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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+ ,16
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+ ,13
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+ ,10
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+ ,17
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+ ,33
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+ ,12
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+ ,35
+ ,37
+ ,16
+ ,12
+ ,9
+ ,21
+ ,11
+ ,31
+ ,32
+ ,14
+ ,8
+ ,9
+ ,18
+ ,11
+ ,37
+ ,34
+ ,13
+ ,11
+ ,15
+ ,15
+ ,11
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
+ ,11
+ ,27
+ ,30
+ ,15
+ ,11
+ ,14
+ ,12
+ ,11
+ ,34
+ ,38
+ ,11
+ ,12
+ ,15
+ ,12
+ ,11
+ ,40
+ ,36
+ ,11
+ ,7
+ ,7
+ ,22
+ ,11
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,11)
+ ,dim=c(7
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(7,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Month'),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 = '4'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '4'
> #'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
Software Connected Separate Learning Happiness Depression Month
1 12 41 38 13 14 12.0 9
2 11 39 32 16 18 11.0 9
3 15 30 35 19 11 14.0 9
4 6 31 33 15 12 12.0 9
5 13 34 37 14 16 21.0 9
6 10 35 29 13 18 12.0 9
7 12 39 31 19 14 22.0 9
8 14 34 36 15 14 11.0 9
9 12 36 35 14 15 10.0 9
10 9 37 38 15 15 13.0 9
11 10 38 31 16 17 10.0 9
12 12 36 34 16 19 8.0 9
13 12 38 35 16 10 15.0 9
14 11 39 38 16 16 14.0 9
15 15 33 37 17 18 10.0 9
16 12 32 33 15 14 14.0 9
17 10 36 32 15 14 14.0 9
18 12 38 38 20 17 11.0 9
19 11 39 38 18 14 10.0 9
20 12 32 32 16 16 13.0 9
21 11 32 33 16 18 9.5 9
22 12 31 31 16 11 14.0 9
23 13 39 38 19 14 12.0 9
24 11 37 39 16 12 14.0 9
25 12 39 32 17 17 11.0 9
26 13 41 32 17 9 9.0 9
27 10 36 35 16 16 11.0 9
28 14 33 37 15 14 15.0 9
29 12 33 33 16 15 14.0 9
30 10 34 33 14 11 13.0 9
31 12 31 31 15 16 9.0 9
32 8 27 32 12 13 15.0 9
33 10 37 31 14 17 10.0 9
34 12 34 37 16 15 11.0 9
35 12 34 30 14 14 13.0 9
36 7 32 33 10 16 8.0 9
37 9 29 31 10 9 20.0 9
38 12 36 33 14 15 12.0 9
39 10 29 31 16 17 10.0 9
40 10 35 33 16 13 10.0 9
41 10 37 32 16 15 9.0 9
42 12 34 33 14 16 14.0 9
43 15 38 32 20 16 8.0 9
44 10 35 33 14 12 14.0 9
45 10 38 28 14 15 11.0 9
46 12 37 35 11 11 13.0 9
47 13 38 39 14 15 9.0 9
48 11 33 34 15 15 11.0 9
49 11 36 38 16 17 15.0 9
50 12 38 32 14 13 11.0 9
51 14 32 38 16 16 10.0 9
52 10 32 30 14 14 14.0 9
53 12 32 33 12 11 18.0 9
54 13 34 38 16 12 14.0 9
55 5 32 32 9 12 11.0 9
56 6 37 35 14 15 14.5 9
57 12 39 34 16 16 13.0 9
58 12 29 34 16 15 9.0 9
59 11 37 36 15 12 10.0 9
60 10 35 34 16 12 15.0 9
61 7 30 28 12 8 20.0 9
62 12 38 34 16 13 12.0 9
63 14 34 35 16 11 12.0 9
64 11 31 35 14 14 14.0 9
65 12 34 31 16 15 13.0 9
66 13 35 37 17 10 11.0 10
67 14 36 35 18 11 17.0 10
68 11 30 27 18 12 12.0 10
69 12 39 40 12 15 13.0 10
70 12 35 37 16 15 14.0 10
71 8 38 36 10 14 13.0 10
72 11 31 38 14 16 15.0 10
73 14 34 39 18 15 13.0 10
74 14 38 41 18 15 10.0 10
75 12 34 27 16 13 11.0 10
76 9 39 30 17 12 19.0 10
77 13 37 37 16 17 13.0 10
78 11 34 31 16 13 17.0 10
79 12 28 31 13 15 13.0 10
80 12 37 27 16 13 9.0 10
81 12 33 36 16 15 11.0 10
82 12 35 37 16 15 9.0 10
83 12 37 33 15 16 12.0 10
84 11 32 34 15 15 12.0 10
85 10 33 31 16 14 13.0 10
86 9 38 39 14 15 13.0 10
87 12 33 34 16 14 12.0 10
88 12 29 32 16 13 15.0 10
89 12 33 33 15 7 22.0 10
90 9 31 36 12 17 13.0 10
91 15 36 32 17 13 15.0 10
92 12 35 41 16 15 13.0 10
93 12 32 28 15 14 15.0 10
94 12 29 30 13 13 12.5 10
95 10 39 36 16 16 11.0 10
96 13 37 35 16 12 16.0 10
97 9 35 31 16 14 11.0 10
98 12 37 34 16 17 11.0 10
99 10 32 36 14 15 10.0 10
100 14 38 36 16 17 10.0 10
101 11 37 35 16 12 16.0 10
102 15 36 37 20 16 12.0 10
103 11 32 28 15 11 11.0 10
104 11 33 39 16 15 16.0 10
105 12 40 32 13 9 19.0 10
106 12 38 35 17 16 11.0 10
107 12 41 39 16 15 16.0 10
108 11 36 35 16 10 15.0 10
109 7 43 42 12 10 24.0 10
110 12 30 34 16 15 14.0 10
111 14 31 33 16 11 15.0 10
112 11 32 41 17 13 11.0 10
113 11 32 33 13 14 15.0 10
114 10 37 34 12 18 12.0 10
115 13 37 32 18 16 10.0 10
116 13 33 40 14 14 14.0 10
117 8 34 40 14 14 13.0 10
118 11 33 35 13 14 9.0 10
119 12 38 36 16 14 15.0 10
120 11 33 37 13 12 15.0 10
121 13 31 27 16 14 14.0 10
122 12 38 39 13 15 11.0 10
123 14 37 38 16 15 8.0 10
124 13 36 31 15 15 11.0 10
125 15 31 33 16 13 11.0 10
126 10 39 32 15 17 8.0 10
127 11 44 39 17 17 10.0 10
128 9 33 36 15 19 11.0 10
129 11 35 33 12 15 13.0 10
130 10 32 33 16 13 11.0 10
131 11 28 32 10 9 20.0 10
132 8 40 37 16 15 10.0 10
133 11 27 30 12 15 15.0 10
134 12 37 38 14 15 12.0 10
135 12 32 29 15 16 14.0 10
136 9 28 22 13 11 23.0 10
137 11 34 35 15 14 14.0 10
138 10 30 35 11 11 16.0 10
139 8 35 34 12 15 11.0 10
140 9 31 35 11 13 12.0 10
141 8 32 34 16 15 10.0 10
142 9 30 37 15 16 14.0 10
143 15 30 35 17 14 12.0 10
144 11 31 23 16 15 12.0 10
145 8 40 31 10 16 11.0 10
146 13 32 27 18 16 12.0 10
147 12 36 36 13 11 13.0 10
148 12 32 31 16 12 11.0 10
149 9 35 32 13 9 19.0 10
150 7 38 39 10 16 12.0 10
151 13 42 37 15 13 17.0 10
152 9 34 38 16 16 9.0 10
153 6 35 39 16 12 12.0 10
154 8 38 34 14 9 19.0 9
155 8 33 31 10 13 18.0 10
156 15 36 32 17 13 15.0 10
157 6 32 37 13 14 14.0 10
158 9 33 36 15 19 11.0 10
159 11 34 32 16 13 9.0 10
160 8 32 38 12 12 18.0 10
161 8 34 36 13 13 16.0 10
162 10 27 26 13 10 24.0 11
163 8 31 26 12 14 14.0 11
164 14 38 33 17 16 20.0 11
165 10 34 39 15 10 18.0 11
166 8 24 30 10 11 23.0 11
167 11 30 33 14 14 12.0 11
168 12 26 25 11 12 14.0 11
169 12 34 38 13 9 16.0 11
170 12 27 37 16 9 18.0 11
171 5 37 31 12 11 20.0 11
172 12 36 37 16 16 12.0 11
173 10 41 35 12 9 12.0 11
174 7 29 25 9 13 17.0 11
175 12 36 28 12 16 13.0 11
176 11 32 35 15 13 9.0 11
177 8 37 33 12 9 16.0 11
178 9 30 30 12 12 18.0 11
179 10 31 31 14 16 10.0 11
180 9 38 37 12 11 14.0 11
181 12 36 36 16 14 11.0 11
182 6 35 30 11 13 9.0 11
183 15 31 36 19 15 11.0 11
