R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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(0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,1,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,1,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0),dim=c(2,86),dimnames=list(c('CorrectAnalysis','T40
'),1:86))
> y <- array(NA,dim=c(2,86),dimnames=list(c('CorrectAnalysis','T40
'),1:86))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal 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, 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
CorrectAnalysis T40\r\r
1 0 1
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 1
9 0 0
10 0 0
11 0 1
12 0 0
13 0 0
14 0 1
15 0 0
16 0 1
17 1 1
18 0 1
19 0 0
20 1 1
21 0 0
22 0 0
23 0 0
24 0 0
25 0 1
26 0 0
27 0 0
28 0 0
29 0 0
30 0 0
31 0 0
32 0 0
33 0 0
34 0 1
35 0 0
36 0 0
37 0 1
38 0 0
39 0 0
40 0 1
41 1 0
42 0 0
43 0 0
44 0 1
45 0 0
46 0 0
47 0 0
48 0 0
49 0 0
50 0 0
51 0 1
52 1 1
53 0 0
54 1 0
55 0 0
56 0 1
57 0 0
58 0 0
59 0 0
60 1 1
61 0 1
62 0 0
63 0 0
64 0 1
65 0 0
66 0 0
67 1 1
68 0 0
69 0 0
70 0 0
71 0 0
72 0 0
73 0 0
74 0 0
75 0 0
76 0 1
77 0 0
78 0 0
79 1 1
80 0 1
81 0 0
82 0 0
83 0 0
84 1 0
85 0 0
86 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `T40\\r\\r`
0.04762 0.21325
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.26087 -0.04762 -0.04762 -0.04762 0.95238
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04762 0.03712 1.283 0.20308
`T40\\r\\r` 0.21325 0.07178 2.971 0.00387 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2946 on 84 degrees of freedom
Multiple R-squared: 0.09509, Adjusted R-squared: 0.08431
F-statistic: 8.826 on 1 and 84 DF, p-value: 0.003871
> 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,] 0.0000000000 0.000000000 1.0000000
[2,] 0.0000000000 0.000000000 1.0000000
[3,] 0.0000000000 0.000000000 1.0000000
[4,] 0.0000000000 0.000000000 1.0000000
[5,] 0.0000000000 0.000000000 1.0000000
[6,] 0.0000000000 0.000000000 1.0000000
[7,] 0.0000000000 0.000000000 1.0000000
[8,] 0.0000000000 0.000000000 1.0000000
[9,] 0.0000000000 0.000000000 1.0000000
[10,] 0.0000000000 0.000000000 1.0000000
[11,] 0.0000000000 0.000000000 1.0000000
[12,] 0.0000000000 0.000000000 1.0000000
[13,] 0.1241092388 0.248218478 0.8758908
[14,] 0.1009544110 0.201908822 0.8990456
[15,] 0.0686204548 0.137240910 0.9313795
[16,] 0.4487798661 0.897559732 0.5512201
[17,] 0.3748719240 0.749743848 0.6251281
[18,] 0.3062237190 0.612447438 0.6937763
[19,] 0.2445452669 0.489090534 0.7554547
[20,] 0.1908770602 0.381754120 0.8091229
[21,] 0.1778271777 0.355654355 0.8221728
[22,] 0.1352649851 0.270529970 0.8647350
[23,] 0.1005897904 0.201179581 0.8994102
[24,] 0.0731319334 0.146263867 0.9268681
[25,] 0.0519835886 0.103967177 0.9480164
[26,] 0.0361298994 0.072259799 0.9638701
[27,] 0.0245558104 0.049111621 0.9754442
[28,] 0.0163223405 0.032644681 0.9836777
[29,] 0.0106123301 0.021224660 0.9893877
[30,] 0.0094073748 0.018814750 0.9905926
[31,] 0.0059679763 0.011935953 0.9940320
[32,] 0.0037051154 0.007410231 0.9962949
[33,] 0.0032088725 0.006417745 0.9967911
[34,] 0.0019433582 0.003886716 0.9980566
