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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> x <- array(list(14
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+ ,dim=c(6
+ ,264)
+ ,dimnames=list(c('Happiness'
+ ,'Connected'
+ ,'Separate'
+ ,'Depression'
+ ,'Learning'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(6,264),dimnames=list(c('Happiness','Connected','Separate','Depression','Learning','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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Connected Separate Depression Learning Month
1 14 41 38 12.0 13 9
2 18 39 32 11.0 16 9
3 11 30 35 14.0 19 9
4 12 31 33 12.0 15 9
5 16 34 37 21.0 14 9
6 18 35 29 12.0 13 9
7 14 39 31 22.0 19 9
8 14 34 36 11.0 15 9
9 15 36 35 10.0 14 9
10 15 37 38 13.0 15 9
11 17 38 31 10.0 16 9
12 19 36 34 8.0 16 9
13 10 38 35 15.0 16 9
14 16 39 38 14.0 16 9
15 18 33 37 10.0 17 9
16 14 32 33 14.0 15 9
17 14 36 32 14.0 15 9
18 17 38 38 11.0 20 9
19 14 39 38 10.0 18 9
20 16 32 32 13.0 16 9
21 18 32 33 9.5 16 9
22 11 31 31 14.0 16 9
23 14 39 38 12.0 19 9
24 12 37 39 14.0 16 9
25 17 39 32 11.0 17 9
26 9 41 32 9.0 17 9
27 16 36 35 11.0 16 9
28 14 33 37 15.0 15 9
29 15 33 33 14.0 16 9
30 11 34 33 13.0 14 9
31 16 31 31 9.0 15 9
32 13 27 32 15.0 12 9
33 17 37 31 10.0 14 9
34 15 34 37 11.0 16 9
35 14 34 30 13.0 14 9
36 16 32 33 8.0 10 9
37 9 29 31 20.0 10 9
38 15 36 33 12.0 14 9
39 17 29 31 10.0 16 9
40 13 35 33 10.0 16 9
41 15 37 32 9.0 16 9
42 16 34 33 14.0 14 9
43 16 38 32 8.0 20 9
44 12 35 33 14.0 14 9
45 15 38 28 11.0 14 9
46 11 37 35 13.0 11 9
47 15 38 39 9.0 14 9
48 15 33 34 11.0 15 9
49 17 36 38 15.0 16 9
50 13 38 32 11.0 14 9
51 16 32 38 10.0 16 9
52 14 32 30 14.0 14 9
53 11 32 33 18.0 12 9
54 12 34 38 14.0 16 9
55 12 32 32 11.0 9 9
56 15 37 35 14.5 14 9
57 16 39 34 13.0 16 9
58 15 29 34 9.0 16 9
59 12 37 36 10.0 15 9
60 12 35 34 15.0 16 9
61 8 30 28 20.0 12 9
62 13 38 34 12.0 16 9
63 11 34 35 12.0 16 9
64 14 31 35 14.0 14 9
65 15 34 31 13.0 16 9
66 10 35 37 11.0 17 10
67 11 36 35 17.0 18 10
68 12 30 27 12.0 18 10
69 15 39 40 13.0 12 10
70 15 35 37 14.0 16 10
71 14 38 36 13.0 10 10
72 16 31 38 15.0 14 10
73 15 34 39 13.0 18 10
74 15 38 41 10.0 18 10
75 13 34 27 11.0 16 10
76 12 39 30 19.0 17 10
77 17 37 37 13.0 16 10
78 13 34 31 17.0 16 10
79 15 28 31 13.0 13 10
80 13 37 27 9.0 16 10
81 15 33 36 11.0 16 10
82 15 35 37 9.0 16 10
83 16 37 33 12.0 15 10
84 15 32 34 12.0 15 10
85 14 33 31 13.0 16 10
86 15 38 39 13.0 14 10
87 14 33 34 12.0 16 10
88 13 29 32 15.0 16 10
89 7 33 33 22.0 15 10
90 17 31 36 13.0 12 10
91 13 36 32 15.0 17 10
92 15 35 41 13.0 16 10
93 14 32 28 15.0 15 10
94 13 29 30 12.5 13 10
95 16 39 36 11.0 16 10
96 12 37 35 16.0 16 10
97 14 35 31 11.0 16 10
98 17 37 34 11.0 16 10
99 15 32 36 10.0 14 10
100 17 38 36 10.0 16 10
101 12 37 35 16.0 16 10
102 16 36 37 12.0 20 10
103 11 32 28 11.0 15 10
104 15 33 39 16.0 16 10
105 9 40 32 19.0 13 10
106 16 38 35 11.0 17 10
107 15 41 39 16.0 16 10
108 10 36 35 15.0 16 10
109 10 43 42 24.0 12 10
110 15 30 34 14.0 16 10
111 11 31 33 15.0 16 10
112 13 32 41 11.0 17 10
113 14 32 33 15.0 13 10
114 18 37 34 12.0 12 10
115 16 37 32 10.0 18 10
116 14 33 40 14.0 14 10
117 14 34 40 13.0 14 10
118 14 33 35 9.0 13 10
119 14 38 36 15.0 16 10
120 12 33 37 15.0 13 10
121 14 31 27 14.0 16 10
122 15 38 39 11.0 13 10
123 15 37 38 8.0 16 10
124 15 36 31 11.0 15 10
125 13 31 33 11.0 16 10
126 17 39 32 8.0 15 10
127 17 44 39 10.0 17 10
128 19 33 36 11.0 15 10
129 15 35 33 13.0 12 10
130 13 32 33 11.0 16 10
131 9 28 32 20.0 10 10
132 15 40 37 10.0 16 10
133 15 27 30 15.0 12 10
134 15 37 38 12.0 14 10
135 16 32 29 14.0 15 10
136 11 28 22 23.0 13 10
137 14 34 35 14.0 15 10
138 11 30 35 16.0 11 10
139 15 35 34 11.0 12 10
140 13 31 35 12.0 11 10
141 15 32 34 10.0 16 10
142 16 30 37 14.0 15 10
143 14 30 35 12.0 17 10
144 15 31 23 12.0 16 10
145 16 40 31 11.0 10 10
146 16 32 27 12.0 18 10
147 11 36 36 13.0 13 10
148 12 32 31 11.0 16 10
149 9 35 32 19.0 13 10
150 16 38 39 12.0 10 10
151 13 42 37 17.0 15 10
152 16 34 38 9.0 16 10
153 12 35 39 12.0 16 10
154 9 38 34 19.0 14 9
155 13 33 31 18.0 10 10
156 13 36 32 15.0 17 10
157 14 32 37 14.0 13 10
158 19 33 36 11.0 15 10
159 13 34 32 9.0 16 10
160 12 32 38 18.0 12 10
161 13 34 36 16.0 13 10
162 10 27 26 24.0 13 11
163 14 31 26 14.0 12 11
164 16 38 33 20.0 17 11
165 10 34 39 18.0 15 11
166 11 24 30 23.0 10 11
167 14 30 33 12.0 14 11
168 12 26 25 14.0 11 11
169 9 34 38 16.0 13 11
170 9 27 37 18.0 16 11
171 11 37 31 20.0 12 11
172 16 36 37 12.0 16 11
173 9 41 35 12.0 12 11
174 13 29 25 17.0 9 11
175 16 36 28 13.0 12 11
176 13 32 35 9.0 15 11
177 9 37 33 16.0 12 11
178 12 30 30 18.0 12 11
179 16 31 31 10.0 14 11
180 11 38 37 14.0 12 11
181 14 36 36 11.0 16 11
182 13 35 30 9.0 11 11
183 15 31 36 11.0 19 11
184 14 38 32 10.0 15 11
185 16 22 28 11.0 8 11
186 13 32 36 19.0 16 11
187 14 36 34 14.0 17 11
188 15 39 31 12.0 12 11
189 13 28 28 14.0 11 11
190 11 32 36 21.0 11 11
191 11 32 36 13.0 14 11
192 14 38 40 10.0 16 11
193 15 32 33 15.0 12 11
194 11 35 37 16.0 16 11
195 15 32 32 14.0 13 11
196 12 37 38 12.0 15 11
197 14 34 31 19.0 16 11
198 14 33 37 15.0 16 11
199 8 33 33 19.0 14 11
200 13 26 32 13.0 16 11
201 9 30 30 17.0 16 11
202 15 24 30 12.0 14 11
203 17 34 31 11.0 11 11
204 13 34 32 14.0 12 11
205 15 33 34 11.0 15 11
206 15 34 36 13.0 15 11
207 14 35 37 12.0 16 11
208 16 35 36 15.0 16 11
209 13 36 33 14.0 11 11
210 16 34 33 12.0 15 11
211 9 34 33 17.0 12 11
212 16 41 44 11.0 12 11
213 11 32 39 18.0 15 11
214 10 30 32 13.0 15 11
215 11 35 35 17.0 16 11
216 15 28 25 13.0 14 11
217 17 33 35 11.0 17 11
218 14 39 34 12.0 14 11
219 8 36 35 22.0 13 11
220 15 36 39 14.0 15 11
221 11 35 33 12.0 13 11
