R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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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
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(3353,1,3186,1,3902,1,4164,1,3499,1,4145,1,3796,1,3711,1,3949,1,3740,1,3243,1,4407,1,4814,1,3908,1,5250,1,3937,1,4004,1,5560,1,3922,1,3759,1,4138,1,4634,1,3996,1,4308,1,4143,0,4429,0,5219,0,4929,0,5755,0,5592,0,4163,0,4962,0,5208,0,4755,0,4491,0,5732,0,5731,0,5040,0,6102,0,4904,0,5369,0,5578,0,4619,0,4731,0,5011,0,5299,0,4146,0,4625,0,4736,0,4219,0,5116,0,4205,0,4121,0,5103,1,4300,1,4578,1,3809,1,5526,1,4247,1,3830,1,4394,1),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),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 = 'No Linear Trend'
> par2 = 'Include Monthly 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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 3353 1 1 0 0 0 0 0 0 0 0 0 0
2 3186 1 0 1 0 0 0 0 0 0 0 0 0
3 3902 1 0 0 1 0 0 0 0 0 0 0 0
4 4164 1 0 0 0 1 0 0 0 0 0 0 0
5 3499 1 0 0 0 0 1 0 0 0 0 0 0
6 4145 1 0 0 0 0 0 1 0 0 0 0 0
7 3796 1 0 0 0 0 0 0 1 0 0 0 0
8 3711 1 0 0 0 0 0 0 0 1 0 0 0
9 3949 1 0 0 0 0 0 0 0 0 1 0 0
10 3740 1 0 0 0 0 0 0 0 0 0 1 0
11 3243 1 0 0 0 0 0 0 0 0 0 0 1
12 4407 1 0 0 0 0 0 0 0 0 0 0 0
13 4814 1 1 0 0 0 0 0 0 0 0 0 0
14 3908 1 0 1 0 0 0 0 0 0 0 0 0
15 5250 1 0 0 1 0 0 0 0 0 0 0 0
16 3937 1 0 0 0 1 0 0 0 0 0 0 0
17 4004 1 0 0 0 0 1 0 0 0 0 0 0
18 5560 1 0 0 0 0 0 1 0 0 0 0 0
19 3922 1 0 0 0 0 0 0 1 0 0 0 0
20 3759 1 0 0 0 0 0 0 0 1 0 0 0
21 4138 1 0 0 0 0 0 0 0 0 1 0 0
22 4634 1 0 0 0 0 0 0 0 0 0 1 0
23 3996 1 0 0 0 0 0 0 0 0 0 0 1
24 4308 1 0 0 0 0 0 0 0 0 0 0 0
25 4143 0 1 0 0 0 0 0 0 0 0 0 0
26 4429 0 0 1 0 0 0 0 0 0 0 0 0
27 5219 0 0 0 1 0 0 0 0 0 0 0 0
28 4929 0 0 0 0 1 0 0 0 0 0 0 0
29 5755 0 0 0 0 0 1 0 0 0 0 0 0
30 5592 0 0 0 0 0 0 1 0 0 0 0 0
31 4163 0 0 0 0 0 0 0 1 0 0 0 0
32 4962 0 0 0 0 0 0 0 0 1 0 0 0
33 5208 0 0 0 0 0 0 0 0 0 1 0 0
34 4755 0 0 0 0 0 0 0 0 0 0 1 0
35 4491 0 0 0 0 0 0 0 0 0 0 0 1
36 5732 0 0 0 0 0 0 0 0 0 0 0 0
37 5731 0 1 0 0 0 0 0 0 0 0 0 0
38 5040 0 0 1 0 0 0 0 0 0 0 0 0
39 6102 0 0 0 1 0 0 0 0 0 0 0 0
40 4904 0 0 0 0 1 0 0 0 0 0 0 0
41 5369 0 0 0 0 0 1 0 0 0 0 0 0
42 5578 0 0 0 0 0 0 1 0 0 0 0 0
43 4619 0 0 0 0 0 0 0 1 0 0 0 0
44 4731 0 0 0 0 0 0 0 0 1 0 0 0
45 5011 0 0 0 0 0 0 0 0 0 1 0 0
46 5299 0 0 0 0 0 0 0 0 0 0 1 0
47 4146 0 0 0 0 0 0 0 0 0 0 0 1
48 4625 0 0 0 0 0 0 0 0 0 0 0 0
49 4736 0 1 0 0 0 0 0 0 0 0 0 0
50 4219 0 0 1 0 0 0 0 0 0 0 0 0
51 5116 0 0 0 1 0 0 0 0 0 0 0 0
52 4205 0 0 0 0 1 0 0 0 0 0 0 0
53 4121 0 0 0 0 0 1 0 0 0 0 0 0
54 5103 1 0 0 0 0 0 1 0 0 0 0 0
55 4300 1 0 0 0 0 0 0 1 0 0 0 0
56 4578 1 0 0 0 0 0 0 0 1 0 0 0
57 3809 1 0 0 0 0 0 0 0 0 1 0 0
58 5526 1 0 0 0 0 0 0 0 0 0 1 0
59 4247 1 0 0 0 0 0 0 0 0 0 0 1
60 3830 1 0 0 0 0 0 0 0 0 0 0 0
61 4394 1 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
5051.6 -785.3 -130.4 -581.1 380.3 -309.7
M5 M6 M7 M8 M9 M10
-187.9 615.2 -420.4 -232.2 -157.4 210.4
M11
-555.8
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-782.83 -323.06 -12.20 258.17 1049.34
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5051.6 243.7 20.730 < 2e-16 ***
