R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
+ ,162556
+ ,1081
+ ,213118
+ ,6282929
+ ,1
+ ,29790
+ ,309
+ ,81767
+ ,4324047
+ ,1
+ ,87550
+ ,458
+ ,153198
+ ,4108272
+ ,0
+ ,84738
+ ,588
+ ,-26007
+ ,-1212617
+ ,1
+ ,54660
+ ,299
+ ,126942
+ ,1485329
+ ,1
+ ,42634
+ ,156
+ ,157214
+ ,1779876
+ ,0
+ ,40949
+ ,481
+ ,129352
+ ,1367203
+ ,1
+ ,42312
+ ,323
+ ,234817
+ ,2519076
+ ,1
+ ,37704
+ ,452
+ ,60448
+ ,912684
+ ,1
+ ,16275
+ ,109
+ ,47818
+ ,1443586
+ ,0
+ ,25830
+ ,115
+ ,245546
+ ,1220017
+ ,0
+ ,12679
+ ,110
+ ,48020
+ ,984885
+ ,1
+ ,18014
+ ,239
+ ,-1710
+ ,1457425
+ ,0
+ ,43556
+ ,247
+ ,32648
+ ,-572920
+ ,1
+ ,24524
+ ,497
+ ,95350
+ ,929144
+ ,0
+ ,6532
+ ,103
+ ,151352
+ ,1151176
+ ,0
+ ,7123
+ ,109
+ ,288170
+ ,790090
+ ,1
+ ,20813
+ ,502
+ ,114337
+ ,774497
+ ,1
+ ,37597
+ ,248
+ ,37884
+ ,990576
+ ,0
+ ,17821
+ ,373
+ ,122844
+ ,454195
+ ,1
+ ,12988
+ ,119
+ ,82340
+ ,876607
+ ,1
+ ,22330
+ ,84
+ ,79801
+ ,711969
+ ,0
+ ,13326
+ ,102
+ ,165548
+ ,702380
+ ,0
+ ,16189
+ ,295
+ ,116384
+ ,264449
+ ,0
+ ,7146
+ ,105
+ ,134028
+ ,450033
+ ,0
+ ,15824
+ ,64
+ ,63838
+ ,541063
+ ,1
+ ,26088
+ ,267
+ ,74996
+ ,588864
+ ,0
+ ,11326
+ ,129
+ ,31080
+ ,-37216
+ ,0
+ ,8568
+ ,37
+ ,32168
+ ,783310
+ ,0
+ ,14416
+ ,361
+ ,49857
+ ,467359
+ ,1
+ ,3369
+ ,28
+ ,87161
+ ,688779
+ ,1
+ ,11819
+ ,85
+ ,106113
+ ,608419
+ ,1
+ ,6620
+ ,44
+ ,80570
+ ,696348
+ ,1
+ ,4519
+ ,49
+ ,102129
+ ,597793
+ ,0
+ ,2220
+ ,22
+ ,301670
+ ,821730
+ ,0
+ ,18562
+ ,155
+ ,102313
+ ,377934
+ ,0
+ ,10327
+ ,91
+ ,88577
+ ,651939
+ ,1
+ ,5336
+ ,81
+ ,112477
+ ,697458
+ ,1
+ ,2365
+ ,79
+ ,191778
+ ,700368
+ ,0
+ ,4069
+ ,145
+ ,79804
+ ,225986
+ ,0
+ ,7710
+ ,816
+ ,128294
+ ,348695
+ ,0
+ ,13718
+ ,61
+ ,96448
+ ,373683
+ ,0
+ ,4525
+ ,226
+ ,93811
+ ,501709
+ ,0
+ ,6869
+ ,105
+ ,117520
+ ,413743
+ ,0
+ ,4628
+ ,62
+ ,69159
+ ,379825
+ ,1
+ ,3653
+ ,24
+ ,101792
+ ,336260
+ ,1
+ ,1265
+ ,26
+ ,210568
+ ,636765
+ ,1
+ ,7489
+ ,322
+ ,136996
+ ,481231
+ ,0
+ ,4901
+ ,84
+ ,121920
+ ,469107)
+ ,dim=c(5
+ ,49)
+ ,dimnames=list(c('Group'
+ ,'Costs'
+ ,'Trades'
+ ,'Dividends'
+ ,'Wealth')
+ ,1:49))
> y <- array(NA,dim=c(5,49),dimnames=list(c('Group','Costs','Trades','Dividends','Wealth'),1:49))
> 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
> 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
Group Costs Trades Dividends Wealth
1 1 162556 1081 213118 6282929
2 1 29790 309 81767 4324047
3 1 87550 458 153198 4108272
4 0 84738 588 -26007 -1212617
5 1 54660 299 126942 1485329
6 1 42634 156 157214 1779876
7 0 40949 481 129352 1367203
8 1 42312 323 234817 2519076
9 1 37704 452 60448 912684
10 1 16275 109 47818 1443586
11 0 25830 115 245546 1220017
12 0 12679 110 48020 984885
13 1 18014 239 -1710 1457425
14 0 43556 247 32648 -572920
15 1 24524 497 95350 929144
16 0 6532 103 151352 1151176
17 0 7123 109 288170 790090
18 1 20813 502 114337 774497
19 1 37597 248 37884 990576
20 0 17821 373 122844 454195
21 1 12988 119 82340 876607
22 1 22330 84 79801 711969
23 0 13326 102 165548 702380
24 0 16189 295 116384 264449
25 0 7146 105 134028 450033
26 0 15824 64 63838 541063
27 1 26088 267 74996 588864
28 0 11326 129 31080 -37216
