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(2.7,0,2.3,0,1.9,0,2.0,0,2.3,0,2.8,0,2.4,0,2.3,0,2.7,0,2.7,0,2.9,0,3.0,0,2.2,0,2.3,0,2.8,0,2.8,0,2.8,0,2.2,0,2.6,0,2.8,0,2.5,0,2.4,0,2.3,0,1.9,0,1.7,0,2.0,0,2.1,0,1.7,0,1.8,0,1.8,0,1.8,0,1.3,0,1.3,0,1.3,1,1.2,1,1.4,1,2.2,1,2.9,1,3.1,1,3.5,1,3.6,1,4.4,1,4.1,1,5.1,1,5.8,1,5.9,1,5.4,1,5.5,1,4.8,1,3.2,1,2.7,1,2.1,1,1.9,1,0.6,1,0.7,1,-0.2,1,-1.0,1,-1.7,1,-0.7,1,-1.0,1),dim=c(2,60),dimnames=list(c('Inflatie','Kredietcrisis'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Inflatie','Kredietcrisis'),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 = '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
Inflatie Kredietcrisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 2.7 0 1 0 0 0 0 0 0 0 0 0 0
2 2.3 0 0 1 0 0 0 0 0 0 0 0 0
3 1.9 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 2.3 0 0 0 0 0 1 0 0 0 0 0 0
6 2.8 0 0 0 0 0 0 1 0 0 0 0 0
7 2.4 0 0 0 0 0 0 0 1 0 0 0 0
8 2.3 0 0 0 0 0 0 0 0 1 0 0 0
9 2.7 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 2.9 0 0 0 0 0 0 0 0 0 0 0 1
12 3.0 0 0 0 0 0 0 0 0 0 0 0 0
13 2.2 0 1 0 0 0 0 0 0 0 0 0 0
14 2.3 0 0 1 0 0 0 0 0 0 0 0 0
15 2.8 0 0 0 1 0 0 0 0 0 0 0 0
16 2.8 0 0 0 0 1 0 0 0 0 0 0 0
17 2.8 0 0 0 0 0 1 0 0 0 0 0 0
18 2.2 0 0 0 0 0 0 1 0 0 0 0 0
19 2.6 0 0 0 0 0 0 0 1 0 0 0 0
20 2.8 0 0 0 0 0 0 0 0 1 0 0 0
21 2.5 0 0 0 0 0 0 0 0 0 1 0 0
22 2.4 0 0 0 0 0 0 0 0 0 0 1 0
23 2.3 0 0 0 0 0 0 0 0 0 0 0 1
24 1.9 0 0 0 0 0 0 0 0 0 0 0 0
25 1.7 0 1 0 0 0 0 0 0 0 0 0 0
26 2.0 0 0 1 0 0 0 0 0 0 0 0 0
27 2.1 0 0 0 1 0 0 0 0 0 0 0 0
28 1.7 0 0 0 0 1 0 0 0 0 0 0 0
29 1.8 0 0 0 0 0 1 0 0 0 0 0 0
30 1.8 0 0 0 0 0 0 1 0 0 0 0 0
31 1.8 0 0 0 0 0 0 0 1 0 0 0 0
32 1.3 0 0 0 0 0 0 0 0 1 0 0 0
33 1.3 0 0 0 0 0 0 0 0 0 1 0 0
34 1.3 1 0 0 0 0 0 0 0 0 0 1 0
35 1.2 1 0 0 0 0 0 0 0 0 0 0 1
36 1.4 1 0 0 0 0 0 0 0 0 0 0 0
37 2.2 1 1 0 0 0 0 0 0 0 0 0 0
38 2.9 1 0 1 0 0 0 0 0 0 0 0 0
39 3.1 1 0 0 1 0 0 0 0 0 0 0 0
40 3.5 1 0 0 0 1 0 0 0 0 0 0 0
41 3.6 1 0 0 0 0 1 0 0 0 0 0 0
42 4.4 1 0 0 0 0 0 1 0 0 0 0 0
43 4.1 1 0 0 0 0 0 0 1 0 0 0 0
44 5.1 1 0 0 0 0 0 0 0 1 0 0 0
45 5.8 1 0 0 0 0 0 0 0 0 1 0 0
46 5.9 1 0 0 0 0 0 0 0 0 0 1 0
47 5.4 1 0 0 0 0 0 0 0 0 0 0 1
48 5.5 1 0 0 0 0 0 0 0 0 0 0 0
49 4.8 1 1 0 0 0 0 0 0 0 0 0 0
50 3.2 1 0 1 0 0 0 0 0 0 0 0 0
51 2.7 1 0 0 1 0 0 0 0 0 0 0 0
52 2.1 1 0 0 0 1 0 0 0 0 0 0 0
53 1.9 1 0 0 0 0 1 0 0 0 0 0 0
54 0.6 1 0 0 0 0 0 1 0 0 0 0 0
55 0.7 1 0 0 0 0 0 0 1 0 0 0 0
56 -0.2 1 0 0 0 0 0 0 0 1 0 0 0
57 -1.0 1 0 0 0 0 0 0 0 0 1 0 0
58 -1.7 1 0 0 0 0 0 0 0 0 0 1 0
59 -0.7 1 0 0 0 0 0 0 0 0 0 0 1
60 -1.0 1 0 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) Kredietcrisis M1 M2 M3
2.0125 0.2458 0.6092 0.4292 0.4092
M4 M5 M6 M7 M8
0.3092 0.3692 0.2492 0.2092 0.1492
M9 M10 M11
0.1492 -0.0400 0.0600
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.91833 -0.63312 -0.01458 0.53833 3.68167
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.0125 0.8114 2.480 0.0168 *
Kredietcrisis 0.2458 0.4508 0.545 0.5881
M1 0.6092 1.0856 0.561 0.5774
M2 0.4292 1.0856 0.395 0.6944
M3 0.4092 1.0856 0.377 0.7079
M4 0.3092 1.0856 0.285 0.7771
