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(1.4,2,1.2,2,1,2,1.7,2,2.4,2,2,2,2.1,2,2,2,1.8,2,2.7,2,2.3,2,1.9,2,2,2,2.3,2,2.8,2,2.4,2,2.3,2,2.7,2,2.7,2,2.9,2,3,2,2.2,2,2.3,2,2.8,2.21,2.8,2.25,2.8,2.25,2.2,2.45,2.6,2.5,2.8,2.5,2.5,2.64,2.4,2.75,2.3,2.93,1.9,3,1.7,3.17,2,3.25,2.1,3.39,1.7,3.5,1.8,3.5,1.8,3.65,1.8,3.75,1.3,3.75,1.3,3.9,1.3,4,1.2,4,1.4,4,2.2,4,2.9,4,3.1,4,3.5,4,3.6,4,4.4,4,4.1,4,5.1,4,5.8,4,5.9,4.18,5.4,4.25,5.5,4.25,4.8,3.97,3.2,3.42,2.7,2.75),dim=c(2,60),dimnames=list(c('Inflatie','rente'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Inflatie','rente'),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 rente M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 1.4 2.00 1 0 0 0 0 0 0 0 0 0 0
2 1.2 2.00 0 1 0 0 0 0 0 0 0 0 0
3 1.0 2.00 0 0 1 0 0 0 0 0 0 0 0
4 1.7 2.00 0 0 0 1 0 0 0 0 0 0 0
5 2.4 2.00 0 0 0 0 1 0 0 0 0 0 0
6 2.0 2.00 0 0 0 0 0 1 0 0 0 0 0
7 2.1 2.00 0 0 0 0 0 0 1 0 0 0 0
8 2.0 2.00 0 0 0 0 0 0 0 1 0 0 0
9 1.8 2.00 0 0 0 0 0 0 0 0 1 0 0
10 2.7 2.00 0 0 0 0 0 0 0 0 0 1 0
11 2.3 2.00 0 0 0 0 0 0 0 0 0 0 1
12 1.9 2.00 0 0 0 0 0 0 0 0 0 0 0
13 2.0 2.00 1 0 0 0 0 0 0 0 0 0 0
14 2.3 2.00 0 1 0 0 0 0 0 0 0 0 0
15 2.8 2.00 0 0 1 0 0 0 0 0 0 0 0
16 2.4 2.00 0 0 0 1 0 0 0 0 0 0 0
17 2.3 2.00 0 0 0 0 1 0 0 0 0 0 0
18 2.7 2.00 0 0 0 0 0 1 0 0 0 0 0
19 2.7 2.00 0 0 0 0 0 0 1 0 0 0 0
20 2.9 2.00 0 0 0 0 0 0 0 1 0 0 0
21 3.0 2.00 0 0 0 0 0 0 0 0 1 0 0
22 2.2 2.00 0 0 0 0 0 0 0 0 0 1 0
23 2.3 2.00 0 0 0 0 0 0 0 0 0 0 1
24 2.8 2.21 0 0 0 0 0 0 0 0 0 0 0
25 2.8 2.25 1 0 0 0 0 0 0 0 0 0 0
26 2.8 2.25 0 1 0 0 0 0 0 0 0 0 0
27 2.2 2.45 0 0 1 0 0 0 0 0 0 0 0
28 2.6 2.50 0 0 0 1 0 0 0 0 0 0 0
29 2.8 2.50 0 0 0 0 1 0 0 0 0 0 0
30 2.5 2.64 0 0 0 0 0 1 0 0 0 0 0
31 2.4 2.75 0 0 0 0 0 0 1 0 0 0 0
32 2.3 2.93 0 0 0 0 0 0 0 1 0 0 0
33 1.9 3.00 0 0 0 0 0 0 0 0 1 0 0
34 1.7 3.17 0 0 0 0 0 0 0 0 0 1 0
35 2.0 3.25 0 0 0 0 0 0 0 0 0 0 1
36 2.1 3.39 0 0 0 0 0 0 0 0 0 0 0
37 1.7 3.50 1 0 0 0 0 0 0 0 0 0 0
38 1.8 3.50 0 1 0 0 0 0 0 0 0 0 0
39 1.8 3.65 0 0 1 0 0 0 0 0 0 0 0
40 1.8 3.75 0 0 0 1 0 0 0 0 0 0 0
41 1.3 3.75 0 0 0 0 1 0 0 0 0 0 0
42 1.3 3.90 0 0 0 0 0 1 0 0 0 0 0
43 1.3 4.00 0 0 0 0 0 0 1 0 0 0 0
44 1.2 4.00 0 0 0 0 0 0 0 1 0 0 0
45 1.4 4.00 0 0 0 0 0 0 0 0 1 0 0
46 2.2 4.00 0 0 0 0 0 0 0 0 0 1 0
47 2.9 4.00 0 0 0 0 0 0 0 0 0 0 1
48 3.1 4.00 0 0 0 0 0 0 0 0 0 0 0
49 3.5 4.00 1 0 0 0 0 0 0 0 0 0 0
50 3.6 4.00 0 1 0 0 0 0 0 0 0 0 0
51 4.4 4.00 0 0 1 0 0 0 0 0 0 0 0
52 4.1 4.00 0 0 0 1 0 0 0 0 0 0 0
53 5.1 4.00 0 0 0 0 1 0 0 0 0 0 0
54 5.8 4.00 0 0 0 0 0 1 0 0 0 0 0
55 5.9 4.18 0 0 0 0 0 0 1 0 0 0 0
56 5.4 4.25 0 0 0 0 0 0 0 1 0 0 0
57 5.5 4.25 0 0 0 0 0 0 0 0 1 0 0
58 4.8 3.97 0 0 0 0 0 0 0 0 0 1 0
59 3.2 3.42 0 0 0 0 0 0 0 0 0 0 1
60 2.7 2.75 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) rente M1 M2 M3 M4
0.94881 0.54745 -0.17431 -0.11431 -0.05263 0.01095
M5 M6 M7 M8 M9 M10
0.27095 0.31920 0.29650 0.14912 0.10146 0.11350
M11
-0.01504
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.13512 -0.70676 0.01406 0.55101 2.36634
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.94881 0.72387 1.311 0.19631
rente 0.54745 0.17399 3.147 0.00287 **
M1 -0.17431 0.74143 -0.235 0.81516
