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 = '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 t
1 2.7 0 1 0 0 0 0 0 0 0 0 0 0 1
2 2.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 1.9 0 0 0 1 0 0 0 0 0 0 0 0 3
4 2.0 0 0 0 0 1 0 0 0 0 0 0 0 4
5 2.3 0 0 0 0 0 1 0 0 0 0 0 0 5
6 2.8 0 0 0 0 0 0 1 0 0 0 0 0 6
7 2.4 0 0 0 0 0 0 0 1 0 0 0 0 7
8 2.3 0 0 0 0 0 0 0 0 1 0 0 0 8
9 2.7 0 0 0 0 0 0 0 0 0 1 0 0 9
10 2.7 0 0 0 0 0 0 0 0 0 0 1 0 10
11 2.9 0 0 0 0 0 0 0 0 0 0 0 1 11
12 3.0 0 0 0 0 0 0 0 0 0 0 0 0 12
13 2.2 0 1 0 0 0 0 0 0 0 0 0 0 13
14 2.3 0 0 1 0 0 0 0 0 0 0 0 0 14
15 2.8 0 0 0 1 0 0 0 0 0 0 0 0 15
16 2.8 0 0 0 0 1 0 0 0 0 0 0 0 16
17 2.8 0 0 0 0 0 1 0 0 0 0 0 0 17
18 2.2 0 0 0 0 0 0 1 0 0 0 0 0 18
19 2.6 0 0 0 0 0 0 0 1 0 0 0 0 19
20 2.8 0 0 0 0 0 0 0 0 1 0 0 0 20
21 2.5 0 0 0 0 0 0 0 0 0 1 0 0 21
22 2.4 0 0 0 0 0 0 0 0 0 0 1 0 22
23 2.3 0 0 0 0 0 0 0 0 0 0 0 1 23
24 1.9 0 0 0 0 0 0 0 0 0 0 0 0 24
25 1.7 0 1 0 0 0 0 0 0 0 0 0 0 25
26 2.0 0 0 1 0 0 0 0 0 0 0 0 0 26
27 2.1 0 0 0 1 0 0 0 0 0 0 0 0 27
28 1.7 0 0 0 0 1 0 0 0 0 0 0 0 28
29 1.8 0 0 0 0 0 1 0 0 0 0 0 0 29
30 1.8 0 0 0 0 0 0 1 0 0 0 0 0 30
31 1.8 0 0 0 0 0 0 0 1 0 0 0 0 31
32 1.3 0 0 0 0 0 0 0 0 1 0 0 0 32
33 1.3 0 0 0 0 0 0 0 0 0 1 0 0 33
34 1.3 1 0 0 0 0 0 0 0 0 0 1 0 34
35 1.2 1 0 0 0 0 0 0 0 0 0 0 1 35
36 1.4 1 0 0 0 0 0 0 0 0 0 0 0 36
37 2.2 1 1 0 0 0 0 0 0 0 0 0 0 37
38 2.9 1 0 1 0 0 0 0 0 0 0 0 0 38
39 3.1 1 0 0 1 0 0 0 0 0 0 0 0 39
40 3.5 1 0 0 0 1 0 0 0 0 0 0 0 40
41 3.6 1 0 0 0 0 1 0 0 0 0 0 0 41
42 4.4 1 0 0 0 0 0 1 0 0 0 0 0 42
43 4.1 1 0 0 0 0 0 0 1 0 0 0 0 43
44 5.1 1 0 0 0 0 0 0 0 1 0 0 0 44
45 5.8 1 0 0 0 0 0 0 0 0 1 0 0 45
46 5.9 1 0 0 0 0 0 0 0 0 0 1 0 46
47 5.4 1 0 0 0 0 0 0 0 0 0 0 1 47
48 5.5 1 0 0 0 0 0 0 0 0 0 0 0 48
49 4.8 1 1 0 0 0 0 0 0 0 0 0 0 49
50 3.2 1 0 1 0 0 0 0 0 0 0 0 0 50
51 2.7 1 0 0 1 0 0 0 0 0 0 0 0 51
52 2.1 1 0 0 0 1 0 0 0 0 0 0 0 52
53 1.9 1 0 0 0 0 1 0 0 0 0 0 0 53
54 0.6 1 0 0 0 0 0 1 0 0 0 0 0 54
55 0.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 -0.2 1 0 0 0 0 0 0 0 1 0 0 0 56
57 -1.0 1 0 0 0 0 0 0 0 0 1 0 0 57
58 -1.7 1 0 0 0 0 0 0 0 0 0 1 0 58
59 -0.7 1 0 0 0 0 0 0 0 0 0 0 1 59
60 -1.0 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Kredietcrisis M1 M2 M3
3.220000 2.258333 0.273750 0.160833 0.207917
M4 M5 M6 M7 M8
0.175000 0.302083 0.249167 0.276250 0.283333
M9 M10 M11 t
0.350417 -0.174167 -0.007083 -0.067083
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-3.1133 -0.7667 0.1067 0.4950 3.6817
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.220000 0.877565 3.669 0.00063 ***
Kredietcrisis 2.258333 0.844437 2.674 0.01033 *
M1 0.273750 1.024115 0.267 0.79043
M2 0.160833 1.021501 0.157 0.87558
M3 0.207917 1.019463 0.204 0.83929
M4 0.175000 1.018005 0.172 0.86427
M5 0.302083 1.017129 0.297 0.76781
M6 0.249167 1.016837 0.245 0.80751
M7 0.276250 1.017129 0.272 0.78715