M5 0.3692 1.0856 0.340 0.7353
M6 0.2492 1.0856 0.230 0.8195
M7 0.2092 1.0856 0.193 0.8480
M8 0.1492 1.0856 0.137 0.8913
M9 0.1492 1.0856 0.137 0.8913
M10 -0.0400 1.0819 -0.037 0.9707
M11 0.0600 1.0819 0.055 0.9560
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.711 on 47 degrees of freedom
Multiple R-squared: 0.01849, Adjusted R-squared: -0.2321
F-statistic: 0.07378 on 12 and 47 DF, p-value: 1
> 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,] 2.264157e-02 4.528315e-02 0.9773584
[2,] 6.014932e-03 1.202986e-02 0.9939851
[3,] 1.755194e-03 3.510387e-03 0.9982448
[4,] 3.557287e-04 7.114574e-04 0.9996443
[5,] 9.059906e-05 1.811981e-04 0.9999094
[6,] 1.682470e-05 3.364940e-05 0.9999832
[7,] 3.274547e-06 6.549094e-06 0.9999967
[8,] 9.531194e-07 1.906239e-06 0.9999990
[9,] 8.632356e-07 1.726471e-06 0.9999991
[10,] 3.766013e-07 7.532026e-07 0.9999996
[11,] 7.557854e-08 1.511571e-07 0.9999999
[12,] 1.390075e-08 2.780150e-08 1.0000000
[13,] 5.065197e-09 1.013039e-08 1.0000000
[14,] 1.979354e-09 3.958707e-09 1.0000000
[15,] 6.642823e-10 1.328565e-09 1.0000000
[16,] 2.160736e-10 4.321472e-10 1.0000000
[17,] 2.654717e-10 5.309435e-10 1.0000000
[18,] 2.839612e-10 5.679224e-10 1.0000000
[19,] 4.919505e-11 9.839009e-11 1.0000000
[20,] 8.431270e-12 1.686254e-11 1.0000000
[21,] 1.378224e-12 2.756448e-12 1.0000000
[22,] 1.062112e-12 2.124225e-12 1.0000000
[23,] 1.390856e-12 2.781713e-12 1.0000000
[24,] 9.342005e-13 1.868401e-12 1.0000000
[25,] 1.041866e-12 2.083732e-12 1.0000000
[26,] 5.807431e-13 1.161486e-12 1.0000000
[27,] 1.961063e-12 3.922125e-12 1.0000000
[28,] 1.621275e-12 3.242549e-12 1.0000000
[29,] 1.974623e-11 3.949245e-11 1.0000000
> postscript(file="/var/www/html/rcomp/tmp/1ux0c1258746794.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2cx1g1258746794.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3v2qi1258746794.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4gza41258746794.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/59t8i1258746794.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
0.07833333 -0.14166667 -0.52166667 -0.32166667 -0.08166667 0.53833333
7 8 9 10 11 12
0.17833333 0.13833333 0.53833333 0.72750000 0.82750000 0.98750000
13 14 15 16 17 18
-0.42166667 -0.14166667 0.37833333 0.47833333 0.41833333 -0.06166667
19 20 21 22 23 24
0.37833333 0.63833333 0.33833333 0.42750000 0.22750000 -0.11250000
25 26 27 28 29 30
-0.92166667 -0.44166667 -0.32166667 -0.62166667 -0.58166667 -0.46166667
31 32 33 34 35 36
-0.42166667 -0.86166667 -0.86166667 -0.91833333 -1.11833333 -0.85833333
37 38 39 40 41 42
-0.66750000 0.21250000 0.43250000 0.93250000 0.97250000 1.89250000
43 44 45 46 47 48
1.63250000 2.69250000 3.39250000 3.68166667 3.08166667 3.24166667
49 50 51 52 53 54
1.93250000 0.51250000 0.03250000 -0.46750000 -0.72750000 -1.90750000
55 56 57 58 59 60
-1.76750000 -2.60750000 -3.40750000 -3.91833333 -3.01833333 -3.25833333
> postscript(file="/var/www/html/rcomp/tmp/6v9nu1258746794.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 0.07833333 NA
1 -0.14166667 0.07833333
2 -0.52166667 -0.14166667
3 -0.32166667 -0.52166667