29 0 8568 37 32168 783310
30 0 14416 361 49857 467359
31 1 3369 28 87161 688779
32 1 11819 85 106113 608419
33 1 6620 44 80570 696348
34 1 4519 49 102129 597793
35 0 2220 22 301670 821730
36 0 18562 155 102313 377934
37 0 10327 91 88577 651939
38 1 5336 81 112477 697458
39 1 2365 79 191778 700368
40 0 4069 145 79804 225986
41 0 7710 816 128294 348695
42 0 13718 61 96448 373683
43 0 4525 226 93811 501709
44 0 6869 105 117520 413743
45 0 4628 62 69159 379825
46 1 3653 24 101792 336260
47 1 1265 26 210568 636765
48 1 7489 322 136996 481231
49 0 4901 84 121920 469107
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Costs Trades Dividends Wealth
4.872e-01 -9.879e-07 -2.344e-04 -1.308e-06 2.343e-07
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6168 -0.3990 -0.2094 0.4753 0.6621
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.872e-01 1.490e-01 3.269 0.00210 **
Costs -9.879e-07 4.285e-06 -0.231 0.81875
Trades -2.344e-04 4.679e-04 -0.501 0.61889
Dividends -1.308e-06 1.095e-06 -1.195 0.23851
Wealth 2.343e-07 8.236e-08 2.844 0.00673 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.471 on 44 degrees of freedom
Multiple R-squared: 0.2027, Adjusted R-squared: 0.1302
F-statistic: 2.797 on 4 and 44 DF, p-value: 0.03739
> 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,] 0.1384740 0.2769480 0.8615260
[2,] 0.4019897 0.8039795 0.5980103
[3,] 0.2621998 0.5243996 0.7378002
[4,] 0.4216400 0.8432800 0.5783600
[5,] 0.5854725 0.8290550 0.4145275
[6,] 0.4987975 0.9975950 0.5012025
[7,] 0.4330139 0.8660278 0.5669861
[8,] 0.4191558 0.8383116 0.5808442
[9,] 0.5321515 0.9356971 0.4678485
[10,] 0.4703247 0.9406494 0.5296753
[11,] 0.4451681 0.8903361 0.5548319
[12,] 0.4011270 0.8022541 0.5988730
[13,] 0.3779114 0.7558227 0.6220886
[14,] 0.3422075 0.6844150 0.6577925
[15,] 0.3412394 0.6824787 0.6587606
[16,] 0.3267721 0.6535441 0.6732279
[17,] 0.2758978 0.5517956 0.7241022
[18,] 0.2438076 0.4876151 0.7561924
[19,] 0.2451464 0.4902928 0.7548536
[20,] 0.3250040 0.6500081 0.6749960
[21,] 0.2825761 0.5651522 0.7174239
[22,] 0.4018787 0.8037574 0.5981213
[23,] 0.3734450 0.7468899 0.6265550
[24,] 0.3328523 0.6657045 0.6671477
[25,] 0.3667848 0.7335695 0.6332152
[26,] 0.3227515 0.6455030 0.6772485
[27,] 0.3103262 0.6206524 0.6896738
[28,] 0.5866521 0.8266957 0.4133479
[29,] 0.4946694 0.9893389 0.5053306
[30,] 0.4390261 0.8780523 0.5609739
[31,] 0.4779726 0.9559453 0.5220274
[32,] 0.3869652 0.7739304 0.6130348
[33,] 0.3189719 0.6379439 0.6810281
[34,] 0.5345276 0.9309449 0.4654724
> postscript(file="/var/wessaorg/rcomp/tmp/127ek1322175464.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2791h1322175464.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/32afp1322175464.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/40dmy1322175464.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5m5r51322175464.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 = 49
Frequency = 1
1 2 3 4 5 6
-0.26642204 -0.29142629 -0.05545204 -0.01559343 0.45494316 0.38013475
7 8 9 10 11 12
-0.48511547 0.34728982 0.52123595 0.27876809 -0.39937087 -0.61682176
13 14 15 16 17 18
0.24293037 -0.20935132 0.56056009 -0.52833076 -0.26278124 0.61913233
19 20 21 22 23 24
