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(264777
+ ,26.4
+ ,267366
+ ,267413
+ ,258863
+ ,29.4
+ ,264777
+ ,267366
+ ,254844
+ ,34.4
+ ,258863
+ ,264777
+ ,254868
+ ,24.4
+ ,254844
+ ,258863
+ ,277267
+ ,26.4
+ ,254868
+ ,254844
+ ,285351
+ ,25.4
+ ,277267
+ ,254868
+ ,286602
+ ,31.4
+ ,285351
+ ,277267
+ ,283042
+ ,27.4
+ ,286602
+ ,285351
+ ,276687
+ ,27.4
+ ,283042
+ ,286602
+ ,277915
+ ,29.4
+ ,276687
+ ,283042
+ ,277128
+ ,32.4
+ ,277915
+ ,276687
+ ,277103
+ ,26.4
+ ,277128
+ ,277915
+ ,275037
+ ,22.4
+ ,277103
+ ,277128
+ ,270150
+ ,19.4
+ ,275037
+ ,277103
+ ,267140
+ ,21.4
+ ,270150
+ ,275037
+ ,264993
+ ,23.4
+ ,267140
+ ,270150
+ ,287259
+ ,23.4
+ ,264993
+ ,267140
+ ,291186
+ ,25.4
+ ,287259
+ ,264993
+ ,292300
+ ,28.4
+ ,291186
+ ,287259
+ ,288186
+ ,27.4
+ ,292300
+ ,291186
+ ,281477
+ ,21.4
+ ,288186
+ ,292300
+ ,282656
+ ,17.4
+ ,281477
+ ,288186
+ ,280190
+ ,24.4
+ ,282656
+ ,281477
+ ,280408
+ ,26.4
+ ,280190
+ ,282656
+ ,276836
+ ,22.4
+ ,280408
+ ,280190
+ ,275216
+ ,14.4
+ ,276836
+ ,280408
+ ,274352
+ ,18.4
+ ,275216
+ ,276836
+ ,271311
+ ,25.4
+ ,274352
+ ,275216
+ ,289802
+ ,29.4
+ ,271311
+ ,274352
+ ,290726
+ ,26.4
+ ,289802
+ ,271311
+ ,292300
+ ,26.4
+ ,290726
+ ,289802
+ ,278506
+ ,20.4
+ ,292300
+ ,290726
+ ,269826
+ ,26.4
+ ,278506
+ ,292300
+ ,265861
+ ,29.4
+ ,269826
+ ,278506
+ ,269034
+ ,33.4
+ ,265861
+ ,269826
+ ,264176
+ ,32.4
+ ,269034
+ ,265861
+ ,255198
+ ,35.4
+ ,264176
+ ,269034
+ ,253353
+ ,34.4
+ ,255198
+ ,264176
+ ,246057
+ ,36.4
+ ,253353
+ ,255198
+ ,235372
+ ,32.4
+ ,246057
+ ,253353
+ ,258556
+ ,34.4
+ ,235372
+ ,246057
+ ,260993
+ ,31.4
+ ,258556
+ ,235372
+ ,254663
+ ,27.4
+ ,260993
+ ,258556
+ ,250643
+ ,27.4
+ ,254663
+ ,260993
+ ,243422
+ ,30.4
+ ,250643
+ ,254663
+ ,247105
+ ,32.4
+ ,243422
+ ,250643
+ ,248541
+ ,32.4
+ ,247105
+ ,243422
+ ,245039
+ ,27.4
+ ,248541
+ ,247105
+ ,237080
+ ,31.4
+ ,245039
+ ,248541
+ ,237085
+ ,29.4
+ ,237080
+ ,245039
+ ,225554
+ ,27.4
+ ,237085
+ ,237080
+ ,226839
+ ,25.4
+ ,225554
+ ,237085
+ ,247934
+ ,26.4
+ ,226839
+ ,225554
+ ,248333
+ ,23.4
+ ,247934
+ ,226839
+ ,246969
+ ,18.4
+ ,248333
+ ,247934
+ ,245098
+ ,22.4
+ ,246969
+ ,248333
+ ,246263
+ ,17.4
+ ,245098
+ ,246969
+ ,255765
+ ,17.4
+ ,246263
+ ,245098
+ ,264319
+ ,11.4
+ ,255765
+ ,246263
+ ,268347
+ ,9.4
+ ,264319
+ ,255765
+ ,273046
+ ,6.4
+ ,268347
+ ,264319
+ ,273963
+ ,0
+ ,273046
+ ,268347
+ ,267430
+ ,7.8
+ ,273963
+ ,273046