184 12 38 32 15 14 10.0 11
185 12 22 28 8 16 11.0 11
186 12 32 36 16 13 19.0 11
187 11 36 34 17 14 14.0 11
188 7 39 31 12 15 12.0 11
189 7 28 28 11 13 14.0 11
190 5 32 36 11 11 21.0 11
191 12 32 36 14 11 13.0 11
192 12 38 40 16 14 10.0 11
193 3 32 33 12 15 15.0 11
194 11 35 37 16 11 16.0 11
195 10 32 32 13 15 14.0 11
196 12 37 38 15 12 12.0 11
197 9 34 31 16 14 19.0 11
198 12 33 37 16 14 15.0 11
199 9 33 33 14 8 19.0 11
200 12 26 32 16 13 13.0 11
201 12 30 30 16 9 17.0 11
202 10 24 30 14 15 12.0 11
203 9 34 31 11 17 11.0 11
204 12 34 32 12 13 14.0 11
205 8 33 34 15 15 11.0 11
206 11 34 36 15 15 13.0 11
207 11 35 37 16 14 12.0 11
208 12 35 36 16 16 15.0 11
209 10 36 33 11 13 14.0 11
210 10 34 33 15 16 12.0 11
211 12 34 33 12 9 17.0 11
212 12 41 44 12 16 11.0 11
213 11 32 39 15 11 18.0 11
214 8 30 32 15 10 13.0 11
215 12 35 35 16 11 17.0 11
216 10 28 25 14 15 13.0 11
217 11 33 35 17 17 11.0 11
218 10 39 34 14 14 12.0 11
219 8 36 35 13 8 22.0 11
220 12 36 39 15 15 14.0 11
221 12 35 33 13 11 12.0 11
222 10 38 36 14 16 12.0 11
223 12 33 32 15 10 17.0 11
224 9 31 32 12 15 9.0 11
225 9 34 36 13 9 21.0 11
226 6 32 36 8 16 10.0 11
227 10 31 32 14 19 11.0 11
228 9 33 34 14 12 12.0 11
229 9 34 33 11 8 23.0 11
230 9 34 35 12 11 13.0 11
231 6 34 30 13 14 12.0 11
232 10 33 38 10 9 16.0 11
233 6 32 34 16 15 9.0 11
234 14 41 33 18 13 17.0 11
235 10 34 32 13 16 9.0 11
236 10 36 31 11 11 14.0 11
237 6 37 30 4 12 17.0 11
238 12 36 27 13 13 13.0 11
239 12 29 31 16 10 11.0 11
240 7 37 30 10 11 12.0 11
241 8 27 32 12 12 10.0 11
242 11 35 35 12 8 19.0 11
243 3 28 28 10 12 16.0 11
244 6 35 33 13 12 16.0 11
245 10 37 31 15 15 14.0 11
246 8 29 35 12 11 20.0 11
247 9 32 35 14 13 15.0 11
248 9 36 32 10 14 23.0 11
249 8 19 21 12 10 20.0 11
250 9 21 20 12 12 16.0 11
251 7 31 34 11 15 14.0 11
252 7 33 32 10 13 17.0 11
253 6 36 34 12 13 11.0 11
254 9 33 32 16 13 13.0 11
255 10 37 33 12 12 17.0 11
256 11 34 33 14 12 15.0 11
257 12 35 37 16 9 21.0 11
258 8 31 32 14 9 18.0 11
259 11 37 34 13 15 15.0 11
260 3 35 30 4 10 8.0 11
261 11 27 30 15 14 12.0 11
262 12 34 38 11 15 12.0 11
263 7 40 36 11 7 22.0 11
264 9 29 32 14 14 12.0 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Happiness Depression
4.611085 -0.017309 0.038988 0.556295 -0.012655 -0.009444
Month
-0.240347
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.0440 -1.1091 0.1987 1.1605 5.1779
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.611085 2.547275 1.810 0.0714 .
Connected -0.017309 0.033827 -0.512 0.6093
Separate 0.038988 0.034467 1.131 0.2590
Learning 0.556295 0.050270 11.066 <2e-16 ***
Happiness -0.012655 0.056251 -0.225 0.8222
Depression -0.009444 0.040278 -0.234 0.8148
Month -0.240347 0.154841 -1.552 0.1218
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.822 on 257 degrees of freedom
Multiple R-squared: 0.3976, Adjusted R-squared: 0.3836
F-statistic: 28.27 on 6 and 257 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.995450535 0.009098931 0.004549465
[2,] 0.990145832 0.019708335 0.009854168
[3,] 0.980264435 0.039471129 0.019735565
[4,] 0.967909535 0.064180930 0.032090465
[5,] 0.962806314 0.074387372 0.037193686
[6,] 0.951752551 0.096494898 0.048247449
[7,] 0.930374876 0.139250249 0.069625124
[8,] 0.897117573 0.205764855 0.102882427
[9,] 0.903345978 0.193308044 0.096654022
[10,] 0.878310105 0.243379791 0.121689895
[11,] 0.834195219 0.331609562 0.165804781
[12,] 0.793945872 0.412108256 0.206054128
[13,] 0.751162852 0.497674295 0.248837148
[14,] 0.694088442 0.611823117 0.305911558
[15,] 0.650680440 0.698639121 0.349319560
[16,] 0.603152547 0.793694906 0.396847453
[17,] 0.651219312 0.697561376 0.348780688
[18,] 0.635559417 0.728881166 0.364440583
[19,] 0.650678627 0.698642746 0.349321373
[20,] 0.590818999 0.818362002 0.409181001
[21,] 0.543928903 0.912142194 0.456071097
[22,] 0.495487672 0.990975345 0.504512328
[23,] 0.539240441 0.921519119 0.460759559
[24,] 0.480884380 0.961768760 0.519115620
[25,] 0.422360775 0.844721551 0.577639225
[26,] 0.413455679 0.826911359 0.586544321
[27,] 0.413089134 0.826178269 0.586910866
[28,] 0.359510358 0.719020716 0.640489642
[29,] 0.342742873 0.685485746 0.657257127
[30,] 0.323062054 0.646124108 0.676937946
[31,] 0.301145913 0.602291827 0.698854087
[32,] 0.273170246 0.546340492 0.726829754
[33,] 0.251018569 0.502037137 0.748981431
[34,] 0.268152189 0.536304378 0.731847811
[35,] 0.231056995 0.462113991 0.768943005
[36,] 0.194316007 0.388632014 0.805683993
[37,] 0.238608438 0.477216876 0.761391562
[38,] 0.236744629 0.473489257 0.763255371
[39,] 0.200318069 0.400636139 0.799681931
[40,] 0.183116383 0.366232766 0.816883617
[41,] 0.170635829 0.341271659 0.829364171
[42,] 0.177023001 0.354046001 0.822976999
[43,] 0.148609622 0.297219245 0.851390378
[44,] 0.154399400 0.308798800 0.845600600
[45,] 0.131997729 0.263995458 0.868002271
[46,] 0.210145183 0.420290365 0.789854817
[47,] 0.467405547 0.934811094 0.532594453
[48,] 0.425123021 0.850246042 0.574876979
[49,] 0.382456205 0.764912410 0.617543795
[50,] 0.342455504 0.684911007 0.657544496
[51,] 0.341260062 0.682520123 0.658739938
[52,] 0.357744072 0.715488144 0.642255928
[53,] 0.319951695 0.639903389 0.680048305
[54,] 0.333850825 0.667701650 0.666149175
[55,] 0.295366173 0.590732346 0.704633827
[56,] 0.265125804 0.530251609 0.734874196
[57,] 0.232080382 0.464160764 0.767919618
[58,] 0.206603840 0.413207680 0.793396160
[59,] 0.191424422 0.382848844 0.808575578
[60,] 0.180700746 0.361401493 0.819299254
[61,] 0.157615686 0.315231373 0.842384314
[62,] 0.143651628 0.287303255 0.856348372
[63,] 0.123285106 0.246570212 0.876714894
[64,] 0.105809482 0.211618965 0.894190518
[65,] 0.089902297 0.179804593 0.910097703
[66,] 0.080484648 0.160969296 0.919515352
[67,] 0.101449784 0.202899568 0.898550216
[68,] 0.089840013 0.179680026 0.910159987
[69,] 0.074945586 0.149891173 0.925054414
[70,] 0.078185683 0.156371366 0.921814317
[71,] 0.069307141 0.138614283 0.930692859
