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.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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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(1
+ ,4
+ ,4
+ ,2
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,5
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+ ,1
+ ,5
+ ,6
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+ ,1
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+ ,6
+ ,6
+ ,4
+ ,1
+ ,6
+ ,6
+ ,2
+ ,1
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+ ,2
+ ,1
+ ,4
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+ ,2
+ ,3
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+ ,3
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+ ,1
+ ,1
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+ ,1
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+ ,1
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+ ,1
+ ,2
+ ,1
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+ ,7
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+ ,1
+ ,2
+ ,1
+ ,1
+ ,1
+ ,1
+ ,5
+ ,1
+ ,1
+ ,2
+ ,5
+ ,1
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+ ,1
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+ ,1
+ ,4
+ ,3
+ ,2
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+ ,5
+ ,2
+ ,1
+ ,2
+ ,5
+ ,3
+ ,2
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+ ,4
+ ,1
+ ,1
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+ ,5
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+ ,2
+ ,2
+ ,7
+ ,3
+ ,1
+ ,1
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+ ,3
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+ ,1
+ ,4
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+ ,1
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+ ,1
+ ,2
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+ ,1
+ ,1
+ ,5
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+ ,1
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+ ,1
+ ,2
+ ,2
+ ,1
+ ,1
+ ,3
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+ ,2
+ ,1
+ ,4
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+ ,5
+ ,2
+ ,5
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+ ,1
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+ ,1
+ ,2
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+ ,4
+ ,1
+ ,2
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+ ,4
+ ,1
+ ,2
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+ ,4
+ ,4
+ ,2
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+ ,4
+ ,1
+ ,5
+ ,5
+ ,4
+ ,2
+ ,2
+ ,5
+ ,4
+ ,1
+ ,3
+ ,6
+ ,2
+ ,1
+ ,6
+ ,5
+ ,4
+ ,1
+ ,4
+ ,5
+ ,2
+ ,1
+ ,5
+ ,7
+ ,2
+ ,1
+ ,1
+ ,1
+ ,1
+ ,1
+ ,2
+ ,3
+ ,3
+ ,1
+ ,2
+ ,5
+ ,2
+ ,1
+ ,2
+ ,5
+ ,1
+ ,1
+ ,6
+ ,6
+ ,3
+ ,1
+ ,2
+ ,4
+ ,3
+ ,1
+ ,2
+ ,2
+ ,2
+ ,2
+ ,1
+ ,4
+ ,5
+ ,1
+ ,5
+ ,5
+ ,2
+ ,1
+ ,3
+ ,5
+ ,5
+ ,3
+ ,6
+ ,5
+ ,4
+ ,1
+ ,1
+ ,5
+ ,1
+ ,1
+ ,2
+ ,2
+ ,2
+ ,1
+ ,3
+ ,5
+ ,2
+ ,1
+ ,2
+ ,1
+ ,3
+ ,2
+ ,3
+ ,3
+ ,4
+ ,1
+ ,7
+ ,7
+ ,2)
+ ,dim=c(4
+ ,157)
+ ,dimnames=list(c('Depressed'
+ ,'cannotdo'
+ ,'worrytoomuch'
+ ,'limitactivity')
+ ,1:157))
> y <- array(NA,dim=c(4,157),dimnames=list(c('Depressed','cannotdo','worrytoomuch','limitactivity'),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
Depressed cannotdo worrytoomuch limitactivity
1 1 4 4 2
2 1 2 2 2
3 1 5 5 4
4 1 4 5 3
5 2 1 1 2
6 1 2 4 1
7 4 5 6 4
8 1 1 5 3
9 1 3 4 1
10 2 5 5 4
11 1 2 7 4
12 1 2 2 4
13 2 2 7 3
14 1 2 5 4
15 1 1 5 1
16 1 4 7 4
17 1 3 3 1
18 1 6 6 4
19 1 1 2 4
20 2 3 6 3
21 1 2 1 2
22 2 5 5 6
23 1 5 4 5
24 2 3 4 4
25 1 3 7 6
26 1 5 7 1
27 1 5 5 2
28 2 4 6 4
29 1 2 5 4
30 1 1 1 1
31 2 4 6 2
32 1 6 4 1
33 1 2 2 2
34 1 3 2 2
35 1 2 6 2
36 2 4 6 6
37 1 2 6 2
38 1 1 1 1
39 1 5 6 4
40 1 5 6 3
41 1 1 1 3
42 1 1 1 1
43 1 2 7 4
44 1 4 2 3
45 1 5 3 4
46 1 3 5 3
47 1 3 3 2
48 1 1 4 1
49 1 2 2 5
50 1 3 3 4
