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.
R is a collaborative project with many contributors.
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(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 = '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 t
1 627 356 1 0 0 0 0 0 0 0 0 0 0 1
2 696 386 0 1 0 0 0 0 0 0 0 0 0 2
3 825 444 0 0 1 0 0 0 0 0 0 0 0 3
4 677 387 0 0 0 1 0 0 0 0 0 0 0 4
5 656 327 0 0 0 0 1 0 0 0 0 0 0 5
6 785 448 0 0 0 0 0 1 0 0 0 0 0 6
7 412 225 0 0 0 0 0 0 1 0 0 0 0 7
8 352 182 0 0 0 0 0 0 0 1 0 0 0 8
9 839 460 0 0 0 0 0 0 0 0 1 0 0 9
10 729 411 0 0 0 0 0 0 0 0 0 1 0 10
11 696 342 0 0 0 0 0 0 0 0 0 0 1 11
12 641 361 0 0 0 0 0 0 0 0 0 0 0 12
13 695 377 1 0 0 0 0 0 0 0 0 0 0 13
14 638 331 0 1 0 0 0 0 0 0 0 0 0 14
15 762 428 0 0 1 0 0 0 0 0 0 0 0 15
16 635 340 0 0 0 1 0 0 0 0 0 0 0 16
17 721 352 0 0 0 0 1 0 0 0 0 0 0 17
18 854 461 0 0 0 0 0 1 0 0 0 0 0 18
19 418 221 0 0 0 0 0 0 1 0 0 0 0 19
20 367 198 0 0 0 0 0 0 0 1 0 0 0 20
21 824 422 0 0 0 0 0 0 0 0 1 0 0 21
22 687 329 0 0 0 0 0 0 0 0 0 1 0 22
23 601 320 0 0 0 0 0 0 0 0 0 0 1 23
24 676 375 0 0 0 0 0 0 0 0 0 0 0 24
25 740 364 1 0 0 0 0 0 0 0 0 0 0 25
26 691 351 0 1 0 0 0 0 0 0 0 0 0 26
27 683 380 0 0 1 0 0 0 0 0 0 0 0 27
28 594 319 0 0 0 1 0 0 0 0 0 0 0 28
29 729 322 0 0 0 0 1 0 0 0 0 0 0 29
30 731 386 0 0 0 0 0 1 0 0 0 0 0 30
31 386 221 0 0 0 0 0 0 1 0 0 0 0 31
32 331 187 0 0 0 0 0 0 0 1 0 0 0 32
33 707 344 0 0 0 0 0 0 0 0 1 0 0 33
34 715 342 0 0 0 0 0 0 0 0 0 1 0 34
35 657 365 0 0 0 0 0 0 0 0 0 0 1 35
36 653 313 0 0 0 0 0 0 0 0 0 0 0 36
37 642 356 1 0 0 0 0 0 0 0 0 0 0 37
38 643 337 0 1 0 0 0 0 0 0 0 0 0 38
39 718 389 0 0 1 0 0 0 0 0 0 0 0 39
40 654 326 0 0 0 1 0 0 0 0 0 0 0 40
41 632 343 0 0 0 0 1 0 0 0 0 0 0 41
42 731 357 0 0 0 0 0 1 0 0 0 0 0 42
43 392 220 0 0 0 0 0 0 1 0 0 0 0 43
44 344 228 0 0 0 0 0 0 0 1 0 0 0 44
45 792 391 0 0 0 0 0 0 0 0 1 0 0 45
46 852 425 0 0 0 0 0 0 0 0 0 1 0 46
47 649 332 0 0 0 0 0 0 0 0 0 0 1 47
48 629 298 0 0 0 0 0 0 0 0 0 0 0 48
49 685 360 1 0 0 0 0 0 0 0 0 0 0 49
50 617 326 0 1 0 0 0 0 0 0 0 0 0 50
51 715 325 0 0 1 0 0 0 0 0 0 0 0 51
52 715 393 0 0 0 1 0 0 0 0 0 0 0 52
53 629 301 0 0 0 0 1 0 0 0 0 0 0 53
54 916 426 0 0 0 0 0 1 0 0 0 0 0 54
55 531 265 0 0 0 0 0 0 1 0 0 0 0 55
56 357 210 0 0 0 0 0 0 0 1 0 0 0 56
57 917 429 0 0 0 0 0 0 0 0 1 0 0 57
58 828 440 0 0 0 0 0 0 0 0 0 1 0 58
59 708 357 0 0 0 0 0 0 0 0 0 0 1 59
60 858 431 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
186.681 1.331 -13.291 -13.141 7.038 -25.940
M5 M6 M7 M8 M9 M10
23.523 37.404 -92.613 -131.964 55.696 27.570
M11 t
-11.824 0.875
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-70.530 -26.509 -1.408 19.826 77.761
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 186.6806 58.6840 3.181 0.002628 **
X 1.3308 0.1528 8.708 2.74e-11 ***
M1 -13.2908 23.4605 -0.567 0.573795
M2 -13.1412 23.4783 -0.560 0.578388
M3 7.0378 24.0145 0.293 0.770793
M4 -25.9403 23.3624 -1.110 0.272621
M5 23.5231 23.7172 0.992 0.326476
M6 37.4037 24.9800 1.497 0.141134