+ ,271993
+ ,7.9
+ ,267430
+ ,273963
+ ,292710
+ ,12
+ ,271993
+ ,267430
+ ,295881
+ ,16.9
+ ,292710
+ ,271993
+ ,293299
+ ,12.3
+ ,295881
+ ,292710)
+ ,dim=c(4
+ ,67)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2')
+ ,1:67))
> y <- array(NA,dim=c(4,67),dimnames=list(c('Y','X','Y1','Y2'),1:67))
> 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
Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 264777 26.4 267366 267413 1 0 0 0 0 0 0 0 0 0 0 1
2 258863 29.4 264777 267366 0 1 0 0 0 0 0 0 0 0 0 2
3 254844 34.4 258863 264777 0 0 1 0 0 0 0 0 0 0 0 3
4 254868 24.4 254844 258863 0 0 0 1 0 0 0 0 0 0 0 4
5 277267 26.4 254868 254844 0 0 0 0 1 0 0 0 0 0 0 5
6 285351 25.4 277267 254868 0 0 0 0 0 1 0 0 0 0 0 6
7 286602 31.4 285351 277267 0 0 0 0 0 0 1 0 0 0 0 7
8 283042 27.4 286602 285351 0 0 0 0 0 0 0 1 0 0 0 8
9 276687 27.4 283042 286602 0 0 0 0 0 0 0 0 1 0 0 9
10 277915 29.4 276687 283042 0 0 0 0 0 0 0 0 0 1 0 10
11 277128 32.4 277915 276687 0 0 0 0 0 0 0 0 0 0 1 11
12 277103 26.4 277128 277915 0 0 0 0 0 0 0 0 0 0 0 12
13 275037 22.4 277103 277128 1 0 0 0 0 0 0 0 0 0 0 13
14 270150 19.4 275037 277103 0 1 0 0 0 0 0 0 0 0 0 14
15 267140 21.4 270150 275037 0 0 1 0 0 0 0 0 0 0 0 15
16 264993 23.4 267140 270150 0 0 0 1 0 0 0 0 0 0 0 16
17 287259 23.4 264993 267140 0 0 0 0 1 0 0 0 0 0 0 17
18 291186 25.4 287259 264993 0 0 0 0 0 1 0 0 0 0 0 18
19 292300 28.4 291186 287259 0 0 0 0 0 0 1 0 0 0 0 19
20 288186 27.4 292300 291186 0 0 0 0 0 0 0 1 0 0 0 20
21 281477 21.4 288186 292300 0 0 0 0 0 0 0 0 1 0 0 21
22 282656 17.4 281477 288186 0 0 0 0 0 0 0 0 0 1 0 22
23 280190 24.4 282656 281477 0 0 0 0 0 0 0 0 0 0 1 23
24 280408 26.4 280190 282656 0 0 0 0 0 0 0 0 0 0 0 24
25 276836 22.4 280408 280190 1 0 0 0 0 0 0 0 0 0 0 25
26 275216 14.4 276836 280408 0 1 0 0 0 0 0 0 0 0 0 26
27 274352 18.4 275216 276836 0 0 1 0 0 0 0 0 0 0 0 27
28 271311 25.4 274352 275216 0 0 0 1 0 0 0 0 0 0 0 28
29 289802 29.4 271311 274352 0 0 0 0 1 0 0 0 0 0 0 29
30 290726 26.4 289802 271311 0 0 0 0 0 1 0 0 0 0 0 30
31 292300 26.4 290726 289802 0 0 0 0 0 0 1 0 0 0 0 31
32 278506 20.4 292300 290726 0 0 0 0 0 0 0 1 0 0 0 32
33 269826 26.4 278506 292300 0 0 0 0 0 0 0 0 1 0 0 33
34 265861 29.4 269826 278506 0 0 0 0 0 0 0 0 0 1 0 34
35 269034 33.4 265861 269826 0 0 0 0 0 0 0 0 0 0 1 35
36 264176 32.4 269034 265861 0 0 0 0 0 0 0 0 0 0 0 36
37 255198 35.4 264176 269034 1 0 0 0 0 0 0 0 0 0 0 37
38 253353 34.4 255198 264176 0 1 0 0 0 0 0 0 0 0 0 38
