R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1.7,0,2.4,0,2.0,0,2.1,0,2.0,0,1.8,0,2.7,0,2.3,0,1.9,0,2.0,0,2.3,0,2.8,0,2.4,0,2.3,0,2.7,0,2.7,0,2.9,0,3.0,0,2.2,0,2.3,0,2.8,0,2.8,0,2.8,0,2.2,0,2.6,0,2.8,0,2.5,0,2.4,0,2.3,0,1.9,0,1.7,0,2.0,0,2.1,0,1.7,0,1.8,0,1.8,0,1.8,0,1.3,0,1.3,0,1.3,0,1.2,0,1.4,0,2.2,1,2.9,1,3.1,1,3.5,1,3.6,1,4.4,1,4.1,1,5.1,1,5.8,1,5.9,1,5.4,1,5.5,1,4.8,1,3.2,1,2.7,1,2.1,1,1.9,1,0.6,1),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 1.7 0 1 0 0 0 0 0 0 0 0 0 0 1
2 2.4 0 0 1 0 0 0 0 0 0 0 0 0 2
3 2.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 2.1 0 0 0 0 1 0 0 0 0 0 0 0 4
5 2.0 0 0 0 0 0 1 0 0 0 0 0 0 5
6 1.8 0 0 0 0 0 0 1 0 0 0 0 0 6
7 2.7 0 0 0 0 0 0 0 1 0 0 0 0 7
8 2.3 0 0 0 0 0 0 0 0 1 0 0 0 8
9 1.9 0 0 0 0 0 0 0 0 0 1 0 0 9
10 2.0 0 0 0 0 0 0 0 0 0 0 1 0 10
11 2.3 0 0 0 0 0 0 0 0 0 0 0 1 11
12 2.8 0 0 0 0 0 0 0 0 0 0 0 0 12
13 2.4 0 1 0 0 0 0 0 0 0 0 0 0 13
14 2.3 0 0 1 0 0 0 0 0 0 0 0 0 14
15 2.7 0 0 0 1 0 0 0 0 0 0 0 0 15
16 2.7 0 0 0 0 1 0 0 0 0 0 0 0 16
17 2.9 0 0 0 0 0 1 0 0 0 0 0 0 17
18 3.0 0 0 0 0 0 0 1 0 0 0 0 0 18
19 2.2 0 0 0 0 0 0 0 1 0 0 0 0 19
20 2.3 0 0 0 0 0 0 0 0 1 0 0 0 20
21 2.8 0 0 0 0 0 0 0 0 0 1 0 0 21
22 2.8 0 0 0 0 0 0 0 0 0 0 1 0 22
23 2.8 0 0 0 0 0 0 0 0 0 0 0 1 23
24 2.2 0 0 0 0 0 0 0 0 0 0 0 0 24
25 2.6 0 1 0 0 0 0 0 0 0 0 0 0 25
26 2.8 0 0 1 0 0 0 0 0 0 0 0 0 26
27 2.5 0 0 0 1 0 0 0 0 0 0 0 0 27
28 2.4 0 0 0 0 1 0 0 0 0 0 0 0 28
29 2.3 0 0 0 0 0 1 0 0 0 0 0 0 29
30 1.9 0 0 0 0 0 0 1 0 0 0 0 0 30
31 1.7 0 0 0 0 0 0 0 1 0 0 0 0 31
32 2.0 0 0 0 0 0 0 0 0 1 0 0 0 32
33 2.1 0 0 0 0 0 0 0 0 0 1 0 0 33
34 1.7 0 0 0 0 0 0 0 0 0 0 1 0 34
35 1.8 0 0 0 0 0 0 0 0 0 0 0 1 35
36 1.8 0 0 0 0 0 0 0 0 0 0 0 0 36
37 1.8 0 1 0 0 0 0 0 0 0 0 0 0 37
38 1.3 0 0 1 0 0 0 0 0 0 0 0 0 38
39 1.3 0 0 0 1 0 0 0 0 0 0 0 0 39
40 1.3 0 0 0 0 1 0 0 0 0 0 0 0 40
41 1.2 0 0 0 0 0 1 0 0 0 0 0 0 41
42 1.4 0 0 0 0 0 0 1 0 0 0 0 0 42
43 2.2 1 0 0 0 0 0 0 1 0 0 0 0 43
44 2.9 1 0 0 0 0 0 0 0 1 0 0 0 44
45 3.1 1 0 0 0 0 0 0 0 0 1 0 0 45
46 3.5 1 0 0 0 0 0 0 0 0 0 1 0 46
47 3.6 1 0 0 0 0 0 0 0 0 0 0 1 47
48 4.4 1 0 0 0 0 0 0 0 0 0 0 0 48
49 4.1 1 1 0 0 0 0 0 0 0 0 0 0 49
50 5.1 1 0 1 0 0 0 0 0 0 0 0 0 50
51 5.8 1 0 0 1 0 0 0 0 0 0 0 0 51
52 5.9 1 0 0 0 1 0 0 0 0 0 0 0 52
53 5.4 1 0 0 0 0 1 0 0 0 0 0 0 53
54 5.5 1 0 0 0 0 0 1 0 0 0 0 0 54
55 4.8 1 0 0 0 0 0 0 1 0 0 0 0 55
56 3.2 1 0 0 0 0 0 0 0 1 0 0 0 56
57 2.7 1 0 0 0 0 0 0 0 0 1 0 0 57
58 2.1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 1.9 1 0 0 0 0 0 0 0 0 0 0 1 59
60 0.6 1 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
2.19333 2.31667 0.39111 0.67222 0.77333 0.81444
M5 M6 M7 M8 M9 M10
