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(825,444,696,627,677,387,825,696,656,327,677,825,785,448,656,677,412,225,785,656,352,182,412,785,839,460,352,412,729,411,839,352,696,342,729,839,641,361,696,729,695,377,641,696,638,331,695,641,762,428,638,695,635,340,762,638,721,352,635,762,854,461,721,635,418,221,854,721,367,198,418,854,824,422,367,418,687,329,824,367,601,320,687,824,676,375,601,687,740,364,676,601,691,351,740,676,683,380,691,740,594,319,683,691,729,322,594,683,731,386,729,594,386,221,731,729,331,187,386,731,707,344,331,386,715,342,707,331,657,365,715,707,653,313,657,715,642,356,653,657,643,337,642,653,718,389,643,642,654,326,718,643,632,343,654,718,731,357,632,654,392,220,731,632,344,228,392,731,792,391,344,392,852,425,792,344,649,332,852,792,629,298,649,852,685,360,629,649,617,326,685,629,715,325,617,685,715,393,715,617,629,301,715,715,916,426,629,715,531,265,916,629,357,210,531,916,917,429,357,531,828,440,917,357,708,357,828,917,858,431,708,828),dim=c(4,58),dimnames=list(c('Y','X','Y-1','Y-2'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y-1','Y-2'),1:58))
> 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 Y-1 Y-2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 825 444 696 627 1 0 0 0 0 0 0 0 0 0 0 1
2 677 387 825 696 0 1 0 0 0 0 0 0 0 0 0 2
3 656 327 677 825 0 0 1 0 0 0 0 0 0 0 0 3
4 785 448 656 677 0 0 0 1 0 0 0 0 0 0 0 4
5 412 225 785 656 0 0 0 0 1 0 0 0 0 0 0 5
6 352 182 412 785 0 0 0 0 0 1 0 0 0 0 0 6
7 839 460 352 412 0 0 0 0 0 0 1 0 0 0 0 7
8 729 411 839 352 0 0 0 0 0 0 0 1 0 0 0 8
9 696 342 729 839 0 0 0 0 0 0 0 0 1 0 0 9
10 641 361 696 729 0 0 0 0 0 0 0 0 0 1 0 10
11 695 377 641 696 0 0 0 0 0 0 0 0 0 0 1 11
12 638 331 695 641 0 0 0 0 0 0 0 0 0 0 0 12
13 762 428 638 695 1 0 0 0 0 0 0 0 0 0 0 13
14 635 340 762 638 0 1 0 0 0 0 0 0 0 0 0 14
15 721 352 635 762 0 0 1 0 0 0 0 0 0 0 0 15
16 854 461 721 635 0 0 0 1 0 0 0 0 0 0 0 16
17 418 221 854 721 0 0 0 0 1 0 0 0 0 0 0 17
18 367 198 418 854 0 0 0 0 0 1 0 0 0 0 0 18
19 824 422 367 418 0 0 0 0 0 0 1 0 0 0 0 19
20 687 329 824 367 0 0 0 0 0 0 0 1 0 0 0 20
21 601 320 687 824 0 0 0 0 0 0 0 0 1 0 0 21
22 676 375 601 687 0 0 0 0 0 0 0 0 0 1 0 22
23 740 364 676 601 0 0 0 0 0 0 0 0 0 0 1 23
24 691 351 740 676 0 0 0 0 0 0 0 0 0 0 0 24
25 683 380 691 740 1 0 0 0 0 0 0 0 0 0 0 25
26 594 319 683 691 0 1 0 0 0 0 0 0 0 0 0 26
27 729 322 594 683 0 0 1 0 0 0 0 0 0 0 0 27
28 731 386 729 594 0 0 0 1 0 0 0 0 0 0 0 28
29 386 221 731 729 0 0 0 0 1 0 0 0 0 0 0 29
30 331 187 386 731 0 0 0 0 0 1 0 0 0 0 0 30
31 707 344 331 386 0 0 0 0 0 0 1 0 0 0 0 31
32 715 342 707 331 0 0 0 0 0 0 0 1 0 0 0 32
33 657 365 715 707 0 0 0 0 0 0 0 0 1 0 0 33
34 653 313 657 715 0 0 0 0 0 0 0 0 0 1 0 34
35 642 356 653 657 0 0 0 0 0 0 0 0 0 0 1 35
36 643 337 642 653 0 0 0 0 0 0 0 0 0 0 0 36
37 718 389 643 642 1 0 0 0 0 0 0 0 0 0 0 37
38 654 326 718 643 0 1 0 0 0 0 0 0 0 0 0 38
39 632 343 654 718 0 0 1 0 0 0 0 0 0 0 0 39
40 731 357 632 654 0 0 0 1 0 0 0 0 0 0 0 40
41 392 220 731 632 0 0 0 0 1 0 0 0 0 0 0 41
42 344 228 392 731 0 0 0 0 0 1 0 0 0 0 0 42
43 792 391 344 392 0 0 0 0 0 0 1 0 0 0 0 43
44 852 425 792 344 0 0 0 0 0 0 0 1 0 0 0 44
45 649 332 852 792 0 0 0 0 0 0 0 0 1 0 0 45
