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 = '2'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '2'
> #'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
Separate Connected Learning Software Happiness Depression
1 38 41 13 12 14 12.0
2 32 39 16 11 18 11.0
3 35 30 19 15 11 14.0
4 33 31 15 6 12 12.0
5 37 34 14 13 16 21.0
6 29 35 13 10 18 12.0
7 31 39 19 12 14 22.0
8 36 34 15 14 14 11.0
9 35 36 14 12 15 10.0
10 38 37 15 9 15 13.0
11 31 38 16 10 17 10.0
12 34 36 16 12 19 8.0
13 35 38 16 12 10 15.0
14 38 39 16 11 16 14.0
15 37 33 17 15 18 10.0
16 33 32 15 12 14 14.0
17 32 36 15 10 14 14.0
18 38 38 20 12 17 11.0
19 38 39 18 11 14 10.0
20 32 32 16 12 16 13.0
21 33 32 16 11 18 9.5
22 31 31 16 12 11 14.0
23 38 39 19 13 14 12.0
24 39 37 16 11 12 14.0
25 32 39 17 12 17 11.0
26 32 41 17 13 9 9.0
27 35 36 16 10 16 11.0
28 37 33 15 14 14 15.0
29 33 33 16 12 15 14.0
30 33 34 14 10 11 13.0
31 31 31 15 12 16 9.0
32 32 27 12 8 13 15.0
33 31 37 14 10 17 10.0
34 37 34 16 12 15 11.0
35 30 34 14 12 14 13.0
36 33 32 10 7 16 8.0
37 31 29 10 9 9 20.0
38 33 36 14 12 15 12.0
39 31 29 16 10 17 10.0
40 33 35 16 10 13 10.0
41 32 37 16 10 15 9.0
42 33 34 14 12 16 14.0
43 32 38 20 15 16 8.0
44 33 35 14 10 12 14.0
45 28 38 14 10 15 11.0
46 35 37 11 12 11 13.0
47 39 38 14 13 15 9.0
48 34 33 15 11 15 11.0
49 38 36 16 11 17 15.0
50 32 38 14 12 13 11.0
51 38 32 16 14 16 10.0
52 30 32 14 10 14 14.0
53 33 32 12 12 11 18.0
54 38 34 16 13 12 14.0
55 32 32 9 5 12 11.0
56 35 37 14 6 15 14.5
57 34 39 16 12 16 13.0
58 34 29 16 12 15 9.0
59 36 37 15 11 12 10.0
60 34 35 16 10 12 15.0
61 28 30 12 7 8 20.0
62 34 38 16 12 13 12.0
63 35 34 16 14 11 12.0
64 35 31 14 11 14 14.0
65 31 34 16 12 15 13.0
66 37 35 17 13 10 11.0
67 35 36 18 14 11 17.0
68 27 30 18 11 12 12.0
69 40 39 12 12 15 13.0
70 37 35 16 12 15 14.0
71 36 38 10 8 14 13.0
72 38 31 14 11 16 15.0
73 39 34 18 14 15 13.0
74 41 38 18 14 15 10.0
75 27 34 16 12 13 11.0
76 30 39 17 9 12 19.0
77 37 37 16 13 17 13.0
78 31 34 16 11 13 17.0
79 31 28 13 12 15 13.0
80 27 37 16 12 13 9.0
81 36 33 16 12 15 11.0
82 37 35 16 12 15 9.0
83 33 37 15 12 16 12.0
84 34 32 15 11 15 12.0
85 31 33 16 10 14 13.0
86 39 38 14 9 15 13.0
87 34 33 16 12 14 12.0
88 32 29 16 12 13 15.0
89 33 33 15 12 7 22.0
90 36 31 12 9 17 13.0
91 32 36 17 15 13 15.0
92 41 35 16 12 15 13.0
93 28 32 15 12 14 15.0
94 30 29 13 12 13 12.5
95 36 39 16 10 16 11.0
96 35 37 16 13 12 16.0
97 31 35 16 9 14 11.0
98 34 37 16 12 17 11.0
99 36 32 14 10 15 10.0
100 36 38 16 14 17 10.0
101 35 37 16 11 12 16.0
102 37 36 20 15 16 12.0
103 28 32 15 11 11 11.0
104 39 33 16 11 15 16.0
105 32 40 13 12 9 19.0
106 35 38 17 12 16 11.0
107 39 41 16 12 15 16.0
108 35 36 16 11 10 15.0
109 42 43 12 7 10 24.0
110 34 30 16 12 15 14.0
111 33 31 16 14 11 15.0
112 41 32 17 11 13 11.0
113 33 32 13 11 14 15.0
114 34 37 12 10 18 12.0
115 32 37 18 13 16 10.0
116 40 33 14 13 14 14.0
117 40 34 14 8 14 13.0
118 35 33 13 11 14 9.0
119 36 38 16 12 14 15.0
120 37 33 13 11 12 15.0
121 27 31 16 13 14 14.0
122 39 38 13 12 15 11.0
123 38 37 16 14 15 8.0
124 31 36 15 13 15 11.0
125 33 31 16 15 13 11.0
126 32 39 15 10 17 8.0
127 39 44 17 11 17 10.0
128 36 33 15 9 19 11.0
129 33 35 12 11 15 13.0
130 33 32 16 10 13 11.0
131 32 28 10 11 9 20.0
132 37 40 16 8 15 10.0
133 30 27 12 11 15 15.0
134 38 37 14 12 15 12.0
135 29 32 15 12 16 14.0
136 22 28 13 9 11 23.0
137 35 34 15 11 14 14.0
138 35 30 11 10 11 16.0
139 34 35 12 8 15 11.0
140 35 31 11 9 13 12.0
141 34 32 16 8 15 10.0
142 37 30 15 9 16 14.0
143 35 30 17 15 14 12.0
144 23 31 16 11 15 12.0
145 31 40 10 8 16 11.0
146 27 32 18 13 16 12.0
147 36 36 13 12 11 13.0
148 31 32 16 12 12 11.0
149 32 35 13 9 9 19.0
150 39 38 10 7 16 12.0
151 37 42 15 13 13 17.0
152 38 34 16 9 16 9.0
153 39 35 16 6 12 12.0
154 34 38 14 8 9 19.0
155 31 33 10 8 13 18.0
156 32 36 17 15 13 15.0
157 37 32 13 6 14 14.0
158 36 33 15 9 19 11.0
159 32 34 16 11 13 9.0
160 38 32 12 8 12 18.0
161 36 34 13 8 13 16.0
162 26 27 13 10 10 24.0
163 26 31 12 8 14 14.0
164 33 38 17 14 16 20.0
165 39 34 15 10 10 18.0
166 30 24 10 8 11 23.0
167 33 30 14 11 14 12.0
168 25 26 11 12 12 14.0
169 38 34 13 12 9 16.0
170 37 27 16 12 9 18.0
171 31 37 12 5 11 20.0
172 37 36 16 12 16 12.0
173 35 41 12 10 9 12.0
174 25 29 9 7 13 17.0
175 28 36 12 12 16 13.0
176 35 32 15 11 13 9.0
177 33 37 12 8 9 16.0
178 30 30 12 9 12 18.0
179 31 31 14 10 16 10.0
180 37 38 12 9 11 14.0
181 36 36 16 12 14 11.0
182 30 35 11 6 13 9.0
183 36 31 19 15 15 11.0
184 32 38 15 12 14 10.0
185 28 22 8 12 16 11.0
186 36 32 16 12 13 19.0
187 34 36 17 11 14 14.0
188 31 39 12 7 15 12.0
189 28 28 11 7 13 14.0
190 36 32 11 5 11 21.0
191 36 32 14 12 11 13.0
192 40 38 16 12 14 10.0
193 33 32 12 3 15 15.0
194 37 35 16 11 11 16.0
195 32 32 13 10 15 14.0
196 38 37 15 12 12 12.0
197 31 34 16 9 14 19.0
198 37 33 16 12 14 15.0
199 33 33 14 9 8 19.0
200 32 26 16 12 13 13.0
201 30 30 16 12 9 17.0
202 30 24 14 10 15 12.0
203 31 34 11 9 17 11.0
204 32 34 12 12 13 14.0
205 34 33 15 8 15 11.0
206 36 34 15 11 15 13.0
207 37 35 16 11 14 12.0
208 36 35 16 12 16 15.0
209 33 36 11 10 13 14.0
210 33 34 15 10 16 12.0
211 33 34 12 12 9 17.0
212 44 41 12 12 16 11.0
213 39 32 15 11 11 18.0
214 32 30 15 8 10 13.0
215 35 35 16 12 11 17.0
216 25 28 14 10 15 13.0
217 35 33 17 11 17 11.0
218 34 39 14 10 14 12.0
219 35 36 13 8 8 22.0
220 39 36 15 12 15 14.0
221 33 35 13 12 11 12.0
222 36 38 14 10 16 12.0
223 32 33 15 12 10 17.0
224 32 31 12 9 15 9.0
225 36 34 13 9 9 21.0
226 36 32 8 6 16 10.0
227 32 31 14 10 19 11.0
228 34 33 14 9 12 12.0
229 33 34 11 9 8 23.0
230 35 34 12 9 11 13.0
231 30 34 13 6 14 12.0
232 38 33 10 10 9 16.0
233 34 32 16 6 15 9.0
234 33 41 18 14 13 17.0
