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(1.6,8.3,1.8,1.6,1.5,7.5,1.6,1.8,1.5,7.2,1.5,1.6,1.3,7.4,1.5,1.5,1.4,8.8,1.3,1.5,1.4,9.3,1.4,1.3,1.3,9.3,1.4,1.4,1.3,8.7,1.3,1.4,1.2,8.2,1.3,1.3,1.1,8.3,1.2,1.3,1.4,8.5,1.1,1.2,1.2,8.6,1.4,1.1,1.5,8.5,1.2,1.4,1.1,8.2,1.5,1.2,1.3,8.1,1.1,1.5,1.5,7.9,1.3,1.1,1.1,8.6,1.5,1.3,1.4,8.7,1.1,1.5,1.3,8.7,1.4,1.1,1.5,8.5,1.3,1.4,1.6,8.4,1.5,1.3,1.7,8.5,1.6,1.5,1.1,8.7,1.7,1.6,1.6,8.7,1.1,1.7,1.3,8.6,1.6,1.1,1.7,8.5,1.3,1.6,1.6,8.3,1.7,1.3,1.7,8,1.6,1.7,1.9,8.2,1.7,1.6,1.8,8.1,1.9,1.7,1.9,8.1,1.8,1.9,1.6,8,1.9,1.8,1.5,7.9,1.6,1.9,1.6,7.9,1.5,1.6,1.6,8,1.6,1.5,1.7,8,1.6,1.6,2,7.9,1.7,1.6,2,8,2,1.7,1.9,7.7,2,2,1.7,7.2,1.9,2,1.8,7.5,1.7,1.9,1.9,7.3,1.8,1.7,1.7,7,1.9,1.8,2,7,1.7,1.9,2.1,7,2,1.7,2.4,7.2,2.1,2,2.5,7.3,2.4,2.1,2.5,7.1,2.5,2.4,2.6,6.8,2.5,2.5,2.2,6.4,2.6,2.5,2.5,6.1,2.2,2.6,2.8,6.5,2.5,2.2,2.8,7.7,2.8,2.5,2.9,7.9,2.8,2.8,3,7.5,2.9,2.8,3.1,6.9,3,2.9,2.9,6.6,3.1,3,2.7,6.9,2.9,3.1),dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = '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 Y1 Y2 t
1 1.6 8.3 1.8 1.6 1
2 1.5 7.5 1.6 1.8 2
3 1.5 7.2 1.5 1.6 3
4 1.3 7.4 1.5 1.5 4
5 1.4 8.8 1.3 1.5 5
6 1.4 9.3 1.4 1.3 6
7 1.3 9.3 1.4 1.4 7
8 1.3 8.7 1.3 1.4 8
9 1.2 8.2 1.3 1.3 9
10 1.1 8.3 1.2 1.3 10
11 1.4 8.5 1.1 1.2 11
12 1.2 8.6 1.4 1.1 12
13 1.5 8.5 1.2 1.4 13
14 1.1 8.2 1.5 1.2 14
15 1.3 8.1 1.1 1.5 15
16 1.5 7.9 1.3 1.1 16
17 1.1 8.6 1.5 1.3 17
18 1.4 8.7 1.1 1.5 18
19 1.3 8.7 1.4 1.1 19
20 1.5 8.5 1.3 1.4 20
21 1.6 8.4 1.5 1.3 21
22 1.7 8.5 1.6 1.5 22
23 1.1 8.7 1.7 1.6 23
24 1.6 8.7 1.1 1.7 24
25 1.3 8.6 1.6 1.1 25
26 1.7 8.5 1.3 1.6 26
27 1.6 8.3 1.7 1.3 27
28 1.7 8.0 1.6 1.7 28
29 1.9 8.2 1.7 1.6 29
30 1.8 8.1 1.9 1.7 30
31 1.9 8.1 1.8 1.9 31
32 1.6 8.0 1.9 1.8 32
33 1.5 7.9 1.6 1.9 33
34 1.6 7.9 1.5 1.6 34
35 1.6 8.0 1.6 1.5 35
36 1.7 8.0 1.6 1.6 36
37 2.0 7.9 1.7 1.6 37
38 2.0 8.0 2.0 1.7 38
39 1.9 7.7 2.0 2.0 39
40 1.7 7.2 1.9 2.0 40
41 1.8 7.5 1.7 1.9 41
42 1.9 7.3 1.8 1.7 42
43 1.7 7.0 1.9 1.8 43
44 2.0 7.0 1.7 1.9 44
45 2.1 7.0 2.0 1.7 45
46 2.4 7.2 2.1 2.0 46
47 2.5 7.3 2.4 2.1 47
48 2.5 7.1 2.5 2.4 48
49 2.6 6.8 2.5 2.5 49
50 2.2 6.4 2.6 2.5 50
51 2.5 6.1 2.2 2.6 51
52 2.8 6.5 2.5 2.2 52
53 2.8 7.7 2.8 2.5 53
54 2.9 7.9 2.8 2.8 54
55 3.0 7.5 2.9 2.8 55
56 3.1 6.9 3.0 2.9 56
57 2.9 6.6 3.1 3.0 57
58 2.7 6.9 2.9 3.1 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 t
0.125898 0.009441 0.393131 0.359986 0.009004
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.55942 -0.08223 0.02162 0.12109 0.36974
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.125898 0.506992 0.248 0.80484
X 0.009441 0.052382 0.180 0.85766
Y1 0.393131 0.123489 3.184 0.00244 **
Y2 0.359986 0.125166 2.876 0.00579 **
t 0.009004 0.002880 3.126 0.00287 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1885 on 53 degrees of freedom
Multiple R-squared: 0.8888, Adjusted R-squared: 0.8804
F-statistic: 105.9 on 4 and 53 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.062795595 0.12559119 0.9372044
[2,] 0.019207898 0.03841580 0.9807921
[3,] 0.006082358 0.01216472 0.9939176
[4,] 0.085815600 0.17163120 0.9141844
