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(627,0,696,0,825,0,677,0,656,0,785,0,412,0,352,0,839,0,729,0,696,0,641,0,695,0,638,0,762,0,635,0,721,0,854,0,418,0,367,0,824,0,687,0,601,0,676,0,740,0,691,0,683,0,594,0,729,0,731,0,386,0,331,0,707,0,715,0,657,0,653,0,642,0,643,0,718,0,654,0,632,0,731,0,392,1,344,1,792,1,852,1,649,1,629,1,685,1,617,1,715,1,715,1,629,1,916,1,531,1,357,1,917,1,828,1,708,1,858,1,775,1,785,1,1006,1,789,1,734,1,906,1,532,1,387,1,991,1,841,1),dim=c(2,70),dimnames=list(c('Y','X'),1:70))
> y <- array(NA,dim=c(2,70),dimnames=list(c('Y','X'),1:70))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X
1 627 0
2 696 0
3 825 0
4 677 0
5 656 0
6 785 0
7 412 0
8 352 0
9 839 0
10 729 0
11 696 0
12 641 0
13 695 0
14 638 0
15 762 0
16 635 0
17 721 0
18 854 0
19 418 0
20 367 0
21 824 0
22 687 0
23 601 0
24 676 0
25 740 0
26 691 0
27 683 0
28 594 0
29 729 0
30 731 0
31 386 0
32 331 0
33 707 0
34 715 0
35 657 0
36 653 0
37 642 0
38 643 0
39 718 0
40 654 0
41 632 0
42 731 0
43 392 1
44 344 1
45 792 1
46 852 1
47 649 1
48 629 1
49 685 1
50 617 1
51 715 1
52 715 1
53 629 1
54 916 1
55 531 1
56 357 1
57 917 1
58 828 1
59 708 1
60 858 1
61 775 1
62 785 1
63 1006 1
64 789 1
65 734 1
66 906 1
67 532 1
68 387 1
69 991 1
70 841 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
653.57 56.43
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-366.00 -46.07 23.71 78.61 296.00
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 653.57 23.89 27.360 <2e-16 ***
X 56.43 37.77 1.494 0.140
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 154.8 on 68 degrees of freedom
Multiple R-squared: 0.03178, Adjusted R-squared: 0.01754
F-statistic: 2.232 on 1 and 68 DF, p-value: 0.1398
> 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,] 0.180055514 0.36011103 0.8199445
[2,] 0.119383772 0.23876754 0.8806162
[3,] 0.483171775 0.96634355 0.5168282
[4,] 0.753579752 0.49284050 0.2464202
[5,] 0.781011149 0.43797770 0.2189889
[6,] 0.709655017 0.58068997 0.2903450
[7,] 0.618433341 0.76313332 0.3815667
[8,] 0.520138027 0.95972395 0.4798620
[9,] 0.426759888 0.85351978 0.5732401
[10,] 0.337433617 0.67486723 0.6625664
[11,] 0.289909058 0.57981812 0.7100909
[12,] 0.219856116 0.43971223 0.7801439
[13,] 0.167781576 0.33556315 0.8322184
[14,] 0.200540819 0.40108164 0.7994592
[15,] 0.313554814 0.62710963 0.6864452
[16,] 0.501524023 0.99695195 0.4984760
[17,] 0.512600896 0.97479821 0.4873991
[18,] 0.438311360 0.87662272 0.5616886
[19,] 0.372965154 0.74593031 0.6270348
[20,] 0.304673301 0.60934660 0.6953267
[21,] 0.259911928 0.51982386 0.7400881
[22,] 0.206039796 0.41207959 0.7939602
[23,] 0.158912698 0.31782540 0.8410873
[24,] 0.124437243 0.24887449 0.8755628
[25,] 0.098360597 0.19672119 0.9016394
[26,] 0.077352750 0.15470550 0.9226472
[27,] 0.144966687 0.28993337 0.8550333
[28,] 0.318479018 0.63695804 0.6815210
[29,] 0.263926414 0.52785283 0.7360736
[30,] 0.216464545 0.43292909 0.7835355
[31,] 0.168393778 0.33678756 0.8316062
[32,] 0.127890412 0.25578082 0.8721096
[33,] 0.095119487 0.19023897 0.9048805
[34,] 0.069135517 0.13827103 0.9308645
[35,] 0.050951424 0.10190285 0.9490486
[36,] 0.035065628 0.07013126 0.9649344
[37,] 0.024313707 0.04862741 0.9756863
[38,] 0.016835148 0.03367030 0.9831649