M7 -92.6131 30.2360 -3.063 0.003656 **
M8 -131.9635 33.2498 -3.969 0.000251 ***
M9 55.6957 24.6317 2.261 0.028524 *
M10 27.5700 23.8069 1.158 0.252814
M11 -11.8235 23.3276 -0.507 0.614683
t 0.8750 0.2812 3.111 0.003198 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 36.76 on 46 degrees of freedom
Multiple R-squared: 0.9498, Adjusted R-squared: 0.9356
F-statistic: 66.9 on 13 and 46 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,] 0.25275732 0.50551465 0.7472427
[2,] 0.16218742 0.32437485 0.8378126
[3,] 0.08193859 0.16387719 0.9180614
[4,] 0.06638633 0.13277266 0.9336137
[5,] 0.04073909 0.08147818 0.9592609
[6,] 0.04402123 0.08804247 0.9559788
[7,] 0.13498867 0.26997734 0.8650113
[8,] 0.09100279 0.18200559 0.9089972
[9,] 0.16627512 0.33255024 0.8337249
[10,] 0.13843151 0.27686302 0.8615685
[11,] 0.18554250 0.37108500 0.8144575
[12,] 0.13238724 0.26477447 0.8676128
[13,] 0.43330021 0.86660042 0.5666998
[14,] 0.39280485 0.78560970 0.6071951
[15,] 0.39306696 0.78613391 0.6069330
[16,] 0.50677432 0.98645136 0.4932257
[17,] 0.41696112 0.83392223 0.5830389
[18,] 0.39925328 0.79850657 0.6007467
[19,] 0.37492412 0.74984824 0.6250759
[20,] 0.37255543 0.74511086 0.6274446
[21,] 0.32300967 0.64601934 0.6769903
[22,] 0.31177833 0.62355667 0.6882217
[23,] 0.27397581 0.54795162 0.7260242
[24,] 0.41744434 0.83488868 0.5825557
[25,] 0.44750613 0.89501226 0.5524939
[26,] 0.39968248 0.79936495 0.6003175
[27,] 0.35205342 0.70410685 0.6479466
> postscript(file="/var/www/html/rcomp/tmp/11x3n1258568494.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qbpa1258568494.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3twhb1258568494.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4g9n31258568494.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5d3641258568494.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
-21.0179676 7.0344174 37.7959742 -2.2471307 6.2605016 -40.5179026
7 8 9 10 11 12
12.3851113 48.0835502 -23.4040258 -40.9456129 56.3958875 -36.5871784
13 14 15 16 17 18
8.5364303 11.7271502 -14.4112280 7.7994616 27.4918293 0.6826358
19 20 21 22 23 24
13.2086983 31.2917859 1.6656584 15.6778441 -19.8267093 -30.7174075
25 26 27 28 29 30
60.3369254 27.6123157 -40.0338682 -15.7539028 64.9153729 -33.0092801
31 32 33 34 35 36
-29.2907849 -0.5692541 -22.0339548 15.8783825 -34.2107329 18.2906982
37 38 39 40 41 42
-37.5164173 -12.2564216 -27.5102595 24.4312410 -70.5302291 -4.9165040
43 44 45 46 47 48
-32.4595006 -52.6302075 -10.0795136 31.9251915 -8.7948865 3.7527285
49 50 51 52 53 54
-10.3389708 -34.1174617 44.1593814 -14.2296690 -28.1374747 77.7610509
55 56 57 58 59 60
36.1564758 -26.1758745 53.8518358 -22.5358052 6.4364411 45.2611592
> postscript(file="/var/www/html/rcomp/tmp/6e7191258568494.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 -21.0179676 NA
1 7.0344174 -21.0179676
2 37.7959742 7.0344174
3 -2.2471307 37.7959742
4 6.2605016 -2.2471307
5 -40.5179026 6.2605016
6 12.3851113 -40.5179026
7 48.0835502 12.3851113
8 -23.4040258 48.0835502
9 -40.9456129 -23.4040258
