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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+ ,11
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+ ,4
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+ ,11
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+ ,11
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+ ,11
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+ ,11
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+ ,7
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+ ,62
+ ,11
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,11)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Month'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '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 Sport1 Month
1 12.0 41 38 13 12 14 53 9
2 11.0 39 32 16 11 18 83 9
3 14.0 30 35 19 15 11 66 9
4 12.0 31 33 15 6 12 67 9
5 21.0 34 37 14 13 16 76 9
6 12.0 35 29 13 10 18 78 9
7 22.0 39 31 19 12 14 53 9
8 11.0 34 36 15 14 14 80 9
9 10.0 36 35 14 12 15 74 9
10 13.0 37 38 15 9 15 76 9
11 10.0 38 31 16 10 17 79 9
12 8.0 36 34 16 12 19 54 9
13 15.0 38 35 16 12 10 67 9
14 14.0 39 38 16 11 16 54 9
15 10.0 33 37 17 15 18 87 9
16 14.0 32 33 15 12 14 58 9
17 14.0 36 32 15 10 14 75 9
18 11.0 38 38 20 12 17 88 9
19 10.0 39 38 18 11 14 64 9
20 13.0 32 32 16 12 16 57 9
21 9.5 32 33 16 11 18 66 9
22 14.0 31 31 16 12 11 68 9
23 12.0 39 38 19 13 14 54 9
24 14.0 37 39 16 11 12 56 9
25 11.0 39 32 17 12 17 86 9
26 9.0 41 32 17 13 9 80 9
27 11.0 36 35 16 10 16 76 9
28 15.0 33 37 15 14 14 69 9
29 14.0 33 33 16 12 15 78 9
30 13.0 34 33 14 10 11 67 9
31 9.0 31 31 15 12 16 80 9
32 15.0 27 32 12 8 13 54 9
33 10.0 37 31 14 10 17 71 9
34 11.0 34 37 16 12 15 84 9
35 13.0 34 30 14 12 14 74 9
36 8.0 32 33 10 7 16 71 9
37 20.0 29 31 10 9 9 63 9
38 12.0 36 33 14 12 15 71 9
39 10.0 29 31 16 10 17 76 9
40 10.0 35 33 16 10 13 69 9
41 9.0 37 32 16 10 15 74 9
42 14.0 34 33 14 12 16 75 9
43 8.0 38 32 20 15 16 54 9
44 14.0 35 33 14 10 12 52 9
45 11.0 38 28 14 10 15 69 9
46 13.0 37 35 11 12 11 68 9
47 9.0 38 39 14 13 15 65 9
48 11.0 33 34 15 11 15 75 9
49 15.0 36 38 16 11 17 74 9
50 11.0 38 32 14 12 13 75 9
51 10.0 32 38 16 14 16 72 9
52 14.0 32 30 14 10 14 67 9
53 18.0 32 33 12 12 11 63 9
54 14.0 34 38 16 13 12 62 9
55 11.0 32 32 9 5 12 63 9
56 14.5 37 35 14 6 15 76 9
57 13.0 39 34 16 12 16 74 9
58 9.0 29 34 16 12 15 67 9
59 10.0 37 36 15 11 12 73 9
60 15.0 35 34 16 10 12 70 9
61 20.0 30 28 12 7 8 53 9
62 12.0 38 34 16 12 13 77 9
63 12.0 34 35 16 14 11 80 9
64 14.0 31 35 14 11 14 52 9
65 13.0 34 31 16 12 15 54 9
66 11.0 35 37 17 13 10 80 10
67 17.0 36 35 18 14 11 66 10
68 12.0 30 27 18 11 12 73 10
69 13.0 39 40 12 12 15 63 10
70 14.0 35 37 16 12 15 69 10
71 13.0 38 36 10 8 14 67 10
72 15.0 31 38 14 11 16 54 10
73 13.0 34 39 18 14 15 81 10
74 10.0 38 41 18 14 15 69 10
75 11.0 34 27 16 12 13 84 10
76 19.0 39 30 17 9 12 80 10
77 13.0 37 37 16 13 17 70 10
78 17.0 34 31 16 11 13 69 10
79 13.0 28 31 13 12 15 77 10
80 9.0 37 27 16 12 13 54 10
81 11.0 33 36 16 12 15 79 10
82 9.0 35 37 16 12 15 71 10
83 12.0 37 33 15 12 16 73 10
84 12.0 32 34 15 11 15 72 10
85 13.0 33 31 16 10 14 77 10
86 13.0 38 39 14 9 15 75 10
87 12.0 33 34 16 12 14 69 10
88 15.0 29 32 16 12 13 54 10
89 22.0 33 33 15 12 7 70 10
90 13.0 31 36 12 9 17 73 10
91 15.0 36 32 17 15 13 54 10
92 13.0 35 41 16 12 15 77 10
93 15.0 32 28 15 12 14 82 10
94 12.5 29 30 13 12 13 80 10
95 11.0 39 36 16 10 16 80 10
96 16.0 37 35 16 13 12 69 10
97 11.0 35 31 16 9 14 78 10
98 11.0 37 34 16 12 17 81 10
99 10.0 32 36 14 10 15 76 10
100 10.0 38 36 16 14 17 76 10
101 16.0 37 35 16 11 12 73 10
102 12.0 36 37 20 15 16 85 10
103 11.0 32 28 15 11 11 66 10
104 16.0 33 39 16 11 15 79 10
105 19.0 40 32 13 12 9 68 10
106 11.0 38 35 17 12 16 76 10
107 16.0 41 39 16 12 15 71 10
108 15.0 36 35 16 11 10 54 10
109 24.0 43 42 12 7 10 46 10
110 14.0 30 34 16 12 15 85 10
111 15.0 31 33 16 14 11 74 10
112 11.0 32 41 17 11 13 88 10
113 15.0 32 33 13 11 14 38 10
114 12.0 37 34 12 10 18 76 10
115 10.0 37 32 18 13 16 86 10
116 14.0 33 40 14 13 14 54 10
117 13.0 34 40 14 8 14 67 10
118 9.0 33 35 13 11 14 69 10
119 15.0 38 36 16 12 14 90 10
120 15.0 33 37 13 11 12 54 10
121 14.0 31 27 16 13 14 76 10
122 11.0 38 39 13 12 15 89 10
123 8.0 37 38 16 14 15 76 10
124 11.0 36 31 15 13 15 73 10
125 11.0 31 33 16 15 13 79 10
126 8.0 39 32 15 10 17 90 10
127 10.0 44 39 17 11 17 74 10
128 11.0 33 36 15 9 19 81 10
129 13.0 35 33 12 11 15 72 10
130 11.0 32 33 16 10 13 71 10
131 20.0 28 32 10 11 9 66 10
132 10.0 40 37 16 8 15 77 10
133 15.0 27 30 12 11 15 65 10
134 12.0 37 38 14 12 15 74 10
135 14.0 32 29 15 12 16 85 10
136 23.0 28 22 13 9 11 54 10
137 14.0 34 35 15 11 14 63 10
138 16.0 30 35 11 10 11 54 10
139 11.0 35 34 12 8 15 64 10
140 12.0 31 35 11 9 13 69 10
141 10.0 32 34 16 8 15 54 10
142 14.0 30 37 15 9 16 84 10
143 12.0 30 35 17 15 14 86 10
144 12.0 31 23 16 11 15 77 10
145 11.0 40 31 10 8 16 89 10
146 12.0 32 27 18 13 16 76 10
147 13.0 36 36 13 12 11 60 10
148 11.0 32 31 16 12 12 75 10
149 19.0 35 32 13 9 9 73 10
150 12.0 38 39 10 7 16 85 10
151 17.0 42 37 15 13 13 79 10
152 9.0 34 38 16 9 16 71 10
153 12.0 35 39 16 6 12 72 10
154 19.0 38 34 14 8 9 69 9
155 18.0 33 31 10 8 13 78 10
156 15.0 36 32 17 15 13 54 10
157 14.0 32 37 13 6 14 69 10
158 11.0 33 36 15 9 19 81 10
159 9.0 34 32 16 11 13 84 10
160 18.0 32 38 12 8 12 84 10
161 16.0 34 36 13 8 13 69 10
162 24.0 27 26 13 10 10 66 11
163 14.0 31 26 12 8 14 81 11
164 20.0 38 33 17 14 16 82 11
165 18.0 34 39 15 10 10 72 11
166 23.0 24 30 10 8 11 54 11
167 12.0 30 33 14 11 14 78 11
168 14.0 26 25 11 12 12 74 11
169 16.0 34 38 13 12 9 82 11
170 18.0 27 37 16 12 9 73 11
171 20.0 37 31 12 5 11 55 11
172 12.0 36 37 16 12 16 72 11
173 12.0 41 35 12 10 9 78 11
174 17.0 29 25 9 7 13 59 11
175 13.0 36 28 12 12 16 72 11
176 9.0 32 35 15 11 13 78 11
177 16.0 37 33 12 8 9 68 11
178 18.0 30 30 12 9 12 69 11
179 10.0 31 31 14 10 16 67 11
180 14.0 38 37 12 9 11 74 11
181 11.0 36 36 16 12 14 54 11
182 9.0 35 30 11 6 13 67 11
183 11.0 31 36 19 15 15 70 11
184 10.0 38 32 15 12 14 80 11
185 11.0 22 28 8 12 16 89 11
186 19.0 32 36 16 12 13 76 11
187 14.0 36 34 17 11 14 74 11
188 12.0 39 31 12 7 15 87 11
189 14.0 28 28 11 7 13 54 11
190 21.0 32 36 11 5 11 61 11
