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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+ ,4
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+ ,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 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '5'
> #'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
Happiness Connected Separate Learning Software Depression Sport1 Month
1 14 41 38 13 12 12.0 53 9
2 18 39 32 16 11 11.0 83 9
3 11 30 35 19 15 14.0 66 9
4 12 31 33 15 6 12.0 67 9
5 16 34 37 14 13 21.0 76 9
6 18 35 29 13 10 12.0 78 9
7 14 39 31 19 12 22.0 53 9
8 14 34 36 15 14 11.0 80 9
9 15 36 35 14 12 10.0 74 9
10 15 37 38 15 9 13.0 76 9
11 17 38 31 16 10 10.0 79 9
12 19 36 34 16 12 8.0 54 9
13 10 38 35 16 12 15.0 67 9
14 16 39 38 16 11 14.0 54 9
15 18 33 37 17 15 10.0 87 9
16 14 32 33 15 12 14.0 58 9
17 14 36 32 15 10 14.0 75 9
18 17 38 38 20 12 11.0 88 9
19 14 39 38 18 11 10.0 64 9
20 16 32 32 16 12 13.0 57 9
21 18 32 33 16 11 9.5 66 9
22 11 31 31 16 12 14.0 68 9
23 14 39 38 19 13 12.0 54 9
24 12 37 39 16 11 14.0 56 9
25 17 39 32 17 12 11.0 86 9
26 9 41 32 17 13 9.0 80 9
27 16 36 35 16 10 11.0 76 9
28 14 33 37 15 14 15.0 69 9
29 15 33 33 16 12 14.0 78 9
30 11 34 33 14 10 13.0 67 9
31 16 31 31 15 12 9.0 80 9
32 13 27 32 12 8 15.0 54 9
33 17 37 31 14 10 10.0 71 9
34 15 34 37 16 12 11.0 84 9
35 14 34 30 14 12 13.0 74 9
36 16 32 33 10 7 8.0 71 9
37 9 29 31 10 9 20.0 63 9
38 15 36 33 14 12 12.0 71 9
39 17 29 31 16 10 10.0 76 9
40 13 35 33 16 10 10.0 69 9
41 15 37 32 16 10 9.0 74 9
42 16 34 33 14 12 14.0 75 9
43 16 38 32 20 15 8.0 54 9
44 12 35 33 14 10 14.0 52 9
45 15 38 28 14 10 11.0 69 9
46 11 37 35 11 12 13.0 68 9
47 15 38 39 14 13 9.0 65 9
48 15 33 34 15 11 11.0 75 9
49 17 36 38 16 11 15.0 74 9
50 13 38 32 14 12 11.0 75 9
51 16 32 38 16 14 10.0 72 9
52 14 32 30 14 10 14.0 67 9
53 11 32 33 12 12 18.0 63 9
54 12 34 38 16 13 14.0 62 9
55 12 32 32 9 5 11.0 63 9
56 15 37 35 14 6 14.5 76 9
57 16 39 34 16 12 13.0 74 9
58 15 29 34 16 12 9.0 67 9
59 12 37 36 15 11 10.0 73 9
60 12 35 34 16 10 15.0 70 9
61 8 30 28 12 7 20.0 53 9
62 13 38 34 16 12 12.0 77 9
63 11 34 35 16 14 12.0 80 9
64 14 31 35 14 11 14.0 52 9
65 15 34 31 16 12 13.0 54 9
66 10 35 37 17 13 11.0 80 10
67 11 36 35 18 14 17.0 66 10
68 12 30 27 18 11 12.0 73 10
69 15 39 40 12 12 13.0 63 10
70 15 35 37 16 12 14.0 69 10
71 14 38 36 10 8 13.0 67 10
72 16 31 38 14 11 15.0 54 10
73 15 34 39 18 14 13.0 81 10
74 15 38 41 18 14 10.0 69 10
75 13 34 27 16 12 11.0 84 10
76 12 39 30 17 9 19.0 80 10
77 17 37 37 16 13 13.0 70 10
78 13 34 31 16 11 17.0 69 10
79 15 28 31 13 12 13.0 77 10
80 13 37 27 16 12 9.0 54 10
81 15 33 36 16 12 11.0 79 10
82 15 35 37 16 12 9.0 71 10
83 16 37 33 15 12 12.0 73 10
84 15 32 34 15 11 12.0 72 10
85 14 33 31 16 10 13.0 77 10
86 15 38 39 14 9 13.0 75 10
87 14 33 34 16 12 12.0 69 10
88 13 29 32 16 12 15.0 54 10
89 7 33 33 15 12 22.0 70 10
90 17 31 36 12 9 13.0 73 10
91 13 36 32 17 15 15.0 54 10
92 15 35 41 16 12 13.0 77 10
93 14 32 28 15 12 15.0 82 10
94 13 29 30 13 12 12.5 80 10
95 16 39 36 16 10 11.0 80 10
96 12 37 35 16 13 16.0 69 10
97 14 35 31 16 9 11.0 78 10
98 17 37 34 16 12 11.0 81 10
99 15 32 36 14 10 10.0 76 10
100 17 38 36 16 14 10.0 76 10
101 12 37 35 16 11 16.0 73 10
102 16 36 37 20 15 12.0 85 10
103 11 32 28 15 11 11.0 66 10
104 15 33 39 16 11 16.0 79 10
105 9 40 32 13 12 19.0 68 10
106 16 38 35 17 12 11.0 76 10
107 15 41 39 16 12 16.0 71 10
108 10 36 35 16 11 15.0 54 10
109 10 43 42 12 7 24.0 46 10
110 15 30 34 16 12 14.0 85 10
111 11 31 33 16 14 15.0 74 10
112 13 32 41 17 11 11.0 88 10
113 14 32 33 13 11 15.0 38 10
114 18 37 34 12 10 12.0 76 10
115 16 37 32 18 13 10.0 86 10
116 14 33 40 14 13 14.0 54 10
117 14 34 40 14 8 13.0 67 10
118 14 33 35 13 11 9.0 69 10
119 14 38 36 16 12 15.0 90 10
120 12 33 37 13 11 15.0 54 10
121 14 31 27 16 13 14.0 76 10
122 15 38 39 13 12 11.0 89 10
123 15 37 38 16 14 8.0 76 10
124 15 36 31 15 13 11.0 73 10
125 13 31 33 16 15 11.0 79 10
126 17 39 32 15 10 8.0 90 10
127 17 44 39 17 11 10.0 74 10
128 19 33 36 15 9 11.0 81 10
129 15 35 33 12 11 13.0 72 10
130 13 32 33 16 10 11.0 71 10
131 9 28 32 10 11 20.0 66 10
132 15 40 37 16 8 10.0 77 10
133 15 27 30 12 11 15.0 65 10
134 15 37 38 14 12 12.0 74 10
135 16 32 29 15 12 14.0 85 10
136 11 28 22 13 9 23.0 54 10
137 14 34 35 15 11 14.0 63 10
138 11 30 35 11 10 16.0 54 10
139 15 35 34 12 8 11.0 64 10
140 13 31 35 11 9 12.0 69 10
141 15 32 34 16 8 10.0 54 10
142 16 30 37 15 9 14.0 84 10
143 14 30 35 17 15 12.0 86 10
144 15 31 23 16 11 12.0 77 10
145 16 40 31 10 8 11.0 89 10
146 16 32 27 18 13 12.0 76 10
147 11 36 36 13 12 13.0 60 10
148 12 32 31 16 12 11.0 75 10
149 9 35 32 13 9 19.0 73 10
150 16 38 39 10 7 12.0 85 10
151 13 42 37 15 13 17.0 79 10
152 16 34 38 16 9 9.0 71 10
153 12 35 39 16 6 12.0 72 10
154 9 38 34 14 8 19.0 69 9
155 13 33 31 10 8 18.0 78 10
156 13 36 32 17 15 15.0 54 10
157 14 32 37 13 6 14.0 69 10
158 19 33 36 15 9 11.0 81 10
159 13 34 32 16 11 9.0 84 10
160 12 32 38 12 8 18.0 84 10
161 13 34 36 13 8 16.0 69 10
162 10 27 26 13 10 24.0 66 11
163 14 31 26 12 8 14.0 81 11
164 16 38 33 17 14 20.0 82 11
165 10 34 39 15 10 18.0 72 11
166 11 24 30 10 8 23.0 54 11
167 14 30 33 14 11 12.0 78 11
168 12 26 25 11 12 14.0 74 11
169 9 34 38 13 12 16.0 82 11
170 9 27 37 16 12 18.0 73 11
171 11 37 31 12 5 20.0 55 11
172 16 36 37 16 12 12.0 72 11
173 9 41 35 12 10 12.0 78 11
174 13 29 25 9 7 17.0 59 11
175 16 36 28 12 12 13.0 72 11
176 13 32 35 15 11 9.0 78 11
177 9 37 33 12 8 16.0 68 11
178 12 30 30 12 9 18.0 69 11
179 16 31 31 14 10 10.0 67 11
180 11 38 37 12 9 14.0 74 11
181 14 36 36 16 12 11.0 54 11
182 13 35 30 11 6 9.0 67 11
183 15 31 36 19 15 11.0 70 11
184 14 38 32 15 12 10.0 80 11
185 16 22 28 8 12 11.0 89 11
186 13 32 36 16 12 19.0 76 11
187 14 36 34 17 11 14.0 74 11
188 15 39 31 12 7 12.0 87 11
189 13 28 28 11 7 14.0 54 11
190 11 32 36 11 5 21.0 61 11
191 11 32 36 14 12 13.0 38 11
192 14 38 40 16 12 10.0 75 11
193 15 32 33 12 3 15.0 69 11
194 11 35 37 16 11 16.0 62 11
195 15 32 32 13 10 14.0 72 11
196 12 37 38 15 12 12.0 70 11
197 14 34 31 16 9 19.0 79 11
198 14 33 37 16 12 15.0 87 11
199 8 33 33 14 9 19.0 62 11
200 13 26 32 16 12 13.0 77 11
201 9 30 30 16 12 17.0 69 11
202 15 24 30 14 10 12.0 69 11
203 17 34 31 11 9 11.0 75 11
204 13 34 32 12 12 14.0 54 11
205 15 33 34 15 8 11.0 72 11
