473090
[231,] 0.24183688 0.48367375 0.7581631
[232,] 0.32753921 0.65507841 0.6724608
[233,] 0.26839407 0.53678815 0.7316059
[234,] 0.22212254 0.44424508 0.7778775
[235,] 0.18120233 0.36240465 0.8187977
[236,] 0.38718026 0.77436052 0.6128197
[237,] 0.63678524 0.72642952 0.3632148
[238,] 0.60410390 0.79179220 0.3958961
[239,] 0.56068339 0.87863322 0.4393166
[240,] 0.59789585 0.80420831 0.4021042
[241,] 0.52546249 0.94907503 0.4745375
[242,] 0.57118817 0.85762367 0.4288118
[243,] 0.75710110 0.48579780 0.2428989
[244,] 0.67312210 0.65375580 0.3268779
[245,] 0.87174312 0.25651377 0.1282569
> postscript(file="/var/wessaorg/rcomp/tmp/15for1384704279.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/23ipe1384704279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3t68z1384704279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/44lzv1384704279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ikqh1384704279.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
5.52785375 4.92993958 -4.94496930 -2.98469755 -1.18764802 2.78243719
7 8 9 10 11 12
6.18471914 -1.33970157 1.18375402 0.72004080 4.38880802 1.41756547
13 14 15 16 17 18
3.45399300 3.04473835 -3.36507291 -1.57483583 2.48572062 0.70780926
19 20 21 22 23 24
2.45696539 -1.39743877 -2.28195145 -1.90541181 2.65994512 0.73240049
25 26 27 28 29 30
4.80794240 7.17330094 0.78329576 -2.38782950 -1.11375892 0.40824281
31 32 33 34 35 36
-2.41158330 -5.69042091 3.82559011 -2.11265798 1.55400193 -1.62987296
37 38 39 40 41 42
-2.67210035 2.20765918 -4.55856757 0.85023867 3.07061209 0.19544727
43 44 45 46 47 48
3.93468022 1.68040756 6.29950875 3.03988631 1.58478337 -1.53535609
49 50 51 52 53 54
-0.29704832 4.60504826 -4.34970158 -0.35226422 -0.90261699 -1.85815151
55 56 57 58 59 60
-0.67634665 2.13486866 4.43089803 -5.60208817 1.69696301 0.68039292
61 62 63 64 65 66
-0.52104779 3.44896801 -0.88268072 -3.23380393 1.13178331 -0.95239068
67 68 69 70 71 72
1.31100579 -1.71932793 2.63624871 -0.70280217 3.49338280 -4.60990005
73 74 75 76 77 78
-3.05415193 0.13615369 2.33437029 6.26033071 1.18999185 1.10505955
79 80 81 82 83 84
-4.82699498 5.76279184 -2.58822152 -0.98244140 2.98448252 -2.43382042
85 86 87 88 89 90
-0.30976963 1.44907832 -1.45056879 -4.12738620 -0.11496356 -3.98893815
91 92 93 94 95 96
2.84134017 -2.63896165 0.23672576 -3.38760880 3.27640077 2.42706807
97 98 99 100 101 102
1.53588254 2.17041668 -3.36602572 2.41372223 2.27893450 -0.59710587
103 104 105 106 107 108
0.40236603 -3.69283643 7.42682796 2.71303829 4.48647917 1.64304825
109 110 111 112 113 114
6.60344279 -4.67327832 -2.75723990 -6.03721224 -0.91762870 2.78209971
115 116 117 118 119 120
2.68965837 -3.37456475 -2.84643434 -1.62834002 2.45433590 -1.86547158
121 122 123 124 125 126
-0.37944889 1.37085195 0.52217858 2.88788190 -3.08134553 4.80952540
127 128 129 130 131 132
6.87344111 -2.75150502 1.49649546 -2.13593301 -4.08348618 3.80528566
133 134 135 136 137 138
-4.97270439 0.97138919 -0.38127612 0.03865609 -0.57498525 -3.64576705
139 140 141 142 143 144
0.98532404 -3.22325506 -2.48107320 -5.97451913 -5.21677650 1.13426735
145 146 147 148 149 150
7.11355645 0.14654116 1.44703861 -1.21480441 2.22216908 1.69981680
151 152 153 154 155 156
6.53546302 -2.57599060 -1.84100476 4.23166771 0.76849706 2.84134017
157 158 159 160 161 162
-3.45216120 -2.75150502 0.01391834 -3.65043736 -0.79977333 -2.79099435
163 164 165 166 167 168
0.36755432 3.99941672 -2.16363956 -7.04948519 -3.91424640 -3.68902863
169 170 171 172 173 174
-1.58586107 -8.33811173 4.93610858 0.10688236 6.66910460 -0.21751027
175 176 177 178 179 180