51 2 2 7 1
52 2 5 7 2
53 1 4 5 4
54 1 4 1 3
55 1 2 2 2
56 2 3 5 3
57 1 6 2 3
58 1 2 4 2
59 2 3 7 2
60 1 2 2 4
61 1 5 5 4
62 1 5 6 2
63 1 5 3 2
64 1 6 7 5
65 2 4 4 4
66 1 2 3 5
67 1 5 5 5
68 2 2 3 2
69 1 1 2 3
70 1 6 6 4
71 1 6 6 2
72 1 3 5 2
73 1 4 2 2
74 3 5 3 5
75 2 2 4 2
76 2 4 6 3
77 1 3 5 2
78 1 2 2 2
79 1 2 5 2
80 1 3 2 2
81 1 3 1 2
82 1 7 2 1
83 1 2 4 3
84 1 2 5 3
85 1 2 5 3
86 1 5 3 3
87 1 1 2 1
88 3 5 7 4
89 1 2 1 1
90 1 1 5 1
91 1 2 5 1
92 1 2 2 3
93 1 0 6 2
94 1 5 2 3
95 1 3 5 5
96 1 2 3 3
97 1 4 3 2
98 1 2 5 2
99 1 2 5 3
100 2 4 5 4
101 1 1 6 4
102 1 5 5 3
103 1 4 5 2
104 2 6 6 3
105 1 2 2 3
106 2 5 5 4
107 2 1 5 2
108 3 7 1 5
109 2 5 5 2
110 2 3 6 2
111 1 4 6 4
112 1 4 3 5
113 1 2 3 0
114 1 1 3 1
115 1 6 5 6
116 1 4 5 1
117 1 2 2 2
118 2 7 3 1
119 1 4 3 4
120 1 4 6 2
121 1 4 5 4
122 1 2 2 1
123 1 5 4 4
124 1 3 2 3
125 1 2 2 1
126 1 3 5 2
127 1 4 5 5
128 2 5 4 3
129 2 6 5 2
130 1 2 1 2
131 1 2 5 4
132 1 2 5 4
133 1 2 5 4
134 4 2 6 4
135 1 5 5 4
136 2 2 5 4
137 1 3 6 2
138 1 6 5 4
139 1 4 5 2
140 1 5 7 2
141 1 1 1 1
142 1 2 3 3
143 1 2 5 2
144 1 2 5 1
145 1 6 6 3
146 1 2 4 3
147 1 2 2 2
148 2 1 4 5
149 1 5 5 2
150 1 3 5 5
151 3 6 5 4
152 1 1 5 1
153 1 2 2 2
154 1 3 5 2
155 1 2 1 3
156 2 3 3 4
157 1 7 7 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) cannotdo worrytoomuch limitactivity
0.74432 0.04318 0.04488 0.07083
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6717 -0.3350 -0.1778 0.0536 2.6167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.74432 0.14215 5.236 5.33e-07 ***
cannotdo 0.04318 0.02878 1.500 0.1356
worrytoomuch 0.04488 0.02636 1.703 0.0907 .
limitactivity 0.07083 0.03526 2.009 0.0463 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.5513 on 153 degrees of freedom
Multiple R-squared: 0.09227, Adjusted R-squared: 0.07447
F-statistic: 5.184 on 3 and 153 DF, p-value: 0.001941
> 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,] 0.99913313 0.0017337345 0.0008668672
[2,] 0.99981921 0.0003615854 0.0001807927
[3,] 0.99953143 0.0009371430 0.0004685715
[4,] 0.99897762 0.0020447585 0.0010223792
[5,] 0.99844419 0.0031116264 0.0015558132
[6,] 0.99833400 0.0033319921 0.0016659961
[7,] 0.99833192 0.0033361557 0.0016680778
[8,] 0.99758262 0.0048347567 0.0024173783
[9,] 0.99560473 0.0087905380 0.0043952690
[10,] 0.99591265 0.0081746930 0.0040873465
[11,] 0.99329341 0.0134131804 0.0067065902
[12,] 0.99459869 0.0108026195 0.0054013098
[13,] 0.99160531 0.0167893898 0.0083946949
[14,] 0.99136678 0.0172664427 0.0086332214
[15,] 0.98652194 0.0269561245 0.0134780623
[16,] 0.98085711 0.0382857733 0.0191428867
[17,] 0.98066204 0.0386759144 0.0193379572
[18,] 0.98056584 0.0388683197 0.0194341598
[19,] 0.98061000 0.0387800094 0.0193900047
[20,] 0.97625700 0.0474860058 0.0237430029
[21,] 0.97011583 0.0597683341 0.0298841670
[22,] 0.96718128 0.0656374307 0.0328187153
[23,] 0.95917678 0.0816464375 0.0408232187
[24,] 0.94462421 0.1107515747 0.0553757874
[25,] 0.94754977 0.1049004643 0.0524502322
[26,] 0.93625153 0.1274969439 0.0637484720
[27,] 0.91697363 0.1660527419 0.0830263709
[28,] 0.89456691 0.2108661883 0.1054330942
[29,] 0.87154539 0.2569092251 0.1284546125
[30,] 0.85123771 0.2975245778 0.1487622889
[31,] 0.82203364 0.3559327267 0.1779663633
[32,] 0.78510834 0.4297833148 0.2148916574
[33,] 0.78088661 0.4382267841 0.2191133921
[34,] 0.76356644 0.4728671258 0.2364335629
[35,] 0.72190543 0.5561891486 0.2780945743
[36,] 0.67657986 0.6468402797 0.3234201398
[37,] 0.65227457 0.6954508686 0.3477254343
[38,] 0.61370887 0.7725822693 0.3862911346
[39,] 0.58843539 0.8231292184 0.4115646092
[40,] 0.55145411 0.8970917773 0.4485458887
[41,] 0.50327243 0.9934551320 0.4967275660
[42,] 0.45223393 0.9044678671 0.5477660664
[43,] 0.41557218 0.8311443682 0.5844278159
[44,] 0.37905775 0.7581154980 0.6209422510
[45,] 0.42583387 0.8516677376 0.5741661312