206 15 34 36 15 11 13.0 74 11
207 14 35 37 16 11 12.0 85 11
208 16 35 36 16 12 15.0 52 11
209 13 36 33 11 10 14.0 70 11
210 16 34 33 15 10 12.0 84 11
211 9 34 33 12 12 17.0 64 11
212 16 41 44 12 12 11.0 84 11
213 11 32 39 15 11 18.0 87 11
214 10 30 32 15 8 13.0 79 11
215 11 35 35 16 12 17.0 67 11
216 15 28 25 14 10 13.0 65 11
217 17 33 35 17 11 11.0 85 11
218 14 39 34 14 10 12.0 83 11
219 8 36 35 13 8 22.0 61 11
220 15 36 39 15 12 14.0 82 11
221 11 35 33 13 12 12.0 76 11
222 16 38 36 14 10 12.0 58 11
223 10 33 32 15 12 17.0 72 11
224 15 31 32 12 9 9.0 72 11
225 9 34 36 13 9 21.0 38 11
226 16 32 36 8 6 10.0 78 11
227 19 31 32 14 10 11.0 54 11
228 12 33 34 14 9 12.0 63 11
229 8 34 33 11 9 23.0 66 11
230 11 34 35 12 9 13.0 70 11
231 14 34 30 13 6 12.0 71 11
232 9 33 38 10 10 16.0 67 11
233 15 32 34 16 6 9.0 58 11
234 13 41 33 18 14 17.0 72 11
235 16 34 32 13 10 9.0 72 11
236 11 36 31 11 10 14.0 70 11
237 12 37 30 4 6 17.0 76 11
238 13 36 27 13 12 13.0 50 11
239 10 29 31 16 12 11.0 72 11
240 11 37 30 10 7 12.0 72 11
241 12 27 32 12 8 10.0 88 11
242 8 35 35 12 11 19.0 53 11
243 12 28 28 10 3 16.0 58 11
244 12 35 33 13 6 16.0 66 11
245 15 37 31 15 10 14.0 82 11
246 11 29 35 12 8 20.0 69 11
247 13 32 35 14 9 15.0 68 11
248 14 36 32 10 9 23.0 44 11
249 10 19 21 12 8 20.0 56 11
250 12 21 20 12 9 16.0 53 11
251 15 31 34 11 7 14.0 70 11
252 13 33 32 10 7 17.0 78 11
253 13 36 34 12 6 11.0 71 11
254 13 33 32 16 9 13.0 72 11
255 12 37 33 12 10 17.0 68 11
256 12 34 33 14 11 15.0 67 11
257 9 35 37 16 12 21.0 75 11
258 9 31 32 14 8 18.0 62 11
259 15 37 34 13 11 15.0 67 11
260 10 35 30 4 3 8.0 83 11
261 14 27 30 15 11 12.0 64 11
262 15 34 38 11 12 12.0 68 11
263 7 40 36 11 7 22.0 62 11
264 14 29 32 14 9 12.0 72 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Depression
18.213769 0.004678 0.011958 0.092053 -0.020360 -0.362320
Sport1 Month
0.025946 -0.321366
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.0110 -1.5196 0.2705 1.3322 5.2929
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.213769 2.636250 6.909 3.86e-11 ***
Connected 0.004678 0.037388 0.125 0.9005
Separate 0.011958 0.038085 0.314 0.7538
Learning 0.092053 0.067129 1.371 0.1715
Software -0.020360 0.068785 -0.296 0.7675
Depression -0.362320 0.039352 -9.207 < 2e-16 ***
Sport1 0.025946 0.012786 2.029 0.0435 *
Month -0.321366 0.171891 -1.870 0.0627 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.008 on 256 degrees of freedom
Multiple R-squared: 0.3716, Adjusted R-squared: 0.3544
F-statistic: 21.63 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,] 0.05116407 0.102328149 0.948835925
[2,] 0.89750134 0.204997317 0.102498658
[3,] 0.98181506 0.036369887 0.018184944
[4,] 0.98186037 0.036279259 0.018139630
[5,] 0.98646549 0.027069013 0.013534507
[6,] 0.97600783 0.047984350 0.023992175
[7,] 0.96656829 0.066863421 0.033431710
[8,] 0.95228610 0.095427793 0.047713896
[9,] 0.93474284 0.130514326 0.065257163
[10,] 0.92905597 0.141888062 0.070944031
[11,] 0.94010999 0.119780016 0.059890008
[12,] 0.96535738 0.069285234 0.034642617
[13,] 0.95037606 0.099247873 0.049623936
[14,] 0.93705131 0.125897373 0.062948686
[15,] 0.91657618 0.166847637 0.083423819
[16,] 0.99894677 0.002106457 0.001053228
[17,] 0.99834157 0.003316861 0.001658431
[18,] 0.99737757 0.005244857 0.002622428
[19,] 0.99597750 0.008044995 0.004022497
[20,] 0.99786455 0.004270897 0.002135448
[21,] 0.99670835 0.006583290 0.003291645
[22,] 0.99509950 0.009801010 0.004900505
[23,] 0.99398815 0.012023709 0.006011855
[24,] 0.99129024 0.017419511 0.008709756
[25,] 0.98822393 0.023552138 0.011776069
[26,] 0.98348660 0.033026807 0.016513403
[27,] 0.98810362 0.023792756 0.011896378
[28,] 0.98355489 0.032890221 0.016445110
[29,] 0.98119680 0.037606409 0.018803204
[30,] 0.98154480 0.036910392 0.018455196
[31,] 0.97589991 0.048200186 0.024100093
[32,] 0.97356983 0.052860340 0.026430170
[33,] 0.96537086 0.069258282 0.034629141
[34,] 0.95882222 0.082355556 0.041177778
[35,] 0.94701737 0.105965251 0.052982626
[36,] 0.95649312 0.087013755 0.043506878
[37,] 0.94441752 0.111164956 0.055582478
[38,] 0.92980315 0.140393692 0.070196846
[39,] 0.94363584 0.112728329 0.056364164
[40,] 0.94035407 0.119291850 0.059645925
[41,] 0.92714845 0.145703109 0.072851555
[42,] 0.91035012 0.179299765 0.089649882
[43,] 0.89585193 0.208296132 0.104148066
[44,] 0.88900377 0.221992469 0.110996234
[45,] 0.88203219 0.235935621 0.117967811
[46,] 0.86278121 0.274437589 0.137218794
[47,] 0.84902258 0.301954843 0.150977421
[48,] 0.82218341 0.355633190 0.177816595
[49,] 0.85854563 0.282908737 0.141454369
[50,] 0.85516232 0.289675351 0.144837676
[51,] 0.87636099 0.247278016 0.123639008
[52,] 0.87514616 0.249707689 0.124853845
[53,] 0.92508408 0.149831832 0.074915916
[54,] 0.91380268 0.172394635 0.086197317
[55,] 0.90342838 0.193143239 0.096571619
[56,] 0.91516689 0.169666212 0.084833106
[57,] 0.91362987 0.172740252 0.086370126
[58,] 0.90578811 0.188423776 0.094211888
[59,] 0.93806160 0.123876802 0.061938401
[60,] 0.94288053 0.114238931 0.057119466
[61,] 0.93571397 0.128572060 0.064286030
[62,] 0.95813644 0.083727119 0.041863559
[63,] 0.94944229 0.101115414 0.050557707
[64,] 0.93825688 0.123486248 0.061743124
[65,] 0.93156528 0.136869448 0.068434724
[66,] 0.91755079 0.164898414 0.082449207
[67,] 0.93416874 0.131662517 0.065831259
[68,] 0.92180535 0.156389308 0.078194654
[69,] 0.91476467 0.170470664 0.085235332
[70,] 0.90786296 0.184274084 0.092137042
[71,] 0.89078176 0.218436476 0.109218238
[72,] 0.87223881 0.255522383 0.127761191
[73,] 0.86707207 0.265855850 0.132927925
[74,] 0.84818969 0.303620618 0.151810309
[75,] 0.82461606 0.350767882 0.175383941
[76,] 0.80159414 0.396811719 0.198405859
[77,] 0.77430006 0.451399876 0.225699938
[78,] 0.74502211 0.509955781 0.254977890
[79,] 0.81567520 0.368649597 0.184324799
[80,] 0.84615567 0.307688665 0.153844333
[81,] 0.82361341 0.352773179 0.176386589
[82,] 0.80036766 0.399264682 0.199632341
[83,] 0.77796332 0.444073357 0.222036678
[84,] 0.75535564 0.489288728 0.244644364
[85,] 0.72950037 0.540999257 0.270499629
[86,] 0.70500265 0.589994696 0.294997348
[87,] 0.67887006 0.642259871 0.321129935
[88,] 0.67690248 0.646195031 0.323097516
[89,] 0.64329617 0.713407656 0.356703828
[90,] 0.63305900 0.733882006 0.366941003
[91,] 0.60988509 0.780229824 0.390114912
[92,] 0.58009698 0.839806033 0.419903016