M2 -0.11431 0.74143 -0.154 0.87814
M3 -0.05263 0.74119 -0.071 0.94370
M4 0.01095 0.74114 0.015 0.98828
M5 0.27095 0.74114 0.366 0.71632
M6 0.31920 0.74117 0.431 0.66868
M7 0.29650 0.74141 0.400 0.69104
M8 0.14912 0.74170 0.201 0.84152
M9 0.10146 0.74180 0.137 0.89179
M10 0.11350 0.74165 0.153 0.87902
M11 -0.01504 0.74122 -0.020 0.98390
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.172 on 47 degrees of freedom
Multiple R-squared: 0.1967, Adjusted R-squared: -0.008336
F-statistic: 0.9594 on 12 and 47 DF, p-value: 0.4994
> 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,] 2.819874e-01 5.639747e-01 0.7180126
[2,] 1.433294e-01 2.866589e-01 0.8566706
[3,] 7.836366e-02 1.567273e-01 0.9216363
[4,] 3.963603e-02 7.927206e-02 0.9603640
[5,] 2.386643e-02 4.773285e-02 0.9761336
[6,] 1.867206e-02 3.734413e-02 0.9813279
[7,] 8.753010e-03 1.750602e-02 0.9912470
[8,] 3.567578e-03 7.135156e-03 0.9964324
[9,] 1.425443e-03 2.850886e-03 0.9985746
[10,] 5.751305e-04 1.150261e-03 0.9994249
[11,] 2.298369e-04 4.596739e-04 0.9997702
[12,] 2.003961e-04 4.007922e-04 0.9997996
[13,] 8.883270e-05 1.776654e-04 0.9999112
[14,] 3.867308e-05 7.734616e-05 0.9999613
[15,] 1.950286e-05 3.900572e-05 0.9999805
[16,] 1.013990e-05 2.027980e-05 0.9999899
[17,] 5.771620e-06 1.154324e-05 0.9999942
[18,] 3.460909e-06 6.921819e-06 0.9999965
[19,] 1.798152e-06 3.596304e-06 0.9999982
[20,] 5.242341e-07 1.048468e-06 0.9999995
[21,] 1.520381e-07 3.040762e-07 0.9999998
[22,] 3.933890e-08 7.867779e-08 1.0000000
[23,] 9.237298e-09 1.847460e-08 1.0000000
[24,] 2.754222e-09 5.508445e-09 1.0000000
[25,] 8.187412e-10 1.637482e-09 1.0000000
[26,] 1.628407e-09 3.256814e-09 1.0000000
[27,] 1.100119e-08 2.200239e-08 1.0000000
[28,] 1.980484e-07 3.960968e-07 0.9999998
[29,] 7.718023e-06 1.543605e-05 0.9999923
> postscript(file="/var/www/html/rcomp/tmp/1ycp81258720375.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2jsdx1258720375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/381z81258720375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/41keg1258720375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/518761258720375.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
-0.46940923 -0.72940923 -0.99108742 -0.35466379 0.08533621 -0.36291144
7 8 9 10 11 12
-0.24021000 -0.19283728 -0.34517292 0.54278309 0.27132238 -0.14371470
13 14 15 16 17 18
0.13059077 0.37059077 0.80891258 0.34533621 -0.01466379 0.33708856
19 20 21 22 23 24
0.35979000 0.70716272 0.85482708 0.04278309 0.27132238 0.64131988
25 26 27 28 29 30
0.79372718 0.73372718 -0.03744189 0.27160903 0.21160903 -0.21328223
31 32 33 34 35 36
-0.35080077 -0.40196984 -0.79262728 -1.09773852 -0.71299558 -0.70467627
37 38 39 40 41 42
-0.99059077 -0.95059077 -1.09438712 -1.21270893 -1.97270893 -2.10307473
43 44 45 46 47 48
-2.13511872 -2.08774601 -1.84008164 -1.05212564 -0.22358635 -0.03862343
49 50 51 52 53 54
0.53568205 0.57568205 1.31400385 0.95042748 1.69042748 2.34217984
55 56 57 58 59 60
2.36633949 1.97539040 2.12305476 1.56429799 0.39393718 0.24569452
> postscript(file="/var/www/html/rcomp/tmp/6ry0c1258720375.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 -0.46940923 NA
