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(8,11.1,8.1,10.9,7.7,10,7.5,9.2,7.6,9.2,7.8,9.5,7.8,9.6,7.8,9.5,7.5,9.1,7.5,8.9,7.1,9,7.5,10.1,7.5,10.3,7.6,10.2,7.7,9.6,7.7,9.2,7.9,9.3,8.1,9.4,8.2,9.4,8.2,9.2,8.2,9,7.9,9,7.3,9,6.9,9.8,6.6,10,6.7,9.8,6.9,9.3,7,9,7.1,9,7.2,9.1,7.1,9.1,6.9,9.1,7,9.2,6.8,8.8,6.4,8.3,6.7,8.4,6.6,8.1,6.4,7.7,6.3,7.9,6.2,7.9,6.5,8,6.8,7.9,6.8,7.6,6.4,7.1,6.1,6.8,5.8,6.5,6.1,6.9,7.2,8.2,7.3,8.7,6.9,8.3,6.1,7.9,5.8,7.5,6.2,7.8,7.1,8.3,7.7,8.4,7.9,8.2,7.7,7.7,7.4,7.2,7.5,7.3,8,8.1),dim=c(2,60),dimnames=list(c('X','Y'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('X','Y'),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 = '2'
> #'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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 11.1 8.0 1 0 0 0 0 0 0 0 0 0 0
2 10.9 8.1 0 1 0 0 0 0 0 0 0 0 0
3 10.0 7.7 0 0 1 0 0 0 0 0 0 0 0
4 9.2 7.5 0 0 0 1 0 0 0 0 0 0 0
5 9.2 7.6 0 0 0 0 1 0 0 0 0 0 0
6 9.5 7.8 0 0 0 0 0 1 0 0 0 0 0
7 9.6 7.8 0 0 0 0 0 0 1 0 0 0 0
8 9.5 7.8 0 0 0 0 0 0 0 1 0 0 0
9 9.1 7.5 0 0 0 0 0 0 0 0 1 0 0
10 8.9 7.5 0 0 0 0 0 0 0 0 0 1 0
11 9.0 7.1 0 0 0 0 0 0 0 0 0 0 1
12 10.1 7.5 0 0 0 0 0 0 0 0 0 0 0
13 10.3 7.5 1 0 0 0 0 0 0 0 0 0 0
14 10.2 7.6 0 1 0 0 0 0 0 0 0 0 0
15 9.6 7.7 0 0 1 0 0 0 0 0 0 0 0
16 9.2 7.7 0 0 0 1 0 0 0 0 0 0 0
17 9.3 7.9 0 0 0 0 1 0 0 0 0 0 0
18 9.4 8.1 0 0 0 0 0 1 0 0 0 0 0
19 9.4 8.2 0 0 0 0 0 0 1 0 0 0 0
20 9.2 8.2 0 0 0 0 0 0 0 1 0 0 0
21 9.0 8.2 0 0 0 0 0 0 0 0 1 0 0
22 9.0 7.9 0 0 0 0 0 0 0 0 0 1 0
23 9.0 7.3 0 0 0 0 0 0 0 0 0 0 1
24 9.8 6.9 0 0 0 0 0 0 0 0 0 0 0
25 10.0 6.6 1 0 0 0 0 0 0 0 0 0 0
26 9.8 6.7 0 1 0 0 0 0 0 0 0 0 0
27 9.3 6.9 0 0 1 0 0 0 0 0 0 0 0
28 9.0 7.0 0 0 0 1 0 0 0 0 0 0 0
29 9.0 7.1 0 0 0 0 1 0 0 0 0 0 0
30 9.1 7.2 0 0 0 0 0 1 0 0 0 0 0
31 9.1 7.1 0 0 0 0 0 0 1 0 0 0 0
32 9.1 6.9 0 0 0 0 0 0 0 1 0 0 0
33 9.2 7.0 0 0 0 0 0 0 0 0 1 0 0
34 8.8 6.8 0 0 0 0 0 0 0 0 0 1 0
35 8.3 6.4 0 0 0 0 0 0 0 0 0 0 1
36 8.4 6.7 0 0 0 0 0 0 0 0 0 0 0
37 8.1 6.6 1 0 0 0 0 0 0 0 0 0 0
38 7.7 6.4 0 1 0 0 0 0 0 0 0 0 0
39 7.9 6.3 0 0 1 0 0 0 0 0 0 0 0
40 7.9 6.2 0 0 0 1 0 0 0 0 0 0 0
41 8.0 6.5 0 0 0 0 1 0 0 0 0 0 0
42 7.9 6.8 0 0 0 0 0 1 0 0 0 0 0
43 7.6 6.8 0 0 0 0 0 0 1 0 0 0 0
44 7.1 6.4 0 0 0 0 0 0 0 1 0 0 0
45 6.8 6.1 0 0 0 0 0 0 0 0 1 0 0
46 6.5 5.8 0 0 0 0 0 0 0 0 0 1 0
47 6.9 6.1 0 0 0 0 0 0 0 0 0 0 1
48 8.2 7.2 0 0 0 0 0 0 0 0 0 0 0
49 8.7 7.3 1 0 0 0 0 0 0 0 0 0 0
50 8.3 6.9 0 1 0 0 0 0 0 0 0 0 0
51 7.9 6.1 0 0 1 0 0 0 0 0 0 0 0
52 7.5 5.8 0 0 0 1 0 0 0 0 0 0 0
53 7.8 6.2 0 0 0 0 1 0 0 0 0 0 0
54 8.3 7.1 0 0 0 0 0 1 0 0 0 0 0
55 8.4 7.7 0 0 0 0 0 0 1 0 0 0 0
56 8.2 7.9 0 0 0 0 0 0 0 1 0 0 0
57 7.7 7.7 0 0 0 0 0 0 0 0 1 0 0
58 7.2 7.4 0 0 0 0 0 0 0 0 0 1 0
59 7.3 7.5 0 0 0 0 0 0 0 0 0 0 1
60 8.1 8.0 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) X M1 M2 M3 M4
1.81401 0.97879 0.77873 0.57745 0.33321 0.05109
M5 M6 M7 M8 M9 M10
-0.06424 -0.21703 -0.35448 -0.47618 -0.59915 -0.66382
M11
-0.44806
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.54430 -0.39249 -0.02591 0.49317 1.23236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.81401 1.16410 1.558 0.126
X 0.97879 0.15382 6.363 7.58e-08 ***
M1 0.77873 0.46479 1.675 0.100
M2 0.57745 0.46507 1.242 0.221
M3 0.33321 0.46730 0.713 0.479
M4 0.05109 0.46917 0.109 0.914