235 32 34 13 10 16 9.0
236 31 36 11 10 11 14.0
237 30 37 4 6 12 17.0
238 27 36 13 12 13 13.0
239 31 29 16 12 10 11.0
240 30 37 10 7 11 12.0
241 32 27 12 8 12 10.0
242 35 35 12 11 8 19.0
243 28 28 10 3 12 16.0
244 33 35 13 6 12 16.0
245 31 37 15 10 15 14.0
246 35 29 12 8 11 20.0
247 35 32 14 9 13 15.0
248 32 36 10 9 14 23.0
249 21 19 12 8 10 20.0
250 20 21 12 9 12 16.0
251 34 31 11 7 15 14.0
252 32 33 10 7 13 17.0
253 34 36 12 6 13 11.0
254 32 33 16 9 13 13.0
255 33 37 12 10 12 17.0
256 33 34 14 11 12 15.0
257 37 35 16 12 9 21.0
258 32 31 14 8 9 18.0
259 34 37 13 11 15 15.0
260 30 35 4 3 10 8.0
261 30 27 15 11 14 12.0
262 38 34 11 12 15 12.0
263 36 40 11 7 7 22.0
264 32 29 14 9 14 12.0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Learning Software Happiness Depression
15.934020 0.414995 0.128945 0.122129 0.032086 0.003787
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.7321 -1.7592 0.1165 2.2627 7.7919
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.934020 2.903033 5.489 9.67e-08 ***
Connected 0.414995 0.054723 7.584 6.09e-13 ***
Learning 0.128945 0.108559 1.188 0.236
Software 0.122129 0.111659 1.094 0.275
Happiness 0.032086 0.100871 0.318 0.751
Depression 0.003787 0.072382 0.052 0.958
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.283 on 258 degrees of freedom
Multiple R-squared: 0.2291, Adjusted R-squared: 0.2142
F-statistic: 15.34 on 5 and 258 DF, p-value: 3.354e-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,] 0.339980797 0.67996159 0.66001920
[2,] 0.574617097 0.85076581 0.42538290
[3,] 0.433930182 0.86786036 0.56606982
[4,] 0.446270593 0.89254119 0.55372941
[5,] 0.379292741 0.75858548 0.62070726
[6,] 0.475120421 0.95024084 0.52487958
[7,] 0.454661458 0.90932292 0.54533854
[8,] 0.391860854 0.78372171 0.60813915
[9,] 0.341213441 0.68242688 0.65878656
[10,] 0.398760463 0.79752093 0.60123954
[11,] 0.355243574 0.71048715 0.64475643
[12,] 0.297456127 0.59491225 0.70254387
[13,] 0.231119146 0.46223829 0.76888085
[14,] 0.230618639 0.46123728 0.76938136
[15,] 0.182665270 0.36533054 0.81733473
[16,] 0.201560481 0.40312096 0.79843952
[17,] 0.225701745 0.45140349 0.77429825
[18,] 0.368480809 0.73696162 0.63151919
[19,] 0.313111519 0.62622304 0.68688848
[20,] 0.285310312 0.57062062 0.71468969
[21,] 0.238779693 0.47755939 0.76122031
[22,] 0.193777904 0.38755581 0.80622210
[23,] 0.175415115 0.35083023 0.82458489
[24,] 0.141696172 0.28339234 0.85830383
[25,] 0.135545708 0.27109142 0.86445429
[26,] 0.131473033 0.26294607 0.86852697
[27,] 0.152847152 0.30569430 0.84715285
[28,] 0.131424137 0.26284827 0.86857586
[29,] 0.105133012 0.21026602 0.89486699
[30,] 0.084906736 0.16981347 0.91509326
[31,] 0.068602167 0.13720433 0.93139783
[32,] 0.053017119 0.10603424 0.94698288
[33,] 0.045642915 0.09128583 0.95435708
[34,] 0.034574526 0.06914905 0.96542547
[35,] 0.042227020 0.08445404 0.95777298
[36,] 0.031942971 0.06388594 0.96805703
[37,] 0.068361757 0.13672351 0.93163824
[38,] 0.054557777 0.10911555 0.94544222
[39,] 0.069897140 0.13979428 0.93010286
[40,] 0.055332308 0.11066462 0.94466769
[41,] 0.064955765 0.12991153 0.93504424
[42,] 0.061323851 0.12264770 0.93867615
[43,] 0.069435581 0.13887116 0.93056442
[44,] 0.067102788 0.13420558 0.93289721
[45,] 0.053463083 0.10692617 0.94653692
[46,] 0.057109428 0.11421886 0.94289057
[47,] 0.048152646 0.09630529 0.95184735
[48,] 0.044596518 0.08919304 0.95540348
[49,] 0.036327996 0.07265599 0.96367200
[50,] 0.028993060 0.05798612 0.97100694
[51,] 0.024232430 0.04846486 0.97576757
[52,] 0.018423452 0.03684690 0.98157655
[53,] 0.020246435 0.04049287 0.97975357
[54,] 0.015860598 0.03172120 0.98413940
[55,] 0.011934942 0.02386988 0.98806506
[56,] 0.010152510 0.02030502 0.98984749
[57,] 0.010396703 0.02079341 0.98960330
[58,] 0.009056241 0.01811248 0.99094376
[59,] 0.006734600 0.01346920 0.99326540
[60,] 0.014843502 0.02968700 0.98515650
[61,] 0.020007002 0.04001400 0.97999300
[62,] 0.018737624 0.03747525 0.98126238
[63,] 0.016078108 0.03215622 0.98392189
[64,] 0.023912088 0.04782418 0.97608791
[65,] 0.028985676 0.05797135 0.97101432
[66,] 0.041506296 0.08301259 0.95849370
[67,] 0.092996172 0.18599234 0.90700383
[68,] 0.110105186 0.22021037 0.88989481
[69,] 0.094818542 0.18963708 0.90518146
[70,] 0.089805413 0.17961083 0.91019459
[71,] 0.080431440 0.16086288 0.91956856
[72,] 0.184703074 0.36940615 0.81529693
[73,] 0.171446063 0.34289213 0.82855394
[74,] 0.164082043 0.32816409 0.83591796
[75,] 0.152018178 0.30403636 0.84798182
[76,] 0.131403274 0.26280655 0.86859673
[77,] 0.119229483 0.23845897 0.88077052
[78,] 0.145829739 0.29165948 0.85417026
[79,] 0.124869814 0.24973963 0.87513019
[80,] 0.106523867 0.21304773 0.89347613
[81,] 0.089929135 0.17985827 0.91007086
[82,] 0.093108060 0.18621612 0.90689194
[83,] 0.098691416 0.19738283 0.90130858
[84,] 0.161804150 0.32360830 0.83819585
[85,] 0.212037299 0.42407460 0.78796270
[86,] 0.201219053 0.40243811 0.79878095
[87,] 0.178735197 0.35747039 0.82126480
[88,] 0.155573343 0.31114669 0.84442666
[89,] 0.147986298 0.29597260 0.85201370
[90,] 0.131239566 0.26247913 0.86876043
[91,] 0.130615073 0.26123015 0.86938493
[92,] 0.112089630 0.22417926 0.88791037
[93,] 0.096867394 0.19373479 0.90313261
[94,] 0.083961489 0.16792298 0.91603851
[95,] 0.101716896 0.20343379 0.89828310
[96,] 0.134528861 0.26905772 0.86547114
[97,] 0.140289438 0.28057888 0.85971056
[98,] 0.122238070 0.24447614 0.87776193
[99,] 0.113856955 0.22771391 0.88614304
[100,] 0.101431025 0.20286205 0.89856898
[101,] 0.149263374 0.29852675 0.85073663
[102,] 0.132069092 0.26413818 0.86793091
[103,] 0.113594803 0.22718961 0.88640520
[104,] 0.233383802 0.46676760 0.76661620
