R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(2
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+ ,3)
+ ,dim=c(7
+ ,159)
+ ,dimnames=list(c('standards'
+ ,'organization'
+ ,'punished'
+ ,'secondrate'
+ ,'mistakes'
+ ,'competent'
+ ,'neat')
+ ,1:159))
> y <- array(NA,dim=c(7,159),dimnames=list(c('standards','organization','punished','secondrate','mistakes','competent','neat'),1:159))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> #'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
mistakes standards organization punished secondrate competent neat
1 3 2 5 2 3 4 4
2 3 2 4 2 4 4 4
3 2 4 4 2 4 5 4
4 2 2 4 2 2 2 4
5 3 3 2 2 2 2 4
6 2 4 5 1 3 4 5
7 1 3 5 1 2 4 4
8 3 3 4 3 3 4 3
9 2 3 3 2 3 4 4
10 2 2 4 1 3 2 4
11 3 4 4 4 3 3 4
12 2 4 2 2 4 4 4
13 2 3 3 3 2 3 4
14 2 3 3 2 2 4 2
15 3 4 4 1 1 4 3
16 1 4 5 1 1 4 4
17 3 3 4 2 3 4 3
18 2 3 2 2 2 2 2
19 3 3 4 2 2 4 4
20 4 4 4 2 3 4 3
21 2 2 4 1 4 4 3
22 3 5 4 2 4 3 4
23 5 4 4 4 3 2 3
24 2 2 4 2 2 4 3
25 2 3 5 2 3 2 4
26 3 4 4 2 4 3 4
27 2 4 4 2 3 4 4
28 2 3 4 2 2 3 4
29 2 4 4 3 1 4 4
30 2 4 4 2 3 4 4
31 3 1 4 1 2 4 5
32 4 4 4 4 4 4 4
33 1 5 2 1 4 4 4
34 3 2 4 2 5 4 4
35 3 4 4 2 2 4 3
36 2 3 5 2 4 5 4
37 1 2 5 2 4 4 3
38 1 4 4 2 2 2 4
39 2 5 3 2 4 4 4
40 2 4 4 2 4 4 3
41 2 4 5 2 2 5 5
42 1 4 4 2 3 4 4
43 2 3 4 2 2 2 3
44 1 4 5 2 4 4 3
45 2 2 4 2 3 4 3
46 2 2 5 1 1 4 4
47 4 4 4 2 2 2 4
48 2 2 4 1 5 5 4
49 2 4 4 2 2 4 4
50 2 4 3 1 4 4 4
51 1 1 4 1 4 4 4
52 2 4 4 2 2 4 4
53 2 2 4 2 2 4 5
54 1 1 2 1 2 3 3
55 5 4 3 5 4 5 3
56 2 3 5 2 3 4 5
57 2 2 4 2 4 4 5
58 2 4 4 1 2 4 4
59 1 3 5 1 3 4 4
60 3 2 3 2 2 2 3
61 1 2 5 2 2 4 4
62 1 3 4 1 3 4 4
63 2 2 5 1 2 4 5
64 3 1 4 2 3 4 4
65 2 3 4 1 2 3 4
66 2 2 5 1 4 4 5
67 2 3 4 2 2 2 4
68 4 3 4 1 5 4 3
69 1 3 5 1 1 4 4
70 2 2 4 2 3 4 4
71 2 3 3 1 2 4 4
72 2 2 4 1 2 4 4
73 2 4 5 3 3 4 4
74 2 4 5 3 4 3 4
75 1 4 5 2 4 4 4
76 2 2 4 2 2 4 3
77 2 3 4 1 3 4 4
78 2 4 5 3 4 4 3
79 2 3 5 2 2 4 5
80 1 4 4 2 2 4 4
81 4 2 5 2 4 4 5
82 2 3 3 2 2 2 5
83 3 3 4 1 4 3 4
84 2 4 4 4 2 5 4
85 1 2 4 1 3 3 4
86 2 4 4 1 4 3 4
87 2 2 4 1 3 4 4
88 1 2 5 1 1 4 5
89 2 4 4 4 3 4 4
90 1 3 4 2 2 4 3
91 2 4 4 2 2 4 4
92 1 2 5 1 1 3 3
93 2 2 3 1 3 4 4
94 2 3 3 1 2 4 4
95 3 3 5 3 3 4 4
96 4 5 5 4 5 5 4
97 1 2 4 4 3 4 4
98 3 3 4 3 4 4 3
99 1 4 4 2 2 2 3
100 1 3 4 2 2 3 3
101 2 4 4 3 3 3 3
102 1 3 4 1 2 3 3
103 3 3 4 3 2 4 2
104 2 2 4 2 2 4 3
105 2 3 5 2 3 2 5
106 1 2 2 2 5 3 2
107 2 3 4 2 2 3 2
108 2 2 2 4 3 4 3
109 1 4 4 3 3 4 3
110 2 2 5 1 1 2 3
111 2 4 3 1 1 3 4
112 4 4 4 2 3 4 4
113 3 1 3 1 4 4 3
114 2 5 4 3 5 5 2
115 5 2 4 2 3 3 3
116 1 3 4 2 3 3 4
117 2 4 2 2 3 4 2
118 1 1 1 1 2 3 4
119 2 5 4 3 3 3 4
120 1 3 3 1 2 2 2
121 1 3 4 1 3 4 3
122 2 3 3 2 2 3 3
123 2 3 3 3 4 4 3
124 2 2 5 2 2 5 4
125 3 2 4 1 2 4 4
126 2 4 3 2 4 3 4
127 1 4 4 1 4 3 3
128 2 3 4 2 3 3 4
129 2 3 4 1 3 3 4
130 3 3 4 2 3 4 4
131 2 4 3 3 4 4 2
132 2 3 4 2 2 3 4
133 2 4 4 1 1 2 5
134 1 4 4 1 3 3 4
135 2 2 4 2 2 2 4
136 2 4 4 2 3 4 4
137 2 2 3 1 2 4 3
138 3 4 4 2 2 4 1
139 1 3 4 3 3 4 4
140 3 3 2 4 2 4 3
141 4 2 2 2 4 4 3
142 2 2 4 4 4 5 3
143 5 5 2 5 2 3 1
144 1 2 4 1 2 4 4
145 2 4 3 3 3 4 5
146 3 3 4 2 4 4 4
147 2 3 3 2 4 5 3
148 2 3 2 2 4 3 4
149 3 3 2 1 1 2 3
150 2 4 4 4 4 4 4
151 1 4 3 2 4 3 4
152 2 4 4 2 3 4 4
153 1 4 4 3 1 5 5
154 2 4 2 1 2 3 2
155 3 5 5 4 2 3 3
156 2 3 4 2 2 3 3
157 2 3 4 2 3 5 4
158 2 4 4 4 3 4 4
159 4 4 3 4 3 2 3
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) standards organization punished secondrate
1.98324 -0.04480 -0.03568 0.35102 0.11261
competent neat
-0.09118 -0.07255
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.83792 -0.57860 -0.07685 0.41515 2.70038
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.98324 0.52311 3.791 0.000216 ***