4.67925634 -3.04279503 3.83565486 -1.85449855 -2.06437547 2.84491381
181 182 183 184 185 186
0.89018376 2.73178815 -4.75807226 4.28033594 -9.12387047 -3.06456995
187 188 189 190 191 192
1.37233737 5.90198501 -2.88317438 -2.15564939 -2.31527424 0.72598518
193 194 195 196 197 198
-1.66492840 -0.36169903 -1.20507318 1.01293554 0.90963113 -2.92976881
199 200 201 202 203 204
-0.12996050 -7.62866250 -2.25975329 -8.52503823 1.21002715 1.41660322
205 206 207 208 209 210
-1.59968322 -1.29345034 -1.08040349 0.04117463 2.76946807 -0.28644133
211 212 213 214 215 216
1.11066738 2.38327033 -4.42764630 -3.55185001 0.51194697 -2.22231551
217 218 219 220 221 222
-2.52368733 4.52233329 2.27009783 -0.65607433 1.42765120 3.00767468
223 224 225 226 227 228
-0.07747931 -2.34038815 0.18702654 -2.70756159 -2.34375988 -1.08608408
229 230 231 232 233 234
1.44019416 -0.26037334 1.47203716 -1.95603123 -2.67811803 6.99947812
235 236 237 238 239 240
0.51050198 3.72266340 6.04264972 5.47327825 -4.08244291 5.05776069
241 242 243 244 245 246
-6.49151860 1.52887252 -2.82335941 1.52493929 3.75979091 -4.93943691
247 248 249 250 251 252
-2.50365532 4.17184947 -8.55732163 -6.30436639 -2.86394968 0.24387284
253 254 255 256 257 258
1.86430316 -0.65790670 3.84292816 0.50518063 -0.22646089 -1.82099504
259 260 261 262 263 264
3.09707103 3.44244367 -5.50670365 -1.47868649 6.11316228 -4.45023617
> postscript(file="/var/wessaorg/rcomp/tmp/6s3qa1384704279.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 5.52785375 NA
1 4.92993958 5.52785375
2 -4.94496930 4.92993958
3 -2.98469755 -4.94496930
4 -1.18764802 -2.98469755
5 2.78243719 -1.18764802
6 6.18471914 2.78243719
7 -1.33970157 6.18471914
8 1.18375402 -1.33970157
9 0.72004080 1.18375402
10 4.38880802 0.72004080
11 1.41756547 4.38880802
12 3.45399300 1.41756547
13 3.04473835 3.45399300
14 -3.36507291 3.04473835
15 -1.57483583 -3.36507291
16 2.48572062 -1.57483583
17 0.70780926 2.48572062
18 2.45696539 0.70780926
19 -1.39743877 2.45696539
20 -2.28195145 -1.39743877
21 -1.90541181 -2.28195145
22 2.65994512 -1.90541181
23 0.73240049 2.65994512
24 4.80794240 0.73240049
25 7.17330094 4.80794240
26 0.78329576 7.17330094
27 -2.38782950 0.78329576
28 -1.11375892 -2.38782950
29 0.40824281 -1.11375892
30 -2.41158330 0.40824281
31 -5.69042091 -2.41158330
32 3.82559011 -5.69042091
33 -2.11265798 3.82559011
34 1.55400193 -2.11265798
35 -1.62987296 1.55400193
36 -2.67210035 -1.62987296
37 2.20765918 -2.67210035
38 -4.55856757 2.20765918
39 0.85023867 -4.55856757
40 3.07061209 0.85023867
41 0.19544727 3.07061209
42 3.93468022 0.19544727
43 1.68040756 3.93468022
44 6.29950875 1.68040756
45 3.03988631 6.29950875
46 1.58478337 3.03988631
47 -1.53535609 1.58478337
48 -0.29704832 -1.53535609
49 4.60504826 -0.29704832
50 -4.34970158 4.60504826
51 -0.35226422 -4.34970158
52 -0.90261699 -0.35226422
53 -1.85815151 -0.90261699
54 -0.67634665 -1.85815151
55 2.13486866 -0.67634665
56 4.43089803 2.13486866
57 -5.60208817 4.43089803
58 1.69696301 -5.60208817
59 0.68039292 1.69696301
60 -0.52104779 0.68039292
61 3.44896801 -0.52104779
62 -0.88268072 3.44896801
63 -3.23380393 -0.88268072
64 1.13178331 -3.23380393
65 -0.95239068 1.13178331
66 1.31100579 -0.95239068
67 -1.71932793 1.31100579
68 2.63624871 -1.71932793
69 -0.70280217 2.63624871
70 3.49338280 -0.70280217
71 -4.60990005 3.49338280
72 -3.05415193 -4.60990005
73 0.13615369 -3.05415193
74 2.33437029 0.13615369
75 6.26033071 2.33437029
76 1.18999185 6.26033071
77 1.10505955 1.18999185
78 -4.82699498 1.10505955
79 5.76279184 -4.82699498
80 -2.58822152 5.76279184