46 629 298 649 852 0 0 0 0 0 0 0 0 0 1 0 46
47 685 360 629 649 0 0 0 0 0 0 0 0 0 0 1 47
48 617 326 685 629 0 0 0 0 0 0 0 0 0 0 0 48
49 715 325 617 685 1 0 0 0 0 0 0 0 0 0 0 49
50 715 393 715 617 0 1 0 0 0 0 0 0 0 0 0 50
51 629 301 715 715 0 0 1 0 0 0 0 0 0 0 0 51
52 916 426 629 715 0 0 0 1 0 0 0 0 0 0 0 52
53 531 265 916 629 0 0 0 0 1 0 0 0 0 0 0 53
54 357 210 531 916 0 0 0 0 0 1 0 0 0 0 0 54
55 917 429 357 531 0 0 0 0 0 0 1 0 0 0 0 55
56 828 440 917 357 0 0 0 0 0 0 0 1 0 0 0 56
57 708 357 828 917 0 0 0 0 0 0 0 0 1 0 0 57
58 858 431 708 828 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X `Y-1` `Y-2` M1 M2
104.97838 1.33317 -0.04041 0.14977 15.96837 -10.36451
M3 M4 M5 M6 M7 M8
23.23242 50.52479 -76.49523 -150.36644 89.96765 92.38042
M9 M10 M11 t
-18.74603 -2.90955 4.94440 0.81937
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-66.552 -22.325 -5.256 22.732 71.094
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 104.97838 95.58221 1.098 0.27833
X 1.33317 0.16879 7.898 7.77e-10 ***
`Y-1` -0.04041 0.11267 -0.359 0.72168
`Y-2` 0.14977 0.10530 1.422 0.16230
M1 15.96837 27.84569 0.573 0.56939
M2 -10.36451 25.65998 -0.404 0.68832
M3 23.23242 27.41612 0.847 0.40158
M4 50.52479 28.80601 1.754 0.08673 .
M5 -76.49523 35.51231 -2.154 0.03702 *
M6 -150.36644 44.61114 -3.371 0.00162 **
M7 89.96765 52.11724 1.726 0.09165 .
M8 92.38042 44.59275 2.072 0.04448 *
M9 -18.74603 30.51787 -0.614 0.54235
M10 -2.90955 28.60456 -0.102 0.91947
M11 4.94440 27.51554 0.180 0.85826
t 0.81937 0.30099 2.722 0.00940 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 37.38 on 42 degrees of freedom
Multiple R-squared: 0.9525, Adjusted R-squared: 0.9355
F-statistic: 56.11 on 15 and 42 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,] 0.008675816 0.01735163 0.9913242
[2,] 0.022240194 0.04448039 0.9777598
[3,] 0.150627306 0.30125461 0.8493727
[4,] 0.121000387 0.24200077 0.8789996
[5,] 0.100446555 0.20089311 0.8995534
[6,] 0.079820952 0.15964190 0.9201790
[7,] 0.082934159 0.16586832 0.9170658
[8,] 0.051755897 0.10351179 0.9482441
[9,] 0.140035209 0.28007042 0.8599648
[10,] 0.187995760 0.37599152 0.8120042
[11,] 0.133533359 0.26706672 0.8664666
[12,] 0.296489936 0.59297987 0.7035101
[13,] 0.212496245 0.42499249 0.7875038
[14,] 0.214404427 0.42880885 0.7855956
[15,] 0.246005973 0.49201195 0.7539940
[16,] 0.283063115 0.56612623 0.7169369
[17,] 0.247846952 0.49569390 0.7521530
[18,] 0.192281524 0.38456305 0.8077185
[19,] 0.169909482 0.33981896 0.8300905
[20,] 0.230885896 0.46177179 0.7691141
[21,] 0.357062269 0.71412454 0.6429377
> postscript(file="/var/www/html/rcomp/tmp/1lly71259094423.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2368d1259094423.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/335281259094423.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wsu71259094423.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5kqvy1259094423.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 = 58
Frequency = 1
1 2 3 4 5 6 7
45.524392 -6.093442 -6.820448 -45.927631 12.926625 48.912509 -22.420723
8 9 10 11 12 13 14
-41.663559 50.248596 -31.595966 -4.879837 13.990187 -18.505469 10.874001
15 16 17 18 19 20 21
22.756442 4.825532 7.479696 22.657576 3.114555 12.970884 -24.704743
