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(19915,23322,19843,22558,19761,19185,20858,17869,21968,21515,23061,17686,22661,18044,22269,20398,21857,22894,21568,22016,21274,25325,20987,27683,19683,17333,19381,20190,19071,22589,20772,14588,22485,14296,24181,12237,23479,7607,22782,9303,22067,9226,21489,9351,20903,21266,20330,21377,19736,22034,19483,22483,19242,15122,20334,18982,21423,19653,22523,16653,21986,23528,21462,24612,20908,24733,20575,21839,20237,22421,19904,26543,19610,27067,19251,31403,18941,25762,20450,29359,21946,34174,23409,20163,22741,25226,22069,25077,21539,29764,21189,21372,20960,34136,20704,29126,19697,17279,19598,16163,19456,8058,20316,17888,21083,7642,22158,7458,21469,4639,20892,10276,20578,3129,20233,20023,19947,3744,20049,7848),dim=c(2,60),dimnames=list(c('X','Y'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('X','Y'),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
X Y
1 19915 23322
2 19843 22558
3 19761 19185
4 20858 17869
5 21968 21515
6 23061 17686
7 22661 18044
8 22269 20398
9 21857 22894
10 21568 22016
11 21274 25325
12 20987 27683
13 19683 17333
14 19381 20190
15 19071 22589
16 20772 14588
17 22485 14296
18 24181 12237
19 23479 7607
20 22782 9303
21 22067 9226
22 21489 9351
23 20903 21266
24 20330 21377
25 19736 22034
26 19483 22483
27 19242 15122
28 20334 18982
29 21423 19653
30 22523 16653
31 21986 23528
32 21462 24612
33 20908 24733
34 20575 21839
35 20237 22421
36 19904 26543
37 19610 27067
38 19251 31403
39 18941 25762
40 20450 29359
41 21946 34174
42 23409 20163
43 22741 25226
44 22069 25077
45 21539 29764
46 21189 21372
47 20960 34136
48 20704 29126
49 19697 17279
50 19598 16163
51 19456 8058
52 20316 17888
53 21083 7642
54 22158 7458
55 21469 4639
56 20892 10276
57 20578 3129
58 20233 20023
59 19947 3744
60 20049 7848
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Y
2.149e+04 -2.667e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1863.6 -1075.2 -89.3 892.9 3015.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.149e+04 4.419e+02 48.639 <2e-16 ***
Y -2.667e-02 2.143e-02 -1.244 0.218
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1237 on 58 degrees of freedom
Multiple R-squared: 0.02599, Adjusted R-squared: 0.009196
F-statistic: 1.548 on 1 and 58 DF, p-value: 0.2185
> 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,] 0.4650041 0.93000826 0.53499587
[2,] 0.6400975 0.71980494 0.35990247
[3,] 0.5878757 0.82424864 0.41212432
[4,] 0.5695595 0.86088109 0.43044055
[5,] 0.5686976 0.86260472 0.43130236
[6,] 0.4787844 0.95756887 0.52121557
[7,] 0.4080108 0.81602153 0.59198924
[8,] 0.3242151 0.64843017 0.67578492
[9,] 0.4443493 0.88869851 0.55565075
[10,] 0.5321191 0.93576184 0.46788092
[11,] 0.6289647 0.74207057 0.37103528
[12,] 0.5514683 0.89706345 0.44853172
[13,] 0.5432316 0.91353673 0.45676837
[14,] 0.7625996 0.47480089 0.23740044
[15,] 0.7923722 0.41525565 0.20762782
[16,] 0.7926992 0.41460166 0.20730083
[17,] 0.7784011 0.44319782 0.22159891
[18,] 0.7640481 0.47190376 0.23595188
[19,] 0.7000360 0.59992792 0.29996396
[20,] 0.6448861 0.71022790 0.35511395
[21,] 0.6325551 0.73488977 0.36744489
[22,] 0.6452007 0.70959869 0.35479934
[23,] 0.7764533 0.44709344 0.22354672
[24,] 0.7328663 0.53426735 0.26713368
[25,] 0.6798605 0.64027902 0.32013951
[26,] 0.7207286 0.55854272 0.27927136
[27,] 0.7383050 0.52338996 0.26169498
[28,] 0.7066165 0.58676702 0.29338351
[29,] 0.6411745 0.71765107 0.35882554
[30,] 0.5691473 0.86170531 0.43085266
[31,] 0.5067424 0.98651527 0.49325763
[32,] 0.4584519 0.91690373 0.54154813
[33,] 0.4422796 0.88455929 0.55772035
[34,] 0.4910719 0.98214377 0.50892812
[35,] 0.6528813 0.69423747 0.34711873
[36,] 0.6261599 0.74768017 0.37384008
[37,] 0.6686999 0.66260016 0.33130008
[38,] 0.8911530 0.21769397 0.10884698
[39,] 0.9554637 0.08907253 0.04453626
[40,] 0.9678130 0.06437398 0.03218699
[41,] 0.9653716 0.06925686 0.03462843
[42,] 0.9523990 0.09520192 0.04760096
[43,] 0.9402391 0.11952181 0.05976091
