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(1.4,0.0,1.6,0.0,1.7,0.0,2.0,0.0,2.0,0.0,2.1,0.0,2.5,0.0,2.5,0.0,2.6,0.0,2.7,0.0,3.7,0.0,4.0,0.0,5.0,0.0,5.1,0.0,5.1,0.0,5.0,0.0,5.1,0.0,4.7,0.0,4.5,0.0,4.5,0.0,4.6,0.0,4.6,0.0,4.6,0.0,4.6,0.0,5.3,0.0,5.4,0.0,5.3,0.0,5.2,0.0,5.0,0.0,4.2,0.0,4.3,0.0,4.3,0.0,4.3,0.0,4.0,0.0,4.0,0.0,4.1,0.0,4.4,0.0,3.6,0.0,3.7,0.0,3.8,0.0,3.3,0.0,3.3,0.0,3.3,0.0,3.5,0.0,3.3,1.0,3.3,1.0,3.4,1.0,3.4,1.0,5.2,1.0,5.3,1.0,4.8,1.0,5.0,1.0,4.6,1.0,4.6,1.0,3.5,1.0,3.5,1.0),dim=c(2,56),dimnames=list(c('IndGez','InvlCrisis'),1:56))
> y <- array(NA,dim=c(2,56),dimnames=list(c('IndGez','InvlCrisis'),1:56))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = '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
IndGez InvlCrisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.4 0 1 0 0 0 0 0 0 0 0 0 0
2 1.6 0 0 1 0 0 0 0 0 0 0 0 0
3 1.7 0 0 0 1 0 0 0 0 0 0 0 0
4 2.0 0 0 0 0 1 0 0 0 0 0 0 0
5 2.0 0 0 0 0 0 1 0 0 0 0 0 0
6 2.1 0 0 0 0 0 0 1 0 0 0 0 0
7 2.5 0 0 0 0 0 0 0 1 0 0 0 0
8 2.5 0 0 0 0 0 0 0 0 1 0 0 0
9 2.6 0 0 0 0 0 0 0 0 0 1 0 0
10 2.7 0 0 0 0 0 0 0 0 0 0 1 0
11 3.7 0 0 0 0 0 0 0 0 0 0 0 1
12 4.0 0 0 0 0 0 0 0 0 0 0 0 0
13 5.0 0 1 0 0 0 0 0 0 0 0 0 0
14 5.1 0 0 1 0 0 0 0 0 0 0 0 0
15 5.1 0 0 0 1 0 0 0 0 0 0 0 0
16 5.0 0 0 0 0 1 0 0 0 0 0 0 0
17 5.1 0 0 0 0 0 1 0 0 0 0 0 0
18 4.7 0 0 0 0 0 0 1 0 0 0 0 0
19 4.5 0 0 0 0 0 0 0 1 0 0 0 0
20 4.5 0 0 0 0 0 0 0 0 1 0 0 0
21 4.6 0 0 0 0 0 0 0 0 0 1 0 0
22 4.6 0 0 0 0 0 0 0 0 0 0 1 0
23 4.6 0 0 0 0 0 0 0 0 0 0 0 1
24 4.6 0 0 0 0 0 0 0 0 0 0 0 0
25 5.3 0 1 0 0 0 0 0 0 0 0 0 0
26 5.4 0 0 1 0 0 0 0 0 0 0 0 0
27 5.3 0 0 0 1 0 0 0 0 0 0 0 0
28 5.2 0 0 0 0 1 0 0 0 0 0 0 0
29 5.0 0 0 0 0 0 1 0 0 0 0 0 0
30 4.2 0 0 0 0 0 0 1 0 0 0 0 0
31 4.3 0 0 0 0 0 0 0 1 0 0 0 0
32 4.3 0 0 0 0 0 0 0 0 1 0 0 0
33 4.3 0 0 0 0 0 0 0 0 0 1 0 0
34 4.0 0 0 0 0 0 0 0 0 0 0 1 0
35 4.0 0 0 0 0 0 0 0 0 0 0 0 1
36 4.1 0 0 0 0 0 0 0 0 0 0 0 0
37 4.4 0 1 0 0 0 0 0 0 0 0 0 0
38 3.6 0 0 1 0 0 0 0 0 0 0 0 0
39 3.7 0 0 0 1 0 0 0 0 0 0 0 0
40 3.8 0 0 0 0 1 0 0 0 0 0 0 0
41 3.3 0 0 0 0 0 1 0 0 0 0 0 0
42 3.3 0 0 0 0 0 0 1 0 0 0 0 0
43 3.3 0 0 0 0 0 0 0 1 0 0 0 0
44 3.5 0 0 0 0 0 0 0 0 1 0 0 0
45 3.3 1 0 0 0 0 0 0 0 0 1 0 0
46 3.3 1 0 0 0 0 0 0 0 0 0 1 0
47 3.4 1 0 0 0 0 0 0 0 0 0 0 1
48 3.4 1 0 0 0 0 0 0 0 0 0 0 0
49 5.2 1 1 0 0 0 0 0 0 0 0 0 0
50 5.3 1 0 1 0 0 0 0 0 0 0 0 0
51 4.8 1 0 0 1 0 0 0 0 0 0 0 0
52 5.0 1 0 0 0 1 0 0 0 0 0 0 0
53 4.6 1 0 0 0 0 1 0 0 0 0 0 0
54 4.6 1 0 0 0 0 0 1 0 0 0 0 0
55 3.5 1 0 0 0 0 0 0 1 0 0 0 0
56 3.5 1 0 0 0 0 0 0 0 1 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) InvlCrisis M1 M2 M3 M4
3.95160 0.29362 0.24968 0.18968 0.10968 0.18968
M5 M6 M7 M8 M9 M10
-0.01032 -0.23032 -0.39032 -0.35032 -0.32500 -0.37500
M11
-0.10000
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.8013 -0.5827 0.2819 0.8603 1.2587
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.95160 0.59664 6.623 4.53e-08 ***
InvlCrisis 0.29362 0.38413 0.764 0.449
