R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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(81.71,84.86,87.703,85.03,90.09,85.61,100.639,85.52,83.042,86.51,89.956,86.66,89.561,87.27,105.38,87.62,86.554,88.17,93.131,87.99,92.812,88.83,102.195,88.75,88.925,88.81,94.184,89.43,94.196,89.5,108.932,89.34,91.134,89.75,97.149,90.26,96.415,90.32,112.432,90.76,92.47,91.53,98.61410515,92.35,97.80117197,93.04,111.8560178,93.35,95.63981455,93.54,104.1120262,95.07,104.0148224,95.39,118.1743476,95.43,102.033431,96.09,109.3138852,96.35,108.1523649,96.6,121.30381,96.62,103.8725146,97.6,112.7185207,97.67,109.0381253,98.23,122.4434864,98.29,106.6325686,98.89,113.8153852,99.88,111.1071252,100.42,130.039536,100.81,109.6121057,101.5,116.8592117,102.59,113.8982545,103.58,128.9375926,103.47,111.8120023,103.77,119.9689463,104.65,117.018539,105.12,132.4743387,104.97,116.3369106,105.58,124.6405636,106.17,121.025249,106.52,137.2054829,107.87,120.0187687,109.63,127.0443429,111.54,124.349043,112.47,143.6114438,111.63),dim=c(2,56),dimnames=list(c('LKI','CPI'),1:56))
> y <- array(NA,dim=c(2,56),dimnames=list(c('LKI','CPI'),1:56))
> 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
> 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
LKI CPI
1 81.71000 84.86
2 87.70300 85.03
3 90.09000 85.61
4 100.63900 85.52
5 83.04200 86.51
6 89.95600 86.66
7 89.56100 87.27
8 105.38000 87.62
9 86.55400 88.17
10 93.13100 87.99
11 92.81200 88.83
12 102.19500 88.75
13 88.92500 88.81
14 94.18400 89.43
15 94.19600 89.50
16 108.93200 89.34
17 91.13400 89.75
18 97.14900 90.26
19 96.41500 90.32
20 112.43200 90.76
21 92.47000 91.53
22 98.61411 92.35
23 97.80117 93.04
24 111.85602 93.35
25 95.63981 93.54
26 104.11203 95.07
27 104.01482 95.39
28 118.17435 95.43
29 102.03343 96.09
30 109.31389 96.35
31 108.15236 96.60
32 121.30381 96.62
33 103.87251 97.60
34 112.71852 97.67
35 109.03813 98.23
36 122.44349 98.29
37 106.63257 98.89
38 113.81539 99.88
39 111.10713 100.42
40 130.03954 100.81
41 109.61211 101.50
42 116.85921 102.59
43 113.89825 103.58
44 128.93759 103.47
45 111.81200 103.77
46 119.96895 104.65
47 117.01854 105.12
48 132.47434 104.97
49 116.33691 105.58
50 124.64056 106.17
51 121.02525 106.52
52 137.20548 107.87
53 120.01877 109.63
54 127.04434 111.54
55 124.34904 112.47
56 143.61144 111.63
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) CPI
-47.776 1.614
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.431 -5.361 -1.970 3.799 15.082
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -47.7763 12.1170 -3.943 0.000234 ***
CPI 1.6143 0.1255 12.862 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.253 on 54 degrees of freedom
Multiple R-squared: 0.7539, Adjusted R-squared: 0.7494
F-statistic: 165.4 on 1 and 54 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,] 0.75346623 0.4930675 0.2465338
[2,] 0.61166762 0.7766648 0.3883324
[3,] 0.46755421 0.9351084 0.5324458
[4,] 0.65934024 0.6813195 0.3406598
[5,] 0.69157976 0.6168405 0.3084202
[6,] 0.58606097 0.8278781 0.4139390
[7,] 0.48473327 0.9694665 0.5152667
[8,] 0.47468432 0.9493686 0.5253157
[9,] 0.45330982 0.9066196 0.5466902
[10,] 0.36492645 0.7298529 0.6350735