M8 0.283333 1.018005 0.278 0.78201
M9 0.350417 1.019463 0.344 0.73262
M10 -0.174167 1.014496 -0.172 0.86444
M11 -0.007083 1.013617 -0.007 0.99445
t -0.067083 0.024377 -2.752 0.00845 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.602 on 46 degrees of freedom
Multiple R-squared: 0.1572, Adjusted R-squared: -0.08094
F-statistic: 0.6602 on 13 and 46 DF, p-value: 0.7898
> 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,] 2.247398e-02 4.494796e-02 0.9775260
[2,] 1.046179e-02 2.092358e-02 0.9895382
[3,] 2.350052e-03 4.700103e-03 0.9976499
[4,] 5.342585e-04 1.068517e-03 0.9994657
[5,] 1.288557e-04 2.577114e-04 0.9998711
[6,] 3.242530e-05 6.485059e-05 0.9999676
[7,] 1.203535e-05 2.407070e-05 0.9999880
[8,] 1.084448e-05 2.168896e-05 0.9999892
[9,] 3.950673e-06 7.901346e-06 0.9999960
[10,] 7.807406e-07 1.561481e-06 0.9999992
[11,] 1.430026e-07 2.860052e-07 0.9999999
[12,] 3.553066e-08 7.106131e-08 1.0000000
[13,] 8.476369e-09 1.695274e-08 1.0000000
[14,] 1.711694e-09 3.423388e-09 1.0000000
[15,] 3.206965e-10 6.413929e-10 1.0000000
[16,] 1.625200e-10 3.250400e-10 1.0000000
[17,] 7.147182e-11 1.429436e-10 1.0000000
[18,] 2.388902e-11 4.777804e-11 1.0000000
[19,] 1.797814e-11 3.595629e-11 1.0000000
[20,] 4.730968e-11 9.461937e-11 1.0000000
[21,] 6.246296e-09 1.249259e-08 1.0000000
[22,] 4.268901e-07 8.537802e-07 0.9999996
[23,] 1.309990e-05 2.619980e-05 0.9999869
[24,] 3.621752e-04 7.243504e-04 0.9996378
[25,] 1.321229e-02 2.642457e-02 0.9867877
[26,] 5.162730e-02 1.032546e-01 0.9483727
[27,] 4.520068e-01 9.040135e-01 0.5479932
> postscript(file="/var/www/html/rcomp/tmp/139nx1258748733.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2uqlz1258748733.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3t9ew1258748733.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4g1qs1258748733.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ivdi1258748734.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
-0.72666667 -0.94666667 -1.32666667 -1.12666667 -0.88666667 -0.26666667
7 8 9 10 11 12
-0.62666667 -0.66666667 -0.26666667 0.32500000 0.42500000 0.58500000
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 0.83000000 0.63000000 0.29000000
25 26 27 28 29 30
-0.11666667 0.36333333 0.48333333 0.18333333 0.22333333 0.34333333
31 32 33 34 35 36
0.38333333 -0.05666667 -0.05666667 -1.72333333 -1.92333333 -1.66333333
37 38 39 40 41 42
-1.07000000 -0.19000000 0.03000000 0.53000000 0.57000000 1.49000000
43 44 45 46 47 48
1.23000000 2.29000000 2.99000000 3.68166667 3.08166667 3.24166667
49 50 51 52 53 54
2.33500000 0.91500000 0.43500000 -0.06500000 -0.32500000 -1.50500000
55 56 57 58 59 60
-1.36500000 -2.20500000 -3.00500000 -3.11333333 -2.21333333 -2.45333333
> postscript(file="/var/www/html/rcomp/tmp/6rxmk1258748734.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 -0.72666667 NA
1 -0.94666667 -0.72666667
2 -1.32666667 -0.94666667
