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(1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,1,0,0,1,0,1,1,0,0,0,1,0,1,0,1,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,0,0,1,1,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,1,0,1,0,1,0,0,1,1,0,1,0,1,0,0,0,1,0,0,1,0,0,1,0,1,0,1,1,0,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,1,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,0,1,0,1,0,0,0,1,0,0,1,0,1,0,1,1,0,0,0,1,0,0,1,0,0,1,0,0,1,1,0,1,0,0,1,0),dim=c(3,86),dimnames=list(c('Treatment4weken','treatment2weken','CorrectAnalysis'),1:86))
> y <- array(NA,dim=c(3,86),dimnames=list(c('Treatment4weken','treatment2weken','CorrectAnalysis'),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
Treatment4weken treatment2weken CorrectAnalysis
1 1 0 0
2 0 1 0
3 0 1 0
4 0 1 0
5 0 1 0
6 0 1 0
7 0 1 0
8 1 0 0
9 0 1 0
10 0 1 0
11 1 0 0
12 0 1 0
13 0 1 0
14 1 0 0
15 0 1 0
16 1 0 0
17 1 0 1
18 1 0 0
19 0 1 0
20 1 0 1
21 0 1 0
22 0 1 0
23 0 1 0
24 0 1 0
25 1 0 0
26 0 1 0
27 0 1 0
28 0 1 0
29 0 1 0
30 0 1 0
31 0 1 0
32 0 1 0
33 0 1 0
34 1 0 0
35 0 1 0
36 0 1 0
37 1 0 0
38 0 1 0
39 0 1 0
40 1 0 0
41 0 1 1
42 0 1 0
43 0 1 0
44 1 0 0
45 0 1 0
46 0 1 0
47 0 1 0
48 0 1 0
49 0 1 0
50 0 1 0
51 1 0 0
52 1 0 1
53 0 1 0
54 0 1 1
55 0 1 0
56 1 0 0
57 0 1 0
58 0 1 0
59 0 1 0
60 1 0 1
61 1 0 0
62 0 1 0
63 0 1 0
64 1 0 0
65 0 1 0
66 0 1 0
67 1 0 1
68 0 1 0
69 0 1 0
70 0 1 0
71 0 1 0
72 0 1 0
73 0 1 0
74 0 1 0
75 0 1 0
76 1 0 0
77 0 1 0
78 0 1 0
79 1 0 1
80 1 0 0
81 0 1 0
82 0 1 0
83 0 1 0
84 0 1 1
85 0 1 0
86 0 1 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) treatment2weken CorrectAnalysis
1.000e+00 -1.000e+00 2.461e-18
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.749e-16 -9.860e-18 -9.860e-18 6.160e-18 6.189e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.000e+00 1.637e-17 6.108e+16 <2e-16 ***
treatment2weken -1.000e+00 1.824e-17 -5.481e+16 <2e-16 ***
CorrectAnalysis 2.461e-18 2.638e-17 9.300e-02 0.926
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 7.124e-17 on 83 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.66e+33 on 2 and 83 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,] 3.381890e-04 6.763780e-04 9.996618e-01
[2,] 9.966435e-01 6.713004e-03 3.356502e-03
[3,] 9.992772e-01 1.445618e-03 7.228089e-04
[4,] 1.000000e+00 4.532244e-22 2.266122e-22
[5,] 4.538112e-02 9.076224e-02 9.546189e-01
[6,] 1.688164e-02 3.376327e-02 9.831184e-01
[7,] 8.867185e-01 2.265631e-01 1.132815e-01
[8,] 1.198418e-01 2.396837e-01 8.801582e-01
[9,] 4.419392e-01 8.838784e-01 5.580608e-01
[10,] 1.000000e+00 1.244751e-13 6.223753e-14
[11,] 2.901653e-01 5.803305e-01 7.098347e-01
[12,] 9.854073e-01 2.918536e-02 1.459268e-02
[13,] 9.700956e-01 5.980880e-02 2.990440e-02
[14,] 7.332412e-03 1.466482e-02 9.926676e-01
[15,] 1.000000e+00 3.660279e-11 1.830139e-11
[16,] 2.294286e-01 4.588571e-01 7.705714e-01