[105,] 0.207581689 0.41516338 0.79241831
[106,] 0.185536512 0.37107302 0.81446349
[107,] 0.192333816 0.38466763 0.80766618
[108,] 0.261502982 0.52300596 0.73849702
[109,] 0.368124848 0.73624970 0.63187515
[110,] 0.344410697 0.68882139 0.65558930
[111,] 0.312262485 0.62452497 0.68773752
[112,] 0.320767659 0.64153532 0.67923234
[113,] 0.412098077 0.82419615 0.58790192
[114,] 0.417804816 0.83560963 0.58219518
[115,] 0.402721136 0.80544227 0.59727886
[116,] 0.421170607 0.84234121 0.57882939
[117,] 0.387077271 0.77415454 0.61292273
[118,] 0.398340863 0.79668173 0.60165914
[119,] 0.371076159 0.74215232 0.62892384
[120,] 0.358461492 0.71692298 0.64153851
[121,] 0.331223660 0.66244732 0.66877634
[122,] 0.300649273 0.60129855 0.69935073
[123,] 0.278114582 0.55622916 0.72188542
[124,] 0.256359165 0.51271833 0.74364083
[125,] 0.236813401 0.47362680 0.76318660
[126,] 0.229866928 0.45973386 0.77013307
[127,] 0.252159775 0.50431955 0.74784022
[128,] 0.454100163 0.90820033 0.54589984
[129,] 0.423146238 0.84629248 0.57685376
[130,] 0.429760193 0.85952039 0.57023981
[131,] 0.395579853 0.79115971 0.60442015
[132,] 0.393972001 0.78794400 0.60602800
[133,] 0.365348370 0.73069674 0.63465163
[134,] 0.410522241 0.82104448 0.58947776
[135,] 0.390585176 0.78117035 0.60941482
[136,] 0.667784638 0.66443072 0.33221536
[137,] 0.695533973 0.60893205 0.30446603
[138,] 0.794256518 0.41148696 0.20574348
[139,] 0.773191444 0.45361711 0.22680856
[140,] 0.761456514 0.47708697 0.23854349
[141,] 0.740828895 0.51834221 0.25917110
[142,] 0.770584153 0.45883169 0.22941585
[143,] 0.743270996 0.51345801 0.25672900
[144,] 0.757935194 0.48412961 0.24206481
[145,] 0.794387378 0.41122524 0.20561262
[146,] 0.772651756 0.45469649 0.22734824
[147,] 0.750151282 0.49969744 0.24984872
[148,] 0.758750832 0.48249834 0.24124917
[149,] 0.791671650 0.41665670 0.20832835
[150,] 0.785692052 0.42861590 0.21430795
[151,] 0.773484482 0.45303104 0.22651552
[152,] 0.832830753 0.33433849 0.16716925
[153,] 0.827784147 0.34443171 0.17221585
[154,] 0.847028874 0.30594225 0.15297113
[155,] 0.889340837 0.22131833 0.11065916
[156,] 0.892690007 0.21461999 0.10730999
[157,] 0.915762359 0.16847528 0.08423764
[158,] 0.907421048 0.18515790 0.09257895
[159,] 0.892281566 0.21543687 0.10771843
[160,] 0.914504696 0.17099061 0.08549530
[161,] 0.923150888 0.15369822 0.07684911
[162,] 0.948328181 0.10334364 0.05167182
[163,] 0.946264067 0.10747187 0.05373593
[164,] 0.938503096 0.12299381 0.06149690
[165,] 0.929216559 0.14156688 0.07078344
[166,] 0.945993786 0.10801243 0.05400621
[167,] 0.972448184 0.05510363 0.02755182
[168,] 0.968182453 0.06363509 0.03181755
[169,] 0.962025610 0.07594878 0.03797439
[170,] 0.954972430 0.09005514 0.04502757
[171,] 0.946831967 0.10633607 0.05316803
[172,] 0.940117641 0.11976472 0.05988236
[173,] 0.928166424 0.14366715 0.07183358
[174,] 0.926006607 0.14798679 0.07399339
[175,] 0.916999282 0.16600144 0.08300072
[176,] 0.926929742 0.14614052 0.07307026
[177,] 0.914503621 0.17099276 0.08549638
[178,] 0.909071315 0.18185737 0.09092868
[179,] 0.895791503 0.20841699 0.10420850
[180,] 0.913671040 0.17265792 0.08632896
[181,] 0.902372096 0.19525581 0.09762790
[182,] 0.922252904 0.15549419 0.07774710
[183,] 0.919550419 0.16089916 0.08044958
[184,] 0.921751871 0.15649626 0.07824813
[185,] 0.910463466 0.17907307 0.08953653
[186,] 0.902171323 0.19565735 0.09782868
[187,] 0.883245112 0.23350978 0.11675489
[188,] 0.872863832 0.25427234 0.12713617
[189,] 0.868621658 0.26275668 0.13137834
[190,] 0.867922624 0.26415475 0.13207738
[191,] 0.843559051 0.31288190 0.15644095
[192,] 0.825741100 0.34851780 0.17425890
[193,] 0.809245719 0.38150856 0.19075428
[194,] 0.786580938 0.42683812 0.21341906
[195,] 0.766600645 0.46679871 0.23339935
[196,] 0.739291557 0.52141689 0.26070844
[197,] 0.704665250 0.59066950 0.29533475
[198,] 0.680375750 0.63924850 0.31962425
[199,] 0.661730336 0.67653933 0.33826966
[200,] 0.626727796 0.74654441 0.37327220
[201,] 0.588253563 0.82349287 0.41174644
[202,] 0.545658199 0.90868360 0.45434180
[203,] 0.500939376 0.99812125 0.49906062
[204,] 0.670534558 0.65893088 0.32946544
[205,] 0.781651092 0.43669782 0.21834891
[206,] 0.745765750 0.50846850 0.25423425
[207,] 0.708191266 0.58361747 0.29180873
[208,] 0.778043732 0.44391254 0.22195627
[209,] 0.749475754 0.50104849 0.25052425
[210,] 0.719759926 0.56048015 0.28024007
[211,] 0.681063583 0.63787283 0.31893642
[212,] 0.726249663 0.54750067 0.27375034
[213,] 0.683922775 0.63215445 0.31607722
[214,] 0.642318627 0.71536275 0.35768137
[215,] 0.597275215 0.80544957 0.40272478
[216,] 0.547770654 0.90445869 0.45222935
[217,] 0.541762521 0.91647496 0.45823748
[218,] 0.623659091 0.75268182 0.37634091
[219,] 0.581193342 0.83761332 0.41880666
[220,] 0.536047874 0.92790425 0.46395213
[221,] 0.480401148 0.96080230 0.51959885
[222,] 0.450390647 0.90078129 0.54960935
[223,] 0.424717813 0.84943563 0.57528219
[224,] 0.595557897 0.80888421 0.40444210
[225,] 0.544129075 0.91174185 0.45587093
[226,] 0.606062178 0.78787564 0.39393782
[227,] 0.547707211 0.90458558 0.45229279
[228,] 0.511905504 0.97618899 0.48809450
[229,] 0.464697215 0.92939443 0.53530278
[230,] 0.746652277 0.50669545 0.25334772
[231,] 0.692315668 0.61536866 0.30768433
[232,] 0.750200736 0.49959853 0.24979926
[233,] 0.745036175 0.50992765 0.25496383
[234,] 0.683690972 0.63261806 0.31630903
[235,] 0.616816003 0.76636799 0.38318400
[236,] 0.539418142 0.92116372 0.46058186
[237,] 0.692243136 0.61551373 0.30775686
[238,] 0.878878566 0.24224287 0.12112143
[239,] 0.867700285 0.26459943 0.13229971
[240,] 0.831010699 0.33797860 0.16898930
[241,] 0.766997933 0.46600413 0.23300207
[242,] 0.932304994 0.13539001 0.06769501