0.71556 0.69667 0.25444 0.09556 0.09667 0.01778
M11 t
0.09889 -0.02111
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.6433 -0.6283 0.0400 0.4850 1.6733
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.19333 0.51571 4.253 0.000102 ***
X 2.31667 0.44222 5.239 3.93e-06 ***
M1 0.39111 0.59861 0.653 0.516774
M2 0.67222 0.59759 1.125 0.266471
M3 0.77333 0.59679 1.296 0.201503
M4 0.81444 0.59622 1.366 0.178579
M5 0.71556 0.59588 1.201 0.235962
M6 0.69667 0.59577 1.169 0.248283
M7 0.25444 0.59616 0.427 0.671509
M8 0.09556 0.59513 0.161 0.873141
M9 0.09667 0.59433 0.163 0.871508
M10 0.01778 0.59376 0.030 0.976244
M11 0.09889 0.59342 0.167 0.868381
t -0.02111 0.01165 -1.812 0.076585 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.9381 on 46 degrees of freedom
Multiple R-squared: 0.4881, Adjusted R-squared: 0.3435
F-statistic: 3.375 on 13 and 46 DF, p-value: 0.001124
> 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,] 4.303708e-02 8.607416e-02 0.9569629
[2,] 2.620330e-02 5.240660e-02 0.9737967
[3,] 4.512343e-02 9.024686e-02 0.9548766
[4,] 2.218834e-02 4.437668e-02 0.9778117
[5,] 1.050637e-02 2.101274e-02 0.9894936
[6,] 4.406279e-03 8.812558e-03 0.9955937
[7,] 1.605797e-03 3.211595e-03 0.9983942
[8,] 2.478811e-03 4.957622e-03 0.9975212
[9,] 8.910194e-04 1.782039e-03 0.9991090
[10,] 3.106086e-04 6.212173e-04 0.9996894
[11,] 1.415601e-04 2.831202e-04 0.9998584
[12,] 7.699629e-05 1.539926e-04 0.9999230
[13,] 4.726583e-05 9.453165e-05 0.9999527
[14,] 7.000288e-05 1.400058e-04 0.9999300
[15,] 9.365437e-05 1.873087e-04 0.9999063
[16,] 5.249326e-05 1.049865e-04 0.9999475
[17,] 3.354450e-05 6.708900e-05 0.9999665
[18,] 3.802492e-05 7.604983e-05 0.9999620
[19,] 6.157441e-05 1.231488e-04 0.9999384
[20,] 2.675906e-04 5.351813e-04 0.9997324
[21,] 2.814925e-04 5.629850e-04 0.9997185
[22,] 3.344998e-04 6.689995e-04 0.9996655
[23,] 2.325529e-04 4.651057e-04 0.9997674
[24,] 1.374196e-04 2.748392e-04 0.9998626
[25,] 7.340837e-05 1.468167e-04 0.9999266
[26,] 2.328329e-05 4.656659e-05 0.9999767
[27,] 2.221716e-03 4.443433e-03 0.9977783
> postscript(file="/var/www/html/rcomp/tmp/15aar1258577335.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2iqwe1258577335.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bfef1258577335.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fmpi1258577335.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5pvju1258577335.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
-8.633333e-01 -4.233333e-01 -9.033333e-01 -8.233333e-01 -8.033333e-01
6 7 8 9 10
-9.633333e-01 4.000000e-01 1.800000e-01 -2.000000e-01 1.665335e-16
11 12 13 14 15
