R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2.08,2.09,2.07,2.04,2.35,2.33,2.37,2.59,2.62,2.6,2.83,2.78,3.01,3.06,3.33,3.32,3.6,3.57,3.57,3.83,3.84,3.8,4.07,4.05,4.272,3.858,4.067,3.964,3.782,4.114,4.009,4.025,4.082,4.044,3.916,4.289,4.296,4.193,3.48,2.934,2.221,1.211,1.28,0.96,0.5,0.687,0.344,0.346,0.334,0.34,0.328,0.344,0.341,0.32,0.314,0.325,0.339,0.329,0.48,0.399,0.37),dim=c(1,61),dimnames=list(c('eonia'),1:61))
> y <- array(NA,dim=c(1,61),dimnames=list(c('eonia'),1:61))
> 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
eonia M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2.080 1 0 0 0 0 0 0 0 0 0 0 1
2 2.090 0 1 0 0 0 0 0 0 0 0 0 2
3 2.070 0 0 1 0 0 0 0 0 0 0 0 3
4 2.040 0 0 0 1 0 0 0 0 0 0 0 4
5 2.350 0 0 0 0 1 0 0 0 0 0 0 5
6 2.330 0 0 0 0 0 1 0 0 0 0 0 6
7 2.370 0 0 0 0 0 0 1 0 0 0 0 7
8 2.590 0 0 0 0 0 0 0 1 0 0 0 8
9 2.620 0 0 0 0 0 0 0 0 1 0 0 9
10 2.600 0 0 0 0 0 0 0 0 0 1 0 10
11 2.830 0 0 0 0 0 0 0 0 0 0 1 11
12 2.780 0 0 0 0 0 0 0 0 0 0 0 12
13 3.010 1 0 0 0 0 0 0 0 0 0 0 13
14 3.060 0 1 0 0 0 0 0 0 0 0 0 14
15 3.330 0 0 1 0 0 0 0 0 0 0 0 15
16 3.320 0 0 0 1 0 0 0 0 0 0 0 16
17 3.600 0 0 0 0 1 0 0 0 0 0 0 17
18 3.570 0 0 0 0 0 1 0 0 0 0 0 18
19 3.570 0 0 0 0 0 0 1 0 0 0 0 19
20 3.830 0 0 0 0 0 0 0 1 0 0 0 20
21 3.840 0 0 0 0 0 0 0 0 1 0 0 21
22 3.800 0 0 0 0 0 0 0 0 0 1 0 22
23 4.070 0 0 0 0 0 0 0 0 0 0 1 23
24 4.050 0 0 0 0 0 0 0 0 0 0 0 24
25 4.272 1 0 0 0 0 0 0 0 0 0 0 25
26 3.858 0 1 0 0 0 0 0 0 0 0 0 26
27 4.067 0 0 1 0 0 0 0 0 0 0 0 27
28 3.964 0 0 0 1 0 0 0 0 0 0 0 28
29 3.782 0 0 0 0 1 0 0 0 0 0 0 29
30 4.114 0 0 0 0 0 1 0 0 0 0 0 30
31 4.009 0 0 0 0 0 0 1 0 0 0 0 31
32 4.025 0 0 0 0 0 0 0 1 0 0 0 32
33 4.082 0 0 0 0 0 0 0 0 1 0 0 33
34 4.044 0 0 0 0 0 0 0 0 0 1 0 34
35 3.916 0 0 0 0 0 0 0 0 0 0 1 35
36 4.289 0 0 0 0 0 0 0 0 0 0 0 36
37 4.296 1 0 0 0 0 0 0 0 0 0 0 37
38 4.193 0 1 0 0 0 0 0 0 0 0 0 38
39 3.480 0 0 1 0 0 0 0 0 0 0 0 39
40 2.934 0 0 0 1 0 0 0 0 0 0 0 40
41 2.221 0 0 0 0 1 0 0 0 0 0 0 41
42 1.211 0 0 0 0 0 1 0 0 0 0 0 42
43 1.280 0 0 0 0 0 0 1 0 0 0 0 43
44 0.960 0 0 0 0 0 0 0 1 0 0 0 44
45 0.500 0 0 0 0 0 0 0 0 1 0 0 45
46 0.687 0 0 0 0 0 0 0 0 0 1 0 46
47 0.344 0 0 0 0 0 0 0 0 0 0 1 47
48 0.346 0 0 0 0 0 0 0 0 0 0 0 48
49 0.334 1 0 0 0 0 0 0 0 0 0 0 49
50 0.340 0 1 0 0 0 0 0 0 0 0 0 50
51 0.328 0 0 1 0 0 0 0 0 0 0 0 51
52 0.344 0 0 0 1 0 0 0 0 0 0 0 52
53 0.341 0 0 0 0 1 0 0 0 0 0 0 53
54 0.320 0 0 0 0 0 1 0 0 0 0 0 54
55 0.314 0 0 0 0 0 0 1 0 0 0 0 55
56 0.325 0 0 0 0 0 0 0 1 0 0 0 56
57 0.339 0 0 0 0 0 0 0 0 1 0 0 57
58 0.329 0 0 0 0 0 0 0 0 0 1 0 58
59 0.480 0 0 0 0 0 0 0 0 0 0 1 59
60 0.399 0 0 0 0 0 0 0 0 0 0 0 60
61 0.370 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
4.15593 -0.22679 -0.15991 -0.16358 -0.24865 -0.26072
M6 M7 M8 M9 M10 M11
-0.36099 -0.31186 -0.22493 -0.24519 -0.17986 -0.09433
t
-0.04953
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.8070 -0.9448 -0.5036 1.1476 2.1995
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.15593 0.70413 5.902 3.53e-07 ***
M1 -0.22679 0.82117 -0.276 0.784
M2 -0.15991 0.86191 -0.186 0.854
M3 -0.16358 0.86081 -0.190 0.850
M4 -0.24865 0.85982 -0.289 0.774
M5 -0.26072 0.85895 -0.304 0.763
M6 -0.36099 0.85820 -0.421 0.676
M7 -0.31186 0.85756 -0.364 0.718
M8 -0.22493 0.85703 -0.262 0.794
