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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+ ,dim=c(6
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:264))
> y <- array(NA,dim=c(6,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression'),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
1 14 41 38 13 12 12.0
2 18 39 32 16 11 11.0
3 11 30 35 19 15 14.0
4 12 31 33 15 6 12.0
5 16 34 37 14 13 21.0
6 18 35 29 13 10 12.0
7 14 39 31 19 12 22.0
8 14 34 36 15 14 11.0
9 15 36 35 14 12 10.0
10 15 37 38 15 9 13.0
11 17 38 31 16 10 10.0
12 19 36 34 16 12 8.0
13 10 38 35 16 12 15.0
14 16 39 38 16 11 14.0
15 18 33 37 17 15 10.0
16 14 32 33 15 12 14.0
17 14 36 32 15 10 14.0
18 17 38 38 20 12 11.0
19 14 39 38 18 11 10.0
20 16 32 32 16 12 13.0
21 18 32 33 16 11 9.5
22 11 31 31 16 12 14.0
23 14 39 38 19 13 12.0
24 12 37 39 16 11 14.0
25 17 39 32 17 12 11.0
26 9 41 32 17 13 9.0
27 16 36 35 16 10 11.0
28 14 33 37 15 14 15.0
29 15 33 33 16 12 14.0
30 11 34 33 14 10 13.0
31 16 31 31 15 12 9.0
32 13 27 32 12 8 15.0
33 17 37 31 14 10 10.0
34 15 34 37 16 12 11.0
35 14 34 30 14 12 13.0
36 16 32 33 10 7 8.0
37 9 29 31 10 9 20.0
38 15 36 33 14 12 12.0
39 17 29 31 16 10 10.0
40 13 35 33 16 10 10.0
41 15 37 32 16 10 9.0
42 16 34 33 14 12 14.0
43 16 38 32 20 15 8.0
44 12 35 33 14 10 14.0
45 15 38 28 14 10 11.0
46 11 37 35 11 12 13.0
47 15 38 39 14 13 9.0
48 15 33 34 15 11 11.0
49 17 36 38 16 11 15.0
50 13 38 32 14 12 11.0
51 16 32 38 16 14 10.0
52 14 32 30 14 10 14.0
53 11 32 33 12 12 18.0
54 12 34 38 16 13 14.0
55 12 32 32 9 5 11.0
56 15 37 35 14 6 14.5
57 16 39 34 16 12 13.0
58 15 29 34 16 12 9.0
59 12 37 36 15 11 10.0
60 12 35 34 16 10 15.0
61 8 30 28 12 7 20.0
62 13 38 34 16 12 12.0
63 11 34 35 16 14 12.0
64 14 31 35 14 11 14.0
65 15 34 31 16 12 13.0
66 10 35 37 17 13 11.0
67 11 36 35 18 14 17.0
68 12 30 27 18 11 12.0
69 15 39 40 12 12 13.0
70 15 35 37 16 12 14.0
71 14 38 36 10 8 13.0
72 16 31 38 14 11 15.0
73 15 34 39 18 14 13.0
74 15 38 41 18 14 10.0
75 13 34 27 16 12 11.0
76 12 39 30 17 9 19.0
77 17 37 37 16 13 13.0
78 13 34 31 16 11 17.0
79 15 28 31 13 12 13.0
80 13 37 27 16 12 9.0
81 15 33 36 16 12 11.0
82 15 35 37 16 12 9.0
83 16 37 33 15 12 12.0
84 15 32 34 15 11 12.0
85 14 33 31 16 10 13.0
86 15 38 39 14 9 13.0
87 14 33 34 16 12 12.0
88 13 29 32 16 12 15.0
89 7 33 33 15 12 22.0
90 17 31 36 12 9 13.0
91 13 36 32 17 15 15.0
92 15 35 41 16 12 13.0
93 14 32 28 15 12 15.0
94 13 29 30 13 12 12.5
95 16 39 36 16 10 11.0
96 12 37 35 16 13 16.0
97 14 35 31 16 9 11.0
98 17 37 34 16 12 11.0
99 15 32 36 14 10 10.0
100 17 38 36 16 14 10.0
101 12 37 35 16 11 16.0
102 16 36 37 20 15 12.0
103 11 32 28 15 11 11.0
104 15 33 39 16 11 16.0
105 9 40 32 13 12 19.0
106 16 38 35 17 12 11.0
107 15 41 39 16 12 16.0
108 10 36 35 16 11 15.0
109 10 43 42 12 7 24.0
110 15 30 34 16 12 14.0
111 11 31 33 16 14 15.0
112 13 32 41 17 11 11.0
113 14 32 33 13 11 15.0
114 18 37 34 12 10 12.0
115 16 37 32 18 13 10.0
116 14 33 40 14 13 14.0
117 14 34 40 14 8 13.0
118 14 33 35 13 11 9.0
119 14 38 36 16 12 15.0
120 12 33 37 13 11 15.0
121 14 31 27 16 13 14.0
122 15 38 39 13 12 11.0
123 15 37 38 16 14 8.0
124 15 36 31 15 13 11.0
125 13 31 33 16 15 11.0
126 17 39 32 15 10 8.0
127 17 44 39 17 11 10.0
128 19 33 36 15 9 11.0
129 15 35 33 12 11 13.0
130 13 32 33 16 10 11.0
131 9 28 32 10 11 20.0
132 15 40 37 16 8 10.0
133 15 27 30 12 11 15.0
134 15 37 38 14 12 12.0
135 16 32 29 15 12 14.0
136 11 28 22 13 9 23.0
137 14 34 35 15 11 14.0
138 11 30 35 11 10 16.0
139 15 35 34 12 8 11.0
140 13 31 35 11 9 12.0
141 15 32 34 16 8 10.0
142 16 30 37 15 9 14.0
143 14 30 35 17 15 12.0
144 15 31 23 16 11 12.0
145 16 40 31 10 8 11.0
146 16 32 27 18 13 12.0
147 11 36 36 13 12 13.0
148 12 32 31 16 12 11.0
149 9 35 32 13 9 19.0
150 16 38 39 10 7 12.0
151 13 42 37 15 13 17.0
152 16 34 38 16 9 9.0
153 12 35 39 16 6 12.0
154 9 38 34 14 8 19.0
155 13 33 31 10 8 18.0
156 13 36 32 17 15 15.0
157 14 32 37 13 6 14.0
158 19 33 36 15 9 11.0
159 13 34 32 16 11 9.0
160 12 32 38 12 8 18.0
161 13 34 36 13 8 16.0
162 10 27 26 13 10 24.0
163 14 31 26 12 8 14.0
164 16 38 33 17 14 20.0
165 10 34 39 15 10 18.0
166 11 24 30 10 8 23.0
167 14 30 33 14 11 12.0
168 12 26 25 11 12 14.0
169 9 34 38 13 12 16.0
170 9 27 37 16 12 18.0
171 11 37 31 12 5 20.0
172 16 36 37 16 12 12.0
173 9 41 35 12 10 12.0
174 13 29 25 9 7 17.0
175 16 36 28 12 12 13.0
176 13 32 35 15 11 9.0
177 9 37 33 12 8 16.0
178 12 30 30 12 9 18.0
179 16 31 31 14 10 10.0
180 11 38 37 12 9 14.0
181 14 36 36 16 12 11.0
182 13 35 30 11 6 9.0
183 15 31 36 19 15 11.0
184 14 38 32 15 12 10.0
185 16 22 28 8 12 11.0
186 13 32 36 16 12 19.0
187 14 36 34 17 11 14.0
188 15 39 31 12 7 12.0
189 13 28 28 11 7 14.0
190 11 32 36 11 5 21.0
191 11 32 36 14 12 13.0
192 14 38 40 16 12 10.0
193 15 32 33 12 3 15.0
194 11 35 37 16 11 16.0
195 15 32 32 13 10 14.0
196 12 37 38 15 12 12.0
197 14 34 31 16 9 19.0
198 14 33 37 16 12 15.0
199 8 33 33 14 9 19.0
200 13 26 32 16 12 13.0
201 9 30 30 16 12 17.0
202 15 24 30 14 10 12.0
203 17 34 31 11 9 11.0
204 13 34 32 12 12 14.0
205 15 33 34 15 8 11.0
206 15 34 36 15 11 13.0
207 14 35 37 16 11 12.0
208 16 35 36 16 12 15.0
209 13 36 33 11 10 14.0
210 16 34 33 15 10 12.0
211 9 34 33 12 12 17.0
212 16 41 44 12 12 11.0
213 11 32 39 15 11 18.0
214 10 30 32 15 8 13.0
215 11 35 35 16 12 17.0
216 15 28 25 14 10 13.0
217 17 33 35 17 11 11.0
218 14 39 34 14 10 12.0
219 8 36 35 13 8 22.0
220 15 36 39 15 12 14.0
221 11 35 33 13 12 12.0
222 16 38 36 14 10 12.0
223 10 33 32 15 12 17.0
224 15 31 32 12 9 9.0
225 9 34 36 13 9 21.0
226 16 32 36 8 6 10.0
227 19 31 32 14 10 11.0
228 12 33 34 14 9 12.0
229 8 34 33 11 9 23.0
230 11 34 35 12 9 13.0
231 14 34 30 13 6 12.0
232 9 33 38 10 10 16.0
233 15 32 34 16 6 9.0
234 13 41 33 18 14 17.0
235 16 34 32 13 10 9.0
236 11 36 31 11 10 14.0
237 12 37 30 4 6 17.0
238 13 36 27 13 12 13.0
239 10 29 31 16 12 11.0
240 11 37 30 10 7 12.0
241 12 27 32 12 8 10.0