191 13.0 32 36 14 12 11 38 11
192 10.0 38 40 16 12 14 75 11
193 15.0 32 33 12 3 15 69 11
194 16.0 35 37 16 11 11 62 11
195 14.0 32 32 13 10 15 72 11
196 12.0 37 38 15 12 12 70 11
197 19.0 34 31 16 9 14 79 11
198 15.0 33 37 16 12 14 87 11
199 19.0 33 33 14 9 8 62 11
200 13.0 26 32 16 12 13 77 11
201 17.0 30 30 16 12 9 69 11
202 12.0 24 30 14 10 15 69 11
203 11.0 34 31 11 9 17 75 11
204 14.0 34 32 12 12 13 54 11
205 11.0 33 34 15 8 15 72 11
206 13.0 34 36 15 11 15 74 11
207 12.0 35 37 16 11 14 85 11
208 15.0 35 36 16 12 16 52 11
209 14.0 36 33 11 10 13 70 11
210 12.0 34 33 15 10 16 84 11
211 17.0 34 33 12 12 9 64 11
212 11.0 41 44 12 12 16 84 11
213 18.0 32 39 15 11 11 87 11
214 13.0 30 32 15 8 10 79 11
215 17.0 35 35 16 12 11 67 11
216 13.0 28 25 14 10 15 65 11
217 11.0 33 35 17 11 17 85 11
218 12.0 39 34 14 10 14 83 11
219 22.0 36 35 13 8 8 61 11
220 14.0 36 39 15 12 15 82 11
221 12.0 35 33 13 12 11 76 11
222 12.0 38 36 14 10 16 58 11
223 17.0 33 32 15 12 10 72 11
224 9.0 31 32 12 9 15 72 11
225 21.0 34 36 13 9 9 38 11
226 10.0 32 36 8 6 16 78 11
227 11.0 31 32 14 10 19 54 11
228 12.0 33 34 14 9 12 63 11
229 23.0 34 33 11 9 8 66 11
230 13.0 34 35 12 9 11 70 11
231 12.0 34 30 13 6 14 71 11
232 16.0 33 38 10 10 9 67 11
233 9.0 32 34 16 6 15 58 11
234 17.0 41 33 18 14 13 72 11
235 9.0 34 32 13 10 16 72 11
236 14.0 36 31 11 10 11 70 11
237 17.0 37 30 4 6 12 76 11
238 13.0 36 27 13 12 13 50 11
239 11.0 29 31 16 12 10 72 11
240 12.0 37 30 10 7 11 72 11
241 10.0 27 32 12 8 12 88 11
242 19.0 35 35 12 11 8 53 11
243 16.0 28 28 10 3 12 58 11
244 16.0 35 33 13 6 12 66 11
245 14.0 37 31 15 10 15 82 11
246 20.0 29 35 12 8 11 69 11
247 15.0 32 35 14 9 13 68 11
248 23.0 36 32 10 9 14 44 11
249 20.0 19 21 12 8 10 56 11
250 16.0 21 20 12 9 12 53 11
251 14.0 31 34 11 7 15 70 11
252 17.0 33 32 10 7 13 78 11
253 11.0 36 34 12 6 13 71 11
254 13.0 33 32 16 9 13 72 11
255 17.0 37 33 12 10 12 68 11
256 15.0 34 33 14 11 12 67 11
257 21.0 35 37 16 12 9 75 11
258 18.0 31 32 14 8 9 62 11
259 15.0 37 34 13 11 15 67 11
260 8.0 35 30 4 3 10 83 11
261 12.0 27 30 15 11 14 64 11
262 12.0 34 38 11 12 15 68 11
263 22.0 40 36 11 7 7 62 11
264 12.0 29 32 14 9 14 72 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Happiness
24.567742 -0.020902 0.004683 -0.063985 -0.010107 -0.686595
Sport1 Month
-0.059370 0.401842
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.317 -1.806 -0.123 1.760 9.878
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.567742 3.642501 6.745 1.02e-10 ***
Connected -0.020902 0.051452 -0.406 0.684906
Separate 0.004683 0.052436 0.089 0.928906
Learning -0.063985 0.092662 -0.691 0.490489
Software -0.010107 0.094702 -0.107 0.915091
Happiness -0.686595 0.074571 -9.207 < 2e-16 ***
Sport1 -0.059370 0.017350 -3.422 0.000723 ***
Month 0.401842 0.236906 1.696 0.091063 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.764 on 256 degrees of freedom
Multiple R-squared: 0.3824, Adjusted R-squared: 0.3655
F-statistic: 22.64 on 7 and 256 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,] 0.85464273 0.2907145413 0.1453572706
[2,] 0.99979649 0.0004070247 0.0002035124
[3,] 0.99950304 0.0009939196 0.0004969598
[4,] 0.99887074 0.0022585271 0.0011292636
[5,] 0.99791672 0.0041665672 0.0020832836
[6,] 0.99591615 0.0081677035 0.0040838518
[7,] 0.99272793 0.0145441309 0.0072720655
[8,] 0.98798322 0.0240335536 0.0120167768
[9,] 0.98827323 0.0234535309 0.0117267655
[10,] 0.98109178 0.0378164498 0.0189082249
[11,] 0.97310600 0.0537880033 0.0268940017
[12,] 0.96187387 0.0762522523 0.0381261261
[13,] 0.95101982 0.0979603580 0.0489801790
[14,] 0.93107610 0.1378478000 0.0689239000
[15,] 0.91030600 0.1793880081 0.0896940041
[16,] 0.96198873 0.0760225359 0.0380112680
[17,] 0.94706731 0.1058653893 0.0529326946
[18,] 0.93508112 0.1298377618 0.0649188809
[19,] 0.92129458 0.1574108484 0.0787054242
[20,] 0.89929330 0.2014134001 0.1007067001
[21,] 0.89904068 0.2019186355 0.1009593177
[22,] 0.87207763 0.2558447465 0.1279223732
[23,] 0.84794046 0.3041190817 0.1520595408
[24,] 0.81248327 0.3750334578 0.1875167289
[25,] 0.77242093 0.4551581354 0.2275790677
[26,] 0.78179474 0.4364105195 0.2182052597
[27,] 0.84339626 0.3132074725 0.1566037363
[28,] 0.80973297 0.3805340586 0.1902670293
[29,] 0.77847414 0.4430517260 0.2215258630
[30,] 0.77848681 0.4430263769 0.2215131885
[31,] 0.76594752 0.4681049584 0.2340524792
[32,] 0.74817521 0.5036495815 0.2518247907
[33,] 0.80736466 0.3852706768 0.1926353384
[34,] 0.77309345 0.4538130942 0.2269065471
[35,] 0.73601343 0.5279731498 0.2639865749
[36,] 0.71283940 0.5743211998 0.2871605999
[37,] 0.73842420 0.5231516065 0.2615758033
[38,] 0.70281323 0.5943735423 0.2971867712
[39,] 0.74722879 0.5055424242 0.2527712121
[40,] 0.71927893 0.5614421478 0.2807210739
[41,] 0.70550675 0.5889865023 0.2944932511
[42,] 0.66999308 0.6600138390 0.3300069195
[43,] 0.67367279 0.6526544124 0.3263272062
[44,] 0.63171065 0.7365786967 0.3682893484
[45,] 0.64350210 0.7129957983 0.3564978992
[46,] 0.66127480 0.6774504013 0.3387252007
[47,] 0.63879332 0.7224133525 0.3612066762
[48,] 0.67031747 0.6593650696 0.3296825348
[49,] 0.68786841 0.6242631878 0.3121315939
[50,] 0.66313608 0.6737278436 0.3368639218
[51,] 0.67548298 0.6490340485 0.3245170242
[52,] 0.63810751 0.7237849812 0.3618924906
[53,] 0.61430310 0.7713938052 0.3856969026
[54,] 0.57289842 0.8542031501 0.4271015750
[55,] 0.53084386 0.9383122715 0.4691561358
[56,] 0.51752553 0.9649489334 0.4824744667
[57,] 0.54371103 0.9125779301 0.4562889650
[58,] 0.52070739 0.9585852233 0.4792926117
[59,] 0.47982213 0.9596442599 0.5201778701
[60,] 0.45199323 0.9039864682 0.5480067659
[61,] 0.41234826 0.8246965240 0.5876517380
[62,] 0.38443425 0.7688684957 0.6155657521
[63,] 0.35172409 0.7034481807 0.6482759096
[64,] 0.34192321 0.6838464259 0.6580767871
[65,] 0.31770665 0.6354132966 0.6822933517
[66,] 0.45073112 0.9014622474 0.5492688763
[67,] 0.42135608 0.8427121686 0.5786439157
[68,] 0.42222215 0.8444443006 0.5777778497
[69,] 0.38439476 0.7687895146 0.6156052427
[70,] 0.51285758 0.9742848312 0.4871424156
[71,] 0.48338646 0.9667729161 0.5166135420
[72,] 0.50987486 0.9802502897 0.4901251448
[73,] 0.47097822 0.9419564496 0.5290217752
[74,] 0.43444426 0.8688885123 0.5655557438