standards -0.04480 0.07529 -0.595 0.552697
organization -0.03568 0.08680 -0.411 0.681624
punished 0.35102 0.07988 4.394 2.08e-05 ***
secondrate 0.11261 0.07157 1.573 0.117714
competent -0.09118 0.08973 -1.016 0.311209
neat -0.07255 0.09282 -0.782 0.435636
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8482 on 152 degrees of freedom
Multiple R-squared: 0.165, Adjusted R-squared: 0.1321
F-statistic: 5.008 on 6 and 152 DF, p-value: 0.0001038
> 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,] 0.35271598 0.70543195 0.6472840
[2,] 0.27796388 0.55592776 0.7220361
[3,] 0.19139294 0.38278588 0.8086071
[4,] 0.17156944 0.34313888 0.8284306
[5,] 0.10494939 0.20989878 0.8950506
[6,] 0.30996206 0.61992412 0.6900379
[7,] 0.28532563 0.57065126 0.7146744
[8,] 0.22896047 0.45792093 0.7710395
[9,] 0.20284912 0.40569824 0.7971509
[10,] 0.19890617 0.39781234 0.8010938
[11,] 0.38087420 0.76174839 0.6191258
[12,] 0.32379184 0.64758368 0.6762082
[13,] 0.26227583 0.52455165 0.7377242
[14,] 0.31553890 0.63107780 0.6844611
[15,] 0.25634593 0.51269186 0.7436541
[16,] 0.26573964 0.53147929 0.7342604
[17,] 0.21617245 0.43234490 0.7838276
[18,] 0.18372395 0.36744791 0.8162760
[19,] 0.14277291 0.28554582 0.8572271
[20,] 0.11560293 0.23120585 0.8843971
[21,] 0.09282292 0.18564583 0.9071771
[22,] 0.22724723 0.45449445 0.7727528
[23,] 0.20571075 0.41142149 0.7942893
[24,] 0.19617855 0.39235711 0.8038214
[25,] 0.16046743 0.32093486 0.8395326
[26,] 0.15157272 0.30314544 0.8484273
[27,] 0.14031928 0.28063855 0.8596807
[28,] 0.29182142 0.58364283 0.7081786
[29,] 0.37010168 0.74020336 0.6298983
[30,] 0.32272072 0.64544144 0.6772793
[31,] 0.28578833 0.57157666 0.7142117
[32,] 0.24177872 0.48355744 0.7582213
[33,] 0.29147530 0.58295061 0.7085247
[34,] 0.25311218 0.50622436 0.7468878
[35,] 0.31263279 0.62526557 0.6873672
[36,] 0.27603961 0.55207922 0.7239604
[37,] 0.24081573 0.48163146 0.7591843
[38,] 0.40687754 0.81375509 0.5931225
[39,] 0.35870819 0.71741639 0.6412918
[40,] 0.31294124 0.62588248 0.6870588
[41,] 0.27179556 0.54359112 0.7282044
[42,] 0.28158570 0.56317140 0.7184143
[43,] 0.24201319 0.48402639 0.7579868
[44,] 0.20768757 0.41537515 0.7923124
[45,] 0.20800415 0.41600830 0.7919959
[46,] 0.26710505 0.53421010 0.7328949
[47,] 0.23115057 0.46230115 0.7688494
[48,] 0.19847857 0.39695714 0.8015214
[49,] 0.17511091 0.35022181 0.8248891
[50,] 0.16264864 0.32529729 0.8373514
[51,] 0.15086019 0.30172038 0.8491398
[52,] 0.17659584 0.35319168 0.8234042
[53,] 0.16489318 0.32978636 0.8351068
[54,] 0.14649507 0.29299015 0.8535049
[55,] 0.14221174 0.28442348 0.8577883
[56,] 0.12087151 0.24174303 0.8791285
[57,] 0.10128988 0.20257977 0.8987101
[58,] 0.08434204 0.16868408 0.9156580
[59,] 0.22490379 0.44980759 0.7750962
[60,] 0.19970305 0.39940611 0.8002969
[61,] 0.17033021 0.34066043 0.8296698
[62,] 0.14775719 0.29551438 0.8522428
[63,] 0.12697779 0.25395558 0.8730222
[64,] 0.11647607 0.23295214 0.8835239
[65,] 0.11151385 0.22302769 0.8884862
[66,] 0.13129466 0.26258932 0.8687053
[67,] 0.10924563 0.21849126 0.8907544
[68,] 0.09091278 0.18182556 0.9090872
[69,] 0.08310387 0.16620774 0.9168961
[70,] 0.06695859 0.13391718 0.9330414
[71,] 0.07301430 0.14602860 0.9269857
[72,] 0.16159747 0.32319494 0.8384025
[73,] 0.13794674 0.27589349 0.8620533
[74,] 0.16164171 0.32328342 0.8383583
[75,] 0.15368977 0.30737954 0.8463102
[76,] 0.15184474 0.30368947 0.8481553
[77,] 0.12730557 0.25461114 0.8726944
[78,] 0.10676485 0.21352970 0.8932352
[79,] 0.09011175 0.18022349 0.9098883
[80,] 0.09030556 0.18061113 0.9096944
[81,] 0.10180860 0.20361721 0.8981914
[82,] 0.08232246 0.16464492 0.9176775
[83,] 0.07581983 0.15163965 0.9241802
