R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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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
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Type 'q()' to quit R.
> x <- array(list(83.4,108.8,113.6,128.4,112.9,121.1,104,119.5,109.9,128.7,99,108.7,106.3,105.5,128.9,119.8,111.1,111.3,102.9,110.6,130,120.1,87,97.5,87.5,107.7,117.6,127.3,103.4,117.2,110.8,119.8,112.6,116.2,102.5,111,112.4,112.4,135.6,130.6,105.1,109.1,127.7,118.8,137,123.9,91,101.6,90.5,112.8,122.4,128,123.3,129.6,124.3,125.8,120,119.5,118.1,115.7,119,113.6,142.7,129.7,123.6,112,129.6,116.8,151.6,127,110.4,112.1,99.2,114.2,130.5,121.1,136.2,131.6,129.7,125,128,120.4,121.6,117.7,135.8,117.5,143.8,120.6,147.5,127.5,136.2,112.3,156.6,124.5,123.3,115.2,104.5,104.7,139.8,130.9,136.5,129.2,112.1,113.5,118.5,125.6,94.4,107.6,102.3,107,111.4,121.6,99.2,110.7,87.8,106.3,115.8,118.6,79.7,104.6),dim=c(2,60),dimnames=list(c('inv','cons'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('inv','cons'),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
inv cons
1 83.4 108.8
2 113.6 128.4
3 112.9 121.1
4 104.0 119.5
5 109.9 128.7
6 99.0 108.7
7 106.3 105.5
8 128.9 119.8
9 111.1 111.3
10 102.9 110.6
11 130.0 120.1
12 87.0 97.5
13 87.5 107.7
14 117.6 127.3
15 103.4 117.2
16 110.8 119.8
17 112.6 116.2
18 102.5 111.0
19 112.4 112.4
20 135.6 130.6
21 105.1 109.1
22 127.7 118.8
23 137.0 123.9
24 91.0 101.6
25 90.5 112.8
26 122.4 128.0
27 123.3 129.6
28 124.3 125.8
29 120.0 119.5
30 118.1 115.7
31 119.0 113.6
32 142.7 129.7
33 123.6 112.0
34 129.6 116.8
35 151.6 127.0
36 110.4 112.1
37 99.2 114.2
38 130.5 121.1
39 136.2 131.6
40 129.7 125.0
41 128.0 120.4
42 121.6 117.7
43 135.8 117.5
44 143.8 120.6
45 147.5 127.5
46 136.2 112.3
47 156.6 124.5
48 123.3 115.2
49 104.5 104.7
50 139.8 130.9
51 136.5 129.2
52 112.1 113.5
53 118.5 125.6
54 94.4 107.6
55 102.3 107.0
56 111.4 121.6
57 99.2 110.7
58 87.8 106.3
59 115.8 118.6
60 79.7 104.6
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) cons
-68.971 1.580
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-24.539 -9.913 1.189 8.161 28.799
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -68.9709 22.0857 -3.123 0.00279 **
cons 1.5805 0.1876 8.425 1.19e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 12.06 on 58 degrees of freedom
Multiple R-squared: 0.5503, Adjusted R-squared: 0.5426
F-statistic: 70.98 on 1 and 58 DF, p-value: 1.193e-11
> 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,] 0.18831369 0.37662738 0.81168631
[2,] 0.18439245 0.36878490 0.81560755
[3,] 0.29829892 0.59659784 0.70170108
[4,] 0.61703498 0.76593004 0.38296502
[5,] 0.54900733 0.90198534 0.45099267
[6,] 0.43450508 0.86901016 0.56549492
[7,] 0.57701384 0.84597232 0.42298616
[8,] 0.49008741 0.98017482 0.50991259
[9,] 0.50457594 0.99084812 0.49542406
[10,] 0.45259477 0.90518954 0.54740523
[11,] 0.40779205 0.81558411 0.59220795
[12,] 0.34357291 0.68714582 0.65642709