81 -0.98244140 -2.58822152
82 2.98448252 -0.98244140
83 -2.43382042 2.98448252
84 -0.30976963 -2.43382042
85 1.44907832 -0.30976963
86 -1.45056879 1.44907832
87 -4.12738620 -1.45056879
88 -0.11496356 -4.12738620
89 -3.98893815 -0.11496356
90 2.84134017 -3.98893815
91 -2.63896165 2.84134017
92 0.23672576 -2.63896165
93 -3.38760880 0.23672576
94 3.27640077 -3.38760880
95 2.42706807 3.27640077
96 1.53588254 2.42706807
97 2.17041668 1.53588254
98 -3.36602572 2.17041668
99 2.41372223 -3.36602572
100 2.27893450 2.41372223
101 -0.59710587 2.27893450
102 0.40236603 -0.59710587
103 -3.69283643 0.40236603
104 7.42682796 -3.69283643
105 2.71303829 7.42682796
106 4.48647917 2.71303829
107 1.64304825 4.48647917
108 6.60344279 1.64304825
109 -4.67327832 6.60344279
110 -2.75723990 -4.67327832
111 -6.03721224 -2.75723990
112 -0.91762870 -6.03721224
113 2.78209971 -0.91762870
114 2.68965837 2.78209971
115 -3.37456475 2.68965837
116 -2.84643434 -3.37456475
117 -1.62834002 -2.84643434
118 2.45433590 -1.62834002
119 -1.86547158 2.45433590
120 -0.37944889 -1.86547158
121 1.37085195 -0.37944889
122 0.52217858 1.37085195
123 2.88788190 0.52217858
124 -3.08134553 2.88788190
125 4.80952540 -3.08134553
126 6.87344111 4.80952540
127 -2.75150502 6.87344111
128 1.49649546 -2.75150502
129 -2.13593301 1.49649546
130 -4.08348618 -2.13593301
131 3.80528566 -4.08348618
132 -4.97270439 3.80528566
133 0.97138919 -4.97270439
134 -0.38127612 0.97138919
135 0.03865609 -0.38127612
136 -0.57498525 0.03865609
137 -3.64576705 -0.57498525
138 0.98532404 -3.64576705
139 -3.22325506 0.98532404
140 -2.48107320 -3.22325506
141 -5.97451913 -2.48107320
142 -5.21677650 -5.97451913
143 1.13426735 -5.21677650
144 7.11355645 1.13426735
145 0.14654116 7.11355645
146 1.44703861 0.14654116
147 -1.21480441 1.44703861
148 2.22216908 -1.21480441
149 1.69981680 2.22216908
150 6.53546302 1.69981680
151 -2.57599060 6.53546302
152 -1.84100476 -2.57599060
153 4.23166771 -1.84100476
154 0.76849706 4.23166771
155 2.84134017 0.76849706
156 -3.45216120 2.84134017
157 -2.75150502 -3.45216120
158 0.01391834 -2.75150502
159 -3.65043736 0.01391834
160 -0.79977333 -3.65043736
161 -2.79099435 -0.79977333
162 0.36755432 -2.79099435
163 3.99941672 0.36755432
164 -2.16363956 3.99941672
165 -7.04948519 -2.16363956
166 -3.91424640 -7.04948519
167 -3.68902863 -3.91424640
168 -1.58586107 -3.68902863
169 -8.33811173 -1.58586107
170 4.93610858 -8.33811173
171 0.10688236 4.93610858
172 6.66910460 0.10688236
173 -0.21751027 6.66910460
174 4.67925634 -0.21751027
175 -3.04279503 4.67925634
176 3.83565486 -3.04279503
177 -1.85449855 3.83565486
178 -2.06437547 -1.85449855
179 2.84491381 -2.06437547
180 0.89018376 2.84491381
181 2.73178815 0.89018376
182 -4.75807226 2.73178815
183 4.28033594 -4.75807226
184 -9.12387047 4.28033594
185 -3.06456995 -9.12387047
186 1.37233737 -3.06456995
187 5.90198501 1.37233737
188 -2.88317438 5.90198501
189 -2.15564939 -2.88317438
190 -2.31527424 -2.15564939
191 0.72598518 -2.31527424
192 -1.66492840 0.72598518
193 -0.36169903 -1.66492840
194 -1.20507318 -0.36169903
195 1.01293554 -1.20507318
196 0.90963113 1.01293554
197 -2.92976881 0.90963113
198 -0.12996050 -2.92976881
199 -7.62866250 -0.12996050
200 -2.25975329 -7.62866250
201 -8.52503823 -2.25975329
202 1.21002715 -8.52503823
203 1.41660322 1.21002715
204 -1.59968322 1.41660322
205 -1.29345034 -1.59968322
206 -1.08040349 -1.29345034
207 0.04117463 -1.08040349
208 2.76946807 0.04117463
209 -0.28644133 2.76946807
210 1.11066738 -0.28644133
211 2.38327033 1.11066738
212 -4.42764630 2.38327033
213 -3.55185001 -4.42764630
