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 = '6'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '6'
> #'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
Depression Connected Separate Learning Software Happiness
1 12.0 41 38 13 12 14
2 11.0 39 32 16 11 18
3 14.0 30 35 19 15 11
4 12.0 31 33 15 6 12
5 21.0 34 37 14 13 16
6 12.0 35 29 13 10 18
7 22.0 39 31 19 12 14
8 11.0 34 36 15 14 14
9 10.0 36 35 14 12 15
10 13.0 37 38 15 9 15
11 10.0 38 31 16 10 17
12 8.0 36 34 16 12 19
13 15.0 38 35 16 12 10
14 14.0 39 38 16 11 16
15 10.0 33 37 17 15 18
16 14.0 32 33 15 12 14
17 14.0 36 32 15 10 14
18 11.0 38 38 20 12 17
19 10.0 39 38 18 11 14
20 13.0 32 32 16 12 16
21 9.5 32 33 16 11 18
22 14.0 31 31 16 12 11
23 12.0 39 38 19 13 14
24 14.0 37 39 16 11 12
25 11.0 39 32 17 12 17
26 9.0 41 32 17 13 9
27 11.0 36 35 16 10 16
28 15.0 33 37 15 14 14
29 14.0 33 33 16 12 15
30 13.0 34 33 14 10 11
31 9.0 31 31 15 12 16
32 15.0 27 32 12 8 13
33 10.0 37 31 14 10 17
34 11.0 34 37 16 12 15
35 13.0 34 30 14 12 14
36 8.0 32 33 10 7 16
37 20.0 29 31 10 9 9
38 12.0 36 33 14 12 15
39 10.0 29 31 16 10 17
40 10.0 35 33 16 10 13
41 9.0 37 32 16 10 15
42 14.0 34 33 14 12 16
43 8.0 38 32 20 15 16
44 14.0 35 33 14 10 12
45 11.0 38 28 14 10 15
46 13.0 37 35 11 12 11
47 9.0 38 39 14 13 15
48 11.0 33 34 15 11 15
49 15.0 36 38 16 11 17
50 11.0 38 32 14 12 13
51 10.0 32 38 16 14 16
52 14.0 32 30 14 10 14
53 18.0 32 33 12 12 11
54 14.0 34 38 16 13 12
55 11.0 32 32 9 5 12
56 14.5 37 35 14 6 15
57 13.0 39 34 16 12 16
58 9.0 29 34 16 12 15
59 10.0 37 36 15 11 12
60 15.0 35 34 16 10 12
61 20.0 30 28 12 7 8
62 12.0 38 34 16 12 13
63 12.0 34 35 16 14 11
64 14.0 31 35 14 11 14
65 13.0 34 31 16 12 15
66 11.0 35 37 17 13 10
67 17.0 36 35 18 14 11
68 12.0 30 27 18 11 12
69 13.0 39 40 12 12 15
70 14.0 35 37 16 12 15
71 13.0 38 36 10 8 14
72 15.0 31 38 14 11 16
73 13.0 34 39 18 14 15
74 10.0 38 41 18 14 15
75 11.0 34 27 16 12 13
76 19.0 39 30 17 9 12
77 13.0 37 37 16 13 17
78 17.0 34 31 16 11 13
79 13.0 28 31 13 12 15
80 9.0 37 27 16 12 13
81 11.0 33 36 16 12 15
82 9.0 35 37 16 12 15
83 12.0 37 33 15 12 16
84 12.0 32 34 15 11 15
85 13.0 33 31 16 10 14
86 13.0 38 39 14 9 15
87 12.0 33 34 16 12 14
88 15.0 29 32 16 12 13
89 22.0 33 33 15 12 7
90 13.0 31 36 12 9 17
91 15.0 36 32 17 15 13
92 13.0 35 41 16 12 15
93 15.0 32 28 15 12 14
94 12.5 29 30 13 12 13
95 11.0 39 36 16 10 16
96 16.0 37 35 16 13 12
97 11.0 35 31 16 9 14
98 11.0 37 34 16 12 17
99 10.0 32 36 14 10 15
100 10.0 38 36 16 14 17
101 16.0 37 35 16 11 12
102 12.0 36 37 20 15 16
103 11.0 32 28 15 11 11
104 16.0 33 39 16 11 15
105 19.0 40 32 13 12 9
106 11.0 38 35 17 12 16
107 16.0 41 39 16 12 15
108 15.0 36 35 16 11 10
109 24.0 43 42 12 7 10
110 14.0 30 34 16 12 15
111 15.0 31 33 16 14 11
112 11.0 32 41 17 11 13
113 15.0 32 33 13 11 14
114 12.0 37 34 12 10 18
115 10.0 37 32 18 13 16
116 14.0 33 40 14 13 14
117 13.0 34 40 14 8 14
118 9.0 33 35 13 11 14
119 15.0 38 36 16 12 14
120 15.0 33 37 13 11 12
121 14.0 31 27 16 13 14
122 11.0 38 39 13 12 15
123 8.0 37 38 16 14 15
124 11.0 36 31 15 13 15
125 11.0 31 33 16 15 13
126 8.0 39 32 15 10 17
127 10.0 44 39 17 11 17
128 11.0 33 36 15 9 19
129 13.0 35 33 12 11 15
130 11.0 32 33 16 10 13
131 20.0 28 32 10 11 9
132 10.0 40 37 16 8 15
133 15.0 27 30 12 11 15
134 12.0 37 38 14 12 15
135 14.0 32 29 15 12 16
136 23.0 28 22 13 9 11
137 14.0 34 35 15 11 14
138 16.0 30 35 11 10 11
139 11.0 35 34 12 8 15
140 12.0 31 35 11 9 13
141 10.0 32 34 16 8 15
142 14.0 30 37 15 9 16
143 12.0 30 35 17 15 14
144 12.0 31 23 16 11 15
145 11.0 40 31 10 8 16
146 12.0 32 27 18 13 16
147 13.0 36 36 13 12 11
148 11.0 32 31 16 12 12
149 19.0 35 32 13 9 9
150 12.0 38 39 10 7 16
151 17.0 42 37 15 13 13
152 9.0 34 38 16 9 16
153 12.0 35 39 16 6 12
154 19.0 38 34 14 8 9
155 18.0 33 31 10 8 13
156 15.0 36 32 17 15 13
157 14.0 32 37 13 6 14
158 11.0 33 36 15 9 19
159 9.0 34 32 16 11 13
160 18.0 32 38 12 8 12
161 16.0 34 36 13 8 13
162 24.0 27 26 13 10 10
163 14.0 31 26 12 8 14
164 20.0 38 33 17 14 16
165 18.0 34 39 15 10 10
166 23.0 24 30 10 8 11
167 12.0 30 33 14 11 14
168 14.0 26 25 11 12 12
169 16.0 34 38 13 12 9
170 18.0 27 37 16 12 9
171 20.0 37 31 12 5 11
172 12.0 36 37 16 12 16
173 12.0 41 35 12 10 9
174 17.0 29 25 9 7 13
175 13.0 36 28 12 12 16
176 9.0 32 35 15 11 13
177 16.0 37 33 12 8 9
178 18.0 30 30 12 9 12
179 10.0 31 31 14 10 16
180 14.0 38 37 12 9 11
181 11.0 36 36 16 12 14
182 9.0 35 30 11 6 13
183 11.0 31 36 19 15 15
184 10.0 38 32 15 12 14
185 11.0 22 28 8 12 16
186 19.0 32 36 16 12 13
187 14.0 36 34 17 11 14
188 12.0 39 31 12 7 15
189 14.0 28 28 11 7 13
190 21.0 32 36 11 5 11
191 13.0 32 36 14 12 11
192 10.0 38 40 16 12 14
193 15.0 32 33 12 3 15
194 16.0 35 37 16 11 11
195 14.0 32 32 13 10 15
196 12.0 37 38 15 12 12
197 19.0 34 31 16 9 14
198 15.0 33 37 16 12 14
199 19.0 33 33 14 9 8
200 13.0 26 32 16 12 13
201 17.0 30 30 16 12 9
202 12.0 24 30 14 10 15
203 11.0 34 31 11 9 17
204 14.0 34 32 12 12 13
205 11.0 33 34 15 8 15
206 13.0 34 36 15 11 15
207 12.0 35 37 16 11 14
208 15.0 35 36 16 12 16
209 14.0 36 33 11 10 13
210 12.0 34 33 15 10 16
211 17.0 34 33 12 12 9
212 11.0 41 44 12 12 16
213 18.0 32 39 15 11 11
214 13.0 30 32 15 8 10
215 17.0 35 35 16 12 11
216 13.0 28 25 14 10 15
217 11.0 33 35 17 11 17
218 12.0 39 34 14 10 14
219 22.0 36 35 13 8 8
220 14.0 36 39 15 12 15
221 12.0 35 33 13 12 11
222 12.0 38 36 14 10 16
223 17.0 33 32 15 12 10
224 9.0 31 32 12 9 15
225 21.0 34 36 13 9 9
226 10.0 32 36 8 6 16
227 11.0 31 32 14 10 19
228 12.0 33 34 14 9 12
229 23.0 34 33 11 9 8
230 13.0 34 35 12 9 11
231 12.0 34 30 13 6 14
232 16.0 33 38 10 10 9
233 9.0 32 34 16 6 15
234 17.0 41 33 18 14 13
235 9.0 34 32 13 10 16
236 14.0 36 31 11 10 11
237 17.0 37 30 4 6 12