M9 -0.24519 0.85663 -0.286 0.776
M10 -0.17986 0.85633 -0.210 0.835
M11 -0.09433 0.85616 -0.110 0.913
t -0.04953 0.00999 -4.958 9.29e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.354 on 48 degrees of freedom
Multiple R-squared: 0.3443, Adjusted R-squared: 0.1804
F-statistic: 2.101 on 12 and 48 DF, p-value: 0.03482
> 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,] 4.067427e-03 8.134854e-03 0.9959325730
[2,] 6.810334e-04 1.362067e-03 0.9993189666
[3,] 1.019635e-04 2.039269e-04 0.9998980365
[4,] 1.348937e-05 2.697875e-05 0.9999865106
[5,] 1.803723e-06 3.607447e-06 0.9999981963
[6,] 2.153938e-07 4.307875e-07 0.9999997846
[7,] 2.556132e-08 5.112263e-08 0.9999999744
[8,] 2.997122e-09 5.994245e-09 0.9999999970
[9,] 4.349217e-10 8.698435e-10 0.9999999996
[10,] 5.523443e-11 1.104689e-10 0.9999999999
[11,] 4.438184e-09 8.876368e-09 0.9999999956
[12,] 3.769477e-09 7.538954e-09 0.9999999962
[13,] 3.609261e-09 7.218522e-09 0.9999999964
[14,] 1.439291e-07 2.878583e-07 0.9999998561
[15,] 6.760641e-08 1.352128e-07 0.9999999324
[16,] 4.488537e-08 8.977073e-08 0.9999999551
[17,] 7.510867e-08 1.502173e-07 0.9999999249
[18,] 7.852909e-08 1.570582e-07 0.9999999215
[19,] 6.655918e-08 1.331184e-07 0.9999999334
[20,] 2.590677e-07 5.181354e-07 0.9999997409
[21,] 3.141760e-07 6.283521e-07 0.9999996858
[22,] 1.250176e-06 2.500351e-06 0.9999987498
[23,] 3.972797e-05 7.945594e-05 0.9999602720
[24,] 9.635503e-03 1.927101e-02 0.9903644972
[25,] 3.698670e-01 7.397340e-01 0.6301330226
[26,] 9.464017e-01 1.071966e-01 0.0535983013
[27,] 9.876962e-01 2.460764e-02 0.0123038205
[28,] 9.974905e-01 5.019078e-03 0.0025095390
[29,] 9.990927e-01 1.814697e-03 0.0009073487
[30,] 9.959436e-01 8.112849e-03 0.0040564246
> postscript(file="/var/www/html/rcomp/tmp/1ltl31293385069.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ltl31293385069.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ltl31293385069.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4w3ko1293385069.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5w3ko1293385069.ps",horizontal=F,onefile=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 = 61
Frequency = 1
1 2 3 4 5 6
-1.79960784 -1.80695294 -1.77375294 -1.66915294 -1.29755294 -1.16775294
7 8 9 10 11 12
-1.12735294 -0.94475294 -0.84495294 -0.88075294 -0.68675294 -0.78155294
13 14 15 16 17 18
-0.27523137 -0.24257647 0.08062353 0.20522353 0.54682353 0.66662353
19 20 21 22 23 24
0.66702353 0.88962353 0.96942353 0.91362353 1.14762353 1.08282353
25 26 27 28 29 30
1.58114510 1.14980000 1.41200000 1.44360000 1.32320000 1.80500000
31 32 33 34 35 36
1.70040000 1.67900000 1.80580000 1.75200000 1.58800000 1.91620000
37 38 39 40 41 42
2.19952157 2.07917647 1.41937647 1.00797647 0.35657647 -0.50362353
43 44 45 46 47 48
-0.43422353 -0.79162353 -1.18182353 -1.01062353 -1.38962353 -1.43242353
49 50 51 52 53 54
-1.16810196 -1.17944706 -1.13824706 -0.98764706 -0.92904706 -0.80024706
55 56 57 58 59 60
-0.80584706 -0.83224706 -0.74844706 -0.77424706 -0.65924706 -0.78504706
61
-0.53772549
> postscript(file="/var/www/html/rcomp/tmp/6w3ko1293385069.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.79960784 NA
1 -1.80695294 -1.79960784
2 -1.77375294 -1.80695294
3 -1.66915294 -1.77375294