0.42555032 -0.32789290 0.45585252 0.49212802 -0.39814057 -0.31178448
25 26 27 28 29 30
-0.38565274 -0.49982797 0.56129080 -0.39640619 -0.61150432 -0.43262400
31 32 33 34 35 36
0.47532995 0.54065526 0.47189744 0.52228335 -0.27777141 -0.38724820
37 38 39 40 41 42
-0.49254610 0.52077737 0.62042111 -0.39775474 -0.20219932 -0.42074267
43 44 45 46 47 48
-0.42459257 -0.39901764 -0.46662275 0.57639885 0.64639049 0.66212317
49
-0.41309941
> postscript(file="/var/wessaorg/rcomp/tmp/6sau61322175464.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 = 49
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.26642204 NA
1 -0.29142629 -0.26642204
2 -0.05545204 -0.29142629
3 -0.01559343 -0.05545204
4 0.45494316 -0.01559343
5 0.38013475 0.45494316
6 -0.48511547 0.38013475
7 0.34728982 -0.48511547
8 0.52123595 0.34728982
9 0.27876809 0.52123595
10 -0.39937087 0.27876809
11 -0.61682176 -0.39937087
12 0.24293037 -0.61682176
13 -0.20935132 0.24293037
14 0.56056009 -0.20935132
15 -0.52833076 0.56056009
16 -0.26278124 -0.52833076
17 0.61913233 -0.26278124
18 0.42555032 0.61913233
19 -0.32789290 0.42555032
20 0.45585252 -0.32789290
21 0.49212802 0.45585252
22 -0.39814057 0.49212802
23 -0.31178448 -0.39814057
24 -0.38565274 -0.31178448
25 -0.49982797 -0.38565274
26 0.56129080 -0.49982797
27 -0.39640619 0.56129080
28 -0.61150432 -0.39640619
29 -0.43262400 -0.61150432
30 0.47532995 -0.43262400
31 0.54065526 0.47532995
32 0.47189744 0.54065526
33 0.52228335 0.47189744
34 -0.27777141 0.52228335
35 -0.38724820 -0.27777141
36 -0.49254610 -0.38724820
37 0.52077737 -0.49254610
38 0.62042111 0.52077737
39 -0.39775474 0.62042111
40 -0.20219932 -0.39775474
41 -0.42074267 -0.20219932
42 -0.42459257 -0.42074267
43 -0.39901764 -0.42459257
44 -0.46662275 -0.39901764
45 0.57639885 -0.46662275
46 0.64639049 0.57639885
47 0.66212317 0.64639049
48 -0.41309941 0.66212317
49 NA -0.41309941
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.29142629 -0.26642204
[2,] -0.05545204 -0.29142629
[3,] -0.01559343 -0.05545204
[4,] 0.45494316 -0.01559343
[5,] 0.38013475 0.45494316
[6,] -0.48511547 0.38013475
[7,] 0.34728982 -0.48511547
[8,] 0.52123595 0.34728982
[9,] 0.27876809 0.52123595
[10,] -0.39937087 0.27876809
[11,] -0.61682176 -0.39937087
[12,] 0.24293037 -0.61682176
[13,] -0.20935132 0.24293037
[14,] 0.56056009 -0.20935132
[15,] -0.52833076 0.56056009
[16,] -0.26278124 -0.52833076
[17,] 0.61913233 -0.26278124
[18,] 0.42555032 0.61913233
[19,] -0.32789290 0.42555032
[20,] 0.45585252 -0.32789290
[21,] 0.49212802 0.45585252
[22,] -0.39814057 0.49212802
[23,] -0.31178448 -0.39814057
[24,] -0.38565274 -0.31178448
[25,] -0.49982797 -0.38565274
[26,] 0.56129080 -0.49982797
[27,] -0.39640619 0.56129080
[28,] -0.61150432 -0.39640619
[29,] -0.43262400 -0.61150432
[30,] 0.47532995 -0.43262400
[31,] 0.54065526 0.47532995
[32,] 0.47189744 0.54065526
[33,] 0.52228335 0.47189744
[34,] -0.27777141 0.52228335
[35,] -0.38724820 -0.27777141
[36,] -0.49254610 -0.38724820
[37,] 0.52077737 -0.49254610
[38,] 0.62042111 0.52077737
[39,] -0.39775474 0.62042111
[40,] -0.20219932 -0.39775474
[41,] -0.42074267 -0.20219932
[42,] -0.42459257 -0.42074267
[43,] -0.39901764 -0.42459257
