R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2
+ ,5
+ ,1
+ ,1
+ ,4
+ ,1
+ ,1
+ ,7
+ ,1
+ ,1
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+ ,1
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+ ,6
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+ ,1
+ ,1
+ ,6
+ ,1
+ ,1
+ ,3
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,6
+ ,1
+ ,1
+ ,5
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+ ,1
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+ ,2
+ ,2
+ ,3
+ ,2
+ ,2
+ ,4
+ ,1
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+ ,2
+ ,6
+ ,2
+ ,1
+ ,6
+ ,2
+ ,1
+ ,4
+ ,1
+ ,2
+ ,6
+ ,1
+ ,1
+ ,6
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+ ,1
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+ ,1
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+ ,1
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+ ,6
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+ ,1
+ ,5
+ ,2
+ ,2
+ ,7
+ ,1
+ ,2
+ ,6
+ ,1
+ ,1
+ ,3
+ ,1
+ ,1
+ ,4
+ ,1
+ ,2
+ ,5
+ ,1
+ ,2
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+ ,2
+ ,1
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+ ,2
+ ,2
+ ,2
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+ ,1
+ ,1
+ ,4
+ ,1
+ ,1
+ ,3
+ ,1
+ ,1
+ ,7
+ ,1
+ ,1
+ ,2
+ ,1
+ ,1
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+ ,1
+ ,1
+ ,2
+ ,1
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+ ,1
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+ ,1
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+ ,1
+ ,1
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+ ,1
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+ ,1
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+ ,1
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+ ,1
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+ ,1
+ ,2
+ ,6
+ ,2
+ ,1
+ ,6
+ ,1
+ ,2
+ ,3
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+ ,5
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,4
+ ,1
+ ,1
+ ,5
+ ,1
+ ,2
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+ ,1
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+ ,6
+ ,1
+ ,1
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+ ,2
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+ ,1
+ ,2
+ ,5
+ ,1
+ ,1
+ ,6
+ ,1
+ ,1
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+ ,1
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+ ,2
+ ,5
+ ,1
+ ,2
+ ,6
+ ,1
+ ,1
+ ,6
+ ,1
+ ,2
+ ,6
+ ,1
+ ,1
+ ,7
+ ,2
+ ,2
+ ,6
+ ,1
+ ,1
+ ,6
+ ,1
+ ,1
+ ,6
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,4
+ ,1
+ ,2
+ ,4
+ ,1
+ ,2
+ ,6
+ ,1
+ ,1
+ ,5
+ ,1
+ ,1
+ ,6
+ ,1
+ ,1
+ ,6
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,7
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,4
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,6
+ ,1
+ ,2
+ ,6
+ ,1
+ ,1
+ ,6
+ ,1
+ ,2
+ ,7
+ ,1
+ ,1
+ ,6
+ ,1
+ ,2
+ ,4
+ ,1
+ ,2
+ ,4
+ ,1
+ ,1
+ ,6
+ ,1
+ ,1
+ ,5
+ ,2
+ ,2
+ ,7
+ ,1
+ ,2
+ ,4
+ ,1
+ ,1
+ ,4
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,7
+ ,1
+ ,2
+ ,5
+ ,1
+ ,2
+ ,6
+ ,1
+ ,1
+ ,6
+ ,2
+ ,2
+ ,6
+ ,1
+ ,2
+ ,5
+ ,1
+ ,2
+ ,7
+ ,1
+ ,2
+ ,4
+ ,1
+ ,1
+ ,6
+ ,1
+ ,1
+ ,6
+ ,1
+ ,2
+ ,7
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,5
+ ,1
+ ,1
+ ,5
+ ,1
+ ,2
+ ,5
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,6
+ ,2
+ ,1
+ ,7
+ ,2
+ ,1
+ ,4
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,6
+ ,1
+ ,2
+ ,7
+ ,1
+ ,1
+ ,6
+ ,1
+ ,2
+ ,7
+ ,1
+ ,2
+ ,4
+ ,2
+ ,2
+ ,6
+ ,1
+ ,1
+ ,4
+ ,1
+ ,1
+ ,4
+ ,1
+ ,2
+ ,7
+ ,1
+ ,1
+ ,4
+ ,1
+ ,2
+ ,7
