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(26
+ ,24
+ ,14
+ ,11
+ ,12
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+ ,16
+ ,17)
+ ,dim=c(6
+ ,159)
+ ,dimnames=list(c('O'
+ ,'CM'
+ ,'D'
+ ,'PE'
+ ,'PC'
+ ,'PS')
+ ,1:159))
> y <- array(NA,dim=c(6,159),dimnames=list(c('O','CM','D','PE','PC','PS'),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 = '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
O CM D PE PC PS
1 26 24 14 11 12 24
2 23 25 11 7 8 25
3 25 17 6 17 8 30
4 23 18 12 10 8 19
5 19 18 8 12 9 22
6 29 16 10 12 7 22
7 25 20 10 11 4 25
8 21 16 11 11 11 23
9 22 18 16 12 7 17
10 25 17 11 13 7 21
11 24 23 13 14 12 19
12 18 30 12 16 10 19
13 22 23 8 11 10 15
14 15 18 12 10 8 16
15 22 15 11 11 8 23
16 28 12 4 15 4 27
17 20 21 9 9 9 22
18 12 15 8 11 8 14
19 24 20 8 17 7 22
20 20 31 14 17 11 23
21 21 27 15 11 9 23
22 20 34 16 18 11 21
23 21 21 9 14 13 19
24 23 31 14 10 8 18
25 28 19 11 11 8 20
26 24 16 8 15 9 23
27 24 20 9 15 6 25
28 24 21 9 13 9 19
29 23 22 9 16 9 24
30 23 17 9 13 6 22
31 29 24 10 9 6 25
32 24 25 16 18 16 26
33 18 26 11 18 5 29
34 25 25 8 12 7 32
35 21 17 9 17 9 25
36 26 32 16 9 6 29
37 22 33 11 9 6 28
38 22 13 16 12 5 17
39 22 32 12 18 12 28
40 23 25 12 12 7 29
41 30 29 14 18 10 26
42 23 22 9 14 9 25
43 17 18 10 15 8 14
44 23 17 9 16 5 25
45 23 20 10 10 8 26
46 25 15 12 11 8 20
47 24 20 14 14 10 18
48 24 33 14 9 6 32
49 23 29 10 12 8 25
50 21 23 14 17 7 25
51 24 26 16 5 4 23
52 24 18 9 12 8 21
53 28 20 10 12 8 20
54 16 11 6 6 4 15
55 20 28 8 24 20 30
56 29 26 13 12 8 24
57 27 22 10 12 8 26
58 22 17 8 14 6 24
59 28 12 7 7 4 22
60 16 14 15 13 8 14
61 25 17 9 12 9 24
62 24 21 10 13 6 24
63 28 19 12 14 7 24
64 24 18 13 8 9 24
65 23 10 10 11 5 19
66 30 29 11 9 5 31
67 24 31 8 11 8 22
68 21 19 9 13 8 27
69 25 9 13 10 6 19
70 25 20 11 11 8 25
71 22 28 8 12 7 20
72 23 19 9 9 7 21
73 26 30 9 15 9 27
74 23 29 15 18 11 23
75 25 26 9 15 6 25
76 21 23 10 12 8 20
77 25 13 14 13 6 21
78 24 21 12 14 9 22
79 29 19 12 10 8 23
80 22 28 11 13 6 25
81 27 23 14 13 10 25
82 26 18 6 11 8 17
83 22 21 12 13 8 19
84 24 20 8 16 10 25
85 27 23 14 8 5 19
86 24 21 11 16 7 20
87 24 21 10 11 5 26
88 29 15 14 9 8 23
89 22 28 12 16 14 27
90 21 19 10 12 7 17
91 24 26 14 14 8 17
92 24 10 5 8 6 19
93 23 16 11 9 5 17
94 20 22 10 15 6 22
95 27 19 9 11 10 21
96 26 31 10 21 12 32
97 25 31 16 14 9 21
98 21 29 13 18 12 21
99 21 19 9 12 7 18
100 19 22 10 13 8 18
101 21 23 10 15 10 23
102 21 15 7 12 6 19
103 16 20 9 19 10 20
104 22 18 8 15 10 21
105 29 23 14 11 10 20
106 15 25 14 11 5 17
107 17 21 8 10 7 18
108 15 24 9 13 10 19
109 21 25 14 15 11 22
110 21 17 14 12 6 15
111 19 13 8 12 7 14
112 24 28 8 16 12 18
113 20 21 8 9 11 24
114 17 25 7 18 11 35
115 23 9 6 8 11 29
116 24 16 8 13 5 21
117 14 19 6 17 8 25
118 19 17 11 9 6 20
119 24 25 14 15 9 22
120 13 20 11 8 4 13
121 22 29 11 7 4 26
122 16 14 11 12 7 17
123 19 22 14 14 11 25
124 25 15 8 6 6 20
125 25 19 20 8 7 19
126 23 20 11 17 8 21
127 24 15 8 10 4 22
128 26 20 11 11 8 24
129 26 18 10 14 9 21
130 25 33 14 11 8 26
131 18 22 11 13 11 24
132 21 16 9 12 8 16
133 26 17 9 11 5 23
134 23 16 8 9 4 18
135 23 21 10 12 8 16
136 22 26 13 20 10 26
137 20 18 13 12 6 19
138 13 18 12 13 9 21
139 24 17 8 12 9 21
140 15 22 13 12 13 22
141 14 30 14 9 9 23
142 22 30 12 15 10 29
143 10 24 14 24 20 21
144 24 21 15 7 5 21
145 22 21 13 17 11 23
146 24 29 16 11 6 27
147 19 31 10 17 9 25
148 20 20 9 11 7 21
149 13 16 9 12 9 10
150 20 22 8 14 10 20
151 22 20 7 11 9 26
152 24 28 16 16 8 24
153 29 38 11 21 7 29
154 12 22 9 14 6 19
155 20 20 11 20 13 24
156 21 17 9 13 6 19
157 24 28 14 11 8 24