M5 -0.06424 0.46572 -0.138 0.891
M6 -0.21703 0.46520 -0.467 0.643
M7 -0.35448 0.46642 -0.760 0.451
M8 -0.47618 0.46552 -1.023 0.312
M9 -0.59915 0.46474 -1.289 0.204
M10 -0.66382 0.46552 -1.426 0.160
M11 -0.44806 0.46836 -0.957 0.344
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.7348 on 47 degrees of freedom
Multiple R-squared: 0.5698, Adjusted R-squared: 0.46
F-statistic: 5.189 on 12 and 47 DF, p-value: 1.873e-05
> 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,] 2.231807e-02 4.463615e-02 0.977681927
[2,] 7.975156e-03 1.595031e-02 0.992024844
[3,] 4.951862e-03 9.903725e-03 0.995048138
[4,] 4.310934e-03 8.621868e-03 0.995689066
[5,] 3.220398e-03 6.440797e-03 0.996779602
[6,] 1.488596e-03 2.977192e-03 0.998511404
[7,] 4.736565e-04 9.473130e-04 0.999526343
[8,] 1.684731e-04 3.369462e-04 0.999831527
[9,] 9.483922e-05 1.896784e-04 0.999905161
[10,] 7.601792e-05 1.520358e-04 0.999923982
[11,] 6.691148e-05 1.338230e-04 0.999933089
[12,] 2.876500e-05 5.753000e-05 0.999971235
[13,] 1.127625e-05 2.255251e-05 0.999988724
[14,] 4.178655e-06 8.357310e-06 0.999995821
[15,] 1.671173e-06 3.342347e-06 0.999998329
[16,] 9.681473e-07 1.936295e-06 0.999999032
[17,] 2.416550e-06 4.833101e-06 0.999997583
[18,] 1.059736e-04 2.119472e-04 0.999894026
[19,] 7.155767e-03 1.431153e-02 0.992844233
[20,] 2.838112e-01 5.676223e-01 0.716188847
[21,] 9.378330e-01 1.243339e-01 0.062166959
[22,] 9.946583e-01 1.068348e-02 0.005341740
[23,] 9.983123e-01 3.375315e-03 0.001687657
[24,] 9.961536e-01 7.692756e-03 0.003846378
[25,] 9.909155e-01 1.816890e-02 0.009084451
[26,] 9.762772e-01 4.744558e-02 0.023722790
[27,] 9.552751e-01 8.944987e-02 0.044724935
[28,] 9.367943e-01 1.264114e-01 0.063205710
[29,] 9.300107e-01 1.399786e-01 0.069989304
> postscript(file="/var/www/html/rcomp/tmp/1hosn1258486329.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2wp991258486329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b67q1258486329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4boxd1258486329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cscb1258486329.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
0.676970547 0.580364656 0.316122020 -0.005999299 0.011455119 0.268485273
7 8 9 10 11 12
0.505939691 0.527636746 0.544242637 0.408909537 0.684666900 0.945091164
13 14 15 16 17 18
0.366363955 0.369758065 -0.083877980 -0.201756662 -0.182180926 -0.125150771
19 20 21 22 23 24
-0.085575035 -0.163877980 -0.240908135 0.117394811 0.488909537 1.232363254
25 26 27 28 29 30
0.947272090 0.850666199 0.399151473 0.283394109 0.300848527 0.455757363
31 32 33 34 35 36
0.691090463 1.008544881 1.133636045 0.994060309 0.669817672 0.028120617
37 38 39 40 41 42
-0.952727910 -0.955697756 -0.413576438 -0.033576438 -0.111879383 -0.352727910
43 44 45 46 47 48
-0.515273492 -0.502061711 -0.385455820 -0.327152875 -0.436546283 -0.661272791
49 50 51 52 53 54
-1.037878682 -0.845091164 -0.217819074 -0.042061711 -0.018243338 -0.246363955
55 56 57 58 59 60
-0.596181627 -0.870241935 -1.051514727 -1.193211781 -1.406847826 -1.544302244
> postscript(file="/var/www/html/rcomp/tmp/6drf01258486329.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 0.676970547 NA
1 0.580364656 0.676970547
2 0.316122020 0.580364656
3 -0.005999299 0.316122020
4 0.011455119 -0.005999299