+ ,2)
+ ,dim=c(3
+ ,157)
+ ,dimnames=list(c('Member'
+ ,'Provision'
+ ,'Illness')
+ ,1:157))
> y <- array(NA,dim=c(3,157),dimnames=list(c('Member','Provision','Illness'),1:157))
> 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'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
Member Provision Illness
1 2 5 1
2 1 4 1
3 1 7 1
4 1 7 1
5 2 5 1
6 2 5 1
7 1 4 1
8 2 4 2
9 1 6 1
10 2 5 1
11 1 1 1
12 2 5 1
13 1 4 2
14 2 6 1
15 2 7 1
16 2 7 1
17 1 2 1
18 1 6 1
19 1 3 1
20 2 6 1
21 2 6 1
22 1 5 1
23 2 6 1
24 2 4 2
25 2 3 2
26 2 4 1
27 2 5 2
28 2 6 2
29 1 6 2
30 1 4 1
31 2 6 1
32 1 6 1
33 2 5 1
34 2 6 1
35 2 4 1
36 1 6 1
37 2 7 1
38 1 5 1
39 1 6 1
40 2 6 2
41 1 5 2
42 2 7 1
43 2 6 1
44 1 3 1
45 1 4 1
46 2 5 1
47 2 4 2
48 1 3 1
49 2 5 1
50 2 5 1
51 1 4 1
52 1 5 1
53 2 1 1
54 2 2 2
55 2 3 1
56 1 4 1
57 1 3 1
58 1 7 1
59 1 2 1
60 1 4 1
61 1 2 1
62 2 5 1
63 2 6 1
64 2 6 1
65 2 6 1
66 1 6 1
67 2 6 1
68 2 6 1
69 1 6 1
70 1 4 1
71 1 4 1
72 2 5 1
73 1 6 1
74 1 6 1
75 1 7 1
76 1 6 1
77 2 6 2
78 1 6 1
79 2 3 1
80 2 5 1
81 2 6 1
82 2 4 1
83 1 5 1
84 2 6 1
85 2 6 1
86 1 3 1
87 2 6 1
88 2 5 1
89 1 6 1
90 1 4 1
91 2 7 1
92 2 5 1
93 2 6 1
94 1 6 1
95 2 6 1
96 1 7 2
97 2 6 1
98 1 6 1
99 1 6 1
100 2 6 1
101 2 2 1
102 1 4 1
103 2 4 1
104 2 6 1
105 1 5 1
106 1 6 1
107 1 6 1
108 1 2 2
109 2 7 1
110 1 1 1
111 1 4 1
112 1 1 1
113 1 6 1
114 2 6 1
115 1 6 1
116 2 7 1
117 1 6 1
118 2 4 1
119 2 4 1
120 1 6 1
121 1 5 2
122 2 7 1
123 2 4 1
124 1 4 1
125 2 6 1
126 2 7 1
127 2 5 1
128 2 6 1
129 1 6 2
130 2 6 1
131 2 5 1
132 2 7 1
133 2 4 1
134 1 6 1
135 1 6 1
136 2 7 1
137 2 6 1
138 2 6 1
139 2 5 1
140 1 5 1
141 2 5 1
142 2 6 1
143 2 6 2
144 1 7 2
145 1 4 1
146 2 6 1
147 2 6 1
148 2 7 1
149 1 6 1
150 2 7 1
151 2 4 2
152 2 6 1
153 1 4 1
154 1 4 1
155 2 7 1
156 1 4 1
157 2 7 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Provision Illness
1.08304 0.07626 0.06973
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.7563 -0.5341 0.3134 0.3897 0.7710
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.08304 0.20183 5.366 2.90e-07 ***
Provision 0.07626 0.02732 2.791 0.00592 **
Illness 0.06973 0.11730 0.594 0.55308
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4893 on 154 degrees of freedom
Multiple R-squared: 0.04934, Adjusted R-squared: 0.03699
F-statistic: 3.996 on 2 and 154 DF, p-value: 0.02033
> 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,] 0.8280744 0.34385122 0.17192561
[2,] 0.8526560 0.29468800 0.14734400
[3,] 0.7651587 0.46968252 0.23484126
[4,] 0.7076411 0.58471774 0.29235887
[5,] 0.7135330 0.57293406 0.28646703
[6,] 0.7309338 0.53813239 0.26906620
[7,] 0.7424694 0.51506111 0.25753055
[8,] 0.7928323 0.41433535 0.20716768
[9,] 0.7818814 0.43623714 0.21811857
[10,] 0.7502129 0.49957427 0.24978714
[11,] 0.7073576 0.58528471 0.29264236
[12,] 0.6556148 0.68877049 0.34438524
[13,] 0.6823362 0.63532760 0.31766380
[14,] 0.6375993 0.72480133 0.36240066
[15,] 0.6133435 0.77331297 0.38665649
[16,] 0.5841225 0.83175509 0.41587754
[17,] 0.5851587 0.82968268 0.41484134
[18,] 0.5571602 0.88567961 0.44283981
[19,] 0.5370721 0.92585574 0.46292787
[20,] 0.5190211 0.96195778 0.48097889
[21,] 0.5431300 0.91374008 0.45687004
[22,] 0.4905553 0.98111052 0.50944474
[23,] 0.4349284 0.86985678 0.56507161
[24,] 0.5653359 0.86932819 0.43466409
[25,] 0.5531190 0.89376192 0.44688096
[26,] 0.5288555 0.94228905 0.47114452
[27,] 0.5592319 0.88153614 0.44076807
[28,] 0.5545489 0.89090224 0.44545112
[29,] 0.5306012 0.93879767 0.46939884
[30,] 0.5401244 0.91975129 0.45987565
[31,] 0.5722365 0.85552701 0.42776350