[84,] 0.06122710 0.12245421 0.9387729
[85,] 0.04982624 0.09965249 0.9501738
[86,] 0.04470300 0.08940600 0.9552970
[87,] 0.06675114 0.13350229 0.9332489
[88,] 0.14377763 0.28755525 0.8562224
[89,] 0.12742739 0.25485477 0.8725726
[90,] 0.15162807 0.30325615 0.8483719
[91,] 0.17827833 0.35655666 0.8217217
[92,] 0.15858378 0.31716756 0.8414162
[93,] 0.15791495 0.31582990 0.8420851
[94,] 0.13795093 0.27590186 0.8620491
[95,] 0.11434297 0.22868595 0.8856570
[96,] 0.09283401 0.18566803 0.9071660
[97,] 0.15483211 0.30966422 0.8451679
[98,] 0.13595570 0.27191140 0.8640443
[99,] 0.15120380 0.30240760 0.8487962
[100,] 0.20757298 0.41514597 0.7924270
[101,] 0.18359711 0.36719421 0.8164029
[102,] 0.15877873 0.31755747 0.8412213
[103,] 0.39961724 0.79923448 0.6003828
[104,] 0.40296474 0.80592947 0.5970353
[105,] 0.36115249 0.72230497 0.6388475
[106,] 0.80150054 0.39699892 0.1984995
[107,] 0.81760389 0.36479222 0.1823961
[108,] 0.78676933 0.42646133 0.2132307
[109,] 0.83579525 0.32840949 0.1642047
[110,] 0.80089592 0.39820817 0.1991041
[111,] 0.88539658 0.22920684 0.1146034
[112,] 0.88260147 0.23479706 0.1173985
[113,] 0.87066584 0.25866833 0.1293342
[114,] 0.85539905 0.28920191 0.1446010
[115,] 0.82178328 0.35643343 0.1782167
[116,] 0.88796230 0.22407541 0.1120377
[117,] 0.85536063 0.28927873 0.1446394
[118,] 0.86079058 0.27841885 0.1392094
[119,] 0.82135924 0.35728153 0.1786408
[120,] 0.78194308 0.43611384 0.2180569
[121,] 0.85469218 0.29061564 0.1453078
[122,] 0.87383955 0.25232089 0.1261604
[123,] 0.83353363 0.33293274 0.1664664
[124,] 0.83633087 0.32733826 0.1636691
[125,] 0.80799643 0.38400715 0.1920036
[126,] 0.75241245 0.49517510 0.2475875
[127,] 0.70471571 0.59056858 0.2952843
[128,] 0.63406944 0.73186112 0.3659306
[129,] 0.57057958 0.85884083 0.4294204
[130,] 0.62984400 0.74031201 0.3701560
[131,] 0.56217354 0.87565293 0.4378265
[132,] 0.73403718 0.53192565 0.2659628
[133,] 0.75494227 0.49011547 0.2450577
[134,] 0.71739959 0.56520082 0.2826004
[135,] 0.74923458 0.50153083 0.2507654
[136,] 0.71007302 0.57985395 0.2899270
[137,] 0.79659753 0.40680494 0.2034025
[138,] 0.68724494 0.62551013 0.3127551
[139,] 0.54331103 0.91337794 0.4566890
[140,] 0.45648089 0.91296177 0.5435191
> postscript(file="/var/www/html/rcomp/tmp/13sj91292850509.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/23sj91292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/33sj91292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ej0u1292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ej0u1292850509.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 = 159
Frequency = 1
1 2 3 4 5
8.997866e-01 7.514974e-01 -6.772588e-02 -2.056314e-01 7.678143e-01
6 7 8 9 10
4.129557e-01 -5.917840e-01 4.853408e-01 -1.267677e-01 3.277377e-02
11 12 13 14 15
1.604990e-01 -2.302568e-01 -4.563493e-01 -1.592589e-01 1.457399e+00
16 17 18 19 20
-4.343718e-01 8.363580e-01 -3.772889e-01 1.021522e+00 1.881158e+00
21 22 23 24 25
2.996295e-02 7.947216e-01 1.996771e+00 -9.583020e-02 -2.377659e-01
26 27 28 29 30
7.499214e-01 -4.629026e-02 -6.965482e-02 -1.720834e-01 -4.629026e-02
31 32 33 34 35
1.355490e+00 1.139063e+00 -8.344394e-01 6.388854e-01 9.937702e-01
36 37 38 39 40
-7.684880e-02 -1.285377e+00 -1.116031e+00 -1.497793e-01 -2.314538e-01
41 42 43 44 45
2.657270e-01 -1.046290e+00 -2.333828e-01 -1.195777e+00 -2.084422e-01
46 47 48 49 50
4.760278e-01 1.883969e+00 8.107890e-02 6.632175e-02 1.564376e-01
51 52 53 54 55
-9.422857e-01 6.632175e-02 4.927294e-02 -9.521442e-01 1.770994e+00
56 57 58 59 60
1.713840e-02 -1.759511e-01 4.173389e-01 -7.043960e-01 6.861398e-01
61 62 63 64 65
-9.876014e-01 -7.400733e-01 4.359674e-01 8.193092e-01 2.813623e-01
66 67 68 69 70