5 0.268485273 0.011455119
6 0.505939691 0.268485273
7 0.527636746 0.505939691
8 0.544242637 0.527636746
9 0.408909537 0.544242637
10 0.684666900 0.408909537
11 0.945091164 0.684666900
12 0.366363955 0.945091164
13 0.369758065 0.366363955
14 -0.083877980 0.369758065
15 -0.201756662 -0.083877980
16 -0.182180926 -0.201756662
17 -0.125150771 -0.182180926
18 -0.085575035 -0.125150771
19 -0.163877980 -0.085575035
20 -0.240908135 -0.163877980
21 0.117394811 -0.240908135
22 0.488909537 0.117394811
23 1.232363254 0.488909537
24 0.947272090 1.232363254
25 0.850666199 0.947272090
26 0.399151473 0.850666199
27 0.283394109 0.399151473
28 0.300848527 0.283394109
29 0.455757363 0.300848527
30 0.691090463 0.455757363
31 1.008544881 0.691090463
32 1.133636045 1.008544881
33 0.994060309 1.133636045
34 0.669817672 0.994060309
35 0.028120617 0.669817672
36 -0.952727910 0.028120617
37 -0.955697756 -0.952727910
38 -0.413576438 -0.955697756
39 -0.033576438 -0.413576438
40 -0.111879383 -0.033576438
41 -0.352727910 -0.111879383
42 -0.515273492 -0.352727910
43 -0.502061711 -0.515273492
44 -0.385455820 -0.502061711
45 -0.327152875 -0.385455820
46 -0.436546283 -0.327152875
47 -0.661272791 -0.436546283
48 -1.037878682 -0.661272791
49 -0.845091164 -1.037878682
50 -0.217819074 -0.845091164
51 -0.042061711 -0.217819074
52 -0.018243338 -0.042061711
53 -0.246363955 -0.018243338
54 -0.596181627 -0.246363955
55 -0.870241935 -0.596181627
56 -1.051514727 -0.870241935
57 -1.193211781 -1.051514727
58 -1.406847826 -1.193211781
59 -1.544302244 -1.406847826
60 NA -1.544302244
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.580364656 0.676970547
[2,] 0.316122020 0.580364656
[3,] -0.005999299 0.316122020
[4,] 0.011455119 -0.005999299
[5,] 0.268485273 0.011455119
[6,] 0.505939691 0.268485273
[7,] 0.527636746 0.505939691
[8,] 0.544242637 0.527636746
[9,] 0.408909537 0.544242637
[10,] 0.684666900 0.408909537
[11,] 0.945091164 0.684666900
[12,] 0.366363955 0.945091164
[13,] 0.369758065 0.366363955
[14,] -0.083877980 0.369758065
[15,] -0.201756662 -0.083877980
[16,] -0.182180926 -0.201756662
[17,] -0.125150771 -0.182180926
[18,] -0.085575035 -0.125150771
[19,] -0.163877980 -0.085575035
[20,] -0.240908135 -0.163877980
[21,] 0.117394811 -0.240908135
[22,] 0.488909537 0.117394811
[23,] 1.232363254 0.488909537
[24,] 0.947272090 1.232363254
[25,] 0.850666199 0.947272090
[26,] 0.399151473 0.850666199
[27,] 0.283394109 0.399151473
[28,] 0.300848527 0.283394109
[29,] 0.455757363 0.300848527
[30,] 0.691090463 0.455757363
[31,] 1.008544881 0.691090463
[32,] 1.133636045 1.008544881
[33,] 0.994060309 1.133636045
[34,] 0.669817672 0.994060309
[35,] 0.028120617 0.669817672
[36,] -0.952727910 0.028120617
[37,] -0.955697756 -0.952727910
[38,] -0.413576438 -0.955697756
[39,] -0.033576438 -0.413576438
[40,] -0.111879383 -0.033576438
[41,] -0.352727910 -0.111879383
[42,] -0.515273492 -0.352727910
[43,] -0.502061711 -0.515273492
[44,] -0.385455820 -0.502061711
[45,] -0.327152875 -0.385455820
[46,] -0.436546283 -0.327152875
[47,] -0.661272791 -0.436546283
[48,] -1.037878682 -0.661272791
[49,] -0.845091164 -1.037878682
[50,] -0.217819074 -0.845091164
[51,] -0.042061711 -0.217819074
[52,] -0.018243338 -0.042061711
[53,] -0.246363955 -0.018243338
[54,] -0.596181627 -0.246363955
[55,] -0.870241935 -0.596181627