[44,] -0.46662275 -0.39901764
[45,] 0.57639885 -0.46662275
[46,] 0.64639049 0.57639885
[47,] 0.66212317 0.64639049
[48,] -0.41309941 0.66212317
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.29142629 -0.26642204
2 -0.05545204 -0.29142629
3 -0.01559343 -0.05545204
4 0.45494316 -0.01559343
5 0.38013475 0.45494316
6 -0.48511547 0.38013475
7 0.34728982 -0.48511547
8 0.52123595 0.34728982
9 0.27876809 0.52123595
10 -0.39937087 0.27876809
11 -0.61682176 -0.39937087
12 0.24293037 -0.61682176
13 -0.20935132 0.24293037
14 0.56056009 -0.20935132
15 -0.52833076 0.56056009
16 -0.26278124 -0.52833076
17 0.61913233 -0.26278124
18 0.42555032 0.61913233
19 -0.32789290 0.42555032
20 0.45585252 -0.32789290
21 0.49212802 0.45585252
22 -0.39814057 0.49212802
23 -0.31178448 -0.39814057
24 -0.38565274 -0.31178448
25 -0.49982797 -0.38565274
26 0.56129080 -0.49982797
27 -0.39640619 0.56129080
28 -0.61150432 -0.39640619
29 -0.43262400 -0.61150432
30 0.47532995 -0.43262400
31 0.54065526 0.47532995
32 0.47189744 0.54065526
33 0.52228335 0.47189744
34 -0.27777141 0.52228335
35 -0.38724820 -0.27777141
36 -0.49254610 -0.38724820
37 0.52077737 -0.49254610
38 0.62042111 0.52077737
39 -0.39775474 0.62042111
40 -0.20219932 -0.39775474
41 -0.42074267 -0.20219932
42 -0.42459257 -0.42074267
43 -0.39901764 -0.42459257
44 -0.46662275 -0.39901764
45 0.57639885 -0.46662275
46 0.64639049 0.57639885
47 0.66212317 0.64639049
48 -0.41309941 0.66212317
> 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/wessaorg/rcomp/tmp/7tpig1322175464.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8y0201322175464.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/98hkf1322175464.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10rmwt1322175464.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11xcjx1322175464.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/wessaorg/rcomp/tmp/12q5vc1322175464.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/wessaorg/rcomp/tmp/13ani31322175464.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/wessaorg/rcomp/tmp/14xc9r1322175464.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/wessaorg/rcomp/tmp/152r801322175464.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/wessaorg/rcomp/tmp/16sa101322175465.tab")
+ }
>
> try(system("convert tmp/127ek1322175464.ps tmp/127ek1322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/2791h1322175464.ps tmp/2791h1322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/32afp1322175464.ps tmp/32afp1322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/40dmy1322175464.ps tmp/40dmy1322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/5m5r51322175464.ps tmp/5m5r51322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sau61322175464.ps tmp/6sau61322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tpig1322175464.ps tmp/7tpig1322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/8y0201322175464.ps tmp/8y0201322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/98hkf1322175464.ps tmp/98hkf1322175464.png",intern=TRUE))
character(0)
> try(system("convert tmp/10rmwt1322175464.ps tmp/10rmwt1322175464.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.070 0.597 3.720