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 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(111.4,91.2,111.5,92.2,111.6,93.2,111.7,94.2,111.8,95.2,111.9,96.2,111.10,97.2,111.11,98.2,111.12,99.2,111.13,100.2,111.14,101.2,111.15,102.2,111.16,103.2,111.17,104.2,111.18,105.2,111.19,106.2,111.20,107.2,111.21,108.2,111.22,109.2,111.23,110.2,111.24,111.2,111.25,112.2,111.26,113.2,111.27,114.2,111.28,115.2,111.29,116.2,111.30,117.2,111.31,118.2,111.32,119.2,111.33,120.2,111.34,121.2,111.35,122.2,111.36,123.2,111.37,124.2,111.38,125.2,111.39,126.2,111.40,127.2,111.41,128.2,111.42,129.2,111.43,130.2,111.44,131.2,111.45,132.2,111.46,133.2,111.47,134.2,111.48,135.2,111.49,136.2,111.50,137.2,111.51,138.2,111.52,139.2,111.53,140.2,111.54,141.2,111.55,142.2,111.56,143.2,111.57,144.2,111.58,145.2,111.59,146.2,111.60,147.2,111.61,148.2,111.62,149.2,111.63,150.2,111.64,151.2),dim=c(2,61),dimnames=list(c('biti','Bosnië'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('biti','Bosnië'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Geen lineaire trend'
> par2 = 'Omvatten niet seizoensgebonden 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
biti Bosni\353
1 111.40 91.2
2 111.50 92.2
3 111.60 93.2
4 111.70 94.2
5 111.80 95.2
6 111.90 96.2
7 111.10 97.2
8 111.11 98.2
9 111.12 99.2
10 111.13 100.2
11 111.14 101.2
12 111.15 102.2
13 111.16 103.2
14 111.17 104.2
15 111.18 105.2
16 111.19 106.2
17 111.20 107.2
18 111.21 108.2
19 111.22 109.2
20 111.23 110.2
21 111.24 111.2
22 111.25 112.2
23 111.26 113.2
24 111.27 114.2
25 111.28 115.2
26 111.29 116.2
27 111.30 117.2
28 111.31 118.2
29 111.32 119.2
30 111.33 120.2
31 111.34 121.2
32 111.35 122.2
33 111.36 123.2
34 111.37 124.2
35 111.38 125.2
36 111.39 126.2
37 111.40 127.2
38 111.41 128.2
39 111.42 129.2
40 111.43 130.2
41 111.44 131.2
42 111.45 132.2
43 111.46 133.2
44 111.47 134.2
45 111.48 135.2
46 111.49 136.2
47 111.50 137.2
48 111.51 138.2
49 111.52 139.2
50 111.53 140.2
51 111.54 141.2
52 111.55 142.2
53 111.56 143.2
54 111.57 144.2
55 111.58 145.2
56 111.59 146.2
57 111.60 147.2
58 111.61 148.2
59 111.62 149.2
60 111.63 150.2
61 111.64 151.2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `Bosni\353`
1.108e+02 4.979e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.17805 -0.10273 -0.02741 0.04790 0.62693
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.108e+02 1.428e-01 775.975 < 2e-16 ***
`Bosni\353` 4.979e-03 1.166e-03 4.271 7.19e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1603 on 59 degrees of freedom
Multiple R-squared: 0.2361, Adjusted R-squared: 0.2232
F-statistic: 18.24 on 1 and 59 DF, p-value: 7.185e-05
> 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,] 5.741744e-41 1.148349e-40 1.000000e+00
[2,] 6.689121e-01 6.621759e-01 3.310879e-01
[3,] 1.000000e+00 0.000000e+00 0.000000e+00
[4,] 1.000000e+00 0.000000e+00 0.000000e+00
[5,] 1.000000e+00 0.000000e+00 0.000000e+00
[6,] 1.000000e+00 0.000000e+00 0.000000e+00
[7,] 1.000000e+00 0.000000e+00 0.000000e+00
[8,] 1.000000e+00 0.000000e+00 0.000000e+00
[9,] 1.000000e+00 0.000000e+00 0.000000e+00
[10,] 1.000000e+00 0.000000e+00 0.000000e+00
[11,] 1.000000e+00 0.000000e+00 0.000000e+00
[12,] 1.000000e+00 0.000000e+00 0.000000e+00
[13,] 1.000000e+00 0.000000e+00 0.000000e+00
[14,] 1.000000e+00 0.000000e+00 0.000000e+00