238 13.0 36 27 13 12 13
239 11.0 29 31 16 12 10
240 12.0 37 30 10 7 11
241 10.0 27 32 12 8 12
242 19.0 35 35 12 11 8
243 16.0 28 28 10 3 12
244 16.0 35 33 13 6 12
245 14.0 37 31 15 10 15
246 20.0 29 35 12 8 11
247 15.0 32 35 14 9 13
248 23.0 36 32 10 9 14
249 20.0 19 21 12 8 10
250 16.0 21 20 12 9 12
251 14.0 31 34 11 7 15
252 17.0 33 32 10 7 13
253 11.0 36 34 12 6 13
254 13.0 33 32 16 9 13
255 17.0 37 33 12 10 12
256 15.0 34 33 14 11 12
257 21.0 35 37 16 12 9
258 18.0 31 32 14 8 9
259 15.0 37 34 13 11 15
260 8.0 35 30 4 3 10
261 12.0 27 30 15 11 14
262 12.0 34 38 11 12 15
263 22.0 40 36 11 7 7
264 12.0 29 32 14 9 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Happiness
27.175257 -0.042872 0.002802 -0.098284 -0.034729 -0.771941
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.5420 -1.7393 -0.0993 1.6779 9.5012
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 27.175257 2.024928 13.420 <2e-16 ***
Connected -0.042872 0.051982 -0.825 0.410
Separate 0.002802 0.053548 0.052 0.958
Learning -0.098284 0.093428 -1.052 0.294
Software -0.034729 0.096238 -0.361 0.718
Happiness -0.771941 0.072254 -10.684 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.824 on 258 degrees of freedom
Multiple R-squared: 0.35, Adjusted R-squared: 0.3374
F-statistic: 27.79 on 5 and 258 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.998635161 0.002729678 0.001364839
[2,] 0.997143565 0.005712871 0.002856435
[3,] 0.997434991 0.005130019 0.002565009
[4,] 0.998243736 0.003512529 0.001756264
[5,] 0.996630785 0.006738429 0.003369215
[6,] 0.993842147 0.012315705 0.006157853
[7,] 0.990815055 0.018369889 0.009184945
[8,] 0.985215840 0.029568319 0.014784160
[9,] 0.976053066 0.047893868 0.023946934
[10,] 0.966908899 0.066182202 0.033091101
[11,] 0.966805299 0.066389402 0.033194701
[12,] 0.951365219 0.097269562 0.048634781
[13,] 0.933269168 0.133461665 0.066730832
[14,] 0.913203096 0.173593807 0.086796904
[15,] 0.889314785 0.221370429 0.110685215
[16,] 0.855756130 0.288487739 0.144243870
[17,] 0.828116774 0.343766453 0.171883226
[18,] 0.941599999 0.116800003 0.058400001
[19,] 0.923303858 0.153392283 0.076696142
[20,] 0.906914244 0.186171512 0.093085756
[21,] 0.884360618 0.231278764 0.115639382
[22,] 0.858274985 0.283450030 0.141725015
[23,] 0.867866356 0.264267288 0.132133644
[24,] 0.843289613 0.313420774 0.156710387
[25,] 0.814620752 0.370758497 0.185379248
[26,] 0.787654814 0.424690371 0.212345186
[27,] 0.745426418 0.509147163 0.254573582
[28,] 0.767087017 0.465825966 0.232912983
[29,] 0.823114519 0.353770962 0.176885481
[30,] 0.787621636 0.424756729 0.212378364
[31,] 0.758286566 0.483426867 0.241713434
[32,] 0.761893961 0.476212078 0.238106039
[33,] 0.755452632 0.489094736 0.244547368
[34,] 0.731878354 0.536243292 0.268121646
[35,] 0.740895558 0.518208884 0.259104442
[36,] 0.700645113 0.598709775 0.299354887
[37,] 0.659104515 0.681790971 0.340895485
[38,] 0.637771110 0.724457781 0.362228890
[39,] 0.655872766 0.688254468 0.344127234
[40,] 0.620628152 0.758743696 0.379371848
[41,] 0.660874328 0.678251344 0.339125672
[42,] 0.636627557 0.726744886 0.363372443
[43,] 0.622211601 0.755576799 0.377788399
[44,] 0.585448665 0.829102670 0.414551335
[45,] 0.593095165 0.813809669 0.406904835
[46,] 0.548696474 0.902607053 0.451303526
[47,] 0.560423941 0.879152117 0.439576059
[48,] 0.563155751 0.873688497 0.436844249
[49,] 0.536682532 0.926634935 0.463317468
[50,] 0.564576769 0.870846462 0.435423231
[51,] 0.594950962 0.810098076 0.405049038
[52,] 0.565200906 0.869598189 0.434799094
[53,] 0.591720686 0.816558627 0.408279314
[54,] 0.555884036 0.888231927 0.444115964
[55,] 0.547576464 0.904847073 0.452423536
[56,] 0.508999462 0.982001076 0.491000538
[57,] 0.470464902 0.940929805 0.529535098
[58,] 0.508066406 0.983867188 0.491933594
[59,] 0.514917335 0.970165329 0.485082665
[60,] 0.497045859 0.994091718 0.502954141
[61,] 0.458867780 0.917735560 0.541132220
[62,] 0.435871721 0.871743443 0.564128279
[63,] 0.396173395 0.792346790 0.603826605
[64,] 0.396899361 0.793798723 0.603100639
[65,] 0.361275229 0.722550458 0.638724771
[66,] 0.340763386 0.681526772 0.659236614
[67,] 0.326902602 0.653805205 0.673097398
[68,] 0.441959347 0.883918695 0.558040653
[69,] 0.425767799 0.851535597 0.574232201
[70,] 0.450384142 0.900768284 0.549615858
[71,] 0.411734520 0.823469040 0.588265480
[72,] 0.456560348 0.913120695 0.543439652
[73,] 0.427150432 0.854300864 0.572849568
[74,] 0.439827467 0.879654934 0.560172533
[75,] 0.403131366 0.806262733 0.596868634
[76,] 0.366989656 0.733979312 0.633010344
[77,] 0.331385137 0.662770273 0.668614863
[78,] 0.298570101 0.597140202 0.701429899
[79,] 0.269275395 0.538550791 0.730724605
[80,] 0.245541359 0.491082718 0.754458641
[81,] 0.308984157 0.617968315 0.691015843
[82,] 0.285101540 0.570203081 0.714898460
[83,] 0.268190273 0.536380546 0.731809727
[84,] 0.239617094 0.479234189 0.760382906
[85,] 0.225115556 0.450231112 0.774884444
[86,] 0.206314922 0.412629844 0.793685078
[87,] 0.180873149 0.361746298 0.819126851
[88,] 0.169519884 0.339039767 0.830480116
[89,] 0.157472527 0.314945055 0.842527473
[90,] 0.136138169 0.272276337 0.863861831
[91,] 0.133309266 0.266618531 0.866690734
[92,] 0.115294446 0.230588891 0.884705554
[93,] 0.106181673 0.212363346 0.893818327
[94,] 0.091400102 0.182800204 0.908599898
[95,] 0.110952190 0.221904380 0.889047810
[96,] 0.122173651 0.244347301 0.877826349
[97,] 0.125535625 0.251071249 0.874464375
[98,] 0.107730905 0.215461809 0.892269095
[99,] 0.124905451 0.249810903 0.875094549
[100,] 0.108988617 0.217977234 0.891011383
[101,] 0.251140564 0.502281127 0.748859436
[102,] 0.233881422 0.467762844 0.766118578
[103,] 0.208381693 0.416763386 0.791618307
[104,] 0.212304333 0.424608667 0.787695667
