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(7.2
+ ,97.78
+ ,7.5
+ ,8.3
+ ,8.9
+ ,7.4
+ ,97.69
+ ,7.2
+ ,7.5
+ ,8.8
+ ,8.8
+ ,96.67
+ ,7.4
+ ,7.2
+ ,8.3
+ ,9.3
+ ,98.29
+ ,8.8
+ ,7.4
+ ,7.5
+ ,9.3
+ ,98.2
+ ,9.3
+ ,8.8
+ ,7.2
+ ,8.7
+ ,98.71
+ ,9.3
+ ,9.3
+ ,7.4
+ ,8.2
+ ,98.54
+ ,8.7
+ ,9.3
+ ,8.8
+ ,8.3
+ ,98.2
+ ,8.2
+ ,8.7
+ ,9.3
+ ,8.5
+ ,96.92
+ ,8.3
+ ,8.2
+ ,9.3
+ ,8.6
+ ,99.06
+ ,8.5
+ ,8.3
+ ,8.7
+ ,8.5
+ ,99.65
+ ,8.6
+ ,8.5
+ ,8.2
+ ,8.2
+ ,99.82
+ ,8.5
+ ,8.6
+ ,8.3
+ ,8.1
+ ,99.99
+ ,8.2
+ ,8.5
+ ,8.5
+ ,7.9
+ ,100.33
+ ,8.1
+ ,8.2
+ ,8.6
+ ,8.6
+ ,99.31
+ ,7.9
+ ,8.1
+ ,8.5
+ ,8.7
+ ,101.1
+ ,8.6
+ ,7.9
+ ,8.2
+ ,8.7
+ ,101.1
+ ,8.7
+ ,8.6
+ ,8.1
+ ,8.5
+ ,100.93
+ ,8.7
+ ,8.7
+ ,7.9
+ ,8.4
+ ,100.85
+ ,8.5
+ ,8.7
+ ,8.6
+ ,8.5
+ ,100.93
+ ,8.4
+ ,8.5
+ ,8.7
+ ,8.7
+ ,99.6
+ ,8.5
+ ,8.4
+ ,8.7
+ ,8.7
+ ,101.88
+ ,8.7
+ ,8.5
+ ,8.5
+ ,8.6
+ ,101.81
+ ,8.7
+ ,8.7
+ ,8.4
+ ,8.5
+ ,102.38
+ ,8.6
+ ,8.7
+ ,8.5
+ ,8.3
+ ,102.74
+ ,8.5
+ ,8.6
+ ,8.7
+ ,8
+ ,102.82
+ ,8.3
+ ,8.5
+ ,8.7
+ ,8.2
+ ,101.72
+ ,8
+ ,8.3
+ ,8.6
+ ,8.1
+ ,103.47
+ ,8.2
+ ,8
+ ,8.5
+ ,8.1
+ ,102.98
+ ,8.1
+ ,8.2
+ ,8.3
+ ,8
+ ,102.68
+ ,8.1
+ ,8.1
+ ,8
+ ,7.9
+ ,102.9
+ ,8
+ ,8.1
+ ,8.2
+ ,7.9
+ ,103.03
+ ,7.9
+ ,8
+ ,8.1
+ ,8
+ ,101.29
+ ,7.9
+ ,7.9
+ ,8.1
+ ,8
+ ,103.69
+ ,8
+ ,7.9
+ ,8
+ ,7.9
+ ,103.68
+ ,8
+ ,8
+ ,7.9
+ ,8
+ ,104.2
+ ,7.9
+ ,8
+ ,7.9
+ ,7.7
+ ,104.08
+ ,8
+ ,7.9
+ ,8
+ ,7.2
+ ,104.16
+ ,7.7
+ ,8
+ ,8
+ ,7.5
+ ,103.05
+ ,7.2
+ ,7.7
+ ,7.9
+ ,7.3
+ ,104.66
+ ,7.5
+ ,7.2
+ ,8
+ ,7
+ ,104.46
+ ,7.3
+ ,7.5
+ ,7.7
+ ,7
+ ,104.95
+ ,7
+ ,7.3
+ ,7.2
+ ,7
+ ,105.85
+ ,7
+ ,7
+ ,7.5
+ ,7.2
+ ,106.23
+ ,7
+ ,7
+ ,7.3
+ ,7.3
+ ,104.86
+ ,7.2
+ ,7
+ ,7
+ ,7.1
+ ,107.44
+ ,7.3
+ ,7.2
+ ,7
+ ,6.8
+ ,108.23
+ ,7.1
+ ,7.3
+ ,7
+ ,6.4
+ ,108.45
+ ,6.8
+ ,7.1
+ ,7.2
+ ,6.1
+ ,109.39
+ ,6.4
+ ,6.8
+ ,7.3
+ ,6.5
+ ,110.15
+ ,6.1
+ ,6.4
+ ,7.1
+ ,7.7
+ ,109.13
+ ,6.5
+ ,6.1
+ ,6.8
+ ,7.9
+ ,110.28
+ ,7.7
+ ,6.5
+ ,6.4
+ ,7.5