158 22 22 13 15 10 22
159 20 31 16 19 16 17
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CM D PE PC PS
16.16348 -0.07051 0.21596 -0.14974 -0.25441 0.42249
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.151 -1.732 0.268 2.226 7.176
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 16.16348 2.00169 8.075 1.87e-13 ***
CM -0.07051 0.06307 -1.118 0.2653
D 0.21596 0.11300 1.911 0.0578 .
PE -0.14974 0.10427 -1.436 0.1530
PC -0.25441 0.13043 -1.951 0.0529 .
PS 0.42249 0.07566 5.584 1.05e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.5 on 153 degrees of freedom
Multiple R-squared: 0.2219, Adjusted R-squared: 0.1964
F-statistic: 8.725 on 5 and 153 DF, p-value: 2.667e-07
> 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,] 0.725054620 0.549890761 0.2749454
[2,] 0.598321152 0.803357697 0.4016788
[3,] 0.488975852 0.977951703 0.5110241
[4,] 0.444478151 0.888956301 0.5555218
[5,] 0.452507565 0.905015131 0.5474924
[6,] 0.692944456 0.614111088 0.3070555
[7,] 0.624361546 0.751276909 0.3756385
[8,] 0.572340257 0.855319487 0.4276597
[9,] 0.506313500 0.987372999 0.4936865
[10,] 0.636441550 0.727116901 0.3635585
[11,] 0.561381672 0.877236656 0.4386183
[12,] 0.566462605 0.867074791 0.4335374
[13,] 0.518595053 0.962809894 0.4814049
[14,] 0.449441537 0.898883073 0.5505585
[15,] 0.390420051 0.780840103 0.6095799
[16,] 0.391051796 0.782103592 0.6089482
[17,] 0.534775206 0.930449589 0.4652248
[18,] 0.472150175 0.944300350 0.5278498
[19,] 0.408598229 0.817196457 0.5914018
[20,] 0.401847031 0.803694062 0.5981530
[21,] 0.340959710 0.681919419 0.6590403
[22,] 0.284307129 0.568614257 0.7156929
[23,] 0.308442850 0.616885699 0.6915572
[24,] 0.259940453 0.519880906 0.7400595
[25,] 0.484013787 0.968027574 0.5159862
[26,] 0.437627660 0.875255320 0.5623723
[27,] 0.402858138 0.805716276 0.5971419
[28,] 0.347214243 0.694428485 0.6527858
[29,] 0.324586176 0.649172351 0.6754138
[30,] 0.275314517 0.550629033 0.7246855
[31,] 0.230568769 0.461137538 0.7694312
[32,] 0.207289923 0.414579847 0.7927101
[33,] 0.358628734 0.717257468 0.6413713
[34,] 0.308567713 0.617135425 0.6914323
[35,] 0.276851864 0.553703729 0.7231481
[36,] 0.235041754 0.470083509 0.7649582
[37,] 0.202514049 0.405028098 0.7974860
[38,] 0.181691106 0.363382211 0.8183089
[39,] 0.169455912 0.338911824 0.8305441
[40,] 0.156496159 0.312992318 0.8435038
[41,] 0.127516697 0.255033394 0.8724833
[42,] 0.118033713 0.236067426 0.8819663
[43,] 0.097268725 0.194537449 0.9027313
[44,] 0.082293743 0.164587485 0.9177063
[45,] 0.137984838 0.275969677 0.8620152
[46,] 0.169490496 0.338980992 0.8305095
[47,] 0.161723148 0.323446295 0.8382769
[48,] 0.216949606 0.433899211 0.7830504
[49,] 0.207883830 0.415767660 0.7921162
[50,] 0.178223051 0.356446101 0.8217769
[51,] 0.188570918 0.377141836 0.8114291
[52,] 0.216660537 0.433321074 0.7833395
[53,] 0.190681473 0.381362947 0.8093185
[54,] 0.159950577 0.319901154 0.8400494
[55,] 0.174689230 0.349378460 0.8253108
[56,] 0.147164667 0.294329335 0.8528353
[57,] 0.121297065 0.242594131 0.8787029
[58,] 0.117576965 0.235153929 0.8824230
[59,] 0.107404081 0.214808162 0.8925959
[60,] 0.107636960 0.215273919 0.8923630
[61,] 0.091358325 0.182716650 0.9086417
[62,] 0.074493248 0.148986496 0.9255068
[63,] 0.060534898 0.121069796 0.9394651
[64,] 0.047799670 0.095599340 0.9522003
[65,] 0.044913726 0.089827453 0.9550863
[66,] 0.036026181 0.072052361 0.9639738
[67,] 0.030004954 0.060009907 0.9699950
[68,] 0.023041442 0.046082884 0.9769586
[69,] 0.018091398 0.036182796 0.9819086
[70,] 0.014485599 0.028971197 0.9855144
[71,] 0.021731879 0.043463758 0.9782681