[35,] 0.0011522608 0.002304522 0.9988477
[36,] 0.0009936526 0.001987305 0.9990063
[37,] 0.0920638340 0.184127668 0.9079362
[38,] 0.0690069202 0.138013840 0.9309931
[39,] 0.0507104277 0.101420855 0.9492896
[40,] 0.0483368640 0.096673728 0.9516631
[41,] 0.0347338191 0.069467638 0.9652662
[42,] 0.0244567871 0.048913574 0.9755432
[43,] 0.0168704534 0.033740907 0.9831295
[44,] 0.0113985486 0.022797097 0.9886015
[45,] 0.0075420580 0.015084116 0.9924579
[46,] 0.0048862663 0.009772533 0.9951137
[47,] 0.0049174868 0.009834974 0.9950825
[48,] 0.0411456795 0.082291359 0.9588543
[49,] 0.0292845991 0.058569198 0.9707154
[50,] 0.3297851984 0.659570397 0.6702148
[51,] 0.2742211124 0.548442225 0.7257789
[52,] 0.2884578796 0.576915759 0.7115421
[53,] 0.2358950341 0.471790068 0.7641050
[54,] 0.1888514812 0.377702962 0.8111485
[55,] 0.1478619823 0.295723965 0.8521380
[56,] 0.3612996183 0.722599237 0.6387004
[57,] 0.3734188146 0.746837629 0.6265812
[58,] 0.3107040111 0.621408022 0.6892960
[59,] 0.2526048022 0.505209604 0.7473952
[60,] 0.2938367696 0.587673539 0.7061632
[61,] 0.2359309825 0.471861965 0.7640690
[62,] 0.1844653348 0.368930670 0.8155347
[63,] 0.3890125314 0.778025063 0.6109875
[64,] 0.3190628124 0.638125625 0.6809372
[65,] 0.2542570582 0.508514116 0.7457429
[66,] 0.1964085800 0.392817160 0.8035914
[67,] 0.1467349473 0.293469895 0.8532651
[68,] 0.1057761492 0.211552298 0.8942239
[69,] 0.0734087300 0.146817460 0.9265913
[70,] 0.0489476267 0.097895253 0.9510524
[71,] 0.0313104709 0.062620942 0.9686895
[72,] 0.0354503946 0.070900789 0.9645496
[73,] 0.0213988898 0.042797780 0.9786011
[74,] 0.0123134682 0.024626936 0.9876865
[75,] 0.0823841650 0.164768330 0.9176158
[76,] 0.0454496371 0.090899274 0.9545504
[77,] 0.0243052803 0.048610561 0.9756947
> postscript(file="/var/wessaorg/rcomp/tmp/110cm1356098968.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/22viy1356098968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3m9zh1356098968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4y0h61356098968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5q1yq1356098968.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 = 86
Frequency = 1
1 2 3 4 5 6
-0.26086957 -0.04761905 -0.04761905 -0.04761905 -0.04761905 -0.04761905
7 8 9 10 11 12
-0.04761905 -0.26086957 -0.04761905 -0.04761905 -0.26086957 -0.04761905
13 14 15 16 17 18
-0.04761905 -0.26086957 -0.04761905 -0.26086957 0.73913043 -0.26086957
19 20 21 22 23 24
-0.04761905 0.73913043 -0.04761905 -0.04761905 -0.04761905 -0.04761905
25 26 27 28 29 30
-0.26086957 -0.04761905 -0.04761905 -0.04761905 -0.04761905 -0.04761905
31 32 33 34 35 36
-0.04761905 -0.04761905 -0.04761905 -0.26086957 -0.04761905 -0.04761905
37 38 39 40 41 42
-0.26086957 -0.04761905 -0.04761905 -0.26086957 0.95238095 -0.04761905
43 44 45 46 47 48
-0.04761905 -0.26086957 -0.04761905 -0.04761905 -0.04761905 -0.04761905
49 50 51 52 53 54
-0.04761905 -0.04761905 -0.26086957 0.73913043 -0.04761905 0.95238095
55 56 57 58 59 60
-0.04761905 -0.26086957 -0.04761905 -0.04761905 -0.04761905 0.73913043
61 62 63 64 65 66
-0.26086957 -0.04761905 -0.04761905 -0.26086957 -0.04761905 -0.04761905