222 16 38 36 12.0 14 11
223 10 33 32 17.0 15 11
224 15 31 32 9.0 12 11
225 9 34 36 21.0 13 11
226 16 32 36 10.0 8 11
227 19 31 32 11.0 14 11
228 12 33 34 12.0 14 11
229 8 34 33 23.0 11 11
230 11 34 35 13.0 12 11
231 14 34 30 12.0 13 11
232 9 33 38 16.0 10 11
233 15 32 34 9.0 16 11
234 13 41 33 17.0 18 11
235 16 34 32 9.0 13 11
236 11 36 31 14.0 11 11
237 12 37 30 17.0 4 11
238 13 36 27 13.0 13 11
239 10 29 31 11.0 16 11
240 11 37 30 12.0 10 11
241 12 27 32 10.0 12 11
242 8 35 35 19.0 12 11
243 12 28 28 16.0 10 11
244 12 35 33 16.0 13 11
245 15 37 31 14.0 15 11
246 11 29 35 20.0 12 11
247 13 32 35 15.0 14 11
248 14 36 32 23.0 10 11
249 10 19 21 20.0 12 11
250 12 21 20 16.0 12 11
251 15 31 34 14.0 11 11
252 13 33 32 17.0 10 11
253 13 36 34 11.0 12 11
254 13 33 32 13.0 16 11
255 12 37 33 17.0 12 11
256 12 34 33 15.0 14 11
257 9 35 37 21.0 16 11
258 9 31 32 18.0 14 11
259 15 37 34 15.0 13 11
260 10 35 30 8.0 4 11
261 14 27 30 12.0 15 11
262 15 34 38 12.0 11 11
263 7 40 36 22.0 11 11
264 14 29 32 12.0 14 11
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Depression Learning Month
19.48810 0.01024 0.01328 -0.38750 0.09026 -0.27477
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9070 -1.6139 0.2422 1.4167 5.0191
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.48810 2.54486 7.658 3.8e-13 ***
Connected 0.01024 0.03743 0.274 0.785
Separate 0.01328 0.03814 0.348 0.728
Depression -0.38750 0.03749 -10.337 < 2e-16 ***
Learning 0.09026 0.05535 1.631 0.104
Month -0.27477 0.17052 -1.611 0.108
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.016 on 258 degrees of freedom
Multiple R-squared: 0.3614, Adjusted R-squared: 0.349
F-statistic: 29.2 on 5 and 258 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.7533768 0.493246348 0.246623174
[2,] 0.6612388 0.677522467 0.338761234
[3,] 0.5551636 0.889672746 0.444836373
[4,] 0.8063901 0.387219836 0.193609918
[5,] 0.9563704 0.087259122 0.043629561
[6,] 0.9508388 0.098322388 0.049161194
[7,] 0.9804873 0.039025364 0.019512682
[8,] 0.9686065 0.062787017 0.031393508
[9,] 0.9573051 0.085389738 0.042694869
[10,] 0.9480560 0.103887952 0.051943976
[11,] 0.9419907 0.116018608 0.058009304
[12,] 0.9267069 0.146586213 0.073293107
[13,] 0.9279483 0.144103498 0.072051749
[14,] 0.9555405 0.088919093 0.044459546
[15,] 0.9423292 0.115341693 0.057670847
[16,] 0.9388954 0.122209193 0.061104597
[17,] 0.9202721 0.159455800 0.079727900
[18,] 0.9980070 0.003986047 0.001993023
[19,] 0.9970776 0.005844886 0.002922443
[20,] 0.9955356 0.008928774 0.004464387
[21,] 0.9935322 0.012935618 0.006467809
[22,] 0.9966871 0.006625747 0.003312873
[23,] 0.9950943 0.009811417 0.004905708
[24,] 0.9932965 0.013407049 0.006703524
[25,] 0.9917220 0.016555945 0.008277973
[26,] 0.9882071 0.023585806 0.011792903
[27,] 0.9841072 0.031785672 0.015892836
[28,] 0.9781939 0.043612259 0.021806129
[29,] 0.9833098 0.033380383 0.016690191
[30,] 0.9774424 0.045115150 0.022557575
[31,] 0.9748508 0.050298391 0.025149196
[32,] 0.9765082 0.046983631 0.023491815
[33,] 0.9699338 0.060132384 0.030066192
[34,] 0.9692209 0.061558296 0.030779148
[35,] 0.9600376 0.079924714 0.039962357
[36,] 0.9581562 0.083687672 0.041843836
[37,] 0.9465299 0.106940123 0.053470062
[38,] 0.9555038 0.088992305 0.044496152
[39,] 0.9436539 0.112692175 0.056346087
[40,] 0.9292558 0.141488441 0.070744221
[41,] 0.9490042 0.101991664 0.050995832
[42,] 0.9447424 0.110515296 0.055257648
[43,] 0.9322322 0.135535676 0.067767838
[44,] 0.9165578 0.166884480 0.083442240
[45,] 0.9047120 0.190575963 0.095287982
[46,] 0.9031270 0.193745904 0.096872952
[47,] 0.8992297 0.201540696 0.100770348
[48,] 0.8889153 0.222169449 0.111084724
[49,] 0.8800130 0.239974097 0.119987048
[50,] 0.8585390 0.282921922 0.141460961
[51,] 0.8856176 0.228764811 0.114382406
[52,] 0.8793571 0.241285739 0.120642869
[53,] 0.9084800 0.183040082 0.091520041
[54,] 0.9026163 0.194767300 0.097383650
[55,] 0.9345091 0.130981891 0.065490946
[56,] 0.9210214 0.157957261 0.078978630
[57,] 0.9064810 0.187037901 0.093518950
[58,] 0.9173979 0.165204158 0.082602079
[59,] 0.9133201 0.173359720 0.086679860
[60,] 0.9055308 0.188938306 0.094469153
[61,] 0.9315932 0.136813664 0.068406832
[62,] 0.9366845 0.126631066 0.063315533
[63,] 0.9289591 0.142081871 0.071040936
[64,] 0.9478170 0.104366073 0.052183037
[65,] 0.9387157 0.122568595 0.061284297
[66,] 0.9262177 0.147564564 0.073782282
[67,] 0.9169033 0.166193395 0.083096698
[68,] 0.9012255 0.197548988 0.098774494
[69,] 0.9182805 0.163439001 0.081719501
[70,] 0.9039435 0.192113072 0.096056536
[71,] 0.8971574 0.205685272 0.102842636
[72,] 0.8982521 0.203495848 0.101747924
[73,] 0.8805435 0.238912979 0.119456490
[74,] 0.8615738 0.276852476 0.138426238
[75,] 0.8560577 0.287884553 0.143942277
[76,] 0.8365411 0.326917800 0.163458900
[77,] 0.8122026 0.375594836 0.187797418
[78,] 0.7898611 0.420277871 0.210138936
[79,] 0.7624950 0.475009962 0.237504981
[80,] 0.7324690 0.535062054 0.267531027
[81,] 0.7983018 0.403396500 0.201698250
[82,] 0.8352896 0.329420881 0.164710440
[83,] 0.8117419 0.376516110 0.188258055
[84,] 0.7884197 0.423160650 0.211580325
[85,] 0.7689899 0.462020280 0.231010140
[86,] 0.7442772 0.511445677 0.255722839
[87,] 0.7213408 0.557318442 0.278659221
[88,] 0.6981536 0.603692704 0.301846352
[89,] 0.6695172 0.660965532 0.330482766
[90,] 0.6728808 0.654238379 0.327119190
[91,] 0.6390208 0.721958434 0.360979217
[92,] 0.6262684 0.747463156 0.373731578
[93,] 0.6009982 0.798003559 0.399001780
[94,] 0.5752953 0.849409357 0.424704679
[95,] 0.6419953 0.716009302 0.358004651
[96,] 0.6359229 0.728154205 0.364077102
[97,] 0.6574253 0.685149496 0.342574748
[98,] 0.6313573 0.737285319 0.368642660
