R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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+ ,dim=c(6
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression')
+ ,1:264))
> y <- array(NA,dim=c(6,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Connected Separate Learning Software Happiness Depression
1 41 38 13 12 14 12.0
2 39 32 16 11 18 11.0
3 30 35 19 15 11 14.0
4 31 33 15 6 12 12.0
5 34 37 14 13 16 21.0
6 35 29 13 10 18 12.0
7 39 31 19 12 14 22.0
8 34 36 15 14 14 11.0
9 36 35 14 12 15 10.0
10 37 38 15 9 15 13.0
11 38 31 16 10 17 10.0
12 36 34 16 12 19 8.0
13 38 35 16 12 10 15.0
14 39 38 16 11 16 14.0
15 33 37 17 15 18 10.0
16 32 33 15 12 14 14.0
17 36 32 15 10 14 14.0
18 38 38 20 12 17 11.0
19 39 38 18 11 14 10.0
20 32 32 16 12 16 13.0
21 32 33 16 11 18 9.5
22 31 31 16 12 11 14.0
23 39 38 19 13 14 12.0
24 37 39 16 11 12 14.0
25 39 32 17 12 17 11.0
26 41 32 17 13 9 9.0
27 36 35 16 10 16 11.0
28 33 37 15 14 14 15.0
29 33 33 16 12 15 14.0
30 34 33 14 10 11 13.0
31 31 31 15 12 16 9.0
32 27 32 12 8 13 15.0
33 37 31 14 10 17 10.0
34 34 37 16 12 15 11.0
35 34 30 14 12 14 13.0
36 32 33 10 7 16 8.0
37 29 31 10 9 9 20.0
38 36 33 14 12 15 12.0
39 29 31 16 10 17 10.0
40 35 33 16 10 13 10.0
41 37 32 16 10 15 9.0
42 34 33 14 12 16 14.0
43 38 32 20 15 16 8.0
44 35 33 14 10 12 14.0
45 38 28 14 10 15 11.0
46 37 35 11 12 11 13.0
47 38 39 14 13 15 9.0
48 33 34 15 11 15 11.0
49 36 38 16 11 17 15.0
50 38 32 14 12 13 11.0
51 32 38 16 14 16 10.0
52 32 30 14 10 14 14.0
53 32 33 12 12 11 18.0
54 34 38 16 13 12 14.0
55 32 32 9 5 12 11.0
56 37 35 14 6 15 14.5
57 39 34 16 12 16 13.0
58 29 34 16 12 15 9.0
59 37 36 15 11 12 10.0
60 35 34 16 10 12 15.0
61 30 28 12 7 8 20.0
62 38 34 16 12 13 12.0
63 34 35 16 14 11 12.0
64 31 35 14 11 14 14.0
65 34 31 16 12 15 13.0
66 35 37 17 13 10 11.0
67 36 35 18 14 11 17.0
68 30 27 18 11 12 12.0
69 39 40 12 12 15 13.0
70 35 37 16 12 15 14.0
71 38 36 10 8 14 13.0
72 31 38 14 11 16 15.0
73 34 39 18 14 15 13.0
74 38 41 18 14 15 10.0
75 34 27 16 12 13 11.0
76 39 30 17 9 12 19.0
77 37 37 16 13 17 13.0
78 34 31 16 11 13 17.0
79 28 31 13 12 15 13.0
80 37 27 16 12 13 9.0
81 33 36 16 12 15 11.0
82 35 37 16 12 15 9.0
83 37 33 15 12 16 12.0
84 32 34 15 11 15 12.0
85 33 31 16 10 14 13.0
86 38 39 14 9 15 13.0
87 33 34 16 12 14 12.0
88 29 32 16 12 13 15.0
89 33 33 15 12 7 22.0
90 31 36 12 9 17 13.0
91 36 32 17 15 13 15.0
92 35 41 16 12 15 13.0
93 32 28 15 12 14 15.0
94 29 30 13 12 13 12.5
95 39 36 16 10 16 11.0
96 37 35 16 13 12 16.0
97 35 31 16 9 14 11.0
98 37 34 16 12 17 11.0
99 32 36 14 10 15 10.0
100 38 36 16 14 17 10.0
101 37 35 16 11 12 16.0
102 36 37 20 15 16 12.0
103 32 28 15 11 11 11.0
104 33 39 16 11 15 16.0
105 40 32 13 12 9 19.0
106 38 35 17 12 16 11.0
107 41 39 16 12 15 16.0
108 36 35 16 11 10 15.0
109 43 42 12 7 10 24.0
110 30 34 16 12 15 14.0
111 31 33 16 14 11 15.0
112 32 41 17 11 13 11.0
113 32 33 13 11 14 15.0
114 37 34 12 10 18 12.0
115 37 32 18 13 16 10.0
116 33 40 14 13 14 14.0
117 34 40 14 8 14 13.0
118 33 35 13 11 14 9.0
119 38 36 16 12 14 15.0
120 33 37 13 11 12 15.0
121 31 27 16 13 14 14.0
122 38 39 13 12 15 11.0
123 37 38 16 14 15 8.0
124 36 31 15 13 15 11.0
125 31 33 16 15 13 11.0
126 39 32 15 10 17 8.0
127 44 39 17 11 17 10.0
128 33 36 15 9 19 11.0
129 35 33 12 11 15 13.0
130 32 33 16 10 13 11.0
131 28 32 10 11 9 20.0
132 40 37 16 8 15 10.0
133 27 30 12 11 15 15.0
134 37 38 14 12 15 12.0
135 32 29 15 12 16 14.0
136 28 22 13 9 11 23.0
137 34 35 15 11 14 14.0
138 30 35 11 10 11 16.0
139 35 34 12 8 15 11.0
140 31 35 11 9 13 12.0
141 32 34 16 8 15 10.0
142 30 37 15 9 16 14.0
143 30 35 17 15 14 12.0
144 31 23 16 11 15 12.0
145 40 31 10 8 16 11.0
146 32 27 18 13 16 12.0
147 36 36 13 12 11 13.0
148 32 31 16 12 12 11.0
149 35 32 13 9 9 19.0
150 38 39 10 7 16 12.0
151 42 37 15 13 13 17.0
152 34 38 16 9 16 9.0
153 35 39 16 6 12 12.0
154 38 34 14 8 9 19.0
155 33 31 10 8 13 18.0
156 36 32 17 15 13 15.0
157 32 37 13 6 14 14.0
158 33 36 15 9 19 11.0
159 34 32 16 11 13 9.0
160 32 38 12 8 12 18.0
161 34 36 13 8 13 16.0
162 27 26 13 10 10 24.0
163 31 26 12 8 14 14.0
164 38 33 17 14 16 20.0
165 34 39 15 10 10 18.0
166 24 30 10 8 11 23.0
167 30 33 14 11 14 12.0
168 26 25 11 12 12 14.0
169 34 38 13 12 9 16.0
170 27 37 16 12 9 18.0
171 37 31 12 5 11 20.0
172 36 37 16 12 16 12.0
173 41 35 12 10 9 12.0
174 29 25 9 7 13 17.0
175 36 28 12 12 16 13.0
176 32 35 15 11 13 9.0
177 37 33 12 8 9 16.0
178 30 30 12 9 12 18.0
179 31 31 14 10 16 10.0
180 38 37 12 9 11 14.0
181 36 36 16 12 14 11.0
182 35 30 11 6 13 9.0
183 31 36 19 15 15 11.0
184 38 32 15 12 14 10.0
185 22 28 8 12 16 11.0
186 32 36 16 12 13 19.0
187 36 34 17 11 14 14.0
188 39 31 12 7 15 12.0
189 28 28 11 7 13 14.0
190 32 36 11 5 11 21.0
191 32 36 14 12 11 13.0
192 38 40 16 12 14 10.0
193 32 33 12 3 15 15.0
194 35 37 16 11 11 16.0
195 32 32 13 10 15 14.0
196 37 38 15 12 12 12.0
197 34 31 16 9 14 19.0
198 33 37 16 12 14 15.0
199 33 33 14 9 8 19.0
200 26 32 16 12 13 13.0
201 30 30 16 12 9 17.0
202 24 30 14 10 15 12.0
203 34 31 11 9 17 11.0
204 34 32 12 12 13 14.0
205 33 34 15 8 15 11.0
206 34 36 15 11 15 13.0
207 35 37 16 11 14 12.0
208 35 36 16 12 16 15.0
209 36 33 11 10 13 14.0
210 34 33 15 10 16 12.0
211 34 33 12 12 9 17.0
212 41 44 12 12 16 11.0
213 32 39 15 11 11 18.0
214 30 32 15 8 10 13.0
215 35 35 16 12 11 17.0
216 28 25 14 10 15 13.0
217 33 35 17 11 17 11.0
218 39 34 14 10 14 12.0
219 36 35 13 8 8 22.0
220 36 39 15 12 15 14.0
221 35 33 13 12 11 12.0
222 38 36 14 10 16 12.0
223 33 32 15 12 10 17.0
224 31 32 12 9 15 9.0
225 34 36 13 9 9 21.0
226 32 36 8 6 16 10.0
227 31 32 14 10 19 11.0
228 33 34 14 9 12 12.0
229 34 33 11 9 8 23.0
