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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> x <- array(list(0,6.3,0,6.2,0,6.1,0,6.3,0,6.5,0,6.6,0,6.5,0,6.2,0,6.2,0,5.9,0,6.1,0,6.1,0,6.1,0,6.1,0,6.1,0,6.4,0,6.7,0,6.9,0,7,0,7,0,6.8,0,6.4,0,5.9,0,5.5,0,5.5,0,5.6,0,5.8,0,5.9,0,6.1,0,6.1,0,6,0,6,0,5.9,0,5.5,0,5.6,0,5.4,0,5.2,0,5.2,0,5.2,0,5.5,1,5.8,1,5.8,1,5.5,1,5.3,1,5.1,1,5.2,1,5.8,1,5.8,1,5.5,1,5,1,4.9,1,5.3,1,6.1,1,6.5,1,6.8,1,6.6,1,6.4,1,6.4),dim=c(2,58),dimnames=list(c('X','Y'),1:58))
> y <- array(NA,dim=c(2,58),dimnames=list(c('X','Y'),1:58))
> 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 = '2'
> #'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 6.3 0 1 0 0 0 0 0 0 0 0 0 0
2 6.2 0 0 1 0 0 0 0 0 0 0 0 0
3 6.1 0 0 0 1 0 0 0 0 0 0 0 0
4 6.3 0 0 0 0 1 0 0 0 0 0 0 0
5 6.5 0 0 0 0 0 1 0 0 0 0 0 0
6 6.6 0 0 0 0 0 0 1 0 0 0 0 0
7 6.5 0 0 0 0 0 0 0 1 0 0 0 0
8 6.2 0 0 0 0 0 0 0 0 1 0 0 0
9 6.2 0 0 0 0 0 0 0 0 0 1 0 0
10 5.9 0 0 0 0 0 0 0 0 0 0 1 0
11 6.1 0 0 0 0 0 0 0 0 0 0 0 1
12 6.1 0 0 0 0 0 0 0 0 0 0 0 0
13 6.1 0 1 0 0 0 0 0 0 0 0 0 0
14 6.1 0 0 1 0 0 0 0 0 0 0 0 0
15 6.1 0 0 0 1 0 0 0 0 0 0 0 0
16 6.4 0 0 0 0 1 0 0 0 0 0 0 0
17 6.7 0 0 0 0 0 1 0 0 0 0 0 0
18 6.9 0 0 0 0 0 0 1 0 0 0 0 0
19 7.0 0 0 0 0 0 0 0 1 0 0 0 0
20 7.0 0 0 0 0 0 0 0 0 1 0 0 0
21 6.8 0 0 0 0 0 0 0 0 0 1 0 0
22 6.4 0 0 0 0 0 0 0 0 0 0 1 0
23 5.9 0 0 0 0 0 0 0 0 0 0 0 1
24 5.5 0 0 0 0 0 0 0 0 0 0 0 0
25 5.5 0 1 0 0 0 0 0 0 0 0 0 0
26 5.6 0 0 1 0 0 0 0 0 0 0 0 0
27 5.8 0 0 0 1 0 0 0 0 0 0 0 0
28 5.9 0 0 0 0 1 0 0 0 0 0 0 0
29 6.1 0 0 0 0 0 1 0 0 0 0 0 0
30 6.1 0 0 0 0 0 0 1 0 0 0 0 0
31 6.0 0 0 0 0 0 0 0 1 0 0 0 0
32 6.0 0 0 0 0 0 0 0 0 1 0 0 0
33 5.9 0 0 0 0 0 0 0 0 0 1 0 0
34 5.5 0 0 0 0 0 0 0 0 0 0 1 0
35 5.6 0 0 0 0 0 0 0 0 0 0 0 1
36 5.4 0 0 0 0 0 0 0 0 0 0 0 0
37 5.2 0 1 0 0 0 0 0 0 0 0 0 0
38 5.2 0 0 1 0 0 0 0 0 0 0 0 0
39 5.2 0 0 0 1 0 0 0 0 0 0 0 0
40 5.5 0 0 0 0 1 0 0 0 0 0 0 0
41 5.8 1 0 0 0 0 1 0 0 0 0 0 0
42 5.8 1 0 0 0 0 0 1 0 0 0 0 0
43 5.5 1 0 0 0 0 0 0 1 0 0 0 0
44 5.3 1 0 0 0 0 0 0 0 1 0 0 0
45 5.1 1 0 0 0 0 0 0 0 0 1 0 0
46 5.2 1 0 0 0 0 0 0 0 0 0 1 0
47 5.8 1 0 0 0 0 0 0 0 0 0 0 1
48 5.8 1 0 0 0 0 0 0 0 0 0 0 0
49 5.5 1 1 0 0 0 0 0 0 0 0 0 0
50 5.0 1 0 1 0 0 0 0 0 0 0 0 0
51 4.9 1 0 0 1 0 0 0 0 0 0 0 0
52 5.3 1 0 0 0 1 0 0 0 0 0 0 0
53 6.1 1 0 0 0 0 1 0 0 0 0 0 0
54 6.5 1 0 0 0 0 0 1 0 0 0 0 0
55 6.8 1 0 0 0 0 0 0 1 0 0 0 0
56 6.6 1 0 0 0 0 0 0 0 1 0 0 0
57 6.4 1 0 0 0 0 0 0 0 0 1 0 0
58 6.4 1 0 0 0 0 0 0 0 0 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
5.8031513 -0.4126050 -0.0006303 -0.1006303 -0.1006303 0.1593697
M5 M6 M7 M8 M9 M10
0.6018908 0.7418908 0.7218908 0.5818908 0.4418908 0.2418908
M11
0.1500000
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.73244 -0.35112 -0.03504 0.36441 0.76756
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.8031513 0.2342281 24.776 < 2e-16 ***
X -0.4126050 0.1343943 -3.070 0.00362 **
M1 -0.0006303 0.3110727 -0.002 0.99839
M2 -0.1006303 0.3110727 -0.323 0.74782
M3 -0.1006303 0.3110727 -0.323 0.74782
M4 0.1593697 0.3110727 0.512 0.61093