[72,] 0.017338675 0.034677350 0.9826613
[73,] 0.016979064 0.033958128 0.9830209
[74,] 0.030488849 0.060977698 0.9695112
[75,] 0.023533425 0.047066850 0.9764666
[76,] 0.019662236 0.039324472 0.9803378
[77,] 0.021640658 0.043281316 0.9783593
[78,] 0.019253005 0.038506011 0.9807470
[79,] 0.014666251 0.029332502 0.9853337
[80,] 0.019233951 0.038467902 0.9807660
[81,] 0.015169624 0.030339249 0.9848304
[82,] 0.011373222 0.022746443 0.9886268
[83,] 0.011502186 0.023004371 0.9884978
[84,] 0.010064054 0.020128107 0.9899359
[85,] 0.007739522 0.015479044 0.9922605
[86,] 0.006416189 0.012832378 0.9935838
[87,] 0.011804411 0.023608823 0.9881956
[88,] 0.010745172 0.021490345 0.9892548
[89,] 0.010282772 0.020565544 0.9897172
[90,] 0.007902055 0.015804109 0.9920979
[91,] 0.005851696 0.011703391 0.9941483
[92,] 0.004498390 0.008996780 0.9955016
[93,] 0.003338836 0.006677672 0.9966612
[94,] 0.002385972 0.004771944 0.9976140
[95,] 0.002564316 0.005128632 0.9974357
[96,] 0.002018108 0.004036215 0.9979819
[97,] 0.008616270 0.017232540 0.9913837
[98,] 0.019316970 0.038633941 0.9806830
[99,] 0.019267220 0.038534440 0.9807328
[100,] 0.024395623 0.048791247 0.9756044
[101,] 0.018938821 0.037877642 0.9810612
[102,] 0.013924237 0.027848475 0.9860758
[103,] 0.010145940 0.020291880 0.9898541
[104,] 0.024832399 0.049664799 0.9751676
[105,] 0.022646378 0.045292757 0.9773536
[106,] 0.062671638 0.125343277 0.9373284
[107,] 0.053689923 0.107379847 0.9463101
[108,] 0.043825172 0.087650343 0.9561748
[109,] 0.127712254 0.255424509 0.8722877
[110,] 0.123183620 0.246367239 0.8768164
[111,] 0.110163603 0.220327206 0.8898364
[112,] 0.190173466 0.380346933 0.8098265
[113,] 0.182394483 0.364788967 0.8176055
[114,] 0.216779914 0.433559828 0.7832201
[115,] 0.210359256 0.420718513 0.7896407
[116,] 0.209304464 0.418608928 0.7906955
[117,] 0.193261851 0.386523701 0.8067381
[118,] 0.159394279 0.318788558 0.8406057
[119,] 0.126401762 0.252803523 0.8735982
[120,] 0.130910825 0.261821650 0.8690892
[121,] 0.185787054 0.371574108 0.8142129
[122,] 0.161991675 0.323983349 0.8380083
[123,] 0.143573171 0.287146342 0.8564268
[124,] 0.125769870 0.251539740 0.8742301
[125,] 0.116506942 0.233013884 0.8834931
[126,] 0.096492568 0.192985136 0.9035074
[127,] 0.130346055 0.260692109 0.8696539
[128,] 0.098735816 0.197471631 0.9012642
[129,] 0.073728387 0.147456773 0.9262716
[130,] 0.180287177 0.360574353 0.8197128
[131,] 0.250321999 0.500643997 0.7496780
[132,] 0.231691330 0.463382660 0.7683087
[133,] 0.470624131 0.941248262 0.5293759
[134,] 0.422400847 0.844801694 0.5775992
[135,] 0.728533458 0.542933083 0.2714665
[136,] 0.679249493 0.641501014 0.3207505
[137,] 0.579343024 0.841313952 0.4206570
[138,] 0.508302393 0.983395215 0.4916976
[139,] 0.558406818 0.883186363 0.4415932
[140,] 0.431110511 0.862221022 0.5688895
[141,] 0.338206148 0.676412296 0.6617939
[142,] 0.242095892 0.484191785 0.7579041
> postscript(file="/var/www/html/rcomp/tmp/1cyem1290503884.ps",horizontal=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/258e71290503884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/358e71290503884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/458e71290503884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gzds1290503884.ps",horizontal=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 6
3.065700914 -1.255005586 0.645684928 1.019614177 -2.830115404 6.088120895
7 8 9 10 11 12
0.189723852 -1.682425132 0.045821274 2.514900796 3.772816268 -1.726988791
13 14 15 16 17 18
3.584524553 -5.712921078 -1.516168652 3.675390786 -2.283768451 -7.065893453
19 20 21 22 23 24