[44,] 0.9297093 0.14058133 0.07029067
[45,] 0.9046234 0.19075320 0.09537660
[46,] 0.8893011 0.22139783 0.11069891
[47,] 0.9296798 0.14064048 0.07032024
[48,] 0.8746655 0.25066898 0.12533449
[49,] 0.7904425 0.41911502 0.20955751
[50,] 0.9052435 0.18951297 0.09475649
[51,] 0.9411276 0.11774472 0.05887236
> postscript(file="/var/www/html/rcomp/tmp/1bzga1261251207.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22wxe1261251207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a6cv1261251207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xg4f1261251207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wn0h1261251207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
-954.68499 -1047.05735 -1218.99973 -157.09139 1050.13064 2041.02884
7 8 9 10 11 12
1650.57505 1321.34539 975.90222 663.49000 457.72580 233.60280
13 14 15 16 17 18
-1346.38404 -1572.20101 -1818.23073 -330.58056 1374.63315 3015.72910
19 20 21 22 23 24
2190.26832 1538.49283 821.43959 246.77277 -21.50904 -591.54919
25 26 27 28 29 30
-1168.03002 -1409.05726 -1846.34123 -651.41281 455.47967 1475.48349
31 32 33 34 35 36
1121.80808 626.71337 75.93988 -334.22977 -656.71051 -879.79575
37 38 39 40 41 42
-1159.82309 -1403.20193 -1863.62143 -258.70600 1365.68788 2455.07902
43 44 45 46 47 48
1922.08592 1246.11278 841.09349 267.31749 378.67460 -10.91904
49 50 51 52 53 54
-1333.82398 -1462.58256 -1820.70559 -698.58475 -204.79839 865.29517
55 56 57 58 59 60
101.12543 -325.56174 -830.13932 -724.65413 -1444.74010 -1233.30532
> postscript(file="/var/www/html/rcomp/tmp/65gpc1261251207.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 -954.68499 NA
1 -1047.05735 -954.68499
2 -1218.99973 -1047.05735
3 -157.09139 -1218.99973
4 1050.13064 -157.09139
5 2041.02884 1050.13064
6 1650.57505 2041.02884
7 1321.34539 1650.57505
8 975.90222 1321.34539
9 663.49000 975.90222
10 457.72580 663.49000
11 233.60280 457.72580
12 -1346.38404 233.60280
13 -1572.20101 -1346.38404
14 -1818.23073 -1572.20101
15 -330.58056 -1818.23073
16 1374.63315 -330.58056
17 3015.72910 1374.63315
18 2190.26832 3015.72910
19 1538.49283 2190.26832
20 821.43959 1538.49283
21 246.77277 821.43959
22 -21.50904 246.77277
23 -591.54919 -21.50904
24 -1168.03002 -591.54919
25 -1409.05726 -1168.03002
26 -1846.34123 -1409.05726
27 -651.41281 -1846.34123
28 455.47967 -651.41281
29 1475.48349 455.47967
30 1121.80808 1475.48349
31 626.71337 1121.80808
32 75.93988 626.71337
33 -334.22977 75.93988
34 -656.71051 -334.22977
35 -879.79575 -656.71051
36 -1159.82309 -879.79575
37 -1403.20193 -1159.82309
38 -1863.62143 -1403.20193
39 -258.70600 -1863.62143
40 1365.68788 -258.70600
41 2455.07902 1365.68788
42 1922.08592 2455.07902
43 1246.11278 1922.08592
44 841.09349 1246.11278
45 267.31749 841.09349
46 378.67460 267.31749
47 -10.91904 378.67460
48 -1333.82398 -10.91904
49 -1462.58256 -1333.82398
50 -1820.70559 -1462.58256
51 -698.58475 -1820.70559
52 -204.79839 -698.58475
53 865.29517 -204.79839
54 101.12543 865.29517
55 -325.56174 101.12543
56 -830.13932 -325.56174
57 -724.65413 -830.13932
58 -1444.74010 -724.65413
59 -1233.30532 -1444.74010
60 NA -1233.30532
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1047.05735 -954.68499
[2,] -1218.99973 -1047.05735
[3,] -157.09139 -1218.99973
[4,] 1050.13064 -157.09139
[5,] 2041.02884 1050.13064
[6,] 1650.57505 2041.02884
[7,] 1321.34539 1650.57505
[8,] 975.90222 1321.34539
[9,] 663.49000 975.90222
[10,] 457.72580 663.49000
[11,] 233.60280 457.72580
[12,] -1346.38404 233.60280
[13,] -1572.20101 -1346.38404
[14,] -1818.23073 -1572.20101
[15,] -330.58056 -1818.23073
[16,] 1374.63315 -330.58056
[17,] 3015.72910 1374.63315
[18,] 2190.26832 3015.72910
[19,] 1538.49283 2190.26832
[20,] 821.43959 1538.49283
[21,] 246.77277 821.43959
[22,] -21.50904 246.77277
[23,] -591.54919 -21.50904
[24,] -1168.03002 -591.54919
[25,] -1409.05726 -1168.03002
[26,] -1846.34123 -1409.05726
[27,] -651.41281 -1846.34123
[28,] 455.47967 -651.41281
[29,] 1475.48349 455.47967