39 246057 36.4 253353 255198 0 0 1 0 0 0 0 0 0 0 0 39
40 235372 32.4 246057 253353 0 0 0 1 0 0 0 0 0 0 0 40
41 258556 34.4 235372 246057 0 0 0 0 1 0 0 0 0 0 0 41
42 260993 31.4 258556 235372 0 0 0 0 0 1 0 0 0 0 0 42
43 254663 27.4 260993 258556 0 0 0 0 0 0 1 0 0 0 0 43
44 250643 27.4 254663 260993 0 0 0 0 0 0 0 1 0 0 0 44
45 243422 30.4 250643 254663 0 0 0 0 0 0 0 0 1 0 0 45
46 247105 32.4 243422 250643 0 0 0 0 0 0 0 0 0 1 0 46
47 248541 32.4 247105 243422 0 0 0 0 0 0 0 0 0 0 1 47
48 245039 27.4 248541 247105 0 0 0 0 0 0 0 0 0 0 0 48
49 237080 31.4 245039 248541 1 0 0 0 0 0 0 0 0 0 0 49
50 237085 29.4 237080 245039 0 1 0 0 0 0 0 0 0 0 0 50
51 225554 27.4 237085 237080 0 0 1 0 0 0 0 0 0 0 0 51
52 226839 25.4 225554 237085 0 0 0 1 0 0 0 0 0 0 0 52
53 247934 26.4 226839 225554 0 0 0 0 1 0 0 0 0 0 0 53
54 248333 23.4 247934 226839 0 0 0 0 0 1 0 0 0 0 0 54
55 246969 18.4 248333 247934 0 0 0 0 0 0 1 0 0 0 0 55
56 245098 22.4 246969 248333 0 0 0 0 0 0 0 1 0 0 0 56
57 246263 17.4 245098 246969 0 0 0 0 0 0 0 0 1 0 0 57
58 255765 17.4 246263 245098 0 0 0 0 0 0 0 0 0 1 0 58
59 264319 11.4 255765 246263 0 0 0 0 0 0 0 0 0 0 1 59
60 268347 9.4 264319 255765 0 0 0 0 0 0 0 0 0 0 0 60
61 273046 6.4 268347 264319 1 0 0 0 0 0 0 0 0 0 0 61
62 273963 0.0 273046 268347 0 1 0 0 0 0 0 0 0 0 0 62
63 267430 7.8 273963 273046 0 0 1 0 0 0 0 0 0 0 0 63
64 271993 7.9 267430 273963 0 0 0 1 0 0 0 0 0 0 0 64
65 292710 12.0 271993 267430 0 0 0 0 1 0 0 0 0 0 0 65
66 295881 16.9 292710 271993 0 0 0 0 0 1 0 0 0 0 0 66
67 293299 12.3 295881 292710 0 0 0 0 0 0 1 0 0 0 0 67
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
3.870e+04 -3.574e+02 8.556e-01 4.014e-02 -3.281e+03 -3.517e+03
M3 M4 M5 M6 M7 M8
-5.821e+03 -2.989e+03 2.088e+04 5.719e+03 9.081e+02 -4.151e+03
M9 M10 M11 t
-5.064e+03 2.531e+03 3.397e+03 -7.823e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8024.78 -1474.74 -89.29 2219.48 4736.57
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.870e+04 1.210e+04 3.197 0.00238 **
X -3.574e+02 8.678e+01 -4.118 0.00014 ***
Y1 8.557e-01 1.501e-01 5.699 6.04e-07 ***
Y2 4.014e-02 1.417e-01 0.283 0.77816
M1 -3.281e+03 2.080e+03 -1.578 0.12085
M2 -3.517e+03 2.283e+03 -1.540 0.12963
M3 -5.821e+03 2.148e+03 -2.710 0.00914 **
M4 -2.989e+03 2.407e+03 -1.242 0.21991
M5 2.088e+04 2.149e+03 9.715 3.43e-13 ***
M6 5.719e+03 3.465e+03 1.650 0.10504
M7 9.081e+02 2.075e+03 0.438 0.66349
M8 -4.151e+03 2.156e+03 -1.926 0.05972 .