[75,] 0.39645455 0.7929091048 0.6035454476
[76,] 0.36090609 0.7218121710 0.6390939145
[77,] 0.33212281 0.6642456110 0.6678771945
[78,] 0.29855855 0.5971171080 0.7014414460
[79,] 0.37287249 0.7457449747 0.6271275126
[80,] 0.34415407 0.6883081425 0.6558459287
[81,] 0.31244727 0.6248945456 0.6875527272
[82,] 0.28123065 0.5624612907 0.7187693546
[83,] 0.26924358 0.5384871592 0.7307564204
[84,] 0.24551005 0.4910201095 0.7544899452
[85,] 0.21741223 0.4348244536 0.7825877732
[86,] 0.19985780 0.3997156058 0.8001421971
[87,] 0.18672898 0.3734579550 0.8132710225
[88,] 0.16278696 0.3255739295 0.8372130353
[89,] 0.16103378 0.3220675597 0.8389662202
[90,] 0.14146684 0.2829336706 0.8585331647
[91,] 0.12983116 0.2596623107 0.8701688447
[92,] 0.11401764 0.2280352704 0.8859823648
[93,] 0.14687203 0.2937440531 0.8531279735
[94,] 0.16408797 0.3281759364 0.8359120318
[95,] 0.16422496 0.3284499107 0.8357750447
[96,] 0.14326548 0.2865309694 0.8567345153
[97,] 0.15568363 0.3113672676 0.8443163662
[98,] 0.14362023 0.2872404580 0.8563797710
[99,] 0.24121102 0.4824220453 0.7587889773
[100,] 0.22984238 0.4596847639 0.7701576181
[101,] 0.20350985 0.4070197032 0.7964901484
[102,] 0.19517058 0.3903411574 0.8048294213
[103,] 0.17155011 0.3431002183 0.8284498909
[104,] 0.15496198 0.3099239561 0.8450380220
[105,] 0.13598167 0.2719633315 0.8640183343
[106,] 0.11813573 0.2362714642 0.8818642679
[107,] 0.10405859 0.2081171757 0.8959414122
[108,] 0.13809381 0.2761876121 0.8619061939
[109,] 0.13860363 0.2772072680 0.8613963660
[110,] 0.12116369 0.2423273788 0.8788363106
[111,] 0.10764525 0.2152904980 0.8923547510
[112,] 0.09406418 0.1881283695 0.9059358152
[113,] 0.11696218 0.2339243562 0.8830378219
[114,] 0.10459786 0.2091957215 0.8954021393
[115,] 0.10058875 0.2011774917 0.8994112542
[116,] 0.09595179 0.1919035882 0.9040482059
[117,] 0.08415865 0.1683172984 0.9158413508
[118,] 0.07505348 0.1501069606 0.9249465197
[119,] 0.06314105 0.1262821093 0.9368589453
[120,] 0.06577042 0.1315408434 0.9342295783
[121,] 0.06501739 0.1300347744 0.9349826128
[122,] 0.06188598 0.1237719662 0.9381140169
[123,] 0.05497896 0.1099579294 0.9450210353
[124,] 0.04607522 0.0921504480 0.9539247760
[125,] 0.04613727 0.0922745322 0.9538627339
[126,] 0.09244913 0.1848982536 0.9075508732
[127,] 0.07842919 0.1568583888 0.9215708056
[128,] 0.06719364 0.1343872831 0.9328063585
[129,] 0.06468591 0.1293718279 0.9353140861
[130,] 0.06337426 0.1267485150 0.9366257425
[131,] 0.07394765 0.1478952963 0.9260523518
[132,] 0.07235771 0.1447154146 0.9276422927
[133,] 0.06103539 0.1220707841 0.9389646079
[134,] 0.05116633 0.1023326644 0.9488336678
[135,] 0.04259034 0.0851806886 0.9574096557
[136,] 0.03515238 0.0703047634 0.9648476183
[137,] 0.03873830 0.0774766079 0.9612616960
[138,] 0.04576445 0.0915289082 0.9542355459
[139,] 0.04179699 0.0835939818 0.9582030091
[140,] 0.03448826 0.0689765297 0.9655117352
[141,] 0.03780867 0.0756173372 0.9621913314
[142,] 0.03984868 0.0796973632 0.9601513184
[143,] 0.04096674 0.0819334766 0.9590332617
[144,] 0.03799754 0.0759950882 0.9620024559
[145,] 0.04286243 0.0857248579 0.9571375710
[146,] 0.03602481 0.0720496106 0.9639751947
[147,] 0.02953664 0.0590732815 0.9704633592
[148,] 0.02522986 0.0504597177 0.9747701411
[149,] 0.04049995 0.0809998908 0.9595000546
[150,] 0.04028644 0.0805728706 0.9597135647
[151,] 0.03394132 0.0678826478 0.9660586761
[152,] 0.08252010 0.1650401935 0.9174799032
[153,] 0.07182344 0.1436468791 0.9281765605
[154,] 0.25288395 0.5057678992 0.7471160504
[155,] 0.23009808 0.4601961550 0.7699019225
[156,] 0.31124129 0.6224825739 0.6887587131
[157,] 0.29224866 0.5844973254 0.7077513373
[158,] 0.27407882 0.5481576455 0.7259211773
[159,] 0.24683361 0.4936672165 0.7531663918
[160,] 0.22069807 0.4413961448 0.7793019276
[161,] 0.21999086 0.4399817274 0.7800091363
[162,] 0.19473459 0.3894691751 0.8052654124
[163,] 0.25652694 0.5130538801 0.7434730600
[164,] 0.24194177 0.4838835498 0.7580582251
[165,] 0.22131066 0.4426213165 0.7786893418
[166,] 0.28993840 0.5798768018 0.7100615991
[167,] 0.26878835 0.5375767051 0.7312116474
[168,] 0.26931775 0.5386354976 0.7306822512
[169,] 0.26435124 0.5287024832 0.7356487584
[170,] 0.24616576 0.4923315162 0.7538342419
[171,] 0.27165353 0.5433070522 0.7283464739
[172,] 0.38133056 0.7626611101 0.6186694450
[173,] 0.36036137 0.7207227359 0.6396386321
[174,] 0.36388826 0.7277765176 0.6361117412
[175,] 0.34610857 0.6922171413 0.6538914293
[176,] 0.43466322 0.8693264336 0.5653367832
[177,] 0.39690414 0.7938082898 0.6030958551
[178,] 0.35979408 0.7195881644 0.6402059178
[179,] 0.33307558 0.6661511642 0.6669244179
[180,] 0.39050658 0.7810131539 0.6094934231
[181,] 0.49901255 0.9980250955 0.5009874523
[182,] 0.52778173 0.9444365480 0.4722182740
[183,] 0.51928545 0.9614290997 0.4807145498
[184,] 0.48407421 0.9681484235 0.5159257883
[185,] 0.45502750 0.9100549969 0.5449725015
[186,] 0.48672891 0.9734578252 0.5132710874
[187,] 0.67579456 0.6484108880 0.3242054440
[188,] 0.67882774 0.6423445281 0.3211722640
[189,] 0.63974488 0.7205102333 0.3602551167
[190,] 0.60123066 0.7975386880 0.3987693440
[191,] 0.55937797 0.8812440634 0.4406220317
[192,] 0.52084902 0.9583019558 0.4791509779
[193,] 0.48474435 0.9694887023 0.5152556488
[194,] 0.46972756 0.9394551144 0.5302724428
[195,] 0.43683239 0.8736647799 0.5631676101
[196,] 0.39435094 0.7887018861 0.6056490569
[197,] 0.35275428 0.7055085614 0.6472457193
[198,] 0.31693947 0.6338789401 0.6830605299
[199,] 0.27851766 0.5570353221 0.7214823389
[200,] 0.25893894 0.5178778825 0.7410610587
[201,] 0.23409180 0.4681836083 0.7659081958
[202,] 0.20197966 0.4039593295 0.7980203352
[203,] 0.23085838 0.4617167635 0.7691416182
[204,] 0.21386076 0.4277215256 0.7861392372
[205,] 0.18292433 0.3658486500 0.8170756750
[206,] 0.15552031 0.3110406291 0.8444796854
[207,] 0.14492731 0.2898546135 0.8550726933
[208,] 0.12024441 0.2404888139 0.8797555930
[209,] 0.11880857 0.2376171381 0.8811914309
[210,] 0.11760356 0.2352071299 0.8823964351
[211,] 0.12397449 0.2479489872 0.8760255064
[212,] 0.10559634 0.2111926731 0.8944036635
[213,] 0.08528019 0.1705603783 0.9147198108
[214,] 0.08773978 0.1754795606 0.9122602197
[215,] 0.07335794 0.1467158845 0.9266420578
[216,] 0.05936370 0.1187273904 0.9406363048