3 -1.12666667 -1.32666667
4 -0.88666667 -1.12666667
5 -0.26666667 -0.88666667
6 -0.62666667 -0.26666667
7 -0.66666667 -0.62666667
8 -0.26666667 -0.66666667
9 0.32500000 -0.26666667
10 0.42500000 0.32500000
11 0.58500000 0.42500000
12 -0.42166667 0.58500000
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 0.83000000 0.33833333
22 0.63000000 0.83000000
23 0.29000000 0.63000000
24 -0.11666667 0.29000000
25 0.36333333 -0.11666667
26 0.48333333 0.36333333
27 0.18333333 0.48333333
28 0.22333333 0.18333333
29 0.34333333 0.22333333
30 0.38333333 0.34333333
31 -0.05666667 0.38333333
32 -0.05666667 -0.05666667
33 -1.72333333 -0.05666667
34 -1.92333333 -1.72333333
35 -1.66333333 -1.92333333
36 -1.07000000 -1.66333333
37 -0.19000000 -1.07000000
38 0.03000000 -0.19000000
39 0.53000000 0.03000000
40 0.57000000 0.53000000
41 1.49000000 0.57000000
42 1.23000000 1.49000000
43 2.29000000 1.23000000
44 2.99000000 2.29000000
45 3.68166667 2.99000000
46 3.08166667 3.68166667
47 3.24166667 3.08166667
48 2.33500000 3.24166667
49 0.91500000 2.33500000
50 0.43500000 0.91500000
51 -0.06500000 0.43500000
52 -0.32500000 -0.06500000
53 -1.50500000 -0.32500000
54 -1.36500000 -1.50500000
55 -2.20500000 -1.36500000
56 -3.00500000 -2.20500000
57 -3.11333333 -3.00500000
58 -2.21333333 -3.11333333
59 -2.45333333 -2.21333333
60 NA -2.45333333
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.94666667 -0.72666667
[2,] -1.32666667 -0.94666667
[3,] -1.12666667 -1.32666667
[4,] -0.88666667 -1.12666667
[5,] -0.26666667 -0.88666667
[6,] -0.62666667 -0.26666667
[7,] -0.66666667 -0.62666667
[8,] -0.26666667 -0.66666667
[9,] 0.32500000 -0.26666667
[10,] 0.42500000 0.32500000
[11,] 0.58500000 0.42500000
[12,] -0.42166667 0.58500000
[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,] 0.83000000 0.33833333
[22,] 0.63000000 0.83000000
[23,] 0.29000000 0.63000000
[24,] -0.11666667 0.29000000
[25,] 0.36333333 -0.11666667
[26,] 0.48333333 0.36333333
[27,] 0.18333333 0.48333333
[28,] 0.22333333 0.18333333
[29,] 0.34333333 0.22333333
[30,] 0.38333333 0.34333333
[31,] -0.05666667 0.38333333
[32,] -0.05666667 -0.05666667
[33,] -1.72333333 -0.05666667
[34,] -1.92333333 -1.72333333
[35,] -1.66333333 -1.92333333
[36,] -1.07000000 -1.66333333
[37,] -0.19000000 -1.07000000
[38,] 0.03000000 -0.19000000
[39,] 0.53000000 0.03000000
[40,] 0.57000000 0.53000000
[41,] 1.49000000 0.57000000
[42,] 1.23000000 1.49000000
[43,] 2.29000000 1.23000000
[44,] 2.99000000 2.29000000
[45,] 3.68166667 2.99000000
[46,] 3.08166667 3.68166667
[47,] 3.24166667 3.08166667
[48,] 2.33500000 3.24166667
[49,] 0.91500000 2.33500000
[50,] 0.43500000 0.91500000
[51,] -0.06500000 0.43500000
[52,] -0.32500000 -0.06500000
[53,] -1.50500000 -0.32500000
[54,] -1.36500000 -1.50500000
[55,] -2.20500000 -1.36500000
[56,] -3.00500000 -2.20500000
[57,] -3.11333333 -3.00500000
[58,] -2.21333333 -3.11333333