[32,] 0.5374800 0.92504004 0.46252002
[33,] 0.5471985 0.90560293 0.45280147
[34,] 0.5731740 0.85365204 0.42682602
[35,] 0.5331244 0.93375119 0.46687559
[36,] 0.5788141 0.84237175 0.42118588
[37,] 0.5477551 0.90448974 0.45224487
[38,] 0.5271735 0.94565293 0.47282646
[39,] 0.5017593 0.99648141 0.49824071
[40,] 0.4890207 0.97804148 0.51097926
[41,] 0.4857092 0.97141837 0.51429081
[42,] 0.4758883 0.95177660 0.52411170
[43,] 0.4493416 0.89868328 0.55065836
[44,] 0.4459685 0.89193693 0.55403154
[45,] 0.4411827 0.88236543 0.55881728
[46,] 0.4309207 0.86184146 0.56907927
[47,] 0.4380986 0.87619728 0.56190136
[48,] 0.5236442 0.95271160 0.47635580
[49,] 0.5488754 0.90224916 0.45112458
[50,] 0.5748544 0.85029114 0.42514557
[51,] 0.5695994 0.86080121 0.43040061
[52,] 0.5509112 0.89817765 0.44908882
[53,] 0.5922911 0.81541771 0.40770885
[54,] 0.5633118 0.87337640 0.43668820
[55,] 0.5545273 0.89094539 0.44547270
[56,] 0.5235177 0.95296469 0.47648234
[57,] 0.5216515 0.95669696 0.47834848
[58,] 0.5055320 0.98893599 0.49446799
[59,] 0.4885761 0.97715214 0.51142393
[60,] 0.4709539 0.94190777 0.52904612
[61,] 0.4965037 0.99300732 0.50349634
[62,] 0.4791859 0.95837186 0.52081407
[63,] 0.4613878 0.92277562 0.53861219
[64,] 0.4872641 0.97452826 0.51273587
[65,] 0.4794457 0.95889135 0.52055432
[66,] 0.4717180 0.94343605 0.52828197
[67,] 0.4679669 0.93593373 0.53203314
[68,] 0.4932102 0.98642031 0.50678985
[69,] 0.5181442 0.96371155 0.48185577
[70,] 0.5626903 0.87461941 0.43730970
[71,] 0.5877538 0.82449245 0.41224623
[72,] 0.5732862 0.85342755 0.42671377
[73,] 0.5997889 0.80042220 0.40021110
[74,] 0.6301079 0.73978410 0.36989205
[75,] 0.6276564 0.74468723 0.37234362
[76,] 0.6121463 0.77570744 0.38785372
[77,] 0.6248818 0.75023645 0.37511823
[78,] 0.6336489 0.73270229 0.36635115
[79,] 0.6174961 0.76500784 0.38250392
[80,] 0.6007477 0.79850454 0.39925227
[81,] 0.5810566 0.83788675 0.41894337
[82,] 0.5634562 0.87308765 0.43654383
[83,] 0.5591948 0.88161036 0.44080518
[84,] 0.5881759 0.82364823 0.41182412
[85,] 0.5823268 0.83534645 0.41767323
[86,] 0.5532093 0.89358137 0.44679069
[87,] 0.5480055 0.90398895 0.45199448
[88,] 0.5290754 0.94184927 0.47092463
[89,] 0.5596168 0.88076643 0.44038321
[90,] 0.5401747 0.91965059 0.45982530
[91,] 0.5876648 0.82467030 0.41233515
[92,] 0.5682280 0.86354401 0.43177201
[93,] 0.5987526 0.80249479 0.40124739
[94,] 0.6313487 0.73730259 0.36865130
[95,] 0.6108973 0.77820534 0.38910267
[96,] 0.6724416 0.65511674 0.32755837
[97,] 0.6642112 0.67157757 0.33578879
[98,] 0.6799597 0.64008061 0.32004030
[99,] 0.6605783 0.67884334 0.33942167
[100,] 0.6702194 0.65956121 0.32978061
[101,] 0.7057861 0.58842780 0.29421390
[102,] 0.7435681 0.51286389 0.25643195
[103,] 0.7159819 0.56803613 0.28401806
[104,] 0.6827658 0.63446849 0.31723425
[105,] 0.6404067 0.71918653 0.35959327
[106,] 0.6341225 0.73175493 0.36587746
[107,] 0.5904494 0.81910111 0.40955056
[108,] 0.6387937 0.72241259 0.36120630
[109,] 0.6109630 0.77807398 0.38903699
[110,] 0.6637837 0.67243265 0.33621632
[111,] 0.6240741 0.75185171 0.37592586
[112,] 0.6829993 0.63400141 0.31700071
[113,] 0.6908481 0.61830381 0.30915190
[114,] 0.7061685 0.58766292 0.29383146
[115,] 0.7670412 0.46591768 0.23295884
[116,] 0.7638831 0.47223374 0.23611687
[117,] 0.7240241 0.55195187 0.27597593
[118,] 0.7442985 0.51140292 0.25570146
[119,] 0.7350246 0.52995072 0.26497536
[120,] 0.7022383 0.59552340 0.29776170
[121,] 0.6550773 0.68984550 0.34492275
[122,] 0.6425322 0.71493561 0.35746780
[123,] 0.6059358 0.78812838 0.39406419
[124,] 0.6603764 0.67924711 0.33962355