4 -0.08166667 -0.32166667
5 0.53833333 -0.08166667
6 0.17833333 0.53833333
7 0.13833333 0.17833333
8 0.53833333 0.13833333
9 0.72750000 0.53833333
10 0.82750000 0.72750000
11 0.98750000 0.82750000
12 -0.42166667 0.98750000
13 -0.14166667 -0.42166667
14 0.37833333 -0.14166667
15 0.47833333 0.37833333
16 0.41833333 0.47833333
17 -0.06166667 0.41833333
18 0.37833333 -0.06166667
19 0.63833333 0.37833333
20 0.33833333 0.63833333
21 0.42750000 0.33833333
22 0.22750000 0.42750000
23 -0.11250000 0.22750000
24 -0.92166667 -0.11250000
25 -0.44166667 -0.92166667
26 -0.32166667 -0.44166667
27 -0.62166667 -0.32166667
28 -0.58166667 -0.62166667
29 -0.46166667 -0.58166667
30 -0.42166667 -0.46166667
31 -0.86166667 -0.42166667
32 -0.86166667 -0.86166667
33 -0.91833333 -0.86166667
34 -1.11833333 -0.91833333
35 -0.85833333 -1.11833333
36 -0.66750000 -0.85833333
37 0.21250000 -0.66750000
38 0.43250000 0.21250000
39 0.93250000 0.43250000
40 0.97250000 0.93250000
41 1.89250000 0.97250000
42 1.63250000 1.89250000
43 2.69250000 1.63250000
44 3.39250000 2.69250000
45 3.68166667 3.39250000
46 3.08166667 3.68166667
47 3.24166667 3.08166667
48 1.93250000 3.24166667
49 0.51250000 1.93250000
50 0.03250000 0.51250000
51 -0.46750000 0.03250000
52 -0.72750000 -0.46750000
53 -1.90750000 -0.72750000
54 -1.76750000 -1.90750000
55 -2.60750000 -1.76750000
56 -3.40750000 -2.60750000
57 -3.91833333 -3.40750000
58 -3.01833333 -3.91833333
59 -3.25833333 -3.01833333
60 NA -3.25833333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.14166667 0.07833333
[2,] -0.52166667 -0.14166667
[3,] -0.32166667 -0.52166667
[4,] -0.08166667 -0.32166667
[5,] 0.53833333 -0.08166667
[6,] 0.17833333 0.53833333
[7,] 0.13833333 0.17833333
[8,] 0.53833333 0.13833333
[9,] 0.72750000 0.53833333
[10,] 0.82750000 0.72750000
[11,] 0.98750000 0.82750000
[12,] -0.42166667 0.98750000
[13,] -0.14166667 -0.42166667
[14,] 0.37833333 -0.14166667
[15,] 0.47833333 0.37833333
[16,] 0.41833333 0.47833333
[17,] -0.06166667 0.41833333
[18,] 0.37833333 -0.06166667
[19,] 0.63833333 0.37833333
[20,] 0.33833333 0.63833333
[21,] 0.42750000 0.33833333
[22,] 0.22750000 0.42750000
[23,] -0.11250000 0.22750000
[24,] -0.92166667 -0.11250000
[25,] -0.44166667 -0.92166667
[26,] -0.32166667 -0.44166667
[27,] -0.62166667 -0.32166667
[28,] -0.58166667 -0.62166667
[29,] -0.46166667 -0.58166667
[30,] -0.42166667 -0.46166667
[31,] -0.86166667 -0.42166667
[32,] -0.86166667 -0.86166667
[33,] -0.91833333 -0.86166667
[34,] -1.11833333 -0.91833333
[35,] -0.85833333 -1.11833333
[36,] -0.66750000 -0.85833333
[37,] 0.21250000 -0.66750000
[38,] 0.43250000 0.21250000
[39,] 0.93250000 0.43250000
[40,] 0.97250000 0.93250000
[41,] 1.89250000 0.97250000
[42,] 1.63250000 1.89250000
[43,] 2.69250000 1.63250000
[44,] 3.39250000 2.69250000
[45,] 3.68166667 3.39250000
[46,] 3.08166667 3.68166667
[47,] 3.24166667 3.08166667
[48,] 1.93250000 3.24166667
[49,] 0.51250000 1.93250000
[50,] 0.03250000 0.51250000
[51,] -0.46750000 0.03250000
[52,] -0.72750000 -0.46750000
[53,] -1.90750000 -0.72750000
[54,] -1.76750000 -1.90750000
[55,] -2.60750000 -1.76750000
[56,] -3.40750000 -2.60750000