214 0.51194697 -3.55185001
215 -2.22231551 0.51194697
216 -2.52368733 -2.22231551
217 4.52233329 -2.52368733
218 2.27009783 4.52233329
219 -0.65607433 2.27009783
220 1.42765120 -0.65607433
221 3.00767468 1.42765120
222 -0.07747931 3.00767468
223 -2.34038815 -0.07747931
224 0.18702654 -2.34038815
225 -2.70756159 0.18702654
226 -2.34375988 -2.70756159
227 -1.08608408 -2.34375988
228 1.44019416 -1.08608408
229 -0.26037334 1.44019416
230 1.47203716 -0.26037334
231 -1.95603123 1.47203716
232 -2.67811803 -1.95603123
233 6.99947812 -2.67811803
234 0.51050198 6.99947812
235 3.72266340 0.51050198
236 6.04264972 3.72266340
237 5.47327825 6.04264972
238 -4.08244291 5.47327825
239 5.05776069 -4.08244291
240 -6.49151860 5.05776069
241 1.52887252 -6.49151860
242 -2.82335941 1.52887252
243 1.52493929 -2.82335941
244 3.75979091 1.52493929
245 -4.93943691 3.75979091
246 -2.50365532 -4.93943691
247 4.17184947 -2.50365532
248 -8.55732163 4.17184947
249 -6.30436639 -8.55732163
250 -2.86394968 -6.30436639
251 0.24387284 -2.86394968
252 1.86430316 0.24387284
253 -0.65790670 1.86430316
254 3.84292816 -0.65790670
255 0.50518063 3.84292816
256 -0.22646089 0.50518063
257 -1.82099504 -0.22646089
258 3.09707103 -1.82099504
259 3.44244367 3.09707103
260 -5.50670365 3.44244367
261 -1.47868649 -5.50670365
262 6.11316228 -1.47868649
263 -4.45023617 6.11316228
264 NA -4.45023617
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.92993958 5.52785375
[2,] -4.94496930 4.92993958
[3,] -2.98469755 -4.94496930
[4,] -1.18764802 -2.98469755
[5,] 2.78243719 -1.18764802
[6,] 6.18471914 2.78243719
[7,] -1.33970157 6.18471914
[8,] 1.18375402 -1.33970157
[9,] 0.72004080 1.18375402
[10,] 4.38880802 0.72004080
[11,] 1.41756547 4.38880802
[12,] 3.45399300 1.41756547
[13,] 3.04473835 3.45399300
[14,] -3.36507291 3.04473835
[15,] -1.57483583 -3.36507291
[16,] 2.48572062 -1.57483583
[17,] 0.70780926 2.48572062
[18,] 2.45696539 0.70780926
[19,] -1.39743877 2.45696539
[20,] -2.28195145 -1.39743877
[21,] -1.90541181 -2.28195145
[22,] 2.65994512 -1.90541181
[23,] 0.73240049 2.65994512
[24,] 4.80794240 0.73240049
[25,] 7.17330094 4.80794240
[26,] 0.78329576 7.17330094
[27,] -2.38782950 0.78329576
[28,] -1.11375892 -2.38782950
[29,] 0.40824281 -1.11375892
[30,] -2.41158330 0.40824281
[31,] -5.69042091 -2.41158330
[32,] 3.82559011 -5.69042091
[33,] -2.11265798 3.82559011
[34,] 1.55400193 -2.11265798
[35,] -1.62987296 1.55400193
[36,] -2.67210035 -1.62987296
[37,] 2.20765918 -2.67210035
[38,] -4.55856757 2.20765918
[39,] 0.85023867 -4.55856757
[40,] 3.07061209 0.85023867
[41,] 0.19544727 3.07061209
[42,] 3.93468022 0.19544727
[43,] 1.68040756 3.93468022
[44,] 6.29950875 1.68040756
[45,] 3.03988631 6.29950875
[46,] 1.58478337 3.03988631
[47,] -1.53535609 1.58478337
[48,] -0.29704832 -1.53535609
[49,] 4.60504826 -0.29704832
[50,] -4.34970158 4.60504826
[51,] -0.35226422 -4.34970158
[52,] -0.90261699 -0.35226422
[53,] -1.85815151 -0.90261699
[54,] -0.67634665 -1.85815151
[55,] 2.13486866 -0.67634665
[56,] 4.43089803 2.13486866
[57,] -5.60208817 4.43089803
[58,] 1.69696301 -5.60208817
[59,] 0.68039292 1.69696301
[60,] -0.52104779 0.68039292
[61,] 3.44896801 -0.52104779
[62,] -0.88268072 3.44896801
[63,] -3.23380393 -0.88268072
[64,] 1.13178331 -3.23380393
[65,] -0.95239068 1.13178331
[66,] 1.31100579 -0.95239068
[67,] -1.71932793 1.31100579
[68,] 2.63624871 -1.71932793
[69,] -0.70280217 2.63624871
[70,] 3.49338280 -0.70280217
[71,] -4.60990005 3.49338280
[72,] -3.05415193 -4.60990005
[73,] 0.13615369 -3.05415193
[74,] 2.33437029 0.13615369
[75,] 6.26033071 2.33437029