[11,] 0.28565939 0.5713188 0.7143406
[12,] 0.45784320 0.9156864 0.5421568
[13,] 0.43588676 0.8717735 0.5641132
[14,] 0.35345393 0.7069079 0.6465461
[15,] 0.28190680 0.5638136 0.7180932
[16,] 0.44540690 0.8908138 0.5545931
[17,] 0.47769413 0.9553883 0.5223059
[18,] 0.41401349 0.8280270 0.5859865
[19,] 0.37364788 0.7472958 0.6263521
[20,] 0.39309355 0.7861871 0.6069064
[21,] 0.41970538 0.8394108 0.5802946
[22,] 0.35295025 0.7059005 0.6470498
[23,] 0.29593713 0.5918743 0.7040629
[24,] 0.38014297 0.7602859 0.6198570
[25,] 0.36677310 0.7335462 0.6332269
[26,] 0.29613119 0.5922624 0.7038688
[27,] 0.23495385 0.4699077 0.7650462
[28,] 0.32910765 0.6582153 0.6708923
[29,] 0.32959288 0.6591858 0.6704071
[30,] 0.26164553 0.5232911 0.7383545
[31,] 0.21205580 0.4241116 0.7879442
[32,] 0.26190786 0.5238157 0.7380921
[33,] 0.24556973 0.4911395 0.7544303
[34,] 0.18537612 0.3707522 0.8146239
[35,] 0.15294501 0.3058900 0.8470550
[36,] 0.30869659 0.6173932 0.6913034
[37,] 0.29673820 0.5934764 0.7032618
[38,] 0.22631045 0.4526209 0.7736895
[39,] 0.20343969 0.4068794 0.7965603
[40,] 0.23067245 0.4613449 0.7693275
[41,] 0.24025380 0.4805076 0.7597462
[42,] 0.17105254 0.3421051 0.8289475
[43,] 0.14653315 0.2930663 0.8534669
[44,] 0.17491018 0.3498204 0.8250898
[45,] 0.15167031 0.3033406 0.8483297
[46,] 0.08603473 0.1720695 0.9139653
[47,] 0.06848632 0.1369726 0.9315137
> postscript(file="/var/www/rcomp/tmp/1zhkf1293190838.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2a81i1293190838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/rcomp/tmp/3a81i1293190838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4a81i1293190838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/53h031293190838.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 = 56
Frequency = 1
1 2 3 4 5 6
-7.499945276 -1.781369670 -0.330641133 10.363642370 -8.831476162 -2.159615333
7 8 9 10 11 12
-3.539314631 11.714693969 -7.999149659 -1.131582654 -2.806562014 6.705578878
13 14 15 16 17 18
-6.661276791 -2.403118700 -2.504116980 12.490164803 -5.969682266 -0.777955449
19 20 21 22 23 24
-1.608811117 13.697913980 -7.507067100 -2.686656088 -4.613429457 8.940995419
25 26 27 28 29 30
-7.581917448 -1.579525347 -2.193292713 11.901662041 -5.304666913 1.556079390
31 32 33 34 35 36
-0.009006196 13.110153681 -5.903117639 2.829890181 -1.754491460 11.554013972
37 38 39 40 41 42
-5.225460514 0.359237554 -3.220723463 15.082125491 -6.459144998 -0.971583644
43 44 45 46 47 48
-5.530659376 9.686247450 -7.923621193 -1.187226999 -4.896337036 10.801601835
49 50 51 52 53 54
-6.320525562 1.030713364 -3.149592636 10.851388720 -9.176425092 -5.234089675
55 56
-9.430652438 11.187727722
> postscript(file="/var/www/rcomp/tmp/63h031293190838.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -7.499945276 NA
1 -1.781369670 -7.499945276
2 -0.330641133 -1.781369670
3 10.363642370 -0.330641133
4 -8.831476162 10.363642370
5 -2.159615333 -8.831476162
6 -3.539314631 -2.159615333
7 11.714693969 -3.539314631
8 -7.999149659 11.714693969
9 -1.131582654 -7.999149659
10 -2.806562014 -1.131582654
11 6.705578878 -2.806562014
12 -6.661276791 6.705578878
13 -2.403118700 -6.661276791