[93,] 0.63431148 0.731377041 0.365688521
[94,] 0.62001253 0.759974933 0.379987467
[95,] 0.63874531 0.722509385 0.361254692
[96,] 0.61138590 0.777228203 0.388614101
[97,] 0.60162264 0.796754721 0.398377360
[98,] 0.63997045 0.720059095 0.360029547
[99,] 0.60980432 0.780391357 0.390195678
[100,] 0.58212151 0.835756986 0.417878493
[101,] 0.58965153 0.820696940 0.410348470
[102,] 0.62027289 0.759454229 0.379727114
[103,] 0.61916900 0.761662010 0.380831005
[104,] 0.69746987 0.605060264 0.302530132
[105,] 0.66704997 0.665900054 0.332950027
[106,] 0.64108377 0.717832454 0.358916227
[107,] 0.60937589 0.781248227 0.390624113
[108,] 0.58653171 0.826936577 0.413468289
[109,] 0.55142796 0.897144087 0.448572043
[110,] 0.51971377 0.960572460 0.480286230
[111,] 0.48965746 0.979314916 0.510342542
[112,] 0.45662360 0.913247191 0.543376404
[113,] 0.43026041 0.860520821 0.569739589
[114,] 0.39723759 0.794475186 0.602762407
[115,] 0.39013666 0.780273324 0.609863338
[116,] 0.35943037 0.718860742 0.640569629
[117,] 0.34281314 0.685626275 0.657186863
[118,] 0.44102303 0.882046060 0.558976970
[119,] 0.42046632 0.840932638 0.579533681
[120,] 0.41098199 0.821963974 0.589018013
[121,] 0.40057328 0.801146557 0.599426721
[122,] 0.37081767 0.741635345 0.629182328
[123,] 0.38637706 0.772754124 0.613622938
[124,] 0.35720496 0.714409929 0.642795036
[125,] 0.36432585 0.728651695 0.635674153
[126,] 0.35184660 0.703693194 0.648153403
[127,] 0.32297397 0.645947942 0.677026029
[128,] 0.29953537 0.599070738 0.700464631
[129,] 0.27339935 0.546798697 0.726600651
[130,] 0.25035483 0.500709657 0.749645171
[131,] 0.22349972 0.446999440 0.776500280
[132,] 0.22713500 0.454270006 0.772864997
[133,] 0.20297556 0.405951125 0.797024437
[134,] 0.18251090 0.365021800 0.817489100
[135,] 0.16910252 0.338205048 0.830897476
[136,] 0.16144890 0.322897801 0.838551100
[137,] 0.16835594 0.336711887 0.831644056
[138,] 0.18618328 0.372366566 0.813816717
[139,] 0.20691188 0.413823754 0.793088123
[140,] 0.20324521 0.406490420 0.796754790
[141,] 0.18046960 0.360939202 0.819530399
[142,] 0.16040892 0.320817848 0.839591076
[143,] 0.17310624 0.346212489 0.826893756
[144,] 0.21253601 0.425072029 0.787463985
[145,] 0.19081008 0.381620167 0.809189916
[146,] 0.17120746 0.342414913 0.828792544
[147,] 0.14934486 0.298689714 0.850655143
[148,] 0.22207031 0.444140622 0.777929689
[149,] 0.24966163 0.499323260 0.750338370
[150,] 0.22382158 0.447643156 0.776178422
[151,] 0.19725202 0.394504031 0.802747984
[152,] 0.17472779 0.349455577 0.825272211
[153,] 0.15400081 0.308001629 0.845999185
[154,] 0.25415404 0.508308082 0.745845959
[155,] 0.25127120 0.502542392 0.748728804
[156,] 0.24855618 0.497112357 0.751443822
[157,] 0.22083807 0.441676132 0.779161934
[158,] 0.19984713 0.399694262 0.800152869
[159,] 0.25498534 0.509970688 0.745014656
[160,] 0.28090106 0.561802123 0.719098938
[161,] 0.25367983 0.507359659 0.746320170
[162,] 0.25073939 0.501478773 0.749260614
[163,] 0.40921940 0.818438806 0.590780597
[164,] 0.39736108 0.794722163 0.602638919
[165,] 0.42250043 0.845000853 0.577499573
[166,] 0.43164993 0.863299857 0.568350072
[167,] 0.48167316 0.963346320 0.518326840
[168,] 0.44820237 0.896404743 0.551797629
[169,] 0.42926210 0.858524205 0.570737898
[170,] 0.42793521 0.855870421 0.572064789
[171,] 0.39063321 0.781266416 0.609366792
[172,] 0.37965364 0.759307274 0.620346363
[173,] 0.34400907 0.688018132 0.655990934
[174,] 0.31591377 0.631827532 0.684086234
[175,] 0.33298793 0.665975850 0.667012075
[176,] 0.32518989 0.650379777 0.674810111
[177,] 0.29178726 0.583574519 0.708212740
[178,] 0.26445503 0.528910069 0.735544966
[179,] 0.23458998 0.469179951 0.765410025
[180,] 0.21435732 0.428714643 0.785642679
[181,] 0.21995270 0.439905408 0.780047296
[182,] 0.20046279 0.400925571 0.799537214
[183,] 0.21848317 0.436966344 0.781516828
[184,] 0.20563821 0.411276410 0.794361795
[185,] 0.20721085 0.414421694 0.792789153
[186,] 0.21539191 0.430783819 0.784608090
[187,] 0.26316478 0.526329565 0.736835217
[188,] 0.24530435 0.490608705 0.754695648
[189,] 0.27558034 0.551160673 0.724419663
[190,] 0.24309729 0.486194580 0.756902710
[191,] 0.27330737 0.546614735 0.726692632
[192,] 0.25312703 0.506254061 0.746872969
[193,] 0.30122132 0.602442635 0.698778682
[194,] 0.26635789 0.532715784 0.733642108
[195,] 0.23437259 0.468745184 0.765627408
[196,] 0.21528931 0.430578618 0.784710691
[197,] 0.18497163 0.369943265 0.815028367
[198,] 0.21052244 0.421044880 0.789477560
[199,] 0.18022139 0.360442789 0.819778606
[200,] 0.18838266 0.376765328 0.811617336
[201,] 0.21012579 0.420251583 0.789874209
[202,] 0.20037417 0.400748333 0.799625833
[203,] 0.17470877 0.349417549 0.825291226
[204,] 0.21499280 0.429985609 0.785007195
[205,] 0.18996154 0.379923072 0.810038464
[206,] 0.18047094 0.360941870 0.819529065
[207,] 0.21026683 0.420533657 0.789733172
[208,] 0.18087110 0.361742195 0.819128902
[209,] 0.16950337 0.339006742 0.830496629
[210,] 0.18283673 0.365673465 0.817163267
[211,] 0.19217016 0.384340312 0.807829844
[212,] 0.18656324 0.373126487 0.813436756
[213,] 0.17250526 0.345010519 0.827494741
[214,] 0.14324448 0.286488952 0.856755524
[215,] 0.14068206 0.281364120 0.859317940
[216,] 0.16946811 0.338936213 0.830531893
[217,] 0.36380892 0.727617836 0.636191082
[218,] 0.33422158 0.668443168 0.665778416
[219,] 0.30805902 0.616118048 0.691940976
[220,] 0.29095154 0.581903087 0.709048456
[221,] 0.25053672 0.501073445 0.749463277
[222,] 0.28834123 0.576682460 0.711658770
[223,] 0.24541401 0.490828013 0.754585994
[224,] 0.20544930 0.410898598 0.794550701
[225,] 0.20460390 0.409207793 0.795396104
[226,] 0.18163323 0.363266465 0.818366767
[227,] 0.15221895 0.304437903 0.847781049
[228,] 0.11831463 0.236629259 0.881685370
[229,] 0.22858434 0.457168672 0.771415664
[230,] 0.21684727 0.433694534 0.783152733
[231,] 0.18756976 0.375139514 0.812430243
[232,] 0.38836947 0.776738935 0.611630533
[233,] 0.34558882 0.691177638 0.654411181
[234,] 0.28519233 0.570384657 0.714807672
[235,] 0.40034497 0.800689945 0.599655028
[236,] 0.31739905 0.634798091 0.682600954
[237,] 0.24040241 0.480804829 0.759597586
[238,] 0.38698569 0.773971379 0.613014310
[239,] 0.28996073 0.579921451 0.710039274
[240,] 0.20308146 0.406162911 0.796918545
[241,] 0.32109956 0.642199129 0.678900436
[242,] 0.83842946 0.323141073 0.161570536
[243,] 0.69847210 0.603055809 0.301527904