[56,] -1.051514727 -0.870241935
[57,] -1.193211781 -1.051514727
[58,] -1.406847826 -1.193211781
[59,] -1.544302244 -1.406847826
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.580364656 0.676970547
2 0.316122020 0.580364656
3 -0.005999299 0.316122020
4 0.011455119 -0.005999299
5 0.268485273 0.011455119
6 0.505939691 0.268485273
7 0.527636746 0.505939691
8 0.544242637 0.527636746
9 0.408909537 0.544242637
10 0.684666900 0.408909537
11 0.945091164 0.684666900
12 0.366363955 0.945091164
13 0.369758065 0.366363955
14 -0.083877980 0.369758065
15 -0.201756662 -0.083877980
16 -0.182180926 -0.201756662
17 -0.125150771 -0.182180926
18 -0.085575035 -0.125150771
19 -0.163877980 -0.085575035
20 -0.240908135 -0.163877980
21 0.117394811 -0.240908135
22 0.488909537 0.117394811
23 1.232363254 0.488909537
24 0.947272090 1.232363254
25 0.850666199 0.947272090
26 0.399151473 0.850666199
27 0.283394109 0.399151473
28 0.300848527 0.283394109
29 0.455757363 0.300848527
30 0.691090463 0.455757363
31 1.008544881 0.691090463
32 1.133636045 1.008544881
33 0.994060309 1.133636045
34 0.669817672 0.994060309
35 0.028120617 0.669817672
36 -0.952727910 0.028120617
37 -0.955697756 -0.952727910
38 -0.413576438 -0.955697756
39 -0.033576438 -0.413576438
40 -0.111879383 -0.033576438
41 -0.352727910 -0.111879383
42 -0.515273492 -0.352727910
43 -0.502061711 -0.515273492
44 -0.385455820 -0.502061711
45 -0.327152875 -0.385455820
46 -0.436546283 -0.327152875
47 -0.661272791 -0.436546283
48 -1.037878682 -0.661272791
49 -0.845091164 -1.037878682
50 -0.217819074 -0.845091164
51 -0.042061711 -0.217819074
52 -0.018243338 -0.042061711
53 -0.246363955 -0.018243338
54 -0.596181627 -0.246363955
55 -0.870241935 -0.596181627
56 -1.051514727 -0.870241935
57 -1.193211781 -1.051514727
58 -1.406847826 -1.193211781
59 -1.544302244 -1.406847826
> 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/7b7fk1258486329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8j7m21258486329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9kik21258486329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ic421258486329.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11sjgj1258486329.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/12k1e81258486329.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/13upci1258486329.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/142pkf1258486329.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/15htoy1258486329.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/163ugc1258486329.tab")
+ }
>
> system("convert tmp/1hosn1258486329.ps tmp/1hosn1258486329.png")
> system("convert tmp/2wp991258486329.ps tmp/2wp991258486329.png")
> system("convert tmp/3b67q1258486329.ps tmp/3b67q1258486329.png")
> system("convert tmp/4boxd1258486329.ps tmp/4boxd1258486329.png")
> system("convert tmp/5cscb1258486329.ps tmp/5cscb1258486329.png")
> system("convert tmp/6drf01258486329.ps tmp/6drf01258486329.png")
> system("convert tmp/7b7fk1258486329.ps tmp/7b7fk1258486329.png")
> system("convert tmp/8j7m21258486329.ps tmp/8j7m21258486329.png")
> system("convert tmp/9kik21258486329.ps tmp/9kik21258486329.png")
> system("convert tmp/10ic421258486329.ps tmp/10ic421258486329.png")
>
>
> proc.time()
user system elapsed
2.469 1.574 3.521