67 68 69 70 71 72
0.73913043 -0.04761905 -0.04761905 -0.04761905 -0.04761905 -0.04761905
73 74 75 76 77 78
-0.04761905 -0.04761905 -0.04761905 -0.26086957 -0.04761905 -0.04761905
79 80 81 82 83 84
0.73913043 -0.26086957 -0.04761905 -0.04761905 -0.04761905 0.95238095
85 86
-0.04761905 -0.04761905
> postscript(file="/var/wessaorg/rcomp/tmp/6q2ge1356098968.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 = 86
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.26086957 NA
1 -0.04761905 -0.26086957
2 -0.04761905 -0.04761905
3 -0.04761905 -0.04761905
4 -0.04761905 -0.04761905
5 -0.04761905 -0.04761905
6 -0.04761905 -0.04761905
7 -0.26086957 -0.04761905
8 -0.04761905 -0.26086957
9 -0.04761905 -0.04761905
10 -0.26086957 -0.04761905
11 -0.04761905 -0.26086957
12 -0.04761905 -0.04761905
13 -0.26086957 -0.04761905
14 -0.04761905 -0.26086957
15 -0.26086957 -0.04761905
16 0.73913043 -0.26086957
17 -0.26086957 0.73913043
18 -0.04761905 -0.26086957
19 0.73913043 -0.04761905
20 -0.04761905 0.73913043
21 -0.04761905 -0.04761905
22 -0.04761905 -0.04761905
23 -0.04761905 -0.04761905
24 -0.26086957 -0.04761905
25 -0.04761905 -0.26086957
26 -0.04761905 -0.04761905
27 -0.04761905 -0.04761905
28 -0.04761905 -0.04761905
29 -0.04761905 -0.04761905
30 -0.04761905 -0.04761905
31 -0.04761905 -0.04761905
32 -0.04761905 -0.04761905
33 -0.26086957 -0.04761905
34 -0.04761905 -0.26086957
35 -0.04761905 -0.04761905
36 -0.26086957 -0.04761905
37 -0.04761905 -0.26086957
38 -0.04761905 -0.04761905
39 -0.26086957 -0.04761905
40 0.95238095 -0.26086957
41 -0.04761905 0.95238095
42 -0.04761905 -0.04761905
43 -0.26086957 -0.04761905
44 -0.04761905 -0.26086957
45 -0.04761905 -0.04761905
46 -0.04761905 -0.04761905
47 -0.04761905 -0.04761905
48 -0.04761905 -0.04761905
49 -0.04761905 -0.04761905
50 -0.26086957 -0.04761905
51 0.73913043 -0.26086957
52 -0.04761905 0.73913043
53 0.95238095 -0.04761905
54 -0.04761905 0.95238095
55 -0.26086957 -0.04761905
56 -0.04761905 -0.26086957
57 -0.04761905 -0.04761905
58 -0.04761905 -0.04761905
59 0.73913043 -0.04761905
60 -0.26086957 0.73913043
61 -0.04761905 -0.26086957
62 -0.04761905 -0.04761905
63 -0.26086957 -0.04761905
64 -0.04761905 -0.26086957
65 -0.04761905 -0.04761905
66 0.73913043 -0.04761905
67 -0.04761905 0.73913043
68 -0.04761905 -0.04761905
69 -0.04761905 -0.04761905
70 -0.04761905 -0.04761905
71 -0.04761905 -0.04761905
72 -0.04761905 -0.04761905
73 -0.04761905 -0.04761905
74 -0.04761905 -0.04761905
75 -0.26086957 -0.04761905
76 -0.04761905 -0.26086957
77 -0.04761905 -0.04761905
78 0.73913043 -0.04761905
79 -0.26086957 0.73913043
80 -0.04761905 -0.26086957
81 -0.04761905 -0.04761905
82 -0.04761905 -0.04761905
83 0.95238095 -0.04761905
84 -0.04761905 0.95238095
85 -0.04761905 -0.04761905
86 NA -0.04761905
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.04761905 -0.26086957
[2,] -0.04761905 -0.04761905
[3,] -0.04761905 -0.04761905
[4,] -0.04761905 -0.04761905
[5,] -0.04761905 -0.04761905
[6,] -0.04761905 -0.04761905
[7,] -0.26086957 -0.04761905
[8,] -0.04761905 -0.26086957
[9,] -0.04761905 -0.04761905
[10,] -0.26086957 -0.04761905
[11,] -0.04761905 -0.26086957
