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 'demo()' for some demos, 'help()' for on-line help, or
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> x <- array(list(103.63,100.30,103.64,98.50,103.66,95.10,103.77,93.10,103.88,92.20,103.91,89.00,103.91,86.40,103.92,84.50,104.05,82.70,104.23,80.80,104.30,81.80,104.31,81.80,104.31,82.90,104.34,83.80,104.55,86.20,104.65,86.10,104.73,86.20,104.75,88.80,104.75,89.60,104.76,87.80,104.94,88.30,105.29,88.60,105.38,91.00,105.43,91.50,105.43,95.40,105.42,98.70,105.52,99.90,105.69,98.60,105.72,100.30,105.74,100.20,105.74,100.40,105.74,101.40,105.95,103.00,106.17,109.10,106.34,111.40,106.37,114.10,106.37,121.80,106.36,127.60,106.44,129.90,106.29,128.00,106.23,123.50,106.23,124.00,106.23,127.40,106.23,127.60,106.34,128.40,106.44,131.40,106.44,135.10,106.48,134.00,106.50,144.50,106.57,147.30,106.40,150.90,106.37,148.70,106.25,141.40,106.21,138.90,106.21,139.80,106.24,145.60,106.19,147.90,106.08,148.50,106.13,151.10,106.09,157.50),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),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 = '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 t
1 103.63 100.3 1
2 103.64 98.5 2
3 103.66 95.1 3
4 103.77 93.1 4
5 103.88 92.2 5
6 103.91 89.0 6
7 103.91 86.4 7
8 103.92 84.5 8
9 104.05 82.7 9
10 104.23 80.8 10
11 104.30 81.8 11
12 104.31 81.8 12
13 104.31 82.9 13
14 104.34 83.8 14
15 104.55 86.2 15
16 104.65 86.1 16
17 104.73 86.2 17
18 104.75 88.8 18
19 104.75 89.6 19
20 104.76 87.8 20
21 104.94 88.3 21
22 105.29 88.6 22
23 105.38 91.0 23
24 105.43 91.5 24
25 105.43 95.4 25
26 105.42 98.7 26
27 105.52 99.9 27
28 105.69 98.6 28
29 105.72 100.3 29
30 105.74 100.2 30
31 105.74 100.4 31
32 105.74 101.4 32
33 105.95 103.0 33
34 106.17 109.1 34
35 106.34 111.4 35
36 106.37 114.1 36
37 106.37 121.8 37
38 106.36 127.6 38
39 106.44 129.9 39
40 106.29 128.0 40
41 106.23 123.5 41
42 106.23 124.0 42
43 106.23 127.4 43
44 106.23 127.6 44
45 106.34 128.4 45
46 106.44 131.4 46
47 106.44 135.1 47
48 106.48 134.0 48
49 106.50 144.5 49
50 106.57 147.3 50
51 106.40 150.9 51
52 106.37 148.7 52
53 106.25 141.4 53
54 106.21 138.9 54
55 106.21 139.8 55
56 106.24 145.6 56
57 106.19 147.9 57
58 106.08 148.5 58
59 106.13 151.1 59
60 106.09 157.5 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X t
105.00359 -0.01564 0.07107
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.72254 -0.22515 0.07195 0.18836 0.69677
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 105.003586 0.368333 285.078 < 2e-16 ***
X -0.015642 0.004946 -3.162 0.00251 **
t 0.071066 0.006770 10.497 6.17e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3443 on 57 degrees of freedom
Multiple R-squared: 0.8776, Adjusted R-squared: 0.8733
F-statistic: 204.4 on 2 and 57 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,] 1.139889e-03 2.279778e-03 9.988601e-01
[2,] 1.259573e-04 2.519147e-04 9.998740e-01
[3,] 5.445442e-05 1.089088e-04 9.999455e-01
[4,] 5.878387e-06 1.175677e-05 9.999941e-01
[5,] 5.860471e-06 1.172094e-05 9.999941e-01
[6,] 1.512160e-06 3.024319e-06 9.999985e-01
[7,] 9.182298e-07 1.836460e-06 9.999991e-01
[8,] 8.368448e-07 1.673690e-06 9.999992e-01
[9,] 4.177224e-07 8.354448e-07 9.999996e-01
[10,] 2.149531e-07 4.299062e-07 9.999998e-01