[217,] 0.04549283 0.0909856682 0.9545071659
[218,] 0.05383137 0.1076627462 0.9461686269
[219,] 0.07440150 0.1488029959 0.9255985021
[220,] 0.07158039 0.1431607866 0.9284196067
[221,] 0.05541527 0.1108305482 0.9445847259
[222,] 0.05160796 0.1032159129 0.9483920435
[223,] 0.10391143 0.2078228591 0.8960885704
[224,] 0.11225603 0.2245120576 0.8877439712
[225,] 0.10961720 0.2192344097 0.8903827951
[226,] 0.08629303 0.1725860580 0.9137069710
[227,] 0.12322734 0.2464546706 0.8767726647
[228,] 0.16636863 0.3327372591 0.8336313705
[229,] 0.27370846 0.5474169173 0.7262915413
[230,] 0.27127792 0.5425558497 0.7287220751
[231,] 0.22602532 0.4520506470 0.7739746765
[232,] 0.27551741 0.5510348234 0.7244825883
[233,] 0.21520425 0.4304084952 0.7847957524
[234,] 0.16152946 0.3230589293 0.8384705353
[235,] 0.22149507 0.4429901491 0.7785049255
[236,] 0.23840259 0.4768051704 0.7615974148
[237,] 0.17341542 0.3468308416 0.8265845792
[238,] 0.21871521 0.4374304295 0.7812847852
[239,] 0.21453833 0.4290766567 0.7854616717
[240,] 0.19598704 0.3919740862 0.8040129569
[241,] 0.22397620 0.4479523924 0.7760238038
[242,] 0.88087247 0.2382550578 0.1191275289
[243,] 0.79490556 0.4101888735 0.2050944367
> postscript(file="/var/fisher/rcomp/tmp/1qydj1384964252.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2lmyw1384964252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3no4c1384964252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/485q21384964252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5irrb1384964252.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
-1.793230064 1.902407774 -0.882838833 -2.453509776 9.877942957 2.333933740
7 8 9 10 11 12
8.581658779 -1.178990143 -1.886330328 1.272927736 -0.047995267 -2.194705601
13 14 15 16 17 18
-0.565125015 1.779375691 1.095476515 0.466890449 1.544265186 1.729713260
19 20 21 22 23 24
-2.872139308 0.849378585 -0.757886852 -0.946740942 -1.381644689 -0.894750880
25 26 27 28 29 30
1.468016364 -6.329056874 0.026762283 2.142349699 2.425782335 -2.100956260
31 32 33 34 35 36
-1.865304719 0.210602677 -0.671831580 -0.215824869 0.408685624 -3.758564694
37 38 39 40 41 42
2.927179633 -0.055075758 -0.414224119 -3.460152471 -2.743622901 2.827197432
43 44 45 46 47 48
-3.917058947 -1.284016285 -1.128811927 -2.159987758 -3.387485680 -0.831104415
49 50 51 52 53 54
4.590674543 -2.144297313 -1.267948196 0.931074348 2.512001469 -0.576336763
55 56 57 58 59 60
-4.059425514 2.692670321 1.995624219 -3.315583632 -3.935389103 0.907939844
61 62 63 64 65 66
1.789587148 -0.906951781 -2.170107345 0.006307340 0.031158868 -4.193129863
67 68 69 70 71 72
1.766638910 -2.249441360 -0.029926854 1.512678398 -0.649608800 2.082348076
73 74 75 76 77 78
1.343040822 -2.295163126 -1.944026766 5.256021032 1.997150110 3.136577081
79 80 81 82 83 84
0.677470761 -5.662435205 -0.930737728 -3.368580653 0.433306034 -0.431959323
85 86 87 88 89 90
0.267127067 0.763949365 -1.201671645 0.146934317 3.992230400 1.758163601
91 92 93 94 95 96
0.387554279 0.968910207 2.513355288 -0.992023206 -0.079574552 1.514169817
97 98 99 100 101 102
-1.641805625 0.654167287 -2.277935746 -0.610935067 1.731437637 1.456365369
103 104 105 106 107 108
-4.506464838 4.045106240 2.269705749 -0.249076023 3.747464971 -1.790693648
109 110 111 112 113 114
6.551505811 2.372145307 0.018488445 -1.760032454 -0.260438213 1.767754841
115 116 117 118 119 120
-0.588131889 -0.238190292 -0.496007380 -4.408417549 3.140252015 -0.681530993
121 122 123 124 125 126
1.215005828 -0.438528156 -4.014393324 -1.254718031 -2.301361815 -1.844527939
127 128 129 130 131 132
-0.584649221 1.840077465 0.443473617 -2.805958876 2.698078657 -1.948276280
133 134 135 136 137 138
1.874713794 -0.281318930 3.059974041 6.577395045 0.384232115 -0.559543587
139 140 141 142 143 144
-2.066494298 -2.285001241 -3.466963701 2.891014595 -0.165455970 -0.040510624
145 146 147 148 149 150
0.094950834 0.737068667 -2.934407460 -3.225492065 2.431727324 0.768094043
151 152 153 154 155 156
3.825628127 -2.737891542 -2.439003898 2.703305033 4.235776568 0.387554279
157 158 159 160 161 162
0.510779322 1.840077465 -3.977548792 3.979691914 1.890885109 7.171877149
163 164 165 166 167 168
0.808223301 8.734885355 1.741505164 5.852418868 -1.265279346 -1.103944164
169 170 171 172 173 174
-0.454458751 1.061532285 3.276480977 -0.003554625 -4.615777471 1.576293909
175 176 177 178 179 180
0.782651197 -4.855451342 -1.303938050 2.693060390 -2.525003353 -1.562247889
181 182 183 184 185 186
-3.440730502 -5.728882123 -1.686440436 -2.900547579 -0.756618993 5.095217039
187 188 189 190 191 192
0.809923241 -0.015265223 -1.627538817 4.440793869 -4.662021911 -3.170878645
193 194 195 196 197 198
1.719958521 -0.061244163 1.037487509 -2.916442575 5.994821368 2.451106365
199 200 201 202 203 204
0.707713809 -0.952092114 -0.080462824 -1.234488123 -0.502801488 -1.406338702
205 206 207 208 209 210
-1.843220051 0.317377959 -0.635937752 1.792816938 -0.503489596 0.601619824
211 212 213 214 215 216
-0.563697601 -0.475321381 3.286960617 -2.913942354 1.255081246 -0.364947290
217 218 219 220 221 222
0.455395147 -0.795099581 3.627590820 1.830203678 -3.393174351 -0.936439061
223 224 225 226 227 228
0.773598289 -4.057506756 1.912285228 -2.298781071 -0.241717037 -3.491218063
229 230 231 232 233 234
4.774141779 -2.873971540 -1.697736419 -1.578087756 -4.651537427 3.208086215
235 236 237 238 239 240
-3.234113456 -1.867313926 1.712763840 -2.514616453 -5.241341170 -3.817294450
241 242 243 244 245 246
-4.261079430 0.098060914 -0.181065527 0.639073825 1.868355515 3.952041264
247 248 249 250 251 252
0.466644541 7.570063887 2.350172528 -0.398154662 0.730186931 2.819144984
253 254 255 256 257 258
-3.425244988 -1.132952053 1.776061517 -0.207937150 4.347488725 0.347081057
259 260 261 262 263 264
1.845885861 -8.316958152 -2.081137523 -1.294045001 2.941213263 -1.657935214
> postscript(file="/var/fisher/rcomp/tmp/6mdn81384964252.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 -1.793230064 NA
1 1.902407774 -1.793230064
2 -0.882838833 1.902407774
3 -2.453509776 -0.882838833
4 9.877942957 -2.453509776
5 2.333933740 9.877942957
6 8.581658779 2.333933740
7 -1.178990143 8.581658779
8 -1.886330328 -1.178990143
9 1.272927736 -1.886330328
10 -0.047995267 1.272927736