[17,] 8.996202e-01 2.007596e-01 1.003798e-01
[18,] 7.716528e-01 4.566943e-01 2.283472e-01
[19,] 4.197135e-01 8.394270e-01 5.802865e-01
[20,] 9.999996e-01 8.772542e-07 4.386271e-07
[21,] 1.930514e-15 3.861028e-15 1.000000e+00
[22,] 5.062027e-03 1.012405e-02 9.949380e-01
[23,] 2.835734e-03 5.671467e-03 9.971643e-01
[24,] 1.000000e+00 6.499800e-10 3.249900e-10
[25,] 1.000000e+00 5.143790e-12 2.571895e-12
[26,] 8.060612e-06 1.612122e-05 9.999919e-01
[27,] 7.556725e-01 4.886550e-01 2.443275e-01
[28,] 6.561023e-01 6.877953e-01 3.438977e-01
[29,] 8.719753e-01 2.560495e-01 1.280247e-01
[30,] 9.941476e-01 1.170476e-02 5.852378e-03
[31,] 9.452685e-01 1.094630e-01 5.473150e-02
[32,] 3.313840e-05 6.627679e-05 9.999669e-01
[33,] 8.261956e-02 1.652391e-01 9.173804e-01
[34,] 4.272890e-14 8.545781e-14 1.000000e+00
[35,] 4.852066e-05 9.704131e-05 9.999515e-01
[36,] 8.461329e-01 3.077342e-01 1.538671e-01
[37,] 1.000000e+00 8.773652e-08 4.386826e-08
[38,] 9.999988e-01 2.374879e-06 1.187440e-06
[39,] 1.247891e-01 2.495782e-01 8.752109e-01
[40,] 1.000000e+00 8.550193e-13 4.275097e-13
[41,] 9.999407e-01 1.186298e-04 5.931491e-05
[42,] 1.000000e+00 1.371088e-17 6.855440e-18
[43,] 9.064619e-01 1.870761e-01 9.353806e-02
[44,] 8.692742e-01 2.614516e-01 1.307258e-01
[45,] 7.422776e-02 1.484555e-01 9.257722e-01
[46,] 2.150216e-05 4.300432e-05 9.999785e-01
[47,] 4.598103e-10 9.196206e-10 1.000000e+00
[48,] 3.588490e-01 7.176980e-01 6.411510e-01
[49,] 6.913293e-01 6.173414e-01 3.086707e-01
[50,] 9.998370e-01 3.259774e-04 1.629887e-04
[51,] 7.648654e-03 1.529731e-02 9.923513e-01
[52,] 1.017943e-01 2.035886e-01 8.982057e-01
[53,] 9.976133e-01 4.773388e-03 2.386694e-03
[54,] 1.000000e+00 2.299299e-10 1.149649e-10
[55,] 1.000000e+00 5.641338e-13 2.820669e-13
[56,] 4.674394e-01 9.348788e-01 5.325606e-01
[57,] 1.000000e+00 2.784518e-08 1.392259e-08
[58,] 6.033042e-02 1.206608e-01 9.396696e-01
[59,] 3.055352e-22 6.110705e-22 1.000000e+00
[60,] 9.865272e-01 2.694550e-02 1.347275e-02
[61,] 3.175335e-03 6.350669e-03 9.968247e-01
[62,] 9.999473e-01 1.054467e-04 5.272336e-05
[63,] 3.841152e-01 7.682305e-01 6.158848e-01
[64,] 5.942886e-01 8.114227e-01 4.057114e-01
[65,] 3.930572e-01 7.861144e-01 6.069428e-01
[66,] 5.515847e-03 1.103169e-02 9.944842e-01
[67,] 5.105458e-01 9.789085e-01 4.894542e-01
[68,] 1.081626e-10 2.163251e-10 1.000000e+00
[69,] 4.069946e-03 8.139891e-03 9.959301e-01
[70,] 9.730258e-01 5.394848e-02 2.697424e-02
[71,] 9.999997e-01 5.227415e-07 2.613707e-07
[72,] 7.318886e-01 5.362228e-01 2.681114e-01
[73,] 6.033482e-01 7.933036e-01 3.966518e-01
[74,] 4.303448e-03 8.606895e-03 9.956966e-01
[75,] 1.000000e+00 0.000000e+00 0.000000e+00
> postscript(file="/var/wessaorg/rcomp/tmp/1fuvt1356095216.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2w88v1356095216.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lzjt1356095216.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ygzn1356095216.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/52g9r1356095216.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