2.550790875 -1.374199457 -2.279472549 -0.599870564 1.750047484 1.926818709
25 26 27 28 29 30
6.033338224 2.055599605 0.513471930 3.582662059 0.989955673 0.269922195
31 32 33 34 35 36
4.681105093 1.925148379 -6.990525182 -1.070246151 -1.635343798 -0.740525566
37 38 39 40 41 42
-3.167725178 -0.815553381 -0.580057086 -2.666622691 7.112645177 0.267984542
43 44 45 46 47 48
-1.687316733 -0.802729223 -1.364861853 2.535335771 3.258990605 -3.505559136
49 50 51 52 53 54
-0.008295928 -2.800904200 -1.736447029 2.122001527 6.469550519 -5.104854379
55 56 57 58 59 60
0.090502186 5.554779758 3.075642094 -1.209352540 3.942019535 -4.348643353
61 62 63 64 65 66
2.038437245 0.491027508 4.322238236 -0.353859273 0.273959011 3.028356949
67 68 69 70 71 72
2.682369085 -3.192675990 1.660237069 0.991407515 1.211144428 0.488876740
73 74 75 76 77 78
3.136833749 1.418560597 1.936535127 -0.318917883 1.330566705 1.817057791
79 80 81 82 83 84
5.400171716 -1.653847333 3.363363023 6.310094043 0.680370099 1.896817677
85 86 87 88 89 90
3.877530328 2.668655499 -0.907842867 4.536467501 -0.230271860 0.412093735
91 92 93 94 95 96
3.595720058 2.158947290 1.026555500 -2.294002581 5.551592610 2.540624353
97 98 99 100 101 102
3.080810081 0.949868732 0.205565808 -1.394710477 -0.628336312 -0.321454924
103 104 105 106 107 108
-3.757477080 1.296008163 7.176321383 -6.687247786 -3.736935687 -4.951395347
109 110 111 112 113 114
-0.674257288 -0.002203115 -0.311584073 5.927141669 -2.403962634 -8.205657944
115 116 117 118 119 120
-2.080351068 1.583449236 -8.100852764 -3.915987716 1.816920720 -7.405601812
121 122 123 124 125 126
-3.413095687 -5.156419250 -4.302995072 2.141647859 0.808548260 1.579809155
127 128 129 130 131 132
0.386815135 2.413895763 4.459935083 0.837675228 -4.382367301 1.093421703
133 134 135 136 137 138
2.293540068 0.997537220 3.230014045 -0.583443218 -2.405685254 -9.121727000
139 140 141 142 143 144
2.521862311 -6.610230896 -9.151463511 -2.101612716 -7.684907638 -0.474168997
145 146 147 148 149 150
0.136655536 -1.807597786 -2.864156660 -2.141129845 -4.117237811 -0.149202898
151 152 153 154 155 156
-1.312828452 0.646885624 5.813647527 -8.960318955 -0.966376287 -0.462613060
157 158 159
0.330099061 0.075760439 2.300347252
> postscript(file="/var/www/html/rcomp/tmp/6gzds1290503884.ps",horizontal=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 3.065700914 NA
1 -1.255005586 3.065700914
2 0.645684928 -1.255005586
3 1.019614177 0.645684928
4 -2.830115404 1.019614177
5 6.088120895 -2.830115404
6 0.189723852 6.088120895
7 -1.682425132 0.189723852
8 0.045821274 -1.682425132
9 2.514900796 0.045821274
10 3.772816268 2.514900796
11 -1.726988791 3.772816268
12 3.584524553 -1.726988791
13 -5.712921078 3.584524553
14 -1.516168652 -5.712921078
15 3.675390786 -1.516168652
16 -2.283768451 3.675390786
17 -7.065893453 -2.283768451
18 2.550790875 -7.065893453
19 -1.374199457 2.550790875
20 -2.279472549 -1.374199457
21 -0.599870564 -2.279472549
22 1.750047484 -0.599870564
23 1.926818709 1.750047484
24 6.033338224 1.926818709
25 2.055599605 6.033338224
26 0.513471930 2.055599605
27 3.582662059 0.513471930
28 0.989955673 3.582662059
29 0.269922195 0.989955673
30 4.681105093 0.269922195
31 1.925148379 4.681105093
32 -6.990525182 1.925148379
33 -1.070246151 -6.990525182
34 -1.635343798 -1.070246151
35 -0.740525566 -1.635343798
36 -3.167725178 -0.740525566
37 -0.815553381 -3.167725178
38 -0.580057086 -0.815553381
39 -2.666622691 -0.580057086
40 7.112645177 -2.666622691
41 0.267984542 7.112645177
42 -1.687316733 0.267984542
43 -0.802729223 -1.687316733
44 -1.364861853 -0.802729223
45 2.535335771 -1.364861853