[15,] 1.000000e+00 0.000000e+00 0.000000e+00
[16,] 1.000000e+00 0.000000e+00 0.000000e+00
[17,] 1.000000e+00 0.000000e+00 0.000000e+00
[18,] 1.000000e+00 0.000000e+00 0.000000e+00
[19,] 1.000000e+00 0.000000e+00 0.000000e+00
[20,] 1.000000e+00 0.000000e+00 0.000000e+00
[21,] 1.000000e+00 0.000000e+00 0.000000e+00
[22,] 1.000000e+00 0.000000e+00 0.000000e+00
[23,] 1.000000e+00 0.000000e+00 0.000000e+00
[24,] 1.000000e+00 0.000000e+00 0.000000e+00
[25,] 1.000000e+00 0.000000e+00 0.000000e+00
[26,] 1.000000e+00 0.000000e+00 0.000000e+00
[27,] 1.000000e+00 0.000000e+00 0.000000e+00
[28,] 1.000000e+00 0.000000e+00 0.000000e+00
[29,] 1.000000e+00 6.916919e-322 3.458460e-322
[30,] 1.000000e+00 2.718219e-315 1.359109e-315
[31,] 1.000000e+00 3.753712e-313 1.876856e-313
[32,] 1.000000e+00 3.056748e-309 1.528374e-309
[33,] 1.000000e+00 1.984840e-276 9.924200e-277
[34,] 1.000000e+00 7.039319e-275 3.519659e-275
[35,] 1.000000e+00 1.581551e-252 7.907753e-253
[36,] 1.000000e+00 5.788494e-244 2.894247e-244
[37,] 1.000000e+00 2.836150e-225 1.418075e-225
[38,] 1.000000e+00 5.284422e-215 2.642211e-215
[39,] 1.000000e+00 7.889474e-205 3.944737e-205
[40,] 1.000000e+00 1.239576e-200 6.197881e-201
[41,] 1.000000e+00 3.616187e-180 1.808093e-180
[42,] 1.000000e+00 2.629970e-173 1.314985e-173
[43,] 1.000000e+00 6.300560e-153 3.150280e-153
[44,] 1.000000e+00 9.862669e-146 4.931335e-146
[45,] 1.000000e+00 5.921077e-126 2.960539e-126
[46,] 1.000000e+00 1.884983e-117 9.424915e-118
[47,] 1.000000e+00 3.402856e-103 1.701428e-103
[48,] 1.000000e+00 7.072122e-90 3.536061e-90
[49,] 1.000000e+00 9.846222e-79 4.923111e-79
[50,] 1.000000e+00 4.334273e-65 2.167136e-65
[51,] 1.000000e+00 2.134400e-51 1.067200e-51
[52,] 1.000000e+00 8.261192e-41 4.130596e-41
> postscript(file="/var/www/html/rcomp/tmp/1o8ri1258725313.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rgn61258725313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3a5vl1258725313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4upti1258725313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5c3n31258725313.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 = 61
Frequency = 1
1 2 3 4 5 6
0.151824432 0.246845584 0.341866737 0.436887890 0.531909043 0.626930196
7 8 9 10 11 12
-0.178048652 -0.173027499 -0.168006346 -0.162985193 -0.157964040 -0.152942887
13 14 15 16 17 18
-0.147921735 -0.142900582 -0.137879429 -0.132858276 -0.127837123 -0.122815970
19 20 21 22 23 24
-0.117794818 -0.112773665 -0.107752512 -0.102731359 -0.097710206 -0.092689053
25 26 27 28 29 30
-0.087667901 -0.082646748 -0.077625595 -0.072604442 -0.067583289 -0.062562136
31 32 33 34 35 36
-0.057540984 -0.052519831 -0.047498678 -0.042477525 -0.037456372 -0.032435219
37 38 39 40 41 42
-0.027414067 -0.022392914 -0.017371761 -0.012350608 -0.007329455 -0.002308302
43 44 45 46 47 48
0.002712850 0.007734003 0.012755156 0.017776309 0.022797462 0.027818614
49 50 51 52 53 54
0.032839767 0.037860920 0.042882073 0.047903226 0.052924379 0.057945531
55 56 57 58 59 60
0.062966684 0.067987837 0.073008990 0.078030143 0.083051296 0.088072448
61
0.093093601
> postscript(file="/var/www/html/rcomp/tmp/6wp081258725313.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 0.151824432 NA
1 0.246845584 0.151824432
2 0.341866737 0.246845584
3 0.436887890 0.341866737
4 0.531909043 0.436887890
5 0.626930196 0.531909043