[39,] 0.024621574 0.04924315 0.9753784
[40,] 0.056239968 0.11247994 0.9437600
[41,] 0.096312059 0.19262412 0.9036879
[42,] 0.127758537 0.25551707 0.8722415
[43,] 0.099158312 0.19831662 0.9008417
[44,] 0.077262437 0.15452487 0.9227376
[45,] 0.056547381 0.11309476 0.9434526
[46,] 0.043978155 0.08795631 0.9560218
[47,] 0.030764869 0.06152974 0.9692351
[48,] 0.020677182 0.04135436 0.9793228
[49,] 0.015002208 0.03000442 0.9849978
[50,] 0.019999989 0.03999998 0.9800000
[51,] 0.023379557 0.04675911 0.9766204
[52,] 0.149885587 0.29977117 0.8501144
[53,] 0.161663548 0.32332710 0.8383365
[54,] 0.125886339 0.25177268 0.8741137
[55,] 0.088655228 0.17731046 0.9113448
[56,] 0.067912759 0.13582552 0.9320872
[57,] 0.041483364 0.08296673 0.9585166
[58,] 0.023538115 0.04707623 0.9764619
[59,] 0.043532703 0.08706541 0.9564673
[60,] 0.022856283 0.04571257 0.9771437
[61,] 0.009569852 0.01913970 0.9904301
> postscript(file="/var/www/html/rcomp/tmp/1uyxz1260387203.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2d0bw1260387203.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3e5te1260387203.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45tfw1260387203.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5nhue1260387203.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 = 70
Frequency = 1
1 2 3 4 5 6
-26.5714286 42.4285714 171.4285714 23.4285714 2.4285714 131.4285714
7 8 9 10 11 12
-241.5714286 -301.5714286 185.4285714 75.4285714 42.4285714 -12.5714286
13 14 15 16 17 18
41.4285714 -15.5714286 108.4285714 -18.5714286 67.4285714 200.4285714
19 20 21 22 23 24
-235.5714286 -286.5714286 170.4285714 33.4285714 -52.5714286 22.4285714
25 26 27 28 29 30
86.4285714 37.4285714 29.4285714 -59.5714286 75.4285714 77.4285714
31 32 33 34 35 36
-267.5714286 -322.5714286 53.4285714 61.4285714 3.4285714 -0.5714286
37 38 39 40 41 42
-11.5714286 -10.5714286 64.4285714 0.4285714 -21.5714286 77.4285714
43 44 45 46 47 48
-318.0000000 -366.0000000 82.0000000 142.0000000 -61.0000000 -81.0000000
49 50 51 52 53 54
-25.0000000 -93.0000000 5.0000000 5.0000000 -81.0000000 206.0000000
55 56 57 58 59 60
-179.0000000 -353.0000000 207.0000000 118.0000000 -2.0000000 148.0000000
61 62 63 64 65 66
65.0000000 75.0000000 296.0000000 79.0000000 24.0000000 196.0000000
67 68 69 70
-178.0000000 -323.0000000 281.0000000 131.0000000
> postscript(file="/var/www/html/rcomp/tmp/69e9n1260387203.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 = 70
Frequency = 1
lag(myerror, k = 1) myerror
0 -26.5714286 NA
1 42.4285714 -26.5714286
2 171.4285714 42.4285714
3 23.4285714 171.4285714
4 2.4285714 23.4285714
5 131.4285714 2.4285714
6 -241.5714286 131.4285714
7 -301.5714286 -241.5714286
8 185.4285714 -301.5714286
9 75.4285714 185.4285714
10 42.4285714 75.4285714
11 -12.5714286 42.4285714
12 41.4285714 -12.5714286
13 -15.5714286 41.4285714
14 108.4285714 -15.5714286
15 -18.5714286 108.4285714
16 67.4285714 -18.5714286
17 200.4285714 67.4285714
18 -235.5714286 200.4285714
19 -286.5714286 -235.5714286
20 170.4285714 -286.5714286
21 33.4285714 170.4285714
22 -52.5714286 33.4285714
23 22.4285714 -52.5714286
24 86.4285714 22.4285714
25 37.4285714 86.4285714
26 29.4285714 37.4285714
27 -59.5714286 29.4285714
28 75.4285714 -59.5714286