230 34 35 12 9 11 13.0
231 34 30 13 6 14 12.0
232 33 38 10 10 9 16.0
233 32 34 16 6 15 9.0
234 41 33 18 14 13 17.0
235 34 32 13 10 16 9.0
236 36 31 11 10 11 14.0
237 37 30 4 6 12 17.0
238 36 27 13 12 13 13.0
239 29 31 16 12 10 11.0
240 37 30 10 7 11 12.0
241 27 32 12 8 12 10.0
242 35 35 12 11 8 19.0
243 28 28 10 3 12 16.0
244 35 33 13 6 12 16.0
245 37 31 15 10 15 14.0
246 29 35 12 8 11 20.0
247 32 35 14 9 13 15.0
248 36 32 10 9 14 23.0
249 19 21 12 8 10 20.0
250 21 20 12 9 12 16.0
251 31 34 11 7 15 14.0
252 33 32 10 7 13 17.0
253 36 34 12 6 13 11.0
254 33 32 16 9 13 13.0
255 37 33 12 10 12 17.0
256 34 33 14 11 12 15.0
257 35 37 16 12 9 21.0
258 31 32 14 8 9 18.0
259 37 34 13 11 15 15.0
260 35 30 4 3 10 8.0
261 27 30 15 11 14 12.0
262 34 38 11 12 15 12.0
263 40 36 11 7 7 22.0
264 29 32 14 9 14 12.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Separate Learning Software Happiness Depression
17.65448 0.43923 0.15029 -0.03690 0.04953 -0.06134
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.8302 -2.4667 -0.0033 2.4210 7.4988
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.65448 2.95858 5.967 7.93e-09 ***
Separate 0.43923 0.05792 7.584 6.09e-13 ***
Learning 0.15029 0.11160 1.347 0.179
Software -0.03690 0.11512 -0.321 0.749
Happiness 0.04953 0.10375 0.477 0.633
Depression -0.06134 0.07437 -0.825 0.410
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.378 on 258 degrees of freedom
Multiple R-squared: 0.2234, Adjusted R-squared: 0.2083
F-statistic: 14.84 on 5 and 258 DF, p-value: 8.451e-13
> 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,] 0.90706032 0.18587937 0.09293968
[2,] 0.83814065 0.32371870 0.16185935
[3,] 0.75007260 0.49985479 0.24992740
[4,] 0.72600099 0.54799802 0.27399901
[5,] 0.78031196 0.43937607 0.21968804
[6,] 0.71510198 0.56979605 0.28489802
[7,] 0.71041351 0.57917297 0.28958649
[8,] 0.69739171 0.60521659 0.30260829
[9,] 0.61880494 0.76239013 0.38119506
[10,] 0.54161824 0.91676353 0.45838176
[11,] 0.50389517 0.99220967 0.49610483
[12,] 0.49405255 0.98810509 0.50594745
[13,] 0.51551481 0.96897039 0.48448519
[14,] 0.45847998 0.91695996 0.54152002
[15,] 0.42520635 0.85041270 0.57479365
[16,] 0.35594091 0.71188182 0.64405909
[17,] 0.38743500 0.77487001 0.61256500
[18,] 0.63401237 0.73197526 0.36598763
[19,] 0.57346789 0.85306422 0.42653211
[20,] 0.53955123 0.92089755 0.46044877
[21,] 0.50098715 0.99802571 0.49901285
[22,] 0.44388463 0.88776925 0.55611537
[23,] 0.44470611 0.88941222 0.55529389
[24,] 0.58968388 0.82063224 0.41031612
[25,] 0.57795874 0.84408252 0.42204126
[26,] 0.54648673 0.90702655 0.45351327
[27,] 0.49650478 0.99300956 0.50349522
[28,] 0.44695492 0.89390984 0.55304508
[29,] 0.40081330 0.80162660 0.59918670
[30,] 0.36942662 0.73885324 0.63057338
[31,] 0.48032662 0.96065324 0.51967338
[32,] 0.42828659 0.85657318 0.57171341
[33,] 0.39751686 0.79503372 0.60248314
[34,] 0.34840681 0.69681362 0.65159319
[35,] 0.31350617 0.62701234 0.68649383
[36,] 0.27667658 0.55335317 0.72332342
[37,] 0.34053341 0.68106682 0.65946659
[38,] 0.35522399 0.71044799 0.64477601
[39,] 0.31992286 0.63984573 0.68007714
[40,] 0.29487549 0.58975097 0.70512451
[41,] 0.25427908 0.50855816 0.74572092
[42,] 0.26616763 0.53233525 0.73383237
[43,] 0.30613027 0.61226055 0.69386973
[44,] 0.27310571 0.54621141 0.72689429
[45,] 0.23753285 0.47506570 0.76246715
[46,] 0.21411959 0.42823918 0.78588041
[47,] 0.18249854 0.36499709 0.81750146
[48,] 0.16926084 0.33852168 0.83073916
[49,] 0.18239435 0.36478870 0.81760565
[50,] 0.28226605 0.56453209 0.71773395
[51,] 0.25366143 0.50732287 0.74633857
[52,] 0.21997862 0.43995725 0.78002138
[53,] 0.19433000 0.38866001 0.80567000
[54,] 0.18876025 0.37752051 0.81123975
[55,] 0.16423075 0.32846150 0.83576925
[56,] 0.16748716 0.33497431 0.83251284
[57,] 0.14301970 0.28603940 0.85698030
[58,] 0.12260311 0.24520623 0.87739689
[59,] 0.10356359 0.20712717 0.89643641
[60,] 0.11073657 0.22147314 0.88926343
[61,] 0.11040086 0.22080172 0.88959914
[62,] 0.09313638 0.18627277 0.90686362
[63,] 0.09811117 0.19622235 0.90188883
[64,] 0.11978674 0.23957348 0.88021326
[65,] 0.11476770 0.22953540 0.88523230
[66,] 0.09605323 0.19210645 0.90394677
[67,] 0.08379034 0.16758067 0.91620966
[68,] 0.12200134 0.24400268 0.87799866
[69,] 0.10490304 0.20980608 0.89509696
[70,] 0.08863794 0.17727588 0.91136206
[71,] 0.11272867 0.22545734 0.88727133
[72,] 0.12893306 0.25786613 0.87106694
[73,] 0.12194508 0.24389015 0.87805492
[74,] 0.10511302 0.21022603 0.89488698
[75,] 0.09941134 0.19882268 0.90058866
[76,] 0.09441416 0.18882833 0.90558584
[77,] 0.08157386 0.16314771 0.91842614
[78,] 0.07214288 0.14428576 0.92785712
[79,] 0.06357359 0.12714717 0.93642641
[80,] 0.07936358 0.15872716 0.92063642
[81,] 0.06600767 0.13201534 0.93399233
[82,] 0.06976152 0.13952304 0.93023848
[83,] 0.06351273 0.12702546 0.93648727
[84,] 0.05650568 0.11301135 0.94349432
[85,] 0.04713421 0.09426842 0.95286579
[86,] 0.04840126 0.09680252 0.95159874
[87,] 0.04798442 0.09596885 0.95201558
[88,] 0.04419020 0.08838040 0.95580980
[89,] 0.03752659 0.07505317 0.96247341
[90,] 0.03324907 0.06649814 0.96675093
[91,] 0.03325139 0.06650277 0.96674861
[92,] 0.03051752 0.06103505 0.96948248
[93,] 0.02737072 0.05474145 0.97262928
[94,] 0.02252372 0.04504745 0.97747628
[95,] 0.01859252 0.03718504 0.98140748
[96,] 0.01866837 0.03733673 0.98133163
[97,] 0.04616699 0.09233398 0.95383301
[98,] 0.04328584 0.08657168 0.95671416
[99,] 0.05360119 0.10720238 0.94639881
[100,] 0.04589466 0.09178932 0.95410534
[101,] 0.07501098 0.15002196 0.92498902
[102,] 0.08863410 0.17726820 0.91136590
[103,] 0.08503407 0.17006813 0.91496593