M9 -5.064e+03 2.368e+03 -2.139 0.03727 *
M10 2.531e+03 2.374e+03 1.066 0.29134
M11 3.397e+03 2.125e+03 1.598 0.11615
t -7.823e+01 3.239e+01 -2.415 0.01934 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3346 on 51 degrees of freedom
Multiple R-squared: 0.9723, Adjusted R-squared: 0.9641
F-statistic: 119.2 on 15 and 51 DF, p-value: < 2.2e-16
> 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,] 2.680486e-02 5.360972e-02 0.97319514
[2,] 8.880504e-03 1.776101e-02 0.99111950
[3,] 2.673903e-03 5.347805e-03 0.99732610
[4,] 1.230030e-03 2.460060e-03 0.99876997
[5,] 6.052314e-04 1.210463e-03 0.99939477
[6,] 3.006987e-04 6.013973e-04 0.99969930
[7,] 8.261037e-05 1.652207e-04 0.99991739
[8,] 2.783313e-04 5.566626e-04 0.99972167
[9,] 3.143893e-04 6.287786e-04 0.99968561
[10,] 1.143581e-04 2.287162e-04 0.99988564
[11,] 3.881316e-05 7.762631e-05 0.99996119
[12,] 4.654258e-05 9.308516e-05 0.99995346
[13,] 1.750839e-04 3.501678e-04 0.99982492
[14,] 5.309877e-02 1.061975e-01 0.94690123
[15,] 3.939411e-02 7.878821e-02 0.96060589
[16,] 7.079976e-02 1.415995e-01 0.92920024
[17,] 1.859827e-01 3.719653e-01 0.81401735
[18,] 1.332252e-01 2.664503e-01 0.86677483
[19,] 1.021690e-01 2.043380e-01 0.89783098
[20,] 1.392513e-01 2.785025e-01 0.86074873
[21,] 3.803403e-01 7.606806e-01 0.61965970
[22,] 5.675329e-01 8.649343e-01 0.43246714
[23,] 5.948199e-01 8.103601e-01 0.40518006
[24,] 7.614314e-01 4.771373e-01 0.23856863
[25,] 8.463377e-01 3.073245e-01 0.15366225
[26,] 8.240865e-01 3.518269e-01 0.17591345
[27,] 7.780596e-01 4.438809e-01 0.22194043
[28,] 7.241074e-01 5.517852e-01 0.27589262
[29,] 7.836873e-01 4.326254e-01 0.21631269
[30,] 9.550770e-01 8.984596e-02 0.04492298
> postscript(file="/var/www/html/rcomp/tmp/126b71258715431.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2j9xb1258715431.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3057f1258715431.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45ju81258715431.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5aa161258715431.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 = 67
Frequency = 1
1 2 3 4 5 6 7
-634.5424 -2945.3504 2369.2654 -257.8413 -790.9240 3005.3160 3473.1827
8 9 10 11 12 13 14
2226.5688 -141.6689 -135.2452 -1433.0220 496.5674 413.3123 -3463.1293
15 16 17 18 19 20 21
888.6200 -525.5653 -89.2881 823.0210 3644.0291 3199.6253 -1187.3314
22 23 24 25 26 27 28
-3049.0176 -4540.1601 1930.0239 200.2419 -917.2212 3560.3283 1071.6579
29 30 31 32 33 34 35
-158.7593 -770.3684 4159.5549 -8024.7778 -1830.0388 -4259.0649 3297.2357
36 37 38 39 40 41 42
-999.2177 -1516.4633 4472.0766 2212.3961 -6338.8381 3207.7465 400.4166
43 44 45 46 47 48 49
-5486.1308 950.0879 -514.1252 2706.7537 494.0007 -2696.6839 -2928.0892
50 51 52 53 54 55 56
3626.7414 -5921.2876 1761.6343 -1210.2606 -4748.6222 -4198.8817 1648.4959
57 58 59 60 61 62 63
3673.1643 4736.5739 2181.9457 1269.3103 4465.5406 -773.1171 -3109.3222
64 65 66 67
4288.9526 -958.5146 1290.2370 -1591.7542
> postscript(file="/var/www/html/rcomp/tmp/6g8ar1258715431.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 = 67
Frequency = 1
lag(myerror, k = 1) myerror
0 -634.5424 NA
1 -2945.3504 -634.5424