[125,] 0.6242801 0.75143971 0.37571986
[126,] 0.6175854 0.76482928 0.38241464
[127,] 0.5628422 0.87431559 0.43715779
[128,] 0.6167735 0.76645299 0.38322649
[129,] 0.6801791 0.63964183 0.31982092
[130,] 0.7582088 0.48358230 0.24179115
[131,] 0.7011443 0.59771148 0.29885574
[132,] 0.6572103 0.68557937 0.34278968
[133,] 0.6124402 0.77511962 0.38755981
[134,] 0.6217430 0.75651409 0.37825704
[135,] 0.6248561 0.75028785 0.37514393
[136,] 0.6369459 0.72610828 0.36305414
[137,] 0.5973126 0.80537482 0.40268741
[138,] 0.5407381 0.91852388 0.45926194
[139,] 0.8991684 0.20166324 0.10083162
[140,] 0.8530379 0.29392424 0.14696212
[141,] 0.8286905 0.34261904 0.17130952
[142,] 0.8133395 0.37332094 0.18666047
[143,] 0.7355033 0.52899333 0.26449667
[144,] 0.8468311 0.30633782 0.15316891
[145,] 0.7396653 0.52066934 0.26033467
[146,] 0.9581563 0.08368734 0.04184367
> postscript(file="/var/www/html/rcomp/tmp/1bzbn1291283830.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2bzbn1291283830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/34qaq1291283830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/44qaq1291283830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/54qaq1291283830.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 = 157
Frequency = 1
1 2 3 4 5 6 7
0.4659148 -0.4578212 -0.6866130 -0.6866130 0.4659148 0.4659148 -0.4578212
8 9 10 11 12 13 14
0.4724507 -0.6103491 0.4659148 -0.2290295 0.4659148 -0.5275493 0.3896509
15 16 17 18 19 20 21
0.3133870 0.3133870 -0.3052934 -0.6103491 -0.3815573 0.3896509 0.3896509
22 23 24 25 26 27 28
-0.5340852 0.3896509 0.4724507 0.5487146 0.5421788 0.3961868 0.3199229
29 30 31 32 33 34 35
-0.6800771 -0.4578212 0.3896509 -0.6103491 0.4659148 0.3896509 0.5421788
36 37 38 39 40 41 42
-0.6103491 0.3133870 -0.5340852 -0.6103491 0.3199229 -0.6038132 0.3133870
43 44 45 46 47 48 49
0.3896509 -0.3815573 -0.4578212 0.4659148 0.4724507 -0.3815573 0.4659148
50 51 52 53 54 55 56
0.4659148 -0.4578212 -0.5340852 0.7709705 0.6249785 0.6184427 -0.4578212
57 58 59 60 61 62 63
-0.3815573 -0.6866130 -0.3052934 -0.4578212 -0.3052934 0.4659148 0.3896509
64 65 66 67 68 69 70
0.3896509 0.3896509 -0.6103491 0.3896509 0.3896509 -0.6103491 -0.4578212
71 72 73 74 75 76 77
-0.4578212 0.4659148 -0.6103491 -0.6103491 -0.6866130 -0.6103491 0.3199229
78 79 80 81 82 83 84
-0.6103491 0.6184427 0.4659148 0.3896509 0.5421788 -0.5340852 0.3896509
85 86 87 88 89 90 91
0.3896509 -0.3815573 0.3896509 0.4659148 -0.6103491 -0.4578212 0.3133870
92 93 94 95 96 97 98
0.4659148 0.3896509 -0.6103491 0.3896509 -0.7563410 0.3896509 -0.6103491
99 100 101 102 103 104 105
-0.6103491 0.3896509 0.6947066 -0.4578212 0.5421788 0.3896509 -0.5340852
106 107 108 109 110 111 112
-0.6103491 -0.6103491 -0.3750215 0.3133870 -0.2290295 -0.4578212 -0.2290295
113 114 115 116 117 118 119
-0.6103491 0.3896509 -0.6103491 0.3133870 -0.6103491 0.5421788 0.5421788
120 121 122 123 124 125 126
-0.6103491 -0.6038132 0.3133870 0.5421788 -0.4578212 0.3896509 0.3133870
127 128 129 130 131 132 133
0.4659148 0.3896509 -0.6800771 0.3896509 0.4659148 0.3133870 0.5421788
134 135 136 137 138 139 140
-0.6103491 -0.6103491 0.3133870 0.3896509 0.3896509 0.4659148 -0.5340852
141 142 143 144 145 146 147
0.4659148 0.3896509 0.3199229 -0.7563410 -0.4578212 0.3896509 0.3896509
148 149 150 151 152 153 154
0.3133870 -0.6103491 0.3133870 0.4724507 0.3896509 -0.4578212 -0.4578212
155 156 157
0.3133870 -0.4578212 0.2436590
> postscript(file="/var/www/html/rcomp/tmp/6wz9t1291283830.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 = 157