[11,] 9.787317e-08 1.957463e-07 9.999999e-01
[12,] 3.813057e-08 7.626114e-08 1.000000e+00
[13,] 1.874312e-08 3.748623e-08 1.000000e+00
[14,] 3.660596e-08 7.321192e-08 1.000000e+00
[15,] 1.898322e-07 3.796645e-07 9.999998e-01
[16,] 2.682764e-07 5.365529e-07 9.999997e-01
[17,] 2.829934e-05 5.659868e-05 9.999717e-01
[18,] 1.301718e-04 2.603436e-04 9.998698e-01
[19,] 1.639228e-04 3.278456e-04 9.998361e-01
[20,] 1.751348e-04 3.502696e-04 9.998249e-01
[21,] 7.411051e-04 1.482210e-03 9.992589e-01
[22,] 2.777230e-03 5.554460e-03 9.972228e-01
[23,] 3.431144e-03 6.862289e-03 9.965689e-01
[24,] 5.146862e-03 1.029372e-02 9.948531e-01
[25,] 9.167909e-03 1.833582e-02 9.908321e-01
[26,] 3.007636e-02 6.015273e-02 9.699236e-01
[27,] 2.210461e-01 4.420922e-01 7.789539e-01
[28,] 3.464599e-01 6.929199e-01 6.535401e-01
[29,] 3.627809e-01 7.255617e-01 6.372191e-01
[30,] 3.428977e-01 6.857953e-01 6.571023e-01
[31,] 2.854034e-01 5.708069e-01 7.145966e-01
[32,] 2.412410e-01 4.824820e-01 7.587590e-01
[33,] 2.854602e-01 5.709205e-01 7.145398e-01
[34,] 2.662562e-01 5.325125e-01 7.337438e-01
[35,] 5.188752e-01 9.622496e-01 4.811248e-01
[36,] 7.753111e-01 4.493778e-01 2.246889e-01
[37,] 9.168727e-01 1.662546e-01 8.312731e-02
[38,] 9.879749e-01 2.405023e-02 1.202511e-02
[39,] 9.997406e-01 5.188169e-04 2.594085e-04
[40,] 9.999716e-01 5.684223e-05 2.842112e-05
[41,] 9.999636e-01 7.277119e-05 3.638560e-05
[42,] 9.999644e-01 7.112609e-05 3.556304e-05
[43,] 9.998951e-01 2.098983e-04 1.049491e-04
[44,] 9.996562e-01 6.876762e-04 3.438381e-04
[45,] 9.999570e-01 8.590778e-05 4.295389e-05
[46,] 9.998119e-01 3.762710e-04 1.881355e-04
[47,] 9.992743e-01 1.451471e-03 7.257354e-04
[48,] 9.981213e-01 3.757434e-03 1.878717e-03
[49,] 9.954697e-01 9.060504e-03 4.530252e-03
> postscript(file="/var/www/html/rcomp/tmp/1t1l71258576024.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ejka1258576024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3npoa1258576024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4vwq01258576024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5dvc21258576024.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
0.124279400 0.035056738 -0.069193751 -0.061544891 -0.036689400 -0.127811411
7 8 9 10 11 12
-0.239547986 -0.330334887 -0.299557549 -0.220344450 -0.205768412 -0.266834768
13 14 15 16 17 18
-0.320694491 -0.347682693 -0.171207307 -0.143837901 -0.133340017 -0.143736152
19 20 21 22 23 24
-0.202288593 -0.291511255 -0.174756414 0.108869949 0.165345335 0.152100176
25 26 27 28 29 30
0.142039152 0.112592692 0.160297207 0.238895742 0.224421454 0.171790859
31 32 33 34 35 36
0.103852983 0.048429020 0.212390493 0.456742731 0.591653879 0.592821983
37 38 39 40 41 42
0.642202050 0.651861571 0.696772718 0.445985817 0.244528696 0.181283537
43 44 45 46 47 48
0.163401316 0.095463439 0.146910998 0.222771820 0.209582317 0.161309330
49 50 51 52 53 54
0.274488095 0.317220439 0.132466697 -0.003012922 -0.308268742 -0.458441078
55 56 57 58 59 60
-0.515429280 -0.465769759 -0.550858612 -0.722539532 -0.702935667 -0.713890710
> postscript(file="/var/www/html/rcomp/tmp/6obj61258576024.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 0.124279400 NA
1 0.035056738 0.124279400
2 -0.069193751 0.035056738
3 -0.061544891 -0.069193751
4 -0.036689400 -0.061544891