[72,] 0.057689388 0.115378775 0.942310612
[73,] 0.047983724 0.095967449 0.952016276
[74,] 0.040705528 0.081411055 0.959294472
[75,] 0.033423415 0.066846829 0.966576585
[76,] 0.031687448 0.063374896 0.968312552
[77,] 0.037065860 0.074131720 0.962934140
[78,] 0.029839895 0.059679790 0.970160105
[79,] 0.023959198 0.047918396 0.976040802
[80,] 0.020148296 0.040296591 0.979851704
[81,] 0.017183216 0.034366433 0.982816784
[82,] 0.026437100 0.052874201 0.973562900
[83,] 0.021986104 0.043972207 0.978013896
[84,] 0.019709550 0.039419100 0.980290450
[85,] 0.020637831 0.041275662 0.979362169
[86,] 0.021212247 0.042424494 0.978787753
[87,] 0.018621363 0.037242726 0.981378637
[88,] 0.023962275 0.047924551 0.976037725
[89,] 0.019272820 0.038545640 0.980727180
[90,] 0.016562797 0.033125595 0.983437203
[91,] 0.018935235 0.037870470 0.981064765
[92,] 0.015826636 0.031653272 0.984173364
[93,] 0.013313705 0.026627410 0.986686295
[94,] 0.010430765 0.020861529 0.989569235
[95,] 0.009295487 0.018590975 0.990704513
[96,] 0.010086191 0.020172382 0.989913809
[97,] 0.007899301 0.015798602 0.992100699
[98,] 0.006178258 0.012356516 0.993821742
[99,] 0.005133879 0.010267758 0.994866121
[100,] 0.007902106 0.015804212 0.992097894
[101,] 0.006151428 0.012302856 0.993848572
[102,] 0.007046797 0.014093595 0.992953203
[103,] 0.007499765 0.014999529 0.992500235
[104,] 0.006243078 0.012486155 0.993756922
[105,] 0.004946391 0.009892782 0.995053609
[106,] 0.003823717 0.007647434 0.996176283
[107,] 0.004194045 0.008388090 0.995805955
[108,] 0.006869124 0.013738248 0.993130876
[109,] 0.005617492 0.011234985 0.994382508
[110,] 0.004371963 0.008743927 0.995628037
[111,] 0.003547021 0.007094041 0.996452979
[112,] 0.003341225 0.006682449 0.996658775
[113,] 0.003396867 0.006793733 0.996603133
[114,] 0.003890501 0.007781001 0.996109499
[115,] 0.004246523 0.008493045 0.995753477
[116,] 0.007958884 0.015917768 0.992041116
[117,] 0.006762442 0.013524884 0.993237558
[118,] 0.005833477 0.011666954 0.994166523
[119,] 0.006654346 0.013308693 0.993345654
[120,] 0.006478163 0.012956327 0.993521837
[121,] 0.006494658 0.012989315 0.993505342
[122,] 0.008580186 0.017160373 0.991419814
[123,] 0.017062884 0.034125768 0.982937116
[124,] 0.016783933 0.033567866 0.983216067
[125,] 0.015960367 0.031920735 0.984039633
[126,] 0.014311767 0.028623533 0.985688233
[127,] 0.011833431 0.023666861 0.988166569
[128,] 0.009603821 0.019207642 0.990396179
[129,] 0.008855124 0.017710247 0.991144876
[130,] 0.008151240 0.016302480 0.991848760
[131,] 0.006884724 0.013769447 0.993115276
[132,] 0.013759297 0.027518595 0.986240703
[133,] 0.014974407 0.029948814 0.985025593
[134,] 0.022708732 0.045417465 0.977291268
[135,] 0.018415754 0.036831508 0.981584246
[136,] 0.014852993 0.029705986 0.985147007
[137,] 0.012615501 0.025231003 0.987384499
[138,] 0.015240213 0.030480427 0.984759787
[139,] 0.013604276 0.027208553 0.986395724
[140,] 0.011583601 0.023167203 0.988416399
[141,] 0.010219806 0.020439612 0.989780194
[142,] 0.013192030 0.026384060 0.986807970
[143,] 0.015360796 0.030721592 0.984639204
[144,] 0.068780610 0.137561220 0.931219390
[145,] 0.068807122 0.137614244 0.931192878
[146,] 0.059902172 0.119804344 0.940097828
[147,] 0.105511321 0.211022643 0.894488679
[148,] 0.150849798 0.301699596 0.849150202
[149,] 0.144669073 0.289338146 0.855330927
[150,] 0.130345752 0.260691504 0.869654248
[151,] 0.119337201 0.238674402 0.880662799
[152,] 0.111697827 0.223395654 0.888302173
[153,] 0.098162198 0.196324396 0.901837802
[154,] 0.087481346 0.174962692 0.912518654
[155,] 0.095218990 0.190437979 0.904781010
[156,] 0.085822043 0.171644086 0.914177957
[157,] 0.073111001 0.146222001 0.926888999
[158,] 0.062590126 0.125180252 0.937409874
[159,] 0.103876887 0.207753774 0.896123113
[160,] 0.105866995 0.211733991 0.894133005
[161,] 0.091761131 0.183522262 0.908238869
[162,] 0.160696674 0.321393348 0.839303326
[163,] 0.140134403 0.280268806 0.859865597
[164,] 0.123519849 0.247039698 0.876480151
[165,] 0.106606039 0.213212078 0.893393961
[166,] 0.142789652 0.285579304 0.857210348
[167,] 0.123299550 0.246599100 0.876700450
[168,] 0.111566681 0.223133362 0.888433319
[169,] 0.095561838 0.191123676 0.904438162
[170,] 0.081259036 0.162518072 0.918740964
[171,] 0.068260546 0.136521092 0.931739454
[172,] 0.057536792 0.115073584 0.942463208
[173,] 0.065994960 0.131989920 0.934005040
[174,] 0.067657651 0.135315301 0.932342349
[175,] 0.062257249 0.124514498 0.937742751
[176,] 0.224882194 0.449764389 0.775117806
[177,] 0.202198454 0.404396908 0.797801546
[178,] 0.182060788 0.364121577 0.817939212
[179,] 0.190620727 0.381241453 0.809379273
[180,] 0.176792639 0.353585277 0.823207361
[181,] 0.253540264 0.507080528 0.746459736
[182,] 0.251807799 0.503615598 0.748192201
[183,] 0.221819560 0.443639121 0.778180440
[184,] 0.587879731 0.824240537 0.412120269
[185,] 0.549497058 0.901005885 0.450502942
[186,] 0.512215286 0.975569427 0.487784714
[187,] 0.485454486 0.970908972 0.514545514
[188,] 0.509603160 0.980793680 0.490396840
[189,] 0.472772966 0.945545931 0.527227034
[190,] 0.447166682 0.894333364 0.552833318
[191,] 0.435157040 0.870314080 0.564842960
[192,] 0.414757560 0.829515120 0.585242440
[193,] 0.389761349 0.779522698 0.610238651
[194,] 0.353196587 0.706393174 0.646803413
[195,] 0.428180182 0.856360365 0.571819818
[196,] 0.463524305 0.927048610 0.536475695
[197,] 0.421148219 0.842296438 0.578851781
[198,] 0.379215847 0.758431693 0.620784153
[199,] 0.341739429 0.683478858 0.658260571
[200,] 0.325533921 0.651067842 0.674466079
[201,] 0.289266812 0.578533624 0.710733188
[202,] 0.362333193 0.724666387 0.637666807
[203,] 0.389617689 0.779235379 0.610382311
[204,] 0.348911717 0.697823434 0.651088283
[205,] 0.364536985 0.729073970 0.635463015
[206,] 0.328522637 0.657045275 0.671477363
[207,] 0.293112096 0.586224192 0.706887904
[208,] 0.256462374 0.512924749 0.743537626
[209,] 0.219462177 0.438924354 0.780537823