14 -2.504116980 -2.403118700
15 12.490164803 -2.504116980
16 -5.969682266 12.490164803
17 -0.777955449 -5.969682266
18 -1.608811117 -0.777955449
19 13.697913980 -1.608811117
20 -7.507067100 13.697913980
21 -2.686656088 -7.507067100
22 -4.613429457 -2.686656088
23 8.940995419 -4.613429457
24 -7.581917448 8.940995419
25 -1.579525347 -7.581917448
26 -2.193292713 -1.579525347
27 11.901662041 -2.193292713
28 -5.304666913 11.901662041
29 1.556079390 -5.304666913
30 -0.009006196 1.556079390
31 13.110153681 -0.009006196
32 -5.903117639 13.110153681
33 2.829890181 -5.903117639
34 -1.754491460 2.829890181
35 11.554013972 -1.754491460
36 -5.225460514 11.554013972
37 0.359237554 -5.225460514
38 -3.220723463 0.359237554
39 15.082125491 -3.220723463
40 -6.459144998 15.082125491
41 -0.971583644 -6.459144998
42 -5.530659376 -0.971583644
43 9.686247450 -5.530659376
44 -7.923621193 9.686247450
45 -1.187226999 -7.923621193
46 -4.896337036 -1.187226999
47 10.801601835 -4.896337036
48 -6.320525562 10.801601835
49 1.030713364 -6.320525562
50 -3.149592636 1.030713364
51 10.851388720 -3.149592636
52 -9.176425092 10.851388720
53 -5.234089675 -9.176425092
54 -9.430652438 -5.234089675
55 11.187727722 -9.430652438
56 NA 11.187727722
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.781369670 -7.499945276
[2,] -0.330641133 -1.781369670
[3,] 10.363642370 -0.330641133
[4,] -8.831476162 10.363642370
[5,] -2.159615333 -8.831476162
[6,] -3.539314631 -2.159615333
[7,] 11.714693969 -3.539314631
[8,] -7.999149659 11.714693969
[9,] -1.131582654 -7.999149659
[10,] -2.806562014 -1.131582654
[11,] 6.705578878 -2.806562014
[12,] -6.661276791 6.705578878
[13,] -2.403118700 -6.661276791
[14,] -2.504116980 -2.403118700
[15,] 12.490164803 -2.504116980
[16,] -5.969682266 12.490164803
[17,] -0.777955449 -5.969682266
[18,] -1.608811117 -0.777955449
[19,] 13.697913980 -1.608811117
[20,] -7.507067100 13.697913980
[21,] -2.686656088 -7.507067100
[22,] -4.613429457 -2.686656088
[23,] 8.940995419 -4.613429457
[24,] -7.581917448 8.940995419
[25,] -1.579525347 -7.581917448
[26,] -2.193292713 -1.579525347
[27,] 11.901662041 -2.193292713
[28,] -5.304666913 11.901662041
[29,] 1.556079390 -5.304666913
[30,] -0.009006196 1.556079390
[31,] 13.110153681 -0.009006196
[32,] -5.903117639 13.110153681
[33,] 2.829890181 -5.903117639
[34,] -1.754491460 2.829890181
[35,] 11.554013972 -1.754491460
[36,] -5.225460514 11.554013972
[37,] 0.359237554 -5.225460514
[38,] -3.220723463 0.359237554
[39,] 15.082125491 -3.220723463
[40,] -6.459144998 15.082125491
[41,] -0.971583644 -6.459144998
[42,] -5.530659376 -0.971583644
[43,] 9.686247450 -5.530659376
[44,] -7.923621193 9.686247450
[45,] -1.187226999 -7.923621193
[46,] -4.896337036 -1.187226999
[47,] 10.801601835 -4.896337036
[48,] -6.320525562 10.801601835
[49,] 1.030713364 -6.320525562
[50,] -3.149592636 1.030713364
[51,] 10.851388720 -3.149592636
[52,] -9.176425092 10.851388720
[53,] -5.234089675 -9.176425092
[54,] -9.430652438 -5.234089675
[55,] 11.187727722 -9.430652438
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.781369670 -7.499945276
2 -0.330641133 -1.781369670
3 10.363642370 -0.330641133
4 -8.831476162 10.363642370