[76,] 1.18999185 6.26033071
[77,] 1.10505955 1.18999185
[78,] -4.82699498 1.10505955
[79,] 5.76279184 -4.82699498
[80,] -2.58822152 5.76279184
[81,] -0.98244140 -2.58822152
[82,] 2.98448252 -0.98244140
[83,] -2.43382042 2.98448252
[84,] -0.30976963 -2.43382042
[85,] 1.44907832 -0.30976963
[86,] -1.45056879 1.44907832
[87,] -4.12738620 -1.45056879
[88,] -0.11496356 -4.12738620
[89,] -3.98893815 -0.11496356
[90,] 2.84134017 -3.98893815
[91,] -2.63896165 2.84134017
[92,] 0.23672576 -2.63896165
[93,] -3.38760880 0.23672576
[94,] 3.27640077 -3.38760880
[95,] 2.42706807 3.27640077
[96,] 1.53588254 2.42706807
[97,] 2.17041668 1.53588254
[98,] -3.36602572 2.17041668
[99,] 2.41372223 -3.36602572
[100,] 2.27893450 2.41372223
[101,] -0.59710587 2.27893450
[102,] 0.40236603 -0.59710587
[103,] -3.69283643 0.40236603
[104,] 7.42682796 -3.69283643
[105,] 2.71303829 7.42682796
[106,] 4.48647917 2.71303829
[107,] 1.64304825 4.48647917
[108,] 6.60344279 1.64304825
[109,] -4.67327832 6.60344279
[110,] -2.75723990 -4.67327832
[111,] -6.03721224 -2.75723990
[112,] -0.91762870 -6.03721224
[113,] 2.78209971 -0.91762870
[114,] 2.68965837 2.78209971
[115,] -3.37456475 2.68965837
[116,] -2.84643434 -3.37456475
[117,] -1.62834002 -2.84643434
[118,] 2.45433590 -1.62834002
[119,] -1.86547158 2.45433590
[120,] -0.37944889 -1.86547158
[121,] 1.37085195 -0.37944889
[122,] 0.52217858 1.37085195
[123,] 2.88788190 0.52217858
[124,] -3.08134553 2.88788190
[125,] 4.80952540 -3.08134553
[126,] 6.87344111 4.80952540
[127,] -2.75150502 6.87344111
[128,] 1.49649546 -2.75150502
[129,] -2.13593301 1.49649546
[130,] -4.08348618 -2.13593301
[131,] 3.80528566 -4.08348618
[132,] -4.97270439 3.80528566
[133,] 0.97138919 -4.97270439
[134,] -0.38127612 0.97138919
[135,] 0.03865609 -0.38127612
[136,] -0.57498525 0.03865609
[137,] -3.64576705 -0.57498525
[138,] 0.98532404 -3.64576705
[139,] -3.22325506 0.98532404
[140,] -2.48107320 -3.22325506
[141,] -5.97451913 -2.48107320
[142,] -5.21677650 -5.97451913
[143,] 1.13426735 -5.21677650
[144,] 7.11355645 1.13426735
[145,] 0.14654116 7.11355645
[146,] 1.44703861 0.14654116
[147,] -1.21480441 1.44703861
[148,] 2.22216908 -1.21480441
[149,] 1.69981680 2.22216908
[150,] 6.53546302 1.69981680
[151,] -2.57599060 6.53546302
[152,] -1.84100476 -2.57599060
[153,] 4.23166771 -1.84100476
[154,] 0.76849706 4.23166771
[155,] 2.84134017 0.76849706
[156,] -3.45216120 2.84134017
[157,] -2.75150502 -3.45216120
[158,] 0.01391834 -2.75150502
[159,] -3.65043736 0.01391834
[160,] -0.79977333 -3.65043736
[161,] -2.79099435 -0.79977333
[162,] 0.36755432 -2.79099435
[163,] 3.99941672 0.36755432
[164,] -2.16363956 3.99941672
[165,] -7.04948519 -2.16363956
[166,] -3.91424640 -7.04948519
[167,] -3.68902863 -3.91424640
[168,] -1.58586107 -3.68902863
[169,] -8.33811173 -1.58586107
[170,] 4.93610858 -8.33811173
[171,] 0.10688236 4.93610858
[172,] 6.66910460 0.10688236
[173,] -0.21751027 6.66910460
[174,] 4.67925634 -0.21751027
[175,] -3.04279503 4.67925634
[176,] 3.83565486 -3.04279503
[177,] -1.85449855 3.83565486
[178,] -2.06437547 -1.85449855
[179,] 2.84491381 -2.06437547
[180,] 0.89018376 2.84491381
[181,] 2.73178815 0.89018376
[182,] -4.75807226 2.73178815
[183,] 4.28033594 -4.75807226
[184,] -9.12387047 4.28033594
[185,] -3.06456995 -9.12387047
[186,] 1.37233737 -3.06456995
[187,] 5.90198501 1.37233737
[188,] -2.88317438 5.90198501
[189,] -2.15564939 -2.88317438
[190,] -2.31527424 -2.15564939
[191,] 0.72598518 -2.31527424
[192,] -1.66492840 0.72598518
[193,] -0.36169903 -1.66492840
[194,] -1.20507318 -0.36169903
[195,] 1.01293554 -1.20507318