> postscript(file="/var/wessaorg/rcomp/tmp/1is291384698824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2s32l1384698824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3y0jg1384698824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/49kz81384698824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mutl1384698824.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
0.0526335830 2.6965030086 -2.9639343028 -2.5103134876 4.6897601030
6 7 8 9 10
3.4989414203 3.2165745854 -1.0969621971 -0.2496701393 0.5917116361
11 12 13 14 15
1.4342441919 3.3724687508 -3.4499081242 2.4641635039 2.1880669812
16 17 18 19 20
0.5653282530 0.0767627849 1.1518500463 -1.4286879685 2.1488587858
21 22 23 24 25
2.6149026142 -2.7575956677 -0.4959162831 -1.5903303003 1.5469706983
26 27 28 29 30
-7.0109873855 0.8359296272 0.6304495505 0.9496681907 -2.9885335743
31 32 33 34 35
0.2115001464 0.2615022343 1.8306006823 -0.3454796245 -0.0935648232
36 37 38 39 40
0.4125694356 -1.9533467414 0.5767246597 1.5541884991 -2.3161719404
41 42 43 44 45
-0.8056231671 2.2069359236 0.0798947722 -1.2416955599 0.2760082564
46 47 48 49 50
-2.7355493782 -0.4152994679 0.0002824792 3.3215903380 -1.8867801516
51 52 53 54 55
0.6416769635 0.4190161892 -0.8389630929 -1.6792954205 -2.2296085043
56 57 58 59 60
1.2020371461 1.6511060356 -0.5697661465 -3.3527734801 -1.5424753954
61 62 63 64 65
-2.8875154920 -1.7843749935 -3.8147378342 0.7734628428 1.2292990055
66 67 68 69 70
-4.9966993536 -1.5119848978 -2.4425601992 1.5543503512 1.4473646487
71 72 73 74 75
0.6057390728 3.3693831874 0.6110644456 -0.2071676048 -1.9045370762
76 77 78 79 80
-0.1145895546 3.0701017796 0.5903889288 1.2581277442 -1.8648198787
81 82 83 84 85
0.1222544596 -0.4161287961 1.7494660885 0.7664862016 -0.0821448951
86 87 88 89 90
1.0144414846 -0.2320459562 0.2867393113 -3.5307800769 3.3190624834
91 92 93 94 95
0.2230185653 0.8296425883 0.6860886859 -0.9935922501 1.0275173048
96 97 98 99 100
-0.7930761296 -0.8624485857 2.0755635753 -0.0141619487 1.8551025814
101 102 103 104 105
-0.9375824901 0.9957708105 -3.3684095075 1.8776217636 -2.4025315148
106 107 108 109 110
1.0966062456 2.0681267158 -2.8022423793 0.8365303502 1.0914866626
111 112 113 114 115
-2.2127826326 -2.2787869696 1.9316891754 3.8951087301 0.4436802049
116 117 118 119 120
1.0145117761 0.2084081657 -1.0751640873 0.2627326803 -0.5359624259
121 122 123 124 125
0.4243899854 0.0796858842 -0.8887746311 0.4360999622 -1.7714348516
126 127 128 129 130
0.8209768094 1.6899179497 4.1013339940 1.4028891977 -1.6703436150
131 132 133 134 135
-1.6763785892 -0.3143202639 2.3824540327 0.7557848189 2.2339716633
136 137 138 139 140
1.5246343608 0.7033298525 -0.9719454984 0.8127815742 -0.8354609107
141 142 143 144 145
0.3557469726 2.1125324828 -0.7020300173 0.6809132397 1.3607092748
146 147 148 149 150
1.5109645730 -2.3979981816 -2.7094932710 -2.5699529396 1.7201501621
151 152 153 154 155
0.3545262446 0.5155108424 -2.5011922464 -2.9378973811 1.2151090849
156 157 158 159 160
0.2230185653 0.6153978130 4.1013339940 -2.7093259386 -0.2037014690
161 162 163 164 165
0.3833597306 0.8741715869 0.8943934816 4.5878108769 -1.8277323455
166 167 168 169 170
2.0248523971 0.0455414693 -0.7151374455 -3.5750515666 -2.8483477879
171 172 173 174 175
0.5939816637 1.9615728299 -4.8660891014 1.8292892142 2.7997254878
176 177 178 179 180
-2.1667442997 -3.1554364872 0.6322390335 1.6051884111 -2.0679038302
181 182 183 184 185
0.0782458387 -1.5691685758 0.4714157000 -0.8281537385 2.0677065945
186 187 188 189 190
1.4246990415 0.5577794424 0.8964994057 0.6567594759 0.8562801402
191 192 193 194 195
-1.5791510815 -0.8861363027 2.3778869283 -1.3453645337 2.0001542448
196 197 198 199 200
-1.9111169194 2.3362103345 0.6733719869 -3.0578307431 -0.6992674560
201 202 203 204 205
-3.0372136947 1.3226418600 3.0017020808 0.5906071547 0.6597727039
206 207 208 209 210
1.3650075195 -0.3914125801 3.5840975008 0.2054828886 1.7587358535
211 212 213 214 215
-2.5938542849 1.5490141003 -1.1872115477 -3.7592616313 -1.0685008318
216 217 218 219 220
1.8298225517 2.1874858709 -0.1586136717 -1.9111812226 1.4948870274
221 222 223 224 225
-2.8135436376 2.4708097475 -2.0609498550 0.2649250350 -0.6589744141
226 227 228 229 230
1.7261903164 5.2928542812 -1.6319756716 -1.4408540138 -2.2838096238
231 232 233 234 235
0.2345776441 -2.9457374534 0.1702865026 0.6542262293 1.1791969507
236 237 238 239 240
-1.7706018044 0.7308918809 0.2904508677 -4.2962529911 -2.5088832022
241 242 243 244 245
-2.7895444783 -2.6327574659 0.2882260757 -0.2269610882 1.5451492166
246 247 248 249 250
0.2814090023 0.3179733061 5.2246215127 -0.1670970468 0.4844230310
251 252 253 254 255
2.1558358875 1.1418369725 -1.0928763595 -0.6633648075 0.2476043343
256 257 258 259 260
-0.6008009529 -1.8507069713 -2.4191968816 2.4652597867 -4.7633385002
261 262 263 264
0.3666458529 1.5230245853 -2.8040521746 0.1771352568
> postscript(file="/var/wessaorg/rcomp/tmp/60xlg1384698824.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 0.0526335830 NA
1 2.6965030086 0.0526335830
2 -2.9639343028 2.6965030086
3 -2.5103134876 -2.9639343028
4 4.6897601030 -2.5103134876
5 3.4989414203 4.6897601030
6 3.2165745854 3.4989414203
7 -1.0969621971 3.2165745854
8 -0.2496701393 -1.0969621971
9 0.5917116361 -0.2496701393
10 1.4342441919 0.5917116361
11 3.3724687508 1.4342441919
12 -3.4499081242 3.3724687508
13 2.4641635039 -3.4499081242
14 2.1880669812 2.4641635039
15 0.5653282530 2.1880669812
16 0.0767627849 0.5653282530
17 1.1518500463 0.0767627849
18 -1.4286879685 1.1518500463
19 2.1488587858 -1.4286879685
20 2.6149026142 2.1488587858
21 -2.7575956677 2.6149026142
22 -0.4959162831 -2.7575956677
23 -1.5903303003 -0.4959162831
24 1.5469706983 -1.5903303003
25 -7.0109873855 1.5469706983
26 0.8359296272 -7.0109873855
27 0.6304495505 0.8359296272
28 0.9496681907 0.6304495505
29 -2.9885335743 0.9496681907
30 0.2115001464 -2.9885335743
31 0.2615022343 0.2115001464
32 1.8306006823 0.2615022343
33 -0.3454796245 1.8306006823
34 -0.0935648232 -0.3454796245
35 0.4125694356 -0.0935648232
36 -1.9533467414 0.4125694356
37 0.5767246597 -1.9533467414
38 1.5541884991 0.5767246597
39 -2.3161719404 1.5541884991
40 -0.8056231671 -2.3161719404
41 2.2069359236 -0.8056231671
42 0.0798947722 2.2069359236
43 -1.2416955599 0.0798947722
44 0.2760082564 -1.2416955599