11 -2.194705601 -0.047995267
12 -0.565125015 -2.194705601
13 1.779375691 -0.565125015
14 1.095476515 1.779375691
15 0.466890449 1.095476515
16 1.544265186 0.466890449
17 1.729713260 1.544265186
18 -2.872139308 1.729713260
19 0.849378585 -2.872139308
20 -0.757886852 0.849378585
21 -0.946740942 -0.757886852
22 -1.381644689 -0.946740942
23 -0.894750880 -1.381644689
24 1.468016364 -0.894750880
25 -6.329056874 1.468016364
26 0.026762283 -6.329056874
27 2.142349699 0.026762283
28 2.425782335 2.142349699
29 -2.100956260 2.425782335
30 -1.865304719 -2.100956260
31 0.210602677 -1.865304719
32 -0.671831580 0.210602677
33 -0.215824869 -0.671831580
34 0.408685624 -0.215824869
35 -3.758564694 0.408685624
36 2.927179633 -3.758564694
37 -0.055075758 2.927179633
38 -0.414224119 -0.055075758
39 -3.460152471 -0.414224119
40 -2.743622901 -3.460152471
41 2.827197432 -2.743622901
42 -3.917058947 2.827197432
43 -1.284016285 -3.917058947
44 -1.128811927 -1.284016285
45 -2.159987758 -1.128811927
46 -3.387485680 -2.159987758
47 -0.831104415 -3.387485680
48 4.590674543 -0.831104415
49 -2.144297313 4.590674543
50 -1.267948196 -2.144297313
51 0.931074348 -1.267948196
52 2.512001469 0.931074348
53 -0.576336763 2.512001469
54 -4.059425514 -0.576336763
55 2.692670321 -4.059425514
56 1.995624219 2.692670321
57 -3.315583632 1.995624219
58 -3.935389103 -3.315583632
59 0.907939844 -3.935389103
60 1.789587148 0.907939844
61 -0.906951781 1.789587148
62 -2.170107345 -0.906951781
63 0.006307340 -2.170107345
64 0.031158868 0.006307340
65 -4.193129863 0.031158868
66 1.766638910 -4.193129863
67 -2.249441360 1.766638910
68 -0.029926854 -2.249441360
69 1.512678398 -0.029926854
70 -0.649608800 1.512678398
71 2.082348076 -0.649608800
72 1.343040822 2.082348076
73 -2.295163126 1.343040822
74 -1.944026766 -2.295163126
75 5.256021032 -1.944026766
76 1.997150110 5.256021032
77 3.136577081 1.997150110
78 0.677470761 3.136577081
79 -5.662435205 0.677470761
80 -0.930737728 -5.662435205
81 -3.368580653 -0.930737728
82 0.433306034 -3.368580653
83 -0.431959323 0.433306034
84 0.267127067 -0.431959323
85 0.763949365 0.267127067
86 -1.201671645 0.763949365
87 0.146934317 -1.201671645
88 3.992230400 0.146934317
89 1.758163601 3.992230400
90 0.387554279 1.758163601
91 0.968910207 0.387554279
92 2.513355288 0.968910207
93 -0.992023206 2.513355288
94 -0.079574552 -0.992023206
95 1.514169817 -0.079574552
96 -1.641805625 1.514169817
97 0.654167287 -1.641805625
98 -2.277935746 0.654167287
99 -0.610935067 -2.277935746
100 1.731437637 -0.610935067
101 1.456365369 1.731437637
102 -4.506464838 1.456365369
103 4.045106240 -4.506464838
104 2.269705749 4.045106240
105 -0.249076023 2.269705749
106 3.747464971 -0.249076023
107 -1.790693648 3.747464971
108 6.551505811 -1.790693648
109 2.372145307 6.551505811
110 0.018488445 2.372145307
111 -1.760032454 0.018488445
112 -0.260438213 -1.760032454
113 1.767754841 -0.260438213
114 -0.588131889 1.767754841
115 -0.238190292 -0.588131889
116 -0.496007380 -0.238190292
117 -4.408417549 -0.496007380
118 3.140252015 -4.408417549
119 -0.681530993 3.140252015
120 1.215005828 -0.681530993
121 -0.438528156 1.215005828
122 -4.014393324 -0.438528156
123 -1.254718031 -4.014393324
124 -2.301361815 -1.254718031
125 -1.844527939 -2.301361815
126 -0.584649221 -1.844527939
127 1.840077465 -0.584649221
128 0.443473617 1.840077465
129 -2.805958876 0.443473617
130 2.698078657 -2.805958876
131 -1.948276280 2.698078657
132 1.874713794 -1.948276280
133 -0.281318930 1.874713794
134 3.059974041 -0.281318930
135 6.577395045 3.059974041
136 0.384232115 6.577395045
137 -0.559543587 0.384232115
138 -2.066494298 -0.559543587
139 -2.285001241 -2.066494298
140 -3.466963701 -2.285001241
141 2.891014595 -3.466963701
142 -0.165455970 2.891014595
143 -0.040510624 -0.165455970
144 0.094950834 -0.040510624
145 0.737068667 0.094950834
146 -2.934407460 0.737068667
147 -3.225492065 -2.934407460
148 2.431727324 -3.225492065
149 0.768094043 2.431727324
150 3.825628127 0.768094043
151 -2.737891542 3.825628127
152 -2.439003898 -2.737891542
153 2.703305033 -2.439003898
154 4.235776568 2.703305033
155 0.387554279 4.235776568
156 0.510779322 0.387554279
157 1.840077465 0.510779322
158 -3.977548792 1.840077465
159 3.979691914 -3.977548792
160 1.890885109 3.979691914
161 7.171877149 1.890885109
162 0.808223301 7.171877149
163 8.734885355 0.808223301
164 1.741505164 8.734885355
165 5.852418868 1.741505164
166 -1.265279346 5.852418868
167 -1.103944164 -1.265279346
168 -0.454458751 -1.103944164
169 1.061532285 -0.454458751
170 3.276480977 1.061532285
171 -0.003554625 3.276480977
172 -4.615777471 -0.003554625
173 1.576293909 -4.615777471
174 0.782651197 1.576293909
175 -4.855451342 0.782651197
176 -1.303938050 -4.855451342
177 2.693060390 -1.303938050
178 -2.525003353 2.693060390
179 -1.562247889 -2.525003353
180 -3.440730502 -1.562247889
181 -5.728882123 -3.440730502
182 -1.686440436 -5.728882123
183 -2.900547579 -1.686440436
184 -0.756618993 -2.900547579
185 5.095217039 -0.756618993
186 0.809923241 5.095217039
187 -0.015265223 0.809923241
188 -1.627538817 -0.015265223
189 4.440793869 -1.627538817
190 -4.662021911 4.440793869
191 -3.170878645 -4.662021911
192 1.719958521 -3.170878645
193 -0.061244163 1.719958521
194 1.037487509 -0.061244163
195 -2.916442575 1.037487509
196 5.994821368 -2.916442575
197 2.451106365 5.994821368
198 0.707713809 2.451106365
199 -0.952092114 0.707713809
200 -0.080462824 -0.952092114
201 -1.234488123 -0.080462824
202 -0.502801488 -1.234488123
203 -1.406338702 -0.502801488
204 -1.843220051 -1.406338702
205 0.317377959 -1.843220051
206 -0.635937752 0.317377959
207 1.792816938 -0.635937752
208 -0.503489596 1.792816938
209 0.601619824 -0.503489596
210 -0.563697601 0.601619824
211 -0.475321381 -0.563697601
212 3.286960617 -0.475321381
213 -2.913942354 3.286960617
214 1.255081246 -2.913942354
215 -0.364947290 1.255081246
216 0.455395147 -0.364947290
217 -0.795099581 0.455395147
218 3.627590820 -0.795099581
219 1.830203678 3.627590820
220 -3.393174351 1.830203678
221 -0.936439061 -3.393174351
222 0.773598289 -0.936439061
223 -4.057506756 0.773598289
224 1.912285228 -4.057506756
225 -2.298781071 1.912285228
226 -0.241717037 -2.298781071
227 -3.491218063 -0.241717037