[104,] 0.11484562 0.22969125 0.88515438
[105,] 0.10190942 0.20381884 0.89809058
[106,] 0.09776991 0.19553982 0.90223009
[107,] 0.09427549 0.18855098 0.90572451
[108,] 0.09422772 0.18845544 0.90577228
[109,] 0.08998087 0.17996174 0.91001913
[110,] 0.07866382 0.15732765 0.92133618
[111,] 0.07480169 0.14960339 0.92519831
[112,] 0.06697335 0.13394670 0.93302665
[113,] 0.05854003 0.11708006 0.94145997
[114,] 0.05318068 0.10636137 0.94681932
[115,] 0.04497361 0.08994722 0.95502639
[116,] 0.04316340 0.08632679 0.95683660
[117,] 0.04079270 0.08158539 0.95920730
[118,] 0.05231786 0.10463573 0.94768214
[119,] 0.09536762 0.19073524 0.90463238
[120,] 0.09133495 0.18266990 0.90866505
[121,] 0.08043591 0.16087182 0.91956409
[122,] 0.07420810 0.14841619 0.92579190
[123,] 0.08136328 0.16272655 0.91863672
[124,] 0.08742346 0.17484692 0.91257654
[125,] 0.10799465 0.21598931 0.89200535
[126,] 0.09466658 0.18933316 0.90533342
[127,] 0.08149702 0.16299404 0.91850298
[128,] 0.07078697 0.14157395 0.92921303
[129,] 0.06001735 0.12003471 0.93998265
[130,] 0.06377863 0.12755726 0.93622137
[131,] 0.05404540 0.10809080 0.94595460
[132,] 0.05332154 0.10664307 0.94667846
[133,] 0.05241475 0.10482950 0.94758525
[134,] 0.07372390 0.14744780 0.92627610
[135,] 0.08727979 0.17455959 0.91272021
[136,] 0.08144308 0.16288617 0.91855692
[137,] 0.14883344 0.29766687 0.85116656
[138,] 0.13712691 0.27425381 0.86287309
[139,] 0.12184304 0.24368608 0.87815696
[140,] 0.10695884 0.21391768 0.89304116
[141,] 0.09876099 0.19752199 0.90123901
[142,] 0.08785354 0.17570709 0.91214646
[143,] 0.14221501 0.28443002 0.85778499
[144,] 0.13341353 0.26682707 0.86658647
[145,] 0.12039475 0.24078950 0.87960525
[146,] 0.13246106 0.26492212 0.86753894
[147,] 0.11488741 0.22977483 0.88511259
[148,] 0.11358672 0.22717344 0.88641328
[149,] 0.11645682 0.23291364 0.88354318
[150,] 0.10864120 0.21728240 0.89135880
[151,] 0.09490520 0.18981040 0.90509480
[152,] 0.09913084 0.19826168 0.90086916
[153,] 0.08533726 0.17067452 0.91466274
[154,] 0.08043022 0.16086045 0.91956978
[155,] 0.07002586 0.14005171 0.92997414
[156,] 0.08641084 0.17282167 0.91358916
[157,] 0.07829769 0.15659538 0.92170231
[158,] 0.14324715 0.28649430 0.85675285
[159,] 0.14387521 0.28775042 0.85612479
[160,] 0.13988477 0.27976953 0.86011523
[161,] 0.12603129 0.25206259 0.87396871
[162,] 0.24470156 0.48940311 0.75529844
[163,] 0.27773756 0.55547513 0.72226244
[164,] 0.24811668 0.49623336 0.75188332
[165,] 0.34080848 0.68161695 0.65919152
[166,] 0.30731863 0.61463726 0.69268137
[167,] 0.36256282 0.72512565 0.63743718
[168,] 0.34808978 0.69617956 0.65191022
[169,] 0.35614173 0.71228346 0.64385827
[170,] 0.32873992 0.65747985 0.67126008
[171,] 0.30323878 0.60647756 0.69676122
[172,] 0.28911811 0.57823622 0.71088189
[173,] 0.26078497 0.52156995 0.73921503
[174,] 0.25770162 0.51540324 0.74229838
[175,] 0.26933465 0.53866931 0.73066535
[176,] 0.32198030 0.64396061 0.67801970
[177,] 0.57332705 0.85334591 0.42667295
[178,] 0.56411745 0.87176511 0.43588255
[179,] 0.55111382 0.89777236 0.44888618
[180,] 0.67913836 0.64172328 0.32086164
[181,] 0.66619756 0.66760487 0.33380244
[182,] 0.65891120 0.68217760 0.34108880
[183,] 0.65131557 0.69736886 0.34868443
[184,] 0.61688089 0.76623822 0.38311911
[185,] 0.58374326 0.83251348 0.41625674
[186,] 0.54350086 0.91299829 0.45649914
[187,] 0.50596956 0.98806089 0.49403044
[188,] 0.46958428 0.93916856 0.53041572
[189,] 0.45268450 0.90536900 0.54731550
[190,] 0.43281400 0.86562800 0.56718600
[191,] 0.39254573 0.78509145 0.60745427
[192,] 0.51308252 0.97383496 0.48691748
[193,] 0.47871510 0.95743021 0.52128490
[194,] 0.66622061 0.66755878 0.33377939
[195,] 0.63020418 0.73959163 0.36979582
[196,] 0.59075408 0.81849184 0.40924592
[197,] 0.55175888 0.89648224 0.44824112
[198,] 0.51306433 0.97387134 0.48693567
[199,] 0.46972188 0.93944376 0.53027812
[200,] 0.42598016 0.85196032 0.57401984
[201,] 0.39950837 0.79901674 0.60049163
[202,] 0.35949756 0.71899512 0.64050244
[203,] 0.32066786 0.64133571 0.67933214
[204,] 0.29118401 0.58236801 0.70881599
[205,] 0.33605422 0.67210843 0.66394578
[206,] 0.31112227 0.62224455 0.68887773
[207,] 0.27142151 0.54284301 0.72857849
[208,] 0.23872670 0.47745341 0.76127330
[209,] 0.21072294 0.42144588 0.78927706
[210,] 0.24959207 0.49918414 0.75040793
[211,] 0.22207978 0.44415957 0.77792022
[212,] 0.19429395 0.38858791 0.80570605
[213,] 0.16564533 0.33129066 0.83435467
[214,] 0.15328326 0.30656653 0.84671674
[215,] 0.12559321 0.25118641 0.87440679
[216,] 0.10803188 0.21606376 0.89196812
[217,] 0.08882403 0.17764806 0.91117597
[218,] 0.09589632 0.19179263 0.90410368
[219,] 0.08676803 0.17353606 0.91323197
[220,] 0.06851123 0.13702246 0.93148877
[221,] 0.05339758 0.10679516 0.94660242
[222,] 0.04097339 0.08194678 0.95902661
[223,] 0.03845828 0.07691655 0.96154172
[224,] 0.05506111 0.11012222 0.94493889
[225,] 0.04357244 0.08714489 0.95642756
[226,] 0.13860248 0.27720496 0.86139752
[227,] 0.11136339 0.22272678 0.88863661
[228,] 0.10298412 0.20596824 0.89701588
[229,] 0.09311138 0.18622276 0.90688862
[230,] 0.22765401 0.45530801 0.77234599
[231,] 0.18863019 0.37726039 0.81136981
[232,] 0.29101838 0.58203676 0.70898162
[233,] 0.35952377 0.71904754 0.64047623
[234,] 0.29564781 0.59129561 0.70435219
[235,] 0.25650202 0.51300405 0.74349798
[236,] 0.20868704 0.41737407 0.79131296
[237,] 0.42913052 0.85826103 0.57086948
[238,] 0.70606357 0.58787287 0.29393643
[239,] 0.67623279 0.64753443 0.32376721
[240,] 0.64917522 0.70164955 0.35082478
[241,] 0.68987207 0.62025587 0.31012793
[242,] 0.62986701 0.74026598 0.37013299
[243,] 0.67825488 0.64349024 0.32174512