242 8 35 35 12 11 19.0
243 12 28 28 10 3 16.0
244 12 35 33 13 6 16.0
245 15 37 31 15 10 14.0
246 11 29 35 12 8 20.0
247 13 32 35 14 9 15.0
248 14 36 32 10 9 23.0
249 10 19 21 12 8 20.0
250 12 21 20 12 9 16.0
251 15 31 34 11 7 14.0
252 13 33 32 10 7 17.0
253 13 36 34 12 6 11.0
254 13 33 32 16 9 13.0
255 12 37 33 12 10 17.0
256 12 34 33 14 11 15.0
257 9 35 37 16 12 21.0
258 9 31 32 14 8 18.0
259 15 37 34 13 11 15.0
260 10 35 30 4 3 8.0
261 14 27 30 15 11 12.0
262 15 34 38 11 12 12.0
263 7 40 36 11 7 22.0
264 14 29 32 14 9 12.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Depression
16.289892 0.017822 0.012218 0.116518 -0.004848 -0.397333
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7533 -1.4312 0.2307 1.4039 5.4279
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.289892 1.598535 10.191 <2e-16 ***
Connected 0.017822 0.037326 0.477 0.6334
Separate 0.012218 0.038410 0.318 0.7507
Learning 0.116518 0.066780 1.745 0.0822 .
Software -0.004848 0.069061 -0.070 0.9441
Depression -0.397333 0.037190 -10.684 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.026 on 258 degrees of freedom
Multiple R-squared: 0.3549, Adjusted R-squared: 0.3424
F-statistic: 28.39 on 5 and 258 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,] 0.5644542 0.871091594 0.435545797
[2,] 0.6121527 0.775694561 0.387847281
[3,] 0.5089213 0.982157401 0.491078701
[4,] 0.7639094 0.472181173 0.236090587
[5,] 0.9449475 0.110104914 0.055052457
[6,] 0.9452679 0.109464216 0.054732108
[7,] 0.9706819 0.058636216 0.029318108
[8,] 0.9552515 0.089496935 0.044748467
[9,] 0.9386284 0.122743259 0.061371630
[10,] 0.9356103 0.128779391 0.064389695
[11,] 0.9225943 0.154811356 0.077405678
[12,] 0.9022768 0.195446321 0.097723160
[13,] 0.9084150 0.183170007 0.091585004
[14,] 0.9455950 0.108810083 0.054405042
[15,] 0.9308823 0.138235449 0.069117725
[16,] 0.9217114 0.156577136 0.078288568
[17,] 0.8986006 0.202798750 0.101399375
[18,] 0.9979086 0.004182726 0.002091363
[19,] 0.9969576 0.006084784 0.003042392
[20,] 0.9953857 0.009228536 0.004614268
[21,] 0.9933690 0.013262013 0.006631006
[22,] 0.9965124 0.006975209 0.003487605
[23,] 0.9948511 0.010297735 0.005148868
[24,] 0.9928506 0.014298800 0.007149400
[25,] 0.9912759 0.017448202 0.008724101
[26,] 0.9876079 0.024784128 0.012392064
[27,] 0.9835337 0.032932534 0.016466267
[28,] 0.9775185 0.044962907 0.022481454
[29,] 0.9825762 0.034847649 0.017423824
[30,] 0.9765671 0.046865887 0.023432944
[31,] 0.9743181 0.051363817 0.025681908
[32,] 0.9754824 0.049035289 0.024517645
[33,] 0.9684552 0.063089539 0.031544770
[34,] 0.9675541 0.064891784 0.032445892
[35,] 0.9582616 0.083476828 0.041738414
[36,] 0.9556475 0.088705037 0.044352519
[37,] 0.9435970 0.112805952 0.056402976
[38,] 0.9539377 0.092124662 0.046062331
[39,] 0.9416065 0.116787041 0.058393521
[40,] 0.9269352 0.146129542 0.073064771
[41,] 0.9481573 0.103685401 0.051842701
[42,] 0.9431589 0.113682183 0.056841091
[43,] 0.9308204 0.138359269 0.069179635
[44,] 0.9151807 0.169638611 0.084819306
[45,] 0.9024087 0.195182511 0.097591256
[46,] 0.8996248 0.200750392 0.100375196
[47,] 0.8950889 0.209822169 0.104911085
[48,] 0.8842744 0.231451184 0.115725592
[49,] 0.8765164 0.246967154 0.123483577
[50,] 0.8542014 0.291597212 0.145798606
[51,] 0.8797972 0.240405530 0.120202765
[52,] 0.8728409 0.254318247 0.127159123
[53,] 0.8992724 0.201455157 0.100727578
[54,] 0.8910068 0.217986377 0.108993188
[55,] 0.9207301 0.158539782 0.079269891
[56,] 0.9057473 0.188505304 0.094252652
[57,] 0.8908062 0.218387603 0.109193802
[58,] 0.9540159 0.091968137 0.045984068
[59,] 0.9526449 0.094710256 0.047355128
[60,] 0.9566435 0.086713045 0.043356522
[61,] 0.9501110 0.099777933 0.049888967
[62,] 0.9432086 0.113582826 0.056791413
[63,] 0.9314189 0.137162214 0.068581107
[64,] 0.9422070 0.115586002 0.057793001
[65,] 0.9310603 0.137879326 0.068939663
[66,] 0.9181255 0.163748952 0.081874476
[67,] 0.9098046 0.180390714 0.090195357
[68,] 0.8931518 0.213696421 0.106848210
[69,] 0.9069842 0.186031659 0.093015829
[70,] 0.8908120 0.218375968 0.109187984
[71,] 0.8828996 0.234200858 0.117100429
[72,] 0.8841704 0.231659170 0.115829585
[73,] 0.8644351 0.271129765 0.135564883
[74,] 0.8440286 0.311942802 0.155971401
[75,] 0.8369392 0.326121664 0.163060832
[76,] 0.8156124 0.368775199 0.184387600
[77,] 0.7891100 0.421780077 0.210890039
[78,] 0.7655020 0.468995935 0.234497968
[79,] 0.7361972 0.527605627 0.263802813
[80,] 0.7045007 0.590998648 0.295499324
[81,] 0.7704333 0.459133381 0.229566691
[82,] 0.8105826 0.378834893 0.189417447
[83,] 0.7843339 0.431332233 0.215666116
[84,] 0.7600454 0.479909104 0.239954552
[85,] 0.7408495 0.518301003 0.259150501
[86,] 0.7123812 0.575237518 0.287618759
[87,] 0.6874747 0.625050581 0.312525290
[88,] 0.6612342 0.677531649 0.338765824
[89,] 0.6311966 0.737606778 0.368803389
[90,] 0.6369796 0.726040899 0.363020450
[91,] 0.6023638 0.795272498 0.397636249
[92,] 0.5939567 0.812086678 0.406043339
[93,] 0.5672707 0.865458583 0.432729291
[94,] 0.5429439 0.914112283 0.457056142
[95,] 0.6017676 0.796464739 0.398232369
[96,] 0.5982342 0.803531679 0.401765840
[97,] 0.6109064 0.778187138 0.389093569
[98,] 0.5845125 0.830974973 0.415487486
[99,] 0.5776654 0.844669100 0.422334550
[100,] 0.6379566 0.724086784 0.362043392
[101,] 0.6123592 0.775281505 0.387640753
[102,] 0.5959493 0.808101426 0.404050713
[103,] 0.5952898 0.809420326 0.404710163
[104,] 0.6054755 0.789048995 0.394524497
[105,] 0.5827301 0.834539772 0.417269886
[106,] 0.6758006 0.648398893 0.324199447
[107,] 0.6474972 0.705005699 0.352502849
[108,] 0.6190453 0.761909436 0.380954718
[109,] 0.5886547 0.822690680 0.411345340
[110,] 0.5674597 0.865080544 0.432540272
[111,] 0.5367283 0.926543423 0.463271712
[112,] 0.5105803 0.978839349 0.489419675
[113,] 0.4815834 0.963166807 0.518416596
[114,] 0.4497877 0.899575417 0.550212291
[115,] 0.4236994 0.847398881 0.576300559