29 77.4285714 75.4285714
30 -267.5714286 77.4285714
31 -322.5714286 -267.5714286
32 53.4285714 -322.5714286
33 61.4285714 53.4285714
34 3.4285714 61.4285714
35 -0.5714286 3.4285714
36 -11.5714286 -0.5714286
37 -10.5714286 -11.5714286
38 64.4285714 -10.5714286
39 0.4285714 64.4285714
40 -21.5714286 0.4285714
41 77.4285714 -21.5714286
42 -318.0000000 77.4285714
43 -366.0000000 -318.0000000
44 82.0000000 -366.0000000
45 142.0000000 82.0000000
46 -61.0000000 142.0000000
47 -81.0000000 -61.0000000
48 -25.0000000 -81.0000000
49 -93.0000000 -25.0000000
50 5.0000000 -93.0000000
51 5.0000000 5.0000000
52 -81.0000000 5.0000000
53 206.0000000 -81.0000000
54 -179.0000000 206.0000000
55 -353.0000000 -179.0000000
56 207.0000000 -353.0000000
57 118.0000000 207.0000000
58 -2.0000000 118.0000000
59 148.0000000 -2.0000000
60 65.0000000 148.0000000
61 75.0000000 65.0000000
62 296.0000000 75.0000000
63 79.0000000 296.0000000
64 24.0000000 79.0000000
65 196.0000000 24.0000000
66 -178.0000000 196.0000000
67 -323.0000000 -178.0000000
68 281.0000000 -323.0000000
69 131.0000000 281.0000000
70 NA 131.0000000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 42.4285714 -26.5714286
[2,] 171.4285714 42.4285714
[3,] 23.4285714 171.4285714
[4,] 2.4285714 23.4285714
[5,] 131.4285714 2.4285714
[6,] -241.5714286 131.4285714
[7,] -301.5714286 -241.5714286
[8,] 185.4285714 -301.5714286
[9,] 75.4285714 185.4285714
[10,] 42.4285714 75.4285714
[11,] -12.5714286 42.4285714
[12,] 41.4285714 -12.5714286
[13,] -15.5714286 41.4285714
[14,] 108.4285714 -15.5714286
[15,] -18.5714286 108.4285714
[16,] 67.4285714 -18.5714286
[17,] 200.4285714 67.4285714
[18,] -235.5714286 200.4285714
[19,] -286.5714286 -235.5714286
[20,] 170.4285714 -286.5714286
[21,] 33.4285714 170.4285714
[22,] -52.5714286 33.4285714
[23,] 22.4285714 -52.5714286
[24,] 86.4285714 22.4285714
[25,] 37.4285714 86.4285714
[26,] 29.4285714 37.4285714
[27,] -59.5714286 29.4285714
[28,] 75.4285714 -59.5714286
[29,] 77.4285714 75.4285714
[30,] -267.5714286 77.4285714
[31,] -322.5714286 -267.5714286
[32,] 53.4285714 -322.5714286
[33,] 61.4285714 53.4285714
[34,] 3.4285714 61.4285714
[35,] -0.5714286 3.4285714
[36,] -11.5714286 -0.5714286
[37,] -10.5714286 -11.5714286
[38,] 64.4285714 -10.5714286
[39,] 0.4285714 64.4285714
[40,] -21.5714286 0.4285714
[41,] 77.4285714 -21.5714286
[42,] -318.0000000 77.4285714
[43,] -366.0000000 -318.0000000
[44,] 82.0000000 -366.0000000
[45,] 142.0000000 82.0000000
[46,] -61.0000000 142.0000000
[47,] -81.0000000 -61.0000000
[48,] -25.0000000 -81.0000000
[49,] -93.0000000 -25.0000000
[50,] 5.0000000 -93.0000000
[51,] 5.0000000 5.0000000
[52,] -81.0000000 5.0000000
[53,] 206.0000000 -81.0000000
[54,] -179.0000000 206.0000000
[55,] -353.0000000 -179.0000000
[56,] 207.0000000 -353.0000000
[57,] 118.0000000 207.0000000
[58,] -2.0000000 118.0000000
[59,] 148.0000000 -2.0000000
[60,] 65.0000000 148.0000000
[61,] 75.0000000 65.0000000
[62,] 296.0000000 75.0000000
[63,] 79.0000000 296.0000000
[64,] 24.0000000 79.0000000
[65,] 196.0000000 24.0000000
[66,] -178.0000000 196.0000000
[67,] -323.0000000 -178.0000000
[68,] 281.0000000 -323.0000000
[69,] 131.0000000 281.0000000
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 42.4285714 -26.5714286