46 3.258990605 2.535335771
47 -3.505559136 3.258990605
48 -0.008295928 -3.505559136
49 -2.800904200 -0.008295928
50 -1.736447029 -2.800904200
51 2.122001527 -1.736447029
52 6.469550519 2.122001527
53 -5.104854379 6.469550519
54 0.090502186 -5.104854379
55 5.554779758 0.090502186
56 3.075642094 5.554779758
57 -1.209352540 3.075642094
58 3.942019535 -1.209352540
59 -4.348643353 3.942019535
60 2.038437245 -4.348643353
61 0.491027508 2.038437245
62 4.322238236 0.491027508
63 -0.353859273 4.322238236
64 0.273959011 -0.353859273
65 3.028356949 0.273959011
66 2.682369085 3.028356949
67 -3.192675990 2.682369085
68 1.660237069 -3.192675990
69 0.991407515 1.660237069
70 1.211144428 0.991407515
71 0.488876740 1.211144428
72 3.136833749 0.488876740
73 1.418560597 3.136833749
74 1.936535127 1.418560597
75 -0.318917883 1.936535127
76 1.330566705 -0.318917883
77 1.817057791 1.330566705
78 5.400171716 1.817057791
79 -1.653847333 5.400171716
80 3.363363023 -1.653847333
81 6.310094043 3.363363023
82 0.680370099 6.310094043
83 1.896817677 0.680370099
84 3.877530328 1.896817677
85 2.668655499 3.877530328
86 -0.907842867 2.668655499
87 4.536467501 -0.907842867
88 -0.230271860 4.536467501
89 0.412093735 -0.230271860
90 3.595720058 0.412093735
91 2.158947290 3.595720058
92 1.026555500 2.158947290
93 -2.294002581 1.026555500
94 5.551592610 -2.294002581
95 2.540624353 5.551592610
96 3.080810081 2.540624353
97 0.949868732 3.080810081
98 0.205565808 0.949868732
99 -1.394710477 0.205565808
100 -0.628336312 -1.394710477
101 -0.321454924 -0.628336312
102 -3.757477080 -0.321454924
103 1.296008163 -3.757477080
104 7.176321383 1.296008163
105 -6.687247786 7.176321383
106 -3.736935687 -6.687247786
107 -4.951395347 -3.736935687
108 -0.674257288 -4.951395347
109 -0.002203115 -0.674257288
110 -0.311584073 -0.002203115
111 5.927141669 -0.311584073
112 -2.403962634 5.927141669
113 -8.205657944 -2.403962634
114 -2.080351068 -8.205657944
115 1.583449236 -2.080351068
116 -8.100852764 1.583449236
117 -3.915987716 -8.100852764
118 1.816920720 -3.915987716
119 -7.405601812 1.816920720
120 -3.413095687 -7.405601812
121 -5.156419250 -3.413095687
122 -4.302995072 -5.156419250
123 2.141647859 -4.302995072
124 0.808548260 2.141647859
125 1.579809155 0.808548260
126 0.386815135 1.579809155
127 2.413895763 0.386815135
128 4.459935083 2.413895763
129 0.837675228 4.459935083
130 -4.382367301 0.837675228
131 1.093421703 -4.382367301
132 2.293540068 1.093421703
133 0.997537220 2.293540068
134 3.230014045 0.997537220
135 -0.583443218 3.230014045
136 -2.405685254 -0.583443218
137 -9.121727000 -2.405685254
138 2.521862311 -9.121727000
139 -6.610230896 2.521862311
140 -9.151463511 -6.610230896
141 -2.101612716 -9.151463511
142 -7.684907638 -2.101612716
143 -0.474168997 -7.684907638
144 0.136655536 -0.474168997
145 -1.807597786 0.136655536
146 -2.864156660 -1.807597786
147 -2.141129845 -2.864156660
148 -4.117237811 -2.141129845
149 -0.149202898 -4.117237811
150 -1.312828452 -0.149202898
151 0.646885624 -1.312828452
152 5.813647527 0.646885624
153 -8.960318955 5.813647527
154 -0.966376287 -8.960318955
155 -0.462613060 -0.966376287
156 0.330099061 -0.462613060
157 0.075760439 0.330099061
158 2.300347252 0.075760439
159 NA 2.300347252
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.255005586 3.065700914
[2,] 0.645684928 -1.255005586
[3,] 1.019614177 0.645684928
[4,] -2.830115404 1.019614177
[5,] 6.088120895 -2.830115404
[6,] 0.189723852 6.088120895
[7,] -1.682425132 0.189723852
[8,] 0.045821274 -1.682425132
[9,] 2.514900796 0.045821274
[10,] 3.772816268 2.514900796
[11,] -1.726988791 3.772816268
