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(7.59
+ ,43.14
+ ,7.59
+ ,7.59
+ ,7.55
+ ,7.55
+ ,7.57
+ ,43.39
+ ,7.59
+ ,7.59
+ ,7.59
+ ,7.55
+ ,7.57
+ ,43.46
+ ,7.57
+ ,7.59
+ ,7.59
+ ,7.59
+ ,7.59
+ ,43.54
+ ,7.57
+ ,7.57
+ ,7.59
+ ,7.59
+ ,7.6
+ ,43.62
+ ,7.59
+ ,7.57
+ ,7.57
+ ,7.59
+ ,7.64
+ ,44.01
+ ,7.6
+ ,7.59
+ ,7.57
+ ,7.57
+ ,7.64
+ ,44.5
+ ,7.64
+ ,7.6
+ ,7.59
+ ,7.57
+ ,7.76
+ ,44.73
+ ,7.64
+ ,7.64
+ ,7.6
+ ,7.59
+ ,7.76
+ ,44.89
+ ,7.76
+ ,7.64
+ ,7.64
+ ,7.6
+ ,7.76
+ ,45.09
+ ,7.76
+ ,7.76
+ ,7.64
+ ,7.64
+ ,7.77
+ ,45.17
+ ,7.76
+ ,7.76
+ ,7.76
+ ,7.64
+ ,7.83
+ ,45.24
+ ,7.77
+ ,7.76
+ ,7.76
+ ,7.76
+ ,7.94
+ ,45.42
+ ,7.83
+ ,7.77
+ ,7.76
+ ,7.76
+ ,7.94
+ ,45.67
+ ,7.94
+ ,7.83
+ ,7.77
+ ,7.76
+ ,7.94
+ ,45.68
+ ,7.94
+ ,7.94
+ ,7.83
+ ,7.77
+ ,8.09
+ ,46.56
+ ,7.94
+ ,7.94
+ ,7.94
+ ,7.83
+ ,8.18
+ ,46.72
+ ,8.09
+ ,7.94
+ ,7.94
+ ,7.94
+ ,8.26
+ ,47.01
+ ,8.18
+ ,8.09
+ ,7.94
+ ,7.94
+ ,8.28
+ ,47.26
+ ,8.26
+ ,8.18
+ ,8.09
+ ,7.94
+ ,8.28
+ ,47.49
+ ,8.28
+ ,8.26
+ ,8.18
+ ,8.09
+ ,8.28
+ ,47.51
+ ,8.28
+ ,8.28
+ ,8.26
+ ,8.18
+ ,8.29
+ ,47.52
+ ,8.28
+ ,8.28
+ ,8.28
+ ,8.26
+ ,8.3
+ ,47.66
+ ,8.29
+ ,8.28
+ ,8.28
+ ,8.28
+ ,8.3
+ ,47.71
+ ,8.3
+ ,8.29
+ ,8.28
+ ,8.28
+ ,8.31
+ ,47.87
+ ,8.3
+ ,8.3
+ ,8.29
+ ,8.28
+ ,8.33
+ ,48
+ ,8.31
+ ,8.3
+ ,8.3
+ ,8.29
+ ,8.33
+ ,48
+ ,8.33
+ ,8.31
+ ,8.3
+ ,8.3
+ ,8.34
+ ,48.05
+ ,8.33
+ ,8.33
+ ,8.31
+ ,8.3
+ ,8.48
+ ,48.25
+ ,8.34
+ ,8.33
+ ,8.33
+ ,8.31
+ ,8.59
+ ,48.72
+ ,8.48
+ ,8.34
+ ,8.33
+ ,8.33
+ ,8.67
+ ,48.94
+ ,8.59
+ ,8.48
+ ,8.34
+ ,8.33
+ ,8.67
+ ,49.16
+ ,8.67
+ ,8.59
+ ,8.48
+ ,8.34
+ ,8.67
+ ,49.18
+ ,8.67
+ ,8.67
+ ,8.59
+ ,8.48
+ ,8.71
+ ,49.25
+ ,8.67
+ ,8.67
+ ,8.67
+ ,8.59
+ ,8.72
+ ,49.34
+ ,8.71
+ ,8.67
+ ,8.67
+ ,8.67
+ ,8.72
+ ,49.49
+ ,8.72
+ ,8.71
+ ,8.67
+ ,8.67
+ ,8.72
+ ,49.57
+ ,8.72
+ ,8.72
+ ,8.71
+ ,8.67
+ ,8.74
+ ,49.63
+ ,8.72
+ ,8.72
+ ,8.72
+ ,8.71
+ ,8.74
+ ,49.67
+ ,8.74
+ ,8.72
+ ,8.72
+ ,8.72
+ ,8.74
+ ,49.7
+ ,8.74
+ ,8.74
+ ,8.72
+ ,8.72
+ ,8.74
+ ,49.8
+ ,8.74
+ ,8.74
+ ,8.74
+ ,8.72
+ ,8.79
+ ,50.09
+ ,8.74
+ ,8.74
+ ,8.74
+ ,8.74
+ ,8.85
+ ,50.49
+ ,8.79
+ ,8.74
+ ,8.74
+ ,8.74
+ ,8.86
+ ,50.73
+ ,8.85
+ ,8.79
+ ,8.74
+ ,8.74
+ ,8.87
+ ,51.12
+ ,8.86
+ ,8.85
+ ,8.79
+ ,8.74
+ ,8.92
+ ,51.15
+ ,8.87
+ ,8.86
+ ,8.85
+ ,8.79
+ ,8.96
+ ,51.41
+ ,8.92
+ ,8.87
+ ,8.86
+ ,8.85
+ ,8.97
+ ,51.61
+ ,8.96
+ ,8.92
+ ,8.87
+ ,8.86
+ ,8.99
+ ,52.06
+ ,8.97
+ ,8.96
+ ,8.92
+ ,8.87
+ ,8.98
+ ,52.17
+ ,8.99
+ ,8.97
+ ,8.96
+ ,8.92
+ ,8.98
+ ,52.18
+ ,8.98
+ ,8.99
+ ,8.97
+ ,8.96
+ ,9.01
+ ,52.19
+ ,8.98
+ ,8.98
+ ,8.99
+ ,8.97
+ ,9.01
+ ,52.74
+ ,9.01