2 2369.2654 -2945.3504
3 -257.8413 2369.2654
4 -790.9240 -257.8413
5 3005.3160 -790.9240
6 3473.1827 3005.3160
7 2226.5688 3473.1827
8 -141.6689 2226.5688
9 -135.2452 -141.6689
10 -1433.0220 -135.2452
11 496.5674 -1433.0220
12 413.3123 496.5674
13 -3463.1293 413.3123
14 888.6200 -3463.1293
15 -525.5653 888.6200
16 -89.2881 -525.5653
17 823.0210 -89.2881
18 3644.0291 823.0210
19 3199.6253 3644.0291
20 -1187.3314 3199.6253
21 -3049.0176 -1187.3314
22 -4540.1601 -3049.0176
23 1930.0239 -4540.1601
24 200.2419 1930.0239
25 -917.2212 200.2419
26 3560.3283 -917.2212
27 1071.6579 3560.3283
28 -158.7593 1071.6579
29 -770.3684 -158.7593
30 4159.5549 -770.3684
31 -8024.7778 4159.5549
32 -1830.0388 -8024.7778
33 -4259.0649 -1830.0388
34 3297.2357 -4259.0649
35 -999.2177 3297.2357
36 -1516.4633 -999.2177
37 4472.0766 -1516.4633
38 2212.3961 4472.0766
39 -6338.8381 2212.3961
40 3207.7465 -6338.8381
41 400.4166 3207.7465
42 -5486.1308 400.4166
43 950.0879 -5486.1308
44 -514.1252 950.0879
45 2706.7537 -514.1252
46 494.0007 2706.7537
47 -2696.6839 494.0007
48 -2928.0892 -2696.6839
49 3626.7414 -2928.0892
50 -5921.2876 3626.7414
51 1761.6343 -5921.2876
52 -1210.2606 1761.6343
53 -4748.6222 -1210.2606
54 -4198.8817 -4748.6222
55 1648.4959 -4198.8817
56 3673.1643 1648.4959
57 4736.5739 3673.1643
58 2181.9457 4736.5739
59 1269.3103 2181.9457
60 4465.5406 1269.3103
61 -773.1171 4465.5406
62 -3109.3222 -773.1171
63 4288.9526 -3109.3222
64 -958.5146 4288.9526
65 1290.2370 -958.5146
66 -1591.7542 1290.2370
67 NA -1591.7542
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2945.3504 -634.5424
[2,] 2369.2654 -2945.3504
[3,] -257.8413 2369.2654
[4,] -790.9240 -257.8413
[5,] 3005.3160 -790.9240
[6,] 3473.1827 3005.3160
[7,] 2226.5688 3473.1827
[8,] -141.6689 2226.5688
[9,] -135.2452 -141.6689
[10,] -1433.0220 -135.2452
[11,] 496.5674 -1433.0220
[12,] 413.3123 496.5674
[13,] -3463.1293 413.3123
[14,] 888.6200 -3463.1293
[15,] -525.5653 888.6200
[16,] -89.2881 -525.5653
[17,] 823.0210 -89.2881
[18,] 3644.0291 823.0210
[19,] 3199.6253 3644.0291
[20,] -1187.3314 3199.6253
[21,] -3049.0176 -1187.3314
[22,] -4540.1601 -3049.0176
[23,] 1930.0239 -4540.1601
[24,] 200.2419 1930.0239
[25,] -917.2212 200.2419
[26,] 3560.3283 -917.2212
[27,] 1071.6579 3560.3283
[28,] -158.7593 1071.6579
[29,] -770.3684 -158.7593
[30,] 4159.5549 -770.3684
[31,] -8024.7778 4159.5549
[32,] -1830.0388 -8024.7778
[33,] -4259.0649 -1830.0388
[34,] 3297.2357 -4259.0649
[35,] -999.2177 3297.2357
[36,] -1516.4633 -999.2177
[37,] 4472.0766 -1516.4633
[38,] 2212.3961 4472.0766
[39,] -6338.8381 2212.3961
[40,] 3207.7465 -6338.8381
[41,] 400.4166 3207.7465
[42,] -5486.1308 400.4166
[43,] 950.0879 -5486.1308
[44,] -514.1252 950.0879
[45,] 2706.7537 -514.1252
[46,] 494.0007 2706.7537
[47,] -2696.6839 494.0007
[48,] -2928.0892 -2696.6839
[49,] 3626.7414 -2928.0892
[50,] -5921.2876 3626.7414
[51,] 1761.6343 -5921.2876
[52,] -1210.2606 1761.6343
[53,] -4748.6222 -1210.2606
[54,] -4198.8817 -4748.6222
[55,] 1648.4959 -4198.8817
[56,] 3673.1643 1648.4959
[57,] 4736.5739 3673.1643
[58,] 2181.9457 4736.5739
[59,] 1269.3103 2181.9457
[60,] 4465.5406 1269.3103
[61,] -773.1171 4465.5406
