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.
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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(902.2,0,891.9,0,874,0,930.9,0,944.2,0,935.9,0,937.1,0,885.1,0,892.4,0,987.3,0,946.3,0,799.6,0,875.4,0,846.2,0,880.6,0,885.7,0,868.9,0,882.5,0,789.6,0,773.3,0,804.3,0,817.8,0,836.7,0,721.8,0,760.8,0,841.4,0,1045.6,0,949.2,1,850.1,1,957.4,0,851.8,0,913.9,0,888,0,973.8,0,927.6,1,833,1,879.5,1,797.3,1,834.5,1,735.1,1,835,1,892.8,1,697.2,1,821.1,1,732.7,1,797.6,1,866.3,1,826.3,1,778.6,1,779.2,1,951,1,692.3,1,841.4,1,857.3,1,760.7,1,841.2,0,810.3,0,1007.4,1,931.3,0,931.2,0),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 902.2 0 1 0 0 0 0 0 0 0 0 0 0 1
2 891.9 0 0 1 0 0 0 0 0 0 0 0 0 2
3 874.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 930.9 0 0 0 0 1 0 0 0 0 0 0 0 4
5 944.2 0 0 0 0 0 1 0 0 0 0 0 0 5
6 935.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 937.1 0 0 0 0 0 0 0 1 0 0 0 0 7
8 885.1 0 0 0 0 0 0 0 0 1 0 0 0 8
9 892.4 0 0 0 0 0 0 0 0 0 1 0 0 9
10 987.3 0 0 0 0 0 0 0 0 0 0 1 0 10
11 946.3 0 0 0 0 0 0 0 0 0 0 0 1 11
12 799.6 0 0 0 0 0 0 0 0 0 0 0 0 12
13 875.4 0 1 0 0 0 0 0 0 0 0 0 0 13
14 846.2 0 0 1 0 0 0 0 0 0 0 0 0 14
15 880.6 0 0 0 1 0 0 0 0 0 0 0 0 15
16 885.7 0 0 0 0 1 0 0 0 0 0 0 0 16
17 868.9 0 0 0 0 0 1 0 0 0 0 0 0 17
18 882.5 0 0 0 0 0 0 1 0 0 0 0 0 18
19 789.6 0 0 0 0 0 0 0 1 0 0 0 0 19
20 773.3 0 0 0 0 0 0 0 0 1 0 0 0 20
21 804.3 0 0 0 0 0 0 0 0 0 1 0 0 21
22 817.8 0 0 0 0 0 0 0 0 0 0 1 0 22
23 836.7 0 0 0 0 0 0 0 0 0 0 0 1 23
24 721.8 0 0 0 0 0 0 0 0 0 0 0 0 24
25 760.8 0 1 0 0 0 0 0 0 0 0 0 0 25
26 841.4 0 0 1 0 0 0 0 0 0 0 0 0 26
27 1045.6 0 0 0 1 0 0 0 0 0 0 0 0 27
28 949.2 1 0 0 0 1 0 0 0 0 0 0 0 28
29 850.1 1 0 0 0 0 1 0 0 0 0 0 0 29
30 957.4 0 0 0 0 0 0 1 0 0 0 0 0 30
31 851.8 0 0 0 0 0 0 0 1 0 0 0 0 31
32 913.9 0 0 0 0 0 0 0 0 1 0 0 0 32
33 888.0 0 0 0 0 0 0 0 0 0 1 0 0 33
34 973.8 0 0 0 0 0 0 0 0 0 0 1 0 34
35 927.6 1 0 0 0 0 0 0 0 0 0 0 1 35
36 833.0 1 0 0 0 0 0 0 0 0 0 0 0 36
37 879.5 1 1 0 0 0 0 0 0 0 0 0 0 37
38 797.3 1 0 1 0 0 0 0 0 0 0 0 0 38
39 834.5 1 0 0 1 0 0 0 0 0 0 0 0 39
40 735.1 1 0 0 0 1 0 0 0 0 0 0 0 40
41 835.0 1 0 0 0 0 1 0 0 0 0 0 0 41
42 892.8 1 0 0 0 0 0 1 0 0 0 0 0 42
43 697.2 1 0 0 0 0 0 0 1 0 0 0 0 43
44 821.1 1 0 0 0 0 0 0 0 1 0 0 0 44
45 732.7 1 0 0 0 0 0 0 0 0 1 0 0 45
46 797.6 1 0 0 0 0 0 0 0 0 0 1 0 46
47 866.3 1 0 0 0 0 0 0 0 0 0 0 1 47
48 826.3 1 0 0 0 0 0 0 0 0 0 0 0 48
49 778.6 1 1 0 0 0 0 0 0 0 0 0 0 49
50 779.2 1 0 1 0 0 0 0 0 0 0 0 0 50
51 951.0 1 0 0 1 0 0 0 0 0 0 0 0 51
52 692.3 1 0 0 0 1 0 0 0 0 0 0 0 52
53 841.4 1 0 0 0 0 1 0 0 0 0 0 0 53
54 857.3 1 0 0 0 0 0 1 0 0 0 0 0 54
55 760.7 1 0 0 0 0 0 0 1 0 0 0 0 55
56 841.2 0 0 0 0 0 0 0 0 1 0 0 0 56
57 810.3 0 0 0 0 0 0 0 0 0 1 0 0 57
58 1007.4 1 0 0 0 0 0 0 0 0 0 1 0 58
59 931.3 0 0 0 0 0 0 0 0 0 0 0 1 59
60 931.2 0 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
852.7012 -45.3792 13.2015 5.4396 91.7176 22.6315
M5 M6 M7 M8 M9 M10
52.2496 80.7717 -16.7902 14.1120 -6.9300 93.7239
M11 t
78.9220 -0.3380
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-122.78821 -41.13821 0.03821 38.10274 128.71170