4 -1.29755294 -1.66915294
5 -1.16775294 -1.29755294
6 -1.12735294 -1.16775294
7 -0.94475294 -1.12735294
8 -0.84495294 -0.94475294
9 -0.88075294 -0.84495294
10 -0.68675294 -0.88075294
11 -0.78155294 -0.68675294
12 -0.27523137 -0.78155294
13 -0.24257647 -0.27523137
14 0.08062353 -0.24257647
15 0.20522353 0.08062353
16 0.54682353 0.20522353
17 0.66662353 0.54682353
18 0.66702353 0.66662353
19 0.88962353 0.66702353
20 0.96942353 0.88962353
21 0.91362353 0.96942353
22 1.14762353 0.91362353
23 1.08282353 1.14762353
24 1.58114510 1.08282353
25 1.14980000 1.58114510
26 1.41200000 1.14980000
27 1.44360000 1.41200000
28 1.32320000 1.44360000
29 1.80500000 1.32320000
30 1.70040000 1.80500000
31 1.67900000 1.70040000
32 1.80580000 1.67900000
33 1.75200000 1.80580000
34 1.58800000 1.75200000
35 1.91620000 1.58800000
36 2.19952157 1.91620000
37 2.07917647 2.19952157
38 1.41937647 2.07917647
39 1.00797647 1.41937647
40 0.35657647 1.00797647
41 -0.50362353 0.35657647
42 -0.43422353 -0.50362353
43 -0.79162353 -0.43422353
44 -1.18182353 -0.79162353
45 -1.01062353 -1.18182353
46 -1.38962353 -1.01062353
47 -1.43242353 -1.38962353
48 -1.16810196 -1.43242353
49 -1.17944706 -1.16810196
50 -1.13824706 -1.17944706
51 -0.98764706 -1.13824706
52 -0.92904706 -0.98764706
53 -0.80024706 -0.92904706
54 -0.80584706 -0.80024706
55 -0.83224706 -0.80584706
56 -0.74844706 -0.83224706
57 -0.77424706 -0.74844706
58 -0.65924706 -0.77424706
59 -0.78504706 -0.65924706
60 -0.53772549 -0.78504706
61 NA -0.53772549
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.80695294 -1.79960784
[2,] -1.77375294 -1.80695294
[3,] -1.66915294 -1.77375294
[4,] -1.29755294 -1.66915294
[5,] -1.16775294 -1.29755294
[6,] -1.12735294 -1.16775294
[7,] -0.94475294 -1.12735294
[8,] -0.84495294 -0.94475294
[9,] -0.88075294 -0.84495294
[10,] -0.68675294 -0.88075294
[11,] -0.78155294 -0.68675294
[12,] -0.27523137 -0.78155294
[13,] -0.24257647 -0.27523137
[14,] 0.08062353 -0.24257647
[15,] 0.20522353 0.08062353
[16,] 0.54682353 0.20522353
[17,] 0.66662353 0.54682353
[18,] 0.66702353 0.66662353
[19,] 0.88962353 0.66702353
[20,] 0.96942353 0.88962353
[21,] 0.91362353 0.96942353
[22,] 1.14762353 0.91362353
[23,] 1.08282353 1.14762353
[24,] 1.58114510 1.08282353
[25,] 1.14980000 1.58114510
[26,] 1.41200000 1.14980000
[27,] 1.44360000 1.41200000
[28,] 1.32320000 1.44360000
[29,] 1.80500000 1.32320000
[30,] 1.70040000 1.80500000
[31,] 1.67900000 1.70040000
[32,] 1.80580000 1.67900000
[33,] 1.75200000 1.80580000
[34,] 1.58800000 1.75200000
[35,] 1.91620000 1.58800000
[36,] 2.19952157 1.91620000
[37,] 2.07917647 2.19952157
[38,] 1.41937647 2.07917647
[39,] 1.00797647 1.41937647
[40,] 0.35657647 1.00797647
[41,] -0.50362353 0.35657647
[42,] -0.43422353 -0.50362353
[43,] -0.79162353 -0.43422353
[44,] -1.18182353 -0.79162353
[45,] -1.01062353 -1.18182353
[46,] -1.38962353 -1.01062353
[47,] -1.43242353 -1.38962353
[48,] -1.16810196 -1.43242353
[49,] -1.17944706 -1.16810196
[50,] -1.13824706 -1.17944706
[51,] -0.98764706 -1.13824706
[52,] -0.92904706 -0.98764706
[53,] -0.80024706 -0.92904706
[54,] -0.80584706 -0.80024706
[55,] -0.83224706 -0.80584706
[56,] -0.74844706 -0.83224706
[57,] -0.77424706 -0.74844706
[58,] -0.65924706 -0.77424706
[59,] -0.78504706 -0.65924706
