R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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
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Type 'q()' to quit R.
> x <- array(list(627,356,696,386,825,444,677,387,656,327,785,448,412,225,352,182,839,460,729,411,696,342,641,361,695,377,638,331,762,428,635,340,721,352,854,461,418,221,367,198,824,422,687,329,601,320,676,375,740,364,691,351,683,380,594,319,729,322,731,386,386,221,331,187,707,344,715,342,657,365,653,313,642,356,643,337,718,389,654,326,632,343,731,357,392,220,344,228,792,391,852,425,649,332,629,298,685,360,617,326,715,325,715,393,629,301,916,426,531,265,357,210,917,429,828,440,708,357,858,431),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> 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 = 'Include Monthly 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
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 627 356 1 0 0 0 0 0 0 0 0 0 0
2 696 386 0 1 0 0 0 0 0 0 0 0 0
3 825 444 0 0 1 0 0 0 0 0 0 0 0
4 677 387 0 0 0 1 0 0 0 0 0 0 0
5 656 327 0 0 0 0 1 0 0 0 0 0 0
6 785 448 0 0 0 0 0 1 0 0 0 0 0
7 412 225 0 0 0 0 0 0 1 0 0 0 0
8 352 182 0 0 0 0 0 0 0 1 0 0 0
9 839 460 0 0 0 0 0 0 0 0 1 0 0
10 729 411 0 0 0 0 0 0 0 0 0 1 0
11 696 342 0 0 0 0 0 0 0 0 0 0 1
12 641 361 0 0 0 0 0 0 0 0 0 0 0
13 695 377 1 0 0 0 0 0 0 0 0 0 0
14 638 331 0 1 0 0 0 0 0 0 0 0 0
15 762 428 0 0 1 0 0 0 0 0 0 0 0
16 635 340 0 0 0 1 0 0 0 0 0 0 0
17 721 352 0 0 0 0 1 0 0 0 0 0 0
18 854 461 0 0 0 0 0 1 0 0 0 0 0
19 418 221 0 0 0 0 0 0 1 0 0 0 0
20 367 198 0 0 0 0 0 0 0 1 0 0 0
21 824 422 0 0 0 0 0 0 0 0 1 0 0
22 687 329 0 0 0 0 0 0 0 0 0 1 0
23 601 320 0 0 0 0 0 0 0 0 0 0 1
24 676 375 0 0 0 0 0 0 0 0 0 0 0
25 740 364 1 0 0 0 0 0 0 0 0 0 0
26 691 351 0 1 0 0 0 0 0 0 0 0 0
27 683 380 0 0 1 0 0 0 0 0 0 0 0
28 594 319 0 0 0 1 0 0 0 0 0 0 0
29 729 322 0 0 0 0 1 0 0 0 0 0 0
30 731 386 0 0 0 0 0 1 0 0 0 0 0
31 386 221 0 0 0 0 0 0 1 0 0 0 0
32 331 187 0 0 0 0 0 0 0 1 0 0 0
33 707 344 0 0 0 0 0 0 0 0 1 0 0
34 715 342 0 0 0 0 0 0 0 0 0 1 0
35 657 365 0 0 0 0 0 0 0 0 0 0 1
36 653 313 0 0 0 0 0 0 0 0 0 0 0
37 642 356 1 0 0 0 0 0 0 0 0 0 0
38 643 337 0 1 0 0 0 0 0 0 0 0 0
39 718 389 0 0 1 0 0 0 0 0 0 0 0
40 654 326 0 0 0 1 0 0 0 0 0 0 0
41 632 343 0 0 0 0 1 0 0 0 0 0 0
42 731 357 0 0 0 0 0 1 0 0 0 0 0
43 392 220 0 0 0 0 0 0 1 0 0 0 0
44 344 228 0 0 0 0 0 0 0 1 0 0 0
45 792 391 0 0 0 0 0 0 0 0 1 0 0
46 852 425 0 0 0 0 0 0 0 0 0 1 0
47 649 332 0 0 0 0 0 0 0 0 0 0 1
48 629 298 0 0 0 0 0 0 0 0 0 0 0
49 685 360 1 0 0 0 0 0 0 0 0 0 0
50 617 326 0 1 0 0 0 0 0 0 0 0 0
51 715 325 0 0 1 0 0 0 0 0 0 0 0
52 715 393 0 0 0 1 0 0 0 0 0 0 0
53 629 301 0 0 0 0 1 0 0 0 0 0 0
54 916 426 0 0 0 0 0 1 0 0 0 0 0
55 531 265 0 0 0 0 0 0 1 0 0 0 0
56 357 210 0 0 0 0 0 0 0 1 0 0 0
57 917 429 0 0 0 0 0 0 0 0 1 0 0
58 828 440 0 0 0 0 0 0 0 0 0 1 0
59 708 357 0 0 0 0 0 0 0 0 0 0 1
60 858 431 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
236.299 1.280 -22.559 -22.370 1.079 -33.072
M5 M6 M7 M8 M9 M10
16.043 35.211 -103.367 -143.341 55.802 27.542
M11
-13.330
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-60.844 -23.692 -1.754 18.470 99.290