22 23 24 25 26 27 28
-22.640988 63.261331 27.070666 -47.944174 -23.091993 71.094204 -21.555642
29 30 31 32 33 34 35
-40.520950 8.618784 -16.392943 14.471461 -19.875071 25.251963 -43.222378
36 37 38 39 40 41 42
-12.612586 -22.037069 26.346602 -66.552388 -5.632024 -28.492453 -42.631071
43 44 45 46 47 48 49
-4.257577 32.473714 -0.907984 -9.424971 -15.159115 -28.448267 42.962320
50 51 52 53 54 55 56
-8.035167 -20.477811 68.289765 48.607082 -37.557798 39.956688 -18.252499
57 58
-4.760798 38.409963
> postscript(file="/var/www/html/rcomp/tmp/6v34j1259094423.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 45.524392 NA
1 -6.093442 45.524392
2 -6.820448 -6.093442
3 -45.927631 -6.820448
4 12.926625 -45.927631
5 48.912509 12.926625
6 -22.420723 48.912509
7 -41.663559 -22.420723
8 50.248596 -41.663559
9 -31.595966 50.248596
10 -4.879837 -31.595966
11 13.990187 -4.879837
12 -18.505469 13.990187
13 10.874001 -18.505469
14 22.756442 10.874001
15 4.825532 22.756442
16 7.479696 4.825532
17 22.657576 7.479696
18 3.114555 22.657576
19 12.970884 3.114555
20 -24.704743 12.970884
21 -22.640988 -24.704743
22 63.261331 -22.640988
23 27.070666 63.261331
24 -47.944174 27.070666
25 -23.091993 -47.944174
26 71.094204 -23.091993
27 -21.555642 71.094204
28 -40.520950 -21.555642
29 8.618784 -40.520950
30 -16.392943 8.618784
31 14.471461 -16.392943
32 -19.875071 14.471461
33 25.251963 -19.875071
34 -43.222378 25.251963
35 -12.612586 -43.222378
36 -22.037069 -12.612586
37 26.346602 -22.037069
38 -66.552388 26.346602
39 -5.632024 -66.552388
40 -28.492453 -5.632024
41 -42.631071 -28.492453
42 -4.257577 -42.631071
43 32.473714 -4.257577
44 -0.907984 32.473714
45 -9.424971 -0.907984
46 -15.159115 -9.424971
47 -28.448267 -15.159115
48 42.962320 -28.448267
49 -8.035167 42.962320
50 -20.477811 -8.035167
51 68.289765 -20.477811
52 48.607082 68.289765
53 -37.557798 48.607082
54 39.956688 -37.557798
55 -18.252499 39.956688
56 -4.760798 -18.252499
57 38.409963 -4.760798
58 NA 38.409963
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -6.093442 45.524392
[2,] -6.820448 -6.093442
[3,] -45.927631 -6.820448
[4,] 12.926625 -45.927631
[5,] 48.912509 12.926625
[6,] -22.420723 48.912509
[7,] -41.663559 -22.420723
[8,] 50.248596 -41.663559
[9,] -31.595966 50.248596
[10,] -4.879837 -31.595966
[11,] 13.990187 -4.879837
[12,] -18.505469 13.990187
[13,] 10.874001 -18.505469
[14,] 22.756442 10.874001
[15,] 4.825532 22.756442
[16,] 7.479696 4.825532
[17,] 22.657576 7.479696
[18,] 3.114555 22.657576
[19,] 12.970884 3.114555
[20,] -24.704743 12.970884
[21,] -22.640988 -24.704743
[22,] 63.261331 -22.640988
[23,] 27.070666 63.261331
[24,] -47.944174 27.070666
[25,] -23.091993 -47.944174
[26,] 71.094204 -23.091993
[27,] -21.555642 71.094204
[28,] -40.520950 -21.555642
[29,] 8.618784 -40.520950
[30,] -16.392943 8.618784
[31,] 14.471461 -16.392943
[32,] -19.875071 14.471461
[33,] 25.251963 -19.875071
[34,] -43.222378 25.251963
[35,] -12.612586 -43.222378
[36,] -22.037069 -12.612586
[37,] 26.346602 -22.037069
[38,] -66.552388 26.346602
[39,] -5.632024 -66.552388
[40,] -28.492453 -5.632024
[41,] -42.631071 -28.492453
[42,] -4.257577 -42.631071
[43,] 32.473714 -4.257577