[105,] 0.196430881 0.392861762 0.803569119
[106,] 0.180206145 0.360412290 0.819793855
[107,] 0.160816959 0.321633917 0.839183041
[108,] 0.141314237 0.282628474 0.858685763
[109,] 0.123493655 0.246987310 0.876506345
[110,] 0.150831623 0.301663245 0.849168377
[111,] 0.141107499 0.282214998 0.858892501
[112,] 0.121955605 0.243911210 0.878044395
[113,] 0.109812047 0.219624094 0.890187953
[114,] 0.099267092 0.198534185 0.900732908
[115,] 0.119355215 0.238710430 0.880644785
[116,] 0.105332759 0.210665517 0.894667241
[117,] 0.103645074 0.207290149 0.896354926
[118,] 0.103993966 0.207987931 0.896006034
[119,] 0.090070776 0.180141552 0.909929224
[120,] 0.080430221 0.160860442 0.919569779
[121,] 0.068178880 0.136357760 0.931821120
[122,] 0.068690990 0.137381980 0.931309010
[123,] 0.067837970 0.135675941 0.932162030
[124,] 0.064136423 0.128272846 0.935863577
[125,] 0.060051160 0.120102320 0.939948840
[126,] 0.050396759 0.100793517 0.949603241
[127,] 0.047973126 0.095946252 0.952026874
[128,] 0.118736756 0.237473512 0.881263244
[129,] 0.103257415 0.206514831 0.896742585
[130,] 0.088093601 0.176187202 0.911906399
[131,] 0.079991294 0.159982587 0.920008706
[132,] 0.076926154 0.153852309 0.923073846
[133,] 0.073762569 0.147525137 0.926237431
[134,] 0.068744546 0.137489093 0.931255454
[135,] 0.059135979 0.118271959 0.940864021
[136,] 0.049857572 0.099715143 0.950142428
[137,] 0.042451715 0.084903430 0.957548285
[138,] 0.035357702 0.070715403 0.964642298
[139,] 0.033966331 0.067932663 0.966033669
[140,] 0.039389653 0.078779305 0.960610347
[141,] 0.035277196 0.070554391 0.964722804
[142,] 0.029080913 0.058161827 0.970919087
[143,] 0.031444917 0.062889833 0.968555083
[144,] 0.030261505 0.060523010 0.969738495
[145,] 0.029488767 0.058977533 0.970511233
[146,] 0.026571387 0.053142773 0.973428613
[147,] 0.029360542 0.058721083 0.970639458
[148,] 0.024979250 0.049958500 0.975020750
[149,] 0.020282826 0.040565652 0.979717174
[150,] 0.017410859 0.034821718 0.982589141
[151,] 0.027146925 0.054293851 0.972853075
[152,] 0.027649497 0.055298994 0.972350503
[153,] 0.024410069 0.048820137 0.975589931
[154,] 0.065902354 0.131804708 0.934097646
[155,] 0.055076305 0.110152611 0.944923695
[156,] 0.196731341 0.393462682 0.803268659
[157,] 0.180426494 0.360852989 0.819573506
[158,] 0.303782202 0.607564405 0.696217798
[159,] 0.279589342 0.559178684 0.720410658
[160,] 0.256533902 0.513067805 0.743466098
[161,] 0.232333763 0.464667526 0.767666237
[162,] 0.207190755 0.414381509 0.792809245
[163,] 0.234256578 0.468513157 0.765743422
[164,] 0.207544986 0.415089971 0.792455014
[165,] 0.275985692 0.551971385 0.724014308
[166,] 0.268495116 0.536990231 0.731504884
[167,] 0.245385810 0.490771620 0.754614190
[168,] 0.315935344 0.631870687 0.684064656
[169,] 0.291637020 0.583274040 0.708362980
[170,] 0.292318930 0.584637860 0.707681070
[171,] 0.272476916 0.544953833 0.727523084
[172,] 0.254115580 0.508231160 0.745884420
[173,] 0.242686782 0.485373563 0.757313218
[174,] 0.324696358 0.649392716 0.675303642
[175,] 0.299299237 0.598598473 0.700700763
[176,] 0.313012949 0.626025899 0.686987051
[177,] 0.289382116 0.578764231 0.710617884
[178,] 0.355664650 0.711329301 0.644335350
[179,] 0.322086665 0.644173330 0.677913335
[180,] 0.290624595 0.581249191 0.709375405
[181,] 0.258928690 0.517857381 0.741071310
[182,] 0.337680855 0.675361711 0.662319145
[183,] 0.333692769 0.667385537 0.666307231
[184,] 0.351498523 0.702997047 0.648501477
[185,] 0.350997993 0.701995986 0.649002007
[186,] 0.315354948 0.630709897 0.684645052
[187,] 0.288275910 0.576551820 0.711724090
[188,] 0.301660346 0.603320691 0.698339654
[189,] 0.413744641 0.827489283 0.586255359
[190,] 0.384883396 0.769766791 0.615116604
[191,] 0.347852479 0.695704958 0.652147521
[192,] 0.317116354 0.634232707 0.682883646
[193,] 0.284695575 0.569391150 0.715304425
[194,] 0.252005117 0.504010234 0.747994883
[195,] 0.220431817 0.440863633 0.779568183
[196,] 0.191621897 0.383243794 0.808378103
[197,] 0.168950230 0.337900460 0.831049770
[198,] 0.143518443 0.287036887 0.856481557
[199,] 0.126697247 0.253394495 0.873302753
[200,] 0.129764036 0.259528071 0.870235964
[201,] 0.108272605 0.216545210 0.891727395
[202,] 0.089458913 0.178917827 0.910541087
[203,] 0.075767655 0.151535309 0.924232345
[204,] 0.063333640 0.126667281 0.936666360
[205,] 0.055844419 0.111688838 0.944155581
[206,] 0.061238410 0.122476821 0.938761590
[207,] 0.049890868 0.099781736 0.950109132
[208,] 0.039638071 0.079276141 0.960361929
[209,] 0.030772391 0.061544781 0.969227609
[210,] 0.025281345 0.050562690 0.974718655
[211,] 0.027912321 0.055824642 0.972087679
[212,] 0.022246935 0.044493870 0.977753065
[213,] 0.031200144 0.062400287 0.968799856
[214,] 0.023645045 0.047290089 0.976354955
[215,] 0.017983430 0.035966860 0.982016570
[216,] 0.019867990 0.039735980 0.980132010
[217,] 0.021616488 0.043232975 0.978383512
[218,] 0.017245254 0.034490507 0.982754746
[219,] 0.013856949 0.027713898 0.986143051
[220,] 0.013327792 0.026655584 0.986672208
[221,] 0.018940767 0.037881534 0.981059233
[222,] 0.017998324 0.035996648 0.982001676
[223,] 0.013293719 0.026587438 0.986706281
[224,] 0.010768725 0.021537449 0.989231275
[225,] 0.010455896 0.020911793 0.989544104
[226,] 0.008615483 0.017230966 0.991384517
[227,] 0.008977724 0.017955448 0.991022276
[228,] 0.007324884 0.014649769 0.992675116
[229,] 0.005781094 0.011562189 0.994218906
[230,] 0.004645559 0.009291117 0.995354441
[231,] 0.022439004 0.044878008 0.977560996
[232,] 0.030254414 0.060508827 0.969745586
[233,] 0.055498715 0.110997431 0.944501285
[234,] 0.042795743 0.085591486 0.957204257
[235,] 0.040267731 0.080535463 0.959732269
[236,] 0.031260290 0.062520579 0.968739710
