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(100,0,95.84395716,0,105.5073942,1,118.1540031,1,101.8612953,1,109.8419174,1,105.6348802,1,112.927078,1,133.0698623,1,125.6756757,1,146.736359,1,142.5803162,1,106.1448241,1,126.5170831,1,132.7893932,1,121.2391637,1,114.5079041,1,146.1499235,1,146.1244263,1,128.5058644,1,155.5838858,1,125.0382458,1,136.8944416,1,142.2233554,1,117.7715451,1,120.627231,1,127.7664457,1,135.1096379,1,105.7113717,1,117.9245283,1,120.754717,1,107.572667,1,130.4436512,1,107.2157063,1,105.0739419,1,130.1121877,1,109.6379398,1,116.7261601,1,97.11881693,0,140.8975013,1,108.2865885,1,97.65425803,0,112.0346762,1,123.0494646,1,112.4171341,1,116.4966854,1,104.6914839,1,122.2335543,1,99.79602244,0,96.71086181,0,112.3151453,1,102.5497195,1,104.5385008,1,122.0805711,1,80.64762876,0,91.40744518,0,99.51555329,0,106.527282,1,98.49566548,0,106.7567568,1),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 = '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
1 100.00000 0
2 95.84396 0
3 105.50739 1
4 118.15400 1
5 101.86130 1
6 109.84192 1
7 105.63488 1
8 112.92708 1
9 133.06986 1
10 125.67568 1
11 146.73636 1
12 142.58032 1
13 106.14482 1
14 126.51708 1
15 132.78939 1
16 121.23916 1
17 114.50790 1
18 146.14992 1
19 146.12443 1
20 128.50586 1
21 155.58389 1
22 125.03825 1
23 136.89444 1
24 142.22336 1
25 117.77155 1
26 120.62723 1
27 127.76645 1
28 135.10964 1
29 105.71137 1
30 117.92453 1
31 120.75472 1
32 107.57267 1
33 130.44365 1
34 107.21571 1
35 105.07394 1
36 130.11219 1
37 109.63794 1
38 116.72616 1
39 97.11882 0
40 140.89750 1
41 108.28659 1
42 97.65426 0
43 112.03468 1
44 123.04946 1
45 112.41713 1
46 116.49669 1
47 104.69148 1
48 122.23355 1
49 99.79602 0
50 96.71086 0
51 112.31515 1
52 102.54972 1
53 104.53850 1
54 122.08057 1
55 80.64763 0
56 91.40745 0
57 99.51555 0
58 106.52728 1
59 98.49567 0
60 106.75676 1
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
95.72 24.90
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-18.7593 -11.3205 0.1295 6.2088 34.9633
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 95.719 4.121 23.225 < 2e-16 ***
X 24.902 4.515 5.516 8.44e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.03 on 58 degrees of freedom
Multiple R-squared: 0.344, Adjusted R-squared: 0.3327
F-statistic: 30.42 on 1 and 58 DF, p-value: 8.442e-07
> 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,] 0.17249457 0.344989146 0.827505427
[2,] 0.07597932 0.151958645 0.924020678
[3,] 0.03501412 0.070028245 0.964985877
[4,] 0.01751472 0.035029435 0.982485282
[5,] 0.20992355 0.419847090 0.790076455
[6,] 0.21219262 0.424385247 0.787807377
[7,] 0.66291587 0.674168267 0.337084134
[8,] 0.80405798 0.391884036 0.195942018
[9,] 0.80025259 0.399494828 0.199747414
[10,] 0.74703107 0.505937851 0.252968925
[11,] 0.73318784 0.533624312 0.266812156
[12,] 0.65566707 0.688665851 0.344332925
[13,] 0.58877934 0.822441325 0.411220662
[14,] 0.77053654 0.458926913 0.229463456
[15,] 0.88507010 0.229859809 0.114929904
[16,] 0.85409869 0.291802615 0.145901308
[17,] 0.98421475 0.031570491 0.015785245
[18,] 0.97655926 0.046881486 0.023440743
[19,] 0.98272788 0.034544250 0.017272125
[20,] 0.99462245 0.010755097 0.005377548