[210,] 0.220650696 0.441301393 0.779349304
[211,] 0.201339285 0.402678570 0.798660715
[212,] 0.242899989 0.485799978 0.757100011
[213,] 0.205271583 0.410543165 0.794728417
[214,] 0.196008214 0.392016429 0.803991786
[215,] 0.170772506 0.341545011 0.829227494
[216,] 0.144414496 0.288828992 0.855585504
[217,] 0.118825059 0.237650118 0.881174941
[218,] 0.097400567 0.194801133 0.902599433
[219,] 0.078796554 0.157593107 0.921203446
[220,] 0.060937982 0.121875965 0.939062018
[221,] 0.046933830 0.093867659 0.953066170
[222,] 0.071177559 0.142355119 0.928822441
[223,] 0.089105945 0.178211890 0.910894055
[224,] 0.269737076 0.539474152 0.730262924
[225,] 0.231976099 0.463952198 0.768023901
[226,] 0.189866259 0.379732518 0.810133741
[227,] 0.184947791 0.369895581 0.815052209
[228,] 0.183953659 0.367907318 0.816046341
[229,] 0.257729359 0.515458719 0.742270641
[230,] 0.262992308 0.525984616 0.737007692
[231,] 0.218157323 0.436314645 0.781842677
[232,] 0.172942833 0.345885666 0.827057167
[233,] 0.247312198 0.494624396 0.752687802
[234,] 0.526218383 0.947563233 0.473781617
[235,] 0.665736121 0.668527758 0.334263879
[236,] 0.596498136 0.807003728 0.403501864
[237,] 0.544694555 0.910610890 0.455305445
[238,] 0.492306779 0.984613558 0.507693221
[239,] 0.406368051 0.812736101 0.593631949
[240,] 0.315801559 0.631603117 0.684198441
[241,] 0.324940320 0.649880641 0.675059680
[242,] 0.451558728 0.903117457 0.548441272
[243,] 0.617335810 0.765328380 0.382664190
[244,] 0.668600555 0.662798889 0.331399445
[245,] 0.701606617 0.596786765 0.298393383
> postscript(file="/var/wessaorg/rcomp/tmp/1qkiw1383330808.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/20eva1383330808.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/38gm51383330808.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/498j21383330808.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5qv4g1383330808.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
1.83883605 -0.58956137 1.40855124 -5.27721603 2.31067409 0.13649599
7 8 9 10 11 12
-1.16619122 2.67361307 1.30672591 -2.32089165 -1.58998198 0.26485630
13 14 15 16 17 18
0.21269994 -0.82046853 2.54590195 0.78429250 -1.10748204 -2.07863603
19 20 21 22 23 24
-1.99614669 0.28285185 -0.76388126 0.25069897 -0.53355303 -0.94469641
25 26 27 28 29 30
-0.15851170 0.75597613 -1.78376463 2.65509298 0.25796196 -0.67220395
31 32 33 34 35 36
0.82304844 -1.59759128 -0.49470111 0.09098494 1.48272669 -1.46558782
37 38 39 40 41 42
0.58520663 1.40359125 -1.74576560 -1.77050745 -1.68103444 1.40051667
43 44 45 46 47 48
1.11430539 -0.63279504 -0.37629305 2.97063257 2.17594693 -0.25306437
49 50 51 52 53 54
-0.85029678 1.44244326 2.02058894 -0.54244752 2.45298950 1.04236402
55 56 57 58 59 60
-2.89259232 -4.63346512 0.32604028 0.10251464 -0.30921393 -1.77492913
61 62 63 64 65 66
-2.40576460 0.26132087 2.12778491 0.24530170 0.34380347 0.72907020
67 68 69 70 71 72
1.33738249 -1.48913349 2.54498289 0.37697466 -0.21643830 0.40343899
73 74 75 76 77 78
1.15965423 1.12258168 0.69590472 -2.82790907 1.42745936 -0.40338223
79 80 81 82 83 84
2.14918028 0.72894384 0.35301125 0.32975279 1.11760801 -0.02058199
85 86 87 88 89 90
-1.44581377 -1.54592827 0.42777697 0.45219427 1.02891733 -0.41222775
91 92 93 94 95 96
3.01706422 0.21157709 1.22902567 2.17544518 -1.53047775 1.47049283
97 98 99 100 101 102
-2.43008393 0.52553551 -0.56115224 2.45542383 -0.52950717 1.16287011
103 104 105 106 107 108
0.15328242 -0.71673177 2.29863821 -0.06509383 0.42174255 -0.58157134
109 110 111 112 113 114
-2.42314469 0.40739309 2.42251405 -1.44084511 1.14667435 0.77281547
115 116 117 118 119 120
0.46882231 2.32532610 -2.66680898 1.02934080 0.46467995 0.98271995
121 122 123 124 125 126
1.68496522 1.99147806 2.31593870 2.15617569 3.41004706 -0.83390735
127 128 129 130 131 132
-1.11398040 -2.04007266 1.74866381 -1.57264365 2.76925635 -3.57425638
133 134 135 136 137 138
1.74604312 1.46630636 1.20590350 -0.45610231 -0.01871821 1.11814796
139 140 141 142 143 144
-1.30921324 0.12299026 -3.59576582 -2.14062146 2.78056572 -0.16531509
145 146 147 148 149 150
0.01954352 0.59610609 2.04209212 0.49267770 -0.78790824 -1.31753706
151 152 153 154 155 156
2.05745739 -2.71388955 -5.75785644 -2.61059935 -0.07347665 3.01706422
157 158 159 160 161 162
-4.01872322 -2.04007266 -0.51792551 -1.48894944 -1.93888284 0.60777163
163 164 165 166 167 168
-0.81051885 2.33822942 -0.94716760 0.07198670 0.76777486 3.67290758
169 170 171 172 173 174
2.17286687 0.44069362 -3.88290411 0.62839775 0.92951438 -0.12158601
175 176 177 178 179 180
3.21391713 0.12713340 -1.02396869 0.02868566 -0.13051750 -0.15619084
181 182 183 184 185 186
0.63263118 -2.40081691 1.88985472 1.37005366 5.17787866 0.62629375
187 188 189 190 191 192
-0.81735419 -1.87321951 -1.39678348 -3.59865259 1.65690718 0.50185219
193 194 195 196 197 198
-6.04402802 -0.41441030 0.42922082 1.11239283 -2.13149093 0.57949250
199 200 201 202 203 204
-1.19011815 0.62172495 0.75609520 -0.20646074 0.61239525 2.99482399
205 206 207 208 209 210
-2.77236977 0.18585167 -0.41422204 0.67840988 1.54674935 -0.69397256
211 212 213 214 215 216
2.93354781 2.65776209 0.03086907 -2.79070855 0.67301067 0.06716228
217 218 219 220 221 222
-0.89863773 -0.11542982 -1.63153867 1.11294974 2.37265065 -0.18540520
223 224 225 226 227 228
1.29899681 -0.07901525 -0.70193467 -0.97037919 -0.12209567 -1.24459606
229 230 231 232 233 234
0.53385388 -0.15689578 -3.48972800 1.82444283 -5.36486289 1.76756334
235 236 237 238 239 240
0.42927279 1.59941545 1.59076699 2.65864460 0.65578663 -0.80688063
241 242 243 244 245 246
-1.17677379 1.87911400 -4.83425490 -3.57691661 -0.55783459 -1.17733162
247 248 249 250 251 252
-1.25990528 1.23968734 -0.81724361 0.24389627 -1.55347490 -0.88156202
253 254 255 256 257 258
-3.07686715 -2.25711001 1.02344143 0.84003465 0.60750107 -2.18253756
259 260 261 262 263 264
1.44723505 -1.55416145 0.27651680 3.32361104 -1.50135489 -1.21054613