[30,] 1121.80808 1475.48349
[31,] 626.71337 1121.80808
[32,] 75.93988 626.71337
[33,] -334.22977 75.93988
[34,] -656.71051 -334.22977
[35,] -879.79575 -656.71051
[36,] -1159.82309 -879.79575
[37,] -1403.20193 -1159.82309
[38,] -1863.62143 -1403.20193
[39,] -258.70600 -1863.62143
[40,] 1365.68788 -258.70600
[41,] 2455.07902 1365.68788
[42,] 1922.08592 2455.07902
[43,] 1246.11278 1922.08592
[44,] 841.09349 1246.11278
[45,] 267.31749 841.09349
[46,] 378.67460 267.31749
[47,] -10.91904 378.67460
[48,] -1333.82398 -10.91904
[49,] -1462.58256 -1333.82398
[50,] -1820.70559 -1462.58256
[51,] -698.58475 -1820.70559
[52,] -204.79839 -698.58475
[53,] 865.29517 -204.79839
[54,] 101.12543 865.29517
[55,] -325.56174 101.12543
[56,] -830.13932 -325.56174
[57,] -724.65413 -830.13932
[58,] -1444.74010 -724.65413
[59,] -1233.30532 -1444.74010
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1047.05735 -954.68499
2 -1218.99973 -1047.05735
3 -157.09139 -1218.99973
4 1050.13064 -157.09139
5 2041.02884 1050.13064
6 1650.57505 2041.02884
7 1321.34539 1650.57505
8 975.90222 1321.34539
9 663.49000 975.90222
10 457.72580 663.49000
11 233.60280 457.72580
12 -1346.38404 233.60280
13 -1572.20101 -1346.38404
14 -1818.23073 -1572.20101
15 -330.58056 -1818.23073
16 1374.63315 -330.58056
17 3015.72910 1374.63315
18 2190.26832 3015.72910
19 1538.49283 2190.26832
20 821.43959 1538.49283
21 246.77277 821.43959
22 -21.50904 246.77277
23 -591.54919 -21.50904
24 -1168.03002 -591.54919
25 -1409.05726 -1168.03002
26 -1846.34123 -1409.05726
27 -651.41281 -1846.34123
28 455.47967 -651.41281
29 1475.48349 455.47967
30 1121.80808 1475.48349
31 626.71337 1121.80808
32 75.93988 626.71337
33 -334.22977 75.93988
34 -656.71051 -334.22977
35 -879.79575 -656.71051
36 -1159.82309 -879.79575
37 -1403.20193 -1159.82309
38 -1863.62143 -1403.20193
39 -258.70600 -1863.62143
40 1365.68788 -258.70600
41 2455.07902 1365.68788
42 1922.08592 2455.07902
43 1246.11278 1922.08592
44 841.09349 1246.11278
45 267.31749 841.09349
46 378.67460 267.31749
47 -10.91904 378.67460
48 -1333.82398 -10.91904
49 -1462.58256 -1333.82398
50 -1820.70559 -1462.58256
51 -698.58475 -1820.70559
52 -204.79839 -698.58475
53 865.29517 -204.79839
54 101.12543 865.29517
55 -325.56174 101.12543
56 -830.13932 -325.56174
57 -724.65413 -830.13932
58 -1444.74010 -724.65413
59 -1233.30532 -1444.74010
> 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/7s0ca1261251207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8flcw1261251207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9w71k1261251207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10vfcu1261251207.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jc1s1261251207.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/1228k41261251207.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/13mic91261251208.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/147m1z1261251208.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/15c46g1261251208.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/165gpj1261251208.tab")
+ }
>
> try(system("convert tmp/1bzga1261251207.ps tmp/1bzga1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/22wxe1261251207.ps tmp/22wxe1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a6cv1261251207.ps tmp/3a6cv1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xg4f1261251207.ps tmp/4xg4f1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wn0h1261251207.ps tmp/5wn0h1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/65gpc1261251207.ps tmp/65gpc1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/7s0ca1261251207.ps tmp/7s0ca1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/8flcw1261251207.ps tmp/8flcw1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/9w71k1261251207.ps tmp/9w71k1261251207.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vfcu1261251207.ps tmp/10vfcu1261251207.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.488 1.569 3.870