X -785.3 134.1 -5.854 4.18e-07 ***
M1 -130.4 311.7 -0.418 0.6775
M2 -581.1 326.4 -1.780 0.0814 .
M3 380.3 326.4 1.165 0.2497
M4 -309.7 326.4 -0.949 0.3475
M5 -187.9 326.4 -0.576 0.5676
M6 615.2 325.3 1.891 0.0646 .
M7 -420.4 325.3 -1.292 0.2024
M8 -232.2 325.3 -0.714 0.4788
M9 -157.4 325.3 -0.484 0.6307
M10 210.4 325.3 0.647 0.5208
M11 -555.8 325.3 -1.709 0.0940 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 514.3 on 48 degrees of freedom
Multiple R-squared: 0.5621, Adjusted R-squared: 0.4526
F-statistic: 5.134 on 12 and 48 DF, p-value: 1.940e-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,] 0.9618506 0.07629882 0.03814941
[2,] 0.9367134 0.12657316 0.06328658
[3,] 0.9659661 0.06806776 0.03403388
[4,] 0.9353249 0.12935019 0.06467510
[5,] 0.9034842 0.19303162 0.09651581
[6,] 0.8493137 0.30137264 0.15068632
[7,] 0.8361669 0.32766617 0.16383308
[8,] 0.8019253 0.39614938 0.19807469
[9,] 0.7226752 0.55464963 0.27732481
[10,] 0.7078590 0.58428199 0.29214099
[11,] 0.6589546 0.68209071 0.34104536
[12,] 0.5788330 0.84233391 0.42116696
[13,] 0.5043418 0.99131646 0.49565823
[14,] 0.6721990 0.65560196 0.32780098
[15,] 0.5815861 0.83682782 0.41841391
[16,] 0.5435670 0.91286602 0.45643301
[17,] 0.4623885 0.92477690 0.53761155
[18,] 0.4069015 0.81380299 0.59309851
[19,] 0.4157195 0.83143910 0.58428045
[20,] 0.3216021 0.64320417 0.67839791
[21,] 0.4608907 0.92178133 0.53910934
[22,] 0.6085408 0.78291838 0.39145919
[23,] 0.6247527 0.75049467 0.37524733
[24,] 0.7022295 0.59554096 0.29777048
[25,] 0.6841488 0.63170234 0.31585117
[26,] 0.8885641 0.22287185 0.11143592
[27,] 0.8118498 0.37630048 0.18815024
[28,] 0.6964433 0.60711342 0.30355671
[29,] 0.5630473 0.87390542 0.43695271
[30,] 0.7095677 0.58086458 0.29043229
> postscript(file="/var/www/html/rcomp/tmp/13b981258620426.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2tyhc1258620426.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fjzy1258620426.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/492zx1258620426.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5p4ia1258620426.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
-782.829932 -499.195918 -744.595918 207.404082 -579.395918 -736.463946
7 8 9 10 11 12
-49.863946 -323.063946 -159.863946 -736.663946 -467.463946 140.736054
13 14 15 16 17 18
678.170068 222.804082 603.404082 -19.595918 -74.395918 678.536054
19 20 21 22 23 24
76.136054 -275.063946 29.136054 157.336054 285.536054 41.736054
25 26 27 28 29 30
-778.170068 -41.536054 -212.936054 187.063946 891.263946 -74.804082
31 32 33 34 35 36
-468.204082 142.595918 313.795918 -507.004082 -4.804082 680.395918
37 38 39 40 41 42
809.829932 569.463946 670.063946 162.063946 505.263946 -88.804082
43 44 45 46 47 48
-12.204082 -88.404082 116.795918 36.995918 -349.804082 -426.604082
49 50 51 52 53 54
-185.170068 -251.536054 -315.936054 -536.936054 -742.736054 221.536054
55 56 57 58 59 60
454.136054 543.936054 -299.863946 1049.336054 536.536054 -436.263946
61
258.170068
> postscript(file="/var/www/html/rcomp/tmp/6meqy1258620426.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 -782.829932 NA
1 -499.195918 -782.829932