[99,] 0.6217030 0.756594050 0.378297025
[100,] 0.6830693 0.633861323 0.316930662
[101,] 0.6543532 0.691293557 0.345646778
[102,] 0.6358288 0.728342457 0.364171229
[103,] 0.6428297 0.714340530 0.357170265
[104,] 0.6504548 0.699090323 0.349545162
[105,] 0.6257883 0.748423320 0.374211660
[106,] 0.7083024 0.583395148 0.291697574
[107,] 0.6807557 0.638488628 0.319244314
[108,] 0.6505918 0.698816326 0.349408163
[109,] 0.6189076 0.762184892 0.381092446
[110,] 0.6007296 0.798540710 0.399270355
[111,] 0.5685462 0.862907645 0.431453823
[112,] 0.5442548 0.911490457 0.455745228
[113,] 0.5151531 0.969693762 0.484846881
[114,] 0.4809045 0.961808995 0.519095503
[115,] 0.4569828 0.913965595 0.543017203
[116,] 0.4231632 0.846326333 0.576836834
[117,] 0.4149628 0.829925531 0.585037234
[118,] 0.3893188 0.778637534 0.610681233
[119,] 0.3705896 0.741179209 0.629410395
[120,] 0.4897301 0.979460262 0.510269869
[121,] 0.4683735 0.936746906 0.531626547
[122,] 0.4594895 0.918979076 0.540510462
[123,] 0.4479405 0.895880962 0.552059519
[124,] 0.4150860 0.830172092 0.584913954
[125,] 0.4255187 0.851037406 0.574481297
[126,] 0.3952906 0.790581163 0.604709419
[127,] 0.4171215 0.834243015 0.582878492
[128,] 0.3996236 0.799247208 0.600376396
[129,] 0.3675491 0.735098293 0.632450854
[130,] 0.3506996 0.701399292 0.649300354
[131,] 0.3206969 0.641393732 0.679303134
[132,] 0.2962525 0.592504928 0.703747536
[133,] 0.2661692 0.532338339 0.733830830
[134,] 0.2839150 0.567830055 0.716084972
[135,] 0.2553061 0.510612247 0.744693876
[136,] 0.2329400 0.465880081 0.767059959
[137,] 0.2249788 0.449957648 0.775021176
[138,] 0.2158979 0.431795773 0.784102114
[139,] 0.2373633 0.474726583 0.762636709
[140,] 0.2582012 0.516402303 0.741798848
[141,] 0.2723508 0.544701568 0.727649216
[142,] 0.2758175 0.551634952 0.724182524
[143,] 0.2499217 0.499843368 0.750078316
[144,] 0.2248534 0.449706799 0.775146600
[145,] 0.2343830 0.468765915 0.765617042
[146,] 0.2747636 0.549527101 0.725236449
[147,] 0.2539229 0.507845840 0.746077080
[148,] 0.2319889 0.463977749 0.768011125
[149,] 0.2057300 0.411459957 0.794270021
[150,] 0.3000086 0.600017267 0.699991366
[151,] 0.3249590 0.649917934 0.675041033
[152,] 0.2929035 0.585806969 0.707096515
[153,] 0.2623003 0.524600660 0.737699670
[154,] 0.2365035 0.473007062 0.763496469
[155,] 0.2132769 0.426553748 0.786723126
[156,] 0.3430878 0.686175618 0.656912191
[157,] 0.3386069 0.677213795 0.661393102
[158,] 0.3322791 0.664558278 0.667720861
[159,] 0.3002755 0.600551083 0.699724459
[160,] 0.2761512 0.552302430 0.723848785
[161,] 0.3306420 0.661284071 0.669357965
[162,] 0.3562772 0.712554448 0.643722776
[163,] 0.3250728 0.650145633 0.674927183
[164,] 0.3198477 0.639695368 0.680152316
[165,] 0.4859356 0.971871252 0.514064374
[166,] 0.4702031 0.940406137 0.529796932
[167,] 0.4961817 0.992363316 0.503818342
[168,] 0.5054470 0.989105946 0.494552973
[169,] 0.5575558 0.884888424 0.442444212
[170,] 0.5251119 0.949776292 0.474888146
[171,] 0.5022238 0.995552387 0.497776193
[172,] 0.5002440 0.999512005 0.499756003
[173,] 0.4645896 0.929179127 0.535410436
[174,] 0.4571784 0.914356863 0.542821569
[175,] 0.4191199 0.838239900 0.580880050
[176,] 0.3887528 0.777505615 0.611247193
[177,] 0.4098448 0.819689601 0.590155199
[178,] 0.4018036 0.803607282 0.598196359
[179,] 0.3661470 0.732293949 0.633853026
[180,] 0.3398254 0.679650856 0.660174572
[181,] 0.3057101 0.611420152 0.694289924
[182,] 0.2860910 0.572181907 0.713909047
[183,] 0.3005357 0.601071443 0.699464279
[184,] 0.2772989 0.554597760 0.722701120
[185,] 0.2991361 0.598272105 0.700863947
[186,] 0.2841343 0.568268603 0.715865699
[187,] 0.2852062 0.570412337 0.714793832
[188,] 0.2920456 0.584091141 0.707954430
[189,] 0.3258729 0.651745747 0.674127127
[190,] 0.2994381 0.598876147 0.700561926
[191,] 0.3351648 0.670329699 0.664835150
[192,] 0.3000487 0.600097312 0.699951344
[193,] 0.3380863 0.676172668 0.661913666
[194,] 0.3171179 0.634235709 0.682882145
[195,] 0.3650017 0.730003494 0.634998253
[196,] 0.3260618 0.652123632 0.673938184
[197,] 0.2904403 0.580880668 0.709559666
[198,] 0.2690138 0.538027515 0.730986243
[199,] 0.2346313 0.469262518 0.765368741
[200,] 0.2759565 0.551912963 0.724043519
[201,] 0.2413606 0.482721278 0.758639361
[202,] 0.2409314 0.481862816 0.759068592
[203,] 0.2575423 0.515084657 0.742457671
[204,] 0.2515258 0.503051569 0.748474216
[205,] 0.2178843 0.435768679 0.782115660
[206,] 0.2795314 0.559062705 0.720468647
[207,] 0.2516673 0.503334573 0.748332713
[208,] 0.2395019 0.479003795 0.760498102
[209,] 0.2554183 0.510836531 0.744581734
[210,] 0.2189741 0.437948229 0.781025886
[211,] 0.2086948 0.417389624 0.791305188
[212,] 0.2053318 0.410663506 0.794668247
[213,] 0.2232129 0.446425834 0.776787083
[214,] 0.2319423 0.463884550 0.768057725
[215,] 0.2230877 0.446175411 0.776912295
[216,] 0.1900985 0.380196916 0.809901542
[217,] 0.1662642 0.332528447 0.833735777
[218,] 0.1934527 0.386905443 0.806547278
[219,] 0.4626768 0.925353504 0.537323248
[220,] 0.4275596 0.855119239 0.572440381
[221,] 0.4059488 0.811897606 0.594051197
[222,] 0.3861846 0.772369195 0.613815402
[223,] 0.3391882 0.678376341 0.660811829
[224,] 0.3631836 0.726367277 0.636816362
[225,] 0.3181828 0.636365548 0.681817226
[226,] 0.2707838 0.541567648 0.729216176
[227,] 0.2802940 0.560587910 0.719706045
[228,] 0.2501499 0.500299899 0.749850051
[229,] 0.2138764 0.427752706 0.786123647
[230,] 0.1722373 0.344474653 0.827762674
[231,] 0.2738279 0.547655780 0.726172110
[232,] 0.2588358 0.517671656 0.741164172
[233,] 0.2426932 0.485386355 0.757306822
[234,] 0.3159524 0.631904756 0.684047622
[235,] 0.2547226 0.509445105 0.745277447
[236,] 0.1994991 0.398998295 0.800500853