[12,] 3.584524553 -1.726988791
[13,] -5.712921078 3.584524553
[14,] -1.516168652 -5.712921078
[15,] 3.675390786 -1.516168652
[16,] -2.283768451 3.675390786
[17,] -7.065893453 -2.283768451
[18,] 2.550790875 -7.065893453
[19,] -1.374199457 2.550790875
[20,] -2.279472549 -1.374199457
[21,] -0.599870564 -2.279472549
[22,] 1.750047484 -0.599870564
[23,] 1.926818709 1.750047484
[24,] 6.033338224 1.926818709
[25,] 2.055599605 6.033338224
[26,] 0.513471930 2.055599605
[27,] 3.582662059 0.513471930
[28,] 0.989955673 3.582662059
[29,] 0.269922195 0.989955673
[30,] 4.681105093 0.269922195
[31,] 1.925148379 4.681105093
[32,] -6.990525182 1.925148379
[33,] -1.070246151 -6.990525182
[34,] -1.635343798 -1.070246151
[35,] -0.740525566 -1.635343798
[36,] -3.167725178 -0.740525566
[37,] -0.815553381 -3.167725178
[38,] -0.580057086 -0.815553381
[39,] -2.666622691 -0.580057086
[40,] 7.112645177 -2.666622691
[41,] 0.267984542 7.112645177
[42,] -1.687316733 0.267984542
[43,] -0.802729223 -1.687316733
[44,] -1.364861853 -0.802729223
[45,] 2.535335771 -1.364861853
[46,] 3.258990605 2.535335771
[47,] -3.505559136 3.258990605
[48,] -0.008295928 -3.505559136
[49,] -2.800904200 -0.008295928
[50,] -1.736447029 -2.800904200
[51,] 2.122001527 -1.736447029
[52,] 6.469550519 2.122001527
[53,] -5.104854379 6.469550519
[54,] 0.090502186 -5.104854379
[55,] 5.554779758 0.090502186
[56,] 3.075642094 5.554779758
[57,] -1.209352540 3.075642094
[58,] 3.942019535 -1.209352540
[59,] -4.348643353 3.942019535
[60,] 2.038437245 -4.348643353
[61,] 0.491027508 2.038437245
[62,] 4.322238236 0.491027508
[63,] -0.353859273 4.322238236
[64,] 0.273959011 -0.353859273
[65,] 3.028356949 0.273959011
[66,] 2.682369085 3.028356949
[67,] -3.192675990 2.682369085
[68,] 1.660237069 -3.192675990
[69,] 0.991407515 1.660237069
[70,] 1.211144428 0.991407515
[71,] 0.488876740 1.211144428
[72,] 3.136833749 0.488876740
[73,] 1.418560597 3.136833749
[74,] 1.936535127 1.418560597
[75,] -0.318917883 1.936535127
[76,] 1.330566705 -0.318917883
[77,] 1.817057791 1.330566705
[78,] 5.400171716 1.817057791
[79,] -1.653847333 5.400171716
[80,] 3.363363023 -1.653847333
[81,] 6.310094043 3.363363023
[82,] 0.680370099 6.310094043
[83,] 1.896817677 0.680370099
[84,] 3.877530328 1.896817677
[85,] 2.668655499 3.877530328
[86,] -0.907842867 2.668655499
[87,] 4.536467501 -0.907842867
[88,] -0.230271860 4.536467501
[89,] 0.412093735 -0.230271860
[90,] 3.595720058 0.412093735
[91,] 2.158947290 3.595720058
[92,] 1.026555500 2.158947290
[93,] -2.294002581 1.026555500
[94,] 5.551592610 -2.294002581
[95,] 2.540624353 5.551592610
[96,] 3.080810081 2.540624353
[97,] 0.949868732 3.080810081
[98,] 0.205565808 0.949868732
[99,] -1.394710477 0.205565808
[100,] -0.628336312 -1.394710477
[101,] -0.321454924 -0.628336312
[102,] -3.757477080 -0.321454924
[103,] 1.296008163 -3.757477080
[104,] 7.176321383 1.296008163
[105,] -6.687247786 7.176321383
[106,] -3.736935687 -6.687247786
[107,] -4.951395347 -3.736935687
[108,] -0.674257288 -4.951395347
[109,] -0.002203115 -0.674257288
[110,] -0.311584073 -0.002203115
[111,] 5.927141669 -0.311584073
[112,] -2.403962634 5.927141669
[113,] -8.205657944 -2.403962634
[114,] -2.080351068 -8.205657944
[115,] 1.583449236 -2.080351068
[116,] -8.100852764 1.583449236
[117,] -3.915987716 -8.100852764
[118,] 1.816920720 -3.915987716
[119,] -7.405601812 1.816920720
[120,] -3.413095687 -7.405601812
[121,] -5.156419250 -3.413095687
[122,] -4.302995072 -5.156419250
[123,] 2.141647859 -4.302995072
[124,] 0.808548260 2.141647859
[125,] 1.579809155 0.808548260
[126,] 0.386815135 1.579809155