+ ,110.17
+ ,7.9
+ ,7.7
+ ,6.1
+ ,6.9
+ ,109.99
+ ,7.5
+ ,7.9
+ ,6.5
+ ,6.6
+ ,109.26
+ ,6.9
+ ,7.5
+ ,7.7
+ ,6.9
+ ,109.11
+ ,6.6
+ ,6.9
+ ,7.9)
+ ,dim=c(5
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(5,56),dimnames=list(c('Y','X','Y1','Y2','Y4'),1:56))
> 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 Y1 Y2 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.2 97.78 7.5 8.3 8.9 1 0 0 0 0 0 0 0 0 0 0 1
2 7.4 97.69 7.2 7.5 8.8 0 1 0 0 0 0 0 0 0 0 0 2
3 8.8 96.67 7.4 7.2 8.3 0 0 1 0 0 0 0 0 0 0 0 3
4 9.3 98.29 8.8 7.4 7.5 0 0 0 1 0 0 0 0 0 0 0 4
5 9.3 98.20 9.3 8.8 7.2 0 0 0 0 1 0 0 0 0 0 0 5
6 8.7 98.71 9.3 9.3 7.4 0 0 0 0 0 1 0 0 0 0 0 6
7 8.2 98.54 8.7 9.3 8.8 0 0 0 0 0 0 1 0 0 0 0 7
8 8.3 98.20 8.2 8.7 9.3 0 0 0 0 0 0 0 1 0 0 0 8
9 8.5 96.92 8.3 8.2 9.3 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 99.06 8.5 8.3 8.7 0 0 0 0 0 0 0 0 0 1 0 10
11 8.5 99.65 8.6 8.5 8.2 0 0 0 0 0 0 0 0 0 0 1 11
12 8.2 99.82 8.5 8.6 8.3 0 0 0 0 0 0 0 0 0 0 0 12
13 8.1 99.99 8.2 8.5 8.5 1 0 0 0 0 0 0 0 0 0 0 13
14 7.9 100.33 8.1 8.2 8.6 0 1 0 0 0 0 0 0 0 0 0 14
15 8.6 99.31 7.9 8.1 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 101.10 8.6 7.9 8.2 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 101.10 8.7 8.6 8.1 0 0 0 0 1 0 0 0 0 0 0 17
18 8.5 100.93 8.7 8.7 7.9 0 0 0 0 0 1 0 0 0 0 0 18
19 8.4 100.85 8.5 8.7 8.6 0 0 0 0 0 0 1 0 0 0 0 19
20 8.5 100.93 8.4 8.5 8.7 0 0 0 0 0 0 0 1 0 0 0 20
21 8.7 99.60 8.5 8.4 8.7 0 0 0 0 0 0 0 0 1 0 0 21
22 8.7 101.88 8.7 8.5 8.5 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 101.81 8.7 8.7 8.4 0 0 0 0 0 0 0 0 0 0 1 23
24 8.5 102.38 8.6 8.7 8.5 0 0 0 0 0 0 0 0 0 0 0 24
25 8.3 102.74 8.5 8.6 8.7 1 0 0 0 0 0 0 0 0 0 0 25
26 8.0 102.82 8.3 8.5 8.7 0 1 0 0 0 0 0 0 0 0 0 26
27 8.2 101.72 8.0 8.3 8.6 0 0 1 0 0 0 0 0 0 0 0 27
28 8.1 103.47 8.2 8.0 8.5 0 0 0 1 0 0 0 0 0 0 0 28
29 8.1 102.98 8.1 8.2 8.3 0 0 0 0 1 0 0 0 0 0 0 29