[237,] 0.1990119 0.398023751 0.800988124
[238,] 0.1553237 0.310647314 0.844676343
[239,] 0.1113998 0.222799560 0.888600220
[240,] 0.4303736 0.860747110 0.569626445
[241,] 0.3408422 0.681684412 0.659157794
[242,] 0.2867562 0.573512355 0.713243822
[243,] 0.3001681 0.600336171 0.699831915
[244,] 0.5721238 0.855752434 0.427876217
[245,] 0.5698936 0.860212775 0.430106387
[246,] 0.7731397 0.453720547 0.226860274
[247,] 0.6935980 0.612804075 0.306402038
> postscript(file="/var/fisher/rcomp/tmp/1bn5p1384817162.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2xrlg1384817162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3e7x81384817162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4xlei1384817162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5289g1384817162.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
-0.46317837 2.97872899 -3.07720024 -2.47485397 5.01905948 3.71781857
7 8 9 10 11 12
2.98375789 -0.93293046 -0.23737809 0.78478005 1.61475242 2.82038753
13 14 15 16 17 18
-3.50086925 2.06153952 2.49601463 0.28990650 0.26221833 1.54825021
19 20 21 22 23 24
-1.66898032 1.82543048 2.45589271 -2.76354300 -0.98423405 -1.93125758
25 26 27 28 29 30
1.88847216 -6.90701619 0.96960982 0.61403601 1.18940704 -3.02782349
31 32 33 34 35 36
0.38920609 0.01267400 1.80550869 -0.03646963 0.01202357 0.41618133
37 38 39 40 41 42
-1.87650752 0.56418971 1.70693607 -2.38108440 -0.77578885 2.35967806
43 44 45 46 47 48
-0.53456032 -1.65056457 0.22261467 -2.81434561 -0.69849430 0.10387687
49 50 51 52 53 54
3.47976895 -1.83051474 0.58323172 0.42001037 -0.88931685 -1.88724734
55 56 57 58 59 60
-2.31777486 1.49613625 1.72716738 -0.72041253 -3.35115989 -1.45685902
61 62 63 64 65 66
-3.02741674 -1.65009154 -3.62240338 0.36384124 0.81822757 -4.86219981
67 68 69 70 71 72
-1.61112527 -2.38091843 1.28326983 1.39056165 0.52715551 2.98626501
73 74 75 76 77 78
0.80622439 -0.42381547 -1.62887684 0.28981852 2.98257485 0.64300304
79 80 81 82 83 84
1.42522308 -2.43460782 0.26182462 -0.54694608 1.73845953 0.77639032
85 86 87 88 89 90
0.10323947 1.12628116 -0.32410913 -0.09406927 -3.34555448 3.41834026
91 92 93 94 95 96
-0.25602449 0.94993070 1.01858908 -0.76548797 1.20036885 -0.82835580
97 98 99 100 101 102
-0.69224888 2.24741881 0.06507935 1.82310993 -0.82835580 1.24428864
103 104 105 106 107 108
-3.53141711 2.15948530 -2.38596152 1.13363701 2.07754428 -3.20561472
109 110 111 112 113 114
0.47825163 1.48162185 -2.12783688 -1.88460133 1.13269096 3.99594765
115 116 117 118 119 120
0.70596832 0.55171350 0.15396933 -1.22912565 0.76061767 -0.93068107
121 122 123 124 125 126
0.56435569 0.44153489 -0.96821524 0.38776532 -1.67784307 1.18125044
127 128 129 130 131 132
1.63155029 4.35208144 1.41721681 -1.68808569 -1.60477797 -0.21065767
133 134 135 136 137 138
2.31400798 0.76230460 2.61780518 1.41977972 0.51762581 -1.30537329
139 140 141 142 143 144
0.62893136 -0.86562211 -0.08886959 2.53203162 -0.39692042 0.84248200
145 146 147 148 149 150
1.79807892 1.59859632 -2.72312970 -2.66152099 -2.33474838 2.09980691
151 152 153 154 155 156
0.57162472 0.45001420 -2.41100614 -2.75706707 1.58228815 -0.25602449
157 158 159 160 161 162
0.69206001 4.35208144 -2.47029169 0.31904067 0.45986020 1.03916376
163 164 165 166 167 168
1.21343460 4.92248491 -1.71072814 1.90003116 0.17518403 -0.63181308
169 170 171 172 173 174
-3.29193523 -2.70272186 0.41057636 1.88008521 -4.78353594 1.68047732
175 176 177 178 179 180
2.74815521 -2.12462740 -3.16599453 0.72055401 1.41650301 -2.00436966
181 182 183 184 185 186
-0.49413399 -1.72791623 0.28630868 -0.75873456 2.47757620 1.64684890
187 188 189 190 191 192
0.60467853 1.29007872 0.30785461 0.87313611 -2.49764674 -0.95525020
193 194 195 196 197 198
2.49771706 -1.55966598 2.03324104 -2.05318295 2.69277540 1.07331773
199 200 201 202 203 204
-3.14303303 -0.56357521 -3.02797483 1.27648685 3.04404714 0.10301261
205 206 207 208 209 210
0.65341542 1.39161118 -0.10967217 3.06611483 0.15950182 2.04395669
211 212 213 214 215 216
-2.74776510 1.70942135 -0.69024288 -3.51428890 -1.14559973 1.68942965
217 218 219 220 221 222
2.45961942 0.06971802 -1.94756415 1.71878042 -2.78577229 2.05339595
223 224 225 226 227 228
-1.99501059 0.19623276 -1.32786279 1.88138956 4.79072220 -1.86882621
229 230 231 232 233 234
-1.33249900 -2.32433600 0.26431739 -3.01092214 -0.20160187 0.63899556
235 236 237 238 239 240
1.07524805 -1.81393347 0.98340865 -0.32881926 -4.35602383 -2.49564002
241 242 243 244 245 246
-2.37529519 -3.00956934 0.17311452 -0.23576610 1.81479661 0.43938797
247 248 249 250 251 252
0.29063871 4.75055492 -0.27223282 0.17055809 2.19743261 1.45627352
253 254 255 256 257 258
-1.10654199 -0.63527360 0.22150701 -0.70328184 -1.62215824 -2.49676697
259 260 261 262 263 264
2.34296474 -4.48362002 0.15550214 1.33857222 -2.82130337 0.19870901
> postscript(file="/var/fisher/rcomp/tmp/6ajyx1384817162.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 -0.46317837 NA
1 2.97872899 -0.46317837
2 -3.07720024 2.97872899
3 -2.47485397 -3.07720024
4 5.01905948 -2.47485397
5 3.71781857 5.01905948
6 2.98375789 3.71781857
7 -0.93293046 2.98375789
8 -0.23737809 -0.93293046
9 0.78478005 -0.23737809
10 1.61475242 0.78478005
11 2.82038753 1.61475242
12 -3.50086925 2.82038753
13 2.06153952 -3.50086925
14 2.49601463 2.06153952
15 0.28990650 2.49601463
16 0.26221833 0.28990650
17 1.54825021 0.26221833
18 -1.66898032 1.54825021
19 1.82543048 -1.66898032
20 2.45589271 1.82543048
21 -2.76354300 2.45589271
22 -0.98423405 -2.76354300
23 -1.93125758 -0.98423405
24 1.88847216 -1.93125758
25 -6.90701619 1.88847216
26 0.96960982 -6.90701619
27 0.61403601 0.96960982
28 1.18940704 0.61403601
29 -3.02782349 1.18940704
30 0.38920609 -3.02782349