5 -0.127811411 -0.036689400
6 -0.239547986 -0.127811411
7 -0.330334887 -0.239547986
8 -0.299557549 -0.330334887
9 -0.220344450 -0.299557549
10 -0.205768412 -0.220344450
11 -0.266834768 -0.205768412
12 -0.320694491 -0.266834768
13 -0.347682693 -0.320694491
14 -0.171207307 -0.347682693
15 -0.143837901 -0.171207307
16 -0.133340017 -0.143837901
17 -0.143736152 -0.133340017
18 -0.202288593 -0.143736152
19 -0.291511255 -0.202288593
20 -0.174756414 -0.291511255
21 0.108869949 -0.174756414
22 0.165345335 0.108869949
23 0.152100176 0.165345335
24 0.142039152 0.152100176
25 0.112592692 0.142039152
26 0.160297207 0.112592692
27 0.238895742 0.160297207
28 0.224421454 0.238895742
29 0.171790859 0.224421454
30 0.103852983 0.171790859
31 0.048429020 0.103852983
32 0.212390493 0.048429020
33 0.456742731 0.212390493
34 0.591653879 0.456742731
35 0.592821983 0.591653879
36 0.642202050 0.592821983
37 0.651861571 0.642202050
38 0.696772718 0.651861571
39 0.445985817 0.696772718
40 0.244528696 0.445985817
41 0.181283537 0.244528696
42 0.163401316 0.181283537
43 0.095463439 0.163401316
44 0.146910998 0.095463439
45 0.222771820 0.146910998
46 0.209582317 0.222771820
47 0.161309330 0.209582317
48 0.274488095 0.161309330
49 0.317220439 0.274488095
50 0.132466697 0.317220439
51 -0.003012922 0.132466697
52 -0.308268742 -0.003012922
53 -0.458441078 -0.308268742
54 -0.515429280 -0.458441078
55 -0.465769759 -0.515429280
56 -0.550858612 -0.465769759
57 -0.722539532 -0.550858612
58 -0.702935667 -0.722539532
59 -0.713890710 -0.702935667
60 NA -0.713890710
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.035056738 0.124279400
[2,] -0.069193751 0.035056738
[3,] -0.061544891 -0.069193751
[4,] -0.036689400 -0.061544891
[5,] -0.127811411 -0.036689400
[6,] -0.239547986 -0.127811411
[7,] -0.330334887 -0.239547986
[8,] -0.299557549 -0.330334887
[9,] -0.220344450 -0.299557549
[10,] -0.205768412 -0.220344450
[11,] -0.266834768 -0.205768412
[12,] -0.320694491 -0.266834768
[13,] -0.347682693 -0.320694491
[14,] -0.171207307 -0.347682693
[15,] -0.143837901 -0.171207307
[16,] -0.133340017 -0.143837901
[17,] -0.143736152 -0.133340017
[18,] -0.202288593 -0.143736152
[19,] -0.291511255 -0.202288593
[20,] -0.174756414 -0.291511255
[21,] 0.108869949 -0.174756414
[22,] 0.165345335 0.108869949
[23,] 0.152100176 0.165345335
[24,] 0.142039152 0.152100176
[25,] 0.112592692 0.142039152
[26,] 0.160297207 0.112592692
[27,] 0.238895742 0.160297207
[28,] 0.224421454 0.238895742
[29,] 0.171790859 0.224421454
[30,] 0.103852983 0.171790859
[31,] 0.048429020 0.103852983
[32,] 0.212390493 0.048429020
[33,] 0.456742731 0.212390493
[34,] 0.591653879 0.456742731
[35,] 0.592821983 0.591653879
[36,] 0.642202050 0.592821983
[37,] 0.651861571 0.642202050
[38,] 0.696772718 0.651861571
[39,] 0.445985817 0.696772718
[40,] 0.244528696 0.445985817
[41,] 0.181283537 0.244528696
[42,] 0.163401316 0.181283537
[43,] 0.095463439 0.163401316
[44,] 0.146910998 0.095463439
[45,] 0.222771820 0.146910998
[46,] 0.209582317 0.222771820
[47,] 0.161309330 0.209582317
[48,] 0.274488095 0.161309330
[49,] 0.317220439 0.274488095
[50,] 0.132466697 0.317220439
[51,] -0.003012922 0.132466697
[52,] -0.308268742 -0.003012922
[53,] -0.458441078 -0.308268742
[54,] -0.515429280 -0.458441078
[55,] -0.465769759 -0.515429280