2 -744.595918 -499.195918
3 207.404082 -744.595918
4 -579.395918 207.404082
5 -736.463946 -579.395918
6 -49.863946 -736.463946
7 -323.063946 -49.863946
8 -159.863946 -323.063946
9 -736.663946 -159.863946
10 -467.463946 -736.663946
11 140.736054 -467.463946
12 678.170068 140.736054
13 222.804082 678.170068
14 603.404082 222.804082
15 -19.595918 603.404082
16 -74.395918 -19.595918
17 678.536054 -74.395918
18 76.136054 678.536054
19 -275.063946 76.136054
20 29.136054 -275.063946
21 157.336054 29.136054
22 285.536054 157.336054
23 41.736054 285.536054
24 -778.170068 41.736054
25 -41.536054 -778.170068
26 -212.936054 -41.536054
27 187.063946 -212.936054
28 891.263946 187.063946
29 -74.804082 891.263946
30 -468.204082 -74.804082
31 142.595918 -468.204082
32 313.795918 142.595918
33 -507.004082 313.795918
34 -4.804082 -507.004082
35 680.395918 -4.804082
36 809.829932 680.395918
37 569.463946 809.829932
38 670.063946 569.463946
39 162.063946 670.063946
40 505.263946 162.063946
41 -88.804082 505.263946
42 -12.204082 -88.804082
43 -88.404082 -12.204082
44 116.795918 -88.404082
45 36.995918 116.795918
46 -349.804082 36.995918
47 -426.604082 -349.804082
48 -185.170068 -426.604082
49 -251.536054 -185.170068
50 -315.936054 -251.536054
51 -536.936054 -315.936054
52 -742.736054 -536.936054
53 221.536054 -742.736054
54 454.136054 221.536054
55 543.936054 454.136054
56 -299.863946 543.936054
57 1049.336054 -299.863946
58 536.536054 1049.336054
59 -436.263946 536.536054
60 258.170068 -436.263946
61 NA 258.170068
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -499.195918 -782.829932
[2,] -744.595918 -499.195918
[3,] 207.404082 -744.595918
[4,] -579.395918 207.404082
[5,] -736.463946 -579.395918
[6,] -49.863946 -736.463946
[7,] -323.063946 -49.863946
[8,] -159.863946 -323.063946
[9,] -736.663946 -159.863946
[10,] -467.463946 -736.663946
[11,] 140.736054 -467.463946
[12,] 678.170068 140.736054
[13,] 222.804082 678.170068
[14,] 603.404082 222.804082
[15,] -19.595918 603.404082
[16,] -74.395918 -19.595918
[17,] 678.536054 -74.395918
[18,] 76.136054 678.536054
[19,] -275.063946 76.136054
[20,] 29.136054 -275.063946
[21,] 157.336054 29.136054
[22,] 285.536054 157.336054
[23,] 41.736054 285.536054
[24,] -778.170068 41.736054
[25,] -41.536054 -778.170068
[26,] -212.936054 -41.536054
[27,] 187.063946 -212.936054
[28,] 891.263946 187.063946
[29,] -74.804082 891.263946
[30,] -468.204082 -74.804082
[31,] 142.595918 -468.204082
[32,] 313.795918 142.595918
[33,] -507.004082 313.795918
[34,] -4.804082 -507.004082
[35,] 680.395918 -4.804082
[36,] 809.829932 680.395918
[37,] 569.463946 809.829932
[38,] 670.063946 569.463946
[39,] 162.063946 670.063946
[40,] 505.263946 162.063946
[41,] -88.804082 505.263946
[42,] -12.204082 -88.804082
[43,] -88.404082 -12.204082
[44,] 116.795918 -88.404082
[45,] 36.995918 116.795918
[46,] -349.804082 36.995918
[47,] -426.604082 -349.804082
[48,] -185.170068 -426.604082
[49,] -251.536054 -185.170068
[50,] -315.936054 -251.536054
[51,] -536.936054 -315.936054
[52,] -742.736054 -536.936054
[53,] 221.536054 -742.736054
[54,] 454.136054 221.536054
[55,] 543.936054 454.136054
[56,] -299.863946 543.936054