[243,] 0.927711594 0.14457681 0.07228841
[244,] 0.870135536 0.25972893 0.12986446
[245,] 0.865234721 0.26953056 0.13476528
[246,] 0.825476418 0.34904716 0.17452358
[247,] 0.840701229 0.31859754 0.15929877
> postscript(file="/var/fisher/rcomp/tmp/10tbo1383469553.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2fh7w1383469553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32af91383469553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4o81v1383469553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5jrxf1383469553.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
1.41469932 -4.14457391 1.92826572 1.10370498 2.97033050 -4.97941658
7 8 9 10 11 12
-5.56685656 1.82130131 0.33621705 3.14730279 -4.57157646 -1.04244252
13 14 15 16 17 18
-0.61017054 1.90823582 2.73171988 -0.11581242 -2.53153315 1.66459622
19 20 21 22 23 24
1.72966646 -1.30514221 -0.23392893 -1.73350516 1.34888774 3.86656918
25 26 27 28 29 30
-4.36356266 -5.05141965 0.28671179 3.22114670 -0.69183851 -0.47255302
31 32 33 34 35 36
-1.74605258 1.86281367 -3.89869096 2.90452874 -3.81306943 1.09811322
37 38 39 40 41 42
0.27799213 -1.67135767 -0.83662282 -1.19824826 -3.08862242 -0.88102862
43 44 45 46 47 48
-4.65834378 -0.92342115 -7.25330134 0.42503974 3.38788539 0.57059824
49 50 51 52 53 54
3.11734709 -3.43338818 4.46196122 -2.74260843 0.35213184 3.86729505
55 56 57 58 59 60
0.58829877 0.63695506 -2.21010615 1.98707770 1.01066395 -0.18509916
61 62 63 64 65 66
-3.11854879 -1.69506619 0.78482636 2.55025708 -3.10304599 2.39888880
67 68 69 70 71 72
-0.32199080 -5.47878281 4.33776107 2.47817180 1.53124981 5.48229789
73 74 75 76 77 78
4.39480469 4.74618739 -7.03129944 -5.86704400 1.46566830 -2.93189429
79 80 81 82 83 84
-0.22624093 -8.26870925 2.31952358 2.49710862 -2.24738376 0.98180573
85 86 87 88 89 90
-2.41170656 3.86125326 0.34782213 0.02852535 -0.33650478 3.95993602
91 92 93 94 95 96
-3.37177191 6.48195916 -5.11959978 -1.57517027 0.04172725 -0.38526422
97 98 99 100 101 102
-3.11199220 -1.40462764 3.24045511 -0.06009378 -0.14100555 1.15649647
103 104 105 106 107 108
-4.88606326 5.42271610 -4.03638780 -0.91648189 1.98062798 0.34194849
109 110 111 112 113 114
5.40719691 1.55314604 0.01844882 7.79187427 0.26042020 -0.68046095
115 116 117 118 119 120
-3.74877434 6.47600872 6.67544790 1.86814953 0.26148580 3.90959718
121 122 123 124 125 126
-5.95189223 3.63138532 2.42664762 -3.91864497 -0.15270289 -3.85005125
127 128 129 130 131 132
0.68737980 2.68651325 -0.88013021 0.04294892 1.44872832 0.90686434
133 134 135 136 137 138
-0.56774615 2.91364748 -4.17998425 -8.76938291 1.17632722 3.56290025
139 140 141 142 143 144
0.49383252 3.22101237 1.22682313 5.01639345 2.09747336 -9.73214475
145 146 147 148 149 150
-4.35533699 -6.68137483 1.58214394 -2.16922383 -1.59502557 4.59299468
151 152 153 154 155 156
-0.36716641 4.24640555 5.31478026 -0.84682610 -1.38062686 -3.37177191
157 158 159 160 161 162
4.87485422 2.68651325 -1.90159538 5.80856326 2.82511705 -4.44821885
163 164 165 166 167 168
-5.82546427 -3.19482683 5.41165075 1.39956179 0.97282666 -5.04589021
169 170 171 172 173 174
4.46494338 5.97549661 -2.87551180 2.03866576 -1.05166712 -5.46578544
175 176 177 178 179 180
-6.44934030 2.05733964 -1.16257851 -1.48357638 -1.37663596 2.24370021
181 182 183 184 185 186
1.10662495 -3.06121694 2.39628931 -3.59063205 -0.11605640 2.76839135
187 188 189 190 191 192
-0.91155313 -4.04780491 -2.29731915 4.32462040 3.11317801 4.28042262
193 194 195 196 197 198
1.33431427 2.72107006 -0.64574902 2.88095989 -2.72729626 3.33646004
199 200 201 202 203 204
0.13810472 1.28108462 -2.26570057 0.55283917 -2.14852847 -1.52688023
205 206 207 208 209 210
0.93698624 2.14802867 2.63996177 1.44229852 -0.98366593 -0.75814055
211 212 213 214 215 216
-0.40989866 7.48326019 6.08742520 0.33482562 0.59515336 -6.11092759
217 218 219 220 221 222
1.24853576 -1.63999764 1.13283274 4.19212227 -0.99907385 0.71082538
223 224 225 226 227 228
-1.41382570 0.03925730 2.81239455 4.47055847 -0.47668106 1.03627261
229 230 231 232 233 234
0.09479639 1.90746696 -2.94756074 5.51103286 1.47486915 -4.46113687
235 236 237 238 239 240
-1.48888781 -2.91949411 -2.98680236 -7.48202789 -0.86006745 -3.83158091
241 242 243 244 245 246
1.91383641 1.32174701 -1.65534531 -0.31353322 -3.97861391 4.07805899
247 248 249 250 251 252
2.40781945 -1.79876346 -5.73990662 -7.74104802 2.39352448 -0.25471016
253 254 255 256 257 258
0.38726816 -1.25749132 -1.50688228 -0.63434300 2.64417573 0.06192520
259 260 261 262 263 264
-0.84663996 -1.69216656 -0.91113412 4.54546800 0.88495924 0.63208017
> postscript(file="/var/fisher/rcomp/tmp/6aa681383469553.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 1.41469932 NA
1 -4.14457391 1.41469932
2 1.92826572 -4.14457391
3 1.10370498 1.92826572
4 2.97033050 1.10370498
5 -4.97941658 2.97033050
6 -5.56685656 -4.97941658
7 1.82130131 -5.56685656
8 0.33621705 1.82130131
9 3.14730279 0.33621705
10 -4.57157646 3.14730279
11 -1.04244252 -4.57157646
12 -0.61017054 -1.04244252
13 1.90823582 -0.61017054
14 2.73171988 1.90823582
15 -0.11581242 2.73171988
16 -2.53153315 -0.11581242
17 1.66459622 -2.53153315
18 1.72966646 1.66459622
19 -1.30514221 1.72966646
20 -0.23392893 -1.30514221
21 -1.73350516 -0.23392893
22 1.34888774 -1.73350516
23 3.86656918 1.34888774
24 -4.36356266 3.86656918
25 -5.05141965 -4.36356266
26 0.28671179 -5.05141965
27 3.22114670 0.28671179
28 -0.69183851 3.22114670
29 -0.47255302 -0.69183851
30 -1.74605258 -0.47255302
31 1.86281367 -1.74605258
32 -3.89869096 1.86281367
33 2.90452874 -3.89869096
34 -3.81306943 2.90452874
35 1.09811322 -3.81306943
36 0.27799213 1.09811322