[46,] 0.42803179 0.8560635769 0.5719682116
[47,] 0.40514687 0.8102937315 0.5948531343
[48,] 0.36074853 0.7214970591 0.6392514704
[49,] 0.31588225 0.6317645028 0.6841177486
[50,] 0.34012423 0.6802484603 0.6598757698
[51,] 0.30584286 0.6116857254 0.6941571373
[52,] 0.26797887 0.5359577380 0.7320211310
[53,] 0.28042942 0.5608588444 0.7195705778
[54,] 0.24538664 0.4907732774 0.7546133613
[55,] 0.23168953 0.4633790610 0.7683104695
[56,] 0.21271810 0.4254362089 0.7872818955
[57,] 0.18379239 0.3675847897 0.8162076052
[58,] 0.19318729 0.3863745816 0.8068127092
[59,] 0.20932254 0.4186450712 0.7906774644
[60,] 0.18668329 0.3733665719 0.8133167140
[61,] 0.18179950 0.3635990006 0.8182004997
[62,] 0.23556391 0.4711278140 0.7644360930
[63,] 0.20198507 0.4039701439 0.7980149280
[64,] 0.19912069 0.3982413722 0.8008793139
[65,] 0.18297983 0.3659596604 0.8170201698
[66,] 0.15966476 0.3193295103 0.8403352449
[67,] 0.13428633 0.2685726587 0.8657136707
[68,] 0.40427835 0.8085566966 0.5957216517
[69,] 0.46230339 0.9246067889 0.5376966055
[70,] 0.46925583 0.9385116583 0.5307441708
[71,] 0.43233152 0.8646630467 0.5676684766
[72,] 0.38778616 0.7755723249 0.6122138375
[73,] 0.35050641 0.7010128260 0.6494935870
[74,] 0.30967183 0.6193436522 0.6903281739
[75,] 0.27022609 0.5404521826 0.7297739087
[76,] 0.23867479 0.4773495889 0.7613252056
[77,] 0.20983203 0.4196640595 0.7901679702
[78,] 0.18544235 0.3708847084 0.8145576458
[79,] 0.16278863 0.3255772648 0.8372113676
[80,] 0.14421208 0.2884241682 0.8557879159
[81,] 0.11967755 0.2393550965 0.8803224517
[82,] 0.30538980 0.6107796083 0.6946101958
[83,] 0.26590763 0.5318152509 0.7340923745
[84,] 0.23056607 0.4611321467 0.7694339266
[85,] 0.19823927 0.3964785473 0.8017607263
[86,] 0.16942469 0.3388493842 0.8305753079
[87,] 0.14397997 0.2879599448 0.8560200276
[88,] 0.12685709 0.2537141791 0.8731429104
[89,] 0.11838574 0.2367714842 0.8816142579
[90,] 0.09903746 0.1980749246 0.9009625377
[91,] 0.08280245 0.1656049025 0.9171975488
[92,] 0.06776585 0.1355316939 0.9322341531
[93,] 0.05626016 0.1125203191 0.9437398404
[94,] 0.05645292 0.1129058336 0.9435470832
[95,] 0.04767350 0.0953470093 0.9523264954
[96,] 0.04215417 0.0843083396 0.9578458302
[97,] 0.03463959 0.0692791888 0.9653604056
[98,] 0.03291988 0.0658397680 0.9670801160
[99,] 0.02581927 0.0516385457 0.9741807272
[100,] 0.02460915 0.0492182970 0.9753908515
[101,] 0.03578175 0.0715635016 0.9642182492
[102,] 0.14141209 0.2828241885 0.8585879058
[103,] 0.15631927 0.3126385362 0.8436807319
[104,] 0.17637150 0.3527429961 0.8236285019
[105,] 0.16300763 0.3260152678 0.8369923661
[106,] 0.14781289 0.2956257834 0.8521871083
[107,] 0.12034467 0.2406893403 0.8796553298
[108,] 0.09637327 0.1927465428 0.9036267286
[109,] 0.10520377 0.2104075414 0.8947962293
[110,] 0.08489101 0.1697820180 0.9151089910
[111,] 0.06646208 0.1329241620 0.9335379190
[112,] 0.09491601 0.1898320128 0.9050839936
[113,] 0.08156697 0.1631339351 0.9184330324
[114,] 0.06652725 0.1330544952 0.9334727524
[115,] 0.06048440 0.1209687912 0.9395156044
[116,] 0.04617476 0.0923495108 0.9538252446
[117,] 0.04112302 0.0822460495 0.9588769753
[118,] 0.03154597 0.0630919359 0.9684540321
[119,] 0.02305340 0.0461067972 0.9769466014
[120,] 0.01691941 0.0338388254 0.9830805873
[121,] 0.01961675 0.0392334906 0.9803832547
[122,] 0.02104348 0.0420869543 0.9789565229
[123,] 0.03112061 0.0622412244 0.9688793878
[124,] 0.02212117 0.0442423471 0.9778788264
[125,] 0.02250057 0.0450011477 0.9774994262
[126,] 0.02531666 0.0506333271 0.9746833365
[127,] 0.03311273 0.0662254630 0.9668872685
[128,] 0.79678250 0.4064349916 0.2032174958
[129,] 0.79844957 0.4031008654 0.2015504327
[130,] 0.80271848 0.3945630450 0.1972815225
[131,] 0.74610873 0.5077825443 0.2538912722
[132,] 0.77622499 0.4475500127 0.2237750063
[133,] 0.71404193 0.5719161326 0.2859580663