[116,] 0.3907855 0.781570977 0.609214511
[117,] 0.3756840 0.751368045 0.624315977
[118,] 0.3515100 0.703019914 0.648490043
[119,] 0.3363461 0.672692112 0.663653944
[120,] 0.4564711 0.912942259 0.543528871
[121,] 0.4403637 0.880727411 0.559636295
[122,] 0.4297694 0.859538812 0.570230594
[123,] 0.4110893 0.822178581 0.588910709
[124,] 0.3814863 0.762972664 0.618513668
[125,] 0.3996440 0.799288088 0.600355956
[126,] 0.3730319 0.746063837 0.626968081
[127,] 0.4018248 0.803649688 0.598175156
[128,] 0.3862287 0.772457353 0.613771324
[129,] 0.3558928 0.711785525 0.644107237
[130,] 0.3365737 0.673147450 0.663426275
[131,] 0.3084399 0.616879734 0.691560133
[132,] 0.2834742 0.566948467 0.716525766
[133,] 0.2544843 0.508968576 0.745515712
[134,] 0.2738253 0.547650658 0.726174671
[135,] 0.2451038 0.490207556 0.754896222
[136,] 0.2237479 0.447495746 0.776252127
[137,] 0.2178661 0.435732100 0.782133950
[138,] 0.2104188 0.420837540 0.789581230
[139,] 0.2274754 0.454950726 0.772524637
[140,] 0.2436115 0.487222996 0.756388502
[141,] 0.2531751 0.506350195 0.746824902
[142,] 0.2620404 0.524080729 0.737959635
[143,] 0.2383099 0.476619748 0.761690126
[144,] 0.2152752 0.430550500 0.784724750
[145,] 0.2278191 0.455638207 0.772180896
[146,] 0.2410689 0.482137809 0.758931095
[147,] 0.2323065 0.464613021 0.767693489
[148,] 0.2057266 0.411453237 0.794273381
[149,] 0.1862642 0.372528351 0.813735824
[150,] 0.2981775 0.596354998 0.701822501
[151,] 0.3105489 0.621097729 0.689451136
[152,] 0.2857238 0.571447641 0.714276180
[153,] 0.2611689 0.522337769 0.738831115
[154,] 0.2357582 0.471516431 0.764241785
[155,] 0.2130562 0.426112446 0.786943777
[156,] 0.3501984 0.700396746 0.649801627
[157,] 0.3420925 0.684184916 0.657907542
[158,] 0.3370356 0.674071181 0.662964409
[159,] 0.3043222 0.608644421 0.695677790
[160,] 0.2783097 0.556619436 0.721690282
[161,] 0.3283539 0.656707858 0.671646071
[162,] 0.3538438 0.707687635 0.646156183
[163,] 0.3240728 0.648145698 0.675927151
[164,] 0.3188247 0.637649453 0.681175274
[165,] 0.4867415 0.973482941 0.513258530
[166,] 0.4696824 0.939364736 0.530317632
[167,] 0.4937325 0.987464986 0.506267507
[168,] 0.5044048 0.991190351 0.495595176
[169,] 0.5601619 0.879676121 0.439838061
[170,] 0.5273816 0.945236792 0.472618396
[171,] 0.5031481 0.993703849 0.496851924
[172,] 0.5035910 0.992818083 0.496409042
[173,] 0.4688748 0.937749695 0.531125153
[174,] 0.4650916 0.930183115 0.534908442
[175,] 0.4267889 0.853577722 0.573211139
[176,] 0.3974186 0.794837116 0.602581442
[177,] 0.4257156 0.851431143 0.574284429
[178,] 0.4180365 0.836072967 0.581963516
[179,] 0.3811903 0.762380695 0.618809653
[180,] 0.3514455 0.702891027 0.648554487
[181,] 0.3166508 0.633301569 0.683349215
[182,] 0.2930486 0.586097266 0.706951367
[183,] 0.3076187 0.615237400 0.692381300
[184,] 0.2852988 0.570597514 0.714701243
[185,] 0.3057064 0.611412852 0.694293574
[186,] 0.2921933 0.584386526 0.707806737
[187,] 0.2910518 0.582103511 0.708948244
[188,] 0.3012510 0.602501942 0.698749029
[189,] 0.3381594 0.676318735 0.661840633
[190,] 0.3097632 0.619526461 0.690236769
[191,] 0.3465774 0.693154833 0.653422584
[192,] 0.3124306 0.624861140 0.687569430
[193,] 0.3582818 0.716563639 0.641718180
[194,] 0.3335436 0.667087266 0.666456367
[195,] 0.3770390 0.754078030 0.622960985
[196,] 0.3370082 0.674016481 0.662991759
[197,] 0.3015231 0.603046284 0.698476858
[198,] 0.2781888 0.556377624 0.721811188
[199,] 0.2434069 0.486813899 0.756593051
[200,] 0.2818609 0.563721887 0.718139057
[201,] 0.2465354 0.493070865 0.753464568
[202,] 0.2453364 0.490672799 0.754663600
[203,] 0.2699935 0.539986938 0.730006531
[204,] 0.2582757 0.516551498 0.741724251
[205,] 0.2245934 0.449186890 0.775406555
[206,] 0.2859880 0.571976058 0.714011971
[207,] 0.2590139 0.518027791 0.740986105
[208,] 0.2449578 0.489915656 0.755042172
[209,] 0.2594672 0.518934482 0.740532759
[210,] 0.2228782 0.445756374 0.777121813
[211,] 0.2124607 0.424921327 0.787539336
[212,] 0.2082583 0.416516647 0.791741676
[213,] 0.2291532 0.458306364 0.770846818
[214,] 0.2368222 0.473644355 0.763177822
[215,] 0.2309813 0.461962640 0.769018680
[216,] 0.1967358 0.393471668 0.803264166
[217,] 0.1722503 0.344500539 0.827749731
[218,] 0.1995562 0.399112396 0.800443802
[219,] 0.4644938 0.928987614 0.535506193
[220,] 0.4307811 0.861562108 0.569218946
[221,] 0.4093448 0.818689632 0.590655184
[222,] 0.3910844 0.782168701 0.608915649
[223,] 0.3456728 0.691345643 0.654327178
[224,] 0.3805538 0.761107656 0.619446172
[225,] 0.3510350 0.702070076 0.648964962
[226,] 0.2997508 0.599501566 0.700249217
[227,] 0.3042746 0.608549159 0.695725421
[228,] 0.2809711 0.561942273 0.719028863
[229,] 0.2362307 0.472461434 0.763769283
[230,] 0.1901912 0.380382408 0.809808796
[231,] 0.3286763 0.657352640 0.671323680
[232,] 0.3185776 0.637155113 0.681422443
[233,] 0.3015672 0.603134485 0.698432757
[234,] 0.4624767 0.924953353 0.537523323
[235,] 0.4536727 0.907345417 0.546327291
[236,] 0.4057032 0.811406410 0.594296795
[237,] 0.4096341 0.819268156 0.590365922
[238,] 0.3317088 0.663417559 0.668291221
[239,] 0.2644131 0.528826154 0.735586923
[240,] 0.5933978 0.813204474 0.406602237
[241,] 0.4965590 0.993118047 0.503440976
[242,] 0.3976279 0.795255783 0.602372108
[243,] 0.5647391 0.870521738 0.435260869
[244,] 0.9175293 0.164941469 0.082470735
[245,] 0.8512692 0.297461614 0.148730807
[246,] 0.7949903 0.410019481 0.205009740
[247,] 0.6962286 0.607542880 0.303771440
> postscript(file="/var/fisher/rcomp/tmp/1pqtz1383469892.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2jufu1383469892.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3t4nd1383469892.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4vmrp1383469892.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5qtim1383469892.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
-0.173419158 3.183795954 -2.830626722 -2.196238574 5.427876366 4.033775133
7 8 9 10 11 12