2.107434e-01 -1.608312e-01 1.962151e+00 -4.791720e-01 -1.358906e-01
71 72 73 74 75
3.368615e-01 3.277385e-01 -3.616301e-01 -5.654185e-01 -1.123225e+00
76 77 78 79 80
-9.583020e-02 2.599267e-01 -5.467937e-01 1.297504e-01 -9.336783e-01
81 82 83 84 85
1.859726e+00 -1.239569e-01 1.056138e+00 -5.445362e-01 -8.760499e-01
86 87 88 89 90
1.009385e-01 2.151265e-01 -4.514206e-01 -7.483246e-01 -1.051030e+00
91 92 93 94 95
6.632175e-02 -6.877001e-01 1.794493e-01 3.368615e-01 5.935697e-01
96 97 98 99 100
1.198105e+00 -1.837925e+00 3.727288e-01 -1.188583e+00 -1.142206e+00
101 102 103 104 105
-5.610354e-01 -7.911892e-01 5.254013e-01 -9.583020e-02 -1.652144e-01
106 107 108 109 110
-1.668749e+00 -2.147580e-01 -9.818311e-01 -1.469859e+00 2.211235e-01
111 112 113 114 115
4.030973e-01 1.953710e+00 9.494855e-01 -6.316580e-01 2.700381e+00
116 117 118 119 120
-1.182267e+00 -2.627479e-01 -9.152698e-01 -4.436836e-01 -9.905944e-01
121 122 123 124 125
-8.126249e-01 -1.778837e-01 -6.629485e-01 1.035750e-01 1.327739e+00
126 127 128 129 130
-2.857559e-01 -9.716130e-01 -1.822668e-01 1.687503e-01 9.089096e-01
131 132 133 134 135
-6.906998e-01 -6.965482e-02 4.201497e-01 -7.864495e-01 -2.056314e-01
136 137 138 139 140
-4.629026e-02 2.195097e-01 8.486670e-01 -1.442108e+00 1.755811e-01
141 142 143 144 145
1.607591e+00 -9.319122e-01 1.677885e+00 -6.722615e-01 -3.604331e-01
146 147 148 149 150
7.962976e-01 -2.207549e-01 -3.662334e-01 1.158892e+00 -8.609366e-01
151 152 153 154 155
-1.285756e+00 -4.629026e-02 -1.008355e+00 1.097048e-01 2.810369e-01
156 157 158 159
-1.422064e-01 8.593336e-05 -7.483246e-01 9.610938e-01
> postscript(file="/var/www/html/rcomp/tmp/6ej0u1292850509.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 = 159
Frequency = 1
lag(myerror, k = 1) myerror
0 8.997866e-01 NA
1 7.514974e-01 8.997866e-01
2 -6.772588e-02 7.514974e-01
3 -2.056314e-01 -6.772588e-02
4 7.678143e-01 -2.056314e-01
5 4.129557e-01 7.678143e-01
6 -5.917840e-01 4.129557e-01
7 4.853408e-01 -5.917840e-01
8 -1.267677e-01 4.853408e-01
9 3.277377e-02 -1.267677e-01
10 1.604990e-01 3.277377e-02
11 -2.302568e-01 1.604990e-01
12 -4.563493e-01 -2.302568e-01
13 -1.592589e-01 -4.563493e-01
14 1.457399e+00 -1.592589e-01
15 -4.343718e-01 1.457399e+00
16 8.363580e-01 -4.343718e-01
17 -3.772889e-01 8.363580e-01
18 1.021522e+00 -3.772889e-01
19 1.881158e+00 1.021522e+00
20 2.996295e-02 1.881158e+00
21 7.947216e-01 2.996295e-02
22 1.996771e+00 7.947216e-01
23 -9.583020e-02 1.996771e+00
24 -2.377659e-01 -9.583020e-02
25 7.499214e-01 -2.377659e-01
26 -4.629026e-02 7.499214e-01
27 -6.965482e-02 -4.629026e-02
28 -1.720834e-01 -6.965482e-02
29 -4.629026e-02 -1.720834e-01
30 1.355490e+00 -4.629026e-02
31 1.139063e+00 1.355490e+00
32 -8.344394e-01 1.139063e+00
33 6.388854e-01 -8.344394e-01
34 9.937702e-01 6.388854e-01
35 -7.684880e-02 9.937702e-01
36 -1.285377e+00 -7.684880e-02
37 -1.116031e+00 -1.285377e+00
38 -1.497793e-01 -1.116031e+00
39 -2.314538e-01 -1.497793e-01
40 2.657270e-01 -2.314538e-01
41 -1.046290e+00 2.657270e-01
42 -2.333828e-01 -1.046290e+00
43 -1.195777e+00 -2.333828e-01
44 -2.084422e-01 -1.195777e+00
45 4.760278e-01 -2.084422e-01
46 1.883969e+00 4.760278e-01
47 8.107890e-02 1.883969e+00
48 6.632175e-02 8.107890e-02
49 1.564376e-01 6.632175e-02
50 -9.422857e-01 1.564376e-01
51 6.632175e-02 -9.422857e-01
52 4.927294e-02 6.632175e-02
53 -9.521442e-01 4.927294e-02
54 1.770994e+00 -9.521442e-01
55 1.713840e-02 1.770994e+00
56 -1.759511e-01 1.713840e-02
57 4.173389e-01 -1.759511e-01
58 -7.043960e-01 4.173389e-01
59 6.861398e-01 -7.043960e-01
60 -9.876014e-01 6.861398e-01
61 -7.400733e-01 -9.876014e-01
62 4.359674e-01 -7.400733e-01
63 8.193092e-01 4.359674e-01
64 2.813623e-01 8.193092e-01