[134,] 0.64507778 0.7098444443 0.3549222222
[135,] 0.57380951 0.8523809749 0.4261904875
[136,] 0.50618878 0.9876224357 0.4938112178
[137,] 0.41498127 0.8299625428 0.5850187286
[138,] 0.35197559 0.7039511879 0.6480244061
[139,] 0.34004627 0.6800925444 0.6599537278
[140,] 0.26309055 0.5261810995 0.7369094503
[141,] 0.18048986 0.3609797180 0.8195101410
[142,] 0.13950595 0.2790119061 0.8604940470
[143,] 0.09675844 0.1935168772 0.9032415614
[144,] 0.46010905 0.9202181075 0.5398909463
> postscript(file="/var/www/html/rcomp/tmp/1u7rl1292759235.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2nhq61292759235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3nhq61292759235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nhq61292759235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5f8791292759235.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
-0.238232389 -0.062111959 -0.467961795 -0.353944299 1.025948256 -0.081031991
7 8 9 10 11 12
2.487160890 -0.224395596 -0.124214892 0.532038205 -0.428167721 -0.203781151
13 14 15 16 17 18
0.642666875 -0.338413093 -0.082726405 -0.514533523 -0.079337578 -0.556022010
19 20 21 22 23 24
-0.160598250 0.644361288 -0.017234645 0.390369013 -0.493919077 0.663281320
25 26 27 28 29 30
-0.613019814 -0.345212636 -0.326292604 0.530343791 -0.338413093 0.096782852
31 32 33 34 35 36
0.672012983 -0.253763595 -0.062111959 -0.105294860 -0.241621215 0.388674600
37 38 39 40 41 42
-0.241621215 0.096782852 -0.512839110 -0.442004514 -0.044886340 0.096782852
43 44 45 46 47 48
-0.428167721 -0.219312357 -0.378207167 -0.310761398 -0.150172174 -0.037849091
49 50 51 52 53 54
-0.274615747 -0.291841366 0.784336066 0.583952768 -0.424778895 -0.174435043
55 56 57 58 59 60
-0.062111959 0.689238602 -0.305678158 -0.151866587 0.670318570 -0.203781151
61 62 63 64 65 66
-0.467961795 -0.371169918 -0.236537976 -0.671733920 0.620098419 -0.319493061
67 68 69 70 71 72
-0.538796391 0.893010727 -0.089763654 -0.556022010 -0.414352819 -0.239926802
73 74 75 76 77 78
-0.148477761 1.550958237 0.848133413 0.601178387 -0.239926802 -0.062111959
79 80 81 82 83 84
-0.196743901 -0.105294860 -0.060417546 -0.207191867 -0.222701183 -0.267578497
85 86 87 88 89 90
-0.267578497 -0.307372571 0.051905538 1.442283576 0.053599951 -0.082726405
91 92 93 94 95 96
-0.125909305 -0.132946555 -0.155255414 -0.262495257 -0.452430590 -0.177823869
97 98 99 100 101 102
-0.193355075 -0.196743901 -0.267578497 0.575221105 -0.340107506 -0.397127200
103 104 105 106 107 108
-0.283109703 0.514812585 -0.132946555 0.532038205 0.846439000 1.554347063
109 110 111 112 113 114
0.673707396 0.715195884 -0.469656209 -0.405858862 0.034679919 0.007028224
115 116 117 118 119 120
-0.652813888 -0.212275107 -0.062111959 0.747930818 -0.335024266 -0.327987017
121 122 123 124 125 126
-0.424778895 0.008722637 -0.423084481 -0.176129456 0.008722637 -0.239926802
127 128 129 130 131 132
-0.495613490 0.647750114 0.630524495 -0.017234645 -0.338413093 -0.338413093
133 134 135 136 137 138
-0.338413093 2.616709593 -0.467961795 0.661586907 -0.284804116 -0.511144696
139 140 141 142 143 144
-0.283109703 -0.416047232 0.096782852 -0.177823869 -0.196743901 -0.125909305
145 146 147 148 149 150
-0.485187415 -0.222701183 -0.062111959 0.678812526 -0.326292604 -0.452430590
151 152 153 154 155 156
1.488855304 -0.082726405 -0.062111959 -0.239926802 -0.088069241 0.708158634
157
-0.502413034
> postscript(file="/var/www/html/rcomp/tmp/6f8791292759235.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 -0.238232389 NA
1 -0.062111959 -0.238232389
2 -0.467961795 -0.062111959
3 -0.353944299 -0.467961795
4 1.025948256 -0.353944299
5 -0.081031991 1.025948256
6 2.487160890 -0.081031991
7 -0.224395596 2.487160890
8 -0.124214892 -0.224395596
9 0.532038205 -0.124214892
10 -0.428167721 0.532038205
11 -0.203781151 -0.428167721
12 0.642666875 -0.203781151