31 0.01267400 0.38920609
32 1.80550869 0.01267400
33 -0.03646963 1.80550869
34 0.01202357 -0.03646963
35 0.41618133 0.01202357
36 -1.87650752 0.41618133
37 0.56418971 -1.87650752
38 1.70693607 0.56418971
39 -2.38108440 1.70693607
40 -0.77578885 -2.38108440
41 2.35967806 -0.77578885
42 -0.53456032 2.35967806
43 -1.65056457 -0.53456032
44 0.22261467 -1.65056457
45 -2.81434561 0.22261467
46 -0.69849430 -2.81434561
47 0.10387687 -0.69849430
48 3.47976895 0.10387687
49 -1.83051474 3.47976895
50 0.58323172 -1.83051474
51 0.42001037 0.58323172
52 -0.88931685 0.42001037
53 -1.88724734 -0.88931685
54 -2.31777486 -1.88724734
55 1.49613625 -2.31777486
56 1.72716738 1.49613625
57 -0.72041253 1.72716738
58 -3.35115989 -0.72041253
59 -1.45685902 -3.35115989
60 -3.02741674 -1.45685902
61 -1.65009154 -3.02741674
62 -3.62240338 -1.65009154
63 0.36384124 -3.62240338
64 0.81822757 0.36384124
65 -4.86219981 0.81822757
66 -1.61112527 -4.86219981
67 -2.38091843 -1.61112527
68 1.28326983 -2.38091843
69 1.39056165 1.28326983
70 0.52715551 1.39056165
71 2.98626501 0.52715551
72 0.80622439 2.98626501
73 -0.42381547 0.80622439
74 -1.62887684 -0.42381547
75 0.28981852 -1.62887684
76 2.98257485 0.28981852
77 0.64300304 2.98257485
78 1.42522308 0.64300304
79 -2.43460782 1.42522308
80 0.26182462 -2.43460782
81 -0.54694608 0.26182462
82 1.73845953 -0.54694608
83 0.77639032 1.73845953
84 0.10323947 0.77639032
85 1.12628116 0.10323947
86 -0.32410913 1.12628116
87 -0.09406927 -0.32410913
88 -3.34555448 -0.09406927
89 3.41834026 -3.34555448
90 -0.25602449 3.41834026
91 0.94993070 -0.25602449
92 1.01858908 0.94993070
93 -0.76548797 1.01858908
94 1.20036885 -0.76548797
95 -0.82835580 1.20036885
96 -0.69224888 -0.82835580
97 2.24741881 -0.69224888
98 0.06507935 2.24741881
99 1.82310993 0.06507935
100 -0.82835580 1.82310993
101 1.24428864 -0.82835580
102 -3.53141711 1.24428864
103 2.15948530 -3.53141711
104 -2.38596152 2.15948530
105 1.13363701 -2.38596152
106 2.07754428 1.13363701
107 -3.20561472 2.07754428
108 0.47825163 -3.20561472
109 1.48162185 0.47825163
110 -2.12783688 1.48162185
111 -1.88460133 -2.12783688
112 1.13269096 -1.88460133
113 3.99594765 1.13269096
114 0.70596832 3.99594765
115 0.55171350 0.70596832
116 0.15396933 0.55171350
117 -1.22912565 0.15396933
118 0.76061767 -1.22912565
119 -0.93068107 0.76061767
120 0.56435569 -0.93068107
121 0.44153489 0.56435569
122 -0.96821524 0.44153489
123 0.38776532 -0.96821524
124 -1.67784307 0.38776532
125 1.18125044 -1.67784307
126 1.63155029 1.18125044
127 4.35208144 1.63155029
128 1.41721681 4.35208144
129 -1.68808569 1.41721681
130 -1.60477797 -1.68808569
131 -0.21065767 -1.60477797
132 2.31400798 -0.21065767
133 0.76230460 2.31400798
134 2.61780518 0.76230460
135 1.41977972 2.61780518
136 0.51762581 1.41977972
137 -1.30537329 0.51762581
138 0.62893136 -1.30537329
139 -0.86562211 0.62893136
140 -0.08886959 -0.86562211
141 2.53203162 -0.08886959
142 -0.39692042 2.53203162
143 0.84248200 -0.39692042
144 1.79807892 0.84248200
145 1.59859632 1.79807892
146 -2.72312970 1.59859632
147 -2.66152099 -2.72312970
148 -2.33474838 -2.66152099
149 2.09980691 -2.33474838
150 0.57162472 2.09980691
151 0.45001420 0.57162472
152 -2.41100614 0.45001420
153 -2.75706707 -2.41100614
154 1.58228815 -2.75706707
155 -0.25602449 1.58228815
156 0.69206001 -0.25602449
157 4.35208144 0.69206001
158 -2.47029169 4.35208144
159 0.31904067 -2.47029169
160 0.45986020 0.31904067
161 1.03916376 0.45986020
162 1.21343460 1.03916376
163 4.92248491 1.21343460
164 -1.71072814 4.92248491
165 1.90003116 -1.71072814
166 0.17518403 1.90003116
167 -0.63181308 0.17518403
168 -3.29193523 -0.63181308
169 -2.70272186 -3.29193523
170 0.41057636 -2.70272186
171 1.88008521 0.41057636
172 -4.78353594 1.88008521
173 1.68047732 -4.78353594
174 2.74815521 1.68047732
175 -2.12462740 2.74815521
176 -3.16599453 -2.12462740
177 0.72055401 -3.16599453
178 1.41650301 0.72055401
179 -2.00436966 1.41650301
180 -0.49413399 -2.00436966
181 -1.72791623 -0.49413399
182 0.28630868 -1.72791623
183 -0.75873456 0.28630868
184 2.47757620 -0.75873456
185 1.64684890 2.47757620
186 0.60467853 1.64684890
187 1.29007872 0.60467853
188 0.30785461 1.29007872
189 0.87313611 0.30785461
190 -2.49764674 0.87313611
191 -0.95525020 -2.49764674
192 2.49771706 -0.95525020
193 -1.55966598 2.49771706
194 2.03324104 -1.55966598
195 -2.05318295 2.03324104
196 2.69277540 -2.05318295
197 1.07331773 2.69277540
198 -3.14303303 1.07331773
199 -0.56357521 -3.14303303
200 -3.02797483 -0.56357521
201 1.27648685 -3.02797483
202 3.04404714 1.27648685
203 0.10301261 3.04404714
204 0.65341542 0.10301261
205 1.39161118 0.65341542
206 -0.10967217 1.39161118
207 3.06611483 -0.10967217
208 0.15950182 3.06611483
209 2.04395669 0.15950182
210 -2.74776510 2.04395669
211 1.70942135 -2.74776510
212 -0.69024288 1.70942135
213 -3.51428890 -0.69024288
214 -1.14559973 -3.51428890
215 1.68942965 -1.14559973
216 2.45961942 1.68942965
217 0.06971802 2.45961942
218 -1.94756415 0.06971802
219 1.71878042 -1.94756415
220 -2.78577229 1.71878042
221 2.05339595 -2.78577229
222 -1.99501059 2.05339595
223 0.19623276 -1.99501059
224 -1.32786279 0.19623276
225 1.88138956 -1.32786279
226 4.79072220 1.88138956
227 -1.86882621 4.79072220
228 -1.33249900 -1.86882621
229 -2.32433600 -1.33249900
230 0.26431739 -2.32433600
231 -3.01092214 0.26431739
232 -0.20160187 -3.01092214
233 0.63899556 -0.20160187
234 1.07524805 0.63899556
235 -1.81393347 1.07524805
236 0.98340865 -1.81393347
237 -0.32881926 0.98340865
238 -4.35602383 -0.32881926
239 -2.49564002 -4.35602383
240 -2.37529519 -2.49564002
241 -3.00956934 -2.37529519
242 0.17311452 -3.00956934
243 -0.23576610 0.17311452