30 8.0 102.68 8.1 8.1 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 7.9 102.90 8.0 8.1 8.2 0 0 0 0 0 0 1 0 0 0 0 31
32 7.9 103.03 7.9 8.0 8.1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.0 101.29 7.9 7.9 8.1 0 0 0 0 0 0 0 0 1 0 0 33
34 8.0 103.69 8.0 7.9 8.0 0 0 0 0 0 0 0 0 0 1 0 34
35 7.9 103.68 8.0 8.0 7.9 0 0 0 0 0 0 0 0 0 0 1 35
36 8.0 104.20 7.9 8.0 7.9 0 0 0 0 0 0 0 0 0 0 0 36
37 7.7 104.08 8.0 7.9 8.0 1 0 0 0 0 0 0 0 0 0 0 37
38 7.2 104.16 7.7 8.0 8.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.5 103.05 7.2 7.7 7.9 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 104.66 7.5 7.2 8.0 0 0 0 1 0 0 0 0 0 0 0 40
41 7.0 104.46 7.3 7.5 7.7 0 0 0 0 1 0 0 0 0 0 0 41
42 7.0 104.95 7.0 7.3 7.2 0 0 0 0 0 1 0 0 0 0 0 42
43 7.0 105.85 7.0 7.0 7.5 0 0 0 0 0 0 1 0 0 0 0 43
44 7.2 106.23 7.0 7.0 7.3 0 0 0 0 0 0 0 1 0 0 0 44
45 7.3 104.86 7.2 7.0 7.0 0 0 0 0 0 0 0 0 1 0 0 45
46 7.1 107.44 7.3 7.2 7.0 0 0 0 0 0 0 0 0 0 1 0 46
47 6.8 108.23 7.1 7.3 7.0 0 0 0 0 0 0 0 0 0 0 1 47
48 6.4 108.45 6.8 7.1 7.2 0 0 0 0 0 0 0 0 0 0 0 48
49 6.1 109.39 6.4 6.8 7.3 1 0 0 0 0 0 0 0 0 0 0 49
50 6.5 110.15 6.1 6.4 7.1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.7 109.13 6.5 6.1 6.8 0 0 1 0 0 0 0 0 0 0 0 51
52 7.9 110.28 7.7 6.5 6.4 0 0 0 1 0 0 0 0 0 0 0 52
53 7.5 110.17 7.9 7.7 6.1 0 0 0 0 1 0 0 0 0 0 0 53
54 6.9 109.99 7.5 7.9 6.5 0 0 0 0 0 1 0 0 0 0 0 54
55 6.6 109.26 6.9 7.5 7.7 0 0 0 0 0 0 1 0 0 0 0 55
56 6.9 109.11 6.6 6.9 7.9 0 0 0 0 0 0 0 1 0 0 0 56
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 Y4 M1
-3.9251543 0.0512518 1.5166349 -0.9119918 0.2789782 -0.1422935
M2 M3 M4 M5 M6 M7
-0.1157237 0.6791325 -0.4269499 0.0675868 0.1068543 0.0377163
M8 M9 M10 M11 t
0.1955317 0.1282279 -0.0737311 0.0001567 -0.0170088
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.270834 -0.068003 -0.004002 0.066348 0.350461
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.9251543 3.1781995 -1.235 0.224210
X 0.0512518 0.0294576 1.740 0.089772 .