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 236.2987 61.4692 3.844 0.000362 ***
X 1.2798 0.1654 7.739 6.27e-10 ***
M1 -22.5587 25.3284 -0.891 0.377654
M2 -22.3698 25.3496 -0.882 0.382025
M3 1.0791 26.0548 0.041 0.967141
M4 -33.0725 25.3055 -1.307 0.197596
M5 16.0430 25.6815 0.625 0.535194
M6 35.2113 27.1780 1.296 0.201448
M7 -103.3675 32.6939 -3.162 0.002746 **
M8 -143.3410 35.9704 -3.985 0.000234 ***
M9 55.8021 26.8097 2.081 0.042869 *
M10 27.5423 25.9120 1.063 0.293250
M11 -13.3303 25.3849 -0.525 0.601962
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 40.01 on 47 degrees of freedom
Multiple R-squared: 0.9392, Adjusted R-squared: 0.9237
F-statistic: 60.49 on 12 and 47 DF, p-value: < 2.2e-16
> 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,] 0.24859878 0.49719756 0.7514012
[2,] 0.16927703 0.33855406 0.8307230
[3,] 0.18325465 0.36650931 0.8167453
[4,] 0.10277232 0.20554464 0.8972277
[5,] 0.05623777 0.11247555 0.9437622
[6,] 0.04607619 0.09215238 0.9539238
[7,] 0.04540917 0.09081834 0.9545908
[8,] 0.08155684 0.16311367 0.9184432
[9,] 0.07212305 0.14424609 0.9278770
[10,] 0.19100046 0.38200093 0.8089995
[11,] 0.15423204 0.30846407 0.8457680
[12,] 0.18037166 0.36074332 0.8196283
[13,] 0.12644593 0.25289187 0.8735541
[14,] 0.24736471 0.49472943 0.7526353
[15,] 0.28645938 0.57291875 0.7135406
[16,] 0.25501126 0.51002252 0.7449887
[17,] 0.21142697 0.42285393 0.7885730
[18,] 0.17755078 0.35510157 0.8224492
[19,] 0.14876788 0.29753577 0.8512321
[20,] 0.15299030 0.30598060 0.8470097
[21,] 0.13969740 0.27939480 0.8603026
[22,] 0.11674783 0.23349565 0.8832522
[23,] 0.07391860 0.14783720 0.9260814
[24,] 0.15744621 0.31489243 0.8425538
[25,] 0.19232202 0.38464403 0.8076780
[26,] 0.39979594 0.79959187 0.6002041
[27,] 0.38477526 0.76955053 0.6152247
[28,] 0.41489952 0.82979903 0.5851005
[29,] 0.40708525 0.81417050 0.5929147
> postscript(file="/var/www/html/rcomp/tmp/1fq1y1258566673.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/242mv1258566673.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32q7c1258566673.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4vj9r1258566673.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gjj91258566673.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 = 60
Frequency = 1
1 2 3 4 5 6
-42.3532379 -11.9365350 19.3855282 -21.5136229 -14.8403751 -59.8659230
7 8 9 10 11 12
-8.8890128 26.1164363 -41.8144718 -60.8439487 35.3357749 -57.3109872
13 14 15 16 17 18
-1.2292991 0.4531491 -23.1374728 -3.3624383 18.1643139 -7.5034847
19 20 21 22 23 24
2.2302369 20.6394373 -8.1815992 2.1006712 -31.5083514 -40.2283613
25 26 27 28 29 30
60.4082626 27.8569003 -40.7064758 -17.4863771 64.5586871 -34.5175518
31 32 33 34 35 36
-29.7697631 -1.2826259 -25.3562291 13.4631095 -33.0999111 16.1200098
37 38 39 40 41 42
-27.3532379 -2.2257256 -17.2247878 33.5549358 -59.3173741 2.5970088
43 44 45 46 47 48
-22.4899506 -40.7549358 -0.5074136 44.2386772 1.1338993 11.3171964
49 50 51 52 53 54
10.5275123 -14.1477888 61.6832082 8.8075025 -8.5652517 99.2899506
55 56 57 58 59 60
58.9184897 -4.7183119 75.8597137 1.0414907 28.1385884 70.1021422