[21,] 0.99170011 0.016599786 0.008299893
[22,] 0.98731390 0.025372206 0.012686103
[23,] 0.98510940 0.029781199 0.014890599
[24,] 0.99182106 0.016357873 0.008178937
[25,] 0.99320034 0.013599328 0.006799664
[26,] 0.98947982 0.021040356 0.010520178
[27,] 0.98462712 0.030745751 0.015372876
[28,] 0.98359592 0.032808162 0.016404081
[29,] 0.98728373 0.025432530 0.012716265
[30,] 0.98606865 0.027862710 0.013931355
[31,] 0.98695813 0.026083746 0.013041873
[32,] 0.99054055 0.018918898 0.009459449
[33,] 0.98699355 0.026012902 0.013006451
[34,] 0.97926717 0.041465654 0.020732827
[35,] 0.96652650 0.066946995 0.033473498
[36,] 0.99863588 0.002728236 0.001364118
[37,] 0.99783655 0.004326892 0.002163446
[38,] 0.99582015 0.008359703 0.004179852
[39,] 0.99238601 0.015227972 0.007613986
[40,] 0.99327730 0.013445405 0.006722702
[41,] 0.98767255 0.024654905 0.012327453
[42,] 0.98108319 0.037833618 0.018916809
[43,] 0.97461711 0.050765776 0.025382888
[44,] 0.98009768 0.039804636 0.019902318
[45,] 0.96849359 0.063012829 0.031506414
[46,] 0.94407574 0.111848521 0.055924260
[47,] 0.90576411 0.188471787 0.094235894
[48,] 0.87574967 0.248500665 0.124250332
[49,] 0.82553606 0.348927874 0.174463937
[50,] 0.86174675 0.276506495 0.138253248
[51,] 0.97402813 0.051943738 0.025971869
> postscript(file="/var/www/html/rcomp/tmp/1glbi1258717880.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wgr01258717880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3qvah1258717880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ga8c1258717880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5etro1258717880.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
4.280979092 0.124936252 -15.113207538 -2.466598638 -18.759306438
6 7 8 9 10
-10.778684338 -14.985721538 -7.693523738 12.449260562 5.055073962
11 12 13 14 15
26.115757262 21.959714462 -14.475777638 5.896481362 12.168791462
16 17 18 19 20
0.618561962 -6.112697638 25.529321762 25.503824562 7.885262662
21 22 23 24 25
34.963284062 4.417644062 16.273839862 21.602753662 -2.849056638
26 27 28 29 30
0.006629262 7.145843962 14.489036162 -14.909230038 -2.696073438
31 32 33 34 35
0.134115262 -13.047934738 9.823049462 -13.404895438 -15.546659838
36 37 38 39 40
9.491585962 -10.982661938 -3.894441638 1.399796022 20.276899562
41 42 43 44 45
-12.334013238 1.935237122 -8.585925538 2.428862862 -8.203467638
46 47 48 49 50
-4.123916338 -15.929117838 1.612952562 4.077001532 0.991840902
51 52 53 54 55
-8.305456438 -18.070882238 -16.082100938 1.459969362 -15.071392148
56 57 58 59 60
-4.311575728 3.796532382 -14.093319738 2.776644572 -13.863844938
> postscript(file="/var/www/html/rcomp/tmp/69kxm1258717880.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 4.280979092 NA
1 0.124936252 4.280979092
2 -15.113207538 0.124936252
3 -2.466598638 -15.113207538
4 -18.759306438 -2.466598638
5 -10.778684338 -18.759306438
6 -14.985721538 -10.778684338
7 -7.693523738 -14.985721538
8 12.449260562 -7.693523738
9 5.055073962 12.449260562
10 26.115757262 5.055073962
11 21.959714462 26.115757262
12 -14.475777638 21.959714462
13 5.896481362 -14.475777638
14 12.168791462 5.896481362
15 0.618561962 12.168791462
16 -6.112697638 0.618561962