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 852.7012 36.8292 23.153 <2e-16 ***
X -45.3792 25.8278 -1.757 0.0856 .
M1 13.2015 44.1489 0.299 0.7663
M2 5.4396 44.0239 0.124 0.9022
M3 91.7176 43.9105 2.089 0.0423 *
M4 22.6315 44.5802 0.508 0.6141
M5 52.2496 44.4334 1.176 0.2457
M6 80.7717 43.6406 1.851 0.0706 .
M7 -16.7902 43.5744 -0.385 0.7018
M8 14.1120 43.5882 0.324 0.7476
M9 -6.9300 43.6056 -0.159 0.8744
M10 93.7239 43.4477 2.157 0.0363 *
M11 78.9220 43.4296 1.817 0.0757 .
t -0.3380 0.7246 -0.467 0.6430
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 68.66 on 46 degrees of freedom
Multiple R-squared: 0.3717, Adjusted R-squared: 0.1941
F-statistic: 2.093 on 13 and 46 DF, p-value: 0.03332
> 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,] 0.04631860 0.09263720 0.9536814
[2,] 0.01352542 0.02705084 0.9864746
[3,] 0.05602325 0.11204651 0.9439767
[4,] 0.03859396 0.07718793 0.9614060
[5,] 0.01747799 0.03495597 0.9825220
[6,] 0.03724778 0.07449555 0.9627522
[7,] 0.02463904 0.04927808 0.9753610
[8,] 0.02884401 0.05768801 0.9711560
[9,] 0.02980524 0.05961047 0.9701948
[10,] 0.04988400 0.09976799 0.9501160
[11,] 0.61322830 0.77354341 0.3867717
[12,] 0.83906460 0.32187079 0.1609354
[13,] 0.79733272 0.40533456 0.2026673
[14,] 0.78730055 0.42539890 0.2126995
[15,] 0.72140857 0.55718287 0.2785914
[16,] 0.73247199 0.53505602 0.2675280
[17,] 0.73701972 0.52596057 0.2629803
[18,] 0.69942439 0.60115122 0.3005756
[19,] 0.67488165 0.65023671 0.3251184
[20,] 0.58703622 0.82592756 0.4129638
[21,] 0.66333348 0.67333303 0.3366665
[22,] 0.62802995 0.74394010 0.3719700
[23,] 0.60800919 0.78398162 0.3919908
[24,] 0.68678693 0.62642614 0.3132131
[25,] 0.60839070 0.78321860 0.3916093
[26,] 0.80777125 0.38445751 0.1922288
[27,] 0.94091131 0.11817739 0.0590887
> postscript(file="/var/www/html/rcomp/tmp/1keta1258573502.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2abe51258573502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lox71258573502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/47d8s1258573502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5z1uh1258573502.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
36.6352830 34.4352830 -69.4047170 56.9194340 40.9394340 4.4552830
7 8 9 10 11 12
103.5552830 20.9911321 49.6711321 44.2552830 18.3952830 -49.0447170
13 14 15 16 17 18
13.8917925 -7.2082075 -58.7482075 15.7759434 -30.3040566 -44.8882075
19 20 21 22 23 24
-39.8882075 -86.7523585 -34.3723585 -121.1882075 -87.1482075 -122.7882075
25 26 27 28 29 30
-96.6516981 -7.9516981 110.3083019 128.7116981 0.3316981 34.0683019
31 32 33 34 35 36
26.3683019 57.9041509 53.3841509 38.8683019 53.1875472 37.8475472
37 38 39 40 41 42
71.4840566 -2.6159434 -51.3559434 -81.3317925 -10.7117925 18.9040566
43 44 45 46 47 48
-78.7959434 14.5399057 -52.4800943 -87.8959434 -4.0559434 35.2040566
49 50 51 52 53 54
-25.3594340 -16.6594340 69.2005660 -120.0752830 -0.2552830 -12.5394340
55 56 57 58 59 60
-11.2394340 -6.6828302 -16.2028302 125.9605660 19.6213208 98.7813208
> postscript(file="/var/www/html/rcomp/tmp/63w3l1258573502.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 36.6352830 NA