[12,] -0.04761905 -0.04761905
[13,] -0.26086957 -0.04761905
[14,] -0.04761905 -0.26086957
[15,] -0.26086957 -0.04761905
[16,] 0.73913043 -0.26086957
[17,] -0.26086957 0.73913043
[18,] -0.04761905 -0.26086957
[19,] 0.73913043 -0.04761905
[20,] -0.04761905 0.73913043
[21,] -0.04761905 -0.04761905
[22,] -0.04761905 -0.04761905
[23,] -0.04761905 -0.04761905
[24,] -0.26086957 -0.04761905
[25,] -0.04761905 -0.26086957
[26,] -0.04761905 -0.04761905
[27,] -0.04761905 -0.04761905
[28,] -0.04761905 -0.04761905
[29,] -0.04761905 -0.04761905
[30,] -0.04761905 -0.04761905
[31,] -0.04761905 -0.04761905
[32,] -0.04761905 -0.04761905
[33,] -0.26086957 -0.04761905
[34,] -0.04761905 -0.26086957
[35,] -0.04761905 -0.04761905
[36,] -0.26086957 -0.04761905
[37,] -0.04761905 -0.26086957
[38,] -0.04761905 -0.04761905
[39,] -0.26086957 -0.04761905
[40,] 0.95238095 -0.26086957
[41,] -0.04761905 0.95238095
[42,] -0.04761905 -0.04761905
[43,] -0.26086957 -0.04761905
[44,] -0.04761905 -0.26086957
[45,] -0.04761905 -0.04761905
[46,] -0.04761905 -0.04761905
[47,] -0.04761905 -0.04761905
[48,] -0.04761905 -0.04761905
[49,] -0.04761905 -0.04761905
[50,] -0.26086957 -0.04761905
[51,] 0.73913043 -0.26086957
[52,] -0.04761905 0.73913043
[53,] 0.95238095 -0.04761905
[54,] -0.04761905 0.95238095
[55,] -0.26086957 -0.04761905
[56,] -0.04761905 -0.26086957
[57,] -0.04761905 -0.04761905
[58,] -0.04761905 -0.04761905
[59,] 0.73913043 -0.04761905
[60,] -0.26086957 0.73913043
[61,] -0.04761905 -0.26086957
[62,] -0.04761905 -0.04761905
[63,] -0.26086957 -0.04761905
[64,] -0.04761905 -0.26086957
[65,] -0.04761905 -0.04761905
[66,] 0.73913043 -0.04761905
[67,] -0.04761905 0.73913043
[68,] -0.04761905 -0.04761905
[69,] -0.04761905 -0.04761905
[70,] -0.04761905 -0.04761905
[71,] -0.04761905 -0.04761905
[72,] -0.04761905 -0.04761905
[73,] -0.04761905 -0.04761905
[74,] -0.04761905 -0.04761905
[75,] -0.26086957 -0.04761905
[76,] -0.04761905 -0.26086957
[77,] -0.04761905 -0.04761905
[78,] 0.73913043 -0.04761905
[79,] -0.26086957 0.73913043
[80,] -0.04761905 -0.26086957
[81,] -0.04761905 -0.04761905
[82,] -0.04761905 -0.04761905
[83,] 0.95238095 -0.04761905
[84,] -0.04761905 0.95238095
[85,] -0.04761905 -0.04761905
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.04761905 -0.26086957
2 -0.04761905 -0.04761905
3 -0.04761905 -0.04761905
4 -0.04761905 -0.04761905
5 -0.04761905 -0.04761905
6 -0.04761905 -0.04761905
7 -0.26086957 -0.04761905
8 -0.04761905 -0.26086957
9 -0.04761905 -0.04761905
10 -0.26086957 -0.04761905
11 -0.04761905 -0.26086957
12 -0.04761905 -0.04761905
13 -0.26086957 -0.04761905
14 -0.04761905 -0.26086957
15 -0.26086957 -0.04761905
16 0.73913043 -0.26086957
17 -0.26086957 0.73913043
18 -0.04761905 -0.26086957
19 0.73913043 -0.04761905
20 -0.04761905 0.73913043
21 -0.04761905 -0.04761905
22 -0.04761905 -0.04761905
23 -0.04761905 -0.04761905
24 -0.26086957 -0.04761905
25 -0.04761905 -0.26086957
26 -0.04761905 -0.04761905
27 -0.04761905 -0.04761905
28 -0.04761905 -0.04761905
29 -0.04761905 -0.04761905
30 -0.04761905 -0.04761905
31 -0.04761905 -0.04761905
32 -0.04761905 -0.04761905
33 -0.26086957 -0.04761905
34 -0.04761905 -0.26086957