6 -0.178048652 0.626930196
7 -0.173027499 -0.178048652
8 -0.168006346 -0.173027499
9 -0.162985193 -0.168006346
10 -0.157964040 -0.162985193
11 -0.152942887 -0.157964040
12 -0.147921735 -0.152942887
13 -0.142900582 -0.147921735
14 -0.137879429 -0.142900582
15 -0.132858276 -0.137879429
16 -0.127837123 -0.132858276
17 -0.122815970 -0.127837123
18 -0.117794818 -0.122815970
19 -0.112773665 -0.117794818
20 -0.107752512 -0.112773665
21 -0.102731359 -0.107752512
22 -0.097710206 -0.102731359
23 -0.092689053 -0.097710206
24 -0.087667901 -0.092689053
25 -0.082646748 -0.087667901
26 -0.077625595 -0.082646748
27 -0.072604442 -0.077625595
28 -0.067583289 -0.072604442
29 -0.062562136 -0.067583289
30 -0.057540984 -0.062562136
31 -0.052519831 -0.057540984
32 -0.047498678 -0.052519831
33 -0.042477525 -0.047498678
34 -0.037456372 -0.042477525
35 -0.032435219 -0.037456372
36 -0.027414067 -0.032435219
37 -0.022392914 -0.027414067
38 -0.017371761 -0.022392914
39 -0.012350608 -0.017371761
40 -0.007329455 -0.012350608
41 -0.002308302 -0.007329455
42 0.002712850 -0.002308302
43 0.007734003 0.002712850
44 0.012755156 0.007734003
45 0.017776309 0.012755156
46 0.022797462 0.017776309
47 0.027818614 0.022797462
48 0.032839767 0.027818614
49 0.037860920 0.032839767
50 0.042882073 0.037860920
51 0.047903226 0.042882073
52 0.052924379 0.047903226
53 0.057945531 0.052924379
54 0.062966684 0.057945531
55 0.067987837 0.062966684
56 0.073008990 0.067987837
57 0.078030143 0.073008990
58 0.083051296 0.078030143
59 0.088072448 0.083051296
60 0.093093601 0.088072448
61 NA 0.093093601
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.246845584 0.151824432
[2,] 0.341866737 0.246845584
[3,] 0.436887890 0.341866737
[4,] 0.531909043 0.436887890
[5,] 0.626930196 0.531909043
[6,] -0.178048652 0.626930196
[7,] -0.173027499 -0.178048652
[8,] -0.168006346 -0.173027499
[9,] -0.162985193 -0.168006346
[10,] -0.157964040 -0.162985193
[11,] -0.152942887 -0.157964040
[12,] -0.147921735 -0.152942887
[13,] -0.142900582 -0.147921735
[14,] -0.137879429 -0.142900582
[15,] -0.132858276 -0.137879429
[16,] -0.127837123 -0.132858276
[17,] -0.122815970 -0.127837123
[18,] -0.117794818 -0.122815970
[19,] -0.112773665 -0.117794818
[20,] -0.107752512 -0.112773665
[21,] -0.102731359 -0.107752512
[22,] -0.097710206 -0.102731359
[23,] -0.092689053 -0.097710206
[24,] -0.087667901 -0.092689053
[25,] -0.082646748 -0.087667901
[26,] -0.077625595 -0.082646748
[27,] -0.072604442 -0.077625595
[28,] -0.067583289 -0.072604442
[29,] -0.062562136 -0.067583289
[30,] -0.057540984 -0.062562136
[31,] -0.052519831 -0.057540984
[32,] -0.047498678 -0.052519831
[33,] -0.042477525 -0.047498678
[34,] -0.037456372 -0.042477525
[35,] -0.032435219 -0.037456372
[36,] -0.027414067 -0.032435219
[37,] -0.022392914 -0.027414067
[38,] -0.017371761 -0.022392914
[39,] -0.012350608 -0.017371761
[40,] -0.007329455 -0.012350608
[41,] -0.002308302 -0.007329455
[42,] 0.002712850 -0.002308302
[43,] 0.007734003 0.002712850
[44,] 0.012755156 0.007734003
[45,] 0.017776309 0.012755156
[46,] 0.022797462 0.017776309
[47,] 0.027818614 0.022797462
[48,] 0.032839767 0.027818614
[49,] 0.037860920 0.032839767
[50,] 0.042882073 0.037860920
[51,] 0.047903226 0.042882073
[52,] 0.052924379 0.047903226
[53,] 0.057945531 0.052924379
[54,] 0.062966684 0.057945531
[55,] 0.067987837 0.062966684