Frequency = 1
lag(myerror, k = 1) myerror
0 0.4659148 NA
1 -0.4578212 0.4659148
2 -0.6866130 -0.4578212
3 -0.6866130 -0.6866130
4 0.4659148 -0.6866130
5 0.4659148 0.4659148
6 -0.4578212 0.4659148
7 0.4724507 -0.4578212
8 -0.6103491 0.4724507
9 0.4659148 -0.6103491
10 -0.2290295 0.4659148
11 0.4659148 -0.2290295
12 -0.5275493 0.4659148
13 0.3896509 -0.5275493
14 0.3133870 0.3896509
15 0.3133870 0.3133870
16 -0.3052934 0.3133870
17 -0.6103491 -0.3052934
18 -0.3815573 -0.6103491
19 0.3896509 -0.3815573
20 0.3896509 0.3896509
21 -0.5340852 0.3896509
22 0.3896509 -0.5340852
23 0.4724507 0.3896509
24 0.5487146 0.4724507
25 0.5421788 0.5487146
26 0.3961868 0.5421788
27 0.3199229 0.3961868
28 -0.6800771 0.3199229
29 -0.4578212 -0.6800771
30 0.3896509 -0.4578212
31 -0.6103491 0.3896509
32 0.4659148 -0.6103491
33 0.3896509 0.4659148
34 0.5421788 0.3896509
35 -0.6103491 0.5421788
36 0.3133870 -0.6103491
37 -0.5340852 0.3133870
38 -0.6103491 -0.5340852
39 0.3199229 -0.6103491
40 -0.6038132 0.3199229
41 0.3133870 -0.6038132
42 0.3896509 0.3133870
43 -0.3815573 0.3896509
44 -0.4578212 -0.3815573
45 0.4659148 -0.4578212
46 0.4724507 0.4659148
47 -0.3815573 0.4724507
48 0.4659148 -0.3815573
49 0.4659148 0.4659148
50 -0.4578212 0.4659148
51 -0.5340852 -0.4578212
52 0.7709705 -0.5340852
53 0.6249785 0.7709705
54 0.6184427 0.6249785
55 -0.4578212 0.6184427
56 -0.3815573 -0.4578212
57 -0.6866130 -0.3815573
58 -0.3052934 -0.6866130
59 -0.4578212 -0.3052934
60 -0.3052934 -0.4578212
61 0.4659148 -0.3052934
62 0.3896509 0.4659148
63 0.3896509 0.3896509
64 0.3896509 0.3896509
65 -0.6103491 0.3896509
66 0.3896509 -0.6103491
67 0.3896509 0.3896509
68 -0.6103491 0.3896509
69 -0.4578212 -0.6103491
70 -0.4578212 -0.4578212
71 0.4659148 -0.4578212
72 -0.6103491 0.4659148
73 -0.6103491 -0.6103491
74 -0.6866130 -0.6103491
75 -0.6103491 -0.6866130
76 0.3199229 -0.6103491
77 -0.6103491 0.3199229
78 0.6184427 -0.6103491
79 0.4659148 0.6184427
80 0.3896509 0.4659148
81 0.5421788 0.3896509
82 -0.5340852 0.5421788
83 0.3896509 -0.5340852
84 0.3896509 0.3896509
85 -0.3815573 0.3896509
86 0.3896509 -0.3815573
87 0.4659148 0.3896509
88 -0.6103491 0.4659148
89 -0.4578212 -0.6103491
90 0.3133870 -0.4578212
91 0.4659148 0.3133870
92 0.3896509 0.4659148
93 -0.6103491 0.3896509
94 0.3896509 -0.6103491
95 -0.7563410 0.3896509
96 0.3896509 -0.7563410
97 -0.6103491 0.3896509
98 -0.6103491 -0.6103491
99 0.3896509 -0.6103491
100 0.6947066 0.3896509
101 -0.4578212 0.6947066
102 0.5421788 -0.4578212
103 0.3896509 0.5421788
104 -0.5340852 0.3896509
105 -0.6103491 -0.5340852
106 -0.6103491 -0.6103491
107 -0.3750215 -0.6103491
108 0.3133870 -0.3750215
109 -0.2290295 0.3133870
110 -0.4578212 -0.2290295
111 -0.2290295 -0.4578212
112 -0.6103491 -0.2290295
113 0.3896509 -0.6103491
114 -0.6103491 0.3896509
115 0.3133870 -0.6103491
116 -0.6103491 0.3133870
117 0.5421788 -0.6103491
118 0.5421788 0.5421788
119 -0.6103491 0.5421788
120 -0.6038132 -0.6103491
121 0.3133870 -0.6038132
122 0.5421788 0.3133870
123 -0.4578212 0.5421788
124 0.3896509 -0.4578212
125 0.3133870 0.3896509
126 0.4659148 0.3133870
127 0.3896509 0.4659148
128 -0.6800771 0.3896509
129 0.3896509 -0.6800771
130 0.4659148 0.3896509
131 0.3133870 0.4659148
132 0.5421788 0.3133870
133 -0.6103491 0.5421788
134 -0.6103491 -0.6103491
135 0.3133870 -0.6103491
136 0.3896509 0.3133870
137 0.3896509 0.3896509
138 0.4659148 0.3896509
139 -0.5340852 0.4659148
140 0.4659148 -0.5340852
141 0.3896509 0.4659148
142 0.3199229 0.3896509
143 -0.7563410 0.3199229
144 -0.4578212 -0.7563410