228 4.774141779 -3.491218063
229 -2.873971540 4.774141779
230 -1.697736419 -2.873971540
231 -1.578087756 -1.697736419
232 -4.651537427 -1.578087756
233 3.208086215 -4.651537427
234 -3.234113456 3.208086215
235 -1.867313926 -3.234113456
236 1.712763840 -1.867313926
237 -2.514616453 1.712763840
238 -5.241341170 -2.514616453
239 -3.817294450 -5.241341170
240 -4.261079430 -3.817294450
241 0.098060914 -4.261079430
242 -0.181065527 0.098060914
243 0.639073825 -0.181065527
244 1.868355515 0.639073825
245 3.952041264 1.868355515
246 0.466644541 3.952041264
247 7.570063887 0.466644541
248 2.350172528 7.570063887
249 -0.398154662 2.350172528
250 0.730186931 -0.398154662
251 2.819144984 0.730186931
252 -3.425244988 2.819144984
253 -1.132952053 -3.425244988
254 1.776061517 -1.132952053
255 -0.207937150 1.776061517
256 4.347488725 -0.207937150
257 0.347081057 4.347488725
258 1.845885861 0.347081057
259 -8.316958152 1.845885861
260 -2.081137523 -8.316958152
261 -1.294045001 -2.081137523
262 2.941213263 -1.294045001
263 -1.657935214 2.941213263
264 NA -1.657935214
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.902407774 -1.793230064
[2,] -0.882838833 1.902407774
[3,] -2.453509776 -0.882838833
[4,] 9.877942957 -2.453509776
[5,] 2.333933740 9.877942957
[6,] 8.581658779 2.333933740
[7,] -1.178990143 8.581658779
[8,] -1.886330328 -1.178990143
[9,] 1.272927736 -1.886330328
[10,] -0.047995267 1.272927736
[11,] -2.194705601 -0.047995267
[12,] -0.565125015 -2.194705601
[13,] 1.779375691 -0.565125015
[14,] 1.095476515 1.779375691
[15,] 0.466890449 1.095476515
[16,] 1.544265186 0.466890449
[17,] 1.729713260 1.544265186
[18,] -2.872139308 1.729713260
[19,] 0.849378585 -2.872139308
[20,] -0.757886852 0.849378585
[21,] -0.946740942 -0.757886852
[22,] -1.381644689 -0.946740942
[23,] -0.894750880 -1.381644689
[24,] 1.468016364 -0.894750880
[25,] -6.329056874 1.468016364
[26,] 0.026762283 -6.329056874
[27,] 2.142349699 0.026762283
[28,] 2.425782335 2.142349699
[29,] -2.100956260 2.425782335
[30,] -1.865304719 -2.100956260
[31,] 0.210602677 -1.865304719
[32,] -0.671831580 0.210602677
[33,] -0.215824869 -0.671831580
[34,] 0.408685624 -0.215824869
[35,] -3.758564694 0.408685624
[36,] 2.927179633 -3.758564694
[37,] -0.055075758 2.927179633
[38,] -0.414224119 -0.055075758
[39,] -3.460152471 -0.414224119
[40,] -2.743622901 -3.460152471
[41,] 2.827197432 -2.743622901
[42,] -3.917058947 2.827197432
[43,] -1.284016285 -3.917058947
[44,] -1.128811927 -1.284016285
[45,] -2.159987758 -1.128811927
[46,] -3.387485680 -2.159987758
[47,] -0.831104415 -3.387485680
[48,] 4.590674543 -0.831104415
[49,] -2.144297313 4.590674543
[50,] -1.267948196 -2.144297313
[51,] 0.931074348 -1.267948196
[52,] 2.512001469 0.931074348
[53,] -0.576336763 2.512001469
[54,] -4.059425514 -0.576336763
[55,] 2.692670321 -4.059425514
[56,] 1.995624219 2.692670321
[57,] -3.315583632 1.995624219
[58,] -3.935389103 -3.315583632
[59,] 0.907939844 -3.935389103
[60,] 1.789587148 0.907939844
[61,] -0.906951781 1.789587148
[62,] -2.170107345 -0.906951781
[63,] 0.006307340 -2.170107345
[64,] 0.031158868 0.006307340
[65,] -4.193129863 0.031158868
[66,] 1.766638910 -4.193129863
[67,] -2.249441360 1.766638910
[68,] -0.029926854 -2.249441360
[69,] 1.512678398 -0.029926854
[70,] -0.649608800 1.512678398
[71,] 2.082348076 -0.649608800
[72,] 1.343040822 2.082348076
[73,] -2.295163126 1.343040822
[74,] -1.944026766 -2.295163126
[75,] 5.256021032 -1.944026766
[76,] 1.997150110 5.256021032
[77,] 3.136577081 1.997150110
[78,] 0.677470761 3.136577081
[79,] -5.662435205 0.677470761
[80,] -0.930737728 -5.662435205
[81,] -3.368580653 -0.930737728
[82,] 0.433306034 -3.368580653
[83,] -0.431959323 0.433306034
[84,] 0.267127067 -0.431959323
[85,] 0.763949365 0.267127067
[86,] -1.201671645 0.763949365
[87,] 0.146934317 -1.201671645
[88,] 3.992230400 0.146934317
[89,] 1.758163601 3.992230400
[90,] 0.387554279 1.758163601
[91,] 0.968910207 0.387554279
[92,] 2.513355288 0.968910207
[93,] -0.992023206 2.513355288
[94,] -0.079574552 -0.992023206
[95,] 1.514169817 -0.079574552
[96,] -1.641805625 1.514169817
[97,] 0.654167287 -1.641805625
[98,] -2.277935746 0.654167287
[99,] -0.610935067 -2.277935746
[100,] 1.731437637 -0.610935067
[101,] 1.456365369 1.731437637
[102,] -4.506464838 1.456365369
[103,] 4.045106240 -4.506464838
[104,] 2.269705749 4.045106240
[105,] -0.249076023 2.269705749
[106,] 3.747464971 -0.249076023
[107,] -1.790693648 3.747464971
[108,] 6.551505811 -1.790693648
[109,] 2.372145307 6.551505811
[110,] 0.018488445 2.372145307
[111,] -1.760032454 0.018488445
[112,] -0.260438213 -1.760032454
[113,] 1.767754841 -0.260438213
[114,] -0.588131889 1.767754841
[115,] -0.238190292 -0.588131889
[116,] -0.496007380 -0.238190292
[117,] -4.408417549 -0.496007380
[118,] 3.140252015 -4.408417549
[119,] -0.681530993 3.140252015
[120,] 1.215005828 -0.681530993
[121,] -0.438528156 1.215005828
[122,] -4.014393324 -0.438528156
[123,] -1.254718031 -4.014393324
[124,] -2.301361815 -1.254718031
[125,] -1.844527939 -2.301361815
[126,] -0.584649221 -1.844527939
[127,] 1.840077465 -0.584649221
[128,] 0.443473617 1.840077465
[129,] -2.805958876 0.443473617
[130,] 2.698078657 -2.805958876
[131,] -1.948276280 2.698078657
[132,] 1.874713794 -1.948276280
[133,] -0.281318930 1.874713794
[134,] 3.059974041 -0.281318930
[135,] 6.577395045 3.059974041
[136,] 0.384232115 6.577395045
[137,] -0.559543587 0.384232115
[138,] -2.066494298 -0.559543587
[139,] -2.285001241 -2.066494298
[140,] -3.466963701 -2.285001241
[141,] 2.891014595 -3.466963701
[142,] -0.165455970 2.891014595
[143,] -0.040510624 -0.165455970
[144,] 0.094950834 -0.040510624
[145,] 0.737068667 0.094950834
[146,] -2.934407460 0.737068667
[147,] -3.225492065 -2.934407460
[148,] 2.431727324 -3.225492065
[149,] 0.768094043 2.431727324
[150,] 3.825628127 0.768094043
[151,] -2.737891542 3.825628127
[152,] -2.439003898 -2.737891542
[153,] 2.703305033 -2.439003898
[154,] 4.235776568 2.703305033
[155,] 0.387554279 4.235776568
[156,] 0.510779322 0.387554279
[157,] 1.840077465 0.510779322
[158,] -3.977548792 1.840077465
[159,] 3.979691914 -3.977548792
[160,] 1.890885109 3.979691914
[161,] 7.171877149 1.890885109
[162,] 0.808223301 7.171877149
[163,] 8.734885355 0.808223301