37 -1.67135767 0.27799213
38 -0.83662282 -1.67135767
39 -1.19824826 -0.83662282
40 -3.08862242 -1.19824826
41 -0.88102862 -3.08862242
42 -4.65834378 -0.88102862
43 -0.92342115 -4.65834378
44 -7.25330134 -0.92342115
45 0.42503974 -7.25330134
46 3.38788539 0.42503974
47 0.57059824 3.38788539
48 3.11734709 0.57059824
49 -3.43338818 3.11734709
50 4.46196122 -3.43338818
51 -2.74260843 4.46196122
52 0.35213184 -2.74260843
53 3.86729505 0.35213184
54 0.58829877 3.86729505
55 0.63695506 0.58829877
56 -2.21010615 0.63695506
57 1.98707770 -2.21010615
58 1.01066395 1.98707770
59 -0.18509916 1.01066395
60 -3.11854879 -0.18509916
61 -1.69506619 -3.11854879
62 0.78482636 -1.69506619
63 2.55025708 0.78482636
64 -3.10304599 2.55025708
65 2.39888880 -3.10304599
66 -0.32199080 2.39888880
67 -5.47878281 -0.32199080
68 4.33776107 -5.47878281
69 2.47817180 4.33776107
70 1.53124981 2.47817180
71 5.48229789 1.53124981
72 4.39480469 5.48229789
73 4.74618739 4.39480469
74 -7.03129944 4.74618739
75 -5.86704400 -7.03129944
76 1.46566830 -5.86704400
77 -2.93189429 1.46566830
78 -0.22624093 -2.93189429
79 -8.26870925 -0.22624093
80 2.31952358 -8.26870925
81 2.49710862 2.31952358
82 -2.24738376 2.49710862
83 0.98180573 -2.24738376
84 -2.41170656 0.98180573
85 3.86125326 -2.41170656
86 0.34782213 3.86125326
87 0.02852535 0.34782213
88 -0.33650478 0.02852535
89 3.95993602 -0.33650478
90 -3.37177191 3.95993602
91 6.48195916 -3.37177191
92 -5.11959978 6.48195916
93 -1.57517027 -5.11959978
94 0.04172725 -1.57517027
95 -0.38526422 0.04172725
96 -3.11199220 -0.38526422
97 -1.40462764 -3.11199220
98 3.24045511 -1.40462764
99 -0.06009378 3.24045511
100 -0.14100555 -0.06009378
101 1.15649647 -0.14100555
102 -4.88606326 1.15649647
103 5.42271610 -4.88606326
104 -4.03638780 5.42271610
105 -0.91648189 -4.03638780
106 1.98062798 -0.91648189
107 0.34194849 1.98062798
108 5.40719691 0.34194849
109 1.55314604 5.40719691
110 0.01844882 1.55314604
111 7.79187427 0.01844882
112 0.26042020 7.79187427
113 -0.68046095 0.26042020
114 -3.74877434 -0.68046095
115 6.47600872 -3.74877434
116 6.67544790 6.47600872
117 1.86814953 6.67544790
118 0.26148580 1.86814953
119 3.90959718 0.26148580
120 -5.95189223 3.90959718
121 3.63138532 -5.95189223
122 2.42664762 3.63138532
123 -3.91864497 2.42664762
124 -0.15270289 -3.91864497
125 -3.85005125 -0.15270289
126 0.68737980 -3.85005125
127 2.68651325 0.68737980
128 -0.88013021 2.68651325
129 0.04294892 -0.88013021
130 1.44872832 0.04294892
131 0.90686434 1.44872832
132 -0.56774615 0.90686434
133 2.91364748 -0.56774615
134 -4.17998425 2.91364748
135 -8.76938291 -4.17998425
136 1.17632722 -8.76938291
137 3.56290025 1.17632722
138 0.49383252 3.56290025
139 3.22101237 0.49383252
140 1.22682313 3.22101237
141 5.01639345 1.22682313
142 2.09747336 5.01639345
143 -9.73214475 2.09747336
144 -4.35533699 -9.73214475
145 -6.68137483 -4.35533699
146 1.58214394 -6.68137483
147 -2.16922383 1.58214394
148 -1.59502557 -2.16922383
149 4.59299468 -1.59502557
150 -0.36716641 4.59299468
151 4.24640555 -0.36716641
152 5.31478026 4.24640555
153 -0.84682610 5.31478026
154 -1.38062686 -0.84682610
155 -3.37177191 -1.38062686
156 4.87485422 -3.37177191
157 2.68651325 4.87485422
158 -1.90159538 2.68651325
159 5.80856326 -1.90159538
160 2.82511705 5.80856326
161 -4.44821885 2.82511705
162 -5.82546427 -4.44821885
163 -3.19482683 -5.82546427
164 5.41165075 -3.19482683
165 1.39956179 5.41165075
166 0.97282666 1.39956179
167 -5.04589021 0.97282666
168 4.46494338 -5.04589021
169 5.97549661 4.46494338
170 -2.87551180 5.97549661
171 2.03866576 -2.87551180
172 -1.05166712 2.03866576
173 -5.46578544 -1.05166712
174 -6.44934030 -5.46578544
175 2.05733964 -6.44934030
176 -1.16257851 2.05733964
177 -1.48357638 -1.16257851
178 -1.37663596 -1.48357638
179 2.24370021 -1.37663596
180 1.10662495 2.24370021
181 -3.06121694 1.10662495
182 2.39628931 -3.06121694
183 -3.59063205 2.39628931
184 -0.11605640 -3.59063205
185 2.76839135 -0.11605640
186 -0.91155313 2.76839135
187 -4.04780491 -0.91155313
188 -2.29731915 -4.04780491
189 4.32462040 -2.29731915
190 3.11317801 4.32462040
191 4.28042262 3.11317801
192 1.33431427 4.28042262
193 2.72107006 1.33431427
194 -0.64574902 2.72107006
195 2.88095989 -0.64574902
196 -2.72729626 2.88095989
197 3.33646004 -2.72729626
198 0.13810472 3.33646004
199 1.28108462 0.13810472
200 -2.26570057 1.28108462
201 0.55283917 -2.26570057
202 -2.14852847 0.55283917
203 -1.52688023 -2.14852847
204 0.93698624 -1.52688023
205 2.14802867 0.93698624
206 2.63996177 2.14802867
207 1.44229852 2.63996177
208 -0.98366593 1.44229852
209 -0.75814055 -0.98366593
210 -0.40989866 -0.75814055
211 7.48326019 -0.40989866
212 6.08742520 7.48326019
213 0.33482562 6.08742520
214 0.59515336 0.33482562
215 -6.11092759 0.59515336
216 1.24853576 -6.11092759
217 -1.63999764 1.24853576
218 1.13283274 -1.63999764
219 4.19212227 1.13283274
220 -0.99907385 4.19212227
221 0.71082538 -0.99907385
222 -1.41382570 0.71082538
223 0.03925730 -1.41382570
224 2.81239455 0.03925730
225 4.47055847 2.81239455
226 -0.47668106 4.47055847
227 1.03627261 -0.47668106
228 0.09479639 1.03627261
229 1.90746696 0.09479639
230 -2.94756074 1.90746696
231 5.51103286 -2.94756074
232 1.47486915 5.51103286
233 -4.46113687 1.47486915
234 -1.48888781 -4.46113687
235 -2.91949411 -1.48888781
236 -2.98680236 -2.91949411
237 -7.48202789 -2.98680236
238 -0.86006745 -7.48202789
239 -3.83158091 -0.86006745
240 1.91383641 -3.83158091
241 1.32174701 1.91383641
242 -1.65534531 1.32174701
243 -0.31353322 -1.65534531
244 -3.97861391 -0.31353322
245 4.07805899 -3.97861391
246 2.40781945 4.07805899
247 -1.79876346 2.40781945
248 -5.73990662 -1.79876346
