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(2.172,286602,2.150,283042,2.533,276687,2.058,277915,2.160,277128,2.260,277103,2.498,275037,2.695,270150,2.799,267140,2.947,264993,2.930,287259,2.318,291186,2.540,292300,2.570,288186,2.669,281477,2.450,282656,2.842,280190,3.440,280408,2.678,276836,2.981,275216,2.260,274352,2.844,271311,2.546,289802,2.456,290726,2.295,292300,2.379,278506,2.479,269826,2.057,265861,2.280,269034,2.351,264176,2.276,255198,2.548,253353,2.311,246057,2.201,235372,2.725,258556,2.408,260993,2.139,254663,1.898,250643,2.539,243422,2.069,247105,2.063,248541,2.565,245039,2.442,237080,2.194,237085,2.798,225554,2.074,226839,2.628,247934,2.289,248333,2.154,246969,2.466,245098,2.137,246263,1.846,255765,2.072,264319,1.786,268347,1.754,273046,2.226,273963,1.947,267430,1.823,271993,2.521,292710,2.072,295881,2.368,294563),dim=c(2,61),dimnames=list(c('Bouw','NWWM'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Bouw','NWWM'),1:61))
> 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
Bouw NWWM
1 2.172 286602
2 2.150 283042
3 2.533 276687
4 2.058 277915
5 2.160 277128
6 2.260 277103
7 2.498 275037
8 2.695 270150
9 2.799 267140
10 2.947 264993
11 2.930 287259
12 2.318 291186
13 2.540 292300
14 2.570 288186
15 2.669 281477
16 2.450 282656
17 2.842 280190
18 3.440 280408
19 2.678 276836
20 2.981 275216
21 2.260 274352
22 2.844 271311
23 2.546 289802
24 2.456 290726
25 2.295 292300
26 2.379 278506
27 2.479 269826
28 2.057 265861
29 2.280 269034
30 2.351 264176
31 2.276 255198
32 2.548 253353
33 2.311 246057
34 2.201 235372
35 2.725 258556
36 2.408 260993
37 2.139 254663
38 1.898 250643
39 2.539 243422
40 2.069 247105
41 2.063 248541
42 2.565 245039
43 2.442 237080
44 2.194 237085
45 2.798 225554
46 2.074 226839
47 2.628 247934
48 2.289 248333
49 2.154 246969
50 2.466 245098
51 2.137 246263
52 1.846 255765
53 2.072 264319
54 1.786 268347
55 1.754 273046
56 2.226 273963
57 1.947 267430
58 1.823 271993
59 2.521 292710
60 2.072 295881
61 2.368 294563
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) NWWM
1.600e+00 2.911e-06
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.6408 -0.2023 -0.0317 0.2105 1.0238
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.600e+00 6.233e-01 2.567 0.0128 *
NWWM 2.911e-06 2.331e-06 1.249 0.2166
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3284 on 59 degrees of freedom
Multiple R-squared: 0.02576, Adjusted R-squared: 0.009246
F-statistic: 1.56 on 1 and 59 DF, p-value: 0.2166
> 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,] 0.21924465 0.43848930 0.78075535
[2,] 0.10260959 0.20521917 0.89739041
[3,] 0.06960714 0.13921428 0.93039286
[4,] 0.04783618 0.09567236 0.95216382
[5,] 0.02624023 0.05248046 0.97375977
[6,] 0.01657599 0.03315199 0.98342401
[7,] 0.47096950 0.94193901 0.52903050
[8,] 0.38309588 0.76619176 0.61690412
[9,] 0.35685516 0.71371032 0.64314484
[10,] 0.30285400 0.60570799 0.69714600
[11,] 0.26160466 0.52320932 0.73839534
[12,] 0.19207157 0.38414314 0.80792843
[13,] 0.22149310 0.44298621 0.77850690
[14,] 0.83124004 0.33751993 0.16875996
[15,] 0.80610668 0.38778664 0.19389332
[16,] 0.88666720 0.22666560 0.11333280
[17,] 0.87965597 0.24068807 0.12034403
[18,] 0.90928916 0.18142167 0.09071084