> postscript(file="/var/www/html/rcomp/tmp/6snc11258566673.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -42.3532379 NA
1 -11.9365350 -42.3532379
2 19.3855282 -11.9365350
3 -21.5136229 19.3855282
4 -14.8403751 -21.5136229
5 -59.8659230 -14.8403751
6 -8.8890128 -59.8659230
7 26.1164363 -8.8890128
8 -41.8144718 26.1164363
9 -60.8439487 -41.8144718
10 35.3357749 -60.8439487
11 -57.3109872 35.3357749
12 -1.2292991 -57.3109872
13 0.4531491 -1.2292991
14 -23.1374728 0.4531491
15 -3.3624383 -23.1374728
16 18.1643139 -3.3624383
17 -7.5034847 18.1643139
18 2.2302369 -7.5034847
19 20.6394373 2.2302369
20 -8.1815992 20.6394373
21 2.1006712 -8.1815992
22 -31.5083514 2.1006712
23 -40.2283613 -31.5083514
24 60.4082626 -40.2283613
25 27.8569003 60.4082626
26 -40.7064758 27.8569003
27 -17.4863771 -40.7064758
28 64.5586871 -17.4863771
29 -34.5175518 64.5586871
30 -29.7697631 -34.5175518
31 -1.2826259 -29.7697631
32 -25.3562291 -1.2826259
33 13.4631095 -25.3562291
34 -33.0999111 13.4631095
35 16.1200098 -33.0999111
36 -27.3532379 16.1200098
37 -2.2257256 -27.3532379
38 -17.2247878 -2.2257256
39 33.5549358 -17.2247878
40 -59.3173741 33.5549358
41 2.5970088 -59.3173741
42 -22.4899506 2.5970088
43 -40.7549358 -22.4899506
44 -0.5074136 -40.7549358
45 44.2386772 -0.5074136
46 1.1338993 44.2386772
47 11.3171964 1.1338993
48 10.5275123 11.3171964
49 -14.1477888 10.5275123
50 61.6832082 -14.1477888
51 8.8075025 61.6832082
52 -8.5652517 8.8075025
53 99.2899506 -8.5652517
54 58.9184897 99.2899506
55 -4.7183119 58.9184897
56 75.8597137 -4.7183119
57 1.0414907 75.8597137
58 28.1385884 1.0414907
59 70.1021422 28.1385884
60 NA 70.1021422
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -11.9365350 -42.3532379
[2,] 19.3855282 -11.9365350
[3,] -21.5136229 19.3855282
[4,] -14.8403751 -21.5136229
[5,] -59.8659230 -14.8403751
[6,] -8.8890128 -59.8659230
[7,] 26.1164363 -8.8890128
[8,] -41.8144718 26.1164363
[9,] -60.8439487 -41.8144718
[10,] 35.3357749 -60.8439487
[11,] -57.3109872 35.3357749
[12,] -1.2292991 -57.3109872
[13,] 0.4531491 -1.2292991
[14,] -23.1374728 0.4531491
[15,] -3.3624383 -23.1374728
[16,] 18.1643139 -3.3624383
[17,] -7.5034847 18.1643139
[18,] 2.2302369 -7.5034847
[19,] 20.6394373 2.2302369
[20,] -8.1815992 20.6394373
[21,] 2.1006712 -8.1815992
[22,] -31.5083514 2.1006712
[23,] -40.2283613 -31.5083514
[24,] 60.4082626 -40.2283613
[25,] 27.8569003 60.4082626
[26,] -40.7064758 27.8569003
[27,] -17.4863771 -40.7064758
[28,] 64.5586871 -17.4863771
[29,] -34.5175518 64.5586871
[30,] -29.7697631 -34.5175518
[31,] -1.2826259 -29.7697631
[32,] -25.3562291 -1.2826259
[33,] 13.4631095 -25.3562291
[34,] -33.0999111 13.4631095
[35,] 16.1200098 -33.0999111
[36,] -27.3532379 16.1200098
[37,] -2.2257256 -27.3532379
[38,] -17.2247878 -2.2257256
[39,] 33.5549358 -17.2247878
[40,] -59.3173741 33.5549358
[41,] 2.5970088 -59.3173741
[42,] -22.4899506 2.5970088
[43,] -40.7549358 -22.4899506
[44,] -0.5074136 -40.7549358
[45,] 44.2386772 -0.5074136
[46,] 1.1338993 44.2386772
[47,] 11.3171964 1.1338993
[48,] 10.5275123 11.3171964
[49,] -14.1477888 10.5275123
[50,] 61.6832082 -14.1477888
[51,] 8.8075025 61.6832082
[52,] -8.5652517 8.8075025
[53,] 99.2899506 -8.5652517