[13,] 0.28140242 0.56280483 0.71859758
[14,] 0.21598584 0.43197168 0.78401416
[15,] 0.18493454 0.36986908 0.81506546
[16,] 0.18739684 0.37479368 0.81260316
[17,] 0.14157858 0.28315715 0.85842142
[18,] 0.17058337 0.34116674 0.82941663
[19,] 0.22162437 0.44324874 0.77837563
[20,] 0.16731638 0.33463277 0.83268362
[21,] 0.24388372 0.48776744 0.75611628
[22,] 0.22589459 0.45178917 0.77410541
[23,] 0.23313696 0.46627391 0.76686304
[24,] 0.20588601 0.41177202 0.79411399
[25,] 0.16871988 0.33743975 0.83128012
[26,] 0.14085583 0.28171166 0.85914417
[27,] 0.13194935 0.26389870 0.86805065
[28,] 0.13376276 0.26752552 0.86623724
[29,] 0.17844854 0.35689708 0.82155146
[30,] 0.20669001 0.41338002 0.79330999
[31,] 0.31956056 0.63912113 0.68043944
[32,] 0.25573028 0.51146056 0.74426972
[33,] 0.26811231 0.53622461 0.73188769
[34,] 0.23121688 0.46243376 0.76878312
[35,] 0.20291248 0.40582496 0.79708752
[36,] 0.16102552 0.32205104 0.83897448
[37,] 0.12578925 0.25157851 0.87421075
[38,] 0.09175747 0.18351493 0.90824253
[39,] 0.12873179 0.25746359 0.87126821
[40,] 0.21121073 0.42242146 0.78878927
[41,] 0.20757312 0.41514623 0.79242688
[42,] 0.54643377 0.90713245 0.45356623
[43,] 0.92888624 0.14222752 0.07111376
[44,] 0.95299314 0.09401372 0.04700686
[45,] 0.97940100 0.04119800 0.02059900
[46,] 0.96395228 0.07209544 0.03604772
[47,] 0.94871244 0.10257513 0.05128756
[48,] 0.94935295 0.10129409 0.05064705
[49,] 0.90657235 0.18685530 0.09342765
[50,] 0.81925982 0.36148037 0.18074018
[51,] 0.90458101 0.19083798 0.09541899
> postscript(file="/var/www/html/rcomp/tmp/1qm3x1258787360.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2szbb1258787360.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3h8ei1258787360.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4bm9v1258787360.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5rvw21258787360.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
-19.5873946 -20.3651671 -9.5275274 -15.8987296 -24.5393167 -3.8293448
7 8 9 10 11 12
8.5282507 8.5271208 4.1613589 -2.9322921 9.1529712 1.8722395
13 14 15 16 17 18
-13.7488462 -14.6266186 -12.8635828 -9.5728792 -2.0830842 -3.9644915
19 20 21 22 23 24
3.7228104 -1.8422640 1.6384558 8.9076194 10.1470766 -0.6078047
25 26 27 28 29 30
-18.8093890 -10.9329677 -12.5617654 -5.5558707 0.1012704 4.2071651
31 32 33 34 35 36
8.4262121 6.6801847 15.5550099 13.9686166 19.8475309 2.1969600
37 38 39 40 41 42
-12.3220870 8.0724726 -2.8227626 1.1085281 6.6788217 4.5461679
43 44 45 46 47 48
19.0622676 22.1627219 14.9572816 27.6808603 28.7987774 10.1974144
49 50 51 52 53 54
7.9926496 1.8835864 1.2704340 1.6842620 -11.0397710 -6.6907963
55 56 57 58 59 60
2.1575028 -11.8177766 -6.7903420 -11.2361481 -2.6762809 -16.6493005
> postscript(file="/var/www/html/rcomp/tmp/6jm1k1258787360.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 -19.5873946 NA
1 -20.3651671 -19.5873946
2 -9.5275274 -20.3651671
3 -15.8987296 -9.5275274
4 -24.5393167 -15.8987296
5 -3.8293448 -24.5393167