249 -7.74104802 -5.73990662
250 2.39352448 -7.74104802
251 -0.25471016 2.39352448
252 0.38726816 -0.25471016
253 -1.25749132 0.38726816
254 -1.50688228 -1.25749132
255 -0.63434300 -1.50688228
256 2.64417573 -0.63434300
257 0.06192520 2.64417573
258 -0.84663996 0.06192520
259 -1.69216656 -0.84663996
260 -0.91113412 -1.69216656
261 4.54546800 -0.91113412
262 0.88495924 4.54546800
263 0.63208017 0.88495924
264 NA 0.63208017
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.14457391 1.41469932
[2,] 1.92826572 -4.14457391
[3,] 1.10370498 1.92826572
[4,] 2.97033050 1.10370498
[5,] -4.97941658 2.97033050
[6,] -5.56685656 -4.97941658
[7,] 1.82130131 -5.56685656
[8,] 0.33621705 1.82130131
[9,] 3.14730279 0.33621705
[10,] -4.57157646 3.14730279
[11,] -1.04244252 -4.57157646
[12,] -0.61017054 -1.04244252
[13,] 1.90823582 -0.61017054
[14,] 2.73171988 1.90823582
[15,] -0.11581242 2.73171988
[16,] -2.53153315 -0.11581242
[17,] 1.66459622 -2.53153315
[18,] 1.72966646 1.66459622
[19,] -1.30514221 1.72966646
[20,] -0.23392893 -1.30514221
[21,] -1.73350516 -0.23392893
[22,] 1.34888774 -1.73350516
[23,] 3.86656918 1.34888774
[24,] -4.36356266 3.86656918
[25,] -5.05141965 -4.36356266
[26,] 0.28671179 -5.05141965
[27,] 3.22114670 0.28671179
[28,] -0.69183851 3.22114670
[29,] -0.47255302 -0.69183851
[30,] -1.74605258 -0.47255302
[31,] 1.86281367 -1.74605258
[32,] -3.89869096 1.86281367
[33,] 2.90452874 -3.89869096
[34,] -3.81306943 2.90452874
[35,] 1.09811322 -3.81306943
[36,] 0.27799213 1.09811322
[37,] -1.67135767 0.27799213
[38,] -0.83662282 -1.67135767
[39,] -1.19824826 -0.83662282
[40,] -3.08862242 -1.19824826
[41,] -0.88102862 -3.08862242
[42,] -4.65834378 -0.88102862
[43,] -0.92342115 -4.65834378
[44,] -7.25330134 -0.92342115
[45,] 0.42503974 -7.25330134
[46,] 3.38788539 0.42503974
[47,] 0.57059824 3.38788539
[48,] 3.11734709 0.57059824
[49,] -3.43338818 3.11734709
[50,] 4.46196122 -3.43338818
[51,] -2.74260843 4.46196122
[52,] 0.35213184 -2.74260843
[53,] 3.86729505 0.35213184
[54,] 0.58829877 3.86729505
[55,] 0.63695506 0.58829877
[56,] -2.21010615 0.63695506
[57,] 1.98707770 -2.21010615
[58,] 1.01066395 1.98707770
[59,] -0.18509916 1.01066395
[60,] -3.11854879 -0.18509916
[61,] -1.69506619 -3.11854879
[62,] 0.78482636 -1.69506619
[63,] 2.55025708 0.78482636
[64,] -3.10304599 2.55025708
[65,] 2.39888880 -3.10304599
[66,] -0.32199080 2.39888880
[67,] -5.47878281 -0.32199080
[68,] 4.33776107 -5.47878281
[69,] 2.47817180 4.33776107
[70,] 1.53124981 2.47817180
[71,] 5.48229789 1.53124981
[72,] 4.39480469 5.48229789
[73,] 4.74618739 4.39480469
[74,] -7.03129944 4.74618739
[75,] -5.86704400 -7.03129944
[76,] 1.46566830 -5.86704400
[77,] -2.93189429 1.46566830
[78,] -0.22624093 -2.93189429
[79,] -8.26870925 -0.22624093
[80,] 2.31952358 -8.26870925
[81,] 2.49710862 2.31952358
[82,] -2.24738376 2.49710862
[83,] 0.98180573 -2.24738376
[84,] -2.41170656 0.98180573
[85,] 3.86125326 -2.41170656
[86,] 0.34782213 3.86125326
[87,] 0.02852535 0.34782213
[88,] -0.33650478 0.02852535
[89,] 3.95993602 -0.33650478
[90,] -3.37177191 3.95993602
[91,] 6.48195916 -3.37177191
[92,] -5.11959978 6.48195916
[93,] -1.57517027 -5.11959978
[94,] 0.04172725 -1.57517027
[95,] -0.38526422 0.04172725
[96,] -3.11199220 -0.38526422
[97,] -1.40462764 -3.11199220
[98,] 3.24045511 -1.40462764
[99,] -0.06009378 3.24045511
[100,] -0.14100555 -0.06009378
[101,] 1.15649647 -0.14100555
[102,] -4.88606326 1.15649647
[103,] 5.42271610 -4.88606326
[104,] -4.03638780 5.42271610
[105,] -0.91648189 -4.03638780
[106,] 1.98062798 -0.91648189
[107,] 0.34194849 1.98062798
[108,] 5.40719691 0.34194849
[109,] 1.55314604 5.40719691
[110,] 0.01844882 1.55314604
[111,] 7.79187427 0.01844882
[112,] 0.26042020 7.79187427
[113,] -0.68046095 0.26042020
[114,] -3.74877434 -0.68046095
[115,] 6.47600872 -3.74877434
[116,] 6.67544790 6.47600872
[117,] 1.86814953 6.67544790
[118,] 0.26148580 1.86814953
[119,] 3.90959718 0.26148580
[120,] -5.95189223 3.90959718
[121,] 3.63138532 -5.95189223
[122,] 2.42664762 3.63138532
[123,] -3.91864497 2.42664762
[124,] -0.15270289 -3.91864497
[125,] -3.85005125 -0.15270289
[126,] 0.68737980 -3.85005125
[127,] 2.68651325 0.68737980
[128,] -0.88013021 2.68651325
[129,] 0.04294892 -0.88013021
[130,] 1.44872832 0.04294892
[131,] 0.90686434 1.44872832
[132,] -0.56774615 0.90686434
[133,] 2.91364748 -0.56774615
[134,] -4.17998425 2.91364748
[135,] -8.76938291 -4.17998425
[136,] 1.17632722 -8.76938291
[137,] 3.56290025 1.17632722
[138,] 0.49383252 3.56290025
[139,] 3.22101237 0.49383252
[140,] 1.22682313 3.22101237
[141,] 5.01639345 1.22682313
[142,] 2.09747336 5.01639345
[143,] -9.73214475 2.09747336
[144,] -4.35533699 -9.73214475
[145,] -6.68137483 -4.35533699
[146,] 1.58214394 -6.68137483
[147,] -2.16922383 1.58214394
[148,] -1.59502557 -2.16922383
[149,] 4.59299468 -1.59502557
[150,] -0.36716641 4.59299468
[151,] 4.24640555 -0.36716641
[152,] 5.31478026 4.24640555
[153,] -0.84682610 5.31478026
[154,] -1.38062686 -0.84682610
[155,] -3.37177191 -1.38062686
[156,] 4.87485422 -3.37177191
[157,] 2.68651325 4.87485422
[158,] -1.90159538 2.68651325
[159,] 5.80856326 -1.90159538
[160,] 2.82511705 5.80856326
[161,] -4.44821885 2.82511705
[162,] -5.82546427 -4.44821885
[163,] -3.19482683 -5.82546427
[164,] 5.41165075 -3.19482683
[165,] 1.39956179 5.41165075
[166,] 0.97282666 1.39956179
[167,] -5.04589021 0.97282666
[168,] 4.46494338 -5.04589021
[169,] 5.97549661 4.46494338
[170,] -2.87551180 5.97549661
[171,] 2.03866576 -2.87551180
[172,] -1.05166712 2.03866576
[173,] -5.46578544 -1.05166712
[174,] -6.44934030 -5.46578544
[175,] 2.05733964 -6.44934030
[176,] -1.16257851 2.05733964