2.400000e-01 8.600000e-01 9.000000e-02 -2.700000e-01 5.000000e-02
16 17 18 19 20
3.000000e-02 3.500000e-01 4.900000e-01 1.533333e-01 4.333333e-01
21 22 23 24 25
9.533333e-01 1.053333e+00 9.933333e-01 5.133333e-01 5.433333e-01
26 27 28 29 30
4.833333e-01 1.033333e-01 -1.666667e-02 3.333333e-03 -3.566667e-01
31 32 33 34 35
-9.333333e-02 3.866667e-01 5.066667e-01 2.066667e-01 2.466667e-01
36 37 38 39 40
3.666667e-01 -3.333333e-03 -7.633333e-01 -8.433333e-01 -8.633333e-01
41 42 43 44 45
-8.433333e-01 -6.033333e-01 -1.656667e+00 -7.766667e-01 -5.566667e-01
46 47 48 49 50
-5.666667e-02 -1.666667e-02 9.033333e-01 2.333333e-01 9.733333e-01
51 52 53 54 55
1.593333e+00 1.673333e+00 1.293333e+00 1.433333e+00 1.196667e+00
56 57 58 59 60
-2.233333e-01 -7.033333e-01 -1.203333e+00 -1.463333e+00 -2.643333e+00
> postscript(file="/var/www/html/rcomp/tmp/6h7sx1258577335.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 -8.633333e-01 NA
1 -4.233333e-01 -8.633333e-01
2 -9.033333e-01 -4.233333e-01
3 -8.233333e-01 -9.033333e-01
4 -8.033333e-01 -8.233333e-01
5 -9.633333e-01 -8.033333e-01
6 4.000000e-01 -9.633333e-01
7 1.800000e-01 4.000000e-01
8 -2.000000e-01 1.800000e-01
9 1.665335e-16 -2.000000e-01
10 2.400000e-01 1.665335e-16
11 8.600000e-01 2.400000e-01
12 9.000000e-02 8.600000e-01
13 -2.700000e-01 9.000000e-02
14 5.000000e-02 -2.700000e-01
15 3.000000e-02 5.000000e-02
16 3.500000e-01 3.000000e-02
17 4.900000e-01 3.500000e-01
18 1.533333e-01 4.900000e-01
19 4.333333e-01 1.533333e-01
20 9.533333e-01 4.333333e-01
21 1.053333e+00 9.533333e-01
22 9.933333e-01 1.053333e+00
23 5.133333e-01 9.933333e-01
24 5.433333e-01 5.133333e-01
25 4.833333e-01 5.433333e-01
26 1.033333e-01 4.833333e-01
27 -1.666667e-02 1.033333e-01
28 3.333333e-03 -1.666667e-02
29 -3.566667e-01 3.333333e-03
30 -9.333333e-02 -3.566667e-01
31 3.866667e-01 -9.333333e-02
32 5.066667e-01 3.866667e-01
33 2.066667e-01 5.066667e-01
34 2.466667e-01 2.066667e-01
35 3.666667e-01 2.466667e-01
36 -3.333333e-03 3.666667e-01
37 -7.633333e-01 -3.333333e-03
38 -8.433333e-01 -7.633333e-01
39 -8.633333e-01 -8.433333e-01
40 -8.433333e-01 -8.633333e-01
41 -6.033333e-01 -8.433333e-01
42 -1.656667e+00 -6.033333e-01
43 -7.766667e-01 -1.656667e+00
44 -5.566667e-01 -7.766667e-01
45 -5.666667e-02 -5.566667e-01
46 -1.666667e-02 -5.666667e-02
47 9.033333e-01 -1.666667e-02
48 2.333333e-01 9.033333e-01
49 9.733333e-01 2.333333e-01
50 1.593333e+00 9.733333e-01
51 1.673333e+00 1.593333e+00
52 1.293333e+00 1.673333e+00
53 1.433333e+00 1.293333e+00
54 1.196667e+00 1.433333e+00
55 -2.233333e-01 1.196667e+00
56 -7.033333e-01 -2.233333e-01
57 -1.203333e+00 -7.033333e-01
58 -1.463333e+00 -1.203333e+00