M1 0.24968 0.79028 0.316 0.754
M2 0.18968 0.79028 0.240 0.811
M3 0.10968 0.79028 0.139 0.890
M4 0.18968 0.79028 0.240 0.811
M5 -0.01032 0.79028 -0.013 0.990
M6 -0.23032 0.79028 -0.291 0.772
M7 -0.39032 0.79028 -0.494 0.624
M8 -0.35032 0.79028 -0.443 0.660
M9 -0.32500 0.83278 -0.390 0.698
M10 -0.37500 0.83278 -0.450 0.655
M11 -0.10000 0.83278 -0.120 0.905
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.178 on 43 degrees of freedom
Multiple R-squared: 0.05998, Adjusted R-squared: -0.2023
F-statistic: 0.2287 on 12 and 43 DF, p-value: 0.9958
> 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,] 0.9999960 7.910006e-06 3.955003e-06
[2,] 0.9999972 5.555348e-06 2.777674e-06
[3,] 0.9999964 7.268290e-06 3.634145e-06
[4,] 0.9999937 1.254946e-05 6.274729e-06
[5,] 0.9999890 2.199984e-05 1.099992e-05
[6,] 0.9999813 3.744838e-05 1.872419e-05
[7,] 0.9999713 5.742734e-05 2.871367e-05
[8,] 0.9999425 1.149264e-04 5.746321e-05
[9,] 0.9998827 2.346028e-04 1.173014e-04
[10,] 0.9998169 3.661960e-04 1.830980e-04
[11,] 0.9997836 4.327248e-04 2.163624e-04
[12,] 0.9997595 4.809024e-04 2.404512e-04
[13,] 0.9996425 7.149983e-04 3.574992e-04
[14,] 0.9995848 8.304614e-04 4.152307e-04
[15,] 0.9989878 2.024425e-03 1.012212e-03
[16,] 0.9985599 2.880209e-03 1.440104e-03
[17,] 0.9978391 4.321892e-03 2.160946e-03
[18,] 0.9978422 4.315623e-03 2.157811e-03
[19,] 0.9974664 5.067265e-03 2.533632e-03
[20,] 0.9974564 5.087181e-03 2.543591e-03
[21,] 0.9991574 1.685169e-03 8.425843e-04
[22,] 0.9968566 6.286704e-03 3.143352e-03
[23,] 0.9944717 1.105668e-02 5.528340e-03
[24,] 0.9809233 3.815350e-02 1.907675e-02
[25,] 0.9453264 1.093473e-01 5.467365e-02
> postscript(file="/var/www/html/rcomp/tmp/1ddwy1258723158.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2xffw1258723158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b3p91258723158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nfft1258723158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5gg7i1258723158.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 = 56
Frequency = 1
1 2 3 4 5 6
-2.80127660 -2.54127660 -2.36127660 -2.14127660 -1.94127660 -1.62127660
7 8 9 10 11 12
-1.06127660 -1.10127660 -1.02659574 -0.87659574 -0.15159574 0.04840426
13 14 15 16 17 18
0.79872340 0.95872340 1.03872340 0.85872340 1.15872340 0.97872340
19 20 21 22 23 24
0.93872340 0.89872340 0.97340426 1.02340426 0.74840426 0.64840426
25 26 27 28 29 30
1.09872340 1.25872340 1.23872340 1.05872340 1.05872340 0.47872340
31 32 33 34 35 36
0.73872340 0.69872340 0.67340426 0.42340426 0.14840426 0.14840426
37 38 39 40 41 42
0.19872340 -0.54127660 -0.36127660 -0.34127660 -0.64127660 -0.42127660
43 44 45 46 47 48
-0.26127660 -0.10127660 -0.62021277 -0.57021277 -0.74521277 -0.84521277
49 50 51 52 53 54
0.70510638 0.86510638 0.44510638 0.56510638 0.36510638 0.58510638
55 56
-0.35489362 -0.39489362
> postscript(file="/var/www/html/rcomp/tmp/61yry1258723158.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.80127660 NA
1 -2.54127660 -2.80127660