[196,] 0.90963113 1.01293554
[197,] -2.92976881 0.90963113
[198,] -0.12996050 -2.92976881
[199,] -7.62866250 -0.12996050
[200,] -2.25975329 -7.62866250
[201,] -8.52503823 -2.25975329
[202,] 1.21002715 -8.52503823
[203,] 1.41660322 1.21002715
[204,] -1.59968322 1.41660322
[205,] -1.29345034 -1.59968322
[206,] -1.08040349 -1.29345034
[207,] 0.04117463 -1.08040349
[208,] 2.76946807 0.04117463
[209,] -0.28644133 2.76946807
[210,] 1.11066738 -0.28644133
[211,] 2.38327033 1.11066738
[212,] -4.42764630 2.38327033
[213,] -3.55185001 -4.42764630
[214,] 0.51194697 -3.55185001
[215,] -2.22231551 0.51194697
[216,] -2.52368733 -2.22231551
[217,] 4.52233329 -2.52368733
[218,] 2.27009783 4.52233329
[219,] -0.65607433 2.27009783
[220,] 1.42765120 -0.65607433
[221,] 3.00767468 1.42765120
[222,] -0.07747931 3.00767468
[223,] -2.34038815 -0.07747931
[224,] 0.18702654 -2.34038815
[225,] -2.70756159 0.18702654
[226,] -2.34375988 -2.70756159
[227,] -1.08608408 -2.34375988
[228,] 1.44019416 -1.08608408
[229,] -0.26037334 1.44019416
[230,] 1.47203716 -0.26037334
[231,] -1.95603123 1.47203716
[232,] -2.67811803 -1.95603123
[233,] 6.99947812 -2.67811803
[234,] 0.51050198 6.99947812
[235,] 3.72266340 0.51050198
[236,] 6.04264972 3.72266340
[237,] 5.47327825 6.04264972
[238,] -4.08244291 5.47327825
[239,] 5.05776069 -4.08244291
[240,] -6.49151860 5.05776069
[241,] 1.52887252 -6.49151860
[242,] -2.82335941 1.52887252
[243,] 1.52493929 -2.82335941
[244,] 3.75979091 1.52493929
[245,] -4.93943691 3.75979091
[246,] -2.50365532 -4.93943691
[247,] 4.17184947 -2.50365532
[248,] -8.55732163 4.17184947
[249,] -6.30436639 -8.55732163
[250,] -2.86394968 -6.30436639
[251,] 0.24387284 -2.86394968
[252,] 1.86430316 0.24387284
[253,] -0.65790670 1.86430316
[254,] 3.84292816 -0.65790670
[255,] 0.50518063 3.84292816
[256,] -0.22646089 0.50518063
[257,] -1.82099504 -0.22646089
[258,] 3.09707103 -1.82099504
[259,] 3.44244367 3.09707103
[260,] -5.50670365 3.44244367
[261,] -1.47868649 -5.50670365
[262,] 6.11316228 -1.47868649
[263,] -4.45023617 6.11316228
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.92993958 5.52785375
2 -4.94496930 4.92993958
3 -2.98469755 -4.94496930
4 -1.18764802 -2.98469755
5 2.78243719 -1.18764802
6 6.18471914 2.78243719
7 -1.33970157 6.18471914
8 1.18375402 -1.33970157
9 0.72004080 1.18375402
10 4.38880802 0.72004080
11 1.41756547 4.38880802
12 3.45399300 1.41756547
13 3.04473835 3.45399300
14 -3.36507291 3.04473835
15 -1.57483583 -3.36507291
16 2.48572062 -1.57483583
17 0.70780926 2.48572062
18 2.45696539 0.70780926
19 -1.39743877 2.45696539
20 -2.28195145 -1.39743877
21 -1.90541181 -2.28195145
22 2.65994512 -1.90541181
23 0.73240049 2.65994512
24 4.80794240 0.73240049
25 7.17330094 4.80794240
26 0.78329576 7.17330094
27 -2.38782950 0.78329576
28 -1.11375892 -2.38782950
29 0.40824281 -1.11375892
30 -2.41158330 0.40824281
31 -5.69042091 -2.41158330
32 3.82559011 -5.69042091
33 -2.11265798 3.82559011
34 1.55400193 -2.11265798
35 -1.62987296 1.55400193
36 -2.67210035 -1.62987296
37 2.20765918 -2.67210035
38 -4.55856757 2.20765918
39 0.85023867 -4.55856757
40 3.07061209 0.85023867
41 0.19544727 3.07061209
42 3.93468022 0.19544727
43 1.68040756 3.93468022
44 6.29950875 1.68040756
45 3.03988631 6.29950875
46 1.58478337 3.03988631
47 -1.53535609 1.58478337
48 -0.29704832 -1.53535609
49 4.60504826 -0.29704832
50 -4.34970158 4.60504826
51 -0.35226422 -4.34970158
52 -0.90261699 -0.35226422
53 -1.85815151 -0.90261699
54 -0.67634665 -1.85815151
55 2.13486866 -0.67634665
56 4.43089803 2.13486866
57 -5.60208817 4.43089803