244 1.81479661 -0.23576610
245 0.43938797 1.81479661
246 0.29063871 0.43938797
247 4.75055492 0.29063871
248 -0.27223282 4.75055492
249 0.17055809 -0.27223282
250 2.19743261 0.17055809
251 1.45627352 2.19743261
252 -1.10654199 1.45627352
253 -0.63527360 -1.10654199
254 0.22150701 -0.63527360
255 -0.70328184 0.22150701
256 -1.62215824 -0.70328184
257 -2.49676697 -1.62215824
258 2.34296474 -2.49676697
259 -4.48362002 2.34296474
260 0.15550214 -4.48362002
261 1.33857222 0.15550214
262 -2.82130337 1.33857222
263 0.19870901 -2.82130337
264 NA 0.19870901
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.97872899 -0.46317837
[2,] -3.07720024 2.97872899
[3,] -2.47485397 -3.07720024
[4,] 5.01905948 -2.47485397
[5,] 3.71781857 5.01905948
[6,] 2.98375789 3.71781857
[7,] -0.93293046 2.98375789
[8,] -0.23737809 -0.93293046
[9,] 0.78478005 -0.23737809
[10,] 1.61475242 0.78478005
[11,] 2.82038753 1.61475242
[12,] -3.50086925 2.82038753
[13,] 2.06153952 -3.50086925
[14,] 2.49601463 2.06153952
[15,] 0.28990650 2.49601463
[16,] 0.26221833 0.28990650
[17,] 1.54825021 0.26221833
[18,] -1.66898032 1.54825021
[19,] 1.82543048 -1.66898032
[20,] 2.45589271 1.82543048
[21,] -2.76354300 2.45589271
[22,] -0.98423405 -2.76354300
[23,] -1.93125758 -0.98423405
[24,] 1.88847216 -1.93125758
[25,] -6.90701619 1.88847216
[26,] 0.96960982 -6.90701619
[27,] 0.61403601 0.96960982
[28,] 1.18940704 0.61403601
[29,] -3.02782349 1.18940704
[30,] 0.38920609 -3.02782349
[31,] 0.01267400 0.38920609
[32,] 1.80550869 0.01267400
[33,] -0.03646963 1.80550869
[34,] 0.01202357 -0.03646963
[35,] 0.41618133 0.01202357
[36,] -1.87650752 0.41618133
[37,] 0.56418971 -1.87650752
[38,] 1.70693607 0.56418971
[39,] -2.38108440 1.70693607
[40,] -0.77578885 -2.38108440
[41,] 2.35967806 -0.77578885
[42,] -0.53456032 2.35967806
[43,] -1.65056457 -0.53456032
[44,] 0.22261467 -1.65056457
[45,] -2.81434561 0.22261467
[46,] -0.69849430 -2.81434561
[47,] 0.10387687 -0.69849430
[48,] 3.47976895 0.10387687
[49,] -1.83051474 3.47976895
[50,] 0.58323172 -1.83051474
[51,] 0.42001037 0.58323172
[52,] -0.88931685 0.42001037
[53,] -1.88724734 -0.88931685
[54,] -2.31777486 -1.88724734
[55,] 1.49613625 -2.31777486
[56,] 1.72716738 1.49613625
[57,] -0.72041253 1.72716738
[58,] -3.35115989 -0.72041253
[59,] -1.45685902 -3.35115989
[60,] -3.02741674 -1.45685902
[61,] -1.65009154 -3.02741674
[62,] -3.62240338 -1.65009154
[63,] 0.36384124 -3.62240338
[64,] 0.81822757 0.36384124
[65,] -4.86219981 0.81822757
[66,] -1.61112527 -4.86219981
[67,] -2.38091843 -1.61112527
[68,] 1.28326983 -2.38091843
[69,] 1.39056165 1.28326983
[70,] 0.52715551 1.39056165
[71,] 2.98626501 0.52715551
[72,] 0.80622439 2.98626501
[73,] -0.42381547 0.80622439
[74,] -1.62887684 -0.42381547
[75,] 0.28981852 -1.62887684
[76,] 2.98257485 0.28981852
[77,] 0.64300304 2.98257485
[78,] 1.42522308 0.64300304
[79,] -2.43460782 1.42522308
[80,] 0.26182462 -2.43460782
[81,] -0.54694608 0.26182462
[82,] 1.73845953 -0.54694608
[83,] 0.77639032 1.73845953
[84,] 0.10323947 0.77639032
[85,] 1.12628116 0.10323947
[86,] -0.32410913 1.12628116
[87,] -0.09406927 -0.32410913
[88,] -3.34555448 -0.09406927
[89,] 3.41834026 -3.34555448
[90,] -0.25602449 3.41834026
[91,] 0.94993070 -0.25602449
[92,] 1.01858908 0.94993070
[93,] -0.76548797 1.01858908
[94,] 1.20036885 -0.76548797
[95,] -0.82835580 1.20036885
[96,] -0.69224888 -0.82835580
[97,] 2.24741881 -0.69224888
[98,] 0.06507935 2.24741881
[99,] 1.82310993 0.06507935
[100,] -0.82835580 1.82310993
[101,] 1.24428864 -0.82835580
[102,] -3.53141711 1.24428864
[103,] 2.15948530 -3.53141711
[104,] -2.38596152 2.15948530
[105,] 1.13363701 -2.38596152
[106,] 2.07754428 1.13363701
[107,] -3.20561472 2.07754428
[108,] 0.47825163 -3.20561472
[109,] 1.48162185 0.47825163
[110,] -2.12783688 1.48162185
[111,] -1.88460133 -2.12783688
[112,] 1.13269096 -1.88460133
[113,] 3.99594765 1.13269096
[114,] 0.70596832 3.99594765
[115,] 0.55171350 0.70596832
[116,] 0.15396933 0.55171350
[117,] -1.22912565 0.15396933
[118,] 0.76061767 -1.22912565
[119,] -0.93068107 0.76061767
[120,] 0.56435569 -0.93068107
[121,] 0.44153489 0.56435569
[122,] -0.96821524 0.44153489
[123,] 0.38776532 -0.96821524
[124,] -1.67784307 0.38776532
[125,] 1.18125044 -1.67784307
[126,] 1.63155029 1.18125044
[127,] 4.35208144 1.63155029
[128,] 1.41721681 4.35208144
[129,] -1.68808569 1.41721681
[130,] -1.60477797 -1.68808569
[131,] -0.21065767 -1.60477797
[132,] 2.31400798 -0.21065767
[133,] 0.76230460 2.31400798
[134,] 2.61780518 0.76230460
[135,] 1.41977972 2.61780518
[136,] 0.51762581 1.41977972
[137,] -1.30537329 0.51762581
[138,] 0.62893136 -1.30537329
[139,] -0.86562211 0.62893136
[140,] -0.08886959 -0.86562211
[141,] 2.53203162 -0.08886959
[142,] -0.39692042 2.53203162
[143,] 0.84248200 -0.39692042
[144,] 1.79807892 0.84248200
[145,] 1.59859632 1.79807892
[146,] -2.72312970 1.59859632
[147,] -2.66152099 -2.72312970
[148,] -2.33474838 -2.66152099
[149,] 2.09980691 -2.33474838
[150,] 0.57162472 2.09980691
[151,] 0.45001420 0.57162472
[152,] -2.41100614 0.45001420
[153,] -2.75706707 -2.41100614
[154,] 1.58228815 -2.75706707
[155,] -0.25602449 1.58228815
[156,] 0.69206001 -0.25602449
[157,] 4.35208144 0.69206001
[158,] -2.47029169 4.35208144
[159,] 0.31904067 -2.47029169
[160,] 0.45986020 0.31904067
[161,] 1.03916376 0.45986020
[162,] 1.21343460 1.03916376
[163,] 4.92248491 1.21343460
[164,] -1.71072814 4.92248491
[165,] 1.90003116 -1.71072814
[166,] 0.17518403 1.90003116
[167,] -0.63181308 0.17518403
[168,] -3.29193523 -0.63181308
[169,] -2.70272186 -3.29193523
[170,] 0.41057636 -2.70272186
[171,] 1.88008521 0.41057636
[172,] -4.78353594 1.88008521
[173,] 1.68047732 -4.78353594