65 2.107434e-01 2.813623e-01
66 -1.608312e-01 2.107434e-01
67 1.962151e+00 -1.608312e-01
68 -4.791720e-01 1.962151e+00
69 -1.358906e-01 -4.791720e-01
70 3.368615e-01 -1.358906e-01
71 3.277385e-01 3.368615e-01
72 -3.616301e-01 3.277385e-01
73 -5.654185e-01 -3.616301e-01
74 -1.123225e+00 -5.654185e-01
75 -9.583020e-02 -1.123225e+00
76 2.599267e-01 -9.583020e-02
77 -5.467937e-01 2.599267e-01
78 1.297504e-01 -5.467937e-01
79 -9.336783e-01 1.297504e-01
80 1.859726e+00 -9.336783e-01
81 -1.239569e-01 1.859726e+00
82 1.056138e+00 -1.239569e-01
83 -5.445362e-01 1.056138e+00
84 -8.760499e-01 -5.445362e-01
85 1.009385e-01 -8.760499e-01
86 2.151265e-01 1.009385e-01
87 -4.514206e-01 2.151265e-01
88 -7.483246e-01 -4.514206e-01
89 -1.051030e+00 -7.483246e-01
90 6.632175e-02 -1.051030e+00
91 -6.877001e-01 6.632175e-02
92 1.794493e-01 -6.877001e-01
93 3.368615e-01 1.794493e-01
94 5.935697e-01 3.368615e-01
95 1.198105e+00 5.935697e-01
96 -1.837925e+00 1.198105e+00
97 3.727288e-01 -1.837925e+00
98 -1.188583e+00 3.727288e-01
99 -1.142206e+00 -1.188583e+00
100 -5.610354e-01 -1.142206e+00
101 -7.911892e-01 -5.610354e-01
102 5.254013e-01 -7.911892e-01
103 -9.583020e-02 5.254013e-01
104 -1.652144e-01 -9.583020e-02
105 -1.668749e+00 -1.652144e-01
106 -2.147580e-01 -1.668749e+00
107 -9.818311e-01 -2.147580e-01
108 -1.469859e+00 -9.818311e-01
109 2.211235e-01 -1.469859e+00
110 4.030973e-01 2.211235e-01
111 1.953710e+00 4.030973e-01
112 9.494855e-01 1.953710e+00
113 -6.316580e-01 9.494855e-01
114 2.700381e+00 -6.316580e-01
115 -1.182267e+00 2.700381e+00
116 -2.627479e-01 -1.182267e+00
117 -9.152698e-01 -2.627479e-01
118 -4.436836e-01 -9.152698e-01
119 -9.905944e-01 -4.436836e-01
120 -8.126249e-01 -9.905944e-01
121 -1.778837e-01 -8.126249e-01
122 -6.629485e-01 -1.778837e-01
123 1.035750e-01 -6.629485e-01
124 1.327739e+00 1.035750e-01
125 -2.857559e-01 1.327739e+00
126 -9.716130e-01 -2.857559e-01
127 -1.822668e-01 -9.716130e-01
128 1.687503e-01 -1.822668e-01
129 9.089096e-01 1.687503e-01
130 -6.906998e-01 9.089096e-01
131 -6.965482e-02 -6.906998e-01
132 4.201497e-01 -6.965482e-02
133 -7.864495e-01 4.201497e-01
134 -2.056314e-01 -7.864495e-01
135 -4.629026e-02 -2.056314e-01
136 2.195097e-01 -4.629026e-02
137 8.486670e-01 2.195097e-01
138 -1.442108e+00 8.486670e-01
139 1.755811e-01 -1.442108e+00
140 1.607591e+00 1.755811e-01
141 -9.319122e-01 1.607591e+00
142 1.677885e+00 -9.319122e-01
143 -6.722615e-01 1.677885e+00
144 -3.604331e-01 -6.722615e-01
145 7.962976e-01 -3.604331e-01
146 -2.207549e-01 7.962976e-01
147 -3.662334e-01 -2.207549e-01
148 1.158892e+00 -3.662334e-01
149 -8.609366e-01 1.158892e+00
150 -1.285756e+00 -8.609366e-01
151 -4.629026e-02 -1.285756e+00
152 -1.008355e+00 -4.629026e-02
153 1.097048e-01 -1.008355e+00
154 2.810369e-01 1.097048e-01
155 -1.422064e-01 2.810369e-01
156 8.593336e-05 -1.422064e-01
157 -7.483246e-01 8.593336e-05
158 9.610938e-01 -7.483246e-01
159 NA 9.610938e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.514974e-01 8.997866e-01
[2,] -6.772588e-02 7.514974e-01
[3,] -2.056314e-01 -6.772588e-02
[4,] 7.678143e-01 -2.056314e-01
[5,] 4.129557e-01 7.678143e-01
[6,] -5.917840e-01 4.129557e-01
[7,] 4.853408e-01 -5.917840e-01
[8,] -1.267677e-01 4.853408e-01
[9,] 3.277377e-02 -1.267677e-01
[10,] 1.604990e-01 3.277377e-02
[11,] -2.302568e-01 1.604990e-01
[12,] -4.563493e-01 -2.302568e-01
[13,] -1.592589e-01 -4.563493e-01
[14,] 1.457399e+00 -1.592589e-01
[15,] -4.343718e-01 1.457399e+00
[16,] 8.363580e-01 -4.343718e-01
[17,] -3.772889e-01 8.363580e-01
[18,] 1.021522e+00 -3.772889e-01
[19,] 1.881158e+00 1.021522e+00
[20,] 2.996295e-02 1.881158e+00
[21,] 7.947216e-01 2.996295e-02
[22,] 1.996771e+00 7.947216e-01