35 -0.04761905 -0.04761905
36 -0.26086957 -0.04761905
37 -0.04761905 -0.26086957
38 -0.04761905 -0.04761905
39 -0.26086957 -0.04761905
40 0.95238095 -0.26086957
41 -0.04761905 0.95238095
42 -0.04761905 -0.04761905
43 -0.26086957 -0.04761905
44 -0.04761905 -0.26086957
45 -0.04761905 -0.04761905
46 -0.04761905 -0.04761905
47 -0.04761905 -0.04761905
48 -0.04761905 -0.04761905
49 -0.04761905 -0.04761905
50 -0.26086957 -0.04761905
51 0.73913043 -0.26086957
52 -0.04761905 0.73913043
53 0.95238095 -0.04761905
54 -0.04761905 0.95238095
55 -0.26086957 -0.04761905
56 -0.04761905 -0.26086957
57 -0.04761905 -0.04761905
58 -0.04761905 -0.04761905
59 0.73913043 -0.04761905
60 -0.26086957 0.73913043
61 -0.04761905 -0.26086957
62 -0.04761905 -0.04761905
63 -0.26086957 -0.04761905
64 -0.04761905 -0.26086957
65 -0.04761905 -0.04761905
66 0.73913043 -0.04761905
67 -0.04761905 0.73913043
68 -0.04761905 -0.04761905
69 -0.04761905 -0.04761905
70 -0.04761905 -0.04761905
71 -0.04761905 -0.04761905
72 -0.04761905 -0.04761905
73 -0.04761905 -0.04761905
74 -0.04761905 -0.04761905
75 -0.26086957 -0.04761905
76 -0.04761905 -0.26086957
77 -0.04761905 -0.04761905
78 0.73913043 -0.04761905
79 -0.26086957 0.73913043
80 -0.04761905 -0.26086957
81 -0.04761905 -0.04761905
82 -0.04761905 -0.04761905
83 0.95238095 -0.04761905
84 -0.04761905 0.95238095
85 -0.04761905 -0.04761905
> 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/wessaorg/rcomp/tmp/71k141356098968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8br3v1356098968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/95hed1356098968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10f8kf1356098968.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/wessaorg/rcomp/tmp/11072t1356098968.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/wessaorg/rcomp/tmp/12wgy91356098968.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/wessaorg/rcomp/tmp/136alt1356098968.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/wessaorg/rcomp/tmp/141zki1356098968.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/wessaorg/rcomp/tmp/1548nz1356098968.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/wessaorg/rcomp/tmp/16s10v1356098968.tab")
+ }
>
> try(system("convert tmp/110cm1356098968.ps tmp/110cm1356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/22viy1356098968.ps tmp/22viy1356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m9zh1356098968.ps tmp/3m9zh1356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y0h61356098968.ps tmp/4y0h61356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q1yq1356098968.ps tmp/5q1yq1356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/6q2ge1356098968.ps tmp/6q2ge1356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/71k141356098968.ps tmp/71k141356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/8br3v1356098968.ps tmp/8br3v1356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/95hed1356098968.ps tmp/95hed1356098968.png",intern=TRUE))
character(0)
> try(system("convert tmp/10f8kf1356098968.ps tmp/10f8kf1356098968.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.371 1.093 7.689