M5 0.6018908 0.3116528 1.931 0.05976 .
M6 0.7418908 0.3116528 2.381 0.02158 *
M7 0.7218908 0.3116528 2.316 0.02515 *
M8 0.5818908 0.3116528 1.867 0.06841 .
M9 0.4418908 0.3116528 1.418 0.16311
M10 0.2418908 0.3116528 0.776 0.44172
M11 0.1500000 0.3278229 0.458 0.64947
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4636 on 45 degrees of freedom
Multiple R-squared: 0.3956, Adjusted R-squared: 0.2345
F-statistic: 2.455 on 12 and 45 DF, p-value: 0.01475
> 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,] 0.011320833 0.022641667 0.9886792
[2,] 0.005147893 0.010295785 0.9948521
[3,] 0.004848410 0.009696820 0.9951516
[4,] 0.013523578 0.027047156 0.9864764
[5,] 0.076096461 0.152192923 0.9239035
[6,] 0.113985444 0.227970889 0.8860146
[7,] 0.119010149 0.238020298 0.8809899
[8,] 0.075210825 0.150421651 0.9247892
[9,] 0.078024023 0.156048045 0.9219760
[10,] 0.109359207 0.218718415 0.8906408
[11,] 0.115828281 0.231656562 0.8841717
[12,] 0.108578589 0.217157178 0.8914214
[13,] 0.102628232 0.205256464 0.8973718
[14,] 0.089497127 0.178994254 0.9105029
[15,] 0.090066621 0.180133242 0.9099334
[16,] 0.098635024 0.197270048 0.9013650
[17,] 0.085184054 0.170368108 0.9148159
[18,] 0.072810231 0.145620463 0.9271898
[19,] 0.063628703 0.127257406 0.9363713
[20,] 0.042681667 0.085363334 0.9573183
[21,] 0.030151564 0.060303127 0.9698484
[22,] 0.030494702 0.060989404 0.9695053
[23,] 0.024985283 0.049970566 0.9750147
[24,] 0.019498556 0.038997111 0.9805014
[25,] 0.012434385 0.024868770 0.9875656
[26,] 0.005405327 0.010810654 0.9945947
[27,] 0.002755501 0.005511002 0.9972445
> postscript(file="/var/www/html/rcomp/tmp/18rai1258662814.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2rvt91258662814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3xll81258662814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4rxo51258662814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5192a1258662814.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 = 58
Frequency = 1
1 2 3 4 5 6
0.49747899 0.49747899 0.39747899 0.33747899 0.09495798 0.05495798
7 8 9 10 11 12
-0.02504202 -0.18504202 -0.04504202 -0.14504202 0.14684874 0.29684874
13 14 15 16 17 18
0.29747899 0.39747899 0.39747899 0.43747899 0.29495798 0.35495798
19 20 21 22 23 24
0.47495798 0.61495798 0.55495798 0.35495798 -0.05315126 -0.30315126
25 26 27 28 29 30
-0.30252101 -0.10252101 0.09747899 -0.06252101 -0.30504202 -0.44504202
31 32 33 34 35 36
-0.52504202 -0.38504202 -0.34504202 -0.54504202 -0.35315126 -0.40315126
37 38 39 40 41 42
-0.60252101 -0.50252101 -0.50252101 -0.46252101 -0.19243697 -0.33243697
43 44 45 46 47 48
-0.61243697 -0.67243697 -0.73243697 -0.43243697 0.25945378 0.40945378
49 50 51 52 53 54
0.11008403 -0.28991597 -0.38991597 -0.24991597 0.10756303 0.36756303
55 56 57 58
0.68756303 0.62756303 0.56756303 0.76756303
> postscript(file="/var/www/html/rcomp/tmp/62nqt1258662814.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 0.49747899 NA
1 0.49747899 0.49747899
2 0.39747899 0.49747899
3 0.33747899 0.39747899