2 171.4285714 42.4285714
3 23.4285714 171.4285714
4 2.4285714 23.4285714
5 131.4285714 2.4285714
6 -241.5714286 131.4285714
7 -301.5714286 -241.5714286
8 185.4285714 -301.5714286
9 75.4285714 185.4285714
10 42.4285714 75.4285714
11 -12.5714286 42.4285714
12 41.4285714 -12.5714286
13 -15.5714286 41.4285714
14 108.4285714 -15.5714286
15 -18.5714286 108.4285714
16 67.4285714 -18.5714286
17 200.4285714 67.4285714
18 -235.5714286 200.4285714
19 -286.5714286 -235.5714286
20 170.4285714 -286.5714286
21 33.4285714 170.4285714
22 -52.5714286 33.4285714
23 22.4285714 -52.5714286
24 86.4285714 22.4285714
25 37.4285714 86.4285714
26 29.4285714 37.4285714
27 -59.5714286 29.4285714
28 75.4285714 -59.5714286
29 77.4285714 75.4285714
30 -267.5714286 77.4285714
31 -322.5714286 -267.5714286
32 53.4285714 -322.5714286
33 61.4285714 53.4285714
34 3.4285714 61.4285714
35 -0.5714286 3.4285714
36 -11.5714286 -0.5714286
37 -10.5714286 -11.5714286
38 64.4285714 -10.5714286
39 0.4285714 64.4285714
40 -21.5714286 0.4285714
41 77.4285714 -21.5714286
42 -318.0000000 77.4285714
43 -366.0000000 -318.0000000
44 82.0000000 -366.0000000
45 142.0000000 82.0000000
46 -61.0000000 142.0000000
47 -81.0000000 -61.0000000
48 -25.0000000 -81.0000000
49 -93.0000000 -25.0000000
50 5.0000000 -93.0000000
51 5.0000000 5.0000000
52 -81.0000000 5.0000000
53 206.0000000 -81.0000000
54 -179.0000000 206.0000000
55 -353.0000000 -179.0000000
56 207.0000000 -353.0000000
57 118.0000000 207.0000000
58 -2.0000000 118.0000000
59 148.0000000 -2.0000000
60 65.0000000 148.0000000
61 75.0000000 65.0000000
62 296.0000000 75.0000000
63 79.0000000 296.0000000
64 24.0000000 79.0000000
65 196.0000000 24.0000000
66 -178.0000000 196.0000000
67 -323.0000000 -178.0000000
68 281.0000000 -323.0000000
69 131.0000000 281.0000000
> 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/7l0pz1260387203.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8p7hj1260387203.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9u6md1260387203.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10rgs71260387203.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119xr71260387203.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/12jj4u1260387203.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/13qv5r1260387203.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/14a8rn1260387203.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/15kzjj1260387203.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/16sx7s1260387203.tab")
+ }
>
> system("convert tmp/1uyxz1260387203.ps tmp/1uyxz1260387203.png")
> system("convert tmp/2d0bw1260387203.ps tmp/2d0bw1260387203.png")
> system("convert tmp/3e5te1260387203.ps tmp/3e5te1260387203.png")
> system("convert tmp/45tfw1260387203.ps tmp/45tfw1260387203.png")
> system("convert tmp/5nhue1260387203.ps tmp/5nhue1260387203.png")
> system("convert tmp/69e9n1260387203.ps tmp/69e9n1260387203.png")
> system("convert tmp/7l0pz1260387203.ps tmp/7l0pz1260387203.png")
> system("convert tmp/8p7hj1260387203.ps tmp/8p7hj1260387203.png")
> system("convert tmp/9u6md1260387203.ps tmp/9u6md1260387203.png")
> system("convert tmp/10rgs71260387203.ps tmp/10rgs71260387203.png")
>
>
> proc.time()
user system elapsed
2.565 1.618 3.517