10 56.3958875 -40.9456129
11 -36.5871784 56.3958875
12 8.5364303 -36.5871784
13 11.7271502 8.5364303
14 -14.4112280 11.7271502
15 7.7994616 -14.4112280
16 27.4918293 7.7994616
17 0.6826358 27.4918293
18 13.2086983 0.6826358
19 31.2917859 13.2086983
20 1.6656584 31.2917859
21 15.6778441 1.6656584
22 -19.8267093 15.6778441
23 -30.7174075 -19.8267093
24 60.3369254 -30.7174075
25 27.6123157 60.3369254
26 -40.0338682 27.6123157
27 -15.7539028 -40.0338682
28 64.9153729 -15.7539028
29 -33.0092801 64.9153729
30 -29.2907849 -33.0092801
31 -0.5692541 -29.2907849
32 -22.0339548 -0.5692541
33 15.8783825 -22.0339548
34 -34.2107329 15.8783825
35 18.2906982 -34.2107329
36 -37.5164173 18.2906982
37 -12.2564216 -37.5164173
38 -27.5102595 -12.2564216
39 24.4312410 -27.5102595
40 -70.5302291 24.4312410
41 -4.9165040 -70.5302291
42 -32.4595006 -4.9165040
43 -52.6302075 -32.4595006
44 -10.0795136 -52.6302075
45 31.9251915 -10.0795136
46 -8.7948865 31.9251915
47 3.7527285 -8.7948865
48 -10.3389708 3.7527285
49 -34.1174617 -10.3389708
50 44.1593814 -34.1174617
51 -14.2296690 44.1593814
52 -28.1374747 -14.2296690
53 77.7610509 -28.1374747
54 36.1564758 77.7610509
55 -26.1758745 36.1564758
56 53.8518358 -26.1758745
57 -22.5358052 53.8518358
58 6.4364411 -22.5358052
59 45.2611592 6.4364411
60 NA 45.2611592
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 7.0344174 -21.0179676
[2,] 37.7959742 7.0344174
[3,] -2.2471307 37.7959742
[4,] 6.2605016 -2.2471307
[5,] -40.5179026 6.2605016
[6,] 12.3851113 -40.5179026
[7,] 48.0835502 12.3851113
[8,] -23.4040258 48.0835502
[9,] -40.9456129 -23.4040258
[10,] 56.3958875 -40.9456129
[11,] -36.5871784 56.3958875
[12,] 8.5364303 -36.5871784
[13,] 11.7271502 8.5364303
[14,] -14.4112280 11.7271502
[15,] 7.7994616 -14.4112280
[16,] 27.4918293 7.7994616
[17,] 0.6826358 27.4918293
[18,] 13.2086983 0.6826358
[19,] 31.2917859 13.2086983
[20,] 1.6656584 31.2917859
[21,] 15.6778441 1.6656584
[22,] -19.8267093 15.6778441
[23,] -30.7174075 -19.8267093
[24,] 60.3369254 -30.7174075
[25,] 27.6123157 60.3369254
[26,] -40.0338682 27.6123157
[27,] -15.7539028 -40.0338682
[28,] 64.9153729 -15.7539028
[29,] -33.0092801 64.9153729
[30,] -29.2907849 -33.0092801
[31,] -0.5692541 -29.2907849
[32,] -22.0339548 -0.5692541
[33,] 15.8783825 -22.0339548
[34,] -34.2107329 15.8783825
[35,] 18.2906982 -34.2107329
[36,] -37.5164173 18.2906982
[37,] -12.2564216 -37.5164173
[38,] -27.5102595 -12.2564216
[39,] 24.4312410 -27.5102595
[40,] -70.5302291 24.4312410
[41,] -4.9165040 -70.5302291
[42,] -32.4595006 -4.9165040
[43,] -52.6302075 -32.4595006
[44,] -10.0795136 -52.6302075
[45,] 31.9251915 -10.0795136
[46,] -8.7948865 31.9251915
[47,] 3.7527285 -8.7948865
[48,] -10.3389708 3.7527285
[49,] -34.1174617 -10.3389708
[50,] 44.1593814 -34.1174617
[51,] -14.2296690 44.1593814
[52,] -28.1374747 -14.2296690
[53,] 77.7610509 -28.1374747
[54,] 36.1564758 77.7610509
[55,] -26.1758745 36.1564758
[56,] 53.8518358 -26.1758745
[57,] -22.5358052 53.8518358
[58,] 6.4364411 -22.5358052