[56,] 0.073008990 0.067987837
[57,] 0.078030143 0.073008990
[58,] 0.083051296 0.078030143
[59,] 0.088072448 0.083051296
[60,] 0.093093601 0.088072448
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.246845584 0.151824432
2 0.341866737 0.246845584
3 0.436887890 0.341866737
4 0.531909043 0.436887890
5 0.626930196 0.531909043
6 -0.178048652 0.626930196
7 -0.173027499 -0.178048652
8 -0.168006346 -0.173027499
9 -0.162985193 -0.168006346
10 -0.157964040 -0.162985193
11 -0.152942887 -0.157964040
12 -0.147921735 -0.152942887
13 -0.142900582 -0.147921735
14 -0.137879429 -0.142900582
15 -0.132858276 -0.137879429
16 -0.127837123 -0.132858276
17 -0.122815970 -0.127837123
18 -0.117794818 -0.122815970
19 -0.112773665 -0.117794818
20 -0.107752512 -0.112773665
21 -0.102731359 -0.107752512
22 -0.097710206 -0.102731359
23 -0.092689053 -0.097710206
24 -0.087667901 -0.092689053
25 -0.082646748 -0.087667901
26 -0.077625595 -0.082646748
27 -0.072604442 -0.077625595
28 -0.067583289 -0.072604442
29 -0.062562136 -0.067583289
30 -0.057540984 -0.062562136
31 -0.052519831 -0.057540984
32 -0.047498678 -0.052519831
33 -0.042477525 -0.047498678
34 -0.037456372 -0.042477525
35 -0.032435219 -0.037456372
36 -0.027414067 -0.032435219
37 -0.022392914 -0.027414067
38 -0.017371761 -0.022392914
39 -0.012350608 -0.017371761
40 -0.007329455 -0.012350608
41 -0.002308302 -0.007329455
42 0.002712850 -0.002308302
43 0.007734003 0.002712850
44 0.012755156 0.007734003
45 0.017776309 0.012755156
46 0.022797462 0.017776309
47 0.027818614 0.022797462
48 0.032839767 0.027818614
49 0.037860920 0.032839767
50 0.042882073 0.037860920
51 0.047903226 0.042882073
52 0.052924379 0.047903226
53 0.057945531 0.052924379
54 0.062966684 0.057945531
55 0.067987837 0.062966684
56 0.073008990 0.067987837
57 0.078030143 0.073008990
58 0.083051296 0.078030143
59 0.088072448 0.083051296
60 0.093093601 0.088072448
> 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/7zr921258725313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8s7rl1258725313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/917ox1258725313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10h0hd1258725313.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112zxj1258725313.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/128mpq1258725313.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/13n4691258725313.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/14e7tb1258725313.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/15obts1258725313.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/16yi7u1258725313.tab")
+ }
>
> system("convert tmp/1o8ri1258725313.ps tmp/1o8ri1258725313.png")
> system("convert tmp/2rgn61258725313.ps tmp/2rgn61258725313.png")
> system("convert tmp/3a5vl1258725313.ps tmp/3a5vl1258725313.png")
> system("convert tmp/4upti1258725313.ps tmp/4upti1258725313.png")
> system("convert tmp/5c3n31258725313.ps tmp/5c3n31258725313.png")
> system("convert tmp/6wp081258725313.ps tmp/6wp081258725313.png")
> system("convert tmp/7zr921258725313.ps tmp/7zr921258725313.png")
> system("convert tmp/8s7rl1258725313.ps tmp/8s7rl1258725313.png")
> system("convert tmp/917ox1258725313.ps tmp/917ox1258725313.png")
> system("convert tmp/10h0hd1258725313.ps tmp/10h0hd1258725313.png")
>
>
> proc.time()
user system elapsed
2.430 1.575 2.878