[5,] 0.045471930 0.09094386 0.9545281
[6,] 0.093256281 0.18651256 0.9067437
[7,] 0.077892629 0.15578526 0.9221074
[8,] 0.045875993 0.09175199 0.9541240
[9,] 0.181575604 0.36315121 0.8184244
[10,] 0.175923853 0.35184771 0.8240761
[11,] 0.138740739 0.27748148 0.8612593
[12,] 0.105288662 0.21057732 0.8947113
[13,] 0.106601910 0.21320382 0.8933981
[14,] 0.145802693 0.29160539 0.8541973
[15,] 0.154422880 0.30884576 0.8455771
[16,] 0.521539275 0.95692145 0.4784607
[17,] 0.506116837 0.98776633 0.4938832
[18,] 0.473733347 0.94746669 0.5262667
[19,] 0.553275664 0.89344867 0.4467243
[20,] 0.514065969 0.97186806 0.4859340
[21,] 0.477315538 0.95463108 0.5226845
[22,] 0.608318461 0.78336308 0.3916815
[23,] 0.553347267 0.89330547 0.4466527
[24,] 0.659809335 0.68038133 0.3401907
[25,] 0.635837445 0.72832511 0.3641626
[26,] 0.652592122 0.69481576 0.3474079
[27,] 0.582355545 0.83528891 0.4176445
[28,] 0.504918790 0.99016242 0.4950812
[29,] 0.420676021 0.84135204 0.5793240
[30,] 0.526553619 0.94689276 0.4734464
[31,] 0.493642967 0.98728593 0.5063570
[32,] 0.431446044 0.86289209 0.5685540
[33,] 0.391939226 0.78387845 0.6080608
[34,] 0.307878400 0.61575680 0.6921216
[35,] 0.239855131 0.47971026 0.7601449
[36,] 0.425076285 0.85015257 0.5749237
[37,] 0.358011705 0.71602341 0.6419883
[38,] 0.450867411 0.90173482 0.5491326
[39,] 0.414719685 0.82943937 0.5852803
[40,] 0.346651850 0.69330370 0.6533482
[41,] 0.240585722 0.48117144 0.7594143
[42,] 0.225725090 0.45145018 0.7742749
[43,] 0.606282762 0.78743448 0.3937172
> postscript(file="/var/www/html/rcomp/tmp/1mw3j1258567276.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ce7z1258567276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3cljx1258567276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45upn1258567276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57g7v1258567276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 58
Frequency = 1
1 2 3 4 5 6
0.103126555 0.008304413 0.113443165 -0.061450167 0.094955138 0.113915071
7 8 9 10 11 12
-0.031087294 0.004886504 -0.063398273 -0.134033029 0.230386731 -0.061501799
13 14 15 16 17 18
0.201068908 -0.251044708 -0.009847817 0.248404771 -0.317830929 0.057476399
19 20 21 22 23 24
-0.025472264 0.098729430 0.148042152 0.126784019 -0.559419597 0.131456591
25 26 27 28 29 30
-0.157176985 0.172709622 0.016337430 0.005484607 0.191278183 -0.031406288
31 32 33 34 35 36
0.026905844 -0.284468343 -0.310587352 -0.072282242 -0.085544587 -0.030546952
37 38 39 40 41 42
0.222080265 0.058194544 -0.155972775 -0.320943055 -0.118154283 0.007414206
43 44 45 46 47 48
-0.274069013 0.059554806 0.104608951 0.246408144 0.182522422 0.028097932
49 50 51 52 53 54
0.085927805 -0.358612740 0.056469502 0.369744522 0.123476737 0.104589021
55 56 57 58
0.160048477 0.181397495 -0.100085725 -0.269294143
> postscript(file="/var/www/html/rcomp/tmp/6dwj71258567276.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 0.103126555 NA
1 0.008304413 0.103126555
2 0.113443165 0.008304413
3 -0.061450167 0.113443165
4 0.094955138 -0.061450167
5 0.113915071 0.094955138
6 -0.031087294 0.113915071
7 0.004886504 -0.031087294
8 -0.063398273 0.004886504