[23,] -9.583020e-02 1.996771e+00
[24,] -2.377659e-01 -9.583020e-02
[25,] 7.499214e-01 -2.377659e-01
[26,] -4.629026e-02 7.499214e-01
[27,] -6.965482e-02 -4.629026e-02
[28,] -1.720834e-01 -6.965482e-02
[29,] -4.629026e-02 -1.720834e-01
[30,] 1.355490e+00 -4.629026e-02
[31,] 1.139063e+00 1.355490e+00
[32,] -8.344394e-01 1.139063e+00
[33,] 6.388854e-01 -8.344394e-01
[34,] 9.937702e-01 6.388854e-01
[35,] -7.684880e-02 9.937702e-01
[36,] -1.285377e+00 -7.684880e-02
[37,] -1.116031e+00 -1.285377e+00
[38,] -1.497793e-01 -1.116031e+00
[39,] -2.314538e-01 -1.497793e-01
[40,] 2.657270e-01 -2.314538e-01
[41,] -1.046290e+00 2.657270e-01
[42,] -2.333828e-01 -1.046290e+00
[43,] -1.195777e+00 -2.333828e-01
[44,] -2.084422e-01 -1.195777e+00
[45,] 4.760278e-01 -2.084422e-01
[46,] 1.883969e+00 4.760278e-01
[47,] 8.107890e-02 1.883969e+00
[48,] 6.632175e-02 8.107890e-02
[49,] 1.564376e-01 6.632175e-02
[50,] -9.422857e-01 1.564376e-01
[51,] 6.632175e-02 -9.422857e-01
[52,] 4.927294e-02 6.632175e-02
[53,] -9.521442e-01 4.927294e-02
[54,] 1.770994e+00 -9.521442e-01
[55,] 1.713840e-02 1.770994e+00
[56,] -1.759511e-01 1.713840e-02
[57,] 4.173389e-01 -1.759511e-01
[58,] -7.043960e-01 4.173389e-01
[59,] 6.861398e-01 -7.043960e-01
[60,] -9.876014e-01 6.861398e-01
[61,] -7.400733e-01 -9.876014e-01
[62,] 4.359674e-01 -7.400733e-01
[63,] 8.193092e-01 4.359674e-01
[64,] 2.813623e-01 8.193092e-01
[65,] 2.107434e-01 2.813623e-01
[66,] -1.608312e-01 2.107434e-01
[67,] 1.962151e+00 -1.608312e-01
[68,] -4.791720e-01 1.962151e+00
[69,] -1.358906e-01 -4.791720e-01
[70,] 3.368615e-01 -1.358906e-01
[71,] 3.277385e-01 3.368615e-01
[72,] -3.616301e-01 3.277385e-01
[73,] -5.654185e-01 -3.616301e-01
[74,] -1.123225e+00 -5.654185e-01
[75,] -9.583020e-02 -1.123225e+00
[76,] 2.599267e-01 -9.583020e-02
[77,] -5.467937e-01 2.599267e-01
[78,] 1.297504e-01 -5.467937e-01
[79,] -9.336783e-01 1.297504e-01
[80,] 1.859726e+00 -9.336783e-01
[81,] -1.239569e-01 1.859726e+00
[82,] 1.056138e+00 -1.239569e-01
[83,] -5.445362e-01 1.056138e+00
[84,] -8.760499e-01 -5.445362e-01
[85,] 1.009385e-01 -8.760499e-01
[86,] 2.151265e-01 1.009385e-01
[87,] -4.514206e-01 2.151265e-01
[88,] -7.483246e-01 -4.514206e-01
[89,] -1.051030e+00 -7.483246e-01
[90,] 6.632175e-02 -1.051030e+00
[91,] -6.877001e-01 6.632175e-02
[92,] 1.794493e-01 -6.877001e-01
[93,] 3.368615e-01 1.794493e-01
[94,] 5.935697e-01 3.368615e-01
[95,] 1.198105e+00 5.935697e-01
[96,] -1.837925e+00 1.198105e+00
[97,] 3.727288e-01 -1.837925e+00
[98,] -1.188583e+00 3.727288e-01
[99,] -1.142206e+00 -1.188583e+00
[100,] -5.610354e-01 -1.142206e+00
[101,] -7.911892e-01 -5.610354e-01
[102,] 5.254013e-01 -7.911892e-01
[103,] -9.583020e-02 5.254013e-01
[104,] -1.652144e-01 -9.583020e-02
[105,] -1.668749e+00 -1.652144e-01
[106,] -2.147580e-01 -1.668749e+00
[107,] -9.818311e-01 -2.147580e-01
[108,] -1.469859e+00 -9.818311e-01
[109,] 2.211235e-01 -1.469859e+00
[110,] 4.030973e-01 2.211235e-01
[111,] 1.953710e+00 4.030973e-01
[112,] 9.494855e-01 1.953710e+00
[113,] -6.316580e-01 9.494855e-01
[114,] 2.700381e+00 -6.316580e-01
[115,] -1.182267e+00 2.700381e+00
[116,] -2.627479e-01 -1.182267e+00
[117,] -9.152698e-01 -2.627479e-01
[118,] -4.436836e-01 -9.152698e-01
[119,] -9.905944e-01 -4.436836e-01
[120,] -8.126249e-01 -9.905944e-01
[121,] -1.778837e-01 -8.126249e-01
[122,] -6.629485e-01 -1.778837e-01
[123,] 1.035750e-01 -6.629485e-01
[124,] 1.327739e+00 1.035750e-01
[125,] -2.857559e-01 1.327739e+00
[126,] -9.716130e-01 -2.857559e-01
[127,] -1.822668e-01 -9.716130e-01
[128,] 1.687503e-01 -1.822668e-01
[129,] 9.089096e-01 1.687503e-01
[130,] -6.906998e-01 9.089096e-01
[131,] -6.965482e-02 -6.906998e-01
[132,] 4.201497e-01 -6.965482e-02