1 34.4352830 36.6352830
2 -69.4047170 34.4352830
3 56.9194340 -69.4047170
4 40.9394340 56.9194340
5 4.4552830 40.9394340
6 103.5552830 4.4552830
7 20.9911321 103.5552830
8 49.6711321 20.9911321
9 44.2552830 49.6711321
10 18.3952830 44.2552830
11 -49.0447170 18.3952830
12 13.8917925 -49.0447170
13 -7.2082075 13.8917925
14 -58.7482075 -7.2082075
15 15.7759434 -58.7482075
16 -30.3040566 15.7759434
17 -44.8882075 -30.3040566
18 -39.8882075 -44.8882075
19 -86.7523585 -39.8882075
20 -34.3723585 -86.7523585
21 -121.1882075 -34.3723585
22 -87.1482075 -121.1882075
23 -122.7882075 -87.1482075
24 -96.6516981 -122.7882075
25 -7.9516981 -96.6516981
26 110.3083019 -7.9516981
27 128.7116981 110.3083019
28 0.3316981 128.7116981
29 34.0683019 0.3316981
30 26.3683019 34.0683019
31 57.9041509 26.3683019
32 53.3841509 57.9041509
33 38.8683019 53.3841509
34 53.1875472 38.8683019
35 37.8475472 53.1875472
36 71.4840566 37.8475472
37 -2.6159434 71.4840566
38 -51.3559434 -2.6159434
39 -81.3317925 -51.3559434
40 -10.7117925 -81.3317925
41 18.9040566 -10.7117925
42 -78.7959434 18.9040566
43 14.5399057 -78.7959434
44 -52.4800943 14.5399057
45 -87.8959434 -52.4800943
46 -4.0559434 -87.8959434
47 35.2040566 -4.0559434
48 -25.3594340 35.2040566
49 -16.6594340 -25.3594340
50 69.2005660 -16.6594340
51 -120.0752830 69.2005660
52 -0.2552830 -120.0752830
53 -12.5394340 -0.2552830
54 -11.2394340 -12.5394340
55 -6.6828302 -11.2394340
56 -16.2028302 -6.6828302
57 125.9605660 -16.2028302
58 19.6213208 125.9605660
59 98.7813208 19.6213208
60 NA 98.7813208
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 34.4352830 36.6352830
[2,] -69.4047170 34.4352830
[3,] 56.9194340 -69.4047170
[4,] 40.9394340 56.9194340
[5,] 4.4552830 40.9394340
[6,] 103.5552830 4.4552830
[7,] 20.9911321 103.5552830
[8,] 49.6711321 20.9911321
[9,] 44.2552830 49.6711321
[10,] 18.3952830 44.2552830
[11,] -49.0447170 18.3952830
[12,] 13.8917925 -49.0447170
[13,] -7.2082075 13.8917925
[14,] -58.7482075 -7.2082075
[15,] 15.7759434 -58.7482075
[16,] -30.3040566 15.7759434
[17,] -44.8882075 -30.3040566
[18,] -39.8882075 -44.8882075
[19,] -86.7523585 -39.8882075
[20,] -34.3723585 -86.7523585
[21,] -121.1882075 -34.3723585
[22,] -87.1482075 -121.1882075
[23,] -122.7882075 -87.1482075
[24,] -96.6516981 -122.7882075
[25,] -7.9516981 -96.6516981
[26,] 110.3083019 -7.9516981
[27,] 128.7116981 110.3083019
[28,] 0.3316981 128.7116981
[29,] 34.0683019 0.3316981
[30,] 26.3683019 34.0683019
[31,] 57.9041509 26.3683019
[32,] 53.3841509 57.9041509
[33,] 38.8683019 53.3841509
[34,] 53.1875472 38.8683019
[35,] 37.8475472 53.1875472
[36,] 71.4840566 37.8475472
[37,] -2.6159434 71.4840566
[38,] -51.3559434 -2.6159434
[39,] -81.3317925 -51.3559434
[40,] -10.7117925 -81.3317925
[41,] 18.9040566 -10.7117925
[42,] -78.7959434 18.9040566
[43,] 14.5399057 -78.7959434
[44,] -52.4800943 14.5399057
[45,] -87.8959434 -52.4800943
[46,] -4.0559434 -87.8959434
[47,] 35.2040566 -4.0559434
[48,] -25.3594340 35.2040566
[49,] -16.6594340 -25.3594340
[50,] 69.2005660 -16.6594340
[51,] -120.0752830 69.2005660
[52,] -0.2552830 -120.0752830
[53,] -12.5394340 -0.2552830
[54,] -11.2394340 -12.5394340