5 -2.159615333 -8.831476162
6 -3.539314631 -2.159615333
7 11.714693969 -3.539314631
8 -7.999149659 11.714693969
9 -1.131582654 -7.999149659
10 -2.806562014 -1.131582654
11 6.705578878 -2.806562014
12 -6.661276791 6.705578878
13 -2.403118700 -6.661276791
14 -2.504116980 -2.403118700
15 12.490164803 -2.504116980
16 -5.969682266 12.490164803
17 -0.777955449 -5.969682266
18 -1.608811117 -0.777955449
19 13.697913980 -1.608811117
20 -7.507067100 13.697913980
21 -2.686656088 -7.507067100
22 -4.613429457 -2.686656088
23 8.940995419 -4.613429457
24 -7.581917448 8.940995419
25 -1.579525347 -7.581917448
26 -2.193292713 -1.579525347
27 11.901662041 -2.193292713
28 -5.304666913 11.901662041
29 1.556079390 -5.304666913
30 -0.009006196 1.556079390
31 13.110153681 -0.009006196
32 -5.903117639 13.110153681
33 2.829890181 -5.903117639
34 -1.754491460 2.829890181
35 11.554013972 -1.754491460
36 -5.225460514 11.554013972
37 0.359237554 -5.225460514
38 -3.220723463 0.359237554
39 15.082125491 -3.220723463
40 -6.459144998 15.082125491
41 -0.971583644 -6.459144998
42 -5.530659376 -0.971583644
43 9.686247450 -5.530659376
44 -7.923621193 9.686247450
45 -1.187226999 -7.923621193
46 -4.896337036 -1.187226999
47 10.801601835 -4.896337036
48 -6.320525562 10.801601835
49 1.030713364 -6.320525562
50 -3.149592636 1.030713364
51 10.851388720 -3.149592636
52 -9.176425092 10.851388720
53 -5.234089675 -9.176425092
54 -9.430652438 -5.234089675
55 11.187727722 -9.430652438
> 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/rcomp/tmp/7drz51293190838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8drz51293190838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9drz51293190838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10oizr1293190838.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/rcomp/tmp/11rjxw1293190838.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/rcomp/tmp/12v1d21293190838.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/rcomp/tmp/1322tw1293190838.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/rcomp/tmp/14utaz1293190838.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/rcomp/tmp/15gu8n1293190838.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/rcomp/tmp/16c46w1293190838.tab")
+ }
>
> try(system("convert tmp/1zhkf1293190838.ps tmp/1zhkf1293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a81i1293190838.ps tmp/2a81i1293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a81i1293190838.ps tmp/3a81i1293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/4a81i1293190838.ps tmp/4a81i1293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/53h031293190838.ps tmp/53h031293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/63h031293190838.ps tmp/63h031293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/7drz51293190838.ps tmp/7drz51293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/8drz51293190838.ps tmp/8drz51293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/9drz51293190838.ps tmp/9drz51293190838.png",intern=TRUE))
character(0)
> try(system("convert tmp/10oizr1293190838.ps tmp/10oizr1293190838.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.080 1.640 4.708