4 0.09495798 0.33747899
5 0.05495798 0.09495798
6 -0.02504202 0.05495798
7 -0.18504202 -0.02504202
8 -0.04504202 -0.18504202
9 -0.14504202 -0.04504202
10 0.14684874 -0.14504202
11 0.29684874 0.14684874
12 0.29747899 0.29684874
13 0.39747899 0.29747899
14 0.39747899 0.39747899
15 0.43747899 0.39747899
16 0.29495798 0.43747899
17 0.35495798 0.29495798
18 0.47495798 0.35495798
19 0.61495798 0.47495798
20 0.55495798 0.61495798
21 0.35495798 0.55495798
22 -0.05315126 0.35495798
23 -0.30315126 -0.05315126
24 -0.30252101 -0.30315126
25 -0.10252101 -0.30252101
26 0.09747899 -0.10252101
27 -0.06252101 0.09747899
28 -0.30504202 -0.06252101
29 -0.44504202 -0.30504202
30 -0.52504202 -0.44504202
31 -0.38504202 -0.52504202
32 -0.34504202 -0.38504202
33 -0.54504202 -0.34504202
34 -0.35315126 -0.54504202
35 -0.40315126 -0.35315126
36 -0.60252101 -0.40315126
37 -0.50252101 -0.60252101
38 -0.50252101 -0.50252101
39 -0.46252101 -0.50252101
40 -0.19243697 -0.46252101
41 -0.33243697 -0.19243697
42 -0.61243697 -0.33243697
43 -0.67243697 -0.61243697
44 -0.73243697 -0.67243697
45 -0.43243697 -0.73243697
46 0.25945378 -0.43243697
47 0.40945378 0.25945378
48 0.11008403 0.40945378
49 -0.28991597 0.11008403
50 -0.38991597 -0.28991597
51 -0.24991597 -0.38991597
52 0.10756303 -0.24991597
53 0.36756303 0.10756303
54 0.68756303 0.36756303
55 0.62756303 0.68756303
56 0.56756303 0.62756303
57 0.76756303 0.56756303
58 NA 0.76756303
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.49747899 0.49747899
[2,] 0.39747899 0.49747899
[3,] 0.33747899 0.39747899
[4,] 0.09495798 0.33747899
[5,] 0.05495798 0.09495798
[6,] -0.02504202 0.05495798
[7,] -0.18504202 -0.02504202
[8,] -0.04504202 -0.18504202
[9,] -0.14504202 -0.04504202
[10,] 0.14684874 -0.14504202
[11,] 0.29684874 0.14684874
[12,] 0.29747899 0.29684874
[13,] 0.39747899 0.29747899
[14,] 0.39747899 0.39747899
[15,] 0.43747899 0.39747899
[16,] 0.29495798 0.43747899
[17,] 0.35495798 0.29495798
[18,] 0.47495798 0.35495798
[19,] 0.61495798 0.47495798
[20,] 0.55495798 0.61495798
[21,] 0.35495798 0.55495798
[22,] -0.05315126 0.35495798
[23,] -0.30315126 -0.05315126
[24,] -0.30252101 -0.30315126
[25,] -0.10252101 -0.30252101
[26,] 0.09747899 -0.10252101
[27,] -0.06252101 0.09747899
[28,] -0.30504202 -0.06252101
[29,] -0.44504202 -0.30504202
[30,] -0.52504202 -0.44504202
[31,] -0.38504202 -0.52504202
[32,] -0.34504202 -0.38504202
[33,] -0.54504202 -0.34504202
[34,] -0.35315126 -0.54504202
[35,] -0.40315126 -0.35315126
[36,] -0.60252101 -0.40315126
[37,] -0.50252101 -0.60252101
[38,] -0.50252101 -0.50252101
[39,] -0.46252101 -0.50252101
[40,] -0.19243697 -0.46252101
[41,] -0.33243697 -0.19243697
[42,] -0.61243697 -0.33243697
[43,] -0.67243697 -0.61243697
[44,] -0.73243697 -0.67243697
[45,] -0.43243697 -0.73243697
[46,] 0.25945378 -0.43243697
[47,] 0.40945378 0.25945378
[48,] 0.11008403 0.40945378
[49,] -0.28991597 0.11008403
[50,] -0.38991597 -0.28991597
[51,] -0.24991597 -0.38991597
[52,] 0.10756303 -0.24991597
[53,] 0.36756303 0.10756303
[54,] 0.68756303 0.36756303