[127,] 2.413895763 0.386815135
[128,] 4.459935083 2.413895763
[129,] 0.837675228 4.459935083
[130,] -4.382367301 0.837675228
[131,] 1.093421703 -4.382367301
[132,] 2.293540068 1.093421703
[133,] 0.997537220 2.293540068
[134,] 3.230014045 0.997537220
[135,] -0.583443218 3.230014045
[136,] -2.405685254 -0.583443218
[137,] -9.121727000 -2.405685254
[138,] 2.521862311 -9.121727000
[139,] -6.610230896 2.521862311
[140,] -9.151463511 -6.610230896
[141,] -2.101612716 -9.151463511
[142,] -7.684907638 -2.101612716
[143,] -0.474168997 -7.684907638
[144,] 0.136655536 -0.474168997
[145,] -1.807597786 0.136655536
[146,] -2.864156660 -1.807597786
[147,] -2.141129845 -2.864156660
[148,] -4.117237811 -2.141129845
[149,] -0.149202898 -4.117237811
[150,] -1.312828452 -0.149202898
[151,] 0.646885624 -1.312828452
[152,] 5.813647527 0.646885624
[153,] -8.960318955 5.813647527
[154,] -0.966376287 -8.960318955
[155,] -0.462613060 -0.966376287
[156,] 0.330099061 -0.462613060
[157,] 0.075760439 0.330099061
[158,] 2.300347252 0.075760439
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.255005586 3.065700914
2 0.645684928 -1.255005586
3 1.019614177 0.645684928
4 -2.830115404 1.019614177
5 6.088120895 -2.830115404
6 0.189723852 6.088120895
7 -1.682425132 0.189723852
8 0.045821274 -1.682425132
9 2.514900796 0.045821274
10 3.772816268 2.514900796
11 -1.726988791 3.772816268
12 3.584524553 -1.726988791
13 -5.712921078 3.584524553
14 -1.516168652 -5.712921078
15 3.675390786 -1.516168652
16 -2.283768451 3.675390786
17 -7.065893453 -2.283768451
18 2.550790875 -7.065893453
19 -1.374199457 2.550790875
20 -2.279472549 -1.374199457
21 -0.599870564 -2.279472549
22 1.750047484 -0.599870564
23 1.926818709 1.750047484
24 6.033338224 1.926818709
25 2.055599605 6.033338224
26 0.513471930 2.055599605
27 3.582662059 0.513471930
28 0.989955673 3.582662059
29 0.269922195 0.989955673
30 4.681105093 0.269922195
31 1.925148379 4.681105093
32 -6.990525182 1.925148379
33 -1.070246151 -6.990525182
34 -1.635343798 -1.070246151
35 -0.740525566 -1.635343798
36 -3.167725178 -0.740525566
37 -0.815553381 -3.167725178
38 -0.580057086 -0.815553381
39 -2.666622691 -0.580057086
40 7.112645177 -2.666622691
41 0.267984542 7.112645177
42 -1.687316733 0.267984542
43 -0.802729223 -1.687316733
44 -1.364861853 -0.802729223
45 2.535335771 -1.364861853
46 3.258990605 2.535335771
47 -3.505559136 3.258990605
48 -0.008295928 -3.505559136
49 -2.800904200 -0.008295928
50 -1.736447029 -2.800904200
51 2.122001527 -1.736447029
52 6.469550519 2.122001527
53 -5.104854379 6.469550519
54 0.090502186 -5.104854379
55 5.554779758 0.090502186
56 3.075642094 5.554779758
57 -1.209352540 3.075642094
58 3.942019535 -1.209352540
59 -4.348643353 3.942019535
60 2.038437245 -4.348643353
61 0.491027508 2.038437245
62 4.322238236 0.491027508
63 -0.353859273 4.322238236
64 0.273959011 -0.353859273
65 3.028356949 0.273959011
66 2.682369085 3.028356949
67 -3.192675990 2.682369085
68 1.660237069 -3.192675990
69 0.991407515 1.660237069
70 1.211144428 0.991407515
71 0.488876740 1.211144428
72 3.136833749 0.488876740
73 1.418560597 3.136833749
74 1.936535127 1.418560597
75 -0.318917883 1.936535127
76 1.330566705 -0.318917883
77 1.817057791 1.330566705
78 5.400171716 1.817057791
79 -1.653847333 5.400171716
80 3.363363023 -1.653847333
81 6.310094043 3.363363023
82 0.680370099 6.310094043
83 1.896817677 0.680370099
84 3.877530328 1.896817677
85 2.668655499 3.877530328
86 -0.907842867 2.668655499
87 4.536467501 -0.907842867
88 -0.230271860 4.536467501
89 0.412093735 -0.230271860
90 3.595720058 0.412093735
91 2.158947290 3.595720058