[59,] -2.45333333 -2.21333333
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.94666667 -0.72666667
2 -1.32666667 -0.94666667
3 -1.12666667 -1.32666667
4 -0.88666667 -1.12666667
5 -0.26666667 -0.88666667
6 -0.62666667 -0.26666667
7 -0.66666667 -0.62666667
8 -0.26666667 -0.66666667
9 0.32500000 -0.26666667
10 0.42500000 0.32500000
11 0.58500000 0.42500000
12 -0.42166667 0.58500000
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 0.83000000 0.33833333
22 0.63000000 0.83000000
23 0.29000000 0.63000000
24 -0.11666667 0.29000000
25 0.36333333 -0.11666667
26 0.48333333 0.36333333
27 0.18333333 0.48333333
28 0.22333333 0.18333333
29 0.34333333 0.22333333
30 0.38333333 0.34333333
31 -0.05666667 0.38333333
32 -0.05666667 -0.05666667
33 -1.72333333 -0.05666667
34 -1.92333333 -1.72333333
35 -1.66333333 -1.92333333
36 -1.07000000 -1.66333333
37 -0.19000000 -1.07000000
38 0.03000000 -0.19000000
39 0.53000000 0.03000000
40 0.57000000 0.53000000
41 1.49000000 0.57000000
42 1.23000000 1.49000000
43 2.29000000 1.23000000
44 2.99000000 2.29000000
45 3.68166667 2.99000000
46 3.08166667 3.68166667
47 3.24166667 3.08166667
48 2.33500000 3.24166667
49 0.91500000 2.33500000
50 0.43500000 0.91500000
51 -0.06500000 0.43500000
52 -0.32500000 -0.06500000
53 -1.50500000 -0.32500000
54 -1.36500000 -1.50500000
55 -2.20500000 -1.36500000
56 -3.00500000 -2.20500000
57 -3.11333333 -3.00500000
58 -2.21333333 -3.11333333
59 -2.45333333 -2.21333333
> 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/7nivz1258748734.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xo3f1258748734.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91mf31258748734.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ojx41258748734.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11h4k01258748734.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/1262av1258748734.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/13ozuc1258748734.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/14mg771258748734.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/15kj0t1258748734.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/16t6041258748734.tab")
+ }
>
> system("convert tmp/139nx1258748733.ps tmp/139nx1258748733.png")
> system("convert tmp/2uqlz1258748733.ps tmp/2uqlz1258748733.png")
> system("convert tmp/3t9ew1258748733.ps tmp/3t9ew1258748733.png")
> system("convert tmp/4g1qs1258748733.ps tmp/4g1qs1258748733.png")
> system("convert tmp/5ivdi1258748734.ps tmp/5ivdi1258748734.png")
> system("convert tmp/6rxmk1258748734.ps tmp/6rxmk1258748734.png")
> system("convert tmp/7nivz1258748734.ps tmp/7nivz1258748734.png")
> system("convert tmp/8xo3f1258748734.ps tmp/8xo3f1258748734.png")
> system("convert tmp/91mf31258748734.ps tmp/91mf31258748734.png")
> system("convert tmp/10ojx41258748734.ps tmp/10ojx41258748734.png")
>
>
> proc.time()
user system elapsed
2.315 1.555 2.787