17 25.529321762 -6.112697638
18 25.503824562 25.529321762
19 7.885262662 25.503824562
20 34.963284062 7.885262662
21 4.417644062 34.963284062
22 16.273839862 4.417644062
23 21.602753662 16.273839862
24 -2.849056638 21.602753662
25 0.006629262 -2.849056638
26 7.145843962 0.006629262
27 14.489036162 7.145843962
28 -14.909230038 14.489036162
29 -2.696073438 -14.909230038
30 0.134115262 -2.696073438
31 -13.047934738 0.134115262
32 9.823049462 -13.047934738
33 -13.404895438 9.823049462
34 -15.546659838 -13.404895438
35 9.491585962 -15.546659838
36 -10.982661938 9.491585962
37 -3.894441638 -10.982661938
38 1.399796022 -3.894441638
39 20.276899562 1.399796022
40 -12.334013238 20.276899562
41 1.935237122 -12.334013238
42 -8.585925538 1.935237122
43 2.428862862 -8.585925538
44 -8.203467638 2.428862862
45 -4.123916338 -8.203467638
46 -15.929117838 -4.123916338
47 1.612952562 -15.929117838
48 4.077001532 1.612952562
49 0.991840902 4.077001532
50 -8.305456438 0.991840902
51 -18.070882238 -8.305456438
52 -16.082100938 -18.070882238
53 1.459969362 -16.082100938
54 -15.071392148 1.459969362
55 -4.311575728 -15.071392148
56 3.796532382 -4.311575728
57 -14.093319738 3.796532382
58 2.776644572 -14.093319738
59 -13.863844938 2.776644572
60 NA -13.863844938
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.124936252 4.280979092
[2,] -15.113207538 0.124936252
[3,] -2.466598638 -15.113207538
[4,] -18.759306438 -2.466598638
[5,] -10.778684338 -18.759306438
[6,] -14.985721538 -10.778684338
[7,] -7.693523738 -14.985721538
[8,] 12.449260562 -7.693523738
[9,] 5.055073962 12.449260562
[10,] 26.115757262 5.055073962
[11,] 21.959714462 26.115757262
[12,] -14.475777638 21.959714462
[13,] 5.896481362 -14.475777638
[14,] 12.168791462 5.896481362
[15,] 0.618561962 12.168791462
[16,] -6.112697638 0.618561962
[17,] 25.529321762 -6.112697638
[18,] 25.503824562 25.529321762
[19,] 7.885262662 25.503824562
[20,] 34.963284062 7.885262662
[21,] 4.417644062 34.963284062
[22,] 16.273839862 4.417644062
[23,] 21.602753662 16.273839862
[24,] -2.849056638 21.602753662
[25,] 0.006629262 -2.849056638
[26,] 7.145843962 0.006629262
[27,] 14.489036162 7.145843962
[28,] -14.909230038 14.489036162
[29,] -2.696073438 -14.909230038
[30,] 0.134115262 -2.696073438
[31,] -13.047934738 0.134115262
[32,] 9.823049462 -13.047934738
[33,] -13.404895438 9.823049462
[34,] -15.546659838 -13.404895438
[35,] 9.491585962 -15.546659838
[36,] -10.982661938 9.491585962
[37,] -3.894441638 -10.982661938
[38,] 1.399796022 -3.894441638
[39,] 20.276899562 1.399796022
[40,] -12.334013238 20.276899562
[41,] 1.935237122 -12.334013238
[42,] -8.585925538 1.935237122
[43,] 2.428862862 -8.585925538
[44,] -8.203467638 2.428862862
[45,] -4.123916338 -8.203467638
[46,] -15.929117838 -4.123916338
[47,] 1.612952562 -15.929117838
[48,] 4.077001532 1.612952562
[49,] 0.991840902 4.077001532
[50,] -8.305456438 0.991840902
[51,] -18.070882238 -8.305456438
[52,] -16.082100938 -18.070882238
[53,] 1.459969362 -16.082100938
[54,] -15.071392148 1.459969362
[55,] -4.311575728 -15.071392148
[56,] 3.796532382 -4.311575728
[57,] -14.093319738 3.796532382
[58,] 2.776644572 -14.093319738
[59,] -13.863844938 2.776644572