[244,] 0.79201233 0.41597535 0.20798767
[245,] 0.70547417 0.58905167 0.29452583
[246,] 0.90997738 0.18004524 0.09002262
[247,] 0.82467508 0.35064984 0.17532492
> postscript(file="/var/fisher/rcomp/tmp/1zxnn1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2h2je1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3w75w1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4lcnq1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5v1rj1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
5.18642757 5.07454109 -5.01565493 -3.04036708 -1.03478369 2.86753431
7 8 9 10 11 12
5.97262612 -1.22322900 1.18161397 0.78693309 4.46506527 0.99944685
13 14 15 16 17 18
3.43538171 2.72224836 -3.18561350 -1.79534505 2.57007612 0.92444154
19 20 21 22 23 24
2.27539195 -1.66681452 -2.45669030 -1.91857601 2.32157766 0.48116006
25 26 27 28 29 30
5.01068733 7.32119967 0.81902299 -2.41711530 -0.99517226 0.36840962
31 32 33 34 35 36
-2.32263598 -5.94198374 3.76564974 -1.93609139 1.61129626 -1.69548416
37 38 39 40 41 42
-2.66045035 2.18274127 -4.53493473 0.78474879 3.06357156 0.25587724
43 44 45 46 47 48
3.53604970 1.38021012 6.24373970 3.01463697 1.40026928 -1.50501812
49 50 51 52 53 54
-0.26595115 4.65970393 -4.41238301 -0.40117528 -0.95052155 -2.00580475
55 56 57 58 59 60
-0.79762445 2.23620256 4.45472912 -5.74107779 1.70379496 0.70173293
61 62 63 64 65 66
-0.66762231 3.54199856 -0.72435267 -3.56041268 0.82194863 -0.80180527
67 68 69 70 71 72
1.28174021 -1.67135718 2.47006394 -0.75208499 3.42948209 -4.91583170
73 74 75 76 77 78
-2.91865428 0.01888295 2.55526036 6.51679180 1.12441311 1.12945695
79 80 81 82 83 84
-4.72717466 5.43258942 -2.49686321 -1.05876233 2.98291406 -2.44368265
85 86 87 88 89 90
-0.20232341 1.49799714 -1.50753641 -4.39553867 0.04208351 -3.98280379
91 92 93 94 95 96
2.56487961 -2.57033318 0.46213132 -3.21954427 3.37979481 2.43455074
97 98 99 100 101 102
1.63810215 2.28252320 -3.33142122 2.41653838 2.36074373 -0.41474933
103 104 105 106 107 108
0.32849085 -3.54477391 7.49881981 2.74253776 4.49212959 1.39847820
109 110 111 112 113 114
6.32945509 -4.43440044 -2.66188990 -5.78112991 -1.47032862 2.82168564
115 116 117 118 119 120
2.88549810 -3.68274658 -2.92859957 -1.71679780 2.79801364 -2.12817140
121 122 123 124 125 126
-0.27336470 1.63632895 0.51448102 2.88647343 -2.96939821 5.05345838
127 128 129 130 131 132
6.83785109 -2.65542138 1.50775771 -2.15391574 -4.02587152 3.85495912
133 134 135 136 137 138
-5.05188681 0.98660037 -0.13750227 -0.07333696 -0.71070492 -3.87516363
139 140 141 142 143 144
0.83514807 -3.25647895 -2.82735634 -5.76203824 -4.98634631 1.23753510
145 146 147 148 149 150
7.40388212 0.20430995 1.27482432 -1.15211739 2.38810929 1.91448863
151 152 153 154 155 156
6.71818711 -2.65823600 -1.82602840 4.32245719 0.98183532 2.56487961
157 158 159 160 161 162
-3.47309433 -2.65542138 0.19954501 -3.34381145 -0.78785323 -2.68247574
163 164 165 166 167 168
0.58251490 4.24682036 -2.06103938 -7.17318921 -3.80462726 -3.58128072
169 170 171 172 173 174
-1.32055569 -8.20953328 4.79228122 0.07570910 6.82827220 -0.33074233
175 176 177 178 179 180
4.69126714 -2.96784730 3.87826343 -1.79308249 -2.18481529 2.93651333
181 182 183 184 185 186
0.55267176 2.64494503 -4.83702941 4.39854125 -8.83023485 -2.90710951
187 188 189 190 191 192
1.42793879 6.17726458 -3.13301776 -2.19223198 -2.87546791 0.73442356
193 194 195 196 197 198
-1.66479939 -0.46817766 -1.17887438 0.98491305 1.12878591 -2.64121454
199 200 201 202 203 204
-0.15187615 -7.51820961 -2.19627148 -8.57338120 1.24095841 1.14429481
205 206 207 208 209 210
-1.61572863 -1.26080354 -0.86212446 -0.30105631 2.78155185 -0.09089295
211 212 213 214 215 216
1.08721292 2.54094531 -4.07367085 -3.36692647 0.50851767 -2.31590482
217 218 219 220 221 222
-2.34390072 4.71924105 2.26706263 -0.48024911 1.53117339 2.74171475
223 224 225 226 227 228
0.02602942 -2.37216300 -0.24613249 -2.62681680 -2.71131292 -1.21859251
229 230 231 232 233 234
1.54434243 -0.24636578 1.47883199 -1.94348599 -2.96249882 7.06112663
235 236 237 238 239 240
0.46491330 3.75907816 6.23720941 5.12880801 -4.05304745 5.11521712
241 242 243 244 245 246
-6.19912612 1.34405897 -2.95813363 1.50555927 3.95976933 -4.85392099
247 248 249 250 251 252
-2.52334925 3.83665302 -8.65519149 -6.52347162 -2.86745679 0.44436816
253 254 255 256 257 258
1.86041101 -0.62892012 3.86480100 0.47844909 -0.02552687 -1.86042192
259 260 261 262 263 264
3.04090823 3.67354961 -5.63723495 -1.56252293 6.14105038 -4.43920609
> postscript(file="/var/fisher/rcomp/tmp/651uy1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 5.18642757 NA
1 5.07454109 5.18642757
2 -5.01565493 5.07454109
3 -3.04036708 -5.01565493
4 -1.03478369 -3.04036708
5 2.86753431 -1.03478369
6 5.97262612 2.86753431
7 -1.22322900 5.97262612
8 1.18161397 -1.22322900
9 0.78693309 1.18161397
10 4.46506527 0.78693309
11 0.99944685 4.46506527
12 3.43538171 0.99944685
13 2.72224836 3.43538171
14 -3.18561350 2.72224836
15 -1.79534505 -3.18561350
16 2.57007612 -1.79534505
17 0.92444154 2.57007612
18 2.27539195 0.92444154
19 -1.66681452 2.27539195
20 -2.45669030 -1.66681452
21 -1.91857601 -2.45669030
22 2.32157766 -1.91857601
23 0.48116006 2.32157766
24 5.01068733 0.48116006
25 7.32119967 5.01068733
26 0.81902299 7.32119967
27 -2.41711530 0.81902299
28 -0.99517226 -2.41711530
29 0.36840962 -0.99517226
30 -2.32263598 0.36840962
31 -5.94198374 -2.32263598
32 3.76564974 -5.94198374
33 -1.93609139 3.76564974
34 1.61129626 -1.93609139
35 -1.69548416 1.61129626
36 -2.66045035 -1.69548416
37 2.18274127 -2.66045035
38 -4.53493473 2.18274127
39 0.78474879 -4.53493473
40 3.06357156 0.78474879
41 0.25587724 3.06357156