[237,] 0.020689852 0.041379705 0.979310148
[238,] 0.032731946 0.065463893 0.967268054
[239,] 0.021949523 0.043899046 0.978050477
[240,] 0.312674042 0.625348083 0.687325958
[241,] 0.324521279 0.649042559 0.675478721
[242,] 0.377801344 0.755602689 0.622198656
[243,] 0.451637015 0.903274030 0.548362985
[244,] 0.928440557 0.143118886 0.071559443
[245,] 0.876883445 0.246233111 0.123116555
[246,] 0.959292680 0.081414640 0.040707320
[247,] 0.932398212 0.135203576 0.067601788
> postscript(file="/var/fisher/rcomp/tmp/1942a1383469974.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/227n61383469974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ez1d1383469974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4qfqy1383469974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/565jo1383469974.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
-1.02234139 1.25661409 -1.10745956 -2.99274460 9.35725161 1.76394870
7 8 9 10 11 12
9.50123304 -2.05081539 -2.35807107 0.67049139 -0.59012698 -1.07093542
13 14 15 16 17 18
-0.93546468 2.69592024 0.22257380 0.80238695 0.90721860 0.85285595
19 20 21 22 23 24
-2.65139381 1.44735571 -0.54629302 -1.45242112 -0.48365061 -0.48039120
25 26 27 28 29 30
0.61768652 -7.43737010 -0.45902013 1.90351056 1.71548471 -2.59543581
31 32 33 34 35 36
-2.69099887 0.38511596 -0.82956772 -1.25285060 -0.20174736 -4.31879914
37 38 39 40 41 42
2.22405787 -0.35246732 -0.97597656 -3.81211247 -3.17968364 2.33372963
43 44 45 46 47 48
-2.79808574 -0.78062233 -1.32217253 -2.69781694 -3.24880474 -1.42033090
49 50 51 52 53 54
4.33924502 -2.80780371 -1.49999660 0.64304937 2.19171020 -0.53674693
55 56 57 58 59 60
-4.57150569 1.97642434 1.74185719 -3.45880587 -4.57026986 0.41314435
61 62 63 64 65 66
1.63050399 -1.61683889 -3.26555315 0.62089729 0.76396063 -4.93667121
67 68 69 70 71 72
2.01675975 -2.55030540 0.55996754 1.79002157 -0.57912485 3.15637428
73 74 75 76 77 78
1.00757313 -1.82654191 -2.76871449 4.65939536 2.45437799 3.18534856
79 80 81 82 83 84
0.21187474 -4.64009796 -1.29292091 -3.20997843 0.56063044 -0.46320308
85 86 87 88 89 90
-0.12031177 0.61227741 -1.05925847 1.00291527 3.44167000 1.66789185
91 92 93 94 95 96
1.50549316 0.77881408 1.81639631 -1.78633382 -0.33320547 1.60027521
97 98 99 100 101 102
-2.06929689 0.42805415 -2.60182056 -0.46521850 1.53081629 1.10216055
103 104 105 106 107 108
-4.53415706 3.66394401 2.09189115 -0.20553258 4.04165088 -1.05593849
109 110 111 112 113 114
7.69249867 1.58406631 -0.38856594 -2.83013024 1.57108893 1.73739942
115 116 117 118 119 120
-1.10698539 0.76209120 -0.36868393 -4.39164264 2.14949867 0.05887101
121 122 123 124 125 126
0.90933975 -1.38181848 -4.05757703 -1.21384984 -2.80995387 -2.64834096
127 128 129 130 131 132
-0.22229517 1.59237165 0.37336249 -2.94072900 2.24784275 -2.13453540
133 134 135 136 137 138
2.03879070 -0.32360451 2.35747705 7.04513799 0.84779810 -0.06738109
139 140 141 142 143 144
-1.73362777 -2.51535577 -2.46910719 2.14512933 -0.98820420 -0.37697038
145 146 147 148 149 150
-0.93548853 0.69266309 -2.54692243 -3.63760764 1.77334189 -0.07837732
151 152 153 154 155 156
3.48268937 -2.58789956 -2.73978286 1.95990948 3.44858233 1.50549316
157 158 159 160 161 162
0.38623414 1.59237165 -4.81745331 2.81072430 1.77229798 7.25384648
163 164 165 166 167 168
0.34535720 8.86953009 1.71409593 6.52165201 -1.41637114 -1.36945085
169 170 171 172 173 174
-1.18215314 0.81539634 4.16856859 0.60483505 -5.04138549 2.16089112
175 176 177 178 179 180
1.23691479 -5.00988756 -1.27672937 2.78212439 -1.85874207 -1.66645262
181 182 183 184 185 186
-1.93624569 -5.43404609 -0.97962404 -2.93757812 -1.75643278 5.12032431
187 188 189 190 191 192
1.13291288 -0.58846291 -0.69381812 4.84191408 -2.62012686 -2.86170882
193 194 195 196 197 198
1.96691027 0.66752689 1.31110265 -2.54114414 5.88783094 1.93233592
199 200 201 202 203 204
1.01113864 -1.12570126 -0.03637403 -0.92798673 -0.28776654 -0.17586096
205 206 207 208 209 210
-1.52451929 0.61693753 -1.01664920 3.56476475 -0.26066169 0.36255499
211 212 213 214 215 216
-0.26642805 -0.59355429 2.43502234 -3.50723859 1.70786010 0.25751133
217 218 219 220 221 222
0.31731839 -1.06805289 4.00113768 1.72900572 -3.58138899 0.42735379
223 224 225 226 227 228
0.76029578 -3.86478327 3.71926222 -2.55850277 1.45427997 -2.90389802
229 230 231 232 233 234
4.75915798 -2.83233758 -1.50840841 -1.58933707 -3.53856611 3.78060698
235 236 237 238 239 240
-2.83121170 -1.79894055 1.19176701 -0.97782297 -5.31010677 -3.95573917
241 242 243 244 245 246
-5.38682534 0.96416961 0.29703846 0.98217554 1.72483395 3.91857209
247 248 249 250 251 252
0.82236924 9.38106775 2.75713524 0.42429353 0.96186979 2.41105100
253 254 255 256 257 258
-3.30409713 -0.92978441 2.10855346 0.21123495 4.15837374 0.66540800
259 260 261 262 263 264
2.55458924 -9.54204834 -1.43829777 -0.74707387 3.16658519 -1.52590037
> postscript(file="/var/fisher/rcomp/tmp/6lxo11383469974.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 -1.02234139 NA
1 1.25661409 -1.02234139
2 -1.10745956 1.25661409
3 -2.99274460 -1.10745956
4 9.35725161 -2.99274460
5 1.76394870 9.35725161
6 9.50123304 1.76394870
7 -2.05081539 9.50123304
8 -2.35807107 -2.05081539
9 0.67049139 -2.35807107
10 -0.59012698 0.67049139
11 -1.07093542 -0.59012698
12 -0.93546468 -1.07093542
13 2.69592024 -0.93546468
14 0.22257380 2.69592024
15 0.80238695 0.22257380
16 0.90721860 0.80238695
17 0.85285595 0.90721860
18 -2.65139381 0.85285595
19 1.44735571 -2.65139381
20 -0.54629302 1.44735571
21 -1.45242112 -0.54629302
22 -0.48365061 -1.45242112
23 -0.48039120 -0.48365061
24 0.61768652 -0.48039120
25 -7.43737010 0.61768652
26 -0.45902013 -7.43737010
27 1.90351056 -0.45902013
28 1.71548471 1.90351056
29 -2.59543581 1.71548471