59 -2.643333e+00 -1.463333e+00
60 NA -2.643333e+00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -4.233333e-01 -8.633333e-01
[2,] -9.033333e-01 -4.233333e-01
[3,] -8.233333e-01 -9.033333e-01
[4,] -8.033333e-01 -8.233333e-01
[5,] -9.633333e-01 -8.033333e-01
[6,] 4.000000e-01 -9.633333e-01
[7,] 1.800000e-01 4.000000e-01
[8,] -2.000000e-01 1.800000e-01
[9,] 1.665335e-16 -2.000000e-01
[10,] 2.400000e-01 1.665335e-16
[11,] 8.600000e-01 2.400000e-01
[12,] 9.000000e-02 8.600000e-01
[13,] -2.700000e-01 9.000000e-02
[14,] 5.000000e-02 -2.700000e-01
[15,] 3.000000e-02 5.000000e-02
[16,] 3.500000e-01 3.000000e-02
[17,] 4.900000e-01 3.500000e-01
[18,] 1.533333e-01 4.900000e-01
[19,] 4.333333e-01 1.533333e-01
[20,] 9.533333e-01 4.333333e-01
[21,] 1.053333e+00 9.533333e-01
[22,] 9.933333e-01 1.053333e+00
[23,] 5.133333e-01 9.933333e-01
[24,] 5.433333e-01 5.133333e-01
[25,] 4.833333e-01 5.433333e-01
[26,] 1.033333e-01 4.833333e-01
[27,] -1.666667e-02 1.033333e-01
[28,] 3.333333e-03 -1.666667e-02
[29,] -3.566667e-01 3.333333e-03
[30,] -9.333333e-02 -3.566667e-01
[31,] 3.866667e-01 -9.333333e-02
[32,] 5.066667e-01 3.866667e-01
[33,] 2.066667e-01 5.066667e-01
[34,] 2.466667e-01 2.066667e-01
[35,] 3.666667e-01 2.466667e-01
[36,] -3.333333e-03 3.666667e-01
[37,] -7.633333e-01 -3.333333e-03
[38,] -8.433333e-01 -7.633333e-01
[39,] -8.633333e-01 -8.433333e-01
[40,] -8.433333e-01 -8.633333e-01
[41,] -6.033333e-01 -8.433333e-01
[42,] -1.656667e+00 -6.033333e-01
[43,] -7.766667e-01 -1.656667e+00
[44,] -5.566667e-01 -7.766667e-01
[45,] -5.666667e-02 -5.566667e-01
[46,] -1.666667e-02 -5.666667e-02
[47,] 9.033333e-01 -1.666667e-02
[48,] 2.333333e-01 9.033333e-01
[49,] 9.733333e-01 2.333333e-01
[50,] 1.593333e+00 9.733333e-01
[51,] 1.673333e+00 1.593333e+00
[52,] 1.293333e+00 1.673333e+00
[53,] 1.433333e+00 1.293333e+00
[54,] 1.196667e+00 1.433333e+00
[55,] -2.233333e-01 1.196667e+00
[56,] -7.033333e-01 -2.233333e-01
[57,] -1.203333e+00 -7.033333e-01
[58,] -1.463333e+00 -1.203333e+00
[59,] -2.643333e+00 -1.463333e+00
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -4.233333e-01 -8.633333e-01
2 -9.033333e-01 -4.233333e-01
3 -8.233333e-01 -9.033333e-01
4 -8.033333e-01 -8.233333e-01
5 -9.633333e-01 -8.033333e-01
6 4.000000e-01 -9.633333e-01
7 1.800000e-01 4.000000e-01
8 -2.000000e-01 1.800000e-01
9 1.665335e-16 -2.000000e-01
10 2.400000e-01 1.665335e-16
11 8.600000e-01 2.400000e-01
12 9.000000e-02 8.600000e-01
13 -2.700000e-01 9.000000e-02
14 5.000000e-02 -2.700000e-01
15 3.000000e-02 5.000000e-02
16 3.500000e-01 3.000000e-02
17 4.900000e-01 3.500000e-01
18 1.533333e-01 4.900000e-01
19 4.333333e-01 1.533333e-01