6 8.5282507 -3.8293448
7 8.5271208 8.5282507
8 4.1613589 8.5271208
9 -2.9322921 4.1613589
10 9.1529712 -2.9322921
11 1.8722395 9.1529712
12 -13.7488462 1.8722395
13 -14.6266186 -13.7488462
14 -12.8635828 -14.6266186
15 -9.5728792 -12.8635828
16 -2.0830842 -9.5728792
17 -3.9644915 -2.0830842
18 3.7228104 -3.9644915
19 -1.8422640 3.7228104
20 1.6384558 -1.8422640
21 8.9076194 1.6384558
22 10.1470766 8.9076194
23 -0.6078047 10.1470766
24 -18.8093890 -0.6078047
25 -10.9329677 -18.8093890
26 -12.5617654 -10.9329677
27 -5.5558707 -12.5617654
28 0.1012704 -5.5558707
29 4.2071651 0.1012704
30 8.4262121 4.2071651
31 6.6801847 8.4262121
32 15.5550099 6.6801847
33 13.9686166 15.5550099
34 19.8475309 13.9686166
35 2.1969600 19.8475309
36 -12.3220870 2.1969600
37 8.0724726 -12.3220870
38 -2.8227626 8.0724726
39 1.1085281 -2.8227626
40 6.6788217 1.1085281
41 4.5461679 6.6788217
42 19.0622676 4.5461679
43 22.1627219 19.0622676
44 14.9572816 22.1627219
45 27.6808603 14.9572816
46 28.7987774 27.6808603
47 10.1974144 28.7987774
48 7.9926496 10.1974144
49 1.8835864 7.9926496
50 1.2704340 1.8835864
51 1.6842620 1.2704340
52 -11.0397710 1.6842620
53 -6.6907963 -11.0397710
54 2.1575028 -6.6907963
55 -11.8177766 2.1575028
56 -6.7903420 -11.8177766
57 -11.2361481 -6.7903420
58 -2.6762809 -11.2361481
59 -16.6493005 -2.6762809
60 NA -16.6493005
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -20.3651671 -19.5873946
[2,] -9.5275274 -20.3651671
[3,] -15.8987296 -9.5275274
[4,] -24.5393167 -15.8987296
[5,] -3.8293448 -24.5393167
[6,] 8.5282507 -3.8293448
[7,] 8.5271208 8.5282507
[8,] 4.1613589 8.5271208
[9,] -2.9322921 4.1613589
[10,] 9.1529712 -2.9322921
[11,] 1.8722395 9.1529712
[12,] -13.7488462 1.8722395
[13,] -14.6266186 -13.7488462
[14,] -12.8635828 -14.6266186
[15,] -9.5728792 -12.8635828
[16,] -2.0830842 -9.5728792
[17,] -3.9644915 -2.0830842
[18,] 3.7228104 -3.9644915
[19,] -1.8422640 3.7228104
[20,] 1.6384558 -1.8422640
[21,] 8.9076194 1.6384558
[22,] 10.1470766 8.9076194
[23,] -0.6078047 10.1470766
[24,] -18.8093890 -0.6078047
[25,] -10.9329677 -18.8093890
[26,] -12.5617654 -10.9329677
[27,] -5.5558707 -12.5617654
[28,] 0.1012704 -5.5558707
[29,] 4.2071651 0.1012704
[30,] 8.4262121 4.2071651
[31,] 6.6801847 8.4262121
[32,] 15.5550099 6.6801847
[33,] 13.9686166 15.5550099
[34,] 19.8475309 13.9686166
[35,] 2.1969600 19.8475309
[36,] -12.3220870 2.1969600
[37,] 8.0724726 -12.3220870
[38,] -2.8227626 8.0724726
[39,] 1.1085281 -2.8227626
[40,] 6.6788217 1.1085281
[41,] 4.5461679 6.6788217
[42,] 19.0622676 4.5461679
[43,] 22.1627219 19.0622676
[44,] 14.9572816 22.1627219
[45,] 27.6808603 14.9572816
[46,] 28.7987774 27.6808603
[47,] 10.1974144 28.7987774
[48,] 7.9926496 10.1974144
[49,] 1.8835864 7.9926496
[50,] 1.2704340 1.8835864
[51,] 1.6842620 1.2704340
[52,] -11.0397710 1.6842620
[53,] -6.6907963 -11.0397710
[54,] 2.1575028 -6.6907963
[55,] -11.8177766 2.1575028
[56,] -6.7903420 -11.8177766
[57,] -11.2361481 -6.7903420
[58,] -2.6762809 -11.2361481