[57,] 1049.336054 -299.863946
[58,] 536.536054 1049.336054
[59,] -436.263946 536.536054
[60,] 258.170068 -436.263946
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -499.195918 -782.829932
2 -744.595918 -499.195918
3 207.404082 -744.595918
4 -579.395918 207.404082
5 -736.463946 -579.395918
6 -49.863946 -736.463946
7 -323.063946 -49.863946
8 -159.863946 -323.063946
9 -736.663946 -159.863946
10 -467.463946 -736.663946
11 140.736054 -467.463946
12 678.170068 140.736054
13 222.804082 678.170068
14 603.404082 222.804082
15 -19.595918 603.404082
16 -74.395918 -19.595918
17 678.536054 -74.395918
18 76.136054 678.536054
19 -275.063946 76.136054
20 29.136054 -275.063946
21 157.336054 29.136054
22 285.536054 157.336054
23 41.736054 285.536054
24 -778.170068 41.736054
25 -41.536054 -778.170068
26 -212.936054 -41.536054
27 187.063946 -212.936054
28 891.263946 187.063946
29 -74.804082 891.263946
30 -468.204082 -74.804082
31 142.595918 -468.204082
32 313.795918 142.595918
33 -507.004082 313.795918
34 -4.804082 -507.004082
35 680.395918 -4.804082
36 809.829932 680.395918
37 569.463946 809.829932
38 670.063946 569.463946
39 162.063946 670.063946
40 505.263946 162.063946
41 -88.804082 505.263946
42 -12.204082 -88.804082
43 -88.404082 -12.204082
44 116.795918 -88.404082
45 36.995918 116.795918
46 -349.804082 36.995918
47 -426.604082 -349.804082
48 -185.170068 -426.604082
49 -251.536054 -185.170068
50 -315.936054 -251.536054
51 -536.936054 -315.936054
52 -742.736054 -536.936054
53 221.536054 -742.736054
54 454.136054 221.536054
55 543.936054 454.136054
56 -299.863946 543.936054
57 1049.336054 -299.863946
58 536.536054 1049.336054
59 -436.263946 536.536054
60 258.170068 -436.263946
> 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/77hjn1258620426.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/89fi11258620426.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95frs1258620426.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10xq8a1258620426.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11g9e41258620426.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/12f14a1258620426.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/13al0c1258620426.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/14vp961258620426.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/1597xu1258620426.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/16hekk1258620426.tab")
+ }
>
> system("convert tmp/13b981258620426.ps tmp/13b981258620426.png")
> system("convert tmp/2tyhc1258620426.ps tmp/2tyhc1258620426.png")
> system("convert tmp/3fjzy1258620426.ps tmp/3fjzy1258620426.png")
> system("convert tmp/492zx1258620426.ps tmp/492zx1258620426.png")
> system("convert tmp/5p4ia1258620426.ps tmp/5p4ia1258620426.png")
> system("convert tmp/6meqy1258620426.ps tmp/6meqy1258620426.png")
> system("convert tmp/77hjn1258620426.ps tmp/77hjn1258620426.png")
> system("convert tmp/89fi11258620426.ps tmp/89fi11258620426.png")
> system("convert tmp/95frs1258620426.ps tmp/95frs1258620426.png")
> system("convert tmp/10xq8a1258620426.ps tmp/10xq8a1258620426.png")
>
>
> proc.time()
user system elapsed
2.418 1.548 3.234