[55,] 0.62756303 0.68756303
[56,] 0.56756303 0.62756303
[57,] 0.76756303 0.56756303
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.49747899 0.49747899
2 0.39747899 0.49747899
3 0.33747899 0.39747899
4 0.09495798 0.33747899
5 0.05495798 0.09495798
6 -0.02504202 0.05495798
7 -0.18504202 -0.02504202
8 -0.04504202 -0.18504202
9 -0.14504202 -0.04504202
10 0.14684874 -0.14504202
11 0.29684874 0.14684874
12 0.29747899 0.29684874
13 0.39747899 0.29747899
14 0.39747899 0.39747899
15 0.43747899 0.39747899
16 0.29495798 0.43747899
17 0.35495798 0.29495798
18 0.47495798 0.35495798
19 0.61495798 0.47495798
20 0.55495798 0.61495798
21 0.35495798 0.55495798
22 -0.05315126 0.35495798
23 -0.30315126 -0.05315126
24 -0.30252101 -0.30315126
25 -0.10252101 -0.30252101
26 0.09747899 -0.10252101
27 -0.06252101 0.09747899
28 -0.30504202 -0.06252101
29 -0.44504202 -0.30504202
30 -0.52504202 -0.44504202
31 -0.38504202 -0.52504202
32 -0.34504202 -0.38504202
33 -0.54504202 -0.34504202
34 -0.35315126 -0.54504202
35 -0.40315126 -0.35315126
36 -0.60252101 -0.40315126
37 -0.50252101 -0.60252101
38 -0.50252101 -0.50252101
39 -0.46252101 -0.50252101
40 -0.19243697 -0.46252101
41 -0.33243697 -0.19243697
42 -0.61243697 -0.33243697
43 -0.67243697 -0.61243697
44 -0.73243697 -0.67243697
45 -0.43243697 -0.73243697
46 0.25945378 -0.43243697
47 0.40945378 0.25945378
48 0.11008403 0.40945378
49 -0.28991597 0.11008403
50 -0.38991597 -0.28991597
51 -0.24991597 -0.38991597
52 0.10756303 -0.24991597
53 0.36756303 0.10756303
54 0.68756303 0.36756303
55 0.62756303 0.68756303
56 0.56756303 0.62756303
57 0.76756303 0.56756303
> 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/7p5ad1258662814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/87g081258662814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91il81258662814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1067pc1258662814.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11fv231258662814.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/12pgyc1258662814.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/1331n41258662814.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/14y3af1258662814.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/15kymk1258662814.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/16rbzj1258662814.tab")
+ }
>
> system("convert tmp/18rai1258662814.ps tmp/18rai1258662814.png")
> system("convert tmp/2rvt91258662814.ps tmp/2rvt91258662814.png")
> system("convert tmp/3xll81258662814.ps tmp/3xll81258662814.png")
> system("convert tmp/4rxo51258662814.ps tmp/4rxo51258662814.png")
> system("convert tmp/5192a1258662814.ps tmp/5192a1258662814.png")
> system("convert tmp/62nqt1258662814.ps tmp/62nqt1258662814.png")
> system("convert tmp/7p5ad1258662814.ps tmp/7p5ad1258662814.png")
> system("convert tmp/87g081258662814.ps tmp/87g081258662814.png")
> system("convert tmp/91il81258662814.ps tmp/91il81258662814.png")
> system("convert tmp/1067pc1258662814.ps tmp/1067pc1258662814.png")
>
>
> proc.time()
user system elapsed
2.357 1.592 2.776