13 -0.338413093 0.642666875
14 -0.082726405 -0.338413093
15 -0.514533523 -0.082726405
16 -0.079337578 -0.514533523
17 -0.556022010 -0.079337578
18 -0.160598250 -0.556022010
19 0.644361288 -0.160598250
20 -0.017234645 0.644361288
21 0.390369013 -0.017234645
22 -0.493919077 0.390369013
23 0.663281320 -0.493919077
24 -0.613019814 0.663281320
25 -0.345212636 -0.613019814
26 -0.326292604 -0.345212636
27 0.530343791 -0.326292604
28 -0.338413093 0.530343791
29 0.096782852 -0.338413093
30 0.672012983 0.096782852
31 -0.253763595 0.672012983
32 -0.062111959 -0.253763595
33 -0.105294860 -0.062111959
34 -0.241621215 -0.105294860
35 0.388674600 -0.241621215
36 -0.241621215 0.388674600
37 0.096782852 -0.241621215
38 -0.512839110 0.096782852
39 -0.442004514 -0.512839110
40 -0.044886340 -0.442004514
41 0.096782852 -0.044886340
42 -0.428167721 0.096782852
43 -0.219312357 -0.428167721
44 -0.378207167 -0.219312357
45 -0.310761398 -0.378207167
46 -0.150172174 -0.310761398
47 -0.037849091 -0.150172174
48 -0.274615747 -0.037849091
49 -0.291841366 -0.274615747
50 0.784336066 -0.291841366
51 0.583952768 0.784336066
52 -0.424778895 0.583952768
53 -0.174435043 -0.424778895
54 -0.062111959 -0.174435043
55 0.689238602 -0.062111959
56 -0.305678158 0.689238602
57 -0.151866587 -0.305678158
58 0.670318570 -0.151866587
59 -0.203781151 0.670318570
60 -0.467961795 -0.203781151
61 -0.371169918 -0.467961795
62 -0.236537976 -0.371169918
63 -0.671733920 -0.236537976
64 0.620098419 -0.671733920
65 -0.319493061 0.620098419
66 -0.538796391 -0.319493061
67 0.893010727 -0.538796391
68 -0.089763654 0.893010727
69 -0.556022010 -0.089763654
70 -0.414352819 -0.556022010
71 -0.239926802 -0.414352819
72 -0.148477761 -0.239926802
73 1.550958237 -0.148477761
74 0.848133413 1.550958237
75 0.601178387 0.848133413
76 -0.239926802 0.601178387
77 -0.062111959 -0.239926802
78 -0.196743901 -0.062111959
79 -0.105294860 -0.196743901
80 -0.060417546 -0.105294860
81 -0.207191867 -0.060417546
82 -0.222701183 -0.207191867
83 -0.267578497 -0.222701183
84 -0.267578497 -0.267578497
85 -0.307372571 -0.267578497
86 0.051905538 -0.307372571
87 1.442283576 0.051905538
88 0.053599951 1.442283576
89 -0.082726405 0.053599951
90 -0.125909305 -0.082726405
91 -0.132946555 -0.125909305
92 -0.155255414 -0.132946555
93 -0.262495257 -0.155255414
94 -0.452430590 -0.262495257
95 -0.177823869 -0.452430590
96 -0.193355075 -0.177823869
97 -0.196743901 -0.193355075
98 -0.267578497 -0.196743901
99 0.575221105 -0.267578497
100 -0.340107506 0.575221105
101 -0.397127200 -0.340107506
102 -0.283109703 -0.397127200
103 0.514812585 -0.283109703
104 -0.132946555 0.514812585
105 0.532038205 -0.132946555
106 0.846439000 0.532038205
107 1.554347063 0.846439000
108 0.673707396 1.554347063
109 0.715195884 0.673707396
110 -0.469656209 0.715195884
111 -0.405858862 -0.469656209
112 0.034679919 -0.405858862
113 0.007028224 0.034679919
114 -0.652813888 0.007028224
115 -0.212275107 -0.652813888
116 -0.062111959 -0.212275107
117 0.747930818 -0.062111959
118 -0.335024266 0.747930818
119 -0.327987017 -0.335024266
120 -0.424778895 -0.327987017
121 0.008722637 -0.424778895
122 -0.423084481 0.008722637
123 -0.176129456 -0.423084481
124 0.008722637 -0.176129456
125 -0.239926802 0.008722637
126 -0.495613490 -0.239926802
127 0.647750114 -0.495613490
128 0.630524495 0.647750114
129 -0.017234645 0.630524495
130 -0.338413093 -0.017234645
131 -0.338413093 -0.338413093
132 -0.338413093 -0.338413093
133 2.616709593 -0.338413093
134 -0.467961795 2.616709593
135 0.661586907 -0.467961795
136 -0.284804116 0.661586907
137 -0.511144696 -0.284804116
138 -0.283109703 -0.511144696
139 -0.416047232 -0.283109703
140 0.096782852 -0.416047232
141 -0.177823869 0.096782852
142 -0.196743901 -0.177823869
143 -0.125909305 -0.196743901
144 -0.485187415 -0.125909305
145 -0.222701183 -0.485187415
