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(14
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+ ,dim=c(6
+ ,264)
+ ,dimnames=list(c('Happiness'
+ ,'Depression'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software')
+ ,1:264))
> y <- array(NA,dim=c(6,264),dimnames=list(c('Happiness','Depression','Connected','Separate','Learning','Software'),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
Happiness Depression Connected Separate Learning Software
1 14 12.0 41 38 13 12
2 18 11.0 39 32 16 11
3 11 14.0 30 35 19 15
4 12 12.0 31 33 15 6
5 16 21.0 34 37 14 13
6 18 12.0 35 29 13 10
7 14 22.0 39 31 19 12
8 14 11.0 34 36 15 14
9 15 10.0 36 35 14 12
10 15 13.0 37 38 15 9
11 17 10.0 38 31 16 10
12 19 8.0 36 34 16 12
13 10 15.0 38 35 16 12
14 16 14.0 39 38 16 11
15 18 10.0 33 37 17 15
16 14 14.0 32 33 15 12
17 14 14.0 36 32 15 10
18 17 11.0 38 38 20 12
19 14 10.0 39 38 18 11
20 16 13.0 32 32 16 12
21 18 9.5 32 33 16 11
22 11 14.0 31 31 16 12
23 14 12.0 39 38 19 13
24 12 14.0 37 39 16 11
25 17 11.0 39 32 17 12
26 9 9.0 41 32 17 13
27 16 11.0 36 35 16 10
28 14 15.0 33 37 15 14
29 15 14.0 33 33 16 12
30 11 13.0 34 33 14 10
31 16 9.0 31 31 15 12
32 13 15.0 27 32 12 8
33 17 10.0 37 31 14 10
34 15 11.0 34 37 16 12
35 14 13.0 34 30 14 12
36 16 8.0 32 33 10 7
37 9 20.0 29 31 10 9
38 15 12.0 36 33 14 12
39 17 10.0 29 31 16 10
40 13 10.0 35 33 16 10
41 15 9.0 37 32 16 10
42 16 14.0 34 33 14 12
43 16 8.0 38 32 20 15
44 12 14.0 35 33 14 10
45 15 11.0 38 28 14 10
46 11 13.0 37 35 11 12
47 15 9.0 38 39 14 13
48 15 11.0 33 34 15 11
49 17 15.0 36 38 16 11
50 13 11.0 38 32 14 12
51 16 10.0 32 38 16 14
52 14 14.0 32 30 14 10
53 11 18.0 32 33 12 12
54 12 14.0 34 38 16 13
55 12 11.0 32 32 9 5
56 15 14.5 37 35 14 6
57 16 13.0 39 34 16 12
58 15 9.0 29 34 16 12
59 12 10.0 37 36 15 11
60 12 15.0 35 34 16 10
61 8 20.0 30 28 12 7
62 13 12.0 38 34 16 12
63 11 12.0 34 35 16 14
64 14 14.0 31 35 14 11
65 15 13.0 34 31 16 12
66 10 11.0 35 37 17 13
67 11 17.0 36 35 18 14
68 12 12.0 30 27 18 11
69 15 13.0 39 40 12 12
70 15 14.0 35 37 16 12
71 14 13.0 38 36 10 8
72 16 15.0 31 38 14 11
73 15 13.0 34 39 18 14
74 15 10.0 38 41 18 14
75 13 11.0 34 27 16 12
76 12 19.0 39 30 17 9
77 17 13.0 37 37 16 13
78 13 17.0 34 31 16 11
79 15 13.0 28 31 13 12
80 13 9.0 37 27 16 12
81 15 11.0 33 36 16 12
82 15 9.0 35 37 16 12
83 16 12.0 37 33 15 12
84 15 12.0 32 34 15 11
85 14 13.0 33 31 16 10
86 15 13.0 38 39 14 9
87 14 12.0 33 34 16 12
88 13 15.0 29 32 16 12
89 7 22.0 33 33 15 12
90 17 13.0 31 36 12 9
91 13 15.0 36 32 17 15
92 15 13.0 35 41 16 12
93 14 15.0 32 28 15 12
94 13 12.5 29 30 13 12
95 16 11.0 39 36 16 10
96 12 16.0 37 35 16 13
97 14 11.0 35 31 16 9
98 17 11.0 37 34 16 12
99 15 10.0 32 36 14 10
100 17 10.0 38 36 16 14
101 12 16.0 37 35 16 11
102 16 12.0 36 37 20 15
103 11 11.0 32 28 15 11
104 15 16.0 33 39 16 11
105 9 19.0 40 32 13 12
106 16 11.0 38 35 17 12
107 15 16.0 41 39 16 12
108 10 15.0 36 35 16 11
109 10 24.0 43 42 12 7
110 15 14.0 30 34 16 12
111 11 15.0 31 33 16 14