> postscript(file="/var/wessaorg/rcomp/tmp/6rsjz1383330808.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 1.83883605 NA
1 -0.58956137 1.83883605
2 1.40855124 -0.58956137
3 -5.27721603 1.40855124
4 2.31067409 -5.27721603
5 0.13649599 2.31067409
6 -1.16619122 0.13649599
7 2.67361307 -1.16619122
8 1.30672591 2.67361307
9 -2.32089165 1.30672591
10 -1.58998198 -2.32089165
11 0.26485630 -1.58998198
12 0.21269994 0.26485630
13 -0.82046853 0.21269994
14 2.54590195 -0.82046853
15 0.78429250 2.54590195
16 -1.10748204 0.78429250
17 -2.07863603 -1.10748204
18 -1.99614669 -2.07863603
19 0.28285185 -1.99614669
20 -0.76388126 0.28285185
21 0.25069897 -0.76388126
22 -0.53355303 0.25069897
23 -0.94469641 -0.53355303
24 -0.15851170 -0.94469641
25 0.75597613 -0.15851170
26 -1.78376463 0.75597613
27 2.65509298 -1.78376463
28 0.25796196 2.65509298
29 -0.67220395 0.25796196
30 0.82304844 -0.67220395
31 -1.59759128 0.82304844
32 -0.49470111 -1.59759128
33 0.09098494 -0.49470111
34 1.48272669 0.09098494
35 -1.46558782 1.48272669
36 0.58520663 -1.46558782
37 1.40359125 0.58520663
38 -1.74576560 1.40359125
39 -1.77050745 -1.74576560
40 -1.68103444 -1.77050745
41 1.40051667 -1.68103444
42 1.11430539 1.40051667
43 -0.63279504 1.11430539
44 -0.37629305 -0.63279504
45 2.97063257 -0.37629305
46 2.17594693 2.97063257
47 -0.25306437 2.17594693
48 -0.85029678 -0.25306437
49 1.44244326 -0.85029678
50 2.02058894 1.44244326
51 -0.54244752 2.02058894
52 2.45298950 -0.54244752
53 1.04236402 2.45298950
54 -2.89259232 1.04236402
55 -4.63346512 -2.89259232
56 0.32604028 -4.63346512
57 0.10251464 0.32604028
58 -0.30921393 0.10251464
59 -1.77492913 -0.30921393
60 -2.40576460 -1.77492913
61 0.26132087 -2.40576460
62 2.12778491 0.26132087
63 0.24530170 2.12778491
64 0.34380347 0.24530170
65 0.72907020 0.34380347
66 1.33738249 0.72907020
67 -1.48913349 1.33738249
68 2.54498289 -1.48913349
69 0.37697466 2.54498289
70 -0.21643830 0.37697466
71 0.40343899 -0.21643830
72 1.15965423 0.40343899
73 1.12258168 1.15965423
74 0.69590472 1.12258168
75 -2.82790907 0.69590472
76 1.42745936 -2.82790907
77 -0.40338223 1.42745936
78 2.14918028 -0.40338223
79 0.72894384 2.14918028
80 0.35301125 0.72894384
81 0.32975279 0.35301125
82 1.11760801 0.32975279
83 -0.02058199 1.11760801
84 -1.44581377 -0.02058199
85 -1.54592827 -1.44581377
86 0.42777697 -1.54592827
87 0.45219427 0.42777697
88 1.02891733 0.45219427
89 -0.41222775 1.02891733
90 3.01706422 -0.41222775
91 0.21157709 3.01706422
92 1.22902567 0.21157709
93 2.17544518 1.22902567
94 -1.53047775 2.17544518
95 1.47049283 -1.53047775
96 -2.43008393 1.47049283
97 0.52553551 -2.43008393
98 -0.56115224 0.52553551
99 2.45542383 -0.56115224
100 -0.52950717 2.45542383
101 1.16287011 -0.52950717
102 0.15328242 1.16287011
103 -0.71673177 0.15328242
104 2.29863821 -0.71673177
105 -0.06509383 2.29863821
106 0.42174255 -0.06509383
107 -0.58157134 0.42174255
108 -2.42314469 -0.58157134
109 0.40739309 -2.42314469
110 2.42251405 0.40739309
111 -1.44084511 2.42251405
112 1.14667435 -1.44084511
113 0.77281547 1.14667435
114 0.46882231 0.77281547
115 2.32532610 0.46882231
116 -2.66680898 2.32532610
117 1.02934080 -2.66680898
118 0.46467995 1.02934080
119 0.98271995 0.46467995
120 1.68496522 0.98271995
121 1.99147806 1.68496522
122 2.31593870 1.99147806
123 2.15617569 2.31593870
124 3.41004706 2.15617569
125 -0.83390735 3.41004706
126 -1.11398040 -0.83390735
127 -2.04007266 -1.11398040
128 1.74866381 -2.04007266
129 -1.57264365 1.74866381
130 2.76925635 -1.57264365
131 -3.57425638 2.76925635
132 1.74604312 -3.57425638
133 1.46630636 1.74604312
134 1.20590350 1.46630636
135 -0.45610231 1.20590350
136 -0.01871821 -0.45610231
137 1.11814796 -0.01871821
138 -1.30921324 1.11814796
139 0.12299026 -1.30921324
140 -3.59576582 0.12299026
141 -2.14062146 -3.59576582
142 2.78056572 -2.14062146
143 -0.16531509 2.78056572
144 0.01954352 -0.16531509
145 0.59610609 0.01954352
146 2.04209212 0.59610609
147 0.49267770 2.04209212
148 -0.78790824 0.49267770
149 -1.31753706 -0.78790824
150 2.05745739 -1.31753706
151 -2.71388955 2.05745739
152 -5.75785644 -2.71388955
153 -2.61059935 -5.75785644
154 -0.07347665 -2.61059935
155 3.01706422 -0.07347665
156 -4.01872322 3.01706422
157 -2.04007266 -4.01872322
158 -0.51792551 -2.04007266
159 -1.48894944 -0.51792551
160 -1.93888284 -1.48894944
161 0.60777163 -1.93888284
162 -0.81051885 0.60777163
163 2.33822942 -0.81051885
164 -0.94716760 2.33822942
165 0.07198670 -0.94716760
166 0.76777486 0.07198670
167 3.67290758 0.76777486
168 2.17286687 3.67290758
169 0.44069362 2.17286687
170 -3.88290411 0.44069362
171 0.62839775 -3.88290411
172 0.92951438 0.62839775
173 -0.12158601 0.92951438
174 3.21391713 -0.12158601
175 0.12713340 3.21391713
176 -1.02396869 0.12713340
177 0.02868566 -1.02396869
178 -0.13051750 0.02868566
179 -0.15619084 -0.13051750
180 0.63263118 -0.15619084
181 -2.40081691 0.63263118
182 1.88985472 -2.40081691
183 1.37005366 1.88985472
184 5.17787866 1.37005366
185 0.62629375 5.17787866
186 -0.81735419 0.62629375
187 -1.87321951 -0.81735419
188 -1.39678348 -1.87321951
189 -3.59865259 -1.39678348
190 1.65690718 -3.59865259
191 0.50185219 1.65690718
192 -6.04402802 0.50185219
193 -0.41441030 -6.04402802
194 0.42922082 -0.41441030
195 1.11239283 0.42922082
196 -2.13149093 1.11239283
197 0.57949250 -2.13149093
198 -1.19011815 0.57949250
199 0.62172495 -1.19011815
200 0.75609520 0.62172495
201 -0.20646074 0.75609520
202 0.61239525 -0.20646074
203 2.99482399 0.61239525
204 -2.77236977 2.99482399
205 0.18585167 -2.77236977
206 -0.41422204 0.18585167
207 0.67840988 -0.41422204
208 1.54674935 0.67840988
209 -0.69397256 1.54674935
210 2.93354781 -0.69397256
211 2.65776209 2.93354781
212 0.03086907 2.65776209
213 -2.79070855 0.03086907
214 0.67301067 -2.79070855
215 0.06716228 0.67301067
216 -0.89863773 0.06716228
217 -0.11542982 -0.89863773
218 -1.63153867 -0.11542982