30 -2.69099887 -2.59543581
31 0.38511596 -2.69099887
32 -0.82956772 0.38511596
33 -1.25285060 -0.82956772
34 -0.20174736 -1.25285060
35 -4.31879914 -0.20174736
36 2.22405787 -4.31879914
37 -0.35246732 2.22405787
38 -0.97597656 -0.35246732
39 -3.81211247 -0.97597656
40 -3.17968364 -3.81211247
41 2.33372963 -3.17968364
42 -2.79808574 2.33372963
43 -0.78062233 -2.79808574
44 -1.32217253 -0.78062233
45 -2.69781694 -1.32217253
46 -3.24880474 -2.69781694
47 -1.42033090 -3.24880474
48 4.33924502 -1.42033090
49 -2.80780371 4.33924502
50 -1.49999660 -2.80780371
51 0.64304937 -1.49999660
52 2.19171020 0.64304937
53 -0.53674693 2.19171020
54 -4.57150569 -0.53674693
55 1.97642434 -4.57150569
56 1.74185719 1.97642434
57 -3.45880587 1.74185719
58 -4.57026986 -3.45880587
59 0.41314435 -4.57026986
60 1.63050399 0.41314435
61 -1.61683889 1.63050399
62 -3.26555315 -1.61683889
63 0.62089729 -3.26555315
64 0.76396063 0.62089729
65 -4.93667121 0.76396063
66 2.01675975 -4.93667121
67 -2.55030540 2.01675975
68 0.55996754 -2.55030540
69 1.79002157 0.55996754
70 -0.57912485 1.79002157
71 3.15637428 -0.57912485
72 1.00757313 3.15637428
73 -1.82654191 1.00757313
74 -2.76871449 -1.82654191
75 4.65939536 -2.76871449
76 2.45437799 4.65939536
77 3.18534856 2.45437799
78 0.21187474 3.18534856
79 -4.64009796 0.21187474
80 -1.29292091 -4.64009796
81 -3.20997843 -1.29292091
82 0.56063044 -3.20997843
83 -0.46320308 0.56063044
84 -0.12031177 -0.46320308
85 0.61227741 -0.12031177
86 -1.05925847 0.61227741
87 1.00291527 -1.05925847
88 3.44167000 1.00291527
89 1.66789185 3.44167000
90 1.50549316 1.66789185
91 0.77881408 1.50549316
92 1.81639631 0.77881408
93 -1.78633382 1.81639631
94 -0.33320547 -1.78633382
95 1.60027521 -0.33320547
96 -2.06929689 1.60027521
97 0.42805415 -2.06929689
98 -2.60182056 0.42805415
99 -0.46521850 -2.60182056
100 1.53081629 -0.46521850
101 1.10216055 1.53081629
102 -4.53415706 1.10216055
103 3.66394401 -4.53415706
104 2.09189115 3.66394401
105 -0.20553258 2.09189115
106 4.04165088 -0.20553258
107 -1.05593849 4.04165088
108 7.69249867 -1.05593849
109 1.58406631 7.69249867
110 -0.38856594 1.58406631
111 -2.83013024 -0.38856594
112 1.57108893 -2.83013024
113 1.73739942 1.57108893
114 -1.10698539 1.73739942
115 0.76209120 -1.10698539
116 -0.36868393 0.76209120
117 -4.39164264 -0.36868393
118 2.14949867 -4.39164264
119 0.05887101 2.14949867
120 0.90933975 0.05887101
121 -1.38181848 0.90933975
122 -4.05757703 -1.38181848
123 -1.21384984 -4.05757703
124 -2.80995387 -1.21384984
125 -2.64834096 -2.80995387
126 -0.22229517 -2.64834096
127 1.59237165 -0.22229517
128 0.37336249 1.59237165
129 -2.94072900 0.37336249
130 2.24784275 -2.94072900
131 -2.13453540 2.24784275
132 2.03879070 -2.13453540
133 -0.32360451 2.03879070
134 2.35747705 -0.32360451
135 7.04513799 2.35747705
136 0.84779810 7.04513799
137 -0.06738109 0.84779810
138 -1.73362777 -0.06738109
139 -2.51535577 -1.73362777
140 -2.46910719 -2.51535577
141 2.14512933 -2.46910719
142 -0.98820420 2.14512933
143 -0.37697038 -0.98820420
144 -0.93548853 -0.37697038
145 0.69266309 -0.93548853
146 -2.54692243 0.69266309
147 -3.63760764 -2.54692243
148 1.77334189 -3.63760764
149 -0.07837732 1.77334189
150 3.48268937 -0.07837732
151 -2.58789956 3.48268937
152 -2.73978286 -2.58789956
153 1.95990948 -2.73978286
154 3.44858233 1.95990948
155 1.50549316 3.44858233
156 0.38623414 1.50549316
157 1.59237165 0.38623414
158 -4.81745331 1.59237165
159 2.81072430 -4.81745331
160 1.77229798 2.81072430
161 7.25384648 1.77229798
162 0.34535720 7.25384648
163 8.86953009 0.34535720
164 1.71409593 8.86953009
165 6.52165201 1.71409593
166 -1.41637114 6.52165201
167 -1.36945085 -1.41637114
168 -1.18215314 -1.36945085
169 0.81539634 -1.18215314
170 4.16856859 0.81539634
171 0.60483505 4.16856859
172 -5.04138549 0.60483505
173 2.16089112 -5.04138549
174 1.23691479 2.16089112
175 -5.00988756 1.23691479
176 -1.27672937 -5.00988756
177 2.78212439 -1.27672937
178 -1.85874207 2.78212439
179 -1.66645262 -1.85874207
180 -1.93624569 -1.66645262
181 -5.43404609 -1.93624569
182 -0.97962404 -5.43404609
183 -2.93757812 -0.97962404
184 -1.75643278 -2.93757812
185 5.12032431 -1.75643278
186 1.13291288 5.12032431
187 -0.58846291 1.13291288
188 -0.69381812 -0.58846291
189 4.84191408 -0.69381812
190 -2.62012686 4.84191408
191 -2.86170882 -2.62012686
192 1.96691027 -2.86170882
193 0.66752689 1.96691027
194 1.31110265 0.66752689
195 -2.54114414 1.31110265
196 5.88783094 -2.54114414
197 1.93233592 5.88783094
198 1.01113864 1.93233592
199 -1.12570126 1.01113864
200 -0.03637403 -1.12570126
201 -0.92798673 -0.03637403
202 -0.28776654 -0.92798673
203 -0.17586096 -0.28776654
204 -1.52451929 -0.17586096
205 0.61693753 -1.52451929
206 -1.01664920 0.61693753
207 3.56476475 -1.01664920
208 -0.26066169 3.56476475
209 0.36255499 -0.26066169
210 -0.26642805 0.36255499
211 -0.59355429 -0.26642805
212 2.43502234 -0.59355429
213 -3.50723859 2.43502234
214 1.70786010 -3.50723859
215 0.25751133 1.70786010
216 0.31731839 0.25751133
217 -1.06805289 0.31731839
218 4.00113768 -1.06805289
219 1.72900572 4.00113768
220 -3.58138899 1.72900572
221 0.42735379 -3.58138899
222 0.76029578 0.42735379
223 -3.86478327 0.76029578
224 3.71926222 -3.86478327
225 -2.55850277 3.71926222
226 1.45427997 -2.55850277
227 -2.90389802 1.45427997
228 4.75915798 -2.90389802
229 -2.83233758 4.75915798
230 -1.50840841 -2.83233758
231 -1.58933707 -1.50840841
232 -3.53856611 -1.58933707
233 3.78060698 -3.53856611
234 -2.83121170 3.78060698
235 -1.79894055 -2.83121170
236 1.19176701 -1.79894055
237 -0.97782297 1.19176701
238 -5.31010677 -0.97782297
239 -3.95573917 -5.31010677
240 -5.38682534 -3.95573917
241 0.96416961 -5.38682534
242 0.29703846 0.96416961