145 0.3896509 -0.4578212
146 0.3896509 0.3896509
147 0.3133870 0.3896509
148 -0.6103491 0.3133870
149 0.3133870 -0.6103491
150 0.4724507 0.3133870
151 0.3896509 0.4724507
152 -0.4578212 0.3896509
153 -0.4578212 -0.4578212
154 0.3133870 -0.4578212
155 -0.4578212 0.3133870
156 0.2436590 -0.4578212
157 NA 0.2436590
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.4578212 0.4659148
[2,] -0.6866130 -0.4578212
[3,] -0.6866130 -0.6866130
[4,] 0.4659148 -0.6866130
[5,] 0.4659148 0.4659148
[6,] -0.4578212 0.4659148
[7,] 0.4724507 -0.4578212
[8,] -0.6103491 0.4724507
[9,] 0.4659148 -0.6103491
[10,] -0.2290295 0.4659148
[11,] 0.4659148 -0.2290295
[12,] -0.5275493 0.4659148
[13,] 0.3896509 -0.5275493
[14,] 0.3133870 0.3896509
[15,] 0.3133870 0.3133870
[16,] -0.3052934 0.3133870
[17,] -0.6103491 -0.3052934
[18,] -0.3815573 -0.6103491
[19,] 0.3896509 -0.3815573
[20,] 0.3896509 0.3896509
[21,] -0.5340852 0.3896509
[22,] 0.3896509 -0.5340852
[23,] 0.4724507 0.3896509
[24,] 0.5487146 0.4724507
[25,] 0.5421788 0.5487146
[26,] 0.3961868 0.5421788
[27,] 0.3199229 0.3961868
[28,] -0.6800771 0.3199229
[29,] -0.4578212 -0.6800771
[30,] 0.3896509 -0.4578212
[31,] -0.6103491 0.3896509
[32,] 0.4659148 -0.6103491
[33,] 0.3896509 0.4659148
[34,] 0.5421788 0.3896509
[35,] -0.6103491 0.5421788
[36,] 0.3133870 -0.6103491
[37,] -0.5340852 0.3133870
[38,] -0.6103491 -0.5340852
[39,] 0.3199229 -0.6103491
[40,] -0.6038132 0.3199229
[41,] 0.3133870 -0.6038132
[42,] 0.3896509 0.3133870
[43,] -0.3815573 0.3896509
[44,] -0.4578212 -0.3815573
[45,] 0.4659148 -0.4578212
[46,] 0.4724507 0.4659148
[47,] -0.3815573 0.4724507
[48,] 0.4659148 -0.3815573
[49,] 0.4659148 0.4659148
[50,] -0.4578212 0.4659148
[51,] -0.5340852 -0.4578212
[52,] 0.7709705 -0.5340852
[53,] 0.6249785 0.7709705
[54,] 0.6184427 0.6249785
[55,] -0.4578212 0.6184427
[56,] -0.3815573 -0.4578212
[57,] -0.6866130 -0.3815573
[58,] -0.3052934 -0.6866130
[59,] -0.4578212 -0.3052934
[60,] -0.3052934 -0.4578212
[61,] 0.4659148 -0.3052934
[62,] 0.3896509 0.4659148
[63,] 0.3896509 0.3896509
[64,] 0.3896509 0.3896509
[65,] -0.6103491 0.3896509
[66,] 0.3896509 -0.6103491
[67,] 0.3896509 0.3896509
[68,] -0.6103491 0.3896509
[69,] -0.4578212 -0.6103491
[70,] -0.4578212 -0.4578212
[71,] 0.4659148 -0.4578212
[72,] -0.6103491 0.4659148
[73,] -0.6103491 -0.6103491
[74,] -0.6866130 -0.6103491
[75,] -0.6103491 -0.6866130
[76,] 0.3199229 -0.6103491
[77,] -0.6103491 0.3199229
[78,] 0.6184427 -0.6103491
[79,] 0.4659148 0.6184427
[80,] 0.3896509 0.4659148
[81,] 0.5421788 0.3896509
[82,] -0.5340852 0.5421788
[83,] 0.3896509 -0.5340852
[84,] 0.3896509 0.3896509
[85,] -0.3815573 0.3896509
[86,] 0.3896509 -0.3815573
[87,] 0.4659148 0.3896509
[88,] -0.6103491 0.4659148
[89,] -0.4578212 -0.6103491
[90,] 0.3133870 -0.4578212
[91,] 0.4659148 0.3133870
[92,] 0.3896509 0.4659148
[93,] -0.6103491 0.3896509
[94,] 0.3896509 -0.6103491
[95,] -0.7563410 0.3896509
[96,] 0.3896509 -0.7563410
[97,] -0.6103491 0.3896509
[98,] -0.6103491 -0.6103491
[99,] 0.3896509 -0.6103491
[100,] 0.6947066 0.3896509
[101,] -0.4578212 0.6947066
[102,] 0.5421788 -0.4578212
[103,] 0.3896509 0.5421788
[104,] -0.5340852 0.3896509
[105,] -0.6103491 -0.5340852
[106,] -0.6103491 -0.6103491
[107,] -0.3750215 -0.6103491
[108,] 0.3133870 -0.3750215
[109,] -0.2290295 0.3133870
[110,] -0.4578212 -0.2290295
[111,] -0.2290295 -0.4578212
[112,] -0.6103491 -0.2290295
[113,] 0.3896509 -0.6103491
[114,] -0.6103491 0.3896509
[115,] 0.3133870 -0.6103491
[116,] -0.6103491 0.3133870
[117,] 0.5421788 -0.6103491
[118,] 0.5421788 0.5421788