[133,] -7.864495e-01 4.201497e-01
[134,] -2.056314e-01 -7.864495e-01
[135,] -4.629026e-02 -2.056314e-01
[136,] 2.195097e-01 -4.629026e-02
[137,] 8.486670e-01 2.195097e-01
[138,] -1.442108e+00 8.486670e-01
[139,] 1.755811e-01 -1.442108e+00
[140,] 1.607591e+00 1.755811e-01
[141,] -9.319122e-01 1.607591e+00
[142,] 1.677885e+00 -9.319122e-01
[143,] -6.722615e-01 1.677885e+00
[144,] -3.604331e-01 -6.722615e-01
[145,] 7.962976e-01 -3.604331e-01
[146,] -2.207549e-01 7.962976e-01
[147,] -3.662334e-01 -2.207549e-01
[148,] 1.158892e+00 -3.662334e-01
[149,] -8.609366e-01 1.158892e+00
[150,] -1.285756e+00 -8.609366e-01
[151,] -4.629026e-02 -1.285756e+00
[152,] -1.008355e+00 -4.629026e-02
[153,] 1.097048e-01 -1.008355e+00
[154,] 2.810369e-01 1.097048e-01
[155,] -1.422064e-01 2.810369e-01
[156,] 8.593336e-05 -1.422064e-01
[157,] -7.483246e-01 8.593336e-05
[158,] 9.610938e-01 -7.483246e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.514974e-01 8.997866e-01
2 -6.772588e-02 7.514974e-01
3 -2.056314e-01 -6.772588e-02
4 7.678143e-01 -2.056314e-01
5 4.129557e-01 7.678143e-01
6 -5.917840e-01 4.129557e-01
7 4.853408e-01 -5.917840e-01
8 -1.267677e-01 4.853408e-01
9 3.277377e-02 -1.267677e-01
10 1.604990e-01 3.277377e-02
11 -2.302568e-01 1.604990e-01
12 -4.563493e-01 -2.302568e-01
13 -1.592589e-01 -4.563493e-01
14 1.457399e+00 -1.592589e-01
15 -4.343718e-01 1.457399e+00
16 8.363580e-01 -4.343718e-01
17 -3.772889e-01 8.363580e-01
18 1.021522e+00 -3.772889e-01
19 1.881158e+00 1.021522e+00
20 2.996295e-02 1.881158e+00
21 7.947216e-01 2.996295e-02
22 1.996771e+00 7.947216e-01
23 -9.583020e-02 1.996771e+00
24 -2.377659e-01 -9.583020e-02
25 7.499214e-01 -2.377659e-01
26 -4.629026e-02 7.499214e-01
27 -6.965482e-02 -4.629026e-02
28 -1.720834e-01 -6.965482e-02
29 -4.629026e-02 -1.720834e-01
30 1.355490e+00 -4.629026e-02
31 1.139063e+00 1.355490e+00
32 -8.344394e-01 1.139063e+00
33 6.388854e-01 -8.344394e-01
34 9.937702e-01 6.388854e-01
35 -7.684880e-02 9.937702e-01
36 -1.285377e+00 -7.684880e-02
37 -1.116031e+00 -1.285377e+00
38 -1.497793e-01 -1.116031e+00
39 -2.314538e-01 -1.497793e-01
40 2.657270e-01 -2.314538e-01
41 -1.046290e+00 2.657270e-01
42 -2.333828e-01 -1.046290e+00
43 -1.195777e+00 -2.333828e-01
44 -2.084422e-01 -1.195777e+00
45 4.760278e-01 -2.084422e-01
46 1.883969e+00 4.760278e-01
47 8.107890e-02 1.883969e+00
48 6.632175e-02 8.107890e-02
49 1.564376e-01 6.632175e-02
50 -9.422857e-01 1.564376e-01
51 6.632175e-02 -9.422857e-01
52 4.927294e-02 6.632175e-02
53 -9.521442e-01 4.927294e-02
54 1.770994e+00 -9.521442e-01
55 1.713840e-02 1.770994e+00
56 -1.759511e-01 1.713840e-02
57 4.173389e-01 -1.759511e-01
58 -7.043960e-01 4.173389e-01
59 6.861398e-01 -7.043960e-01
60 -9.876014e-01 6.861398e-01
61 -7.400733e-01 -9.876014e-01
62 4.359674e-01 -7.400733e-01
63 8.193092e-01 4.359674e-01
64 2.813623e-01 8.193092e-01
65 2.107434e-01 2.813623e-01
66 -1.608312e-01 2.107434e-01
67 1.962151e+00 -1.608312e-01
68 -4.791720e-01 1.962151e+00
69 -1.358906e-01 -4.791720e-01
70 3.368615e-01 -1.358906e-01
71 3.277385e-01 3.368615e-01
72 -3.616301e-01 3.277385e-01
73 -5.654185e-01 -3.616301e-01
74 -1.123225e+00 -5.654185e-01
75 -9.583020e-02 -1.123225e+00
76 2.599267e-01 -9.583020e-02
77 -5.467937e-01 2.599267e-01
78 1.297504e-01 -5.467937e-01
79 -9.336783e-01 1.297504e-01
80 1.859726e+00 -9.336783e-01
81 -1.239569e-01 1.859726e+00
82 1.056138e+00 -1.239569e-01
83 -5.445362e-01 1.056138e+00
84 -8.760499e-01 -5.445362e-01
85 1.009385e-01 -8.760499e-01
86 2.151265e-01 1.009385e-01
87 -4.514206e-01 2.151265e-01
88 -7.483246e-01 -4.514206e-01
89 -1.051030e+00 -7.483246e-01
90 6.632175e-02 -1.051030e+00
91 -6.877001e-01 6.632175e-02
92 1.794493e-01 -6.877001e-01