45 -2.7355493782 0.2760082564
46 -0.4152994679 -2.7355493782
47 0.0002824792 -0.4152994679
48 3.3215903380 0.0002824792
49 -1.8867801516 3.3215903380
50 0.6416769635 -1.8867801516
51 0.4190161892 0.6416769635
52 -0.8389630929 0.4190161892
53 -1.6792954205 -0.8389630929
54 -2.2296085043 -1.6792954205
55 1.2020371461 -2.2296085043
56 1.6511060356 1.2020371461
57 -0.5697661465 1.6511060356
58 -3.3527734801 -0.5697661465
59 -1.5424753954 -3.3527734801
60 -2.8875154920 -1.5424753954
61 -1.7843749935 -2.8875154920
62 -3.8147378342 -1.7843749935
63 0.7734628428 -3.8147378342
64 1.2292990055 0.7734628428
65 -4.9966993536 1.2292990055
66 -1.5119848978 -4.9966993536
67 -2.4425601992 -1.5119848978
68 1.5543503512 -2.4425601992
69 1.4473646487 1.5543503512
70 0.6057390728 1.4473646487
71 3.3693831874 0.6057390728
72 0.6110644456 3.3693831874
73 -0.2071676048 0.6110644456
74 -1.9045370762 -0.2071676048
75 -0.1145895546 -1.9045370762
76 3.0701017796 -0.1145895546
77 0.5903889288 3.0701017796
78 1.2581277442 0.5903889288
79 -1.8648198787 1.2581277442
80 0.1222544596 -1.8648198787
81 -0.4161287961 0.1222544596
82 1.7494660885 -0.4161287961
83 0.7664862016 1.7494660885
84 -0.0821448951 0.7664862016
85 1.0144414846 -0.0821448951
86 -0.2320459562 1.0144414846
87 0.2867393113 -0.2320459562
88 -3.5307800769 0.2867393113
89 3.3190624834 -3.5307800769
90 0.2230185653 3.3190624834
91 0.8296425883 0.2230185653
92 0.6860886859 0.8296425883
93 -0.9935922501 0.6860886859
94 1.0275173048 -0.9935922501
95 -0.7930761296 1.0275173048
96 -0.8624485857 -0.7930761296
97 2.0755635753 -0.8624485857
98 -0.0141619487 2.0755635753
99 1.8551025814 -0.0141619487
100 -0.9375824901 1.8551025814
101 0.9957708105 -0.9375824901
102 -3.3684095075 0.9957708105
103 1.8776217636 -3.3684095075
104 -2.4025315148 1.8776217636
105 1.0966062456 -2.4025315148
106 2.0681267158 1.0966062456
107 -2.8022423793 2.0681267158
108 0.8365303502 -2.8022423793
109 1.0914866626 0.8365303502
110 -2.2127826326 1.0914866626
111 -2.2787869696 -2.2127826326
112 1.9316891754 -2.2787869696
113 3.8951087301 1.9316891754
114 0.4436802049 3.8951087301
115 1.0145117761 0.4436802049
116 0.2084081657 1.0145117761
117 -1.0751640873 0.2084081657
118 0.2627326803 -1.0751640873
119 -0.5359624259 0.2627326803
120 0.4243899854 -0.5359624259
121 0.0796858842 0.4243899854
122 -0.8887746311 0.0796858842
123 0.4360999622 -0.8887746311
124 -1.7714348516 0.4360999622
125 0.8209768094 -1.7714348516
126 1.6899179497 0.8209768094
127 4.1013339940 1.6899179497
128 1.4028891977 4.1013339940
129 -1.6703436150 1.4028891977
130 -1.6763785892 -1.6703436150
131 -0.3143202639 -1.6763785892
132 2.3824540327 -0.3143202639
133 0.7557848189 2.3824540327
134 2.2339716633 0.7557848189
135 1.5246343608 2.2339716633
136 0.7033298525 1.5246343608
137 -0.9719454984 0.7033298525
138 0.8127815742 -0.9719454984
139 -0.8354609107 0.8127815742
140 0.3557469726 -0.8354609107
141 2.1125324828 0.3557469726
142 -0.7020300173 2.1125324828
143 0.6809132397 -0.7020300173
144 1.3607092748 0.6809132397
145 1.5109645730 1.3607092748
146 -2.3979981816 1.5109645730
147 -2.7094932710 -2.3979981816
148 -2.5699529396 -2.7094932710
149 1.7201501621 -2.5699529396
150 0.3545262446 1.7201501621
151 0.5155108424 0.3545262446
152 -2.5011922464 0.5155108424
153 -2.9378973811 -2.5011922464
154 1.2151090849 -2.9378973811
155 0.2230185653 1.2151090849
156 0.6153978130 0.2230185653
157 4.1013339940 0.6153978130
158 -2.7093259386 4.1013339940
159 -0.2037014690 -2.7093259386
160 0.3833597306 -0.2037014690
161 0.8741715869 0.3833597306
162 0.8943934816 0.8741715869
163 4.5878108769 0.8943934816
164 -1.8277323455 4.5878108769
165 2.0248523971 -1.8277323455
166 0.0455414693 2.0248523971
167 -0.7151374455 0.0455414693
168 -3.5750515666 -0.7151374455
169 -2.8483477879 -3.5750515666
170 0.5939816637 -2.8483477879
171 1.9615728299 0.5939816637
172 -4.8660891014 1.9615728299
173 1.8292892142 -4.8660891014
174 2.7997254878 1.8292892142
175 -2.1667442997 2.7997254878
176 -3.1554364872 -2.1667442997
177 0.6322390335 -3.1554364872
178 1.6051884111 0.6322390335
179 -2.0679038302 1.6051884111
180 0.0782458387 -2.0679038302
181 -1.5691685758 0.0782458387
182 0.4714157000 -1.5691685758
183 -0.8281537385 0.4714157000
184 2.0677065945 -0.8281537385
185 1.4246990415 2.0677065945
186 0.5577794424 1.4246990415
187 0.8964994057 0.5577794424
188 0.6567594759 0.8964994057
189 0.8562801402 0.6567594759
190 -1.5791510815 0.8562801402
191 -0.8861363027 -1.5791510815
192 2.3778869283 -0.8861363027
193 -1.3453645337 2.3778869283
194 2.0001542448 -1.3453645337
195 -1.9111169194 2.0001542448
196 2.3362103345 -1.9111169194
197 0.6733719869 2.3362103345
198 -3.0578307431 0.6733719869
199 -0.6992674560 -3.0578307431
200 -3.0372136947 -0.6992674560
201 1.3226418600 -3.0372136947
202 3.0017020808 1.3226418600
203 0.5906071547 3.0017020808
204 0.6597727039 0.5906071547
205 1.3650075195 0.6597727039
206 -0.3914125801 1.3650075195
207 3.5840975008 -0.3914125801
208 0.2054828886 3.5840975008
209 1.7587358535 0.2054828886
210 -2.5938542849 1.7587358535
211 1.5490141003 -2.5938542849
212 -1.1872115477 1.5490141003
213 -3.7592616313 -1.1872115477
214 -1.0685008318 -3.7592616313
215 1.8298225517 -1.0685008318
216 2.1874858709 1.8298225517
217 -0.1586136717 2.1874858709
218 -1.9111812226 -0.1586136717
219 1.4948870274 -1.9111812226
220 -2.8135436376 1.4948870274
221 2.4708097475 -2.8135436376
222 -2.0609498550 2.4708097475
223 0.2649250350 -2.0609498550
224 -0.6589744141 0.2649250350
225 1.7261903164 -0.6589744141
226 5.2928542812 1.7261903164
227 -1.6319756716 5.2928542812
228 -1.4408540138 -1.6319756716
229 -2.2838096238 -1.4408540138
230 0.2345776441 -2.2838096238
231 -2.9457374534 0.2345776441
232 0.1702865026 -2.9457374534
233 0.6542262293 0.1702865026
234 1.1791969507 0.6542262293
235 -1.7706018044 1.1791969507
236 0.7308918809 -1.7706018044
237 0.2904508677 0.7308918809
238 -4.2962529911 0.2904508677
239 -2.5088832022 -4.2962529911
240 -2.7895444783 -2.5088832022
241 -2.6327574659 -2.7895444783
242 0.2882260757 -2.6327574659
243 -0.2269610882 0.2882260757
244 1.5451492166 -0.2269610882
245 0.2814090023 1.5451492166
246 0.3179733061 0.2814090023
247 5.2246215127 0.3179733061
248 -0.1670970468 5.2246215127
249 0.4844230310 -0.1670970468
250 2.1558358875 0.4844230310