219 1.11294974 -1.63153867
220 2.37265065 1.11294974
221 -0.18540520 2.37265065
222 1.29899681 -0.18540520
223 -0.07901525 1.29899681
224 -0.70193467 -0.07901525
225 -0.97037919 -0.70193467
226 -0.12209567 -0.97037919
227 -1.24459606 -0.12209567
228 0.53385388 -1.24459606
229 -0.15689578 0.53385388
230 -3.48972800 -0.15689578
231 1.82444283 -3.48972800
232 -5.36486289 1.82444283
233 1.76756334 -5.36486289
234 0.42927279 1.76756334
235 1.59941545 0.42927279
236 1.59076699 1.59941545
237 2.65864460 1.59076699
238 0.65578663 2.65864460
239 -0.80688063 0.65578663
240 -1.17677379 -0.80688063
241 1.87911400 -1.17677379
242 -4.83425490 1.87911400
243 -3.57691661 -4.83425490
244 -0.55783459 -3.57691661
245 -1.17733162 -0.55783459
246 -1.25990528 -1.17733162
247 1.23968734 -1.25990528
248 -0.81724361 1.23968734
249 0.24389627 -0.81724361
250 -1.55347490 0.24389627
251 -0.88156202 -1.55347490
252 -3.07686715 -0.88156202
253 -2.25711001 -3.07686715
254 1.02344143 -2.25711001
255 0.84003465 1.02344143
256 0.60750107 0.84003465
257 -2.18253756 0.60750107
258 1.44723505 -2.18253756
259 -1.55416145 1.44723505
260 0.27651680 -1.55416145
261 3.32361104 0.27651680
262 -1.50135489 3.32361104
263 -1.21054613 -1.50135489
264 NA -1.21054613
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.58956137 1.83883605
[2,] 1.40855124 -0.58956137
[3,] -5.27721603 1.40855124
[4,] 2.31067409 -5.27721603
[5,] 0.13649599 2.31067409
[6,] -1.16619122 0.13649599
[7,] 2.67361307 -1.16619122
[8,] 1.30672591 2.67361307
[9,] -2.32089165 1.30672591
[10,] -1.58998198 -2.32089165
[11,] 0.26485630 -1.58998198
[12,] 0.21269994 0.26485630
[13,] -0.82046853 0.21269994
[14,] 2.54590195 -0.82046853
[15,] 0.78429250 2.54590195
[16,] -1.10748204 0.78429250
[17,] -2.07863603 -1.10748204
[18,] -1.99614669 -2.07863603
[19,] 0.28285185 -1.99614669
[20,] -0.76388126 0.28285185
[21,] 0.25069897 -0.76388126
[22,] -0.53355303 0.25069897
[23,] -0.94469641 -0.53355303
[24,] -0.15851170 -0.94469641
[25,] 0.75597613 -0.15851170
[26,] -1.78376463 0.75597613
[27,] 2.65509298 -1.78376463
[28,] 0.25796196 2.65509298
[29,] -0.67220395 0.25796196
[30,] 0.82304844 -0.67220395
[31,] -1.59759128 0.82304844
[32,] -0.49470111 -1.59759128
[33,] 0.09098494 -0.49470111
[34,] 1.48272669 0.09098494
[35,] -1.46558782 1.48272669
[36,] 0.58520663 -1.46558782
[37,] 1.40359125 0.58520663
[38,] -1.74576560 1.40359125
[39,] -1.77050745 -1.74576560
[40,] -1.68103444 -1.77050745
[41,] 1.40051667 -1.68103444
[42,] 1.11430539 1.40051667
[43,] -0.63279504 1.11430539
[44,] -0.37629305 -0.63279504
[45,] 2.97063257 -0.37629305
[46,] 2.17594693 2.97063257
[47,] -0.25306437 2.17594693
[48,] -0.85029678 -0.25306437
[49,] 1.44244326 -0.85029678
[50,] 2.02058894 1.44244326
[51,] -0.54244752 2.02058894
[52,] 2.45298950 -0.54244752
[53,] 1.04236402 2.45298950
[54,] -2.89259232 1.04236402
[55,] -4.63346512 -2.89259232
[56,] 0.32604028 -4.63346512
[57,] 0.10251464 0.32604028
[58,] -0.30921393 0.10251464
[59,] -1.77492913 -0.30921393
[60,] -2.40576460 -1.77492913
[61,] 0.26132087 -2.40576460
[62,] 2.12778491 0.26132087
[63,] 0.24530170 2.12778491
[64,] 0.34380347 0.24530170
[65,] 0.72907020 0.34380347
[66,] 1.33738249 0.72907020
[67,] -1.48913349 1.33738249
[68,] 2.54498289 -1.48913349
[69,] 0.37697466 2.54498289
[70,] -0.21643830 0.37697466
[71,] 0.40343899 -0.21643830
[72,] 1.15965423 0.40343899
[73,] 1.12258168 1.15965423
[74,] 0.69590472 1.12258168
[75,] -2.82790907 0.69590472
[76,] 1.42745936 -2.82790907
[77,] -0.40338223 1.42745936
[78,] 2.14918028 -0.40338223
[79,] 0.72894384 2.14918028
[80,] 0.35301125 0.72894384
[81,] 0.32975279 0.35301125
[82,] 1.11760801 0.32975279
[83,] -0.02058199 1.11760801
[84,] -1.44581377 -0.02058199
[85,] -1.54592827 -1.44581377
[86,] 0.42777697 -1.54592827
[87,] 0.45219427 0.42777697
[88,] 1.02891733 0.45219427
[89,] -0.41222775 1.02891733
[90,] 3.01706422 -0.41222775
[91,] 0.21157709 3.01706422
[92,] 1.22902567 0.21157709
[93,] 2.17544518 1.22902567
[94,] -1.53047775 2.17544518
[95,] 1.47049283 -1.53047775
[96,] -2.43008393 1.47049283
[97,] 0.52553551 -2.43008393
[98,] -0.56115224 0.52553551
[99,] 2.45542383 -0.56115224
[100,] -0.52950717 2.45542383
[101,] 1.16287011 -0.52950717
[102,] 0.15328242 1.16287011
[103,] -0.71673177 0.15328242
[104,] 2.29863821 -0.71673177
[105,] -0.06509383 2.29863821
[106,] 0.42174255 -0.06509383
[107,] -0.58157134 0.42174255
[108,] -2.42314469 -0.58157134
[109,] 0.40739309 -2.42314469
[110,] 2.42251405 0.40739309
[111,] -1.44084511 2.42251405
[112,] 1.14667435 -1.44084511
[113,] 0.77281547 1.14667435
[114,] 0.46882231 0.77281547
[115,] 2.32532610 0.46882231
[116,] -2.66680898 2.32532610
[117,] 1.02934080 -2.66680898
[118,] 0.46467995 1.02934080
[119,] 0.98271995 0.46467995
[120,] 1.68496522 0.98271995
[121,] 1.99147806 1.68496522
[122,] 2.31593870 1.99147806
[123,] 2.15617569 2.31593870
[124,] 3.41004706 2.15617569
[125,] -0.83390735 3.41004706
[126,] -1.11398040 -0.83390735
[127,] -2.04007266 -1.11398040
[128,] 1.74866381 -2.04007266
[129,] -1.57264365 1.74866381
[130,] 2.76925635 -1.57264365
[131,] -3.57425638 2.76925635
[132,] 1.74604312 -3.57425638
[133,] 1.46630636 1.74604312
[134,] 1.20590350 1.46630636
[135,] -0.45610231 1.20590350
[136,] -0.01871821 -0.45610231
[137,] 1.11814796 -0.01871821
[138,] -1.30921324 1.11814796
[139,] 0.12299026 -1.30921324
[140,] -3.59576582 0.12299026
[141,] -2.14062146 -3.59576582
[142,] 2.78056572 -2.14062146
[143,] -0.16531509 2.78056572
[144,] 0.01954352 -0.16531509
[145,] 0.59610609 0.01954352
[146,] 2.04209212 0.59610609
[147,] 0.49267770 2.04209212
[148,] -0.78790824 0.49267770
[149,] -1.31753706 -0.78790824
[150,] 2.05745739 -1.31753706
[151,] -2.71388955 2.05745739
[152,] -5.75785644 -2.71388955
[153,] -2.61059935 -5.75785644
[154,] -0.07347665 -2.61059935
[155,] 3.01706422 -0.07347665
[156,] -4.01872322 3.01706422
[157,] -2.04007266 -4.01872322
[158,] -0.51792551 -2.04007266