3.221970168 -0.644905423 0.041158336 1.047619701 1.811654390 3.025674080
13 14 15 16 17 18
-3.240856215 2.302487530 2.735177149 0.609694147 0.540929612 1.667085845
19 20 21 22 23 24
-1.519880537 2.108060962 2.700329645 -2.464566616 -0.832036597 -1.674087207
25 26 27 28 29 30
2.072125908 -6.753335105 1.195758959 0.950030117 1.475354465 -2.716459979
31 32 33 34 35 36
0.665286678 0.438514945 2.062512180 0.216662460 0.329889800 0.784046602
37 38 39 40 41 42
-1.360361227 0.860260032 1.972048559 -2.159316658 -0.580074884 2.690569107
43 44 45 46 47 48
-0.437061546 -1.336948591 0.478677225 -2.435110026 -0.435841256 0.382807737
49 50 51 52 53 54
3.753285216 -1.560498194 0.852450884 0.753169788 -0.451419677 -1.598708475
55 56 57 58 59 60
-1.904914639 1.782246724 1.958874171 -0.452241937 -3.110247294 -1.184869730
61 62 63 64 65 66
-2.584261484 -1.420637218 -3.351872683 0.714749997 1.084635699 -4.912829160
67 68 69 70 71 72
-1.633887233 -2.430423703 1.351639290 1.390839774 0.631976376 3.075429304
73 74 75 76 77 78
0.763552522 -0.524168434 -1.661158685 0.260681196 2.962711725 0.669119488
79 80 81 82 83 84
1.541119469 -2.509289334 0.246701920 -0.595825040 1.725920350 0.797962275
85 86 87 88 89 90
0.092761150 1.134098349 -0.331529346 -0.043808390 -3.229463725 3.528539242
91 92 93 94 95 96
-0.270533333 0.944635270 1.068116537 -0.663150702 1.130076351 -0.820853616
97 98 99 100 101 102
-0.742395986 2.199851394 0.090530624 1.769957209 -0.830549739 1.126824027
103 104 105 106 107 108
-3.526063376 2.191865016 -2.300959532 1.053293826 2.054140483 -3.210061128
109 110 111 112 113 114
0.602339505 1.516601302 -2.081973300 -1.917932103 1.235215265 4.053560665
115 116 117 118 119 120
0.598766047 0.627713544 0.188318699 -1.191039858 0.746925900 -0.831477852
121 122 123 124 125 126
0.589152988 0.470494716 -1.031322913 0.375692794 -1.666457090 1.103467112
127 128 129 130 131 132
1.495311814 4.348675843 1.503602724 -1.708518973 -1.345061414 -0.306992195
133 134 135 136 137 138
2.477494901 0.781349031 2.658565689 1.609865881 0.544767166 -1.128056240
139 140 141 142 143 144
0.682174728 -0.740057728 -0.127765944 2.581921569 -0.392256432 0.833662481
145 146 147 148 149 150
1.862756729 1.543629272 -2.662542553 -2.674387078 -2.226395845 2.193141695
151 152 153 154 155 156
0.579453812 0.395234464 -2.457350292 -2.425662509 1.768838489 -0.270533333
157 158 159 160 161 162
0.764770452 4.348675843 -2.521762100 0.468098649 0.545707237 0.980996938
163 164 165 166 167 168
1.043202997 4.663422385 -1.919620586 1.928115357 -0.037658583 -0.719560891
169 170 171 172 173 174
-3.459336287 -2.877255780 0.244637716 1.578352274 -5.029943517 1.627769182
175 176 177 178 179 180
2.551718639 -2.406254499 -3.354585721 0.606332943 1.169441626 -2.211096701
181 182 183 184 185 186
-0.806762803 -1.956797671 -0.052665071 -1.074349265 2.472627186 1.443187197
187 188 189 190 191 192
0.288305687 1.040026988 0.183901996 0.786507231 -2.707774364 -1.288610456
193 194 195 196 197 198
2.312948879 -1.819342362 1.845252126 -2.335169077 2.454089291 0.823815886
199 200 201 202 203 204
-3.319488690 -0.785009592 -3.242528267 1.101076457 2.858016128 -0.064176792
205 206 207 208 209 210
0.368263552 1.135216318 -0.408674213 2.800390623 -0.005215841 1.769688950
211 212 213 214 215 216
-2.884395789 1.472458674 -0.879129376 -3.759170028 -1.392725566 1.488212550
217 218 219 220 221 222
2.137553636 -0.215118699 -2.093720249 1.465100537 -3.005400285 1.778267104
223 224 225 226 227 228
-2.203910653 -0.011921068 -1.462779887 1.770247026 4.554556703 -2.113037315
229 230 231 232 233 234
-1.398424089 -2.512707596 0.019986576 -3.101656512 -0.534795030 0.301440666
235 236 237 238 239 240
0.822944163 -1.980780070 1.001849639 -0.552581584 -4.620922355 -2.679075762
241 242 243 244 245 246
-2.548149869 -3.136835270 0.075693783 -0.445156804 1.535325923 0.352882954
247 248 249 250 251 252
0.084565262 4.694668756 -0.297850907 0.094240041 2.057129960 1.354439579
253 254 255 256 257 258
-1.345342970 -0.924304797 0.052443365 -0.916945992 -1.827829486 -2.673808680
259 260 261 262 263 264
2.133889507 -4.553048062 -0.064058312 1.184368078 -2.949036277 -0.017315247
> postscript(file="/var/fisher/rcomp/tmp/6wamm1383469892.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 -0.173419158 NA
1 3.183795954 -0.173419158
2 -2.830626722 3.183795954
3 -2.196238574 -2.830626722
4 5.427876366 -2.196238574
5 4.033775133 5.427876366
6 3.221970168 4.033775133
7 -0.644905423 3.221970168
8 0.041158336 -0.644905423
9 1.047619701 0.041158336
10 1.811654390 1.047619701
11 3.025674080 1.811654390
12 -3.240856215 3.025674080
13 2.302487530 -3.240856215
14 2.735177149 2.302487530
15 0.609694147 2.735177149
16 0.540929612 0.609694147
17 1.667085845 0.540929612
18 -1.519880537 1.667085845
19 2.108060962 -1.519880537
20 2.700329645 2.108060962
21 -2.464566616 2.700329645
22 -0.832036597 -2.464566616
23 -1.674087207 -0.832036597
24 2.072125908 -1.674087207
25 -6.753335105 2.072125908
26 1.195758959 -6.753335105
27 0.950030117 1.195758959
28 1.475354465 0.950030117
29 -2.716459979 1.475354465
30 0.665286678 -2.716459979
31 0.438514945 0.665286678
32 2.062512180 0.438514945
33 0.216662460 2.062512180
34 0.329889800 0.216662460
35 0.784046602 0.329889800
36 -1.360361227 0.784046602
37 0.860260032 -1.360361227
38 1.972048559 0.860260032
39 -2.159316658 1.972048559
40 -0.580074884 -2.159316658
41 2.690569107 -0.580074884
42 -0.437061546 2.690569107
43 -1.336948591 -0.437061546
44 0.478677225 -1.336948591
45 -2.435110026 0.478677225
46 -0.435841256 -2.435110026
47 0.382807737 -0.435841256
48 3.753285216 0.382807737
49 -1.560498194 3.753285216
50 0.852450884 -1.560498194
51 0.753169788 0.852450884
52 -0.451419677 0.753169788
53 -1.598708475 -0.451419677
54 -1.904914639 -1.598708475
55 1.782246724 -1.904914639
56 1.958874171 1.782246724
57 -0.452241937 1.958874171
58 -3.110247294 -0.452241937
59 -1.184869730 -3.110247294