-1.749470e-16 6.188744e-16 -9.862748e-18 -9.862748e-18 -9.862748e-18
6 7 8 9 10
-9.862748e-18 -9.862748e-18 8.623414e-18 -9.862748e-18 -9.862748e-18
11 12 13 14 15
8.623414e-18 -9.862748e-18 -9.862748e-18 8.623414e-18 -9.862748e-18
16 17 18 19 20
8.623414e-18 6.162054e-18 8.623414e-18 -9.862748e-18 6.162054e-18
21 22 23 24 25
-9.862748e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18 8.623414e-18
26 27 28 29 30
-9.862748e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18
31 32 33 34 35
-9.862748e-18 -9.862748e-18 -9.862748e-18 8.623414e-18 -9.862748e-18
36 37 38 39 40
-9.862748e-18 8.623414e-18 -9.862748e-18 -9.862748e-18 8.623414e-18
41 42 43 44 45
-1.232411e-17 -9.862748e-18 -9.862748e-18 8.623414e-18 -9.862748e-18
46 47 48 49 50
-9.862748e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18
51 52 53 54 55
8.623414e-18 6.162054e-18 -9.862748e-18 -1.232411e-17 -9.862748e-18
56 57 58 59 60
8.623414e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18 6.162054e-18
61 62 63 64 65
8.623414e-18 -9.862748e-18 -9.862748e-18 8.623414e-18 -9.862748e-18
66 67 68 69 70
-9.862748e-18 6.162054e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18
71 72 73 74 75
-9.862748e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18 -9.862748e-18
76 77 78 79 80
8.623414e-18 -9.862748e-18 -9.862748e-18 6.162054e-18 8.623414e-18
81 82 83 84 85
-9.862748e-18 -9.862748e-18 -9.862748e-18 -1.232411e-17 -9.862748e-18
86
-9.862748e-18
> postscript(file="/var/wessaorg/rcomp/tmp/6rjy11356095216.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 -1.749470e-16 NA
1 6.188744e-16 -1.749470e-16
2 -9.862748e-18 6.188744e-16
3 -9.862748e-18 -9.862748e-18
4 -9.862748e-18 -9.862748e-18
5 -9.862748e-18 -9.862748e-18
6 -9.862748e-18 -9.862748e-18
7 8.623414e-18 -9.862748e-18
8 -9.862748e-18 8.623414e-18
9 -9.862748e-18 -9.862748e-18
10 8.623414e-18 -9.862748e-18
11 -9.862748e-18 8.623414e-18
12 -9.862748e-18 -9.862748e-18
13 8.623414e-18 -9.862748e-18
14 -9.862748e-18 8.623414e-18
15 8.623414e-18 -9.862748e-18
16 6.162054e-18 8.623414e-18
17 8.623414e-18 6.162054e-18
18 -9.862748e-18 8.623414e-18
19 6.162054e-18 -9.862748e-18
20 -9.862748e-18 6.162054e-18
21 -9.862748e-18 -9.862748e-18
22 -9.862748e-18 -9.862748e-18
23 -9.862748e-18 -9.862748e-18
24 8.623414e-18 -9.862748e-18
25 -9.862748e-18 8.623414e-18
26 -9.862748e-18 -9.862748e-18
27 -9.862748e-18 -9.862748e-18
28 -9.862748e-18 -9.862748e-18
29 -9.862748e-18 -9.862748e-18
30 -9.862748e-18 -9.862748e-18
31 -9.862748e-18 -9.862748e-18
32 -9.862748e-18 -9.862748e-18
33 8.623414e-18 -9.862748e-18
34 -9.862748e-18 8.623414e-18
35 -9.862748e-18 -9.862748e-18
36 8.623414e-18 -9.862748e-18
37 -9.862748e-18 8.623414e-18