42 3.53604970 0.25587724
43 1.38021012 3.53604970
44 6.24373970 1.38021012
45 3.01463697 6.24373970
46 1.40026928 3.01463697
47 -1.50501812 1.40026928
48 -0.26595115 -1.50501812
49 4.65970393 -0.26595115
50 -4.41238301 4.65970393
51 -0.40117528 -4.41238301
52 -0.95052155 -0.40117528
53 -2.00580475 -0.95052155
54 -0.79762445 -2.00580475
55 2.23620256 -0.79762445
56 4.45472912 2.23620256
57 -5.74107779 4.45472912
58 1.70379496 -5.74107779
59 0.70173293 1.70379496
60 -0.66762231 0.70173293
61 3.54199856 -0.66762231
62 -0.72435267 3.54199856
63 -3.56041268 -0.72435267
64 0.82194863 -3.56041268
65 -0.80180527 0.82194863
66 1.28174021 -0.80180527
67 -1.67135718 1.28174021
68 2.47006394 -1.67135718
69 -0.75208499 2.47006394
70 3.42948209 -0.75208499
71 -4.91583170 3.42948209
72 -2.91865428 -4.91583170
73 0.01888295 -2.91865428
74 2.55526036 0.01888295
75 6.51679180 2.55526036
76 1.12441311 6.51679180
77 1.12945695 1.12441311
78 -4.72717466 1.12945695
79 5.43258942 -4.72717466
80 -2.49686321 5.43258942
81 -1.05876233 -2.49686321
82 2.98291406 -1.05876233
83 -2.44368265 2.98291406
84 -0.20232341 -2.44368265
85 1.49799714 -0.20232341
86 -1.50753641 1.49799714
87 -4.39553867 -1.50753641
88 0.04208351 -4.39553867
89 -3.98280379 0.04208351
90 2.56487961 -3.98280379
91 -2.57033318 2.56487961
92 0.46213132 -2.57033318
93 -3.21954427 0.46213132
94 3.37979481 -3.21954427
95 2.43455074 3.37979481
96 1.63810215 2.43455074
97 2.28252320 1.63810215
98 -3.33142122 2.28252320
99 2.41653838 -3.33142122
100 2.36074373 2.41653838
101 -0.41474933 2.36074373
102 0.32849085 -0.41474933
103 -3.54477391 0.32849085
104 7.49881981 -3.54477391
105 2.74253776 7.49881981
106 4.49212959 2.74253776
107 1.39847820 4.49212959
108 6.32945509 1.39847820
109 -4.43440044 6.32945509
110 -2.66188990 -4.43440044
111 -5.78112991 -2.66188990
112 -1.47032862 -5.78112991
113 2.82168564 -1.47032862
114 2.88549810 2.82168564
115 -3.68274658 2.88549810
116 -2.92859957 -3.68274658
117 -1.71679780 -2.92859957
118 2.79801364 -1.71679780
119 -2.12817140 2.79801364
120 -0.27336470 -2.12817140
121 1.63632895 -0.27336470
122 0.51448102 1.63632895
123 2.88647343 0.51448102
124 -2.96939821 2.88647343
125 5.05345838 -2.96939821
126 6.83785109 5.05345838
127 -2.65542138 6.83785109
128 1.50775771 -2.65542138
129 -2.15391574 1.50775771
130 -4.02587152 -2.15391574
131 3.85495912 -4.02587152
132 -5.05188681 3.85495912
133 0.98660037 -5.05188681
134 -0.13750227 0.98660037
135 -0.07333696 -0.13750227
136 -0.71070492 -0.07333696
137 -3.87516363 -0.71070492
138 0.83514807 -3.87516363
139 -3.25647895 0.83514807
140 -2.82735634 -3.25647895
141 -5.76203824 -2.82735634
142 -4.98634631 -5.76203824
143 1.23753510 -4.98634631
144 7.40388212 1.23753510
145 0.20430995 7.40388212
146 1.27482432 0.20430995
147 -1.15211739 1.27482432
148 2.38810929 -1.15211739
149 1.91448863 2.38810929
150 6.71818711 1.91448863
151 -2.65823600 6.71818711
152 -1.82602840 -2.65823600
153 4.32245719 -1.82602840
154 0.98183532 4.32245719
155 2.56487961 0.98183532
156 -3.47309433 2.56487961
157 -2.65542138 -3.47309433
158 0.19954501 -2.65542138
159 -3.34381145 0.19954501
160 -0.78785323 -3.34381145
161 -2.68247574 -0.78785323
162 0.58251490 -2.68247574
163 4.24682036 0.58251490
164 -2.06103938 4.24682036
165 -7.17318921 -2.06103938
166 -3.80462726 -7.17318921
167 -3.58128072 -3.80462726
168 -1.32055569 -3.58128072
169 -8.20953328 -1.32055569
170 4.79228122 -8.20953328
171 0.07570910 4.79228122
172 6.82827220 0.07570910
173 -0.33074233 6.82827220
174 4.69126714 -0.33074233
175 -2.96784730 4.69126714
176 3.87826343 -2.96784730
177 -1.79308249 3.87826343
178 -2.18481529 -1.79308249
179 2.93651333 -2.18481529
180 0.55267176 2.93651333
181 2.64494503 0.55267176
182 -4.83702941 2.64494503
183 4.39854125 -4.83702941
184 -8.83023485 4.39854125
185 -2.90710951 -8.83023485
186 1.42793879 -2.90710951
187 6.17726458 1.42793879
188 -3.13301776 6.17726458
189 -2.19223198 -3.13301776
190 -2.87546791 -2.19223198
191 0.73442356 -2.87546791
192 -1.66479939 0.73442356
193 -0.46817766 -1.66479939
194 -1.17887438 -0.46817766
195 0.98491305 -1.17887438
196 1.12878591 0.98491305
197 -2.64121454 1.12878591
198 -0.15187615 -2.64121454
199 -7.51820961 -0.15187615
200 -2.19627148 -7.51820961
201 -8.57338120 -2.19627148
202 1.24095841 -8.57338120
203 1.14429481 1.24095841
204 -1.61572863 1.14429481
205 -1.26080354 -1.61572863
206 -0.86212446 -1.26080354
207 -0.30105631 -0.86212446
208 2.78155185 -0.30105631
209 -0.09089295 2.78155185
210 1.08721292 -0.09089295
211 2.54094531 1.08721292
212 -4.07367085 2.54094531
213 -3.36692647 -4.07367085
214 0.50851767 -3.36692647
215 -2.31590482 0.50851767
216 -2.34390072 -2.31590482
217 4.71924105 -2.34390072
218 2.26706263 4.71924105
219 -0.48024911 2.26706263
220 1.53117339 -0.48024911
221 2.74171475 1.53117339
222 0.02602942 2.74171475
223 -2.37216300 0.02602942
224 -0.24613249 -2.37216300
225 -2.62681680 -0.24613249
226 -2.71131292 -2.62681680
227 -1.21859251 -2.71131292
228 1.54434243 -1.21859251
229 -0.24636578 1.54434243
230 1.47883199 -0.24636578
231 -1.94348599 1.47883199
232 -2.96249882 -1.94348599
233 7.06112663 -2.96249882
234 0.46491330 7.06112663
235 3.75907816 0.46491330
236 6.23720941 3.75907816
237 5.12880801 6.23720941
238 -4.05304745 5.12880801
239 5.11521712 -4.05304745
240 -6.19912612 5.11521712
241 1.34405897 -6.19912612
242 -2.95813363 1.34405897
243 1.50555927 -2.95813363
244 3.95976933 1.50555927
245 -4.85392099 3.95976933
246 -2.52334925 -4.85392099
247 3.83665302 -2.52334925
248 -8.65519149 3.83665302
249 -6.52347162 -8.65519149
250 -2.86745679 -6.52347162