[60,] -0.53772549 -0.78504706
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.80695294 -1.79960784
2 -1.77375294 -1.80695294
3 -1.66915294 -1.77375294
4 -1.29755294 -1.66915294
5 -1.16775294 -1.29755294
6 -1.12735294 -1.16775294
7 -0.94475294 -1.12735294
8 -0.84495294 -0.94475294
9 -0.88075294 -0.84495294
10 -0.68675294 -0.88075294
11 -0.78155294 -0.68675294
12 -0.27523137 -0.78155294
13 -0.24257647 -0.27523137
14 0.08062353 -0.24257647
15 0.20522353 0.08062353
16 0.54682353 0.20522353
17 0.66662353 0.54682353
18 0.66702353 0.66662353
19 0.88962353 0.66702353
20 0.96942353 0.88962353
21 0.91362353 0.96942353
22 1.14762353 0.91362353
23 1.08282353 1.14762353
24 1.58114510 1.08282353
25 1.14980000 1.58114510
26 1.41200000 1.14980000
27 1.44360000 1.41200000
28 1.32320000 1.44360000
29 1.80500000 1.32320000
30 1.70040000 1.80500000
31 1.67900000 1.70040000
32 1.80580000 1.67900000
33 1.75200000 1.80580000
34 1.58800000 1.75200000
35 1.91620000 1.58800000
36 2.19952157 1.91620000
37 2.07917647 2.19952157
38 1.41937647 2.07917647
39 1.00797647 1.41937647
40 0.35657647 1.00797647
41 -0.50362353 0.35657647
42 -0.43422353 -0.50362353
43 -0.79162353 -0.43422353
44 -1.18182353 -0.79162353
45 -1.01062353 -1.18182353
46 -1.38962353 -1.01062353
47 -1.43242353 -1.38962353
48 -1.16810196 -1.43242353
49 -1.17944706 -1.16810196
50 -1.13824706 -1.17944706
51 -0.98764706 -1.13824706
52 -0.92904706 -0.98764706
53 -0.80024706 -0.92904706
54 -0.80584706 -0.80024706
55 -0.83224706 -0.80584706
56 -0.74844706 -0.83224706
57 -0.77424706 -0.74844706
58 -0.65924706 -0.77424706
59 -0.78504706 -0.65924706
60 -0.53772549 -0.78504706
> 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/76u1q1293385069.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8zl1c1293385069.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9zl1c1293385069.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10acie1293385069.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11vdgk1293385069.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/12zdxq1293385069.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/13vnvz1293385069.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/14y6tn1293385069.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/15k6at1293385069.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/16npqz1293385069.tab")
+ }
> try(system("convert tmp/1ltl31293385069.ps tmp/1ltl31293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ltl31293385069.ps tmp/2ltl31293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ltl31293385069.ps tmp/3ltl31293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/4w3ko1293385069.ps tmp/4w3ko1293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/5w3ko1293385069.ps tmp/5w3ko1293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/6w3ko1293385069.ps tmp/6w3ko1293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/76u1q1293385069.ps tmp/76u1q1293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/8zl1c1293385069.ps tmp/8zl1c1293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zl1c1293385069.ps tmp/9zl1c1293385069.png",intern=TRUE))
character(0)
> try(system("convert tmp/10acie1293385069.ps tmp/10acie1293385069.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.432 1.603 5.628