[57,] -3.91833333 -3.40750000
[58,] -3.01833333 -3.91833333
[59,] -3.25833333 -3.01833333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.14166667 0.07833333
2 -0.52166667 -0.14166667
3 -0.32166667 -0.52166667
4 -0.08166667 -0.32166667
5 0.53833333 -0.08166667
6 0.17833333 0.53833333
7 0.13833333 0.17833333
8 0.53833333 0.13833333
9 0.72750000 0.53833333
10 0.82750000 0.72750000
11 0.98750000 0.82750000
12 -0.42166667 0.98750000
13 -0.14166667 -0.42166667
14 0.37833333 -0.14166667
15 0.47833333 0.37833333
16 0.41833333 0.47833333
17 -0.06166667 0.41833333
18 0.37833333 -0.06166667
19 0.63833333 0.37833333
20 0.33833333 0.63833333
21 0.42750000 0.33833333
22 0.22750000 0.42750000
23 -0.11250000 0.22750000
24 -0.92166667 -0.11250000
25 -0.44166667 -0.92166667
26 -0.32166667 -0.44166667
27 -0.62166667 -0.32166667
28 -0.58166667 -0.62166667
29 -0.46166667 -0.58166667
30 -0.42166667 -0.46166667
31 -0.86166667 -0.42166667
32 -0.86166667 -0.86166667
33 -0.91833333 -0.86166667
34 -1.11833333 -0.91833333
35 -0.85833333 -1.11833333
36 -0.66750000 -0.85833333
37 0.21250000 -0.66750000
38 0.43250000 0.21250000
39 0.93250000 0.43250000
40 0.97250000 0.93250000
41 1.89250000 0.97250000
42 1.63250000 1.89250000
43 2.69250000 1.63250000
44 3.39250000 2.69250000
45 3.68166667 3.39250000
46 3.08166667 3.68166667
47 3.24166667 3.08166667
48 1.93250000 3.24166667
49 0.51250000 1.93250000
50 0.03250000 0.51250000
51 -0.46750000 0.03250000
52 -0.72750000 -0.46750000
53 -1.90750000 -0.72750000
54 -1.76750000 -1.90750000
55 -2.60750000 -1.76750000
56 -3.40750000 -2.60750000
57 -3.91833333 -3.40750000
58 -3.01833333 -3.91833333
59 -3.25833333 -3.01833333
> 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/7ncrt1258746794.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/846ss1258746794.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9s4ua1258746794.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1005xr1258746794.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110e9r1258746794.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/12kuq01258746794.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/13fnjh1258746794.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/14rz7l1258746794.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/152j2n1258746794.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/165wyp1258746794.tab")
+ }
>
> system("convert tmp/1ux0c1258746794.ps tmp/1ux0c1258746794.png")
> system("convert tmp/2cx1g1258746794.ps tmp/2cx1g1258746794.png")
> system("convert tmp/3v2qi1258746794.ps tmp/3v2qi1258746794.png")
> system("convert tmp/4gza41258746794.ps tmp/4gza41258746794.png")
> system("convert tmp/59t8i1258746794.ps tmp/59t8i1258746794.png")
> system("convert tmp/6v9nu1258746794.ps tmp/6v9nu1258746794.png")
> system("convert tmp/7ncrt1258746794.ps tmp/7ncrt1258746794.png")
> system("convert tmp/846ss1258746794.ps tmp/846ss1258746794.png")
> system("convert tmp/9s4ua1258746794.ps tmp/9s4ua1258746794.png")
> system("convert tmp/1005xr1258746794.ps tmp/1005xr1258746794.png")
>
>
> proc.time()
user system elapsed
2.402 1.546 2.787