[164,] 1.741505164 8.734885355
[165,] 5.852418868 1.741505164
[166,] -1.265279346 5.852418868
[167,] -1.103944164 -1.265279346
[168,] -0.454458751 -1.103944164
[169,] 1.061532285 -0.454458751
[170,] 3.276480977 1.061532285
[171,] -0.003554625 3.276480977
[172,] -4.615777471 -0.003554625
[173,] 1.576293909 -4.615777471
[174,] 0.782651197 1.576293909
[175,] -4.855451342 0.782651197
[176,] -1.303938050 -4.855451342
[177,] 2.693060390 -1.303938050
[178,] -2.525003353 2.693060390
[179,] -1.562247889 -2.525003353
[180,] -3.440730502 -1.562247889
[181,] -5.728882123 -3.440730502
[182,] -1.686440436 -5.728882123
[183,] -2.900547579 -1.686440436
[184,] -0.756618993 -2.900547579
[185,] 5.095217039 -0.756618993
[186,] 0.809923241 5.095217039
[187,] -0.015265223 0.809923241
[188,] -1.627538817 -0.015265223
[189,] 4.440793869 -1.627538817
[190,] -4.662021911 4.440793869
[191,] -3.170878645 -4.662021911
[192,] 1.719958521 -3.170878645
[193,] -0.061244163 1.719958521
[194,] 1.037487509 -0.061244163
[195,] -2.916442575 1.037487509
[196,] 5.994821368 -2.916442575
[197,] 2.451106365 5.994821368
[198,] 0.707713809 2.451106365
[199,] -0.952092114 0.707713809
[200,] -0.080462824 -0.952092114
[201,] -1.234488123 -0.080462824
[202,] -0.502801488 -1.234488123
[203,] -1.406338702 -0.502801488
[204,] -1.843220051 -1.406338702
[205,] 0.317377959 -1.843220051
[206,] -0.635937752 0.317377959
[207,] 1.792816938 -0.635937752
[208,] -0.503489596 1.792816938
[209,] 0.601619824 -0.503489596
[210,] -0.563697601 0.601619824
[211,] -0.475321381 -0.563697601
[212,] 3.286960617 -0.475321381
[213,] -2.913942354 3.286960617
[214,] 1.255081246 -2.913942354
[215,] -0.364947290 1.255081246
[216,] 0.455395147 -0.364947290
[217,] -0.795099581 0.455395147
[218,] 3.627590820 -0.795099581
[219,] 1.830203678 3.627590820
[220,] -3.393174351 1.830203678
[221,] -0.936439061 -3.393174351
[222,] 0.773598289 -0.936439061
[223,] -4.057506756 0.773598289
[224,] 1.912285228 -4.057506756
[225,] -2.298781071 1.912285228
[226,] -0.241717037 -2.298781071
[227,] -3.491218063 -0.241717037
[228,] 4.774141779 -3.491218063
[229,] -2.873971540 4.774141779
[230,] -1.697736419 -2.873971540
[231,] -1.578087756 -1.697736419
[232,] -4.651537427 -1.578087756
[233,] 3.208086215 -4.651537427
[234,] -3.234113456 3.208086215
[235,] -1.867313926 -3.234113456
[236,] 1.712763840 -1.867313926
[237,] -2.514616453 1.712763840
[238,] -5.241341170 -2.514616453
[239,] -3.817294450 -5.241341170
[240,] -4.261079430 -3.817294450
[241,] 0.098060914 -4.261079430
[242,] -0.181065527 0.098060914
[243,] 0.639073825 -0.181065527
[244,] 1.868355515 0.639073825
[245,] 3.952041264 1.868355515
[246,] 0.466644541 3.952041264
[247,] 7.570063887 0.466644541
[248,] 2.350172528 7.570063887
[249,] -0.398154662 2.350172528
[250,] 0.730186931 -0.398154662
[251,] 2.819144984 0.730186931
[252,] -3.425244988 2.819144984
[253,] -1.132952053 -3.425244988
[254,] 1.776061517 -1.132952053
[255,] -0.207937150 1.776061517
[256,] 4.347488725 -0.207937150
[257,] 0.347081057 4.347488725
[258,] 1.845885861 0.347081057
[259,] -8.316958152 1.845885861
[260,] -2.081137523 -8.316958152
[261,] -1.294045001 -2.081137523
[262,] 2.941213263 -1.294045001
[263,] -1.657935214 2.941213263
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.902407774 -1.793230064
2 -0.882838833 1.902407774
3 -2.453509776 -0.882838833
4 9.877942957 -2.453509776
5 2.333933740 9.877942957
6 8.581658779 2.333933740
7 -1.178990143 8.581658779
8 -1.886330328 -1.178990143
9 1.272927736 -1.886330328
10 -0.047995267 1.272927736
11 -2.194705601 -0.047995267
12 -0.565125015 -2.194705601
13 1.779375691 -0.565125015
14 1.095476515 1.779375691
15 0.466890449 1.095476515
16 1.544265186 0.466890449
17 1.729713260 1.544265186
18 -2.872139308 1.729713260
19 0.849378585 -2.872139308
20 -0.757886852 0.849378585
21 -0.946740942 -0.757886852
22 -1.381644689 -0.946740942
23 -0.894750880 -1.381644689
24 1.468016364 -0.894750880
25 -6.329056874 1.468016364
26 0.026762283 -6.329056874
27 2.142349699 0.026762283
28 2.425782335 2.142349699
29 -2.100956260 2.425782335
30 -1.865304719 -2.100956260
31 0.210602677 -1.865304719
32 -0.671831580 0.210602677
33 -0.215824869 -0.671831580
34 0.408685624 -0.215824869
35 -3.758564694 0.408685624
36 2.927179633 -3.758564694
37 -0.055075758 2.927179633
38 -0.414224119 -0.055075758
39 -3.460152471 -0.414224119
40 -2.743622901 -3.460152471
41 2.827197432 -2.743622901
42 -3.917058947 2.827197432
43 -1.284016285 -3.917058947
44 -1.128811927 -1.284016285
45 -2.159987758 -1.128811927
46 -3.387485680 -2.159987758
47 -0.831104415 -3.387485680
48 4.590674543 -0.831104415
49 -2.144297313 4.590674543
50 -1.267948196 -2.144297313
51 0.931074348 -1.267948196
52 2.512001469 0.931074348
53 -0.576336763 2.512001469
54 -4.059425514 -0.576336763
55 2.692670321 -4.059425514
56 1.995624219 2.692670321
57 -3.315583632 1.995624219
58 -3.935389103 -3.315583632
59 0.907939844 -3.935389103
60 1.789587148 0.907939844
61 -0.906951781 1.789587148
62 -2.170107345 -0.906951781
63 0.006307340 -2.170107345
64 0.031158868 0.006307340
65 -4.193129863 0.031158868
66 1.766638910 -4.193129863
67 -2.249441360 1.766638910
68 -0.029926854 -2.249441360
69 1.512678398 -0.029926854
70 -0.649608800 1.512678398
71 2.082348076 -0.649608800
72 1.343040822 2.082348076
73 -2.295163126 1.343040822
74 -1.944026766 -2.295163126
75 5.256021032 -1.944026766
76 1.997150110 5.256021032
77 3.136577081 1.997150110
78 0.677470761 3.136577081
79 -5.662435205 0.677470761
80 -0.930737728 -5.662435205
81 -3.368580653 -0.930737728
82 0.433306034 -3.368580653
83 -0.431959323 0.433306034
84 0.267127067 -0.431959323
85 0.763949365 0.267127067
86 -1.201671645 0.763949365
87 0.146934317 -1.201671645
88 3.992230400 0.146934317
89 1.758163601 3.992230400
90 0.387554279 1.758163601
91 0.968910207 0.387554279
92 2.513355288 0.968910207
93 -0.992023206 2.513355288
94 -0.079574552 -0.992023206
95 1.514169817 -0.079574552
96 -1.641805625 1.514169817
97 0.654167287 -1.641805625
98 -2.277935746 0.654167287
99 -0.610935067 -2.277935746
100 1.731437637 -0.610935067
101 1.456365369 1.731437637
102 -4.506464838 1.456365369
103 4.045106240 -4.506464838
104 2.269705749 4.045106240
105 -0.249076023 2.269705749