[59,] -16.6493005 -2.6762809
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -20.3651671 -19.5873946
2 -9.5275274 -20.3651671
3 -15.8987296 -9.5275274
4 -24.5393167 -15.8987296
5 -3.8293448 -24.5393167
6 8.5282507 -3.8293448
7 8.5271208 8.5282507
8 4.1613589 8.5271208
9 -2.9322921 4.1613589
10 9.1529712 -2.9322921
11 1.8722395 9.1529712
12 -13.7488462 1.8722395
13 -14.6266186 -13.7488462
14 -12.8635828 -14.6266186
15 -9.5728792 -12.8635828
16 -2.0830842 -9.5728792
17 -3.9644915 -2.0830842
18 3.7228104 -3.9644915
19 -1.8422640 3.7228104
20 1.6384558 -1.8422640
21 8.9076194 1.6384558
22 10.1470766 8.9076194
23 -0.6078047 10.1470766
24 -18.8093890 -0.6078047
25 -10.9329677 -18.8093890
26 -12.5617654 -10.9329677
27 -5.5558707 -12.5617654
28 0.1012704 -5.5558707
29 4.2071651 0.1012704
30 8.4262121 4.2071651
31 6.6801847 8.4262121
32 15.5550099 6.6801847
33 13.9686166 15.5550099
34 19.8475309 13.9686166
35 2.1969600 19.8475309
36 -12.3220870 2.1969600
37 8.0724726 -12.3220870
38 -2.8227626 8.0724726
39 1.1085281 -2.8227626
40 6.6788217 1.1085281
41 4.5461679 6.6788217
42 19.0622676 4.5461679
43 22.1627219 19.0622676
44 14.9572816 22.1627219
45 27.6808603 14.9572816
46 28.7987774 27.6808603
47 10.1974144 28.7987774
48 7.9926496 10.1974144
49 1.8835864 7.9926496
50 1.2704340 1.8835864
51 1.6842620 1.2704340
52 -11.0397710 1.6842620
53 -6.6907963 -11.0397710
54 2.1575028 -6.6907963
55 -11.8177766 2.1575028
56 -6.7903420 -11.8177766
57 -11.2361481 -6.7903420
58 -2.6762809 -11.2361481
59 -16.6493005 -2.6762809
> 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/72f5o1258787360.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/86xmy1258787360.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9wwq41258787360.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10593c1258787360.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11moy71258787361.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/12er421258787361.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/1306ky1258787361.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/14gtpf1258787361.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/15n4e71258787361.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/162bjh1258787361.tab")
+ }
>
> system("convert tmp/1qm3x1258787360.ps tmp/1qm3x1258787360.png")
> system("convert tmp/2szbb1258787360.ps tmp/2szbb1258787360.png")
> system("convert tmp/3h8ei1258787360.ps tmp/3h8ei1258787360.png")
> system("convert tmp/4bm9v1258787360.ps tmp/4bm9v1258787360.png")
> system("convert tmp/5rvw21258787360.ps tmp/5rvw21258787360.png")
> system("convert tmp/6jm1k1258787360.ps tmp/6jm1k1258787360.png")
> system("convert tmp/72f5o1258787360.ps tmp/72f5o1258787360.png")
> system("convert tmp/86xmy1258787360.ps tmp/86xmy1258787360.png")
> system("convert tmp/9wwq41258787360.ps tmp/9wwq41258787360.png")
> system("convert tmp/10593c1258787360.ps tmp/10593c1258787360.png")
>
>
> proc.time()
user system elapsed
2.474 1.583 6.752