[19,] 0.89688131 0.20623739 0.10311869
[20,] 0.87532147 0.24935705 0.12467853
[21,] 0.84186748 0.31626504 0.15813252
[22,] 0.81659118 0.36681764 0.18340882
[23,] 0.80131824 0.39736352 0.19868176
[24,] 0.85221747 0.29556505 0.14778253
[25,] 0.82882673 0.34234655 0.17117327
[26,] 0.79633736 0.40732528 0.20366264
[27,] 0.75899604 0.48200792 0.24100396
[28,] 0.73553956 0.52892089 0.26446044
[29,] 0.68080767 0.63838465 0.31919233
[30,] 0.62899919 0.74200161 0.37100081
[31,] 0.70971373 0.58057254 0.29028627
[32,] 0.66837133 0.66325733 0.33162867
[33,] 0.62502685 0.74994630 0.37497315
[34,] 0.67856456 0.64287088 0.32143544
[35,] 0.65878257 0.68243487 0.34121743
[36,] 0.62193654 0.75612691 0.37806346
[37,] 0.58286513 0.83426974 0.41713487
[38,] 0.57754976 0.84490048 0.42245024
[39,] 0.51944443 0.96111113 0.48055557
[40,] 0.43976613 0.87953225 0.56023387
[41,] 0.64154598 0.71690804 0.35845402
[42,] 0.57274215 0.85451569 0.42725785
[43,] 0.70532315 0.58935370 0.29467685
[44,] 0.66301818 0.67396364 0.33698182
[45,] 0.59001100 0.81997799 0.40998900
[46,] 0.79836544 0.40326912 0.20163456
[47,] 0.87468125 0.25063750 0.12531875
[48,] 0.83253210 0.33493580 0.16746790
[49,] 0.82312433 0.35375134 0.17687567
[50,] 0.76266646 0.47466707 0.23733354
[51,] 0.76346161 0.47307677 0.23653839
[52,] 0.69988145 0.60023710 0.30011855
> postscript(file="/var/www/html/rcomp/tmp/1dpp21291331112.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ny651291331112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ny651291331112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ny651291331112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yq5q1291331112.ps",horizontal=F,onefile=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 = 61
Frequency = 1
1 2 3 4 5 6
-0.262270627 -0.273906463 0.127594733 -0.350980321 -0.246689142 -0.146616360
7 8 9 10 11 12
0.097398348 0.308625783 0.421388741 0.575639264 0.493816661 -0.129615943
13 14 15 16 17 18
0.089140889 0.131117903 0.249649693 0.027217292 0.426396513 1.023761853
19 20 21 22 23 24
0.272160952 0.579877229 -0.138607424 0.454245785 0.102413271 0.009723247
25 26 27 28 29 30
-0.155859111 -0.031700889 0.093569038 -0.316887729 -0.103125226 -0.017982219
31 32 33 34 35 36
-0.066844730 0.210526585 -0.005232703 -0.084125656 0.372379185 0.048284391
37 38 39 40 41 42
-0.202287194 -0.431583841 0.230438525 -0.250283726 -0.260464327 0.251730982
43 44 45 46 47 48
0.151901875 -0.096112681 0.541457310 -0.186283687 0.306302821 -0.033858780
49 50 51 52 53 54
-0.164887792 0.152559217 -0.179832427 -0.498495427 -0.297398532 -0.595125176
55 56 57 58 59 60
-0.640805289 -0.171474935 -0.431455530 -0.568739710 0.068947264 -0.389284411
61
-0.089447342
> postscript(file="/var/www/html/rcomp/tmp/6yq5q1291331112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.262270627 NA
1 -0.273906463 -0.262270627
2 0.127594733 -0.273906463
3 -0.350980321 0.127594733
4 -0.246689142 -0.350980321
5 -0.146616360 -0.246689142
6 0.097398348 -0.146616360
7 0.308625783 0.097398348
8 0.421388741 0.308625783
9 0.575639264 0.421388741
10 0.493816661 0.575639264
11 -0.129615943 0.493816661
12 0.089140889 -0.129615943
13 0.131117903 0.089140889
14 0.249649693 0.131117903
15 0.027217292 0.249649693
16 0.426396513 0.027217292
17 1.023761853 0.426396513