[119,] -0.6103491 0.5421788
[120,] -0.6038132 -0.6103491
[121,] 0.3133870 -0.6038132
[122,] 0.5421788 0.3133870
[123,] -0.4578212 0.5421788
[124,] 0.3896509 -0.4578212
[125,] 0.3133870 0.3896509
[126,] 0.4659148 0.3133870
[127,] 0.3896509 0.4659148
[128,] -0.6800771 0.3896509
[129,] 0.3896509 -0.6800771
[130,] 0.4659148 0.3896509
[131,] 0.3133870 0.4659148
[132,] 0.5421788 0.3133870
[133,] -0.6103491 0.5421788
[134,] -0.6103491 -0.6103491
[135,] 0.3133870 -0.6103491
[136,] 0.3896509 0.3133870
[137,] 0.3896509 0.3896509
[138,] 0.4659148 0.3896509
[139,] -0.5340852 0.4659148
[140,] 0.4659148 -0.5340852
[141,] 0.3896509 0.4659148
[142,] 0.3199229 0.3896509
[143,] -0.7563410 0.3199229
[144,] -0.4578212 -0.7563410
[145,] 0.3896509 -0.4578212
[146,] 0.3896509 0.3896509
[147,] 0.3133870 0.3896509
[148,] -0.6103491 0.3133870
[149,] 0.3133870 -0.6103491
[150,] 0.4724507 0.3133870
[151,] 0.3896509 0.4724507
[152,] -0.4578212 0.3896509
[153,] -0.4578212 -0.4578212
[154,] 0.3133870 -0.4578212
[155,] -0.4578212 0.3133870
[156,] 0.2436590 -0.4578212
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.4578212 0.4659148
2 -0.6866130 -0.4578212
3 -0.6866130 -0.6866130
4 0.4659148 -0.6866130
5 0.4659148 0.4659148
6 -0.4578212 0.4659148
7 0.4724507 -0.4578212
8 -0.6103491 0.4724507
9 0.4659148 -0.6103491
10 -0.2290295 0.4659148
11 0.4659148 -0.2290295
12 -0.5275493 0.4659148
13 0.3896509 -0.5275493
14 0.3133870 0.3896509
15 0.3133870 0.3133870
16 -0.3052934 0.3133870
17 -0.6103491 -0.3052934
18 -0.3815573 -0.6103491
19 0.3896509 -0.3815573
20 0.3896509 0.3896509
21 -0.5340852 0.3896509
22 0.3896509 -0.5340852
23 0.4724507 0.3896509
24 0.5487146 0.4724507
25 0.5421788 0.5487146
26 0.3961868 0.5421788
27 0.3199229 0.3961868
28 -0.6800771 0.3199229
29 -0.4578212 -0.6800771
30 0.3896509 -0.4578212
31 -0.6103491 0.3896509
32 0.4659148 -0.6103491
33 0.3896509 0.4659148
34 0.5421788 0.3896509
35 -0.6103491 0.5421788
36 0.3133870 -0.6103491
37 -0.5340852 0.3133870
38 -0.6103491 -0.5340852
39 0.3199229 -0.6103491
40 -0.6038132 0.3199229
41 0.3133870 -0.6038132
42 0.3896509 0.3133870
43 -0.3815573 0.3896509
44 -0.4578212 -0.3815573
45 0.4659148 -0.4578212
46 0.4724507 0.4659148
47 -0.3815573 0.4724507
48 0.4659148 -0.3815573
49 0.4659148 0.4659148
50 -0.4578212 0.4659148
51 -0.5340852 -0.4578212
52 0.7709705 -0.5340852
53 0.6249785 0.7709705
54 0.6184427 0.6249785
55 -0.4578212 0.6184427
56 -0.3815573 -0.4578212
57 -0.6866130 -0.3815573
58 -0.3052934 -0.6866130
59 -0.4578212 -0.3052934
60 -0.3052934 -0.4578212
61 0.4659148 -0.3052934
62 0.3896509 0.4659148
63 0.3896509 0.3896509
64 0.3896509 0.3896509
65 -0.6103491 0.3896509
66 0.3896509 -0.6103491
67 0.3896509 0.3896509
68 -0.6103491 0.3896509
69 -0.4578212 -0.6103491
70 -0.4578212 -0.4578212
71 0.4659148 -0.4578212
72 -0.6103491 0.4659148
73 -0.6103491 -0.6103491
74 -0.6866130 -0.6103491
75 -0.6103491 -0.6866130
76 0.3199229 -0.6103491
77 -0.6103491 0.3199229
78 0.6184427 -0.6103491
79 0.4659148 0.6184427
80 0.3896509 0.4659148
81 0.5421788 0.3896509
82 -0.5340852 0.5421788
83 0.3896509 -0.5340852
84 0.3896509 0.3896509
85 -0.3815573 0.3896509
86 0.3896509 -0.3815573
87 0.4659148 0.3896509
88 -0.6103491 0.4659148
89 -0.4578212 -0.6103491
90 0.3133870 -0.4578212
91 0.4659148 0.3133870
92 0.3896509 0.4659148
93 -0.6103491 0.3896509
94 0.3896509 -0.6103491
95 -0.7563410 0.3896509
96 0.3896509 -0.7563410
97 -0.6103491 0.3896509
98 -0.6103491 -0.6103491
99 0.3896509 -0.6103491
100 0.6947066 0.3896509
101 -0.4578212 0.6947066
102 0.5421788 -0.4578212