58 1.69696301 -5.60208817
59 0.68039292 1.69696301
60 -0.52104779 0.68039292
61 3.44896801 -0.52104779
62 -0.88268072 3.44896801
63 -3.23380393 -0.88268072
64 1.13178331 -3.23380393
65 -0.95239068 1.13178331
66 1.31100579 -0.95239068
67 -1.71932793 1.31100579
68 2.63624871 -1.71932793
69 -0.70280217 2.63624871
70 3.49338280 -0.70280217
71 -4.60990005 3.49338280
72 -3.05415193 -4.60990005
73 0.13615369 -3.05415193
74 2.33437029 0.13615369
75 6.26033071 2.33437029
76 1.18999185 6.26033071
77 1.10505955 1.18999185
78 -4.82699498 1.10505955
79 5.76279184 -4.82699498
80 -2.58822152 5.76279184
81 -0.98244140 -2.58822152
82 2.98448252 -0.98244140
83 -2.43382042 2.98448252
84 -0.30976963 -2.43382042
85 1.44907832 -0.30976963
86 -1.45056879 1.44907832
87 -4.12738620 -1.45056879
88 -0.11496356 -4.12738620
89 -3.98893815 -0.11496356
90 2.84134017 -3.98893815
91 -2.63896165 2.84134017
92 0.23672576 -2.63896165
93 -3.38760880 0.23672576
94 3.27640077 -3.38760880
95 2.42706807 3.27640077
96 1.53588254 2.42706807
97 2.17041668 1.53588254
98 -3.36602572 2.17041668
99 2.41372223 -3.36602572
100 2.27893450 2.41372223
101 -0.59710587 2.27893450
102 0.40236603 -0.59710587
103 -3.69283643 0.40236603
104 7.42682796 -3.69283643
105 2.71303829 7.42682796
106 4.48647917 2.71303829
107 1.64304825 4.48647917
108 6.60344279 1.64304825
109 -4.67327832 6.60344279
110 -2.75723990 -4.67327832
111 -6.03721224 -2.75723990
112 -0.91762870 -6.03721224
113 2.78209971 -0.91762870
114 2.68965837 2.78209971
115 -3.37456475 2.68965837
116 -2.84643434 -3.37456475
117 -1.62834002 -2.84643434
118 2.45433590 -1.62834002
119 -1.86547158 2.45433590
120 -0.37944889 -1.86547158
121 1.37085195 -0.37944889
122 0.52217858 1.37085195
123 2.88788190 0.52217858
124 -3.08134553 2.88788190
125 4.80952540 -3.08134553
126 6.87344111 4.80952540
127 -2.75150502 6.87344111
128 1.49649546 -2.75150502
129 -2.13593301 1.49649546
130 -4.08348618 -2.13593301
131 3.80528566 -4.08348618
132 -4.97270439 3.80528566
133 0.97138919 -4.97270439
134 -0.38127612 0.97138919
135 0.03865609 -0.38127612
136 -0.57498525 0.03865609
137 -3.64576705 -0.57498525
138 0.98532404 -3.64576705
139 -3.22325506 0.98532404
140 -2.48107320 -3.22325506
141 -5.97451913 -2.48107320
142 -5.21677650 -5.97451913
143 1.13426735 -5.21677650
144 7.11355645 1.13426735
145 0.14654116 7.11355645
146 1.44703861 0.14654116
147 -1.21480441 1.44703861
148 2.22216908 -1.21480441
149 1.69981680 2.22216908
150 6.53546302 1.69981680
151 -2.57599060 6.53546302
152 -1.84100476 -2.57599060
153 4.23166771 -1.84100476
154 0.76849706 4.23166771
155 2.84134017 0.76849706
156 -3.45216120 2.84134017
157 -2.75150502 -3.45216120
158 0.01391834 -2.75150502
159 -3.65043736 0.01391834
160 -0.79977333 -3.65043736
161 -2.79099435 -0.79977333
162 0.36755432 -2.79099435
163 3.99941672 0.36755432
164 -2.16363956 3.99941672
165 -7.04948519 -2.16363956
166 -3.91424640 -7.04948519
167 -3.68902863 -3.91424640
168 -1.58586107 -3.68902863
169 -8.33811173 -1.58586107
170 4.93610858 -8.33811173
171 0.10688236 4.93610858
172 6.66910460 0.10688236
173 -0.21751027 6.66910460
174 4.67925634 -0.21751027
175 -3.04279503 4.67925634
176 3.83565486 -3.04279503
177 -1.85449855 3.83565486
178 -2.06437547 -1.85449855
179 2.84491381 -2.06437547
180 0.89018376 2.84491381
181 2.73178815 0.89018376
182 -4.75807226 2.73178815
183 4.28033594 -4.75807226
184 -9.12387047 4.28033594
185 -3.06456995 -9.12387047
186 1.37233737 -3.06456995
187 5.90198501 1.37233737
188 -2.88317438 5.90198501
189 -2.15564939 -2.88317438
190 -2.31527424 -2.15564939
191 0.72598518 -2.31527424