1 -0.72940923 -0.46940923
2 -0.99108742 -0.72940923
3 -0.35466379 -0.99108742
4 0.08533621 -0.35466379
5 -0.36291144 0.08533621
6 -0.24021000 -0.36291144
7 -0.19283728 -0.24021000
8 -0.34517292 -0.19283728
9 0.54278309 -0.34517292
10 0.27132238 0.54278309
11 -0.14371470 0.27132238
12 0.13059077 -0.14371470
13 0.37059077 0.13059077
14 0.80891258 0.37059077
15 0.34533621 0.80891258
16 -0.01466379 0.34533621
17 0.33708856 -0.01466379
18 0.35979000 0.33708856
19 0.70716272 0.35979000
20 0.85482708 0.70716272
21 0.04278309 0.85482708
22 0.27132238 0.04278309
23 0.64131988 0.27132238
24 0.79372718 0.64131988
25 0.73372718 0.79372718
26 -0.03744189 0.73372718
27 0.27160903 -0.03744189
28 0.21160903 0.27160903
29 -0.21328223 0.21160903
30 -0.35080077 -0.21328223
31 -0.40196984 -0.35080077
32 -0.79262728 -0.40196984
33 -1.09773852 -0.79262728
34 -0.71299558 -1.09773852
35 -0.70467627 -0.71299558
36 -0.99059077 -0.70467627
37 -0.95059077 -0.99059077
38 -1.09438712 -0.95059077
39 -1.21270893 -1.09438712
40 -1.97270893 -1.21270893
41 -2.10307473 -1.97270893
42 -2.13511872 -2.10307473
43 -2.08774601 -2.13511872
44 -1.84008164 -2.08774601
45 -1.05212564 -1.84008164
46 -0.22358635 -1.05212564
47 -0.03862343 -0.22358635
48 0.53568205 -0.03862343
49 0.57568205 0.53568205
50 1.31400385 0.57568205
51 0.95042748 1.31400385
52 1.69042748 0.95042748
53 2.34217984 1.69042748
54 2.36633949 2.34217984
55 1.97539040 2.36633949
56 2.12305476 1.97539040
57 1.56429799 2.12305476
58 0.39393718 1.56429799
59 0.24569452 0.39393718
60 NA 0.24569452
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.72940923 -0.46940923
[2,] -0.99108742 -0.72940923
[3,] -0.35466379 -0.99108742
[4,] 0.08533621 -0.35466379
[5,] -0.36291144 0.08533621
[6,] -0.24021000 -0.36291144
[7,] -0.19283728 -0.24021000
[8,] -0.34517292 -0.19283728
[9,] 0.54278309 -0.34517292
[10,] 0.27132238 0.54278309
[11,] -0.14371470 0.27132238
[12,] 0.13059077 -0.14371470
[13,] 0.37059077 0.13059077
[14,] 0.80891258 0.37059077
[15,] 0.34533621 0.80891258
[16,] -0.01466379 0.34533621
[17,] 0.33708856 -0.01466379
[18,] 0.35979000 0.33708856
[19,] 0.70716272 0.35979000
[20,] 0.85482708 0.70716272
[21,] 0.04278309 0.85482708
[22,] 0.27132238 0.04278309
[23,] 0.64131988 0.27132238
[24,] 0.79372718 0.64131988
[25,] 0.73372718 0.79372718
[26,] -0.03744189 0.73372718
[27,] 0.27160903 -0.03744189
[28,] 0.21160903 0.27160903
[29,] -0.21328223 0.21160903
[30,] -0.35080077 -0.21328223
[31,] -0.40196984 -0.35080077
[32,] -0.79262728 -0.40196984
[33,] -1.09773852 -0.79262728
[34,] -0.71299558 -1.09773852
[35,] -0.70467627 -0.71299558
[36,] -0.99059077 -0.70467627
[37,] -0.95059077 -0.99059077
[38,] -1.09438712 -0.95059077
[39,] -1.21270893 -1.09438712
[40,] -1.97270893 -1.21270893
[41,] -2.10307473 -1.97270893
[42,] -2.13511872 -2.10307473
[43,] -2.08774601 -2.13511872
[44,] -1.84008164 -2.08774601
[45,] -1.05212564 -1.84008164
[46,] -0.22358635 -1.05212564
[47,] -0.03862343 -0.22358635
[48,] 0.53568205 -0.03862343
[49,] 0.57568205 0.53568205
[50,] 1.31400385 0.57568205
[51,] 0.95042748 1.31400385
[52,] 1.69042748 0.95042748
[53,] 2.34217984 1.69042748
[54,] 2.36633949 2.34217984