18 0.272160952 1.023761853
19 0.579877229 0.272160952
20 -0.138607424 0.579877229
21 0.454245785 -0.138607424
22 0.102413271 0.454245785
23 0.009723247 0.102413271
24 -0.155859111 0.009723247
25 -0.031700889 -0.155859111
26 0.093569038 -0.031700889
27 -0.316887729 0.093569038
28 -0.103125226 -0.316887729
29 -0.017982219 -0.103125226
30 -0.066844730 -0.017982219
31 0.210526585 -0.066844730
32 -0.005232703 0.210526585
33 -0.084125656 -0.005232703
34 0.372379185 -0.084125656
35 0.048284391 0.372379185
36 -0.202287194 0.048284391
37 -0.431583841 -0.202287194
38 0.230438525 -0.431583841
39 -0.250283726 0.230438525
40 -0.260464327 -0.250283726
41 0.251730982 -0.260464327
42 0.151901875 0.251730982
43 -0.096112681 0.151901875
44 0.541457310 -0.096112681
45 -0.186283687 0.541457310
46 0.306302821 -0.186283687
47 -0.033858780 0.306302821
48 -0.164887792 -0.033858780
49 0.152559217 -0.164887792
50 -0.179832427 0.152559217
51 -0.498495427 -0.179832427
52 -0.297398532 -0.498495427
53 -0.595125176 -0.297398532
54 -0.640805289 -0.595125176
55 -0.171474935 -0.640805289
56 -0.431455530 -0.171474935
57 -0.568739710 -0.431455530
58 0.068947264 -0.568739710
59 -0.389284411 0.068947264
60 -0.089447342 -0.389284411
61 NA -0.089447342
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.273906463 -0.262270627
[2,] 0.127594733 -0.273906463
[3,] -0.350980321 0.127594733
[4,] -0.246689142 -0.350980321
[5,] -0.146616360 -0.246689142
[6,] 0.097398348 -0.146616360
[7,] 0.308625783 0.097398348
[8,] 0.421388741 0.308625783
[9,] 0.575639264 0.421388741
[10,] 0.493816661 0.575639264
[11,] -0.129615943 0.493816661
[12,] 0.089140889 -0.129615943
[13,] 0.131117903 0.089140889
[14,] 0.249649693 0.131117903
[15,] 0.027217292 0.249649693
[16,] 0.426396513 0.027217292
[17,] 1.023761853 0.426396513
[18,] 0.272160952 1.023761853
[19,] 0.579877229 0.272160952
[20,] -0.138607424 0.579877229
[21,] 0.454245785 -0.138607424
[22,] 0.102413271 0.454245785
[23,] 0.009723247 0.102413271
[24,] -0.155859111 0.009723247
[25,] -0.031700889 -0.155859111
[26,] 0.093569038 -0.031700889
[27,] -0.316887729 0.093569038
[28,] -0.103125226 -0.316887729
[29,] -0.017982219 -0.103125226
[30,] -0.066844730 -0.017982219
[31,] 0.210526585 -0.066844730
[32,] -0.005232703 0.210526585
[33,] -0.084125656 -0.005232703
[34,] 0.372379185 -0.084125656
[35,] 0.048284391 0.372379185
[36,] -0.202287194 0.048284391
[37,] -0.431583841 -0.202287194
[38,] 0.230438525 -0.431583841
[39,] -0.250283726 0.230438525
[40,] -0.260464327 -0.250283726
[41,] 0.251730982 -0.260464327
[42,] 0.151901875 0.251730982
[43,] -0.096112681 0.151901875
[44,] 0.541457310 -0.096112681
[45,] -0.186283687 0.541457310
[46,] 0.306302821 -0.186283687
[47,] -0.033858780 0.306302821
[48,] -0.164887792 -0.033858780
[49,] 0.152559217 -0.164887792
[50,] -0.179832427 0.152559217
[51,] -0.498495427 -0.179832427
[52,] -0.297398532 -0.498495427
[53,] -0.595125176 -0.297398532
[54,] -0.640805289 -0.595125176
[55,] -0.171474935 -0.640805289
[56,] -0.431455530 -0.171474935
[57,] -0.568739710 -0.431455530
[58,] 0.068947264 -0.568739710
[59,] -0.389284411 0.068947264
[60,] -0.089447342 -0.389284411
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.273906463 -0.262270627
2 0.127594733 -0.273906463