243 0.98217554 0.29703846
244 1.72483395 0.98217554
245 3.91857209 1.72483395
246 0.82236924 3.91857209
247 9.38106775 0.82236924
248 2.75713524 9.38106775
249 0.42429353 2.75713524
250 0.96186979 0.42429353
251 2.41105100 0.96186979
252 -3.30409713 2.41105100
253 -0.92978441 -3.30409713
254 2.10855346 -0.92978441
255 0.21123495 2.10855346
256 4.15837374 0.21123495
257 0.66540800 4.15837374
258 2.55458924 0.66540800
259 -9.54204834 2.55458924
260 -1.43829777 -9.54204834
261 -0.74707387 -1.43829777
262 3.16658519 -0.74707387
263 -1.52590037 3.16658519
264 NA -1.52590037
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.25661409 -1.02234139
[2,] -1.10745956 1.25661409
[3,] -2.99274460 -1.10745956
[4,] 9.35725161 -2.99274460
[5,] 1.76394870 9.35725161
[6,] 9.50123304 1.76394870
[7,] -2.05081539 9.50123304
[8,] -2.35807107 -2.05081539
[9,] 0.67049139 -2.35807107
[10,] -0.59012698 0.67049139
[11,] -1.07093542 -0.59012698
[12,] -0.93546468 -1.07093542
[13,] 2.69592024 -0.93546468
[14,] 0.22257380 2.69592024
[15,] 0.80238695 0.22257380
[16,] 0.90721860 0.80238695
[17,] 0.85285595 0.90721860
[18,] -2.65139381 0.85285595
[19,] 1.44735571 -2.65139381
[20,] -0.54629302 1.44735571
[21,] -1.45242112 -0.54629302
[22,] -0.48365061 -1.45242112
[23,] -0.48039120 -0.48365061
[24,] 0.61768652 -0.48039120
[25,] -7.43737010 0.61768652
[26,] -0.45902013 -7.43737010
[27,] 1.90351056 -0.45902013
[28,] 1.71548471 1.90351056
[29,] -2.59543581 1.71548471
[30,] -2.69099887 -2.59543581
[31,] 0.38511596 -2.69099887
[32,] -0.82956772 0.38511596
[33,] -1.25285060 -0.82956772
[34,] -0.20174736 -1.25285060
[35,] -4.31879914 -0.20174736
[36,] 2.22405787 -4.31879914
[37,] -0.35246732 2.22405787
[38,] -0.97597656 -0.35246732
[39,] -3.81211247 -0.97597656
[40,] -3.17968364 -3.81211247
[41,] 2.33372963 -3.17968364
[42,] -2.79808574 2.33372963
[43,] -0.78062233 -2.79808574
[44,] -1.32217253 -0.78062233
[45,] -2.69781694 -1.32217253
[46,] -3.24880474 -2.69781694
[47,] -1.42033090 -3.24880474
[48,] 4.33924502 -1.42033090
[49,] -2.80780371 4.33924502
[50,] -1.49999660 -2.80780371
[51,] 0.64304937 -1.49999660
[52,] 2.19171020 0.64304937
[53,] -0.53674693 2.19171020
[54,] -4.57150569 -0.53674693
[55,] 1.97642434 -4.57150569
[56,] 1.74185719 1.97642434
[57,] -3.45880587 1.74185719
[58,] -4.57026986 -3.45880587
[59,] 0.41314435 -4.57026986
[60,] 1.63050399 0.41314435
[61,] -1.61683889 1.63050399
[62,] -3.26555315 -1.61683889
[63,] 0.62089729 -3.26555315
[64,] 0.76396063 0.62089729
[65,] -4.93667121 0.76396063
[66,] 2.01675975 -4.93667121
[67,] -2.55030540 2.01675975
[68,] 0.55996754 -2.55030540
[69,] 1.79002157 0.55996754
[70,] -0.57912485 1.79002157
[71,] 3.15637428 -0.57912485
[72,] 1.00757313 3.15637428
[73,] -1.82654191 1.00757313
[74,] -2.76871449 -1.82654191
[75,] 4.65939536 -2.76871449
[76,] 2.45437799 4.65939536
[77,] 3.18534856 2.45437799
[78,] 0.21187474 3.18534856
[79,] -4.64009796 0.21187474
[80,] -1.29292091 -4.64009796
[81,] -3.20997843 -1.29292091
[82,] 0.56063044 -3.20997843
[83,] -0.46320308 0.56063044
[84,] -0.12031177 -0.46320308
[85,] 0.61227741 -0.12031177
[86,] -1.05925847 0.61227741
[87,] 1.00291527 -1.05925847
[88,] 3.44167000 1.00291527
[89,] 1.66789185 3.44167000
[90,] 1.50549316 1.66789185
[91,] 0.77881408 1.50549316
[92,] 1.81639631 0.77881408
[93,] -1.78633382 1.81639631
[94,] -0.33320547 -1.78633382
[95,] 1.60027521 -0.33320547
[96,] -2.06929689 1.60027521
[97,] 0.42805415 -2.06929689
[98,] -2.60182056 0.42805415
[99,] -0.46521850 -2.60182056
[100,] 1.53081629 -0.46521850
[101,] 1.10216055 1.53081629
[102,] -4.53415706 1.10216055
[103,] 3.66394401 -4.53415706
[104,] 2.09189115 3.66394401
[105,] -0.20553258 2.09189115
[106,] 4.04165088 -0.20553258
[107,] -1.05593849 4.04165088
[108,] 7.69249867 -1.05593849
[109,] 1.58406631 7.69249867
[110,] -0.38856594 1.58406631
[111,] -2.83013024 -0.38856594
[112,] 1.57108893 -2.83013024
[113,] 1.73739942 1.57108893
[114,] -1.10698539 1.73739942
[115,] 0.76209120 -1.10698539
[116,] -0.36868393 0.76209120
[117,] -4.39164264 -0.36868393
[118,] 2.14949867 -4.39164264
[119,] 0.05887101 2.14949867
[120,] 0.90933975 0.05887101
[121,] -1.38181848 0.90933975
[122,] -4.05757703 -1.38181848
[123,] -1.21384984 -4.05757703
[124,] -2.80995387 -1.21384984
[125,] -2.64834096 -2.80995387
[126,] -0.22229517 -2.64834096
[127,] 1.59237165 -0.22229517
[128,] 0.37336249 1.59237165
[129,] -2.94072900 0.37336249
[130,] 2.24784275 -2.94072900
[131,] -2.13453540 2.24784275
[132,] 2.03879070 -2.13453540
[133,] -0.32360451 2.03879070
[134,] 2.35747705 -0.32360451
[135,] 7.04513799 2.35747705
[136,] 0.84779810 7.04513799
[137,] -0.06738109 0.84779810
[138,] -1.73362777 -0.06738109
[139,] -2.51535577 -1.73362777
[140,] -2.46910719 -2.51535577
[141,] 2.14512933 -2.46910719
[142,] -0.98820420 2.14512933
[143,] -0.37697038 -0.98820420
[144,] -0.93548853 -0.37697038
[145,] 0.69266309 -0.93548853
[146,] -2.54692243 0.69266309
[147,] -3.63760764 -2.54692243
[148,] 1.77334189 -3.63760764
[149,] -0.07837732 1.77334189
[150,] 3.48268937 -0.07837732
[151,] -2.58789956 3.48268937
[152,] -2.73978286 -2.58789956
[153,] 1.95990948 -2.73978286
[154,] 3.44858233 1.95990948
[155,] 1.50549316 3.44858233
[156,] 0.38623414 1.50549316
[157,] 1.59237165 0.38623414
[158,] -4.81745331 1.59237165
[159,] 2.81072430 -4.81745331
[160,] 1.77229798 2.81072430
[161,] 7.25384648 1.77229798
[162,] 0.34535720 7.25384648
[163,] 8.86953009 0.34535720
[164,] 1.71409593 8.86953009
[165,] 6.52165201 1.71409593
[166,] -1.41637114 6.52165201
[167,] -1.36945085 -1.41637114
[168,] -1.18215314 -1.36945085
[169,] 0.81539634 -1.18215314
[170,] 4.16856859 0.81539634
[171,] 0.60483505 4.16856859
[172,] -5.04138549 0.60483505
[173,] 2.16089112 -5.04138549