106 3.747464971 -0.249076023
107 -1.790693648 3.747464971
108 6.551505811 -1.790693648
109 2.372145307 6.551505811
110 0.018488445 2.372145307
111 -1.760032454 0.018488445
112 -0.260438213 -1.760032454
113 1.767754841 -0.260438213
114 -0.588131889 1.767754841
115 -0.238190292 -0.588131889
116 -0.496007380 -0.238190292
117 -4.408417549 -0.496007380
118 3.140252015 -4.408417549
119 -0.681530993 3.140252015
120 1.215005828 -0.681530993
121 -0.438528156 1.215005828
122 -4.014393324 -0.438528156
123 -1.254718031 -4.014393324
124 -2.301361815 -1.254718031
125 -1.844527939 -2.301361815
126 -0.584649221 -1.844527939
127 1.840077465 -0.584649221
128 0.443473617 1.840077465
129 -2.805958876 0.443473617
130 2.698078657 -2.805958876
131 -1.948276280 2.698078657
132 1.874713794 -1.948276280
133 -0.281318930 1.874713794
134 3.059974041 -0.281318930
135 6.577395045 3.059974041
136 0.384232115 6.577395045
137 -0.559543587 0.384232115
138 -2.066494298 -0.559543587
139 -2.285001241 -2.066494298
140 -3.466963701 -2.285001241
141 2.891014595 -3.466963701
142 -0.165455970 2.891014595
143 -0.040510624 -0.165455970
144 0.094950834 -0.040510624
145 0.737068667 0.094950834
146 -2.934407460 0.737068667
147 -3.225492065 -2.934407460
148 2.431727324 -3.225492065
149 0.768094043 2.431727324
150 3.825628127 0.768094043
151 -2.737891542 3.825628127
152 -2.439003898 -2.737891542
153 2.703305033 -2.439003898
154 4.235776568 2.703305033
155 0.387554279 4.235776568
156 0.510779322 0.387554279
157 1.840077465 0.510779322
158 -3.977548792 1.840077465
159 3.979691914 -3.977548792
160 1.890885109 3.979691914
161 7.171877149 1.890885109
162 0.808223301 7.171877149
163 8.734885355 0.808223301
164 1.741505164 8.734885355
165 5.852418868 1.741505164
166 -1.265279346 5.852418868
167 -1.103944164 -1.265279346
168 -0.454458751 -1.103944164
169 1.061532285 -0.454458751
170 3.276480977 1.061532285
171 -0.003554625 3.276480977
172 -4.615777471 -0.003554625
173 1.576293909 -4.615777471
174 0.782651197 1.576293909
175 -4.855451342 0.782651197
176 -1.303938050 -4.855451342
177 2.693060390 -1.303938050
178 -2.525003353 2.693060390
179 -1.562247889 -2.525003353
180 -3.440730502 -1.562247889
181 -5.728882123 -3.440730502
182 -1.686440436 -5.728882123
183 -2.900547579 -1.686440436
184 -0.756618993 -2.900547579
185 5.095217039 -0.756618993
186 0.809923241 5.095217039
187 -0.015265223 0.809923241
188 -1.627538817 -0.015265223
189 4.440793869 -1.627538817
190 -4.662021911 4.440793869
191 -3.170878645 -4.662021911
192 1.719958521 -3.170878645
193 -0.061244163 1.719958521
194 1.037487509 -0.061244163
195 -2.916442575 1.037487509
196 5.994821368 -2.916442575
197 2.451106365 5.994821368
198 0.707713809 2.451106365
199 -0.952092114 0.707713809
200 -0.080462824 -0.952092114
201 -1.234488123 -0.080462824
202 -0.502801488 -1.234488123
203 -1.406338702 -0.502801488
204 -1.843220051 -1.406338702
205 0.317377959 -1.843220051
206 -0.635937752 0.317377959
207 1.792816938 -0.635937752
208 -0.503489596 1.792816938
209 0.601619824 -0.503489596
210 -0.563697601 0.601619824
211 -0.475321381 -0.563697601
212 3.286960617 -0.475321381
213 -2.913942354 3.286960617
214 1.255081246 -2.913942354
215 -0.364947290 1.255081246
216 0.455395147 -0.364947290
217 -0.795099581 0.455395147
218 3.627590820 -0.795099581
219 1.830203678 3.627590820
220 -3.393174351 1.830203678
221 -0.936439061 -3.393174351
222 0.773598289 -0.936439061
223 -4.057506756 0.773598289
224 1.912285228 -4.057506756
225 -2.298781071 1.912285228
226 -0.241717037 -2.298781071
227 -3.491218063 -0.241717037
228 4.774141779 -3.491218063
229 -2.873971540 4.774141779
230 -1.697736419 -2.873971540
231 -1.578087756 -1.697736419
232 -4.651537427 -1.578087756
233 3.208086215 -4.651537427
234 -3.234113456 3.208086215
235 -1.867313926 -3.234113456
236 1.712763840 -1.867313926
237 -2.514616453 1.712763840
238 -5.241341170 -2.514616453
239 -3.817294450 -5.241341170
240 -4.261079430 -3.817294450
241 0.098060914 -4.261079430
242 -0.181065527 0.098060914
243 0.639073825 -0.181065527
244 1.868355515 0.639073825
245 3.952041264 1.868355515
246 0.466644541 3.952041264
247 7.570063887 0.466644541
248 2.350172528 7.570063887
249 -0.398154662 2.350172528
250 0.730186931 -0.398154662
251 2.819144984 0.730186931
252 -3.425244988 2.819144984
253 -1.132952053 -3.425244988
254 1.776061517 -1.132952053
255 -0.207937150 1.776061517
256 4.347488725 -0.207937150
257 0.347081057 4.347488725
258 1.845885861 0.347081057
259 -8.316958152 1.845885861
260 -2.081137523 -8.316958152
261 -1.294045001 -2.081137523
262 2.941213263 -1.294045001
263 -1.657935214 2.941213263
> 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/7ubx21384964252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/83v261384964252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/92yuf1384964252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10khxo1384964252.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11b4go1384964252.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/1265q31384964252.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/13mrfb1384964252.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/14adq61384964252.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/15r9r71384964252.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/16o9c91384964252.tab")
+ }
>
> try(system("convert tmp/1qydj1384964252.ps tmp/1qydj1384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lmyw1384964252.ps tmp/2lmyw1384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/3no4c1384964252.ps tmp/3no4c1384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/485q21384964252.ps tmp/485q21384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/5irrb1384964252.ps tmp/5irrb1384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mdn81384964252.ps tmp/6mdn81384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ubx21384964252.ps tmp/7ubx21384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/83v261384964252.ps tmp/83v261384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/92yuf1384964252.ps tmp/92yuf1384964252.png",intern=TRUE))
character(0)
> try(system("convert tmp/10khxo1384964252.ps tmp/10khxo1384964252.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.593 1.874 13.462