38 -9.862748e-18 -9.862748e-18
39 8.623414e-18 -9.862748e-18
40 -1.232411e-17 8.623414e-18
41 -9.862748e-18 -1.232411e-17
42 -9.862748e-18 -9.862748e-18
43 8.623414e-18 -9.862748e-18
44 -9.862748e-18 8.623414e-18
45 -9.862748e-18 -9.862748e-18
46 -9.862748e-18 -9.862748e-18
47 -9.862748e-18 -9.862748e-18
48 -9.862748e-18 -9.862748e-18
49 -9.862748e-18 -9.862748e-18
50 8.623414e-18 -9.862748e-18
51 6.162054e-18 8.623414e-18
52 -9.862748e-18 6.162054e-18
53 -1.232411e-17 -9.862748e-18
54 -9.862748e-18 -1.232411e-17
55 8.623414e-18 -9.862748e-18
56 -9.862748e-18 8.623414e-18
57 -9.862748e-18 -9.862748e-18
58 -9.862748e-18 -9.862748e-18
59 6.162054e-18 -9.862748e-18
60 8.623414e-18 6.162054e-18
61 -9.862748e-18 8.623414e-18
62 -9.862748e-18 -9.862748e-18
63 8.623414e-18 -9.862748e-18
64 -9.862748e-18 8.623414e-18
65 -9.862748e-18 -9.862748e-18
66 6.162054e-18 -9.862748e-18
67 -9.862748e-18 6.162054e-18
68 -9.862748e-18 -9.862748e-18
69 -9.862748e-18 -9.862748e-18
70 -9.862748e-18 -9.862748e-18
71 -9.862748e-18 -9.862748e-18
72 -9.862748e-18 -9.862748e-18
73 -9.862748e-18 -9.862748e-18
74 -9.862748e-18 -9.862748e-18
75 8.623414e-18 -9.862748e-18
76 -9.862748e-18 8.623414e-18
77 -9.862748e-18 -9.862748e-18
78 6.162054e-18 -9.862748e-18
79 8.623414e-18 6.162054e-18
80 -9.862748e-18 8.623414e-18
81 -9.862748e-18 -9.862748e-18
82 -9.862748e-18 -9.862748e-18
83 -1.232411e-17 -9.862748e-18
84 -9.862748e-18 -1.232411e-17
85 -9.862748e-18 -9.862748e-18
86 NA -9.862748e-18
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 6.188744e-16 -1.749470e-16
[2,] -9.862748e-18 6.188744e-16
[3,] -9.862748e-18 -9.862748e-18
[4,] -9.862748e-18 -9.862748e-18
[5,] -9.862748e-18 -9.862748e-18
[6,] -9.862748e-18 -9.862748e-18
[7,] 8.623414e-18 -9.862748e-18
[8,] -9.862748e-18 8.623414e-18
[9,] -9.862748e-18 -9.862748e-18
[10,] 8.623414e-18 -9.862748e-18
[11,] -9.862748e-18 8.623414e-18
[12,] -9.862748e-18 -9.862748e-18
[13,] 8.623414e-18 -9.862748e-18
[14,] -9.862748e-18 8.623414e-18
[15,] 8.623414e-18 -9.862748e-18
[16,] 6.162054e-18 8.623414e-18
[17,] 8.623414e-18 6.162054e-18
[18,] -9.862748e-18 8.623414e-18
[19,] 6.162054e-18 -9.862748e-18
[20,] -9.862748e-18 6.162054e-18
[21,] -9.862748e-18 -9.862748e-18
[22,] -9.862748e-18 -9.862748e-18
[23,] -9.862748e-18 -9.862748e-18
[24,] 8.623414e-18 -9.862748e-18
[25,] -9.862748e-18 8.623414e-18
[26,] -9.862748e-18 -9.862748e-18
[27,] -9.862748e-18 -9.862748e-18
[28,] -9.862748e-18 -9.862748e-18
[29,] -9.862748e-18 -9.862748e-18
[30,] -9.862748e-18 -9.862748e-18
[31,] -9.862748e-18 -9.862748e-18
[32,] -9.862748e-18 -9.862748e-18
[33,] 8.623414e-18 -9.862748e-18
[34,] -9.862748e-18 8.623414e-18
[35,] -9.862748e-18 -9.862748e-18
[36,] 8.623414e-18 -9.862748e-18
[37,] -9.862748e-18 8.623414e-18
[38,] -9.862748e-18 -9.862748e-18
[39,] 8.623414e-18 -9.862748e-18
[40,] -1.232411e-17 8.623414e-18