251 0.44436816 -2.86745679
252 1.86041101 0.44436816
253 -0.62892012 1.86041101
254 3.86480100 -0.62892012
255 0.47844909 3.86480100
256 -0.02552687 0.47844909
257 -1.86042192 -0.02552687
258 3.04090823 -1.86042192
259 3.67354961 3.04090823
260 -5.63723495 3.67354961
261 -1.56252293 -5.63723495
262 6.14105038 -1.56252293
263 -4.43920609 6.14105038
264 NA -4.43920609
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 5.07454109 5.18642757
[2,] -5.01565493 5.07454109
[3,] -3.04036708 -5.01565493
[4,] -1.03478369 -3.04036708
[5,] 2.86753431 -1.03478369
[6,] 5.97262612 2.86753431
[7,] -1.22322900 5.97262612
[8,] 1.18161397 -1.22322900
[9,] 0.78693309 1.18161397
[10,] 4.46506527 0.78693309
[11,] 0.99944685 4.46506527
[12,] 3.43538171 0.99944685
[13,] 2.72224836 3.43538171
[14,] -3.18561350 2.72224836
[15,] -1.79534505 -3.18561350
[16,] 2.57007612 -1.79534505
[17,] 0.92444154 2.57007612
[18,] 2.27539195 0.92444154
[19,] -1.66681452 2.27539195
[20,] -2.45669030 -1.66681452
[21,] -1.91857601 -2.45669030
[22,] 2.32157766 -1.91857601
[23,] 0.48116006 2.32157766
[24,] 5.01068733 0.48116006
[25,] 7.32119967 5.01068733
[26,] 0.81902299 7.32119967
[27,] -2.41711530 0.81902299
[28,] -0.99517226 -2.41711530
[29,] 0.36840962 -0.99517226
[30,] -2.32263598 0.36840962
[31,] -5.94198374 -2.32263598
[32,] 3.76564974 -5.94198374
[33,] -1.93609139 3.76564974
[34,] 1.61129626 -1.93609139
[35,] -1.69548416 1.61129626
[36,] -2.66045035 -1.69548416
[37,] 2.18274127 -2.66045035
[38,] -4.53493473 2.18274127
[39,] 0.78474879 -4.53493473
[40,] 3.06357156 0.78474879
[41,] 0.25587724 3.06357156
[42,] 3.53604970 0.25587724
[43,] 1.38021012 3.53604970
[44,] 6.24373970 1.38021012
[45,] 3.01463697 6.24373970
[46,] 1.40026928 3.01463697
[47,] -1.50501812 1.40026928
[48,] -0.26595115 -1.50501812
[49,] 4.65970393 -0.26595115
[50,] -4.41238301 4.65970393
[51,] -0.40117528 -4.41238301
[52,] -0.95052155 -0.40117528
[53,] -2.00580475 -0.95052155
[54,] -0.79762445 -2.00580475
[55,] 2.23620256 -0.79762445
[56,] 4.45472912 2.23620256
[57,] -5.74107779 4.45472912
[58,] 1.70379496 -5.74107779
[59,] 0.70173293 1.70379496
[60,] -0.66762231 0.70173293
[61,] 3.54199856 -0.66762231
[62,] -0.72435267 3.54199856
[63,] -3.56041268 -0.72435267
[64,] 0.82194863 -3.56041268
[65,] -0.80180527 0.82194863
[66,] 1.28174021 -0.80180527
[67,] -1.67135718 1.28174021
[68,] 2.47006394 -1.67135718
[69,] -0.75208499 2.47006394
[70,] 3.42948209 -0.75208499
[71,] -4.91583170 3.42948209
[72,] -2.91865428 -4.91583170
[73,] 0.01888295 -2.91865428
[74,] 2.55526036 0.01888295
[75,] 6.51679180 2.55526036
[76,] 1.12441311 6.51679180
[77,] 1.12945695 1.12441311
[78,] -4.72717466 1.12945695
[79,] 5.43258942 -4.72717466
[80,] -2.49686321 5.43258942
[81,] -1.05876233 -2.49686321
[82,] 2.98291406 -1.05876233
[83,] -2.44368265 2.98291406
[84,] -0.20232341 -2.44368265
[85,] 1.49799714 -0.20232341
[86,] -1.50753641 1.49799714
[87,] -4.39553867 -1.50753641
[88,] 0.04208351 -4.39553867
[89,] -3.98280379 0.04208351
[90,] 2.56487961 -3.98280379
[91,] -2.57033318 2.56487961
[92,] 0.46213132 -2.57033318
[93,] -3.21954427 0.46213132
[94,] 3.37979481 -3.21954427
[95,] 2.43455074 3.37979481
[96,] 1.63810215 2.43455074
[97,] 2.28252320 1.63810215
[98,] -3.33142122 2.28252320
[99,] 2.41653838 -3.33142122
[100,] 2.36074373 2.41653838
[101,] -0.41474933 2.36074373
[102,] 0.32849085 -0.41474933
[103,] -3.54477391 0.32849085
[104,] 7.49881981 -3.54477391
[105,] 2.74253776 7.49881981
[106,] 4.49212959 2.74253776
[107,] 1.39847820 4.49212959
[108,] 6.32945509 1.39847820
[109,] -4.43440044 6.32945509
[110,] -2.66188990 -4.43440044
[111,] -5.78112991 -2.66188990
[112,] -1.47032862 -5.78112991
[113,] 2.82168564 -1.47032862
[114,] 2.88549810 2.82168564
[115,] -3.68274658 2.88549810
[116,] -2.92859957 -3.68274658
[117,] -1.71679780 -2.92859957
[118,] 2.79801364 -1.71679780
[119,] -2.12817140 2.79801364
[120,] -0.27336470 -2.12817140
[121,] 1.63632895 -0.27336470
[122,] 0.51448102 1.63632895
[123,] 2.88647343 0.51448102
[124,] -2.96939821 2.88647343
[125,] 5.05345838 -2.96939821
[126,] 6.83785109 5.05345838
[127,] -2.65542138 6.83785109
[128,] 1.50775771 -2.65542138
[129,] -2.15391574 1.50775771
[130,] -4.02587152 -2.15391574
[131,] 3.85495912 -4.02587152
[132,] -5.05188681 3.85495912
[133,] 0.98660037 -5.05188681
[134,] -0.13750227 0.98660037
[135,] -0.07333696 -0.13750227
[136,] -0.71070492 -0.07333696
[137,] -3.87516363 -0.71070492
[138,] 0.83514807 -3.87516363
[139,] -3.25647895 0.83514807
[140,] -2.82735634 -3.25647895
[141,] -5.76203824 -2.82735634
[142,] -4.98634631 -5.76203824
[143,] 1.23753510 -4.98634631
[144,] 7.40388212 1.23753510
[145,] 0.20430995 7.40388212
[146,] 1.27482432 0.20430995
[147,] -1.15211739 1.27482432
[148,] 2.38810929 -1.15211739
[149,] 1.91448863 2.38810929
[150,] 6.71818711 1.91448863
[151,] -2.65823600 6.71818711
[152,] -1.82602840 -2.65823600
[153,] 4.32245719 -1.82602840
[154,] 0.98183532 4.32245719
[155,] 2.56487961 0.98183532
[156,] -3.47309433 2.56487961
[157,] -2.65542138 -3.47309433
[158,] 0.19954501 -2.65542138
[159,] -3.34381145 0.19954501
[160,] -0.78785323 -3.34381145
[161,] -2.68247574 -0.78785323
[162,] 0.58251490 -2.68247574
[163,] 4.24682036 0.58251490
[164,] -2.06103938 4.24682036
[165,] -7.17318921 -2.06103938
[166,] -3.80462726 -7.17318921
[167,] -3.58128072 -3.80462726
[168,] -1.32055569 -3.58128072
[169,] -8.20953328 -1.32055569
[170,] 4.79228122 -8.20953328
[171,] 0.07570910 4.79228122
[172,] 6.82827220 0.07570910
[173,] -0.33074233 6.82827220
[174,] 4.69126714 -0.33074233
[175,] -2.96784730 4.69126714
[176,] 3.87826343 -2.96784730
[177,] -1.79308249 3.87826343