93 3.368615e-01 1.794493e-01
94 5.935697e-01 3.368615e-01
95 1.198105e+00 5.935697e-01
96 -1.837925e+00 1.198105e+00
97 3.727288e-01 -1.837925e+00
98 -1.188583e+00 3.727288e-01
99 -1.142206e+00 -1.188583e+00
100 -5.610354e-01 -1.142206e+00
101 -7.911892e-01 -5.610354e-01
102 5.254013e-01 -7.911892e-01
103 -9.583020e-02 5.254013e-01
104 -1.652144e-01 -9.583020e-02
105 -1.668749e+00 -1.652144e-01
106 -2.147580e-01 -1.668749e+00
107 -9.818311e-01 -2.147580e-01
108 -1.469859e+00 -9.818311e-01
109 2.211235e-01 -1.469859e+00
110 4.030973e-01 2.211235e-01
111 1.953710e+00 4.030973e-01
112 9.494855e-01 1.953710e+00
113 -6.316580e-01 9.494855e-01
114 2.700381e+00 -6.316580e-01
115 -1.182267e+00 2.700381e+00
116 -2.627479e-01 -1.182267e+00
117 -9.152698e-01 -2.627479e-01
118 -4.436836e-01 -9.152698e-01
119 -9.905944e-01 -4.436836e-01
120 -8.126249e-01 -9.905944e-01
121 -1.778837e-01 -8.126249e-01
122 -6.629485e-01 -1.778837e-01
123 1.035750e-01 -6.629485e-01
124 1.327739e+00 1.035750e-01
125 -2.857559e-01 1.327739e+00
126 -9.716130e-01 -2.857559e-01
127 -1.822668e-01 -9.716130e-01
128 1.687503e-01 -1.822668e-01
129 9.089096e-01 1.687503e-01
130 -6.906998e-01 9.089096e-01
131 -6.965482e-02 -6.906998e-01
132 4.201497e-01 -6.965482e-02
133 -7.864495e-01 4.201497e-01
134 -2.056314e-01 -7.864495e-01
135 -4.629026e-02 -2.056314e-01
136 2.195097e-01 -4.629026e-02
137 8.486670e-01 2.195097e-01
138 -1.442108e+00 8.486670e-01
139 1.755811e-01 -1.442108e+00
140 1.607591e+00 1.755811e-01
141 -9.319122e-01 1.607591e+00
142 1.677885e+00 -9.319122e-01
143 -6.722615e-01 1.677885e+00
144 -3.604331e-01 -6.722615e-01
145 7.962976e-01 -3.604331e-01
146 -2.207549e-01 7.962976e-01
147 -3.662334e-01 -2.207549e-01
148 1.158892e+00 -3.662334e-01
149 -8.609366e-01 1.158892e+00
150 -1.285756e+00 -8.609366e-01
151 -4.629026e-02 -1.285756e+00
152 -1.008355e+00 -4.629026e-02
153 1.097048e-01 -1.008355e+00
154 2.810369e-01 1.097048e-01
155 -1.422064e-01 2.810369e-01
156 8.593336e-05 -1.422064e-01
157 -7.483246e-01 8.593336e-05
158 9.610938e-01 -7.483246e-01
> 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/76shf1292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z1z01292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9z1z01292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10z1z01292850509.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11dtwr1292850509.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/12hudx1292850509.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/13d4to1292850509.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/14l7y01292850509.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/15j58h1292850509.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/1655on1292850509.tab")
+ }
>
> try(system("convert tmp/13sj91292850509.ps tmp/13sj91292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/23sj91292850509.ps tmp/23sj91292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/33sj91292850509.ps tmp/33sj91292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ej0u1292850509.ps tmp/4ej0u1292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ej0u1292850509.ps tmp/5ej0u1292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ej0u1292850509.ps tmp/6ej0u1292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/76shf1292850509.ps tmp/76shf1292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/8z1z01292850509.ps tmp/8z1z01292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/9z1z01292850509.ps tmp/9z1z01292850509.png",intern=TRUE))
character(0)
> try(system("convert tmp/10z1z01292850509.ps tmp/10z1z01292850509.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.128 1.907 10.019