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.124936252 4.280979092
2 -15.113207538 0.124936252
3 -2.466598638 -15.113207538
4 -18.759306438 -2.466598638
5 -10.778684338 -18.759306438
6 -14.985721538 -10.778684338
7 -7.693523738 -14.985721538
8 12.449260562 -7.693523738
9 5.055073962 12.449260562
10 26.115757262 5.055073962
11 21.959714462 26.115757262
12 -14.475777638 21.959714462
13 5.896481362 -14.475777638
14 12.168791462 5.896481362
15 0.618561962 12.168791462
16 -6.112697638 0.618561962
17 25.529321762 -6.112697638
18 25.503824562 25.529321762
19 7.885262662 25.503824562
20 34.963284062 7.885262662
21 4.417644062 34.963284062
22 16.273839862 4.417644062
23 21.602753662 16.273839862
24 -2.849056638 21.602753662
25 0.006629262 -2.849056638
26 7.145843962 0.006629262
27 14.489036162 7.145843962
28 -14.909230038 14.489036162
29 -2.696073438 -14.909230038
30 0.134115262 -2.696073438
31 -13.047934738 0.134115262
32 9.823049462 -13.047934738
33 -13.404895438 9.823049462
34 -15.546659838 -13.404895438
35 9.491585962 -15.546659838
36 -10.982661938 9.491585962
37 -3.894441638 -10.982661938
38 1.399796022 -3.894441638
39 20.276899562 1.399796022
40 -12.334013238 20.276899562
41 1.935237122 -12.334013238
42 -8.585925538 1.935237122
43 2.428862862 -8.585925538
44 -8.203467638 2.428862862
45 -4.123916338 -8.203467638
46 -15.929117838 -4.123916338
47 1.612952562 -15.929117838
48 4.077001532 1.612952562
49 0.991840902 4.077001532
50 -8.305456438 0.991840902
51 -18.070882238 -8.305456438
52 -16.082100938 -18.070882238
53 1.459969362 -16.082100938
54 -15.071392148 1.459969362
55 -4.311575728 -15.071392148
56 3.796532382 -4.311575728
57 -14.093319738 3.796532382
58 2.776644572 -14.093319738
59 -13.863844938 2.776644572
> 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/7x1el1258717880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80kuy1258717880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9l3u31258717880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/106i101258717880.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hp3l1258717880.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/12qmw51258717880.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/13oibr1258717880.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/14136k1258717880.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/15oqps1258717880.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/169kg11258717880.tab")
+ }
>
> system("convert tmp/1glbi1258717880.ps tmp/1glbi1258717880.png")
> system("convert tmp/2wgr01258717880.ps tmp/2wgr01258717880.png")
> system("convert tmp/3qvah1258717880.ps tmp/3qvah1258717880.png")
> system("convert tmp/4ga8c1258717880.ps tmp/4ga8c1258717880.png")
> system("convert tmp/5etro1258717880.ps tmp/5etro1258717880.png")
> system("convert tmp/69kxm1258717880.ps tmp/69kxm1258717880.png")
> system("convert tmp/7x1el1258717880.ps tmp/7x1el1258717880.png")
> system("convert tmp/80kuy1258717880.ps tmp/80kuy1258717880.png")
> system("convert tmp/9l3u31258717880.ps tmp/9l3u31258717880.png")
> system("convert tmp/106i101258717880.ps tmp/106i101258717880.png")
>
>
> proc.time()
user system elapsed
2.472 1.571 4.249