Y1 1.5166349 0.0991370 15.298 < 2e-16 ***
Y2 -0.9119918 0.1103876 -8.262 4.25e-10 ***
Y4 0.2789782 0.0681766 4.092 0.000208 ***
M1 -0.1422935 0.1010806 -1.408 0.167134
M2 -0.1157237 0.1038187 -1.115 0.271814
M3 0.6791325 0.1098942 6.180 2.91e-07 ***
M4 -0.4269499 0.1306582 -3.268 0.002267 **
M5 0.0675868 0.1039885 0.650 0.519538
M6 0.1068543 0.1084520 0.985 0.330566
M7 0.0377163 0.0993789 0.380 0.706361
M8 0.1955317 0.1022139 1.913 0.063115 .
M9 0.1282279 0.1262947 1.015 0.316217
M10 -0.0737311 0.1080958 -0.682 0.499213
M11 0.0001567 0.1042280 0.002 0.998808
t -0.0170088 0.0062224 -2.733 0.009373 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1463 on 39 degrees of freedom
Multiple R-squared: 0.9726, Adjusted R-squared: 0.9613
F-statistic: 86.45 on 16 and 39 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,] 0.11859704 0.23719408 0.8814030
[2,] 0.15864194 0.31728389 0.8413581
[3,] 0.08519169 0.17038337 0.9148083
[4,] 0.03755343 0.07510686 0.9624466
[5,] 0.06137235 0.12274470 0.9386277
[6,] 0.03188573 0.06377146 0.9681143
[7,] 0.01689460 0.03378919 0.9831054
[8,] 0.28295610 0.56591219 0.7170439
[9,] 0.19484790 0.38969579 0.8051521
[10,] 0.15643208 0.31286417 0.8435679
[11,] 0.18090361 0.36180723 0.8190964
[12,] 0.14019685 0.28039371 0.8598031
[13,] 0.12348387 0.24696774 0.8765161
[14,] 0.09553246 0.19106493 0.9044675
[15,] 0.06625229 0.13250457 0.9337477
[16,] 0.03357354 0.06714708 0.9664265
[17,] 0.17883775 0.35767550 0.8211623
> postscript(file="/var/www/html/rcomp/tmp/1guz31258557357.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ziui1258557357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3isy61258557357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ehgy1258557357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5yj701258557357.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 = 56
Frequency = 1
1 2 3 4 5
-0.0150817784 -0.0667353107 0.1702587445 -0.0073858408 0.1218635053
6 7 8 9 10
-0.1263333373 -0.0120621878 0.0361901193 -0.2215542714 -0.0570063219
11 12 13 14 15
-0.0738999206 -0.1504823221 0.2081028421 -0.1687156723 0.0457394221
16 17 18 19 20
0.0167406201 0.0538413448 -0.0127097174 0.0855795373 -0.0179598764
21 22 23 24 25
0.0916550475 0.0374364789 0.0944413073 0.1061589950 0.0516793423
26 27 28 29 30
-0.0498540352 -0.2708344906 0.1135393933 0.0509823939 -0.0634064770
31 32 33 34 35
0.0073328232 -0.0517743696 0.1305172877 0.1027149885 0.0654455266
36 37 38 39 40
0.3076236211 -0.0976843097 -0.0651558406 0.0265040114 -0.0718043271
41 42 43 44 45
-0.1784638743 0.1862452182 -0.1310255658 -0.0355122035 -0.0006180638
46 47 48 49 50
-0.0831451455 -0.0859869133 -0.2633002940 -0.1470160963 0.3504608588
51 52 53 54 55
0.0283323127 -0.0510898456 -0.0482233696 0.0162043135 0.0501753931
56
0.0690563302
> postscript(file="/var/www/html/rcomp/tmp/6ooo91258557357.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 = 56
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0150817784 NA
1 -0.0667353107 -0.0150817784
2 0.1702587445 -0.0667353107
3 -0.0073858408 0.1702587445
4 0.1218635053 -0.0073858408
5 -0.1263333373 0.1218635053
6 -0.0120621878 -0.1263333373
7 0.0361901193 -0.0120621878
8 -0.2215542714 0.0361901193