[174,] 2.74815521 1.68047732
[175,] -2.12462740 2.74815521
[176,] -3.16599453 -2.12462740
[177,] 0.72055401 -3.16599453
[178,] 1.41650301 0.72055401
[179,] -2.00436966 1.41650301
[180,] -0.49413399 -2.00436966
[181,] -1.72791623 -0.49413399
[182,] 0.28630868 -1.72791623
[183,] -0.75873456 0.28630868
[184,] 2.47757620 -0.75873456
[185,] 1.64684890 2.47757620
[186,] 0.60467853 1.64684890
[187,] 1.29007872 0.60467853
[188,] 0.30785461 1.29007872
[189,] 0.87313611 0.30785461
[190,] -2.49764674 0.87313611
[191,] -0.95525020 -2.49764674
[192,] 2.49771706 -0.95525020
[193,] -1.55966598 2.49771706
[194,] 2.03324104 -1.55966598
[195,] -2.05318295 2.03324104
[196,] 2.69277540 -2.05318295
[197,] 1.07331773 2.69277540
[198,] -3.14303303 1.07331773
[199,] -0.56357521 -3.14303303
[200,] -3.02797483 -0.56357521
[201,] 1.27648685 -3.02797483
[202,] 3.04404714 1.27648685
[203,] 0.10301261 3.04404714
[204,] 0.65341542 0.10301261
[205,] 1.39161118 0.65341542
[206,] -0.10967217 1.39161118
[207,] 3.06611483 -0.10967217
[208,] 0.15950182 3.06611483
[209,] 2.04395669 0.15950182
[210,] -2.74776510 2.04395669
[211,] 1.70942135 -2.74776510
[212,] -0.69024288 1.70942135
[213,] -3.51428890 -0.69024288
[214,] -1.14559973 -3.51428890
[215,] 1.68942965 -1.14559973
[216,] 2.45961942 1.68942965
[217,] 0.06971802 2.45961942
[218,] -1.94756415 0.06971802
[219,] 1.71878042 -1.94756415
[220,] -2.78577229 1.71878042
[221,] 2.05339595 -2.78577229
[222,] -1.99501059 2.05339595
[223,] 0.19623276 -1.99501059
[224,] -1.32786279 0.19623276
[225,] 1.88138956 -1.32786279
[226,] 4.79072220 1.88138956
[227,] -1.86882621 4.79072220
[228,] -1.33249900 -1.86882621
[229,] -2.32433600 -1.33249900
[230,] 0.26431739 -2.32433600
[231,] -3.01092214 0.26431739
[232,] -0.20160187 -3.01092214
[233,] 0.63899556 -0.20160187
[234,] 1.07524805 0.63899556
[235,] -1.81393347 1.07524805
[236,] 0.98340865 -1.81393347
[237,] -0.32881926 0.98340865
[238,] -4.35602383 -0.32881926
[239,] -2.49564002 -4.35602383
[240,] -2.37529519 -2.49564002
[241,] -3.00956934 -2.37529519
[242,] 0.17311452 -3.00956934
[243,] -0.23576610 0.17311452
[244,] 1.81479661 -0.23576610
[245,] 0.43938797 1.81479661
[246,] 0.29063871 0.43938797
[247,] 4.75055492 0.29063871
[248,] -0.27223282 4.75055492
[249,] 0.17055809 -0.27223282
[250,] 2.19743261 0.17055809
[251,] 1.45627352 2.19743261
[252,] -1.10654199 1.45627352
[253,] -0.63527360 -1.10654199
[254,] 0.22150701 -0.63527360
[255,] -0.70328184 0.22150701
[256,] -1.62215824 -0.70328184
[257,] -2.49676697 -1.62215824
[258,] 2.34296474 -2.49676697
[259,] -4.48362002 2.34296474
[260,] 0.15550214 -4.48362002
[261,] 1.33857222 0.15550214
[262,] -2.82130337 1.33857222
[263,] 0.19870901 -2.82130337
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.97872899 -0.46317837
2 -3.07720024 2.97872899
3 -2.47485397 -3.07720024
4 5.01905948 -2.47485397
5 3.71781857 5.01905948
6 2.98375789 3.71781857
7 -0.93293046 2.98375789
8 -0.23737809 -0.93293046
9 0.78478005 -0.23737809
10 1.61475242 0.78478005
11 2.82038753 1.61475242
12 -3.50086925 2.82038753
13 2.06153952 -3.50086925
14 2.49601463 2.06153952
15 0.28990650 2.49601463
16 0.26221833 0.28990650
17 1.54825021 0.26221833
18 -1.66898032 1.54825021
19 1.82543048 -1.66898032
20 2.45589271 1.82543048
21 -2.76354300 2.45589271
22 -0.98423405 -2.76354300
23 -1.93125758 -0.98423405
24 1.88847216 -1.93125758
25 -6.90701619 1.88847216
26 0.96960982 -6.90701619
27 0.61403601 0.96960982
28 1.18940704 0.61403601
29 -3.02782349 1.18940704
30 0.38920609 -3.02782349
31 0.01267400 0.38920609
32 1.80550869 0.01267400
33 -0.03646963 1.80550869
34 0.01202357 -0.03646963
35 0.41618133 0.01202357
36 -1.87650752 0.41618133
37 0.56418971 -1.87650752
38 1.70693607 0.56418971
39 -2.38108440 1.70693607
40 -0.77578885 -2.38108440
41 2.35967806 -0.77578885
42 -0.53456032 2.35967806
43 -1.65056457 -0.53456032
44 0.22261467 -1.65056457
45 -2.81434561 0.22261467
46 -0.69849430 -2.81434561
47 0.10387687 -0.69849430
48 3.47976895 0.10387687
49 -1.83051474 3.47976895
50 0.58323172 -1.83051474
51 0.42001037 0.58323172
52 -0.88931685 0.42001037
53 -1.88724734 -0.88931685
54 -2.31777486 -1.88724734
55 1.49613625 -2.31777486
56 1.72716738 1.49613625
57 -0.72041253 1.72716738
58 -3.35115989 -0.72041253
59 -1.45685902 -3.35115989
60 -3.02741674 -1.45685902
61 -1.65009154 -3.02741674
62 -3.62240338 -1.65009154
63 0.36384124 -3.62240338
64 0.81822757 0.36384124
65 -4.86219981 0.81822757
66 -1.61112527 -4.86219981
67 -2.38091843 -1.61112527
68 1.28326983 -2.38091843
69 1.39056165 1.28326983
70 0.52715551 1.39056165
71 2.98626501 0.52715551
72 0.80622439 2.98626501
73 -0.42381547 0.80622439
74 -1.62887684 -0.42381547
75 0.28981852 -1.62887684
76 2.98257485 0.28981852
77 0.64300304 2.98257485
78 1.42522308 0.64300304
79 -2.43460782 1.42522308
80 0.26182462 -2.43460782
81 -0.54694608 0.26182462
82 1.73845953 -0.54694608
83 0.77639032 1.73845953
84 0.10323947 0.77639032
85 1.12628116 0.10323947
86 -0.32410913 1.12628116
87 -0.09406927 -0.32410913
88 -3.34555448 -0.09406927
89 3.41834026 -3.34555448
90 -0.25602449 3.41834026
91 0.94993070 -0.25602449
92 1.01858908 0.94993070
93 -0.76548797 1.01858908
94 1.20036885 -0.76548797
95 -0.82835580 1.20036885
96 -0.69224888 -0.82835580
97 2.24741881 -0.69224888
98 0.06507935 2.24741881
99 1.82310993 0.06507935
100 -0.82835580 1.82310993
101 1.24428864 -0.82835580
102 -3.53141711 1.24428864
103 2.15948530 -3.53141711
104 -2.38596152 2.15948530
105 1.13363701 -2.38596152
106 2.07754428 1.13363701
107 -3.20561472 2.07754428
108 0.47825163 -3.20561472
109 1.48162185 0.47825163
110 -2.12783688 1.48162185
111 -1.88460133 -2.12783688
112 1.13269096 -1.88460133
113 3.99594765 1.13269096