60 -2.584261484 -1.184869730
61 -1.420637218 -2.584261484
62 -3.351872683 -1.420637218
63 0.714749997 -3.351872683
64 1.084635699 0.714749997
65 -4.912829160 1.084635699
66 -1.633887233 -4.912829160
67 -2.430423703 -1.633887233
68 1.351639290 -2.430423703
69 1.390839774 1.351639290
70 0.631976376 1.390839774
71 3.075429304 0.631976376
72 0.763552522 3.075429304
73 -0.524168434 0.763552522
74 -1.661158685 -0.524168434
75 0.260681196 -1.661158685
76 2.962711725 0.260681196
77 0.669119488 2.962711725
78 1.541119469 0.669119488
79 -2.509289334 1.541119469
80 0.246701920 -2.509289334
81 -0.595825040 0.246701920
82 1.725920350 -0.595825040
83 0.797962275 1.725920350
84 0.092761150 0.797962275
85 1.134098349 0.092761150
86 -0.331529346 1.134098349
87 -0.043808390 -0.331529346
88 -3.229463725 -0.043808390
89 3.528539242 -3.229463725
90 -0.270533333 3.528539242
91 0.944635270 -0.270533333
92 1.068116537 0.944635270
93 -0.663150702 1.068116537
94 1.130076351 -0.663150702
95 -0.820853616 1.130076351
96 -0.742395986 -0.820853616
97 2.199851394 -0.742395986
98 0.090530624 2.199851394
99 1.769957209 0.090530624
100 -0.830549739 1.769957209
101 1.126824027 -0.830549739
102 -3.526063376 1.126824027
103 2.191865016 -3.526063376
104 -2.300959532 2.191865016
105 1.053293826 -2.300959532
106 2.054140483 1.053293826
107 -3.210061128 2.054140483
108 0.602339505 -3.210061128
109 1.516601302 0.602339505
110 -2.081973300 1.516601302
111 -1.917932103 -2.081973300
112 1.235215265 -1.917932103
113 4.053560665 1.235215265
114 0.598766047 4.053560665
115 0.627713544 0.598766047
116 0.188318699 0.627713544
117 -1.191039858 0.188318699
118 0.746925900 -1.191039858
119 -0.831477852 0.746925900
120 0.589152988 -0.831477852
121 0.470494716 0.589152988
122 -1.031322913 0.470494716
123 0.375692794 -1.031322913
124 -1.666457090 0.375692794
125 1.103467112 -1.666457090
126 1.495311814 1.103467112
127 4.348675843 1.495311814
128 1.503602724 4.348675843
129 -1.708518973 1.503602724
130 -1.345061414 -1.708518973
131 -0.306992195 -1.345061414
132 2.477494901 -0.306992195
133 0.781349031 2.477494901
134 2.658565689 0.781349031
135 1.609865881 2.658565689
136 0.544767166 1.609865881
137 -1.128056240 0.544767166
138 0.682174728 -1.128056240
139 -0.740057728 0.682174728
140 -0.127765944 -0.740057728
141 2.581921569 -0.127765944
142 -0.392256432 2.581921569
143 0.833662481 -0.392256432
144 1.862756729 0.833662481
145 1.543629272 1.862756729
146 -2.662542553 1.543629272
147 -2.674387078 -2.662542553
148 -2.226395845 -2.674387078
149 2.193141695 -2.226395845
150 0.579453812 2.193141695
151 0.395234464 0.579453812
152 -2.457350292 0.395234464
153 -2.425662509 -2.457350292
154 1.768838489 -2.425662509
155 -0.270533333 1.768838489
156 0.764770452 -0.270533333
157 4.348675843 0.764770452
158 -2.521762100 4.348675843
159 0.468098649 -2.521762100
160 0.545707237 0.468098649
161 0.980996938 0.545707237
162 1.043202997 0.980996938
163 4.663422385 1.043202997
164 -1.919620586 4.663422385
165 1.928115357 -1.919620586
166 -0.037658583 1.928115357
167 -0.719560891 -0.037658583
168 -3.459336287 -0.719560891
169 -2.877255780 -3.459336287
170 0.244637716 -2.877255780
171 1.578352274 0.244637716
172 -5.029943517 1.578352274
173 1.627769182 -5.029943517
174 2.551718639 1.627769182
175 -2.406254499 2.551718639
176 -3.354585721 -2.406254499
177 0.606332943 -3.354585721
178 1.169441626 0.606332943
179 -2.211096701 1.169441626
180 -0.806762803 -2.211096701
181 -1.956797671 -0.806762803
182 -0.052665071 -1.956797671
183 -1.074349265 -0.052665071
184 2.472627186 -1.074349265
185 1.443187197 2.472627186
186 0.288305687 1.443187197
187 1.040026988 0.288305687
188 0.183901996 1.040026988
189 0.786507231 0.183901996
190 -2.707774364 0.786507231
191 -1.288610456 -2.707774364
192 2.312948879 -1.288610456
193 -1.819342362 2.312948879
194 1.845252126 -1.819342362
195 -2.335169077 1.845252126
196 2.454089291 -2.335169077
197 0.823815886 2.454089291
198 -3.319488690 0.823815886
199 -0.785009592 -3.319488690
200 -3.242528267 -0.785009592
201 1.101076457 -3.242528267
202 2.858016128 1.101076457
203 -0.064176792 2.858016128
204 0.368263552 -0.064176792
205 1.135216318 0.368263552
206 -0.408674213 1.135216318
207 2.800390623 -0.408674213
208 -0.005215841 2.800390623
209 1.769688950 -0.005215841
210 -2.884395789 1.769688950
211 1.472458674 -2.884395789
212 -0.879129376 1.472458674
213 -3.759170028 -0.879129376
214 -1.392725566 -3.759170028
215 1.488212550 -1.392725566
216 2.137553636 1.488212550
217 -0.215118699 2.137553636
218 -2.093720249 -0.215118699
219 1.465100537 -2.093720249
220 -3.005400285 1.465100537
221 1.778267104 -3.005400285
222 -2.203910653 1.778267104
223 -0.011921068 -2.203910653
224 -1.462779887 -0.011921068
225 1.770247026 -1.462779887
226 4.554556703 1.770247026
227 -2.113037315 4.554556703
228 -1.398424089 -2.113037315
229 -2.512707596 -1.398424089
230 0.019986576 -2.512707596
231 -3.101656512 0.019986576
232 -0.534795030 -3.101656512
233 0.301440666 -0.534795030
234 0.822944163 0.301440666
235 -1.980780070 0.822944163
236 1.001849639 -1.980780070
237 -0.552581584 1.001849639
238 -4.620922355 -0.552581584
239 -2.679075762 -4.620922355
240 -2.548149869 -2.679075762
241 -3.136835270 -2.548149869
242 0.075693783 -3.136835270
243 -0.445156804 0.075693783
244 1.535325923 -0.445156804
245 0.352882954 1.535325923
246 0.084565262 0.352882954
247 4.694668756 0.084565262
248 -0.297850907 4.694668756
249 0.094240041 -0.297850907
250 2.057129960 0.094240041
251 1.354439579 2.057129960
252 -1.345342970 1.354439579
253 -0.924304797 -1.345342970
254 0.052443365 -0.924304797
255 -0.916945992 0.052443365
256 -1.827829486 -0.916945992
257 -2.673808680 -1.827829486
258 2.133889507 -2.673808680
259 -4.553048062 2.133889507
260 -0.064058312 -4.553048062
261 1.184368078 -0.064058312
262 -2.949036277 1.184368078
263 -0.017315247 -2.949036277