9 -0.0570063219 -0.2215542714
10 -0.0738999206 -0.0570063219
11 -0.1504823221 -0.0738999206
12 0.2081028421 -0.1504823221
13 -0.1687156723 0.2081028421
14 0.0457394221 -0.1687156723
15 0.0167406201 0.0457394221
16 0.0538413448 0.0167406201
17 -0.0127097174 0.0538413448
18 0.0855795373 -0.0127097174
19 -0.0179598764 0.0855795373
20 0.0916550475 -0.0179598764
21 0.0374364789 0.0916550475
22 0.0944413073 0.0374364789
23 0.1061589950 0.0944413073
24 0.0516793423 0.1061589950
25 -0.0498540352 0.0516793423
26 -0.2708344906 -0.0498540352
27 0.1135393933 -0.2708344906
28 0.0509823939 0.1135393933
29 -0.0634064770 0.0509823939
30 0.0073328232 -0.0634064770
31 -0.0517743696 0.0073328232
32 0.1305172877 -0.0517743696
33 0.1027149885 0.1305172877
34 0.0654455266 0.1027149885
35 0.3076236211 0.0654455266
36 -0.0976843097 0.3076236211
37 -0.0651558406 -0.0976843097
38 0.0265040114 -0.0651558406
39 -0.0718043271 0.0265040114
40 -0.1784638743 -0.0718043271
41 0.1862452182 -0.1784638743
42 -0.1310255658 0.1862452182
43 -0.0355122035 -0.1310255658
44 -0.0006180638 -0.0355122035
45 -0.0831451455 -0.0006180638
46 -0.0859869133 -0.0831451455
47 -0.2633002940 -0.0859869133
48 -0.1470160963 -0.2633002940
49 0.3504608588 -0.1470160963
50 0.0283323127 0.3504608588
51 -0.0510898456 0.0283323127
52 -0.0482233696 -0.0510898456
53 0.0162043135 -0.0482233696
54 0.0501753931 0.0162043135
55 0.0690563302 0.0501753931
56 NA 0.0690563302
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0667353107 -0.0150817784
[2,] 0.1702587445 -0.0667353107
[3,] -0.0073858408 0.1702587445
[4,] 0.1218635053 -0.0073858408
[5,] -0.1263333373 0.1218635053
[6,] -0.0120621878 -0.1263333373
[7,] 0.0361901193 -0.0120621878
[8,] -0.2215542714 0.0361901193
[9,] -0.0570063219 -0.2215542714
[10,] -0.0738999206 -0.0570063219
[11,] -0.1504823221 -0.0738999206
[12,] 0.2081028421 -0.1504823221
[13,] -0.1687156723 0.2081028421
[14,] 0.0457394221 -0.1687156723
[15,] 0.0167406201 0.0457394221
[16,] 0.0538413448 0.0167406201
[17,] -0.0127097174 0.0538413448
[18,] 0.0855795373 -0.0127097174
[19,] -0.0179598764 0.0855795373
[20,] 0.0916550475 -0.0179598764
[21,] 0.0374364789 0.0916550475
[22,] 0.0944413073 0.0374364789
[23,] 0.1061589950 0.0944413073
[24,] 0.0516793423 0.1061589950
[25,] -0.0498540352 0.0516793423
[26,] -0.2708344906 -0.0498540352
[27,] 0.1135393933 -0.2708344906
[28,] 0.0509823939 0.1135393933
[29,] -0.0634064770 0.0509823939
[30,] 0.0073328232 -0.0634064770
[31,] -0.0517743696 0.0073328232
[32,] 0.1305172877 -0.0517743696
[33,] 0.1027149885 0.1305172877
[34,] 0.0654455266 0.1027149885
[35,] 0.3076236211 0.0654455266
[36,] -0.0976843097 0.3076236211
[37,] -0.0651558406 -0.0976843097
[38,] 0.0265040114 -0.0651558406
[39,] -0.0718043271 0.0265040114
[40,] -0.1784638743 -0.0718043271
[41,] 0.1862452182 -0.1784638743
[42,] -0.1310255658 0.1862452182
[43,] -0.0355122035 -0.1310255658
[44,] -0.0006180638 -0.0355122035
[45,] -0.0831451455 -0.0006180638
[46,] -0.0859869133 -0.0831451455
[47,] -0.2633002940 -0.0859869133
[48,] -0.1470160963 -0.2633002940
[49,] 0.3504608588 -0.1470160963
[50,] 0.0283323127 0.3504608588
[51,] -0.0510898456 0.0283323127
[52,] -0.0482233696 -0.0510898456
[53,] 0.0162043135 -0.0482233696