[178,] -2.18481529 -1.79308249
[179,] 2.93651333 -2.18481529
[180,] 0.55267176 2.93651333
[181,] 2.64494503 0.55267176
[182,] -4.83702941 2.64494503
[183,] 4.39854125 -4.83702941
[184,] -8.83023485 4.39854125
[185,] -2.90710951 -8.83023485
[186,] 1.42793879 -2.90710951
[187,] 6.17726458 1.42793879
[188,] -3.13301776 6.17726458
[189,] -2.19223198 -3.13301776
[190,] -2.87546791 -2.19223198
[191,] 0.73442356 -2.87546791
[192,] -1.66479939 0.73442356
[193,] -0.46817766 -1.66479939
[194,] -1.17887438 -0.46817766
[195,] 0.98491305 -1.17887438
[196,] 1.12878591 0.98491305
[197,] -2.64121454 1.12878591
[198,] -0.15187615 -2.64121454
[199,] -7.51820961 -0.15187615
[200,] -2.19627148 -7.51820961
[201,] -8.57338120 -2.19627148
[202,] 1.24095841 -8.57338120
[203,] 1.14429481 1.24095841
[204,] -1.61572863 1.14429481
[205,] -1.26080354 -1.61572863
[206,] -0.86212446 -1.26080354
[207,] -0.30105631 -0.86212446
[208,] 2.78155185 -0.30105631
[209,] -0.09089295 2.78155185
[210,] 1.08721292 -0.09089295
[211,] 2.54094531 1.08721292
[212,] -4.07367085 2.54094531
[213,] -3.36692647 -4.07367085
[214,] 0.50851767 -3.36692647
[215,] -2.31590482 0.50851767
[216,] -2.34390072 -2.31590482
[217,] 4.71924105 -2.34390072
[218,] 2.26706263 4.71924105
[219,] -0.48024911 2.26706263
[220,] 1.53117339 -0.48024911
[221,] 2.74171475 1.53117339
[222,] 0.02602942 2.74171475
[223,] -2.37216300 0.02602942
[224,] -0.24613249 -2.37216300
[225,] -2.62681680 -0.24613249
[226,] -2.71131292 -2.62681680
[227,] -1.21859251 -2.71131292
[228,] 1.54434243 -1.21859251
[229,] -0.24636578 1.54434243
[230,] 1.47883199 -0.24636578
[231,] -1.94348599 1.47883199
[232,] -2.96249882 -1.94348599
[233,] 7.06112663 -2.96249882
[234,] 0.46491330 7.06112663
[235,] 3.75907816 0.46491330
[236,] 6.23720941 3.75907816
[237,] 5.12880801 6.23720941
[238,] -4.05304745 5.12880801
[239,] 5.11521712 -4.05304745
[240,] -6.19912612 5.11521712
[241,] 1.34405897 -6.19912612
[242,] -2.95813363 1.34405897
[243,] 1.50555927 -2.95813363
[244,] 3.95976933 1.50555927
[245,] -4.85392099 3.95976933
[246,] -2.52334925 -4.85392099
[247,] 3.83665302 -2.52334925
[248,] -8.65519149 3.83665302
[249,] -6.52347162 -8.65519149
[250,] -2.86745679 -6.52347162
[251,] 0.44436816 -2.86745679
[252,] 1.86041101 0.44436816
[253,] -0.62892012 1.86041101
[254,] 3.86480100 -0.62892012
[255,] 0.47844909 3.86480100
[256,] -0.02552687 0.47844909
[257,] -1.86042192 -0.02552687
[258,] 3.04090823 -1.86042192
[259,] 3.67354961 3.04090823
[260,] -5.63723495 3.67354961
[261,] -1.56252293 -5.63723495
[262,] 6.14105038 -1.56252293
[263,] -4.43920609 6.14105038
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 5.07454109 5.18642757
2 -5.01565493 5.07454109
3 -3.04036708 -5.01565493
4 -1.03478369 -3.04036708
5 2.86753431 -1.03478369
6 5.97262612 2.86753431
7 -1.22322900 5.97262612
8 1.18161397 -1.22322900
9 0.78693309 1.18161397
10 4.46506527 0.78693309
11 0.99944685 4.46506527
12 3.43538171 0.99944685
13 2.72224836 3.43538171
14 -3.18561350 2.72224836
15 -1.79534505 -3.18561350
16 2.57007612 -1.79534505
17 0.92444154 2.57007612
18 2.27539195 0.92444154
19 -1.66681452 2.27539195
20 -2.45669030 -1.66681452
21 -1.91857601 -2.45669030
22 2.32157766 -1.91857601
23 0.48116006 2.32157766
24 5.01068733 0.48116006
25 7.32119967 5.01068733
26 0.81902299 7.32119967
27 -2.41711530 0.81902299
28 -0.99517226 -2.41711530
29 0.36840962 -0.99517226
30 -2.32263598 0.36840962
31 -5.94198374 -2.32263598
32 3.76564974 -5.94198374
33 -1.93609139 3.76564974
34 1.61129626 -1.93609139
35 -1.69548416 1.61129626
36 -2.66045035 -1.69548416
37 2.18274127 -2.66045035
38 -4.53493473 2.18274127
39 0.78474879 -4.53493473
40 3.06357156 0.78474879
41 0.25587724 3.06357156
42 3.53604970 0.25587724
43 1.38021012 3.53604970
44 6.24373970 1.38021012
45 3.01463697 6.24373970
46 1.40026928 3.01463697
47 -1.50501812 1.40026928
48 -0.26595115 -1.50501812
49 4.65970393 -0.26595115
50 -4.41238301 4.65970393
51 -0.40117528 -4.41238301
52 -0.95052155 -0.40117528
53 -2.00580475 -0.95052155
54 -0.79762445 -2.00580475
55 2.23620256 -0.79762445
56 4.45472912 2.23620256
57 -5.74107779 4.45472912
58 1.70379496 -5.74107779
59 0.70173293 1.70379496
60 -0.66762231 0.70173293
61 3.54199856 -0.66762231
62 -0.72435267 3.54199856
63 -3.56041268 -0.72435267
64 0.82194863 -3.56041268
65 -0.80180527 0.82194863
66 1.28174021 -0.80180527
67 -1.67135718 1.28174021
68 2.47006394 -1.67135718
69 -0.75208499 2.47006394
70 3.42948209 -0.75208499
71 -4.91583170 3.42948209
72 -2.91865428 -4.91583170
73 0.01888295 -2.91865428
74 2.55526036 0.01888295
75 6.51679180 2.55526036
76 1.12441311 6.51679180
77 1.12945695 1.12441311
78 -4.72717466 1.12945695
79 5.43258942 -4.72717466
80 -2.49686321 5.43258942
81 -1.05876233 -2.49686321
82 2.98291406 -1.05876233
83 -2.44368265 2.98291406
84 -0.20232341 -2.44368265
85 1.49799714 -0.20232341
86 -1.50753641 1.49799714
87 -4.39553867 -1.50753641
88 0.04208351 -4.39553867
89 -3.98280379 0.04208351
90 2.56487961 -3.98280379
91 -2.57033318 2.56487961
92 0.46213132 -2.57033318
93 -3.21954427 0.46213132
94 3.37979481 -3.21954427
95 2.43455074 3.37979481
96 1.63810215 2.43455074
97 2.28252320 1.63810215
98 -3.33142122 2.28252320
99 2.41653838 -3.33142122
100 2.36074373 2.41653838
101 -0.41474933 2.36074373
102 0.32849085 -0.41474933
103 -3.54477391 0.32849085
104 7.49881981 -3.54477391
105 2.74253776 7.49881981
106 4.49212959 2.74253776
107 1.39847820 4.49212959
108 6.32945509 1.39847820
109 -4.43440044 6.32945509
110 -2.66188990 -4.43440044
111 -5.78112991 -2.66188990
112 -1.47032862 -5.78112991
113 2.82168564 -1.47032862
114 2.88549810 2.82168564
115 -3.68274658 2.88549810