92 1.026555500 2.158947290
93 -2.294002581 1.026555500
94 5.551592610 -2.294002581
95 2.540624353 5.551592610
96 3.080810081 2.540624353
97 0.949868732 3.080810081
98 0.205565808 0.949868732
99 -1.394710477 0.205565808
100 -0.628336312 -1.394710477
101 -0.321454924 -0.628336312
102 -3.757477080 -0.321454924
103 1.296008163 -3.757477080
104 7.176321383 1.296008163
105 -6.687247786 7.176321383
106 -3.736935687 -6.687247786
107 -4.951395347 -3.736935687
108 -0.674257288 -4.951395347
109 -0.002203115 -0.674257288
110 -0.311584073 -0.002203115
111 5.927141669 -0.311584073
112 -2.403962634 5.927141669
113 -8.205657944 -2.403962634
114 -2.080351068 -8.205657944
115 1.583449236 -2.080351068
116 -8.100852764 1.583449236
117 -3.915987716 -8.100852764
118 1.816920720 -3.915987716
119 -7.405601812 1.816920720
120 -3.413095687 -7.405601812
121 -5.156419250 -3.413095687
122 -4.302995072 -5.156419250
123 2.141647859 -4.302995072
124 0.808548260 2.141647859
125 1.579809155 0.808548260
126 0.386815135 1.579809155
127 2.413895763 0.386815135
128 4.459935083 2.413895763
129 0.837675228 4.459935083
130 -4.382367301 0.837675228
131 1.093421703 -4.382367301
132 2.293540068 1.093421703
133 0.997537220 2.293540068
134 3.230014045 0.997537220
135 -0.583443218 3.230014045
136 -2.405685254 -0.583443218
137 -9.121727000 -2.405685254
138 2.521862311 -9.121727000
139 -6.610230896 2.521862311
140 -9.151463511 -6.610230896
141 -2.101612716 -9.151463511
142 -7.684907638 -2.101612716
143 -0.474168997 -7.684907638
144 0.136655536 -0.474168997
145 -1.807597786 0.136655536
146 -2.864156660 -1.807597786
147 -2.141129845 -2.864156660
148 -4.117237811 -2.141129845
149 -0.149202898 -4.117237811
150 -1.312828452 -0.149202898
151 0.646885624 -1.312828452
152 5.813647527 0.646885624
153 -8.960318955 5.813647527
154 -0.966376287 -8.960318955
155 -0.462613060 -0.966376287
156 0.330099061 -0.462613060
157 0.075760439 0.330099061
158 2.300347252 0.075760439
> 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/788uv1290503884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/888uv1290503884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91hcg1290503884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101hcg1290503884.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11xrr71290503884.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/1219qc1290503884.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/13xj631290503884.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/14ikmr1290503884.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/15lk3x1290503884.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/160u061290503884.tab")
+ }
>
> try(system("convert tmp/1cyem1290503884.ps tmp/1cyem1290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/258e71290503884.ps tmp/258e71290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/358e71290503884.ps tmp/358e71290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/458e71290503884.ps tmp/458e71290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gzds1290503884.ps tmp/5gzds1290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/6gzds1290503884.ps tmp/6gzds1290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/788uv1290503884.ps tmp/788uv1290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/888uv1290503884.ps tmp/888uv1290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/91hcg1290503884.ps tmp/91hcg1290503884.png",intern=TRUE))
character(0)
> try(system("convert tmp/101hcg1290503884.ps tmp/101hcg1290503884.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.184 1.831 40.070