[174,] 1.23691479 2.16089112
[175,] -5.00988756 1.23691479
[176,] -1.27672937 -5.00988756
[177,] 2.78212439 -1.27672937
[178,] -1.85874207 2.78212439
[179,] -1.66645262 -1.85874207
[180,] -1.93624569 -1.66645262
[181,] -5.43404609 -1.93624569
[182,] -0.97962404 -5.43404609
[183,] -2.93757812 -0.97962404
[184,] -1.75643278 -2.93757812
[185,] 5.12032431 -1.75643278
[186,] 1.13291288 5.12032431
[187,] -0.58846291 1.13291288
[188,] -0.69381812 -0.58846291
[189,] 4.84191408 -0.69381812
[190,] -2.62012686 4.84191408
[191,] -2.86170882 -2.62012686
[192,] 1.96691027 -2.86170882
[193,] 0.66752689 1.96691027
[194,] 1.31110265 0.66752689
[195,] -2.54114414 1.31110265
[196,] 5.88783094 -2.54114414
[197,] 1.93233592 5.88783094
[198,] 1.01113864 1.93233592
[199,] -1.12570126 1.01113864
[200,] -0.03637403 -1.12570126
[201,] -0.92798673 -0.03637403
[202,] -0.28776654 -0.92798673
[203,] -0.17586096 -0.28776654
[204,] -1.52451929 -0.17586096
[205,] 0.61693753 -1.52451929
[206,] -1.01664920 0.61693753
[207,] 3.56476475 -1.01664920
[208,] -0.26066169 3.56476475
[209,] 0.36255499 -0.26066169
[210,] -0.26642805 0.36255499
[211,] -0.59355429 -0.26642805
[212,] 2.43502234 -0.59355429
[213,] -3.50723859 2.43502234
[214,] 1.70786010 -3.50723859
[215,] 0.25751133 1.70786010
[216,] 0.31731839 0.25751133
[217,] -1.06805289 0.31731839
[218,] 4.00113768 -1.06805289
[219,] 1.72900572 4.00113768
[220,] -3.58138899 1.72900572
[221,] 0.42735379 -3.58138899
[222,] 0.76029578 0.42735379
[223,] -3.86478327 0.76029578
[224,] 3.71926222 -3.86478327
[225,] -2.55850277 3.71926222
[226,] 1.45427997 -2.55850277
[227,] -2.90389802 1.45427997
[228,] 4.75915798 -2.90389802
[229,] -2.83233758 4.75915798
[230,] -1.50840841 -2.83233758
[231,] -1.58933707 -1.50840841
[232,] -3.53856611 -1.58933707
[233,] 3.78060698 -3.53856611
[234,] -2.83121170 3.78060698
[235,] -1.79894055 -2.83121170
[236,] 1.19176701 -1.79894055
[237,] -0.97782297 1.19176701
[238,] -5.31010677 -0.97782297
[239,] -3.95573917 -5.31010677
[240,] -5.38682534 -3.95573917
[241,] 0.96416961 -5.38682534
[242,] 0.29703846 0.96416961
[243,] 0.98217554 0.29703846
[244,] 1.72483395 0.98217554
[245,] 3.91857209 1.72483395
[246,] 0.82236924 3.91857209
[247,] 9.38106775 0.82236924
[248,] 2.75713524 9.38106775
[249,] 0.42429353 2.75713524
[250,] 0.96186979 0.42429353
[251,] 2.41105100 0.96186979
[252,] -3.30409713 2.41105100
[253,] -0.92978441 -3.30409713
[254,] 2.10855346 -0.92978441
[255,] 0.21123495 2.10855346
[256,] 4.15837374 0.21123495
[257,] 0.66540800 4.15837374
[258,] 2.55458924 0.66540800
[259,] -9.54204834 2.55458924
[260,] -1.43829777 -9.54204834
[261,] -0.74707387 -1.43829777
[262,] 3.16658519 -0.74707387
[263,] -1.52590037 3.16658519
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.25661409 -1.02234139
2 -1.10745956 1.25661409
3 -2.99274460 -1.10745956
4 9.35725161 -2.99274460
5 1.76394870 9.35725161
6 9.50123304 1.76394870
7 -2.05081539 9.50123304
8 -2.35807107 -2.05081539
9 0.67049139 -2.35807107
10 -0.59012698 0.67049139
11 -1.07093542 -0.59012698
12 -0.93546468 -1.07093542
13 2.69592024 -0.93546468
14 0.22257380 2.69592024
15 0.80238695 0.22257380
16 0.90721860 0.80238695
17 0.85285595 0.90721860
18 -2.65139381 0.85285595
19 1.44735571 -2.65139381
20 -0.54629302 1.44735571
21 -1.45242112 -0.54629302
22 -0.48365061 -1.45242112
23 -0.48039120 -0.48365061
24 0.61768652 -0.48039120
25 -7.43737010 0.61768652
26 -0.45902013 -7.43737010
27 1.90351056 -0.45902013
28 1.71548471 1.90351056
29 -2.59543581 1.71548471
30 -2.69099887 -2.59543581
31 0.38511596 -2.69099887
32 -0.82956772 0.38511596
33 -1.25285060 -0.82956772
34 -0.20174736 -1.25285060
35 -4.31879914 -0.20174736
36 2.22405787 -4.31879914
37 -0.35246732 2.22405787
38 -0.97597656 -0.35246732
39 -3.81211247 -0.97597656
40 -3.17968364 -3.81211247
41 2.33372963 -3.17968364
42 -2.79808574 2.33372963
43 -0.78062233 -2.79808574
44 -1.32217253 -0.78062233
45 -2.69781694 -1.32217253
46 -3.24880474 -2.69781694
47 -1.42033090 -3.24880474
48 4.33924502 -1.42033090
49 -2.80780371 4.33924502
50 -1.49999660 -2.80780371
51 0.64304937 -1.49999660
52 2.19171020 0.64304937
53 -0.53674693 2.19171020
54 -4.57150569 -0.53674693
55 1.97642434 -4.57150569
56 1.74185719 1.97642434
57 -3.45880587 1.74185719
58 -4.57026986 -3.45880587
59 0.41314435 -4.57026986
60 1.63050399 0.41314435
61 -1.61683889 1.63050399
62 -3.26555315 -1.61683889
63 0.62089729 -3.26555315
64 0.76396063 0.62089729
65 -4.93667121 0.76396063
66 2.01675975 -4.93667121
67 -2.55030540 2.01675975
68 0.55996754 -2.55030540
69 1.79002157 0.55996754
70 -0.57912485 1.79002157
71 3.15637428 -0.57912485
72 1.00757313 3.15637428
73 -1.82654191 1.00757313
74 -2.76871449 -1.82654191
75 4.65939536 -2.76871449
76 2.45437799 4.65939536
77 3.18534856 2.45437799
78 0.21187474 3.18534856
79 -4.64009796 0.21187474
80 -1.29292091 -4.64009796
81 -3.20997843 -1.29292091
82 0.56063044 -3.20997843
83 -0.46320308 0.56063044
84 -0.12031177 -0.46320308
85 0.61227741 -0.12031177
86 -1.05925847 0.61227741
87 1.00291527 -1.05925847
88 3.44167000 1.00291527
89 1.66789185 3.44167000
90 1.50549316 1.66789185
91 0.77881408 1.50549316
92 1.81639631 0.77881408
93 -1.78633382 1.81639631
94 -0.33320547 -1.78633382
95 1.60027521 -0.33320547
96 -2.06929689 1.60027521
97 0.42805415 -2.06929689
98 -2.60182056 0.42805415
99 -0.46521850 -2.60182056
100 1.53081629 -0.46521850
101 1.10216055 1.53081629
102 -4.53415706 1.10216055
103 3.66394401 -4.53415706
104 2.09189115 3.66394401
105 -0.20553258 2.09189115
106 4.04165088 -0.20553258
107 -1.05593849 4.04165088
108 7.69249867 -1.05593849
109 1.58406631 7.69249867
110 -0.38856594 1.58406631
111 -2.83013024 -0.38856594
112 1.57108893 -2.83013024