112 13 11.0 32 41 17 11
113 14 15.0 32 33 13 11
114 18 12.0 37 34 12 10
115 16 10.0 37 32 18 13
116 14 14.0 33 40 14 13
117 14 13.0 34 40 14 8
118 14 9.0 33 35 13 11
119 14 15.0 38 36 16 12
120 12 15.0 33 37 13 11
121 14 14.0 31 27 16 13
122 15 11.0 38 39 13 12
123 15 8.0 37 38 16 14
124 15 11.0 36 31 15 13
125 13 11.0 31 33 16 15
126 17 8.0 39 32 15 10
127 17 10.0 44 39 17 11
128 19 11.0 33 36 15 9
129 15 13.0 35 33 12 11
130 13 11.0 32 33 16 10
131 9 20.0 28 32 10 11
132 15 10.0 40 37 16 8
133 15 15.0 27 30 12 11
134 15 12.0 37 38 14 12
135 16 14.0 32 29 15 12
136 11 23.0 28 22 13 9
137 14 14.0 34 35 15 11
138 11 16.0 30 35 11 10
139 15 11.0 35 34 12 8
140 13 12.0 31 35 11 9
141 15 10.0 32 34 16 8
142 16 14.0 30 37 15 9
143 14 12.0 30 35 17 15
144 15 12.0 31 23 16 11
145 16 11.0 40 31 10 8
146 16 12.0 32 27 18 13
147 11 13.0 36 36 13 12
148 12 11.0 32 31 16 12
149 9 19.0 35 32 13 9
150 16 12.0 38 39 10 7
151 13 17.0 42 37 15 13
152 16 9.0 34 38 16 9
153 12 12.0 35 39 16 6
154 9 19.0 38 34 14 8
155 13 18.0 33 31 10 8
156 13 15.0 36 32 17 15
157 14 14.0 32 37 13 6
158 19 11.0 33 36 15 9
159 13 9.0 34 32 16 11
160 12 18.0 32 38 12 8
161 13 16.0 34 36 13 8
162 10 24.0 27 26 13 10
163 14 14.0 31 26 12 8
164 16 20.0 38 33 17 14
165 10 18.0 34 39 15 10
166 11 23.0 24 30 10 8
167 14 12.0 30 33 14 11
168 12 14.0 26 25 11 12
169 9 16.0 34 38 13 12
170 9 18.0 27 37 16 12
171 11 20.0 37 31 12 5
172 16 12.0 36 37 16 12
173 9 12.0 41 35 12 10
174 13 17.0 29 25 9 7
175 16 13.0 36 28 12 12
176 13 9.0 32 35 15 11
177 9 16.0 37 33 12 8
178 12 18.0 30 30 12 9
179 16 10.0 31 31 14 10
180 11 14.0 38 37 12 9
181 14 11.0 36 36 16 12
182 13 9.0 35 30 11 6
183 15 11.0 31 36 19 15
184 14 10.0 38 32 15 12
185 16 11.0 22 28 8 12
186 13 19.0 32 36 16 12
187 14 14.0 36 34 17 11
188 15 12.0 39 31 12 7
189 13 14.0 28 28 11 7
190 11 21.0 32 36 11 5
191 11 13.0 32 36 14 12
192 14 10.0 38 40 16 12
193 15 15.0 32 33 12 3
194 11 16.0 35 37 16 11
195 15 14.0 32 32 13 10
196 12 12.0 37 38 15 12
197 14 19.0 34 31 16 9
198 14 15.0 33 37 16 12
199 8 19.0 33 33 14 9
200 13 13.0 26 32 16 12
201 9 17.0 30 30 16 12
202 15 12.0 24 30 14 10
203 17 11.0 34 31 11 9
204 13 14.0 34 32 12 12
205 15 11.0 33 34 15 8
206 15 13.0 34 36 15 11
207 14 12.0 35 37 16 11
208 16 15.0 35 36 16 12
209 13 14.0 36 33 11 10
210 16 12.0 34 33 15 10
211 9 17.0 34 33 12 12
212 16 11.0 41 44 12 12
213 11 18.0 32 39 15 11
214 10 13.0 30 32 15 8
215 11 17.0 35 35 16 12
216 15 13.0 28 25 14 10
217 17 11.0 33 35 17 11
218 14 12.0 39 34 14 10
219 8 22.0 36 35 13 8
220 15 14.0 36 39 15 12
221 11 12.0 35 33 13 12
222 16 12.0 38 36 14 10
223 10 17.0 33 32 15 12
224 15 9.0 31 32 12 9
225 9 21.0 34 36 13 9
226 16 10.0 32 36 8 6
227 19 11.0 31 32 14 10
228 12 12.0 33 34 14 9
229 8 23.0 34 33 11 9
230 11 13.0 34 35 12 9
231 14 12.0 34 30 13 6
232 9 16.0 33 38 10 10
233 15 9.0 32 34 16 6
234 13 17.0 41 33 18 14
235 16 9.0 34 32 13 10
236 11 14.0 36 31 11 10
237 12 17.0 37 30 4 6
238 13 13.0 36 27 13 12
239 10 11.0 29 31 16 12