264 NA -0.017315247
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.183795954 -0.173419158
[2,] -2.830626722 3.183795954
[3,] -2.196238574 -2.830626722
[4,] 5.427876366 -2.196238574
[5,] 4.033775133 5.427876366
[6,] 3.221970168 4.033775133
[7,] -0.644905423 3.221970168
[8,] 0.041158336 -0.644905423
[9,] 1.047619701 0.041158336
[10,] 1.811654390 1.047619701
[11,] 3.025674080 1.811654390
[12,] -3.240856215 3.025674080
[13,] 2.302487530 -3.240856215
[14,] 2.735177149 2.302487530
[15,] 0.609694147 2.735177149
[16,] 0.540929612 0.609694147
[17,] 1.667085845 0.540929612
[18,] -1.519880537 1.667085845
[19,] 2.108060962 -1.519880537
[20,] 2.700329645 2.108060962
[21,] -2.464566616 2.700329645
[22,] -0.832036597 -2.464566616
[23,] -1.674087207 -0.832036597
[24,] 2.072125908 -1.674087207
[25,] -6.753335105 2.072125908
[26,] 1.195758959 -6.753335105
[27,] 0.950030117 1.195758959
[28,] 1.475354465 0.950030117
[29,] -2.716459979 1.475354465
[30,] 0.665286678 -2.716459979
[31,] 0.438514945 0.665286678
[32,] 2.062512180 0.438514945
[33,] 0.216662460 2.062512180
[34,] 0.329889800 0.216662460
[35,] 0.784046602 0.329889800
[36,] -1.360361227 0.784046602
[37,] 0.860260032 -1.360361227
[38,] 1.972048559 0.860260032
[39,] -2.159316658 1.972048559
[40,] -0.580074884 -2.159316658
[41,] 2.690569107 -0.580074884
[42,] -0.437061546 2.690569107
[43,] -1.336948591 -0.437061546
[44,] 0.478677225 -1.336948591
[45,] -2.435110026 0.478677225
[46,] -0.435841256 -2.435110026
[47,] 0.382807737 -0.435841256
[48,] 3.753285216 0.382807737
[49,] -1.560498194 3.753285216
[50,] 0.852450884 -1.560498194
[51,] 0.753169788 0.852450884
[52,] -0.451419677 0.753169788
[53,] -1.598708475 -0.451419677
[54,] -1.904914639 -1.598708475
[55,] 1.782246724 -1.904914639
[56,] 1.958874171 1.782246724
[57,] -0.452241937 1.958874171
[58,] -3.110247294 -0.452241937
[59,] -1.184869730 -3.110247294
[60,] -2.584261484 -1.184869730
[61,] -1.420637218 -2.584261484
[62,] -3.351872683 -1.420637218
[63,] 0.714749997 -3.351872683
[64,] 1.084635699 0.714749997
[65,] -4.912829160 1.084635699
[66,] -1.633887233 -4.912829160
[67,] -2.430423703 -1.633887233
[68,] 1.351639290 -2.430423703
[69,] 1.390839774 1.351639290
[70,] 0.631976376 1.390839774
[71,] 3.075429304 0.631976376
[72,] 0.763552522 3.075429304
[73,] -0.524168434 0.763552522
[74,] -1.661158685 -0.524168434
[75,] 0.260681196 -1.661158685
[76,] 2.962711725 0.260681196
[77,] 0.669119488 2.962711725
[78,] 1.541119469 0.669119488
[79,] -2.509289334 1.541119469
[80,] 0.246701920 -2.509289334
[81,] -0.595825040 0.246701920
[82,] 1.725920350 -0.595825040
[83,] 0.797962275 1.725920350
[84,] 0.092761150 0.797962275
[85,] 1.134098349 0.092761150
[86,] -0.331529346 1.134098349
[87,] -0.043808390 -0.331529346
[88,] -3.229463725 -0.043808390
[89,] 3.528539242 -3.229463725
[90,] -0.270533333 3.528539242
[91,] 0.944635270 -0.270533333
[92,] 1.068116537 0.944635270
[93,] -0.663150702 1.068116537
[94,] 1.130076351 -0.663150702
[95,] -0.820853616 1.130076351
[96,] -0.742395986 -0.820853616
[97,] 2.199851394 -0.742395986
[98,] 0.090530624 2.199851394
[99,] 1.769957209 0.090530624
[100,] -0.830549739 1.769957209
[101,] 1.126824027 -0.830549739
[102,] -3.526063376 1.126824027
[103,] 2.191865016 -3.526063376
[104,] -2.300959532 2.191865016
[105,] 1.053293826 -2.300959532
[106,] 2.054140483 1.053293826
[107,] -3.210061128 2.054140483
[108,] 0.602339505 -3.210061128
[109,] 1.516601302 0.602339505
[110,] -2.081973300 1.516601302
[111,] -1.917932103 -2.081973300
[112,] 1.235215265 -1.917932103
[113,] 4.053560665 1.235215265
[114,] 0.598766047 4.053560665
[115,] 0.627713544 0.598766047
[116,] 0.188318699 0.627713544
[117,] -1.191039858 0.188318699
[118,] 0.746925900 -1.191039858
[119,] -0.831477852 0.746925900
[120,] 0.589152988 -0.831477852
[121,] 0.470494716 0.589152988
[122,] -1.031322913 0.470494716
[123,] 0.375692794 -1.031322913
[124,] -1.666457090 0.375692794
[125,] 1.103467112 -1.666457090
[126,] 1.495311814 1.103467112
[127,] 4.348675843 1.495311814
[128,] 1.503602724 4.348675843
[129,] -1.708518973 1.503602724
[130,] -1.345061414 -1.708518973
[131,] -0.306992195 -1.345061414
[132,] 2.477494901 -0.306992195
[133,] 0.781349031 2.477494901
[134,] 2.658565689 0.781349031
[135,] 1.609865881 2.658565689
[136,] 0.544767166 1.609865881
[137,] -1.128056240 0.544767166
[138,] 0.682174728 -1.128056240
[139,] -0.740057728 0.682174728
[140,] -0.127765944 -0.740057728
[141,] 2.581921569 -0.127765944
[142,] -0.392256432 2.581921569
[143,] 0.833662481 -0.392256432
[144,] 1.862756729 0.833662481
[145,] 1.543629272 1.862756729
[146,] -2.662542553 1.543629272
[147,] -2.674387078 -2.662542553
[148,] -2.226395845 -2.674387078
[149,] 2.193141695 -2.226395845
[150,] 0.579453812 2.193141695
[151,] 0.395234464 0.579453812
[152,] -2.457350292 0.395234464
[153,] -2.425662509 -2.457350292
[154,] 1.768838489 -2.425662509
[155,] -0.270533333 1.768838489
[156,] 0.764770452 -0.270533333
[157,] 4.348675843 0.764770452
[158,] -2.521762100 4.348675843
[159,] 0.468098649 -2.521762100
[160,] 0.545707237 0.468098649
[161,] 0.980996938 0.545707237
[162,] 1.043202997 0.980996938
[163,] 4.663422385 1.043202997
[164,] -1.919620586 4.663422385
[165,] 1.928115357 -1.919620586
[166,] -0.037658583 1.928115357
[167,] -0.719560891 -0.037658583
[168,] -3.459336287 -0.719560891
[169,] -2.877255780 -3.459336287
[170,] 0.244637716 -2.877255780
[171,] 1.578352274 0.244637716
[172,] -5.029943517 1.578352274
[173,] 1.627769182 -5.029943517
[174,] 2.551718639 1.627769182
[175,] -2.406254499 2.551718639
[176,] -3.354585721 -2.406254499
[177,] 0.606332943 -3.354585721
[178,] 1.169441626 0.606332943
[179,] -2.211096701 1.169441626
[180,] -0.806762803 -2.211096701
[181,] -1.956797671 -0.806762803
[182,] -0.052665071 -1.956797671
[183,] -1.074349265 -0.052665071
[184,] 2.472627186 -1.074349265
[185,] 1.443187197 2.472627186
[186,] 0.288305687 1.443187197