[59,] 45.2611592 6.4364411
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 7.0344174 -21.0179676
2 37.7959742 7.0344174
3 -2.2471307 37.7959742
4 6.2605016 -2.2471307
5 -40.5179026 6.2605016
6 12.3851113 -40.5179026
7 48.0835502 12.3851113
8 -23.4040258 48.0835502
9 -40.9456129 -23.4040258
10 56.3958875 -40.9456129
11 -36.5871784 56.3958875
12 8.5364303 -36.5871784
13 11.7271502 8.5364303
14 -14.4112280 11.7271502
15 7.7994616 -14.4112280
16 27.4918293 7.7994616
17 0.6826358 27.4918293
18 13.2086983 0.6826358
19 31.2917859 13.2086983
20 1.6656584 31.2917859
21 15.6778441 1.6656584
22 -19.8267093 15.6778441
23 -30.7174075 -19.8267093
24 60.3369254 -30.7174075
25 27.6123157 60.3369254
26 -40.0338682 27.6123157
27 -15.7539028 -40.0338682
28 64.9153729 -15.7539028
29 -33.0092801 64.9153729
30 -29.2907849 -33.0092801
31 -0.5692541 -29.2907849
32 -22.0339548 -0.5692541
33 15.8783825 -22.0339548
34 -34.2107329 15.8783825
35 18.2906982 -34.2107329
36 -37.5164173 18.2906982
37 -12.2564216 -37.5164173
38 -27.5102595 -12.2564216
39 24.4312410 -27.5102595
40 -70.5302291 24.4312410
41 -4.9165040 -70.5302291
42 -32.4595006 -4.9165040
43 -52.6302075 -32.4595006
44 -10.0795136 -52.6302075
45 31.9251915 -10.0795136
46 -8.7948865 31.9251915
47 3.7527285 -8.7948865
48 -10.3389708 3.7527285
49 -34.1174617 -10.3389708
50 44.1593814 -34.1174617
51 -14.2296690 44.1593814
52 -28.1374747 -14.2296690
53 77.7610509 -28.1374747
54 36.1564758 77.7610509
55 -26.1758745 36.1564758
56 53.8518358 -26.1758745
57 -22.5358052 53.8518358
58 6.4364411 -22.5358052
59 45.2611592 6.4364411
> 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/71e2h1258568494.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8lzmf1258568494.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ickc1258568494.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1038ln1258568494.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115o571258568494.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/12gbqu1258568494.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/13i6501258568494.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/147jl21258568494.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/15vhnq1258568494.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/16l67y1258568494.tab")
+ }
>
> system("convert tmp/11x3n1258568494.ps tmp/11x3n1258568494.png")
> system("convert tmp/2qbpa1258568494.ps tmp/2qbpa1258568494.png")
> system("convert tmp/3twhb1258568494.ps tmp/3twhb1258568494.png")
> system("convert tmp/4g9n31258568494.ps tmp/4g9n31258568494.png")
> system("convert tmp/5d3641258568494.ps tmp/5d3641258568494.png")
> system("convert tmp/6e7191258568494.ps tmp/6e7191258568494.png")
> system("convert tmp/71e2h1258568494.ps tmp/71e2h1258568494.png")
> system("convert tmp/8lzmf1258568494.ps tmp/8lzmf1258568494.png")
> system("convert tmp/9ickc1258568494.ps tmp/9ickc1258568494.png")
> system("convert tmp/1038ln1258568494.ps tmp/1038ln1258568494.png")
>
>
> proc.time()
user system elapsed
2.400 1.571 2.820