[159,] -1.48894944 -0.51792551
[160,] -1.93888284 -1.48894944
[161,] 0.60777163 -1.93888284
[162,] -0.81051885 0.60777163
[163,] 2.33822942 -0.81051885
[164,] -0.94716760 2.33822942
[165,] 0.07198670 -0.94716760
[166,] 0.76777486 0.07198670
[167,] 3.67290758 0.76777486
[168,] 2.17286687 3.67290758
[169,] 0.44069362 2.17286687
[170,] -3.88290411 0.44069362
[171,] 0.62839775 -3.88290411
[172,] 0.92951438 0.62839775
[173,] -0.12158601 0.92951438
[174,] 3.21391713 -0.12158601
[175,] 0.12713340 3.21391713
[176,] -1.02396869 0.12713340
[177,] 0.02868566 -1.02396869
[178,] -0.13051750 0.02868566
[179,] -0.15619084 -0.13051750
[180,] 0.63263118 -0.15619084
[181,] -2.40081691 0.63263118
[182,] 1.88985472 -2.40081691
[183,] 1.37005366 1.88985472
[184,] 5.17787866 1.37005366
[185,] 0.62629375 5.17787866
[186,] -0.81735419 0.62629375
[187,] -1.87321951 -0.81735419
[188,] -1.39678348 -1.87321951
[189,] -3.59865259 -1.39678348
[190,] 1.65690718 -3.59865259
[191,] 0.50185219 1.65690718
[192,] -6.04402802 0.50185219
[193,] -0.41441030 -6.04402802
[194,] 0.42922082 -0.41441030
[195,] 1.11239283 0.42922082
[196,] -2.13149093 1.11239283
[197,] 0.57949250 -2.13149093
[198,] -1.19011815 0.57949250
[199,] 0.62172495 -1.19011815
[200,] 0.75609520 0.62172495
[201,] -0.20646074 0.75609520
[202,] 0.61239525 -0.20646074
[203,] 2.99482399 0.61239525
[204,] -2.77236977 2.99482399
[205,] 0.18585167 -2.77236977
[206,] -0.41422204 0.18585167
[207,] 0.67840988 -0.41422204
[208,] 1.54674935 0.67840988
[209,] -0.69397256 1.54674935
[210,] 2.93354781 -0.69397256
[211,] 2.65776209 2.93354781
[212,] 0.03086907 2.65776209
[213,] -2.79070855 0.03086907
[214,] 0.67301067 -2.79070855
[215,] 0.06716228 0.67301067
[216,] -0.89863773 0.06716228
[217,] -0.11542982 -0.89863773
[218,] -1.63153867 -0.11542982
[219,] 1.11294974 -1.63153867
[220,] 2.37265065 1.11294974
[221,] -0.18540520 2.37265065
[222,] 1.29899681 -0.18540520
[223,] -0.07901525 1.29899681
[224,] -0.70193467 -0.07901525
[225,] -0.97037919 -0.70193467
[226,] -0.12209567 -0.97037919
[227,] -1.24459606 -0.12209567
[228,] 0.53385388 -1.24459606
[229,] -0.15689578 0.53385388
[230,] -3.48972800 -0.15689578
[231,] 1.82444283 -3.48972800
[232,] -5.36486289 1.82444283
[233,] 1.76756334 -5.36486289
[234,] 0.42927279 1.76756334
[235,] 1.59941545 0.42927279
[236,] 1.59076699 1.59941545
[237,] 2.65864460 1.59076699
[238,] 0.65578663 2.65864460
[239,] -0.80688063 0.65578663
[240,] -1.17677379 -0.80688063
[241,] 1.87911400 -1.17677379
[242,] -4.83425490 1.87911400
[243,] -3.57691661 -4.83425490
[244,] -0.55783459 -3.57691661
[245,] -1.17733162 -0.55783459
[246,] -1.25990528 -1.17733162
[247,] 1.23968734 -1.25990528
[248,] -0.81724361 1.23968734
[249,] 0.24389627 -0.81724361
[250,] -1.55347490 0.24389627
[251,] -0.88156202 -1.55347490
[252,] -3.07686715 -0.88156202
[253,] -2.25711001 -3.07686715
[254,] 1.02344143 -2.25711001
[255,] 0.84003465 1.02344143
[256,] 0.60750107 0.84003465
[257,] -2.18253756 0.60750107
[258,] 1.44723505 -2.18253756
[259,] -1.55416145 1.44723505
[260,] 0.27651680 -1.55416145
[261,] 3.32361104 0.27651680
[262,] -1.50135489 3.32361104
[263,] -1.21054613 -1.50135489
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.58956137 1.83883605
2 1.40855124 -0.58956137
3 -5.27721603 1.40855124
4 2.31067409 -5.27721603
5 0.13649599 2.31067409
6 -1.16619122 0.13649599
7 2.67361307 -1.16619122
8 1.30672591 2.67361307
9 -2.32089165 1.30672591
10 -1.58998198 -2.32089165
11 0.26485630 -1.58998198
12 0.21269994 0.26485630
13 -0.82046853 0.21269994
14 2.54590195 -0.82046853
15 0.78429250 2.54590195
16 -1.10748204 0.78429250
17 -2.07863603 -1.10748204
18 -1.99614669 -2.07863603
19 0.28285185 -1.99614669
20 -0.76388126 0.28285185
21 0.25069897 -0.76388126
22 -0.53355303 0.25069897
23 -0.94469641 -0.53355303
24 -0.15851170 -0.94469641
25 0.75597613 -0.15851170
26 -1.78376463 0.75597613
27 2.65509298 -1.78376463
28 0.25796196 2.65509298
29 -0.67220395 0.25796196
30 0.82304844 -0.67220395
31 -1.59759128 0.82304844
32 -0.49470111 -1.59759128
33 0.09098494 -0.49470111
34 1.48272669 0.09098494
35 -1.46558782 1.48272669
36 0.58520663 -1.46558782
37 1.40359125 0.58520663
38 -1.74576560 1.40359125
39 -1.77050745 -1.74576560
40 -1.68103444 -1.77050745
41 1.40051667 -1.68103444
42 1.11430539 1.40051667
43 -0.63279504 1.11430539
44 -0.37629305 -0.63279504
45 2.97063257 -0.37629305
46 2.17594693 2.97063257
47 -0.25306437 2.17594693
48 -0.85029678 -0.25306437
49 1.44244326 -0.85029678
50 2.02058894 1.44244326
51 -0.54244752 2.02058894
52 2.45298950 -0.54244752
53 1.04236402 2.45298950
54 -2.89259232 1.04236402
55 -4.63346512 -2.89259232
56 0.32604028 -4.63346512
57 0.10251464 0.32604028
58 -0.30921393 0.10251464
59 -1.77492913 -0.30921393
60 -2.40576460 -1.77492913
61 0.26132087 -2.40576460
62 2.12778491 0.26132087
63 0.24530170 2.12778491
64 0.34380347 0.24530170
65 0.72907020 0.34380347
66 1.33738249 0.72907020
67 -1.48913349 1.33738249
68 2.54498289 -1.48913349
69 0.37697466 2.54498289
70 -0.21643830 0.37697466
71 0.40343899 -0.21643830
72 1.15965423 0.40343899
73 1.12258168 1.15965423
74 0.69590472 1.12258168
75 -2.82790907 0.69590472
76 1.42745936 -2.82790907
77 -0.40338223 1.42745936
78 2.14918028 -0.40338223
79 0.72894384 2.14918028
80 0.35301125 0.72894384
81 0.32975279 0.35301125
82 1.11760801 0.32975279
83 -0.02058199 1.11760801
84 -1.44581377 -0.02058199
85 -1.54592827 -1.44581377
86 0.42777697 -1.54592827
87 0.45219427 0.42777697
88 1.02891733 0.45219427
89 -0.41222775 1.02891733
90 3.01706422 -0.41222775
91 0.21157709 3.01706422
92 1.22902567 0.21157709
93 2.17544518 1.22902567
94 -1.53047775 2.17544518
95 1.47049283 -1.53047775
96 -2.43008393 1.47049283
97 0.52553551 -2.43008393
98 -0.56115224 0.52553551
99 2.45542383 -0.56115224
100 -0.52950717 2.45542383
101 1.16287011 -0.52950717
102 0.15328242 1.16287011
103 -0.71673177 0.15328242
104 2.29863821 -0.71673177
105 -0.06509383 2.29863821