[187,] 1.040026988 0.288305687
[188,] 0.183901996 1.040026988
[189,] 0.786507231 0.183901996
[190,] -2.707774364 0.786507231
[191,] -1.288610456 -2.707774364
[192,] 2.312948879 -1.288610456
[193,] -1.819342362 2.312948879
[194,] 1.845252126 -1.819342362
[195,] -2.335169077 1.845252126
[196,] 2.454089291 -2.335169077
[197,] 0.823815886 2.454089291
[198,] -3.319488690 0.823815886
[199,] -0.785009592 -3.319488690
[200,] -3.242528267 -0.785009592
[201,] 1.101076457 -3.242528267
[202,] 2.858016128 1.101076457
[203,] -0.064176792 2.858016128
[204,] 0.368263552 -0.064176792
[205,] 1.135216318 0.368263552
[206,] -0.408674213 1.135216318
[207,] 2.800390623 -0.408674213
[208,] -0.005215841 2.800390623
[209,] 1.769688950 -0.005215841
[210,] -2.884395789 1.769688950
[211,] 1.472458674 -2.884395789
[212,] -0.879129376 1.472458674
[213,] -3.759170028 -0.879129376
[214,] -1.392725566 -3.759170028
[215,] 1.488212550 -1.392725566
[216,] 2.137553636 1.488212550
[217,] -0.215118699 2.137553636
[218,] -2.093720249 -0.215118699
[219,] 1.465100537 -2.093720249
[220,] -3.005400285 1.465100537
[221,] 1.778267104 -3.005400285
[222,] -2.203910653 1.778267104
[223,] -0.011921068 -2.203910653
[224,] -1.462779887 -0.011921068
[225,] 1.770247026 -1.462779887
[226,] 4.554556703 1.770247026
[227,] -2.113037315 4.554556703
[228,] -1.398424089 -2.113037315
[229,] -2.512707596 -1.398424089
[230,] 0.019986576 -2.512707596
[231,] -3.101656512 0.019986576
[232,] -0.534795030 -3.101656512
[233,] 0.301440666 -0.534795030
[234,] 0.822944163 0.301440666
[235,] -1.980780070 0.822944163
[236,] 1.001849639 -1.980780070
[237,] -0.552581584 1.001849639
[238,] -4.620922355 -0.552581584
[239,] -2.679075762 -4.620922355
[240,] -2.548149869 -2.679075762
[241,] -3.136835270 -2.548149869
[242,] 0.075693783 -3.136835270
[243,] -0.445156804 0.075693783
[244,] 1.535325923 -0.445156804
[245,] 0.352882954 1.535325923
[246,] 0.084565262 0.352882954
[247,] 4.694668756 0.084565262
[248,] -0.297850907 4.694668756
[249,] 0.094240041 -0.297850907
[250,] 2.057129960 0.094240041
[251,] 1.354439579 2.057129960
[252,] -1.345342970 1.354439579
[253,] -0.924304797 -1.345342970
[254,] 0.052443365 -0.924304797
[255,] -0.916945992 0.052443365
[256,] -1.827829486 -0.916945992
[257,] -2.673808680 -1.827829486
[258,] 2.133889507 -2.673808680
[259,] -4.553048062 2.133889507
[260,] -0.064058312 -4.553048062
[261,] 1.184368078 -0.064058312
[262,] -2.949036277 1.184368078
[263,] -0.017315247 -2.949036277
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.183795954 -0.173419158
2 -2.830626722 3.183795954
3 -2.196238574 -2.830626722
4 5.427876366 -2.196238574
5 4.033775133 5.427876366
6 3.221970168 4.033775133
7 -0.644905423 3.221970168
8 0.041158336 -0.644905423
9 1.047619701 0.041158336
10 1.811654390 1.047619701
11 3.025674080 1.811654390
12 -3.240856215 3.025674080
13 2.302487530 -3.240856215
14 2.735177149 2.302487530
15 0.609694147 2.735177149
16 0.540929612 0.609694147
17 1.667085845 0.540929612
18 -1.519880537 1.667085845
19 2.108060962 -1.519880537
20 2.700329645 2.108060962
21 -2.464566616 2.700329645
22 -0.832036597 -2.464566616
23 -1.674087207 -0.832036597
24 2.072125908 -1.674087207
25 -6.753335105 2.072125908
26 1.195758959 -6.753335105
27 0.950030117 1.195758959
28 1.475354465 0.950030117
29 -2.716459979 1.475354465
30 0.665286678 -2.716459979
31 0.438514945 0.665286678
32 2.062512180 0.438514945
33 0.216662460 2.062512180
34 0.329889800 0.216662460
35 0.784046602 0.329889800
36 -1.360361227 0.784046602
37 0.860260032 -1.360361227
38 1.972048559 0.860260032
39 -2.159316658 1.972048559
40 -0.580074884 -2.159316658
41 2.690569107 -0.580074884
42 -0.437061546 2.690569107
43 -1.336948591 -0.437061546
44 0.478677225 -1.336948591
45 -2.435110026 0.478677225
46 -0.435841256 -2.435110026
47 0.382807737 -0.435841256
48 3.753285216 0.382807737
49 -1.560498194 3.753285216
50 0.852450884 -1.560498194
51 0.753169788 0.852450884
52 -0.451419677 0.753169788
53 -1.598708475 -0.451419677
54 -1.904914639 -1.598708475
55 1.782246724 -1.904914639
56 1.958874171 1.782246724
57 -0.452241937 1.958874171
58 -3.110247294 -0.452241937
59 -1.184869730 -3.110247294
60 -2.584261484 -1.184869730
61 -1.420637218 -2.584261484
62 -3.351872683 -1.420637218
63 0.714749997 -3.351872683
64 1.084635699 0.714749997
65 -4.912829160 1.084635699
66 -1.633887233 -4.912829160
67 -2.430423703 -1.633887233
68 1.351639290 -2.430423703
69 1.390839774 1.351639290
70 0.631976376 1.390839774
71 3.075429304 0.631976376
72 0.763552522 3.075429304
73 -0.524168434 0.763552522
74 -1.661158685 -0.524168434
75 0.260681196 -1.661158685
76 2.962711725 0.260681196
77 0.669119488 2.962711725
78 1.541119469 0.669119488
79 -2.509289334 1.541119469
80 0.246701920 -2.509289334
81 -0.595825040 0.246701920
82 1.725920350 -0.595825040
83 0.797962275 1.725920350
84 0.092761150 0.797962275
85 1.134098349 0.092761150
86 -0.331529346 1.134098349
87 -0.043808390 -0.331529346
88 -3.229463725 -0.043808390
89 3.528539242 -3.229463725
90 -0.270533333 3.528539242
91 0.944635270 -0.270533333
92 1.068116537 0.944635270
93 -0.663150702 1.068116537
94 1.130076351 -0.663150702
95 -0.820853616 1.130076351
96 -0.742395986 -0.820853616
97 2.199851394 -0.742395986
98 0.090530624 2.199851394
99 1.769957209 0.090530624
100 -0.830549739 1.769957209
101 1.126824027 -0.830549739
102 -3.526063376 1.126824027
103 2.191865016 -3.526063376
104 -2.300959532 2.191865016
105 1.053293826 -2.300959532
106 2.054140483 1.053293826
107 -3.210061128 2.054140483
108 0.602339505 -3.210061128
109 1.516601302 0.602339505
110 -2.081973300 1.516601302
111 -1.917932103 -2.081973300
112 1.235215265 -1.917932103
113 4.053560665 1.235215265
114 0.598766047 4.053560665
115 0.627713544 0.598766047
116 0.188318699 0.627713544
117 -1.191039858 0.188318699
118 0.746925900 -1.191039858
119 -0.831477852 0.746925900
120 0.589152988 -0.831477852