[55,] 1.97539040 2.36633949
[56,] 2.12305476 1.97539040
[57,] 1.56429799 2.12305476
[58,] 0.39393718 1.56429799
[59,] 0.24569452 0.39393718
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.72940923 -0.46940923
2 -0.99108742 -0.72940923
3 -0.35466379 -0.99108742
4 0.08533621 -0.35466379
5 -0.36291144 0.08533621
6 -0.24021000 -0.36291144
7 -0.19283728 -0.24021000
8 -0.34517292 -0.19283728
9 0.54278309 -0.34517292
10 0.27132238 0.54278309
11 -0.14371470 0.27132238
12 0.13059077 -0.14371470
13 0.37059077 0.13059077
14 0.80891258 0.37059077
15 0.34533621 0.80891258
16 -0.01466379 0.34533621
17 0.33708856 -0.01466379
18 0.35979000 0.33708856
19 0.70716272 0.35979000
20 0.85482708 0.70716272
21 0.04278309 0.85482708
22 0.27132238 0.04278309
23 0.64131988 0.27132238
24 0.79372718 0.64131988
25 0.73372718 0.79372718
26 -0.03744189 0.73372718
27 0.27160903 -0.03744189
28 0.21160903 0.27160903
29 -0.21328223 0.21160903
30 -0.35080077 -0.21328223
31 -0.40196984 -0.35080077
32 -0.79262728 -0.40196984
33 -1.09773852 -0.79262728
34 -0.71299558 -1.09773852
35 -0.70467627 -0.71299558
36 -0.99059077 -0.70467627
37 -0.95059077 -0.99059077
38 -1.09438712 -0.95059077
39 -1.21270893 -1.09438712
40 -1.97270893 -1.21270893
41 -2.10307473 -1.97270893
42 -2.13511872 -2.10307473
43 -2.08774601 -2.13511872
44 -1.84008164 -2.08774601
45 -1.05212564 -1.84008164
46 -0.22358635 -1.05212564
47 -0.03862343 -0.22358635
48 0.53568205 -0.03862343
49 0.57568205 0.53568205
50 1.31400385 0.57568205
51 0.95042748 1.31400385
52 1.69042748 0.95042748
53 2.34217984 1.69042748
54 2.36633949 2.34217984
55 1.97539040 2.36633949
56 2.12305476 1.97539040
57 1.56429799 2.12305476
58 0.39393718 1.56429799
59 0.24569452 0.39393718
> 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/7vdve1258720375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8gwej1258720375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9rq1t1258720375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10y4pb1258720375.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11mv9m1258720375.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/1277b81258720375.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/13nly31258720375.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/146pco1258720375.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/15habu1258720375.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/16a3651258720375.tab")
+ }
>
> system("convert tmp/1ycp81258720375.ps tmp/1ycp81258720375.png")
> system("convert tmp/2jsdx1258720375.ps tmp/2jsdx1258720375.png")
> system("convert tmp/381z81258720375.ps tmp/381z81258720375.png")
> system("convert tmp/41keg1258720375.ps tmp/41keg1258720375.png")
> system("convert tmp/518761258720375.ps tmp/518761258720375.png")
> system("convert tmp/6ry0c1258720375.ps tmp/6ry0c1258720375.png")
> system("convert tmp/7vdve1258720375.ps tmp/7vdve1258720375.png")
> system("convert tmp/8gwej1258720375.ps tmp/8gwej1258720375.png")
> system("convert tmp/9rq1t1258720375.ps tmp/9rq1t1258720375.png")
> system("convert tmp/10y4pb1258720375.ps tmp/10y4pb1258720375.png")
>
>
> proc.time()
user system elapsed
2.377 1.524 2.810