[62,] -3109.3222 -773.1171
[63,] 4288.9526 -3109.3222
[64,] -958.5146 4288.9526
[65,] 1290.2370 -958.5146
[66,] -1591.7542 1290.2370
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2945.3504 -634.5424
2 2369.2654 -2945.3504
3 -257.8413 2369.2654
4 -790.9240 -257.8413
5 3005.3160 -790.9240
6 3473.1827 3005.3160
7 2226.5688 3473.1827
8 -141.6689 2226.5688
9 -135.2452 -141.6689
10 -1433.0220 -135.2452
11 496.5674 -1433.0220
12 413.3123 496.5674
13 -3463.1293 413.3123
14 888.6200 -3463.1293
15 -525.5653 888.6200
16 -89.2881 -525.5653
17 823.0210 -89.2881
18 3644.0291 823.0210
19 3199.6253 3644.0291
20 -1187.3314 3199.6253
21 -3049.0176 -1187.3314
22 -4540.1601 -3049.0176
23 1930.0239 -4540.1601
24 200.2419 1930.0239
25 -917.2212 200.2419
26 3560.3283 -917.2212
27 1071.6579 3560.3283
28 -158.7593 1071.6579
29 -770.3684 -158.7593
30 4159.5549 -770.3684
31 -8024.7778 4159.5549
32 -1830.0388 -8024.7778
33 -4259.0649 -1830.0388
34 3297.2357 -4259.0649
35 -999.2177 3297.2357
36 -1516.4633 -999.2177
37 4472.0766 -1516.4633
38 2212.3961 4472.0766
39 -6338.8381 2212.3961
40 3207.7465 -6338.8381
41 400.4166 3207.7465
42 -5486.1308 400.4166
43 950.0879 -5486.1308
44 -514.1252 950.0879
45 2706.7537 -514.1252
46 494.0007 2706.7537
47 -2696.6839 494.0007
48 -2928.0892 -2696.6839
49 3626.7414 -2928.0892
50 -5921.2876 3626.7414
51 1761.6343 -5921.2876
52 -1210.2606 1761.6343
53 -4748.6222 -1210.2606
54 -4198.8817 -4748.6222
55 1648.4959 -4198.8817
56 3673.1643 1648.4959
57 4736.5739 3673.1643
58 2181.9457 4736.5739
59 1269.3103 2181.9457
60 4465.5406 1269.3103
61 -773.1171 4465.5406
62 -3109.3222 -773.1171
63 4288.9526 -3109.3222
64 -958.5146 4288.9526
65 1290.2370 -958.5146
66 -1591.7542 1290.2370
> 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/7q1uk1258715431.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8l5p81258715431.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/91coe1258715431.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10yyks1258715431.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11uzga1258715431.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/123bkv1258715431.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/130ru51258715431.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/14oufc1258715431.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/15yf1i1258715431.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/16mlmu1258715431.tab")
+ }
>
> system("convert tmp/126b71258715431.ps tmp/126b71258715431.png")
> system("convert tmp/2j9xb1258715431.ps tmp/2j9xb1258715431.png")
> system("convert tmp/3057f1258715431.ps tmp/3057f1258715431.png")
> system("convert tmp/45ju81258715431.ps tmp/45ju81258715431.png")
> system("convert tmp/5aa161258715431.ps tmp/5aa161258715431.png")
> system("convert tmp/6g8ar1258715431.ps tmp/6g8ar1258715431.png")
> system("convert tmp/7q1uk1258715431.ps tmp/7q1uk1258715431.png")
> system("convert tmp/8l5p81258715431.ps tmp/8l5p81258715431.png")
> system("convert tmp/91coe1258715431.ps tmp/91coe1258715431.png")
> system("convert tmp/10yyks1258715431.ps tmp/10yyks1258715431.png")
>
>
> proc.time()
user system elapsed
2.485 1.560 2.895