121 0.470494716 0.589152988
122 -1.031322913 0.470494716
123 0.375692794 -1.031322913
124 -1.666457090 0.375692794
125 1.103467112 -1.666457090
126 1.495311814 1.103467112
127 4.348675843 1.495311814
128 1.503602724 4.348675843
129 -1.708518973 1.503602724
130 -1.345061414 -1.708518973
131 -0.306992195 -1.345061414
132 2.477494901 -0.306992195
133 0.781349031 2.477494901
134 2.658565689 0.781349031
135 1.609865881 2.658565689
136 0.544767166 1.609865881
137 -1.128056240 0.544767166
138 0.682174728 -1.128056240
139 -0.740057728 0.682174728
140 -0.127765944 -0.740057728
141 2.581921569 -0.127765944
142 -0.392256432 2.581921569
143 0.833662481 -0.392256432
144 1.862756729 0.833662481
145 1.543629272 1.862756729
146 -2.662542553 1.543629272
147 -2.674387078 -2.662542553
148 -2.226395845 -2.674387078
149 2.193141695 -2.226395845
150 0.579453812 2.193141695
151 0.395234464 0.579453812
152 -2.457350292 0.395234464
153 -2.425662509 -2.457350292
154 1.768838489 -2.425662509
155 -0.270533333 1.768838489
156 0.764770452 -0.270533333
157 4.348675843 0.764770452
158 -2.521762100 4.348675843
159 0.468098649 -2.521762100
160 0.545707237 0.468098649
161 0.980996938 0.545707237
162 1.043202997 0.980996938
163 4.663422385 1.043202997
164 -1.919620586 4.663422385
165 1.928115357 -1.919620586
166 -0.037658583 1.928115357
167 -0.719560891 -0.037658583
168 -3.459336287 -0.719560891
169 -2.877255780 -3.459336287
170 0.244637716 -2.877255780
171 1.578352274 0.244637716
172 -5.029943517 1.578352274
173 1.627769182 -5.029943517
174 2.551718639 1.627769182
175 -2.406254499 2.551718639
176 -3.354585721 -2.406254499
177 0.606332943 -3.354585721
178 1.169441626 0.606332943
179 -2.211096701 1.169441626
180 -0.806762803 -2.211096701
181 -1.956797671 -0.806762803
182 -0.052665071 -1.956797671
183 -1.074349265 -0.052665071
184 2.472627186 -1.074349265
185 1.443187197 2.472627186
186 0.288305687 1.443187197
187 1.040026988 0.288305687
188 0.183901996 1.040026988
189 0.786507231 0.183901996
190 -2.707774364 0.786507231
191 -1.288610456 -2.707774364
192 2.312948879 -1.288610456
193 -1.819342362 2.312948879
194 1.845252126 -1.819342362
195 -2.335169077 1.845252126
196 2.454089291 -2.335169077
197 0.823815886 2.454089291
198 -3.319488690 0.823815886
199 -0.785009592 -3.319488690
200 -3.242528267 -0.785009592
201 1.101076457 -3.242528267
202 2.858016128 1.101076457
203 -0.064176792 2.858016128
204 0.368263552 -0.064176792
205 1.135216318 0.368263552
206 -0.408674213 1.135216318
207 2.800390623 -0.408674213
208 -0.005215841 2.800390623
209 1.769688950 -0.005215841
210 -2.884395789 1.769688950
211 1.472458674 -2.884395789
212 -0.879129376 1.472458674
213 -3.759170028 -0.879129376
214 -1.392725566 -3.759170028
215 1.488212550 -1.392725566
216 2.137553636 1.488212550
217 -0.215118699 2.137553636
218 -2.093720249 -0.215118699
219 1.465100537 -2.093720249
220 -3.005400285 1.465100537
221 1.778267104 -3.005400285
222 -2.203910653 1.778267104
223 -0.011921068 -2.203910653
224 -1.462779887 -0.011921068
225 1.770247026 -1.462779887
226 4.554556703 1.770247026
227 -2.113037315 4.554556703
228 -1.398424089 -2.113037315
229 -2.512707596 -1.398424089
230 0.019986576 -2.512707596
231 -3.101656512 0.019986576
232 -0.534795030 -3.101656512
233 0.301440666 -0.534795030
234 0.822944163 0.301440666
235 -1.980780070 0.822944163
236 1.001849639 -1.980780070
237 -0.552581584 1.001849639
238 -4.620922355 -0.552581584
239 -2.679075762 -4.620922355
240 -2.548149869 -2.679075762
241 -3.136835270 -2.548149869
242 0.075693783 -3.136835270
243 -0.445156804 0.075693783
244 1.535325923 -0.445156804
245 0.352882954 1.535325923
246 0.084565262 0.352882954
247 4.694668756 0.084565262
248 -0.297850907 4.694668756
249 0.094240041 -0.297850907
250 2.057129960 0.094240041
251 1.354439579 2.057129960
252 -1.345342970 1.354439579
253 -0.924304797 -1.345342970
254 0.052443365 -0.924304797
255 -0.916945992 0.052443365
256 -1.827829486 -0.916945992
257 -2.673808680 -1.827829486
258 2.133889507 -2.673808680
259 -4.553048062 2.133889507
260 -0.064058312 -4.553048062
261 1.184368078 -0.064058312
262 -2.949036277 1.184368078
263 -0.017315247 -2.949036277
> 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/fisher/rcomp/tmp/7y8z11383469892.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8cqi61383469892.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9rebx1383469892.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10wk2n1383469892.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/fisher/rcomp/tmp/11bfii1383469892.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/fisher/rcomp/tmp/12kn3n1383469892.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/fisher/rcomp/tmp/135i4y1383469893.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/fisher/rcomp/tmp/14z1uc1383469893.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/fisher/rcomp/tmp/15f2iw1383469893.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/fisher/rcomp/tmp/16jd0y1383469893.tab")
+ }
>
> try(system("convert tmp/1pqtz1383469892.ps tmp/1pqtz1383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jufu1383469892.ps tmp/2jufu1383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t4nd1383469892.ps tmp/3t4nd1383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/4vmrp1383469892.ps tmp/4vmrp1383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qtim1383469892.ps tmp/5qtim1383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wamm1383469892.ps tmp/6wamm1383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/7y8z11383469892.ps tmp/7y8z11383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/8cqi61383469892.ps tmp/8cqi61383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rebx1383469892.ps tmp/9rebx1383469892.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wk2n1383469892.ps tmp/10wk2n1383469892.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.79 1.77 12.57