240 11 12.0 37 30 10 7
241 12 10.0 27 32 12 8
242 8 19.0 35 35 12 11
243 12 16.0 28 28 10 3
244 12 16.0 35 33 13 6
245 15 14.0 37 31 15 10
246 11 20.0 29 35 12 8
247 13 15.0 32 35 14 9
248 14 23.0 36 32 10 9
249 10 20.0 19 21 12 8
250 12 16.0 21 20 12 9
251 15 14.0 31 34 11 7
252 13 17.0 33 32 10 7
253 13 11.0 36 34 12 6
254 13 13.0 33 32 16 9
255 12 17.0 37 33 12 10
256 12 15.0 34 33 14 11
257 9 21.0 35 37 16 12
258 9 18.0 31 32 14 8
259 15 15.0 37 34 13 11
260 10 8.0 35 30 4 3
261 14 12.0 27 30 15 11
262 15 12.0 34 38 11 12
263 7 22.0 40 36 11 7
264 14 12.0 29 32 14 9
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Depression Connected Separate Learning Software
16.289892 -0.397333 0.017822 0.012218 0.116518 -0.004848
> (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 ***
Depression -0.397333 0.037190 -10.684 <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
---
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/wessaorg/rcomp/tmp/1fzx11384960908.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ijho1384960908.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3o56q1384960908.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/44dxt1384960908.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/58m4f1384960908.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/wessaorg/rcomp/tmp/65zh01384960908.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/wessaorg/rcomp/tmp/7mdmj1384960908.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/85q1b1384960908.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/92a7u1384960908.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10m7u21384960908.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11uzn91384960908.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/123o7j1384960908.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/13fw501384960908.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/14epoj1384960908.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/15xwl51384960908.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/16zxek1384960909.tab")
+ }
>
> try(system("convert tmp/1fzx11384960908.ps tmp/1fzx11384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ijho1384960908.ps tmp/2ijho1384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/3o56q1384960908.ps tmp/3o56q1384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/44dxt1384960908.ps tmp/44dxt1384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/58m4f1384960908.ps tmp/58m4f1384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/65zh01384960908.ps tmp/65zh01384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mdmj1384960908.ps tmp/7mdmj1384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/85q1b1384960908.ps tmp/85q1b1384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/92a7u1384960908.ps tmp/92a7u1384960908.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m7u21384960908.ps tmp/10m7u21384960908.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.467 2.461 16.913