+ ,8.98
+ ,8.98
+ ,8.99
+ ,9.03
+ ,53.05
+ ,9.01
+ ,9.01
+ ,8.98
+ ,8.98
+ ,9.05
+ ,53.38
+ ,9.03
+ ,9.01
+ ,9.01
+ ,8.98
+ ,9.05
+ ,53.78
+ ,9.05
+ ,9.03
+ ,9.01
+ ,9.01)
+ ,dim=c(6
+ ,56)
+ ,dimnames=list(c('Y'
+ ,'X'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:56))
> y <- array(NA,dim=c(6,56),dimnames=list(c('Y','X','Y1','Y2','Y3','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 Y3 Y4 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.59 43.14 7.59 7.59 7.55 7.55 1 0 0 0 0 0 0 0 0 0 0 1
2 7.57 43.39 7.59 7.59 7.59 7.55 0 1 0 0 0 0 0 0 0 0 0 2
3 7.57 43.46 7.57 7.59 7.59 7.59 0 0 1 0 0 0 0 0 0 0 0 3
4 7.59 43.54 7.57 7.57 7.59 7.59 0 0 0 1 0 0 0 0 0 0 0 4
5 7.60 43.62 7.59 7.57 7.57 7.59 0 0 0 0 1 0 0 0 0 0 0 5
6 7.64 44.01 7.60 7.59 7.57 7.57 0 0 0 0 0 1 0 0 0 0 0 6
7 7.64 44.50 7.64 7.60 7.59 7.57 0 0 0 0 0 0 1 0 0 0 0 7
8 7.76 44.73 7.64 7.64 7.60 7.59 0 0 0 0 0 0 0 1 0 0 0 8
9 7.76 44.89 7.76 7.64 7.64 7.60 0 0 0 0 0 0 0 0 1 0 0 9
10 7.76 45.09 7.76 7.76 7.64 7.64 0 0 0 0 0 0 0 0 0 1 0 10
11 7.77 45.17 7.76 7.76 7.76 7.64 0 0 0 0 0 0 0 0 0 0 1 11
12 7.83 45.24 7.77 7.76 7.76 7.76 0 0 0 0 0 0 0 0 0 0 0 12
13 7.94 45.42 7.83 7.77 7.76 7.76 1 0 0 0 0 0 0 0 0 0 0 13
14 7.94 45.67 7.94 7.83 7.77 7.76 0 1 0 0 0 0 0 0 0 0 0 14
15 7.94 45.68 7.94 7.94 7.83 7.77 0 0 1 0 0 0 0 0 0 0 0 15
16 8.09 46.56 7.94 7.94 7.94 7.83 0 0 0 1 0 0 0 0 0 0 0 16
17 8.18 46.72 8.09 7.94 7.94 7.94 0 0 0 0 1 0 0 0 0 0 0 17
18 8.26 47.01 8.18 8.09 7.94 7.94 0 0 0 0 0 1 0 0 0 0 0 18
19 8.28 47.26 8.26 8.18 8.09 7.94 0 0 0 0 0 0 1 0 0 0 0 19
20 8.28 47.49 8.28 8.26 8.18 8.09 0 0 0 0 0 0 0 1 0 0 0 20
21 8.28 47.51 8.28 8.28 8.26 8.18 0 0 0 0 0 0 0 0 1 0 0 21
22 8.29 47.52 8.28 8.28 8.28 8.26 0 0 0 0 0 0 0 0 0 1 0 22
23 8.30 47.66 8.29 8.28 8.28 8.28 0 0 0 0 0 0 0 0 0 0 1 23
24 8.30 47.71 8.30 8.29 8.28 8.28 0 0 0 0 0 0 0 0 0 0 0 24
25 8.31 47.87 8.30 8.30 8.29 8.28 1 0 0 0 0 0 0 0 0 0 0 25
26 8.33 48.00 8.31 8.30 8.30 8.29 0 1 0 0 0 0 0 0 0 0 0 26
27 8.33 48.00 8.33 8.31 8.30 8.30 0 0 1 0 0 0 0 0 0 0 0 27
28 8.34 48.05 8.33 8.33 8.31 8.30 0 0 0 1 0 0 0 0 0 0 0 28
29 8.48 48.25 8.34 8.33 8.33 8.31 0 0 0 0 1 0 0 0 0 0 0 29
30 8.59 48.72 8.48 8.34 8.33 8.33 0 0 0 0 0 1 0 0 0 0 0 30
31 8.67 48.94 8.59 8.48 8.34 8.33 0 0 0 0 0 0 1 0 0 0 0 31
32 8.67 49.16 8.67 8.59 8.48 8.34 0 0 0 0 0 0 0 1 0 0 0 32
33 8.67 49.18 8.67 8.67 8.59 8.48 0 0 0 0 0 0 0 0 1 0 0 33
34 8.71 49.25 8.67 8.67 8.67 8.59 0 0 0 0 0 0 0 0 0 1 0 34
35 8.72 49.34 8.71 8.67 8.67 8.67 0 0 0 0 0 0 0 0 0 0 1 35
36 8.72 49.49 8.72 8.71 8.67 8.67 0 0 0 0 0 0 0 0 0 0 0 36
37 8.72 49.57 8.72 8.72 8.71 8.67 1 0 0 0 0 0 0 0 0 0 0 37