113 1.73739942 1.57108893
114 -1.10698539 1.73739942
115 0.76209120 -1.10698539
116 -0.36868393 0.76209120
117 -4.39164264 -0.36868393
118 2.14949867 -4.39164264
119 0.05887101 2.14949867
120 0.90933975 0.05887101
121 -1.38181848 0.90933975
122 -4.05757703 -1.38181848
123 -1.21384984 -4.05757703
124 -2.80995387 -1.21384984
125 -2.64834096 -2.80995387
126 -0.22229517 -2.64834096
127 1.59237165 -0.22229517
128 0.37336249 1.59237165
129 -2.94072900 0.37336249
130 2.24784275 -2.94072900
131 -2.13453540 2.24784275
132 2.03879070 -2.13453540
133 -0.32360451 2.03879070
134 2.35747705 -0.32360451
135 7.04513799 2.35747705
136 0.84779810 7.04513799
137 -0.06738109 0.84779810
138 -1.73362777 -0.06738109
139 -2.51535577 -1.73362777
140 -2.46910719 -2.51535577
141 2.14512933 -2.46910719
142 -0.98820420 2.14512933
143 -0.37697038 -0.98820420
144 -0.93548853 -0.37697038
145 0.69266309 -0.93548853
146 -2.54692243 0.69266309
147 -3.63760764 -2.54692243
148 1.77334189 -3.63760764
149 -0.07837732 1.77334189
150 3.48268937 -0.07837732
151 -2.58789956 3.48268937
152 -2.73978286 -2.58789956
153 1.95990948 -2.73978286
154 3.44858233 1.95990948
155 1.50549316 3.44858233
156 0.38623414 1.50549316
157 1.59237165 0.38623414
158 -4.81745331 1.59237165
159 2.81072430 -4.81745331
160 1.77229798 2.81072430
161 7.25384648 1.77229798
162 0.34535720 7.25384648
163 8.86953009 0.34535720
164 1.71409593 8.86953009
165 6.52165201 1.71409593
166 -1.41637114 6.52165201
167 -1.36945085 -1.41637114
168 -1.18215314 -1.36945085
169 0.81539634 -1.18215314
170 4.16856859 0.81539634
171 0.60483505 4.16856859
172 -5.04138549 0.60483505
173 2.16089112 -5.04138549
174 1.23691479 2.16089112
175 -5.00988756 1.23691479
176 -1.27672937 -5.00988756
177 2.78212439 -1.27672937
178 -1.85874207 2.78212439
179 -1.66645262 -1.85874207
180 -1.93624569 -1.66645262
181 -5.43404609 -1.93624569
182 -0.97962404 -5.43404609
183 -2.93757812 -0.97962404
184 -1.75643278 -2.93757812
185 5.12032431 -1.75643278
186 1.13291288 5.12032431
187 -0.58846291 1.13291288
188 -0.69381812 -0.58846291
189 4.84191408 -0.69381812
190 -2.62012686 4.84191408
191 -2.86170882 -2.62012686
192 1.96691027 -2.86170882
193 0.66752689 1.96691027
194 1.31110265 0.66752689
195 -2.54114414 1.31110265
196 5.88783094 -2.54114414
197 1.93233592 5.88783094
198 1.01113864 1.93233592
199 -1.12570126 1.01113864
200 -0.03637403 -1.12570126
201 -0.92798673 -0.03637403
202 -0.28776654 -0.92798673
203 -0.17586096 -0.28776654
204 -1.52451929 -0.17586096
205 0.61693753 -1.52451929
206 -1.01664920 0.61693753
207 3.56476475 -1.01664920
208 -0.26066169 3.56476475
209 0.36255499 -0.26066169
210 -0.26642805 0.36255499
211 -0.59355429 -0.26642805
212 2.43502234 -0.59355429
213 -3.50723859 2.43502234
214 1.70786010 -3.50723859
215 0.25751133 1.70786010
216 0.31731839 0.25751133
217 -1.06805289 0.31731839
218 4.00113768 -1.06805289
219 1.72900572 4.00113768
220 -3.58138899 1.72900572
221 0.42735379 -3.58138899
222 0.76029578 0.42735379
223 -3.86478327 0.76029578
224 3.71926222 -3.86478327
225 -2.55850277 3.71926222
226 1.45427997 -2.55850277
227 -2.90389802 1.45427997
228 4.75915798 -2.90389802
229 -2.83233758 4.75915798
230 -1.50840841 -2.83233758
231 -1.58933707 -1.50840841
232 -3.53856611 -1.58933707
233 3.78060698 -3.53856611
234 -2.83121170 3.78060698
235 -1.79894055 -2.83121170
236 1.19176701 -1.79894055
237 -0.97782297 1.19176701
238 -5.31010677 -0.97782297
239 -3.95573917 -5.31010677
240 -5.38682534 -3.95573917
241 0.96416961 -5.38682534
242 0.29703846 0.96416961
243 0.98217554 0.29703846
244 1.72483395 0.98217554
245 3.91857209 1.72483395
246 0.82236924 3.91857209
247 9.38106775 0.82236924
248 2.75713524 9.38106775
249 0.42429353 2.75713524
250 0.96186979 0.42429353
251 2.41105100 0.96186979
252 -3.30409713 2.41105100
253 -0.92978441 -3.30409713
254 2.10855346 -0.92978441
255 0.21123495 2.10855346
256 4.15837374 0.21123495
257 0.66540800 4.15837374
258 2.55458924 0.66540800
259 -9.54204834 2.55458924
260 -1.43829777 -9.54204834
261 -0.74707387 -1.43829777
262 3.16658519 -0.74707387
263 -1.52590037 3.16658519
> 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/72odt1383469974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8nm8z1383469974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/94xzs1383469974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/103zzq1383469974.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11anvb1383469974.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/12q0ru1383469974.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/13tohj1383469975.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/14lb1d1383469975.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/15ad9y1383469975.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/16xakq1383469975.tab")
+ }
>
> try(system("convert tmp/1942a1383469974.ps tmp/1942a1383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/227n61383469974.ps tmp/227n61383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ez1d1383469974.ps tmp/3ez1d1383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qfqy1383469974.ps tmp/4qfqy1383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/565jo1383469974.ps tmp/565jo1383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lxo11383469974.ps tmp/6lxo11383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/72odt1383469974.ps tmp/72odt1383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nm8z1383469974.ps tmp/8nm8z1383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/94xzs1383469974.ps tmp/94xzs1383469974.png",intern=TRUE))
character(0)
> try(system("convert tmp/103zzq1383469974.ps tmp/103zzq1383469974.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.046 1.639 11.672