20 9.533333e-01 4.333333e-01
21 1.053333e+00 9.533333e-01
22 9.933333e-01 1.053333e+00
23 5.133333e-01 9.933333e-01
24 5.433333e-01 5.133333e-01
25 4.833333e-01 5.433333e-01
26 1.033333e-01 4.833333e-01
27 -1.666667e-02 1.033333e-01
28 3.333333e-03 -1.666667e-02
29 -3.566667e-01 3.333333e-03
30 -9.333333e-02 -3.566667e-01
31 3.866667e-01 -9.333333e-02
32 5.066667e-01 3.866667e-01
33 2.066667e-01 5.066667e-01
34 2.466667e-01 2.066667e-01
35 3.666667e-01 2.466667e-01
36 -3.333333e-03 3.666667e-01
37 -7.633333e-01 -3.333333e-03
38 -8.433333e-01 -7.633333e-01
39 -8.633333e-01 -8.433333e-01
40 -8.433333e-01 -8.633333e-01
41 -6.033333e-01 -8.433333e-01
42 -1.656667e+00 -6.033333e-01
43 -7.766667e-01 -1.656667e+00
44 -5.566667e-01 -7.766667e-01
45 -5.666667e-02 -5.566667e-01
46 -1.666667e-02 -5.666667e-02
47 9.033333e-01 -1.666667e-02
48 2.333333e-01 9.033333e-01
49 9.733333e-01 2.333333e-01
50 1.593333e+00 9.733333e-01
51 1.673333e+00 1.593333e+00
52 1.293333e+00 1.673333e+00
53 1.433333e+00 1.293333e+00
54 1.196667e+00 1.433333e+00
55 -2.233333e-01 1.196667e+00
56 -7.033333e-01 -2.233333e-01
57 -1.203333e+00 -7.033333e-01
58 -1.463333e+00 -1.203333e+00
59 -2.643333e+00 -1.463333e+00
> 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/7jmum1258577335.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hjy61258577335.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9vfbz1258577335.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10usaw1258577335.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11d6eo1258577335.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/12mk5r1258577335.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/13wu6y1258577335.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/14w6c11258577335.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/154jd81258577336.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/166jcr1258577336.tab")
+ }
>
> system("convert tmp/15aar1258577335.ps tmp/15aar1258577335.png")
> system("convert tmp/2iqwe1258577335.ps tmp/2iqwe1258577335.png")
> system("convert tmp/3bfef1258577335.ps tmp/3bfef1258577335.png")
> system("convert tmp/4fmpi1258577335.ps tmp/4fmpi1258577335.png")
> system("convert tmp/5pvju1258577335.ps tmp/5pvju1258577335.png")
> system("convert tmp/6h7sx1258577335.ps tmp/6h7sx1258577335.png")
> system("convert tmp/7jmum1258577335.ps tmp/7jmum1258577335.png")
> system("convert tmp/8hjy61258577335.ps tmp/8hjy61258577335.png")
> system("convert tmp/9vfbz1258577335.ps tmp/9vfbz1258577335.png")
> system("convert tmp/10usaw1258577335.ps tmp/10usaw1258577335.png")
>
>
> proc.time()
user system elapsed
2.409 1.557 2.864