251 1.1418369725 2.1558358875
252 -1.0928763595 1.1418369725
253 -0.6633648075 -1.0928763595
254 0.2476043343 -0.6633648075
255 -0.6008009529 0.2476043343
256 -1.8507069713 -0.6008009529
257 -2.4191968816 -1.8507069713
258 2.4652597867 -2.4191968816
259 -4.7633385002 2.4652597867
260 0.3666458529 -4.7633385002
261 1.5230245853 0.3666458529
262 -2.8040521746 1.5230245853
263 0.1771352568 -2.8040521746
264 NA 0.1771352568
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.6965030086 0.0526335830
[2,] -2.9639343028 2.6965030086
[3,] -2.5103134876 -2.9639343028
[4,] 4.6897601030 -2.5103134876
[5,] 3.4989414203 4.6897601030
[6,] 3.2165745854 3.4989414203
[7,] -1.0969621971 3.2165745854
[8,] -0.2496701393 -1.0969621971
[9,] 0.5917116361 -0.2496701393
[10,] 1.4342441919 0.5917116361
[11,] 3.3724687508 1.4342441919
[12,] -3.4499081242 3.3724687508
[13,] 2.4641635039 -3.4499081242
[14,] 2.1880669812 2.4641635039
[15,] 0.5653282530 2.1880669812
[16,] 0.0767627849 0.5653282530
[17,] 1.1518500463 0.0767627849
[18,] -1.4286879685 1.1518500463
[19,] 2.1488587858 -1.4286879685
[20,] 2.6149026142 2.1488587858
[21,] -2.7575956677 2.6149026142
[22,] -0.4959162831 -2.7575956677
[23,] -1.5903303003 -0.4959162831
[24,] 1.5469706983 -1.5903303003
[25,] -7.0109873855 1.5469706983
[26,] 0.8359296272 -7.0109873855
[27,] 0.6304495505 0.8359296272
[28,] 0.9496681907 0.6304495505
[29,] -2.9885335743 0.9496681907
[30,] 0.2115001464 -2.9885335743
[31,] 0.2615022343 0.2115001464
[32,] 1.8306006823 0.2615022343
[33,] -0.3454796245 1.8306006823
[34,] -0.0935648232 -0.3454796245
[35,] 0.4125694356 -0.0935648232
[36,] -1.9533467414 0.4125694356
[37,] 0.5767246597 -1.9533467414
[38,] 1.5541884991 0.5767246597
[39,] -2.3161719404 1.5541884991
[40,] -0.8056231671 -2.3161719404
[41,] 2.2069359236 -0.8056231671
[42,] 0.0798947722 2.2069359236
[43,] -1.2416955599 0.0798947722
[44,] 0.2760082564 -1.2416955599
[45,] -2.7355493782 0.2760082564
[46,] -0.4152994679 -2.7355493782
[47,] 0.0002824792 -0.4152994679
[48,] 3.3215903380 0.0002824792
[49,] -1.8867801516 3.3215903380
[50,] 0.6416769635 -1.8867801516
[51,] 0.4190161892 0.6416769635
[52,] -0.8389630929 0.4190161892
[53,] -1.6792954205 -0.8389630929
[54,] -2.2296085043 -1.6792954205
[55,] 1.2020371461 -2.2296085043
[56,] 1.6511060356 1.2020371461
[57,] -0.5697661465 1.6511060356
[58,] -3.3527734801 -0.5697661465
[59,] -1.5424753954 -3.3527734801
[60,] -2.8875154920 -1.5424753954
[61,] -1.7843749935 -2.8875154920
[62,] -3.8147378342 -1.7843749935
[63,] 0.7734628428 -3.8147378342
[64,] 1.2292990055 0.7734628428
[65,] -4.9966993536 1.2292990055
[66,] -1.5119848978 -4.9966993536
[67,] -2.4425601992 -1.5119848978
[68,] 1.5543503512 -2.4425601992
[69,] 1.4473646487 1.5543503512
[70,] 0.6057390728 1.4473646487
[71,] 3.3693831874 0.6057390728
[72,] 0.6110644456 3.3693831874
[73,] -0.2071676048 0.6110644456
[74,] -1.9045370762 -0.2071676048
[75,] -0.1145895546 -1.9045370762
[76,] 3.0701017796 -0.1145895546
[77,] 0.5903889288 3.0701017796
[78,] 1.2581277442 0.5903889288
[79,] -1.8648198787 1.2581277442
[80,] 0.1222544596 -1.8648198787
[81,] -0.4161287961 0.1222544596
[82,] 1.7494660885 -0.4161287961
[83,] 0.7664862016 1.7494660885
[84,] -0.0821448951 0.7664862016
[85,] 1.0144414846 -0.0821448951
[86,] -0.2320459562 1.0144414846
[87,] 0.2867393113 -0.2320459562
[88,] -3.5307800769 0.2867393113
[89,] 3.3190624834 -3.5307800769
[90,] 0.2230185653 3.3190624834
[91,] 0.8296425883 0.2230185653
[92,] 0.6860886859 0.8296425883
[93,] -0.9935922501 0.6860886859
[94,] 1.0275173048 -0.9935922501
[95,] -0.7930761296 1.0275173048
[96,] -0.8624485857 -0.7930761296
[97,] 2.0755635753 -0.8624485857
[98,] -0.0141619487 2.0755635753
[99,] 1.8551025814 -0.0141619487
[100,] -0.9375824901 1.8551025814
[101,] 0.9957708105 -0.9375824901
[102,] -3.3684095075 0.9957708105
[103,] 1.8776217636 -3.3684095075
[104,] -2.4025315148 1.8776217636
[105,] 1.0966062456 -2.4025315148
[106,] 2.0681267158 1.0966062456
[107,] -2.8022423793 2.0681267158
[108,] 0.8365303502 -2.8022423793
[109,] 1.0914866626 0.8365303502
[110,] -2.2127826326 1.0914866626
[111,] -2.2787869696 -2.2127826326
[112,] 1.9316891754 -2.2787869696
[113,] 3.8951087301 1.9316891754
[114,] 0.4436802049 3.8951087301
[115,] 1.0145117761 0.4436802049
[116,] 0.2084081657 1.0145117761
[117,] -1.0751640873 0.2084081657
[118,] 0.2627326803 -1.0751640873
[119,] -0.5359624259 0.2627326803
[120,] 0.4243899854 -0.5359624259
[121,] 0.0796858842 0.4243899854
[122,] -0.8887746311 0.0796858842
[123,] 0.4360999622 -0.8887746311
[124,] -1.7714348516 0.4360999622
[125,] 0.8209768094 -1.7714348516
[126,] 1.6899179497 0.8209768094
[127,] 4.1013339940 1.6899179497
[128,] 1.4028891977 4.1013339940
[129,] -1.6703436150 1.4028891977
[130,] -1.6763785892 -1.6703436150
[131,] -0.3143202639 -1.6763785892
[132,] 2.3824540327 -0.3143202639
[133,] 0.7557848189 2.3824540327
[134,] 2.2339716633 0.7557848189
[135,] 1.5246343608 2.2339716633
[136,] 0.7033298525 1.5246343608
[137,] -0.9719454984 0.7033298525
[138,] 0.8127815742 -0.9719454984
[139,] -0.8354609107 0.8127815742
[140,] 0.3557469726 -0.8354609107
[141,] 2.1125324828 0.3557469726
[142,] -0.7020300173 2.1125324828
[143,] 0.6809132397 -0.7020300173
[144,] 1.3607092748 0.6809132397
[145,] 1.5109645730 1.3607092748
[146,] -2.3979981816 1.5109645730
[147,] -2.7094932710 -2.3979981816
[148,] -2.5699529396 -2.7094932710
[149,] 1.7201501621 -2.5699529396
[150,] 0.3545262446 1.7201501621
[151,] 0.5155108424 0.3545262446
[152,] -2.5011922464 0.5155108424
[153,] -2.9378973811 -2.5011922464
[154,] 1.2151090849 -2.9378973811
[155,] 0.2230185653 1.2151090849
[156,] 0.6153978130 0.2230185653
[157,] 4.1013339940 0.6153978130
[158,] -2.7093259386 4.1013339940
[159,] -0.2037014690 -2.7093259386
[160,] 0.3833597306 -0.2037014690
[161,] 0.8741715869 0.3833597306
[162,] 0.8943934816 0.8741715869
[163,] 4.5878108769 0.8943934816
[164,] -1.8277323455 4.5878108769
[165,] 2.0248523971 -1.8277323455
[166,] 0.0455414693 2.0248523971
[167,] -0.7151374455 0.0455414693
[168,] -3.5750515666 -0.7151374455
[169,] -2.8483477879 -3.5750515666
[170,] 0.5939816637 -2.8483477879
[171,] 1.9615728299 0.5939816637
[172,] -4.8660891014 1.9615728299
[173,] 1.8292892142 -4.8660891014
[174,] 2.7997254878 1.8292892142
[175,] -2.1667442997 2.7997254878
[176,] -3.1554364872 -2.1667442997