146 -0.062111959 -0.222701183
147 0.678812526 -0.062111959
148 -0.326292604 0.678812526
149 -0.452430590 -0.326292604
150 1.488855304 -0.452430590
151 -0.082726405 1.488855304
152 -0.062111959 -0.082726405
153 -0.239926802 -0.062111959
154 -0.088069241 -0.239926802
155 0.708158634 -0.088069241
156 -0.502413034 0.708158634
157 NA -0.502413034
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.062111959 -0.238232389
[2,] -0.467961795 -0.062111959
[3,] -0.353944299 -0.467961795
[4,] 1.025948256 -0.353944299
[5,] -0.081031991 1.025948256
[6,] 2.487160890 -0.081031991
[7,] -0.224395596 2.487160890
[8,] -0.124214892 -0.224395596
[9,] 0.532038205 -0.124214892
[10,] -0.428167721 0.532038205
[11,] -0.203781151 -0.428167721
[12,] 0.642666875 -0.203781151
[13,] -0.338413093 0.642666875
[14,] -0.082726405 -0.338413093
[15,] -0.514533523 -0.082726405
[16,] -0.079337578 -0.514533523
[17,] -0.556022010 -0.079337578
[18,] -0.160598250 -0.556022010
[19,] 0.644361288 -0.160598250
[20,] -0.017234645 0.644361288
[21,] 0.390369013 -0.017234645
[22,] -0.493919077 0.390369013
[23,] 0.663281320 -0.493919077
[24,] -0.613019814 0.663281320
[25,] -0.345212636 -0.613019814
[26,] -0.326292604 -0.345212636
[27,] 0.530343791 -0.326292604
[28,] -0.338413093 0.530343791
[29,] 0.096782852 -0.338413093
[30,] 0.672012983 0.096782852
[31,] -0.253763595 0.672012983
[32,] -0.062111959 -0.253763595
[33,] -0.105294860 -0.062111959
[34,] -0.241621215 -0.105294860
[35,] 0.388674600 -0.241621215
[36,] -0.241621215 0.388674600
[37,] 0.096782852 -0.241621215
[38,] -0.512839110 0.096782852
[39,] -0.442004514 -0.512839110
[40,] -0.044886340 -0.442004514
[41,] 0.096782852 -0.044886340
[42,] -0.428167721 0.096782852
[43,] -0.219312357 -0.428167721
[44,] -0.378207167 -0.219312357
[45,] -0.310761398 -0.378207167
[46,] -0.150172174 -0.310761398
[47,] -0.037849091 -0.150172174
[48,] -0.274615747 -0.037849091
[49,] -0.291841366 -0.274615747
[50,] 0.784336066 -0.291841366
[51,] 0.583952768 0.784336066
[52,] -0.424778895 0.583952768
[53,] -0.174435043 -0.424778895
[54,] -0.062111959 -0.174435043
[55,] 0.689238602 -0.062111959
[56,] -0.305678158 0.689238602
[57,] -0.151866587 -0.305678158
[58,] 0.670318570 -0.151866587
[59,] -0.203781151 0.670318570
[60,] -0.467961795 -0.203781151
[61,] -0.371169918 -0.467961795
[62,] -0.236537976 -0.371169918
[63,] -0.671733920 -0.236537976
[64,] 0.620098419 -0.671733920
[65,] -0.319493061 0.620098419
[66,] -0.538796391 -0.319493061
[67,] 0.893010727 -0.538796391
[68,] -0.089763654 0.893010727
[69,] -0.556022010 -0.089763654
[70,] -0.414352819 -0.556022010
[71,] -0.239926802 -0.414352819
[72,] -0.148477761 -0.239926802
[73,] 1.550958237 -0.148477761
[74,] 0.848133413 1.550958237
[75,] 0.601178387 0.848133413
[76,] -0.239926802 0.601178387
[77,] -0.062111959 -0.239926802
[78,] -0.196743901 -0.062111959
[79,] -0.105294860 -0.196743901
[80,] -0.060417546 -0.105294860
[81,] -0.207191867 -0.060417546
[82,] -0.222701183 -0.207191867
[83,] -0.267578497 -0.222701183
[84,] -0.267578497 -0.267578497
[85,] -0.307372571 -0.267578497
[86,] 0.051905538 -0.307372571
[87,] 1.442283576 0.051905538
[88,] 0.053599951 1.442283576
[89,] -0.082726405 0.053599951
[90,] -0.125909305 -0.082726405
[91,] -0.132946555 -0.125909305
[92,] -0.155255414 -0.132946555
[93,] -0.262495257 -0.155255414
[94,] -0.452430590 -0.262495257
[95,] -0.177823869 -0.452430590
[96,] -0.193355075 -0.177823869
[97,] -0.196743901 -0.193355075
[98,] -0.267578497 -0.196743901
[99,] 0.575221105 -0.267578497
[100,] -0.340107506 0.575221105
[101,] -0.397127200 -0.340107506
[102,] -0.283109703 -0.397127200
[103,] 0.514812585 -0.283109703
[104,] -0.132946555 0.514812585
[105,] 0.532038205 -0.132946555
[106,] 0.846439000 0.532038205
[107,] 1.554347063 0.846439000
[108,] 0.673707396 1.554347063
[109,] 0.715195884 0.673707396
[110,] -0.469656209 0.715195884