[54,] 58.9184897 99.2899506
[55,] -4.7183119 58.9184897
[56,] 75.8597137 -4.7183119
[57,] 1.0414907 75.8597137
[58,] 28.1385884 1.0414907
[59,] 70.1021422 28.1385884
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -11.9365350 -42.3532379
2 19.3855282 -11.9365350
3 -21.5136229 19.3855282
4 -14.8403751 -21.5136229
5 -59.8659230 -14.8403751
6 -8.8890128 -59.8659230
7 26.1164363 -8.8890128
8 -41.8144718 26.1164363
9 -60.8439487 -41.8144718
10 35.3357749 -60.8439487
11 -57.3109872 35.3357749
12 -1.2292991 -57.3109872
13 0.4531491 -1.2292991
14 -23.1374728 0.4531491
15 -3.3624383 -23.1374728
16 18.1643139 -3.3624383
17 -7.5034847 18.1643139
18 2.2302369 -7.5034847
19 20.6394373 2.2302369
20 -8.1815992 20.6394373
21 2.1006712 -8.1815992
22 -31.5083514 2.1006712
23 -40.2283613 -31.5083514
24 60.4082626 -40.2283613
25 27.8569003 60.4082626
26 -40.7064758 27.8569003
27 -17.4863771 -40.7064758
28 64.5586871 -17.4863771
29 -34.5175518 64.5586871
30 -29.7697631 -34.5175518
31 -1.2826259 -29.7697631
32 -25.3562291 -1.2826259
33 13.4631095 -25.3562291
34 -33.0999111 13.4631095
35 16.1200098 -33.0999111
36 -27.3532379 16.1200098
37 -2.2257256 -27.3532379
38 -17.2247878 -2.2257256
39 33.5549358 -17.2247878
40 -59.3173741 33.5549358
41 2.5970088 -59.3173741
42 -22.4899506 2.5970088
43 -40.7549358 -22.4899506
44 -0.5074136 -40.7549358
45 44.2386772 -0.5074136
46 1.1338993 44.2386772
47 11.3171964 1.1338993
48 10.5275123 11.3171964
49 -14.1477888 10.5275123
50 61.6832082 -14.1477888
51 8.8075025 61.6832082
52 -8.5652517 8.8075025
53 99.2899506 -8.5652517
54 58.9184897 99.2899506
55 -4.7183119 58.9184897
56 75.8597137 -4.7183119
57 1.0414907 75.8597137
58 28.1385884 1.0414907
59 70.1021422 28.1385884
> 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/7ege41258566673.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/859h51258566673.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9uwm91258566673.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ifqd1258566673.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mx9z1258566673.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/12sh7j1258566673.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/13hyqk1258566673.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/14ofls1258566673.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/15gi711258566673.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/166t0j1258566674.tab")
+ }
>
> system("convert tmp/1fq1y1258566673.ps tmp/1fq1y1258566673.png")
> system("convert tmp/242mv1258566673.ps tmp/242mv1258566673.png")
> system("convert tmp/32q7c1258566673.ps tmp/32q7c1258566673.png")
> system("convert tmp/4vj9r1258566673.ps tmp/4vj9r1258566673.png")
> system("convert tmp/5gjj91258566673.ps tmp/5gjj91258566673.png")
> system("convert tmp/6snc11258566673.ps tmp/6snc11258566673.png")
> system("convert tmp/7ege41258566673.ps tmp/7ege41258566673.png")
> system("convert tmp/859h51258566673.ps tmp/859h51258566673.png")
> system("convert tmp/9uwm91258566673.ps tmp/9uwm91258566673.png")
> system("convert tmp/10ifqd1258566673.ps tmp/10ifqd1258566673.png")
>
>
> proc.time()
user system elapsed
2.412 1.583 2.859