116 -2.92859957 -3.68274658
117 -1.71679780 -2.92859957
118 2.79801364 -1.71679780
119 -2.12817140 2.79801364
120 -0.27336470 -2.12817140
121 1.63632895 -0.27336470
122 0.51448102 1.63632895
123 2.88647343 0.51448102
124 -2.96939821 2.88647343
125 5.05345838 -2.96939821
126 6.83785109 5.05345838
127 -2.65542138 6.83785109
128 1.50775771 -2.65542138
129 -2.15391574 1.50775771
130 -4.02587152 -2.15391574
131 3.85495912 -4.02587152
132 -5.05188681 3.85495912
133 0.98660037 -5.05188681
134 -0.13750227 0.98660037
135 -0.07333696 -0.13750227
136 -0.71070492 -0.07333696
137 -3.87516363 -0.71070492
138 0.83514807 -3.87516363
139 -3.25647895 0.83514807
140 -2.82735634 -3.25647895
141 -5.76203824 -2.82735634
142 -4.98634631 -5.76203824
143 1.23753510 -4.98634631
144 7.40388212 1.23753510
145 0.20430995 7.40388212
146 1.27482432 0.20430995
147 -1.15211739 1.27482432
148 2.38810929 -1.15211739
149 1.91448863 2.38810929
150 6.71818711 1.91448863
151 -2.65823600 6.71818711
152 -1.82602840 -2.65823600
153 4.32245719 -1.82602840
154 0.98183532 4.32245719
155 2.56487961 0.98183532
156 -3.47309433 2.56487961
157 -2.65542138 -3.47309433
158 0.19954501 -2.65542138
159 -3.34381145 0.19954501
160 -0.78785323 -3.34381145
161 -2.68247574 -0.78785323
162 0.58251490 -2.68247574
163 4.24682036 0.58251490
164 -2.06103938 4.24682036
165 -7.17318921 -2.06103938
166 -3.80462726 -7.17318921
167 -3.58128072 -3.80462726
168 -1.32055569 -3.58128072
169 -8.20953328 -1.32055569
170 4.79228122 -8.20953328
171 0.07570910 4.79228122
172 6.82827220 0.07570910
173 -0.33074233 6.82827220
174 4.69126714 -0.33074233
175 -2.96784730 4.69126714
176 3.87826343 -2.96784730
177 -1.79308249 3.87826343
178 -2.18481529 -1.79308249
179 2.93651333 -2.18481529
180 0.55267176 2.93651333
181 2.64494503 0.55267176
182 -4.83702941 2.64494503
183 4.39854125 -4.83702941
184 -8.83023485 4.39854125
185 -2.90710951 -8.83023485
186 1.42793879 -2.90710951
187 6.17726458 1.42793879
188 -3.13301776 6.17726458
189 -2.19223198 -3.13301776
190 -2.87546791 -2.19223198
191 0.73442356 -2.87546791
192 -1.66479939 0.73442356
193 -0.46817766 -1.66479939
194 -1.17887438 -0.46817766
195 0.98491305 -1.17887438
196 1.12878591 0.98491305
197 -2.64121454 1.12878591
198 -0.15187615 -2.64121454
199 -7.51820961 -0.15187615
200 -2.19627148 -7.51820961
201 -8.57338120 -2.19627148
202 1.24095841 -8.57338120
203 1.14429481 1.24095841
204 -1.61572863 1.14429481
205 -1.26080354 -1.61572863
206 -0.86212446 -1.26080354
207 -0.30105631 -0.86212446
208 2.78155185 -0.30105631
209 -0.09089295 2.78155185
210 1.08721292 -0.09089295
211 2.54094531 1.08721292
212 -4.07367085 2.54094531
213 -3.36692647 -4.07367085
214 0.50851767 -3.36692647
215 -2.31590482 0.50851767
216 -2.34390072 -2.31590482
217 4.71924105 -2.34390072
218 2.26706263 4.71924105
219 -0.48024911 2.26706263
220 1.53117339 -0.48024911
221 2.74171475 1.53117339
222 0.02602942 2.74171475
223 -2.37216300 0.02602942
224 -0.24613249 -2.37216300
225 -2.62681680 -0.24613249
226 -2.71131292 -2.62681680
227 -1.21859251 -2.71131292
228 1.54434243 -1.21859251
229 -0.24636578 1.54434243
230 1.47883199 -0.24636578
231 -1.94348599 1.47883199
232 -2.96249882 -1.94348599
233 7.06112663 -2.96249882
234 0.46491330 7.06112663
235 3.75907816 0.46491330
236 6.23720941 3.75907816
237 5.12880801 6.23720941
238 -4.05304745 5.12880801
239 5.11521712 -4.05304745
240 -6.19912612 5.11521712
241 1.34405897 -6.19912612
242 -2.95813363 1.34405897
243 1.50555927 -2.95813363
244 3.95976933 1.50555927
245 -4.85392099 3.95976933
246 -2.52334925 -4.85392099
247 3.83665302 -2.52334925
248 -8.65519149 3.83665302
249 -6.52347162 -8.65519149
250 -2.86745679 -6.52347162
251 0.44436816 -2.86745679
252 1.86041101 0.44436816
253 -0.62892012 1.86041101
254 3.86480100 -0.62892012
255 0.47844909 3.86480100
256 -0.02552687 0.47844909
257 -1.86042192 -0.02552687
258 3.04090823 -1.86042192
259 3.67354961 3.04090823
260 -5.63723495 3.67354961
261 -1.56252293 -5.63723495
262 6.14105038 -1.56252293
263 -4.43920609 6.14105038
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7rpx91383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8qb7x1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9a7ag1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/105ann1383469242.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11sf7e1383469242.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1262h61383469242.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13sfa61383469243.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14bs3c1383469243.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/156fe51383469243.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16mrl31383469243.tab")
+ }
>
> try(system("convert tmp/1zxnn1383469242.ps tmp/1zxnn1383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/2h2je1383469242.ps tmp/2h2je1383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w75w1383469242.ps tmp/3w75w1383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lcnq1383469242.ps tmp/4lcnq1383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/5v1rj1383469242.ps tmp/5v1rj1383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/651uy1383469242.ps tmp/651uy1383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rpx91383469242.ps tmp/7rpx91383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qb7x1383469242.ps tmp/8qb7x1383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/9a7ag1383469242.ps tmp/9a7ag1383469242.png",intern=TRUE))
character(0)
> try(system("convert tmp/105ann1383469242.ps tmp/105ann1383469242.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.259 1.911 13.161