[177,] 0.6322390335 -3.1554364872
[178,] 1.6051884111 0.6322390335
[179,] -2.0679038302 1.6051884111
[180,] 0.0782458387 -2.0679038302
[181,] -1.5691685758 0.0782458387
[182,] 0.4714157000 -1.5691685758
[183,] -0.8281537385 0.4714157000
[184,] 2.0677065945 -0.8281537385
[185,] 1.4246990415 2.0677065945
[186,] 0.5577794424 1.4246990415
[187,] 0.8964994057 0.5577794424
[188,] 0.6567594759 0.8964994057
[189,] 0.8562801402 0.6567594759
[190,] -1.5791510815 0.8562801402
[191,] -0.8861363027 -1.5791510815
[192,] 2.3778869283 -0.8861363027
[193,] -1.3453645337 2.3778869283
[194,] 2.0001542448 -1.3453645337
[195,] -1.9111169194 2.0001542448
[196,] 2.3362103345 -1.9111169194
[197,] 0.6733719869 2.3362103345
[198,] -3.0578307431 0.6733719869
[199,] -0.6992674560 -3.0578307431
[200,] -3.0372136947 -0.6992674560
[201,] 1.3226418600 -3.0372136947
[202,] 3.0017020808 1.3226418600
[203,] 0.5906071547 3.0017020808
[204,] 0.6597727039 0.5906071547
[205,] 1.3650075195 0.6597727039
[206,] -0.3914125801 1.3650075195
[207,] 3.5840975008 -0.3914125801
[208,] 0.2054828886 3.5840975008
[209,] 1.7587358535 0.2054828886
[210,] -2.5938542849 1.7587358535
[211,] 1.5490141003 -2.5938542849
[212,] -1.1872115477 1.5490141003
[213,] -3.7592616313 -1.1872115477
[214,] -1.0685008318 -3.7592616313
[215,] 1.8298225517 -1.0685008318
[216,] 2.1874858709 1.8298225517
[217,] -0.1586136717 2.1874858709
[218,] -1.9111812226 -0.1586136717
[219,] 1.4948870274 -1.9111812226
[220,] -2.8135436376 1.4948870274
[221,] 2.4708097475 -2.8135436376
[222,] -2.0609498550 2.4708097475
[223,] 0.2649250350 -2.0609498550
[224,] -0.6589744141 0.2649250350
[225,] 1.7261903164 -0.6589744141
[226,] 5.2928542812 1.7261903164
[227,] -1.6319756716 5.2928542812
[228,] -1.4408540138 -1.6319756716
[229,] -2.2838096238 -1.4408540138
[230,] 0.2345776441 -2.2838096238
[231,] -2.9457374534 0.2345776441
[232,] 0.1702865026 -2.9457374534
[233,] 0.6542262293 0.1702865026
[234,] 1.1791969507 0.6542262293
[235,] -1.7706018044 1.1791969507
[236,] 0.7308918809 -1.7706018044
[237,] 0.2904508677 0.7308918809
[238,] -4.2962529911 0.2904508677
[239,] -2.5088832022 -4.2962529911
[240,] -2.7895444783 -2.5088832022
[241,] -2.6327574659 -2.7895444783
[242,] 0.2882260757 -2.6327574659
[243,] -0.2269610882 0.2882260757
[244,] 1.5451492166 -0.2269610882
[245,] 0.2814090023 1.5451492166
[246,] 0.3179733061 0.2814090023
[247,] 5.2246215127 0.3179733061
[248,] -0.1670970468 5.2246215127
[249,] 0.4844230310 -0.1670970468
[250,] 2.1558358875 0.4844230310
[251,] 1.1418369725 2.1558358875
[252,] -1.0928763595 1.1418369725
[253,] -0.6633648075 -1.0928763595
[254,] 0.2476043343 -0.6633648075
[255,] -0.6008009529 0.2476043343
[256,] -1.8507069713 -0.6008009529
[257,] -2.4191968816 -1.8507069713
[258,] 2.4652597867 -2.4191968816
[259,] -4.7633385002 2.4652597867
[260,] 0.3666458529 -4.7633385002
[261,] 1.5230245853 0.3666458529
[262,] -2.8040521746 1.5230245853
[263,] 0.1771352568 -2.8040521746
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.6965030086 0.0526335830
2 -2.9639343028 2.6965030086
3 -2.5103134876 -2.9639343028
4 4.6897601030 -2.5103134876
5 3.4989414203 4.6897601030
6 3.2165745854 3.4989414203
7 -1.0969621971 3.2165745854
8 -0.2496701393 -1.0969621971
9 0.5917116361 -0.2496701393
10 1.4342441919 0.5917116361
11 3.3724687508 1.4342441919
12 -3.4499081242 3.3724687508
13 2.4641635039 -3.4499081242
14 2.1880669812 2.4641635039
15 0.5653282530 2.1880669812
16 0.0767627849 0.5653282530
17 1.1518500463 0.0767627849
18 -1.4286879685 1.1518500463
19 2.1488587858 -1.4286879685
20 2.6149026142 2.1488587858
21 -2.7575956677 2.6149026142
22 -0.4959162831 -2.7575956677
23 -1.5903303003 -0.4959162831
24 1.5469706983 -1.5903303003
25 -7.0109873855 1.5469706983
26 0.8359296272 -7.0109873855
27 0.6304495505 0.8359296272
28 0.9496681907 0.6304495505
29 -2.9885335743 0.9496681907
30 0.2115001464 -2.9885335743
31 0.2615022343 0.2115001464
32 1.8306006823 0.2615022343
33 -0.3454796245 1.8306006823
34 -0.0935648232 -0.3454796245
35 0.4125694356 -0.0935648232
36 -1.9533467414 0.4125694356
37 0.5767246597 -1.9533467414
38 1.5541884991 0.5767246597
39 -2.3161719404 1.5541884991
40 -0.8056231671 -2.3161719404
41 2.2069359236 -0.8056231671
42 0.0798947722 2.2069359236
43 -1.2416955599 0.0798947722
44 0.2760082564 -1.2416955599
45 -2.7355493782 0.2760082564
46 -0.4152994679 -2.7355493782
47 0.0002824792 -0.4152994679
48 3.3215903380 0.0002824792
49 -1.8867801516 3.3215903380
50 0.6416769635 -1.8867801516
51 0.4190161892 0.6416769635
52 -0.8389630929 0.4190161892
53 -1.6792954205 -0.8389630929
54 -2.2296085043 -1.6792954205
55 1.2020371461 -2.2296085043
56 1.6511060356 1.2020371461
57 -0.5697661465 1.6511060356
58 -3.3527734801 -0.5697661465
59 -1.5424753954 -3.3527734801
60 -2.8875154920 -1.5424753954
61 -1.7843749935 -2.8875154920
62 -3.8147378342 -1.7843749935
63 0.7734628428 -3.8147378342
64 1.2292990055 0.7734628428
65 -4.9966993536 1.2292990055
66 -1.5119848978 -4.9966993536
67 -2.4425601992 -1.5119848978
68 1.5543503512 -2.4425601992
69 1.4473646487 1.5543503512
70 0.6057390728 1.4473646487
71 3.3693831874 0.6057390728
72 0.6110644456 3.3693831874
73 -0.2071676048 0.6110644456
74 -1.9045370762 -0.2071676048
75 -0.1145895546 -1.9045370762
76 3.0701017796 -0.1145895546
77 0.5903889288 3.0701017796
78 1.2581277442 0.5903889288
79 -1.8648198787 1.2581277442
80 0.1222544596 -1.8648198787
81 -0.4161287961 0.1222544596
82 1.7494660885 -0.4161287961
83 0.7664862016 1.7494660885
84 -0.0821448951 0.7664862016
85 1.0144414846 -0.0821448951
86 -0.2320459562 1.0144414846
87 0.2867393113 -0.2320459562
88 -3.5307800769 0.2867393113
89 3.3190624834 -3.5307800769
90 0.2230185653 3.3190624834
91 0.8296425883 0.2230185653
92 0.6860886859 0.8296425883
93 -0.9935922501 0.6860886859
94 1.0275173048 -0.9935922501
95 -0.7930761296 1.0275173048
96 -0.8624485857 -0.7930761296
97 2.0755635753 -0.8624485857
98 -0.0141619487 2.0755635753
99 1.8551025814 -0.0141619487
100 -0.9375824901 1.8551025814
101 0.9957708105 -0.9375824901
102 -3.3684095075 0.9957708105
103 1.8776217636 -3.3684095075
104 -2.4025315148 1.8776217636
105 1.0966062456 -2.4025315148
106 2.0681267158 1.0966062456
107 -2.8022423793 2.0681267158
108 0.8365303502 -2.8022423793
109 1.0914866626 0.8365303502
110 -2.2127826326 1.0914866626
111 -2.2787869696 -2.2127826326