[177,] -1.48357638 -1.16257851
[178,] -1.37663596 -1.48357638
[179,] 2.24370021 -1.37663596
[180,] 1.10662495 2.24370021
[181,] -3.06121694 1.10662495
[182,] 2.39628931 -3.06121694
[183,] -3.59063205 2.39628931
[184,] -0.11605640 -3.59063205
[185,] 2.76839135 -0.11605640
[186,] -0.91155313 2.76839135
[187,] -4.04780491 -0.91155313
[188,] -2.29731915 -4.04780491
[189,] 4.32462040 -2.29731915
[190,] 3.11317801 4.32462040
[191,] 4.28042262 3.11317801
[192,] 1.33431427 4.28042262
[193,] 2.72107006 1.33431427
[194,] -0.64574902 2.72107006
[195,] 2.88095989 -0.64574902
[196,] -2.72729626 2.88095989
[197,] 3.33646004 -2.72729626
[198,] 0.13810472 3.33646004
[199,] 1.28108462 0.13810472
[200,] -2.26570057 1.28108462
[201,] 0.55283917 -2.26570057
[202,] -2.14852847 0.55283917
[203,] -1.52688023 -2.14852847
[204,] 0.93698624 -1.52688023
[205,] 2.14802867 0.93698624
[206,] 2.63996177 2.14802867
[207,] 1.44229852 2.63996177
[208,] -0.98366593 1.44229852
[209,] -0.75814055 -0.98366593
[210,] -0.40989866 -0.75814055
[211,] 7.48326019 -0.40989866
[212,] 6.08742520 7.48326019
[213,] 0.33482562 6.08742520
[214,] 0.59515336 0.33482562
[215,] -6.11092759 0.59515336
[216,] 1.24853576 -6.11092759
[217,] -1.63999764 1.24853576
[218,] 1.13283274 -1.63999764
[219,] 4.19212227 1.13283274
[220,] -0.99907385 4.19212227
[221,] 0.71082538 -0.99907385
[222,] -1.41382570 0.71082538
[223,] 0.03925730 -1.41382570
[224,] 2.81239455 0.03925730
[225,] 4.47055847 2.81239455
[226,] -0.47668106 4.47055847
[227,] 1.03627261 -0.47668106
[228,] 0.09479639 1.03627261
[229,] 1.90746696 0.09479639
[230,] -2.94756074 1.90746696
[231,] 5.51103286 -2.94756074
[232,] 1.47486915 5.51103286
[233,] -4.46113687 1.47486915
[234,] -1.48888781 -4.46113687
[235,] -2.91949411 -1.48888781
[236,] -2.98680236 -2.91949411
[237,] -7.48202789 -2.98680236
[238,] -0.86006745 -7.48202789
[239,] -3.83158091 -0.86006745
[240,] 1.91383641 -3.83158091
[241,] 1.32174701 1.91383641
[242,] -1.65534531 1.32174701
[243,] -0.31353322 -1.65534531
[244,] -3.97861391 -0.31353322
[245,] 4.07805899 -3.97861391
[246,] 2.40781945 4.07805899
[247,] -1.79876346 2.40781945
[248,] -5.73990662 -1.79876346
[249,] -7.74104802 -5.73990662
[250,] 2.39352448 -7.74104802
[251,] -0.25471016 2.39352448
[252,] 0.38726816 -0.25471016
[253,] -1.25749132 0.38726816
[254,] -1.50688228 -1.25749132
[255,] -0.63434300 -1.50688228
[256,] 2.64417573 -0.63434300
[257,] 0.06192520 2.64417573
[258,] -0.84663996 0.06192520
[259,] -1.69216656 -0.84663996
[260,] -0.91113412 -1.69216656
[261,] 4.54546800 -0.91113412
[262,] 0.88495924 4.54546800
[263,] 0.63208017 0.88495924
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.14457391 1.41469932
2 1.92826572 -4.14457391
3 1.10370498 1.92826572
4 2.97033050 1.10370498
5 -4.97941658 2.97033050
6 -5.56685656 -4.97941658
7 1.82130131 -5.56685656
8 0.33621705 1.82130131
9 3.14730279 0.33621705
10 -4.57157646 3.14730279
11 -1.04244252 -4.57157646
12 -0.61017054 -1.04244252
13 1.90823582 -0.61017054
14 2.73171988 1.90823582
15 -0.11581242 2.73171988
16 -2.53153315 -0.11581242
17 1.66459622 -2.53153315
18 1.72966646 1.66459622
19 -1.30514221 1.72966646
20 -0.23392893 -1.30514221
21 -1.73350516 -0.23392893
22 1.34888774 -1.73350516
23 3.86656918 1.34888774
24 -4.36356266 3.86656918
25 -5.05141965 -4.36356266
26 0.28671179 -5.05141965
27 3.22114670 0.28671179
28 -0.69183851 3.22114670
29 -0.47255302 -0.69183851
30 -1.74605258 -0.47255302
31 1.86281367 -1.74605258
32 -3.89869096 1.86281367
33 2.90452874 -3.89869096
34 -3.81306943 2.90452874
35 1.09811322 -3.81306943
36 0.27799213 1.09811322
37 -1.67135767 0.27799213
38 -0.83662282 -1.67135767
39 -1.19824826 -0.83662282
40 -3.08862242 -1.19824826
41 -0.88102862 -3.08862242
42 -4.65834378 -0.88102862
43 -0.92342115 -4.65834378
44 -7.25330134 -0.92342115
45 0.42503974 -7.25330134
46 3.38788539 0.42503974
47 0.57059824 3.38788539
48 3.11734709 0.57059824
49 -3.43338818 3.11734709
50 4.46196122 -3.43338818
51 -2.74260843 4.46196122
52 0.35213184 -2.74260843
53 3.86729505 0.35213184
54 0.58829877 3.86729505
55 0.63695506 0.58829877
56 -2.21010615 0.63695506
57 1.98707770 -2.21010615
58 1.01066395 1.98707770
59 -0.18509916 1.01066395
60 -3.11854879 -0.18509916
61 -1.69506619 -3.11854879
62 0.78482636 -1.69506619
63 2.55025708 0.78482636
64 -3.10304599 2.55025708
65 2.39888880 -3.10304599
66 -0.32199080 2.39888880
67 -5.47878281 -0.32199080
68 4.33776107 -5.47878281
69 2.47817180 4.33776107
70 1.53124981 2.47817180
71 5.48229789 1.53124981
72 4.39480469 5.48229789
73 4.74618739 4.39480469
74 -7.03129944 4.74618739
75 -5.86704400 -7.03129944
76 1.46566830 -5.86704400
77 -2.93189429 1.46566830
78 -0.22624093 -2.93189429
79 -8.26870925 -0.22624093
80 2.31952358 -8.26870925
81 2.49710862 2.31952358
82 -2.24738376 2.49710862
83 0.98180573 -2.24738376
84 -2.41170656 0.98180573
85 3.86125326 -2.41170656
86 0.34782213 3.86125326
87 0.02852535 0.34782213
88 -0.33650478 0.02852535
89 3.95993602 -0.33650478
90 -3.37177191 3.95993602
91 6.48195916 -3.37177191
92 -5.11959978 6.48195916
93 -1.57517027 -5.11959978
94 0.04172725 -1.57517027
95 -0.38526422 0.04172725
96 -3.11199220 -0.38526422
97 -1.40462764 -3.11199220
98 3.24045511 -1.40462764
99 -0.06009378 3.24045511
100 -0.14100555 -0.06009378
101 1.15649647 -0.14100555
102 -4.88606326 1.15649647
103 5.42271610 -4.88606326
104 -4.03638780 5.42271610
105 -0.91648189 -4.03638780
106 1.98062798 -0.91648189
107 0.34194849 1.98062798
108 5.40719691 0.34194849
109 1.55314604 5.40719691
110 0.01844882 1.55314604
111 7.79187427 0.01844882
112 0.26042020 7.79187427
113 -0.68046095 0.26042020
114 -3.74877434 -0.68046095