106 0.42174255 -0.06509383
107 -0.58157134 0.42174255
108 -2.42314469 -0.58157134
109 0.40739309 -2.42314469
110 2.42251405 0.40739309
111 -1.44084511 2.42251405
112 1.14667435 -1.44084511
113 0.77281547 1.14667435
114 0.46882231 0.77281547
115 2.32532610 0.46882231
116 -2.66680898 2.32532610
117 1.02934080 -2.66680898
118 0.46467995 1.02934080
119 0.98271995 0.46467995
120 1.68496522 0.98271995
121 1.99147806 1.68496522
122 2.31593870 1.99147806
123 2.15617569 2.31593870
124 3.41004706 2.15617569
125 -0.83390735 3.41004706
126 -1.11398040 -0.83390735
127 -2.04007266 -1.11398040
128 1.74866381 -2.04007266
129 -1.57264365 1.74866381
130 2.76925635 -1.57264365
131 -3.57425638 2.76925635
132 1.74604312 -3.57425638
133 1.46630636 1.74604312
134 1.20590350 1.46630636
135 -0.45610231 1.20590350
136 -0.01871821 -0.45610231
137 1.11814796 -0.01871821
138 -1.30921324 1.11814796
139 0.12299026 -1.30921324
140 -3.59576582 0.12299026
141 -2.14062146 -3.59576582
142 2.78056572 -2.14062146
143 -0.16531509 2.78056572
144 0.01954352 -0.16531509
145 0.59610609 0.01954352
146 2.04209212 0.59610609
147 0.49267770 2.04209212
148 -0.78790824 0.49267770
149 -1.31753706 -0.78790824
150 2.05745739 -1.31753706
151 -2.71388955 2.05745739
152 -5.75785644 -2.71388955
153 -2.61059935 -5.75785644
154 -0.07347665 -2.61059935
155 3.01706422 -0.07347665
156 -4.01872322 3.01706422
157 -2.04007266 -4.01872322
158 -0.51792551 -2.04007266
159 -1.48894944 -0.51792551
160 -1.93888284 -1.48894944
161 0.60777163 -1.93888284
162 -0.81051885 0.60777163
163 2.33822942 -0.81051885
164 -0.94716760 2.33822942
165 0.07198670 -0.94716760
166 0.76777486 0.07198670
167 3.67290758 0.76777486
168 2.17286687 3.67290758
169 0.44069362 2.17286687
170 -3.88290411 0.44069362
171 0.62839775 -3.88290411
172 0.92951438 0.62839775
173 -0.12158601 0.92951438
174 3.21391713 -0.12158601
175 0.12713340 3.21391713
176 -1.02396869 0.12713340
177 0.02868566 -1.02396869
178 -0.13051750 0.02868566
179 -0.15619084 -0.13051750
180 0.63263118 -0.15619084
181 -2.40081691 0.63263118
182 1.88985472 -2.40081691
183 1.37005366 1.88985472
184 5.17787866 1.37005366
185 0.62629375 5.17787866
186 -0.81735419 0.62629375
187 -1.87321951 -0.81735419
188 -1.39678348 -1.87321951
189 -3.59865259 -1.39678348
190 1.65690718 -3.59865259
191 0.50185219 1.65690718
192 -6.04402802 0.50185219
193 -0.41441030 -6.04402802
194 0.42922082 -0.41441030
195 1.11239283 0.42922082
196 -2.13149093 1.11239283
197 0.57949250 -2.13149093
198 -1.19011815 0.57949250
199 0.62172495 -1.19011815
200 0.75609520 0.62172495
201 -0.20646074 0.75609520
202 0.61239525 -0.20646074
203 2.99482399 0.61239525
204 -2.77236977 2.99482399
205 0.18585167 -2.77236977
206 -0.41422204 0.18585167
207 0.67840988 -0.41422204
208 1.54674935 0.67840988
209 -0.69397256 1.54674935
210 2.93354781 -0.69397256
211 2.65776209 2.93354781
212 0.03086907 2.65776209
213 -2.79070855 0.03086907
214 0.67301067 -2.79070855
215 0.06716228 0.67301067
216 -0.89863773 0.06716228
217 -0.11542982 -0.89863773
218 -1.63153867 -0.11542982
219 1.11294974 -1.63153867
220 2.37265065 1.11294974
221 -0.18540520 2.37265065
222 1.29899681 -0.18540520
223 -0.07901525 1.29899681
224 -0.70193467 -0.07901525
225 -0.97037919 -0.70193467
226 -0.12209567 -0.97037919
227 -1.24459606 -0.12209567
228 0.53385388 -1.24459606
229 -0.15689578 0.53385388
230 -3.48972800 -0.15689578
231 1.82444283 -3.48972800
232 -5.36486289 1.82444283
233 1.76756334 -5.36486289
234 0.42927279 1.76756334
235 1.59941545 0.42927279
236 1.59076699 1.59941545
237 2.65864460 1.59076699
238 0.65578663 2.65864460
239 -0.80688063 0.65578663
240 -1.17677379 -0.80688063
241 1.87911400 -1.17677379
242 -4.83425490 1.87911400
243 -3.57691661 -4.83425490
244 -0.55783459 -3.57691661
245 -1.17733162 -0.55783459
246 -1.25990528 -1.17733162
247 1.23968734 -1.25990528
248 -0.81724361 1.23968734
249 0.24389627 -0.81724361
250 -1.55347490 0.24389627
251 -0.88156202 -1.55347490
252 -3.07686715 -0.88156202
253 -2.25711001 -3.07686715
254 1.02344143 -2.25711001
255 0.84003465 1.02344143
256 0.60750107 0.84003465
257 -2.18253756 0.60750107
258 1.44723505 -2.18253756
259 -1.55416145 1.44723505
260 0.27651680 -1.55416145
261 3.32361104 0.27651680
262 -1.50135489 3.32361104
263 -1.21054613 -1.50135489
> 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/7mab11383330808.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8d8nl1383330808.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/95jj91383330808.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10g71u1383330808.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11i40z1383330808.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/12ebsg1383330808.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/13bi0r1383330808.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/14lra81383330808.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/15fvft1383330809.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/16ngcp1383330809.tab")
+ }
>
> try(system("convert tmp/1qkiw1383330808.ps tmp/1qkiw1383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/20eva1383330808.ps tmp/20eva1383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/38gm51383330808.ps tmp/38gm51383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/498j21383330808.ps tmp/498j21383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/5qv4g1383330808.ps tmp/5qv4g1383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rsjz1383330808.ps tmp/6rsjz1383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/7mab11383330808.ps tmp/7mab11383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d8nl1383330808.ps tmp/8d8nl1383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/95jj91383330808.ps tmp/95jj91383330808.png",intern=TRUE))
character(0)
> try(system("convert tmp/10g71u1383330808.ps tmp/10g71u1383330808.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.731 2.720 18.610