112 1.9316891754 -2.2787869696
113 3.8951087301 1.9316891754
114 0.4436802049 3.8951087301
115 1.0145117761 0.4436802049
116 0.2084081657 1.0145117761
117 -1.0751640873 0.2084081657
118 0.2627326803 -1.0751640873
119 -0.5359624259 0.2627326803
120 0.4243899854 -0.5359624259
121 0.0796858842 0.4243899854
122 -0.8887746311 0.0796858842
123 0.4360999622 -0.8887746311
124 -1.7714348516 0.4360999622
125 0.8209768094 -1.7714348516
126 1.6899179497 0.8209768094
127 4.1013339940 1.6899179497
128 1.4028891977 4.1013339940
129 -1.6703436150 1.4028891977
130 -1.6763785892 -1.6703436150
131 -0.3143202639 -1.6763785892
132 2.3824540327 -0.3143202639
133 0.7557848189 2.3824540327
134 2.2339716633 0.7557848189
135 1.5246343608 2.2339716633
136 0.7033298525 1.5246343608
137 -0.9719454984 0.7033298525
138 0.8127815742 -0.9719454984
139 -0.8354609107 0.8127815742
140 0.3557469726 -0.8354609107
141 2.1125324828 0.3557469726
142 -0.7020300173 2.1125324828
143 0.6809132397 -0.7020300173
144 1.3607092748 0.6809132397
145 1.5109645730 1.3607092748
146 -2.3979981816 1.5109645730
147 -2.7094932710 -2.3979981816
148 -2.5699529396 -2.7094932710
149 1.7201501621 -2.5699529396
150 0.3545262446 1.7201501621
151 0.5155108424 0.3545262446
152 -2.5011922464 0.5155108424
153 -2.9378973811 -2.5011922464
154 1.2151090849 -2.9378973811
155 0.2230185653 1.2151090849
156 0.6153978130 0.2230185653
157 4.1013339940 0.6153978130
158 -2.7093259386 4.1013339940
159 -0.2037014690 -2.7093259386
160 0.3833597306 -0.2037014690
161 0.8741715869 0.3833597306
162 0.8943934816 0.8741715869
163 4.5878108769 0.8943934816
164 -1.8277323455 4.5878108769
165 2.0248523971 -1.8277323455
166 0.0455414693 2.0248523971
167 -0.7151374455 0.0455414693
168 -3.5750515666 -0.7151374455
169 -2.8483477879 -3.5750515666
170 0.5939816637 -2.8483477879
171 1.9615728299 0.5939816637
172 -4.8660891014 1.9615728299
173 1.8292892142 -4.8660891014
174 2.7997254878 1.8292892142
175 -2.1667442997 2.7997254878
176 -3.1554364872 -2.1667442997
177 0.6322390335 -3.1554364872
178 1.6051884111 0.6322390335
179 -2.0679038302 1.6051884111
180 0.0782458387 -2.0679038302
181 -1.5691685758 0.0782458387
182 0.4714157000 -1.5691685758
183 -0.8281537385 0.4714157000
184 2.0677065945 -0.8281537385
185 1.4246990415 2.0677065945
186 0.5577794424 1.4246990415
187 0.8964994057 0.5577794424
188 0.6567594759 0.8964994057
189 0.8562801402 0.6567594759
190 -1.5791510815 0.8562801402
191 -0.8861363027 -1.5791510815
192 2.3778869283 -0.8861363027
193 -1.3453645337 2.3778869283
194 2.0001542448 -1.3453645337
195 -1.9111169194 2.0001542448
196 2.3362103345 -1.9111169194
197 0.6733719869 2.3362103345
198 -3.0578307431 0.6733719869
199 -0.6992674560 -3.0578307431
200 -3.0372136947 -0.6992674560
201 1.3226418600 -3.0372136947
202 3.0017020808 1.3226418600
203 0.5906071547 3.0017020808
204 0.6597727039 0.5906071547
205 1.3650075195 0.6597727039
206 -0.3914125801 1.3650075195
207 3.5840975008 -0.3914125801
208 0.2054828886 3.5840975008
209 1.7587358535 0.2054828886
210 -2.5938542849 1.7587358535
211 1.5490141003 -2.5938542849
212 -1.1872115477 1.5490141003
213 -3.7592616313 -1.1872115477
214 -1.0685008318 -3.7592616313
215 1.8298225517 -1.0685008318
216 2.1874858709 1.8298225517
217 -0.1586136717 2.1874858709
218 -1.9111812226 -0.1586136717
219 1.4948870274 -1.9111812226
220 -2.8135436376 1.4948870274
221 2.4708097475 -2.8135436376
222 -2.0609498550 2.4708097475
223 0.2649250350 -2.0609498550
224 -0.6589744141 0.2649250350
225 1.7261903164 -0.6589744141
226 5.2928542812 1.7261903164
227 -1.6319756716 5.2928542812
228 -1.4408540138 -1.6319756716
229 -2.2838096238 -1.4408540138
230 0.2345776441 -2.2838096238
231 -2.9457374534 0.2345776441
232 0.1702865026 -2.9457374534
233 0.6542262293 0.1702865026
234 1.1791969507 0.6542262293
235 -1.7706018044 1.1791969507
236 0.7308918809 -1.7706018044
237 0.2904508677 0.7308918809
238 -4.2962529911 0.2904508677
239 -2.5088832022 -4.2962529911
240 -2.7895444783 -2.5088832022
241 -2.6327574659 -2.7895444783
242 0.2882260757 -2.6327574659
243 -0.2269610882 0.2882260757
244 1.5451492166 -0.2269610882
245 0.2814090023 1.5451492166
246 0.3179733061 0.2814090023
247 5.2246215127 0.3179733061
248 -0.1670970468 5.2246215127
249 0.4844230310 -0.1670970468
250 2.1558358875 0.4844230310
251 1.1418369725 2.1558358875
252 -1.0928763595 1.1418369725
253 -0.6633648075 -1.0928763595
254 0.2476043343 -0.6633648075
255 -0.6008009529 0.2476043343
256 -1.8507069713 -0.6008009529
257 -2.4191968816 -1.8507069713
258 2.4652597867 -2.4191968816
259 -4.7633385002 2.4652597867
260 0.3666458529 -4.7633385002
261 1.5230245853 0.3666458529
262 -2.8040521746 1.5230245853
263 0.1771352568 -2.8040521746
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7zwgp1384698824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8hyb31384698824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9e7vt1384698824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10b80b1384698824.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11ubdl1384698824.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12opg81384698824.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13y3zy1384698824.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14r8zn1384698824.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/150gvy1384698824.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/167ii61384698824.tab")
+ }
>
> try(system("convert tmp/1is291384698824.ps tmp/1is291384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/2s32l1384698824.ps tmp/2s32l1384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/3y0jg1384698824.ps tmp/3y0jg1384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/49kz81384698824.ps tmp/49kz81384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mutl1384698824.ps tmp/5mutl1384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/60xlg1384698824.ps tmp/60xlg1384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/7zwgp1384698824.ps tmp/7zwgp1384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hyb31384698824.ps tmp/8hyb31384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/9e7vt1384698824.ps tmp/9e7vt1384698824.png",intern=TRUE))
character(0)
> try(system("convert tmp/10b80b1384698824.ps tmp/10b80b1384698824.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.247 2.873 19.108