2 -2.36127660 -2.54127660
3 -2.14127660 -2.36127660
4 -1.94127660 -2.14127660
5 -1.62127660 -1.94127660
6 -1.06127660 -1.62127660
7 -1.10127660 -1.06127660
8 -1.02659574 -1.10127660
9 -0.87659574 -1.02659574
10 -0.15159574 -0.87659574
11 0.04840426 -0.15159574
12 0.79872340 0.04840426
13 0.95872340 0.79872340
14 1.03872340 0.95872340
15 0.85872340 1.03872340
16 1.15872340 0.85872340
17 0.97872340 1.15872340
18 0.93872340 0.97872340
19 0.89872340 0.93872340
20 0.97340426 0.89872340
21 1.02340426 0.97340426
22 0.74840426 1.02340426
23 0.64840426 0.74840426
24 1.09872340 0.64840426
25 1.25872340 1.09872340
26 1.23872340 1.25872340
27 1.05872340 1.23872340
28 1.05872340 1.05872340
29 0.47872340 1.05872340
30 0.73872340 0.47872340
31 0.69872340 0.73872340
32 0.67340426 0.69872340
33 0.42340426 0.67340426
34 0.14840426 0.42340426
35 0.14840426 0.14840426
36 0.19872340 0.14840426
37 -0.54127660 0.19872340
38 -0.36127660 -0.54127660
39 -0.34127660 -0.36127660
40 -0.64127660 -0.34127660
41 -0.42127660 -0.64127660
42 -0.26127660 -0.42127660
43 -0.10127660 -0.26127660
44 -0.62021277 -0.10127660
45 -0.57021277 -0.62021277
46 -0.74521277 -0.57021277
47 -0.84521277 -0.74521277
48 0.70510638 -0.84521277
49 0.86510638 0.70510638
50 0.44510638 0.86510638
51 0.56510638 0.44510638
52 0.36510638 0.56510638
53 0.58510638 0.36510638
54 -0.35489362 0.58510638
55 -0.39489362 -0.35489362
56 NA -0.39489362
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.54127660 -2.80127660
[2,] -2.36127660 -2.54127660
[3,] -2.14127660 -2.36127660
[4,] -1.94127660 -2.14127660
[5,] -1.62127660 -1.94127660
[6,] -1.06127660 -1.62127660
[7,] -1.10127660 -1.06127660
[8,] -1.02659574 -1.10127660
[9,] -0.87659574 -1.02659574
[10,] -0.15159574 -0.87659574
[11,] 0.04840426 -0.15159574
[12,] 0.79872340 0.04840426
[13,] 0.95872340 0.79872340
[14,] 1.03872340 0.95872340
[15,] 0.85872340 1.03872340
[16,] 1.15872340 0.85872340
[17,] 0.97872340 1.15872340
[18,] 0.93872340 0.97872340
[19,] 0.89872340 0.93872340
[20,] 0.97340426 0.89872340
[21,] 1.02340426 0.97340426
[22,] 0.74840426 1.02340426
[23,] 0.64840426 0.74840426
[24,] 1.09872340 0.64840426
[25,] 1.25872340 1.09872340
[26,] 1.23872340 1.25872340
[27,] 1.05872340 1.23872340
[28,] 1.05872340 1.05872340
[29,] 0.47872340 1.05872340
[30,] 0.73872340 0.47872340
[31,] 0.69872340 0.73872340
[32,] 0.67340426 0.69872340
[33,] 0.42340426 0.67340426
[34,] 0.14840426 0.42340426
[35,] 0.14840426 0.14840426
[36,] 0.19872340 0.14840426
[37,] -0.54127660 0.19872340
[38,] -0.36127660 -0.54127660
[39,] -0.34127660 -0.36127660
[40,] -0.64127660 -0.34127660
[41,] -0.42127660 -0.64127660
[42,] -0.26127660 -0.42127660
[43,] -0.10127660 -0.26127660
[44,] -0.62021277 -0.10127660
[45,] -0.57021277 -0.62021277
[46,] -0.74521277 -0.57021277
[47,] -0.84521277 -0.74521277
[48,] 0.70510638 -0.84521277
[49,] 0.86510638 0.70510638
[50,] 0.44510638 0.86510638
[51,] 0.56510638 0.44510638
[52,] 0.36510638 0.56510638
[53,] 0.58510638 0.36510638