[44,] -0.907984 32.473714
[45,] -9.424971 -0.907984
[46,] -15.159115 -9.424971
[47,] -28.448267 -15.159115
[48,] 42.962320 -28.448267
[49,] -8.035167 42.962320
[50,] -20.477811 -8.035167
[51,] 68.289765 -20.477811
[52,] 48.607082 68.289765
[53,] -37.557798 48.607082
[54,] 39.956688 -37.557798
[55,] -18.252499 39.956688
[56,] -4.760798 -18.252499
[57,] 38.409963 -4.760798
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -6.093442 45.524392
2 -6.820448 -6.093442
3 -45.927631 -6.820448
4 12.926625 -45.927631
5 48.912509 12.926625
6 -22.420723 48.912509
7 -41.663559 -22.420723
8 50.248596 -41.663559
9 -31.595966 50.248596
10 -4.879837 -31.595966
11 13.990187 -4.879837
12 -18.505469 13.990187
13 10.874001 -18.505469
14 22.756442 10.874001
15 4.825532 22.756442
16 7.479696 4.825532
17 22.657576 7.479696
18 3.114555 22.657576
19 12.970884 3.114555
20 -24.704743 12.970884
21 -22.640988 -24.704743
22 63.261331 -22.640988
23 27.070666 63.261331
24 -47.944174 27.070666
25 -23.091993 -47.944174
26 71.094204 -23.091993
27 -21.555642 71.094204
28 -40.520950 -21.555642
29 8.618784 -40.520950
30 -16.392943 8.618784
31 14.471461 -16.392943
32 -19.875071 14.471461
33 25.251963 -19.875071
34 -43.222378 25.251963
35 -12.612586 -43.222378
36 -22.037069 -12.612586
37 26.346602 -22.037069
38 -66.552388 26.346602
39 -5.632024 -66.552388
40 -28.492453 -5.632024
41 -42.631071 -28.492453
42 -4.257577 -42.631071
43 32.473714 -4.257577
44 -0.907984 32.473714
45 -9.424971 -0.907984
46 -15.159115 -9.424971
47 -28.448267 -15.159115
48 42.962320 -28.448267
49 -8.035167 42.962320
50 -20.477811 -8.035167
51 68.289765 -20.477811
52 48.607082 68.289765
53 -37.557798 48.607082
54 39.956688 -37.557798
55 -18.252499 39.956688
56 -4.760798 -18.252499
57 38.409963 -4.760798
> 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/7rnde1259094423.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/85wma1259094423.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9j1ub1259094423.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10avos1259094423.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ifqd1259094423.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/121lrf1259094423.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/13elmf1259094423.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/14lnuv1259094423.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/15qhqj1259094423.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/16v6eg1259094424.tab")
+ }
>
> system("convert tmp/1lly71259094423.ps tmp/1lly71259094423.png")
> system("convert tmp/2368d1259094423.ps tmp/2368d1259094423.png")
> system("convert tmp/335281259094423.ps tmp/335281259094423.png")
> system("convert tmp/4wsu71259094423.ps tmp/4wsu71259094423.png")
> system("convert tmp/5kqvy1259094423.ps tmp/5kqvy1259094423.png")
> system("convert tmp/6v34j1259094423.ps tmp/6v34j1259094423.png")
> system("convert tmp/7rnde1259094423.ps tmp/7rnde1259094423.png")
> system("convert tmp/85wma1259094423.ps tmp/85wma1259094423.png")
> system("convert tmp/9j1ub1259094423.ps tmp/9j1ub1259094423.png")
> system("convert tmp/10avos1259094423.ps tmp/10avos1259094423.png")
>
>
> proc.time()
user system elapsed
2.391 1.597 3.064