[111,] -0.405858862 -0.469656209
[112,] 0.034679919 -0.405858862
[113,] 0.007028224 0.034679919
[114,] -0.652813888 0.007028224
[115,] -0.212275107 -0.652813888
[116,] -0.062111959 -0.212275107
[117,] 0.747930818 -0.062111959
[118,] -0.335024266 0.747930818
[119,] -0.327987017 -0.335024266
[120,] -0.424778895 -0.327987017
[121,] 0.008722637 -0.424778895
[122,] -0.423084481 0.008722637
[123,] -0.176129456 -0.423084481
[124,] 0.008722637 -0.176129456
[125,] -0.239926802 0.008722637
[126,] -0.495613490 -0.239926802
[127,] 0.647750114 -0.495613490
[128,] 0.630524495 0.647750114
[129,] -0.017234645 0.630524495
[130,] -0.338413093 -0.017234645
[131,] -0.338413093 -0.338413093
[132,] -0.338413093 -0.338413093
[133,] 2.616709593 -0.338413093
[134,] -0.467961795 2.616709593
[135,] 0.661586907 -0.467961795
[136,] -0.284804116 0.661586907
[137,] -0.511144696 -0.284804116
[138,] -0.283109703 -0.511144696
[139,] -0.416047232 -0.283109703
[140,] 0.096782852 -0.416047232
[141,] -0.177823869 0.096782852
[142,] -0.196743901 -0.177823869
[143,] -0.125909305 -0.196743901
[144,] -0.485187415 -0.125909305
[145,] -0.222701183 -0.485187415
[146,] -0.062111959 -0.222701183
[147,] 0.678812526 -0.062111959
[148,] -0.326292604 0.678812526
[149,] -0.452430590 -0.326292604
[150,] 1.488855304 -0.452430590
[151,] -0.082726405 1.488855304
[152,] -0.062111959 -0.082726405
[153,] -0.239926802 -0.062111959
[154,] -0.088069241 -0.239926802
[155,] 0.708158634 -0.088069241
[156,] -0.502413034 0.708158634
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.062111959 -0.238232389
2 -0.467961795 -0.062111959
3 -0.353944299 -0.467961795
4 1.025948256 -0.353944299
5 -0.081031991 1.025948256
6 2.487160890 -0.081031991
7 -0.224395596 2.487160890
8 -0.124214892 -0.224395596
9 0.532038205 -0.124214892
10 -0.428167721 0.532038205
11 -0.203781151 -0.428167721
12 0.642666875 -0.203781151
13 -0.338413093 0.642666875
14 -0.082726405 -0.338413093
15 -0.514533523 -0.082726405
16 -0.079337578 -0.514533523
17 -0.556022010 -0.079337578
18 -0.160598250 -0.556022010
19 0.644361288 -0.160598250
20 -0.017234645 0.644361288
21 0.390369013 -0.017234645
22 -0.493919077 0.390369013
23 0.663281320 -0.493919077
24 -0.613019814 0.663281320
25 -0.345212636 -0.613019814
26 -0.326292604 -0.345212636
27 0.530343791 -0.326292604
28 -0.338413093 0.530343791
29 0.096782852 -0.338413093
30 0.672012983 0.096782852
31 -0.253763595 0.672012983
32 -0.062111959 -0.253763595
33 -0.105294860 -0.062111959
34 -0.241621215 -0.105294860
35 0.388674600 -0.241621215
36 -0.241621215 0.388674600
37 0.096782852 -0.241621215
38 -0.512839110 0.096782852
39 -0.442004514 -0.512839110
40 -0.044886340 -0.442004514
41 0.096782852 -0.044886340
42 -0.428167721 0.096782852
43 -0.219312357 -0.428167721
44 -0.378207167 -0.219312357
45 -0.310761398 -0.378207167
46 -0.150172174 -0.310761398
47 -0.037849091 -0.150172174
48 -0.274615747 -0.037849091
49 -0.291841366 -0.274615747
50 0.784336066 -0.291841366
51 0.583952768 0.784336066
52 -0.424778895 0.583952768
53 -0.174435043 -0.424778895
54 -0.062111959 -0.174435043
55 0.689238602 -0.062111959
56 -0.305678158 0.689238602
57 -0.151866587 -0.305678158
58 0.670318570 -0.151866587
59 -0.203781151 0.670318570
60 -0.467961795 -0.203781151
61 -0.371169918 -0.467961795
62 -0.236537976 -0.371169918
63 -0.671733920 -0.236537976
64 0.620098419 -0.671733920
65 -0.319493061 0.620098419
66 -0.538796391 -0.319493061
67 0.893010727 -0.538796391
68 -0.089763654 0.893010727
69 -0.556022010 -0.089763654
70 -0.414352819 -0.556022010
71 -0.239926802 -0.414352819
72 -0.148477761 -0.239926802
73 1.550958237 -0.148477761
74 0.848133413 1.550958237
75 0.601178387 0.848133413
76 -0.239926802 0.601178387
77 -0.062111959 -0.239926802
78 -0.196743901 -0.062111959
79 -0.105294860 -0.196743901
80 -0.060417546 -0.105294860
81 -0.207191867 -0.060417546