[41,] -9.862748e-18 -1.232411e-17
[42,] -9.862748e-18 -9.862748e-18
[43,] 8.623414e-18 -9.862748e-18
[44,] -9.862748e-18 8.623414e-18
[45,] -9.862748e-18 -9.862748e-18
[46,] -9.862748e-18 -9.862748e-18
[47,] -9.862748e-18 -9.862748e-18
[48,] -9.862748e-18 -9.862748e-18
[49,] -9.862748e-18 -9.862748e-18
[50,] 8.623414e-18 -9.862748e-18
[51,] 6.162054e-18 8.623414e-18
[52,] -9.862748e-18 6.162054e-18
[53,] -1.232411e-17 -9.862748e-18
[54,] -9.862748e-18 -1.232411e-17
[55,] 8.623414e-18 -9.862748e-18
[56,] -9.862748e-18 8.623414e-18
[57,] -9.862748e-18 -9.862748e-18
[58,] -9.862748e-18 -9.862748e-18
[59,] 6.162054e-18 -9.862748e-18
[60,] 8.623414e-18 6.162054e-18
[61,] -9.862748e-18 8.623414e-18
[62,] -9.862748e-18 -9.862748e-18
[63,] 8.623414e-18 -9.862748e-18
[64,] -9.862748e-18 8.623414e-18
[65,] -9.862748e-18 -9.862748e-18
[66,] 6.162054e-18 -9.862748e-18
[67,] -9.862748e-18 6.162054e-18
[68,] -9.862748e-18 -9.862748e-18
[69,] -9.862748e-18 -9.862748e-18
[70,] -9.862748e-18 -9.862748e-18
[71,] -9.862748e-18 -9.862748e-18
[72,] -9.862748e-18 -9.862748e-18
[73,] -9.862748e-18 -9.862748e-18
[74,] -9.862748e-18 -9.862748e-18
[75,] 8.623414e-18 -9.862748e-18
[76,] -9.862748e-18 8.623414e-18
[77,] -9.862748e-18 -9.862748e-18
[78,] 6.162054e-18 -9.862748e-18
[79,] 8.623414e-18 6.162054e-18
[80,] -9.862748e-18 8.623414e-18
[81,] -9.862748e-18 -9.862748e-18
[82,] -9.862748e-18 -9.862748e-18
[83,] -1.232411e-17 -9.862748e-18
[84,] -9.862748e-18 -1.232411e-17
[85,] -9.862748e-18 -9.862748e-18
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 6.188744e-16 -1.749470e-16
2 -9.862748e-18 6.188744e-16
3 -9.862748e-18 -9.862748e-18
4 -9.862748e-18 -9.862748e-18
5 -9.862748e-18 -9.862748e-18
6 -9.862748e-18 -9.862748e-18
7 8.623414e-18 -9.862748e-18
8 -9.862748e-18 8.623414e-18
9 -9.862748e-18 -9.862748e-18
10 8.623414e-18 -9.862748e-18
11 -9.862748e-18 8.623414e-18
12 -9.862748e-18 -9.862748e-18
13 8.623414e-18 -9.862748e-18
14 -9.862748e-18 8.623414e-18
15 8.623414e-18 -9.862748e-18
16 6.162054e-18 8.623414e-18
17 8.623414e-18 6.162054e-18
18 -9.862748e-18 8.623414e-18
19 6.162054e-18 -9.862748e-18
20 -9.862748e-18 6.162054e-18
21 -9.862748e-18 -9.862748e-18
22 -9.862748e-18 -9.862748e-18
23 -9.862748e-18 -9.862748e-18
24 8.623414e-18 -9.862748e-18
25 -9.862748e-18 8.623414e-18
26 -9.862748e-18 -9.862748e-18
27 -9.862748e-18 -9.862748e-18
28 -9.862748e-18 -9.862748e-18
29 -9.862748e-18 -9.862748e-18
30 -9.862748e-18 -9.862748e-18
31 -9.862748e-18 -9.862748e-18
32 -9.862748e-18 -9.862748e-18
33 8.623414e-18 -9.862748e-18
34 -9.862748e-18 8.623414e-18
35 -9.862748e-18 -9.862748e-18
36 8.623414e-18 -9.862748e-18
37 -9.862748e-18 8.623414e-18
38 -9.862748e-18 -9.862748e-18
39 8.623414e-18 -9.862748e-18
40 -1.232411e-17 8.623414e-18
41 -9.862748e-18 -1.232411e-17
42 -9.862748e-18 -9.862748e-18
43 8.623414e-18 -9.862748e-18
44 -9.862748e-18 8.623414e-18