114 0.70596832 3.99594765
115 0.55171350 0.70596832
116 0.15396933 0.55171350
117 -1.22912565 0.15396933
118 0.76061767 -1.22912565
119 -0.93068107 0.76061767
120 0.56435569 -0.93068107
121 0.44153489 0.56435569
122 -0.96821524 0.44153489
123 0.38776532 -0.96821524
124 -1.67784307 0.38776532
125 1.18125044 -1.67784307
126 1.63155029 1.18125044
127 4.35208144 1.63155029
128 1.41721681 4.35208144
129 -1.68808569 1.41721681
130 -1.60477797 -1.68808569
131 -0.21065767 -1.60477797
132 2.31400798 -0.21065767
133 0.76230460 2.31400798
134 2.61780518 0.76230460
135 1.41977972 2.61780518
136 0.51762581 1.41977972
137 -1.30537329 0.51762581
138 0.62893136 -1.30537329
139 -0.86562211 0.62893136
140 -0.08886959 -0.86562211
141 2.53203162 -0.08886959
142 -0.39692042 2.53203162
143 0.84248200 -0.39692042
144 1.79807892 0.84248200
145 1.59859632 1.79807892
146 -2.72312970 1.59859632
147 -2.66152099 -2.72312970
148 -2.33474838 -2.66152099
149 2.09980691 -2.33474838
150 0.57162472 2.09980691
151 0.45001420 0.57162472
152 -2.41100614 0.45001420
153 -2.75706707 -2.41100614
154 1.58228815 -2.75706707
155 -0.25602449 1.58228815
156 0.69206001 -0.25602449
157 4.35208144 0.69206001
158 -2.47029169 4.35208144
159 0.31904067 -2.47029169
160 0.45986020 0.31904067
161 1.03916376 0.45986020
162 1.21343460 1.03916376
163 4.92248491 1.21343460
164 -1.71072814 4.92248491
165 1.90003116 -1.71072814
166 0.17518403 1.90003116
167 -0.63181308 0.17518403
168 -3.29193523 -0.63181308
169 -2.70272186 -3.29193523
170 0.41057636 -2.70272186
171 1.88008521 0.41057636
172 -4.78353594 1.88008521
173 1.68047732 -4.78353594
174 2.74815521 1.68047732
175 -2.12462740 2.74815521
176 -3.16599453 -2.12462740
177 0.72055401 -3.16599453
178 1.41650301 0.72055401
179 -2.00436966 1.41650301
180 -0.49413399 -2.00436966
181 -1.72791623 -0.49413399
182 0.28630868 -1.72791623
183 -0.75873456 0.28630868
184 2.47757620 -0.75873456
185 1.64684890 2.47757620
186 0.60467853 1.64684890
187 1.29007872 0.60467853
188 0.30785461 1.29007872
189 0.87313611 0.30785461
190 -2.49764674 0.87313611
191 -0.95525020 -2.49764674
192 2.49771706 -0.95525020
193 -1.55966598 2.49771706
194 2.03324104 -1.55966598
195 -2.05318295 2.03324104
196 2.69277540 -2.05318295
197 1.07331773 2.69277540
198 -3.14303303 1.07331773
199 -0.56357521 -3.14303303
200 -3.02797483 -0.56357521
201 1.27648685 -3.02797483
202 3.04404714 1.27648685
203 0.10301261 3.04404714
204 0.65341542 0.10301261
205 1.39161118 0.65341542
206 -0.10967217 1.39161118
207 3.06611483 -0.10967217
208 0.15950182 3.06611483
209 2.04395669 0.15950182
210 -2.74776510 2.04395669
211 1.70942135 -2.74776510
212 -0.69024288 1.70942135
213 -3.51428890 -0.69024288
214 -1.14559973 -3.51428890
215 1.68942965 -1.14559973
216 2.45961942 1.68942965
217 0.06971802 2.45961942
218 -1.94756415 0.06971802
219 1.71878042 -1.94756415
220 -2.78577229 1.71878042
221 2.05339595 -2.78577229
222 -1.99501059 2.05339595
223 0.19623276 -1.99501059
224 -1.32786279 0.19623276
225 1.88138956 -1.32786279
226 4.79072220 1.88138956
227 -1.86882621 4.79072220
228 -1.33249900 -1.86882621
229 -2.32433600 -1.33249900
230 0.26431739 -2.32433600
231 -3.01092214 0.26431739
232 -0.20160187 -3.01092214
233 0.63899556 -0.20160187
234 1.07524805 0.63899556
235 -1.81393347 1.07524805
236 0.98340865 -1.81393347
237 -0.32881926 0.98340865
238 -4.35602383 -0.32881926
239 -2.49564002 -4.35602383
240 -2.37529519 -2.49564002
241 -3.00956934 -2.37529519
242 0.17311452 -3.00956934
243 -0.23576610 0.17311452
244 1.81479661 -0.23576610
245 0.43938797 1.81479661
246 0.29063871 0.43938797
247 4.75055492 0.29063871
248 -0.27223282 4.75055492
249 0.17055809 -0.27223282
250 2.19743261 0.17055809
251 1.45627352 2.19743261
252 -1.10654199 1.45627352
253 -0.63527360 -1.10654199
254 0.22150701 -0.63527360
255 -0.70328184 0.22150701
256 -1.62215824 -0.70328184
257 -2.49676697 -1.62215824
258 2.34296474 -2.49676697
259 -4.48362002 2.34296474
260 0.15550214 -4.48362002
261 1.33857222 0.15550214
262 -2.82130337 1.33857222
263 0.19870901 -2.82130337
> 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/7hd3i1384817162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8st331384817162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/94qoi1384817162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10w15a1384817162.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11j4in1384817162.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/1218wk1384817162.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/13539v1384817162.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/14z8991384817162.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/15gktf1384817162.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/16sere1384817162.tab")
+ }
>
> try(system("convert tmp/1bn5p1384817162.ps tmp/1bn5p1384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xrlg1384817162.ps tmp/2xrlg1384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/3e7x81384817162.ps tmp/3e7x81384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xlei1384817162.ps tmp/4xlei1384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/5289g1384817162.ps tmp/5289g1384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ajyx1384817162.ps tmp/6ajyx1384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hd3i1384817162.ps tmp/7hd3i1384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/8st331384817162.ps tmp/8st331384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/94qoi1384817162.ps tmp/94qoi1384817162.png",intern=TRUE))
character(0)
> try(system("convert tmp/10w15a1384817162.ps tmp/10w15a1384817162.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.363 1.746 13.109