9 -0.134033029 -0.063398273
10 0.230386731 -0.134033029
11 -0.061501799 0.230386731
12 0.201068908 -0.061501799
13 -0.251044708 0.201068908
14 -0.009847817 -0.251044708
15 0.248404771 -0.009847817
16 -0.317830929 0.248404771
17 0.057476399 -0.317830929
18 -0.025472264 0.057476399
19 0.098729430 -0.025472264
20 0.148042152 0.098729430
21 0.126784019 0.148042152
22 -0.559419597 0.126784019
23 0.131456591 -0.559419597
24 -0.157176985 0.131456591
25 0.172709622 -0.157176985
26 0.016337430 0.172709622
27 0.005484607 0.016337430
28 0.191278183 0.005484607
29 -0.031406288 0.191278183
30 0.026905844 -0.031406288
31 -0.284468343 0.026905844
32 -0.310587352 -0.284468343
33 -0.072282242 -0.310587352
34 -0.085544587 -0.072282242
35 -0.030546952 -0.085544587
36 0.222080265 -0.030546952
37 0.058194544 0.222080265
38 -0.155972775 0.058194544
39 -0.320943055 -0.155972775
40 -0.118154283 -0.320943055
41 0.007414206 -0.118154283
42 -0.274069013 0.007414206
43 0.059554806 -0.274069013
44 0.104608951 0.059554806
45 0.246408144 0.104608951
46 0.182522422 0.246408144
47 0.028097932 0.182522422
48 0.085927805 0.028097932
49 -0.358612740 0.085927805
50 0.056469502 -0.358612740
51 0.369744522 0.056469502
52 0.123476737 0.369744522
53 0.104589021 0.123476737
54 0.160048477 0.104589021
55 0.181397495 0.160048477
56 -0.100085725 0.181397495
57 -0.269294143 -0.100085725
58 NA -0.269294143
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.008304413 0.103126555
[2,] 0.113443165 0.008304413
[3,] -0.061450167 0.113443165
[4,] 0.094955138 -0.061450167
[5,] 0.113915071 0.094955138
[6,] -0.031087294 0.113915071
[7,] 0.004886504 -0.031087294
[8,] -0.063398273 0.004886504
[9,] -0.134033029 -0.063398273
[10,] 0.230386731 -0.134033029
[11,] -0.061501799 0.230386731
[12,] 0.201068908 -0.061501799
[13,] -0.251044708 0.201068908
[14,] -0.009847817 -0.251044708
[15,] 0.248404771 -0.009847817
[16,] -0.317830929 0.248404771
[17,] 0.057476399 -0.317830929
[18,] -0.025472264 0.057476399
[19,] 0.098729430 -0.025472264
[20,] 0.148042152 0.098729430
[21,] 0.126784019 0.148042152
[22,] -0.559419597 0.126784019
[23,] 0.131456591 -0.559419597
[24,] -0.157176985 0.131456591
[25,] 0.172709622 -0.157176985
[26,] 0.016337430 0.172709622
[27,] 0.005484607 0.016337430
[28,] 0.191278183 0.005484607
[29,] -0.031406288 0.191278183
[30,] 0.026905844 -0.031406288
[31,] -0.284468343 0.026905844
[32,] -0.310587352 -0.284468343
[33,] -0.072282242 -0.310587352
[34,] -0.085544587 -0.072282242
[35,] -0.030546952 -0.085544587
[36,] 0.222080265 -0.030546952
[37,] 0.058194544 0.222080265
[38,] -0.155972775 0.058194544
[39,] -0.320943055 -0.155972775
[40,] -0.118154283 -0.320943055
[41,] 0.007414206 -0.118154283
[42,] -0.274069013 0.007414206
[43,] 0.059554806 -0.274069013
[44,] 0.104608951 0.059554806
[45,] 0.246408144 0.104608951
[46,] 0.182522422 0.246408144
[47,] 0.028097932 0.182522422
[48,] 0.085927805 0.028097932
[49,] -0.358612740 0.085927805
[50,] 0.056469502 -0.358612740
[51,] 0.369744522 0.056469502
[52,] 0.123476737 0.369744522
[53,] 0.104589021 0.123476737
[54,] 0.160048477 0.104589021
[55,] 0.181397495 0.160048477