45 -9.862748e-18 -9.862748e-18
46 -9.862748e-18 -9.862748e-18
47 -9.862748e-18 -9.862748e-18
48 -9.862748e-18 -9.862748e-18
49 -9.862748e-18 -9.862748e-18
50 8.623414e-18 -9.862748e-18
51 6.162054e-18 8.623414e-18
52 -9.862748e-18 6.162054e-18
53 -1.232411e-17 -9.862748e-18
54 -9.862748e-18 -1.232411e-17
55 8.623414e-18 -9.862748e-18
56 -9.862748e-18 8.623414e-18
57 -9.862748e-18 -9.862748e-18
58 -9.862748e-18 -9.862748e-18
59 6.162054e-18 -9.862748e-18
60 8.623414e-18 6.162054e-18
61 -9.862748e-18 8.623414e-18
62 -9.862748e-18 -9.862748e-18
63 8.623414e-18 -9.862748e-18
64 -9.862748e-18 8.623414e-18
65 -9.862748e-18 -9.862748e-18
66 6.162054e-18 -9.862748e-18
67 -9.862748e-18 6.162054e-18
68 -9.862748e-18 -9.862748e-18
69 -9.862748e-18 -9.862748e-18
70 -9.862748e-18 -9.862748e-18
71 -9.862748e-18 -9.862748e-18
72 -9.862748e-18 -9.862748e-18
73 -9.862748e-18 -9.862748e-18
74 -9.862748e-18 -9.862748e-18
75 8.623414e-18 -9.862748e-18
76 -9.862748e-18 8.623414e-18
77 -9.862748e-18 -9.862748e-18
78 6.162054e-18 -9.862748e-18
79 8.623414e-18 6.162054e-18
80 -9.862748e-18 8.623414e-18
81 -9.862748e-18 -9.862748e-18
82 -9.862748e-18 -9.862748e-18
83 -1.232411e-17 -9.862748e-18
84 -9.862748e-18 -1.232411e-17
85 -9.862748e-18 -9.862748e-18
> 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/7ctc61356095216.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8qx6j1356095216.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9sxhx1356095216.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wddv1356095216.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114xvs1356095216.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/12ul9d1356095216.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/1364vf1356095216.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/14489j1356095216.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/15ufvd1356095216.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/164pjy1356095216.tab")
+ }
>
> try(system("convert tmp/1fuvt1356095216.ps tmp/1fuvt1356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w88v1356095216.ps tmp/2w88v1356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lzjt1356095216.ps tmp/3lzjt1356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ygzn1356095216.ps tmp/4ygzn1356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/52g9r1356095216.ps tmp/52g9r1356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/6rjy11356095216.ps tmp/6rjy11356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ctc61356095216.ps tmp/7ctc61356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/8qx6j1356095216.ps tmp/8qx6j1356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sxhx1356095216.ps tmp/9sxhx1356095216.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wddv1356095216.ps tmp/10wddv1356095216.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.309 1.015 8.425