103 0.3896509 0.5421788
104 -0.5340852 0.3896509
105 -0.6103491 -0.5340852
106 -0.6103491 -0.6103491
107 -0.3750215 -0.6103491
108 0.3133870 -0.3750215
109 -0.2290295 0.3133870
110 -0.4578212 -0.2290295
111 -0.2290295 -0.4578212
112 -0.6103491 -0.2290295
113 0.3896509 -0.6103491
114 -0.6103491 0.3896509
115 0.3133870 -0.6103491
116 -0.6103491 0.3133870
117 0.5421788 -0.6103491
118 0.5421788 0.5421788
119 -0.6103491 0.5421788
120 -0.6038132 -0.6103491
121 0.3133870 -0.6038132
122 0.5421788 0.3133870
123 -0.4578212 0.5421788
124 0.3896509 -0.4578212
125 0.3133870 0.3896509
126 0.4659148 0.3133870
127 0.3896509 0.4659148
128 -0.6800771 0.3896509
129 0.3896509 -0.6800771
130 0.4659148 0.3896509
131 0.3133870 0.4659148
132 0.5421788 0.3133870
133 -0.6103491 0.5421788
134 -0.6103491 -0.6103491
135 0.3133870 -0.6103491
136 0.3896509 0.3133870
137 0.3896509 0.3896509
138 0.4659148 0.3896509
139 -0.5340852 0.4659148
140 0.4659148 -0.5340852
141 0.3896509 0.4659148
142 0.3199229 0.3896509
143 -0.7563410 0.3199229
144 -0.4578212 -0.7563410
145 0.3896509 -0.4578212
146 0.3896509 0.3896509
147 0.3133870 0.3896509
148 -0.6103491 0.3133870
149 0.3133870 -0.6103491
150 0.4724507 0.3133870
151 0.3896509 0.4724507
152 -0.4578212 0.3896509
153 -0.4578212 -0.4578212
154 0.3133870 -0.4578212
155 -0.4578212 0.3133870
156 0.2436590 -0.4578212
> 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/www/html/rcomp/tmp/7wz9t1291283830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8prrw1291283830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9prrw1291283830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10prrw1291283830.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$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/www/html/rcomp/tmp/11l1o51291283830.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12p1ns1291283830.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, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/133b2j1291283830.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,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/146t1p1291283830.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,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/159czd1291283830.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,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ 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,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ 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,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ 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/www/html/rcomp/tmp/16dcgj1291283830.tab")
+ }
>
> try(system("convert tmp/1bzbn1291283830.ps tmp/1bzbn1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bzbn1291283830.ps tmp/2bzbn1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/34qaq1291283830.ps tmp/34qaq1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/44qaq1291283830.ps tmp/44qaq1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/54qaq1291283830.ps tmp/54qaq1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/6wz9t1291283830.ps tmp/6wz9t1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/7wz9t1291283830.ps tmp/7wz9t1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/8prrw1291283830.ps tmp/8prrw1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/9prrw1291283830.ps tmp/9prrw1291283830.png",intern=TRUE))
character(0)
> try(system("convert tmp/10prrw1291283830.ps tmp/10prrw1291283830.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.817 1.749 8.858