38 8.74 49.63 8.72 8.72 8.72 8.71 0 1 0 0 0 0 0 0 0 0 0 38
39 8.74 49.67 8.74 8.72 8.72 8.72 0 0 1 0 0 0 0 0 0 0 0 39
40 8.74 49.70 8.74 8.74 8.72 8.72 0 0 0 1 0 0 0 0 0 0 0 40
41 8.74 49.80 8.74 8.74 8.74 8.72 0 0 0 0 1 0 0 0 0 0 0 41
42 8.79 50.09 8.74 8.74 8.74 8.74 0 0 0 0 0 1 0 0 0 0 0 42
43 8.85 50.49 8.79 8.74 8.74 8.74 0 0 0 0 0 0 1 0 0 0 0 43
44 8.86 50.73 8.85 8.79 8.74 8.74 0 0 0 0 0 0 0 1 0 0 0 44
45 8.87 51.12 8.86 8.85 8.79 8.74 0 0 0 0 0 0 0 0 1 0 0 45
46 8.92 51.15 8.87 8.86 8.85 8.79 0 0 0 0 0 0 0 0 0 1 0 46
47 8.96 51.41 8.92 8.87 8.86 8.85 0 0 0 0 0 0 0 0 0 0 1 47
48 8.97 51.61 8.96 8.92 8.87 8.86 0 0 0 0 0 0 0 0 0 0 0 48
49 8.99 52.06 8.97 8.96 8.92 8.87 1 0 0 0 0 0 0 0 0 0 0 49
50 8.98 52.17 8.99 8.97 8.96 8.92 0 1 0 0 0 0 0 0 0 0 0 50
51 8.98 52.18 8.98 8.99 8.97 8.96 0 0 1 0 0 0 0 0 0 0 0 51
52 9.01 52.19 8.98 8.98 8.99 8.97 0 0 0 1 0 0 0 0 0 0 0 52
53 9.01 52.74 9.01 8.98 8.98 8.99 0 0 0 0 1 0 0 0 0 0 0 53
54 9.03 53.05 9.01 9.01 8.98 8.98 0 0 0 0 0 1 0 0 0 0 0 54
55 9.05 53.38 9.03 9.01 9.01 8.98 0 0 0 0 0 0 1 0 0 0 0 55
56 9.05 53.78 9.05 9.03 9.01 9.01 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 Y3 Y4
0.0140552 0.0139606 1.2365426 -0.4196239 0.2225845 -0.1183842
M1 M2 M3 M4 M5 M6
0.0076132 -0.0235157 -0.0147316 0.0209833 0.0172571 0.0312602
M7 M8 M9 M10 M11 t
-0.0004753 -0.0028285 -0.0261444 0.0080860 -0.0083841 -0.0003824
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.04489 -0.02236 -0.00766 0.01774 0.09852
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0140552 0.9845148 0.014 0.989
X 0.0139606 0.0190855 0.731 0.469
Y1 1.2365426 0.1637119 7.553 4.44e-09 ***
Y2 -0.4196239 0.2543192 -1.650 0.107
Y3 0.2225845 0.2551162 0.872 0.388
Y4 -0.1183842 0.1721329 -0.688 0.496
M1 0.0076132 0.0261931 0.291 0.773
M2 -0.0235157 0.0264704 -0.888 0.380
M3 -0.0147316 0.0265010 -0.556 0.582
M4 0.0209833 0.0271706 0.772 0.445
M5 0.0172571 0.0270818 0.637 0.528
M6 0.0312602 0.0268221 1.165 0.251
M7 -0.0004753 0.0280462 -0.017 0.987
M8 -0.0028285 0.0278017 -0.102 0.919
M9 -0.0261444 0.0293969 -0.889 0.379
M10 0.0080860 0.0280240 0.289 0.775
M11 -0.0083841 0.0282444 -0.297 0.768
t -0.0003824 0.0040243 -0.095 0.925
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03868 on 38 degrees of freedom
Multiple R-squared: 0.9957, Adjusted R-squared: 0.9937
F-statistic: 515 on 17 and 38 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,] 0.8300458 0.339908356 0.169954178
[2,] 0.7912650 0.417470064 0.208735032
[3,] 0.6808163 0.638367498 0.319183749
[4,] 0.6146778 0.770644473 0.385322236
[5,] 0.7595045 0.480990940 0.240495470