[56,] -0.550858612 -0.465769759
[57,] -0.722539532 -0.550858612
[58,] -0.702935667 -0.722539532
[59,] -0.713890710 -0.702935667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.035056738 0.124279400
2 -0.069193751 0.035056738
3 -0.061544891 -0.069193751
4 -0.036689400 -0.061544891
5 -0.127811411 -0.036689400
6 -0.239547986 -0.127811411
7 -0.330334887 -0.239547986
8 -0.299557549 -0.330334887
9 -0.220344450 -0.299557549
10 -0.205768412 -0.220344450
11 -0.266834768 -0.205768412
12 -0.320694491 -0.266834768
13 -0.347682693 -0.320694491
14 -0.171207307 -0.347682693
15 -0.143837901 -0.171207307
16 -0.133340017 -0.143837901
17 -0.143736152 -0.133340017
18 -0.202288593 -0.143736152
19 -0.291511255 -0.202288593
20 -0.174756414 -0.291511255
21 0.108869949 -0.174756414
22 0.165345335 0.108869949
23 0.152100176 0.165345335
24 0.142039152 0.152100176
25 0.112592692 0.142039152
26 0.160297207 0.112592692
27 0.238895742 0.160297207
28 0.224421454 0.238895742
29 0.171790859 0.224421454
30 0.103852983 0.171790859
31 0.048429020 0.103852983
32 0.212390493 0.048429020
33 0.456742731 0.212390493
34 0.591653879 0.456742731
35 0.592821983 0.591653879
36 0.642202050 0.592821983
37 0.651861571 0.642202050
38 0.696772718 0.651861571
39 0.445985817 0.696772718
40 0.244528696 0.445985817
41 0.181283537 0.244528696
42 0.163401316 0.181283537
43 0.095463439 0.163401316
44 0.146910998 0.095463439
45 0.222771820 0.146910998
46 0.209582317 0.222771820
47 0.161309330 0.209582317
48 0.274488095 0.161309330
49 0.317220439 0.274488095
50 0.132466697 0.317220439
51 -0.003012922 0.132466697
52 -0.308268742 -0.003012922
53 -0.458441078 -0.308268742
54 -0.515429280 -0.458441078
55 -0.465769759 -0.515429280
56 -0.550858612 -0.465769759
57 -0.722539532 -0.550858612
58 -0.702935667 -0.722539532
59 -0.713890710 -0.702935667
> 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/7x8rc1258576024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8k7tf1258576024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9r92r1258576024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1019uw1258576024.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ey9i1258576024.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/12doqd1258576024.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/13r25l1258576024.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/1462de1258576024.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/1542go1258576024.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/16jw751258576024.tab")
+ }
>
> system("convert tmp/1t1l71258576024.ps tmp/1t1l71258576024.png")
> system("convert tmp/2ejka1258576024.ps tmp/2ejka1258576024.png")
> system("convert tmp/3npoa1258576024.ps tmp/3npoa1258576024.png")
> system("convert tmp/4vwq01258576024.ps tmp/4vwq01258576024.png")
> system("convert tmp/5dvc21258576024.ps tmp/5dvc21258576024.png")
> system("convert tmp/6obj61258576024.ps tmp/6obj61258576024.png")
> system("convert tmp/7x8rc1258576024.ps tmp/7x8rc1258576024.png")
> system("convert tmp/8k7tf1258576024.ps tmp/8k7tf1258576024.png")
> system("convert tmp/9r92r1258576024.ps tmp/9r92r1258576024.png")
> system("convert tmp/1019uw1258576024.ps tmp/1019uw1258576024.png")
>
>
> proc.time()
user system elapsed
2.423 1.564 3.425