3 -0.350980321 0.127594733
4 -0.246689142 -0.350980321
5 -0.146616360 -0.246689142
6 0.097398348 -0.146616360
7 0.308625783 0.097398348
8 0.421388741 0.308625783
9 0.575639264 0.421388741
10 0.493816661 0.575639264
11 -0.129615943 0.493816661
12 0.089140889 -0.129615943
13 0.131117903 0.089140889
14 0.249649693 0.131117903
15 0.027217292 0.249649693
16 0.426396513 0.027217292
17 1.023761853 0.426396513
18 0.272160952 1.023761853
19 0.579877229 0.272160952
20 -0.138607424 0.579877229
21 0.454245785 -0.138607424
22 0.102413271 0.454245785
23 0.009723247 0.102413271
24 -0.155859111 0.009723247
25 -0.031700889 -0.155859111
26 0.093569038 -0.031700889
27 -0.316887729 0.093569038
28 -0.103125226 -0.316887729
29 -0.017982219 -0.103125226
30 -0.066844730 -0.017982219
31 0.210526585 -0.066844730
32 -0.005232703 0.210526585
33 -0.084125656 -0.005232703
34 0.372379185 -0.084125656
35 0.048284391 0.372379185
36 -0.202287194 0.048284391
37 -0.431583841 -0.202287194
38 0.230438525 -0.431583841
39 -0.250283726 0.230438525
40 -0.260464327 -0.250283726
41 0.251730982 -0.260464327
42 0.151901875 0.251730982
43 -0.096112681 0.151901875
44 0.541457310 -0.096112681
45 -0.186283687 0.541457310
46 0.306302821 -0.186283687
47 -0.033858780 0.306302821
48 -0.164887792 -0.033858780
49 0.152559217 -0.164887792
50 -0.179832427 0.152559217
51 -0.498495427 -0.179832427
52 -0.297398532 -0.498495427
53 -0.595125176 -0.297398532
54 -0.640805289 -0.595125176
55 -0.171474935 -0.640805289
56 -0.431455530 -0.171474935
57 -0.568739710 -0.431455530
58 0.068947264 -0.568739710
59 -0.389284411 0.068947264
60 -0.089447342 -0.389284411
> 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/79zmb1291331112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89zmb1291331112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/99zmb1291331112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1018mw1291331112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nrk21291331112.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/12qrj81291331112.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/13fax11291331112.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/14q1xm1291331112.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/15t2ds1291331112.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/16pubj1291331112.tab")
+ }
>
> try(system("convert tmp/1dpp21291331112.ps tmp/1dpp21291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ny651291331112.ps tmp/2ny651291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ny651291331112.ps tmp/3ny651291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ny651291331112.ps tmp/4ny651291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yq5q1291331112.ps tmp/5yq5q1291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yq5q1291331112.ps tmp/6yq5q1291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/79zmb1291331112.ps tmp/79zmb1291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/89zmb1291331112.ps tmp/89zmb1291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/99zmb1291331112.ps tmp/99zmb1291331112.png",intern=TRUE))
character(0)
> try(system("convert tmp/1018mw1291331112.ps tmp/1018mw1291331112.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.532 1.713 6.302