[56,] -0.100085725 0.181397495
[57,] -0.269294143 -0.100085725
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.008304413 0.103126555
2 0.113443165 0.008304413
3 -0.061450167 0.113443165
4 0.094955138 -0.061450167
5 0.113915071 0.094955138
6 -0.031087294 0.113915071
7 0.004886504 -0.031087294
8 -0.063398273 0.004886504
9 -0.134033029 -0.063398273
10 0.230386731 -0.134033029
11 -0.061501799 0.230386731
12 0.201068908 -0.061501799
13 -0.251044708 0.201068908
14 -0.009847817 -0.251044708
15 0.248404771 -0.009847817
16 -0.317830929 0.248404771
17 0.057476399 -0.317830929
18 -0.025472264 0.057476399
19 0.098729430 -0.025472264
20 0.148042152 0.098729430
21 0.126784019 0.148042152
22 -0.559419597 0.126784019
23 0.131456591 -0.559419597
24 -0.157176985 0.131456591
25 0.172709622 -0.157176985
26 0.016337430 0.172709622
27 0.005484607 0.016337430
28 0.191278183 0.005484607
29 -0.031406288 0.191278183
30 0.026905844 -0.031406288
31 -0.284468343 0.026905844
32 -0.310587352 -0.284468343
33 -0.072282242 -0.310587352
34 -0.085544587 -0.072282242
35 -0.030546952 -0.085544587
36 0.222080265 -0.030546952
37 0.058194544 0.222080265
38 -0.155972775 0.058194544
39 -0.320943055 -0.155972775
40 -0.118154283 -0.320943055
41 0.007414206 -0.118154283
42 -0.274069013 0.007414206
43 0.059554806 -0.274069013
44 0.104608951 0.059554806
45 0.246408144 0.104608951
46 0.182522422 0.246408144
47 0.028097932 0.182522422
48 0.085927805 0.028097932
49 -0.358612740 0.085927805
50 0.056469502 -0.358612740
51 0.369744522 0.056469502
52 0.123476737 0.369744522
53 0.104589021 0.123476737
54 0.160048477 0.104589021
55 0.181397495 0.160048477
56 -0.100085725 0.181397495
57 -0.269294143 -0.100085725
> 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/7auk01258567277.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/83jl41258567277.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xjfh1258567277.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/102bhe1258567277.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11jyh11258567277.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/12rt001258567277.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/13ae161258567277.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/1488i61258567277.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/1520iv1258567277.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/16v3yr1258567277.tab")
+ }
>
> system("convert tmp/1mw3j1258567276.ps tmp/1mw3j1258567276.png")
> system("convert tmp/2ce7z1258567276.ps tmp/2ce7z1258567276.png")
> system("convert tmp/3cljx1258567276.ps tmp/3cljx1258567276.png")
> system("convert tmp/45upn1258567276.ps tmp/45upn1258567276.png")
> system("convert tmp/57g7v1258567276.ps tmp/57g7v1258567276.png")
> system("convert tmp/6dwj71258567276.ps tmp/6dwj71258567276.png")
> system("convert tmp/7auk01258567277.ps tmp/7auk01258567277.png")
> system("convert tmp/83jl41258567277.ps tmp/83jl41258567277.png")
> system("convert tmp/9xjfh1258567277.ps tmp/9xjfh1258567277.png")
> system("convert tmp/102bhe1258567277.ps tmp/102bhe1258567277.png")
>
>
> proc.time()
user system elapsed
2.477 1.609 5.894