82 -0.222701183 -0.207191867
83 -0.267578497 -0.222701183
84 -0.267578497 -0.267578497
85 -0.307372571 -0.267578497
86 0.051905538 -0.307372571
87 1.442283576 0.051905538
88 0.053599951 1.442283576
89 -0.082726405 0.053599951
90 -0.125909305 -0.082726405
91 -0.132946555 -0.125909305
92 -0.155255414 -0.132946555
93 -0.262495257 -0.155255414
94 -0.452430590 -0.262495257
95 -0.177823869 -0.452430590
96 -0.193355075 -0.177823869
97 -0.196743901 -0.193355075
98 -0.267578497 -0.196743901
99 0.575221105 -0.267578497
100 -0.340107506 0.575221105
101 -0.397127200 -0.340107506
102 -0.283109703 -0.397127200
103 0.514812585 -0.283109703
104 -0.132946555 0.514812585
105 0.532038205 -0.132946555
106 0.846439000 0.532038205
107 1.554347063 0.846439000
108 0.673707396 1.554347063
109 0.715195884 0.673707396
110 -0.469656209 0.715195884
111 -0.405858862 -0.469656209
112 0.034679919 -0.405858862
113 0.007028224 0.034679919
114 -0.652813888 0.007028224
115 -0.212275107 -0.652813888
116 -0.062111959 -0.212275107
117 0.747930818 -0.062111959
118 -0.335024266 0.747930818
119 -0.327987017 -0.335024266
120 -0.424778895 -0.327987017
121 0.008722637 -0.424778895
122 -0.423084481 0.008722637
123 -0.176129456 -0.423084481
124 0.008722637 -0.176129456
125 -0.239926802 0.008722637
126 -0.495613490 -0.239926802
127 0.647750114 -0.495613490
128 0.630524495 0.647750114
129 -0.017234645 0.630524495
130 -0.338413093 -0.017234645
131 -0.338413093 -0.338413093
132 -0.338413093 -0.338413093
133 2.616709593 -0.338413093
134 -0.467961795 2.616709593
135 0.661586907 -0.467961795
136 -0.284804116 0.661586907
137 -0.511144696 -0.284804116
138 -0.283109703 -0.511144696
139 -0.416047232 -0.283109703
140 0.096782852 -0.416047232
141 -0.177823869 0.096782852
142 -0.196743901 -0.177823869
143 -0.125909305 -0.196743901
144 -0.485187415 -0.125909305
145 -0.222701183 -0.485187415
146 -0.062111959 -0.222701183
147 0.678812526 -0.062111959
148 -0.326292604 0.678812526
149 -0.452430590 -0.326292604
150 1.488855304 -0.452430590
151 -0.082726405 1.488855304
152 -0.062111959 -0.082726405
153 -0.239926802 -0.062111959
154 -0.088069241 -0.239926802
155 0.708158634 -0.088069241
156 -0.502413034 0.708158634
> 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/78zpc1292759235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88zpc1292759235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9186f1292759235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10186f1292759235.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114r431292759235.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/128r3q1292759235.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/13mj0h1292759235.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/14p2zn1292759235.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/15s2ft1292759235.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/16elwh1292759235.tab")
+ }
>
> try(system("convert tmp/1u7rl1292759235.ps tmp/1u7rl1292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nhq61292759235.ps tmp/2nhq61292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nhq61292759235.ps tmp/3nhq61292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nhq61292759235.ps tmp/4nhq61292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/5f8791292759235.ps tmp/5f8791292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/6f8791292759235.ps tmp/6f8791292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/78zpc1292759235.ps tmp/78zpc1292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/88zpc1292759235.ps tmp/88zpc1292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/9186f1292759235.ps tmp/9186f1292759235.png",intern=TRUE))
character(0)
> try(system("convert tmp/10186f1292759235.ps tmp/10186f1292759235.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.870 1.763 9.265