[54,] -0.35489362 0.58510638
[55,] -0.39489362 -0.35489362
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.54127660 -2.80127660
2 -2.36127660 -2.54127660
3 -2.14127660 -2.36127660
4 -1.94127660 -2.14127660
5 -1.62127660 -1.94127660
6 -1.06127660 -1.62127660
7 -1.10127660 -1.06127660
8 -1.02659574 -1.10127660
9 -0.87659574 -1.02659574
10 -0.15159574 -0.87659574
11 0.04840426 -0.15159574
12 0.79872340 0.04840426
13 0.95872340 0.79872340
14 1.03872340 0.95872340
15 0.85872340 1.03872340
16 1.15872340 0.85872340
17 0.97872340 1.15872340
18 0.93872340 0.97872340
19 0.89872340 0.93872340
20 0.97340426 0.89872340
21 1.02340426 0.97340426
22 0.74840426 1.02340426
23 0.64840426 0.74840426
24 1.09872340 0.64840426
25 1.25872340 1.09872340
26 1.23872340 1.25872340
27 1.05872340 1.23872340
28 1.05872340 1.05872340
29 0.47872340 1.05872340
30 0.73872340 0.47872340
31 0.69872340 0.73872340
32 0.67340426 0.69872340
33 0.42340426 0.67340426
34 0.14840426 0.42340426
35 0.14840426 0.14840426
36 0.19872340 0.14840426
37 -0.54127660 0.19872340
38 -0.36127660 -0.54127660
39 -0.34127660 -0.36127660
40 -0.64127660 -0.34127660
41 -0.42127660 -0.64127660
42 -0.26127660 -0.42127660
43 -0.10127660 -0.26127660
44 -0.62021277 -0.10127660
45 -0.57021277 -0.62021277
46 -0.74521277 -0.57021277
47 -0.84521277 -0.74521277
48 0.70510638 -0.84521277
49 0.86510638 0.70510638
50 0.44510638 0.86510638
51 0.56510638 0.44510638
52 0.36510638 0.56510638
53 0.58510638 0.36510638
54 -0.35489362 0.58510638
55 -0.39489362 -0.35489362
> 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/7d52a1258723158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/80kag1258723158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hscv1258723158.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wchd1258723159.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11bufe1258723159.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/12colb1258723159.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/13iii81258723159.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/14ntp21258723159.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/15kl8j1258723159.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/165ul71258723159.tab")
+ }
>
> system("convert tmp/1ddwy1258723158.ps tmp/1ddwy1258723158.png")
> system("convert tmp/2xffw1258723158.ps tmp/2xffw1258723158.png")
> system("convert tmp/3b3p91258723158.ps tmp/3b3p91258723158.png")
> system("convert tmp/4nfft1258723158.ps tmp/4nfft1258723158.png")
> system("convert tmp/5gg7i1258723158.ps tmp/5gg7i1258723158.png")
> system("convert tmp/61yry1258723158.ps tmp/61yry1258723158.png")
> system("convert tmp/7d52a1258723158.ps tmp/7d52a1258723158.png")
> system("convert tmp/80kag1258723158.ps tmp/80kag1258723158.png")
> system("convert tmp/9hscv1258723158.ps tmp/9hscv1258723158.png")
> system("convert tmp/10wchd1258723159.ps tmp/10wchd1258723159.png")
>
>
> proc.time()
user system elapsed
2.311 1.503 2.700