[55,] -6.6828302 -11.2394340
[56,] -16.2028302 -6.6828302
[57,] 125.9605660 -16.2028302
[58,] 19.6213208 125.9605660
[59,] 98.7813208 19.6213208
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 34.4352830 36.6352830
2 -69.4047170 34.4352830
3 56.9194340 -69.4047170
4 40.9394340 56.9194340
5 4.4552830 40.9394340
6 103.5552830 4.4552830
7 20.9911321 103.5552830
8 49.6711321 20.9911321
9 44.2552830 49.6711321
10 18.3952830 44.2552830
11 -49.0447170 18.3952830
12 13.8917925 -49.0447170
13 -7.2082075 13.8917925
14 -58.7482075 -7.2082075
15 15.7759434 -58.7482075
16 -30.3040566 15.7759434
17 -44.8882075 -30.3040566
18 -39.8882075 -44.8882075
19 -86.7523585 -39.8882075
20 -34.3723585 -86.7523585
21 -121.1882075 -34.3723585
22 -87.1482075 -121.1882075
23 -122.7882075 -87.1482075
24 -96.6516981 -122.7882075
25 -7.9516981 -96.6516981
26 110.3083019 -7.9516981
27 128.7116981 110.3083019
28 0.3316981 128.7116981
29 34.0683019 0.3316981
30 26.3683019 34.0683019
31 57.9041509 26.3683019
32 53.3841509 57.9041509
33 38.8683019 53.3841509
34 53.1875472 38.8683019
35 37.8475472 53.1875472
36 71.4840566 37.8475472
37 -2.6159434 71.4840566
38 -51.3559434 -2.6159434
39 -81.3317925 -51.3559434
40 -10.7117925 -81.3317925
41 18.9040566 -10.7117925
42 -78.7959434 18.9040566
43 14.5399057 -78.7959434
44 -52.4800943 14.5399057
45 -87.8959434 -52.4800943
46 -4.0559434 -87.8959434
47 35.2040566 -4.0559434
48 -25.3594340 35.2040566
49 -16.6594340 -25.3594340
50 69.2005660 -16.6594340
51 -120.0752830 69.2005660
52 -0.2552830 -120.0752830
53 -12.5394340 -0.2552830
54 -11.2394340 -12.5394340
55 -6.6828302 -11.2394340
56 -16.2028302 -6.6828302
57 125.9605660 -16.2028302
58 19.6213208 125.9605660
59 98.7813208 19.6213208
> 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/756pg1258573502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v1v21258573502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9543t1258573502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gmjb1258573502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/111tet1258573502.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/12f2s21258573502.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/1373oa1258573502.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/14cyo21258573502.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/15rafp1258573502.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/16nij61258573502.tab")
+ }
>
> system("convert tmp/1keta1258573502.ps tmp/1keta1258573502.png")
> system("convert tmp/2abe51258573502.ps tmp/2abe51258573502.png")
> system("convert tmp/3lox71258573502.ps tmp/3lox71258573502.png")
> system("convert tmp/47d8s1258573502.ps tmp/47d8s1258573502.png")
> system("convert tmp/5z1uh1258573502.ps tmp/5z1uh1258573502.png")
> system("convert tmp/63w3l1258573502.ps tmp/63w3l1258573502.png")
> system("convert tmp/756pg1258573502.ps tmp/756pg1258573502.png")
> system("convert tmp/8v1v21258573502.ps tmp/8v1v21258573502.png")
> system("convert tmp/9543t1258573502.ps tmp/9543t1258573502.png")
> system("convert tmp/10gmjb1258573502.ps tmp/10gmjb1258573502.png")
>
>
> proc.time()
user system elapsed
2.548 1.616 3.671