192 -1.66492840 0.72598518
193 -0.36169903 -1.66492840
194 -1.20507318 -0.36169903
195 1.01293554 -1.20507318
196 0.90963113 1.01293554
197 -2.92976881 0.90963113
198 -0.12996050 -2.92976881
199 -7.62866250 -0.12996050
200 -2.25975329 -7.62866250
201 -8.52503823 -2.25975329
202 1.21002715 -8.52503823
203 1.41660322 1.21002715
204 -1.59968322 1.41660322
205 -1.29345034 -1.59968322
206 -1.08040349 -1.29345034
207 0.04117463 -1.08040349
208 2.76946807 0.04117463
209 -0.28644133 2.76946807
210 1.11066738 -0.28644133
211 2.38327033 1.11066738
212 -4.42764630 2.38327033
213 -3.55185001 -4.42764630
214 0.51194697 -3.55185001
215 -2.22231551 0.51194697
216 -2.52368733 -2.22231551
217 4.52233329 -2.52368733
218 2.27009783 4.52233329
219 -0.65607433 2.27009783
220 1.42765120 -0.65607433
221 3.00767468 1.42765120
222 -0.07747931 3.00767468
223 -2.34038815 -0.07747931
224 0.18702654 -2.34038815
225 -2.70756159 0.18702654
226 -2.34375988 -2.70756159
227 -1.08608408 -2.34375988
228 1.44019416 -1.08608408
229 -0.26037334 1.44019416
230 1.47203716 -0.26037334
231 -1.95603123 1.47203716
232 -2.67811803 -1.95603123
233 6.99947812 -2.67811803
234 0.51050198 6.99947812
235 3.72266340 0.51050198
236 6.04264972 3.72266340
237 5.47327825 6.04264972
238 -4.08244291 5.47327825
239 5.05776069 -4.08244291
240 -6.49151860 5.05776069
241 1.52887252 -6.49151860
242 -2.82335941 1.52887252
243 1.52493929 -2.82335941
244 3.75979091 1.52493929
245 -4.93943691 3.75979091
246 -2.50365532 -4.93943691
247 4.17184947 -2.50365532
248 -8.55732163 4.17184947
249 -6.30436639 -8.55732163
250 -2.86394968 -6.30436639
251 0.24387284 -2.86394968
252 1.86430316 0.24387284
253 -0.65790670 1.86430316
254 3.84292816 -0.65790670
255 0.50518063 3.84292816
256 -0.22646089 0.50518063
257 -1.82099504 -0.22646089
258 3.09707103 -1.82099504
259 3.44244367 3.09707103
260 -5.50670365 3.44244367
261 -1.47868649 -5.50670365
262 6.11316228 -1.47868649
263 -4.45023617 6.11316228
> 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/744f81384704279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/88nj41384704279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9yfq71384704279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10tifb1384704279.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hais1384704279.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/12yqch1384704279.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/13vn221384704280.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/14dtmn1384704280.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/15acxo1384704280.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/16kqdw1384704280.tab")
+ }
>
> try(system("convert tmp/15for1384704279.ps tmp/15for1384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/23ipe1384704279.ps tmp/23ipe1384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t68z1384704279.ps tmp/3t68z1384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/44lzv1384704279.ps tmp/44lzv1384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ikqh1384704279.ps tmp/5ikqh1384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/6s3qa1384704279.ps tmp/6s3qa1384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/744f81384704279.ps tmp/744f81384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/88nj41384704279.ps tmp/88nj41384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/9yfq71384704279.ps tmp/9yfq71384704279.png",intern=TRUE))
character(0)
> try(system("convert tmp/10tifb1384704279.ps tmp/10tifb1384704279.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.924 2.751 18.659