[54,] 0.0501753931 0.0162043135
[55,] 0.0690563302 0.0501753931
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0667353107 -0.0150817784
2 0.1702587445 -0.0667353107
3 -0.0073858408 0.1702587445
4 0.1218635053 -0.0073858408
5 -0.1263333373 0.1218635053
6 -0.0120621878 -0.1263333373
7 0.0361901193 -0.0120621878
8 -0.2215542714 0.0361901193
9 -0.0570063219 -0.2215542714
10 -0.0738999206 -0.0570063219
11 -0.1504823221 -0.0738999206
12 0.2081028421 -0.1504823221
13 -0.1687156723 0.2081028421
14 0.0457394221 -0.1687156723
15 0.0167406201 0.0457394221
16 0.0538413448 0.0167406201
17 -0.0127097174 0.0538413448
18 0.0855795373 -0.0127097174
19 -0.0179598764 0.0855795373
20 0.0916550475 -0.0179598764
21 0.0374364789 0.0916550475
22 0.0944413073 0.0374364789
23 0.1061589950 0.0944413073
24 0.0516793423 0.1061589950
25 -0.0498540352 0.0516793423
26 -0.2708344906 -0.0498540352
27 0.1135393933 -0.2708344906
28 0.0509823939 0.1135393933
29 -0.0634064770 0.0509823939
30 0.0073328232 -0.0634064770
31 -0.0517743696 0.0073328232
32 0.1305172877 -0.0517743696
33 0.1027149885 0.1305172877
34 0.0654455266 0.1027149885
35 0.3076236211 0.0654455266
36 -0.0976843097 0.3076236211
37 -0.0651558406 -0.0976843097
38 0.0265040114 -0.0651558406
39 -0.0718043271 0.0265040114
40 -0.1784638743 -0.0718043271
41 0.1862452182 -0.1784638743
42 -0.1310255658 0.1862452182
43 -0.0355122035 -0.1310255658
44 -0.0006180638 -0.0355122035
45 -0.0831451455 -0.0006180638
46 -0.0859869133 -0.0831451455
47 -0.2633002940 -0.0859869133
48 -0.1470160963 -0.2633002940
49 0.3504608588 -0.1470160963
50 0.0283323127 0.3504608588
51 -0.0510898456 0.0283323127
52 -0.0482233696 -0.0510898456
53 0.0162043135 -0.0482233696
54 0.0501753931 0.0162043135
55 0.0690563302 0.0501753931
> 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/7m09e1258557357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8p92j1258557357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9e7fm1258557357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/1023ko1258557357.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11kh0u1258557357.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/12oyv11258557358.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/13fbun1258557358.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/14nm6f1258557358.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/1532al1258557358.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/16y4851258557358.tab")
+ }
>
> system("convert tmp/1guz31258557357.ps tmp/1guz31258557357.png")
> system("convert tmp/2ziui1258557357.ps tmp/2ziui1258557357.png")
> system("convert tmp/3isy61258557357.ps tmp/3isy61258557357.png")
> system("convert tmp/4ehgy1258557357.ps tmp/4ehgy1258557357.png")
> system("convert tmp/5yj701258557357.ps tmp/5yj701258557357.png")
> system("convert tmp/6ooo91258557357.ps tmp/6ooo91258557357.png")
> system("convert tmp/7m09e1258557357.ps tmp/7m09e1258557357.png")
> system("convert tmp/8p92j1258557357.ps tmp/8p92j1258557357.png")
> system("convert tmp/9e7fm1258557357.ps tmp/9e7fm1258557357.png")
> system("convert tmp/1023ko1258557357.ps tmp/1023ko1258557357.png")
>
>
> proc.time()
user system elapsed
2.358 1.667 3.108