115 6.47600872 -3.74877434
116 6.67544790 6.47600872
117 1.86814953 6.67544790
118 0.26148580 1.86814953
119 3.90959718 0.26148580
120 -5.95189223 3.90959718
121 3.63138532 -5.95189223
122 2.42664762 3.63138532
123 -3.91864497 2.42664762
124 -0.15270289 -3.91864497
125 -3.85005125 -0.15270289
126 0.68737980 -3.85005125
127 2.68651325 0.68737980
128 -0.88013021 2.68651325
129 0.04294892 -0.88013021
130 1.44872832 0.04294892
131 0.90686434 1.44872832
132 -0.56774615 0.90686434
133 2.91364748 -0.56774615
134 -4.17998425 2.91364748
135 -8.76938291 -4.17998425
136 1.17632722 -8.76938291
137 3.56290025 1.17632722
138 0.49383252 3.56290025
139 3.22101237 0.49383252
140 1.22682313 3.22101237
141 5.01639345 1.22682313
142 2.09747336 5.01639345
143 -9.73214475 2.09747336
144 -4.35533699 -9.73214475
145 -6.68137483 -4.35533699
146 1.58214394 -6.68137483
147 -2.16922383 1.58214394
148 -1.59502557 -2.16922383
149 4.59299468 -1.59502557
150 -0.36716641 4.59299468
151 4.24640555 -0.36716641
152 5.31478026 4.24640555
153 -0.84682610 5.31478026
154 -1.38062686 -0.84682610
155 -3.37177191 -1.38062686
156 4.87485422 -3.37177191
157 2.68651325 4.87485422
158 -1.90159538 2.68651325
159 5.80856326 -1.90159538
160 2.82511705 5.80856326
161 -4.44821885 2.82511705
162 -5.82546427 -4.44821885
163 -3.19482683 -5.82546427
164 5.41165075 -3.19482683
165 1.39956179 5.41165075
166 0.97282666 1.39956179
167 -5.04589021 0.97282666
168 4.46494338 -5.04589021
169 5.97549661 4.46494338
170 -2.87551180 5.97549661
171 2.03866576 -2.87551180
172 -1.05166712 2.03866576
173 -5.46578544 -1.05166712
174 -6.44934030 -5.46578544
175 2.05733964 -6.44934030
176 -1.16257851 2.05733964
177 -1.48357638 -1.16257851
178 -1.37663596 -1.48357638
179 2.24370021 -1.37663596
180 1.10662495 2.24370021
181 -3.06121694 1.10662495
182 2.39628931 -3.06121694
183 -3.59063205 2.39628931
184 -0.11605640 -3.59063205
185 2.76839135 -0.11605640
186 -0.91155313 2.76839135
187 -4.04780491 -0.91155313
188 -2.29731915 -4.04780491
189 4.32462040 -2.29731915
190 3.11317801 4.32462040
191 4.28042262 3.11317801
192 1.33431427 4.28042262
193 2.72107006 1.33431427
194 -0.64574902 2.72107006
195 2.88095989 -0.64574902
196 -2.72729626 2.88095989
197 3.33646004 -2.72729626
198 0.13810472 3.33646004
199 1.28108462 0.13810472
200 -2.26570057 1.28108462
201 0.55283917 -2.26570057
202 -2.14852847 0.55283917
203 -1.52688023 -2.14852847
204 0.93698624 -1.52688023
205 2.14802867 0.93698624
206 2.63996177 2.14802867
207 1.44229852 2.63996177
208 -0.98366593 1.44229852
209 -0.75814055 -0.98366593
210 -0.40989866 -0.75814055
211 7.48326019 -0.40989866
212 6.08742520 7.48326019
213 0.33482562 6.08742520
214 0.59515336 0.33482562
215 -6.11092759 0.59515336
216 1.24853576 -6.11092759
217 -1.63999764 1.24853576
218 1.13283274 -1.63999764
219 4.19212227 1.13283274
220 -0.99907385 4.19212227
221 0.71082538 -0.99907385
222 -1.41382570 0.71082538
223 0.03925730 -1.41382570
224 2.81239455 0.03925730
225 4.47055847 2.81239455
226 -0.47668106 4.47055847
227 1.03627261 -0.47668106
228 0.09479639 1.03627261
229 1.90746696 0.09479639
230 -2.94756074 1.90746696
231 5.51103286 -2.94756074
232 1.47486915 5.51103286
233 -4.46113687 1.47486915
234 -1.48888781 -4.46113687
235 -2.91949411 -1.48888781
236 -2.98680236 -2.91949411
237 -7.48202789 -2.98680236
238 -0.86006745 -7.48202789
239 -3.83158091 -0.86006745
240 1.91383641 -3.83158091
241 1.32174701 1.91383641
242 -1.65534531 1.32174701
243 -0.31353322 -1.65534531
244 -3.97861391 -0.31353322
245 4.07805899 -3.97861391
246 2.40781945 4.07805899
247 -1.79876346 2.40781945
248 -5.73990662 -1.79876346
249 -7.74104802 -5.73990662
250 2.39352448 -7.74104802
251 -0.25471016 2.39352448
252 0.38726816 -0.25471016
253 -1.25749132 0.38726816
254 -1.50688228 -1.25749132
255 -0.63434300 -1.50688228
256 2.64417573 -0.63434300
257 0.06192520 2.64417573
258 -0.84663996 0.06192520
259 -1.69216656 -0.84663996
260 -0.91113412 -1.69216656
261 4.54546800 -0.91113412
262 0.88495924 4.54546800
263 0.63208017 0.88495924
> 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/7msvl1383469553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/871zl1383469553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9687n1383469553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10lkwu1383469553.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11iu7s1383469553.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/12pe2z1383469553.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/13sjgg1383469553.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/14zb371383469553.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/15nio81383469553.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/16szh01383469553.tab")
+ }
>
> try(system("convert tmp/10tbo1383469553.ps tmp/10tbo1383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fh7w1383469553.ps tmp/2fh7w1383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/32af91383469553.ps tmp/32af91383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/4o81v1383469553.ps tmp/4o81v1383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/5jrxf1383469553.ps tmp/5jrxf1383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/6aa681383469553.ps tmp/6aa681383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/7msvl1383469553.ps tmp/7msvl1383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/871zl1383469553.ps tmp/871zl1383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/9687n1383469553.ps tmp/9687n1383469553.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lkwu1383469553.ps tmp/10lkwu1383469553.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.819 1.571 11.403