[6,] 0.6748965 0.650207021 0.325103511
[7,] 0.6968271 0.606345776 0.303172888
[8,] 0.9871315 0.025736936 0.012868468
[9,] 0.9968990 0.006202053 0.003101027
[10,] 0.9934245 0.013151011 0.006575505
[11,] 0.9950396 0.009920806 0.004960403
[12,] 0.9855182 0.028963635 0.014481818
[13,] 0.9751527 0.049694604 0.024847302
[14,] 0.9815180 0.036964063 0.018482032
[15,] 0.9374076 0.125184838 0.062592419
> postscript(file="/var/www/html/rcomp/tmp/1qm801258561579.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2u4fx1258561579.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/379wh1258561579.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4s9zd1258561579.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5izct1258561579.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
-0.0206719310 -0.0215541346 -0.0014668364 -0.0263086951 -0.0335960915
6 7 8 9 10
-0.0190020770 -0.0434420882 0.0930093869 -0.0416306926 -0.0231805598
11 12 13 14 15
-0.0241550322 0.0287066449 0.0589665649 -0.0260803486 -0.0006342297
16 17 18 19 20
0.0843666533 0.0037824433 0.0177679093 -0.0281493336 -0.0220605719
21 22 23 24 25
0.0025987743 -0.0163697735 -0.0014694793 -0.0183384526 -0.0158326116
26 27 28 29 30
0.0204564105 -0.0072960374 -0.0271599656 0.0985232505 0.0217890100
31 32 33 34 35
0.0513373415 -0.0317411396 0.0173373159 0.0177275844 0.0033326831
36 37 38 39 40
-0.0023436330 -0.0153984795 0.0377847365 0.0052776247 -0.0220812458
41 42 43 44 45
-0.0238203848 0.0108780050 0.0355844745 -0.0082418096 0.0216946025
46 47 48 49 50
0.0218227489 0.0222918285 -0.0080245594 -0.0070635428 -0.0106066638
51 52 53 54 55
0.0041194788 -0.0088167468 -0.0448892175 -0.0314328472 -0.0153303942
56
-0.0309658659
> postscript(file="/var/www/html/rcomp/tmp/6v03o1258561579.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 -0.0206719310 NA
1 -0.0215541346 -0.0206719310
2 -0.0014668364 -0.0215541346
3 -0.0263086951 -0.0014668364
4 -0.0335960915 -0.0263086951
5 -0.0190020770 -0.0335960915
6 -0.0434420882 -0.0190020770
7 0.0930093869 -0.0434420882
8 -0.0416306926 0.0930093869
9 -0.0231805598 -0.0416306926
10 -0.0241550322 -0.0231805598
11 0.0287066449 -0.0241550322
12 0.0589665649 0.0287066449
13 -0.0260803486 0.0589665649
14 -0.0006342297 -0.0260803486
15 0.0843666533 -0.0006342297
16 0.0037824433 0.0843666533
17 0.0177679093 0.0037824433
18 -0.0281493336 0.0177679093
19 -0.0220605719 -0.0281493336
20 0.0025987743 -0.0220605719
21 -0.0163697735 0.0025987743
22 -0.0014694793 -0.0163697735
23 -0.0183384526 -0.0014694793
24 -0.0158326116 -0.0183384526
25 0.0204564105 -0.0158326116
26 -0.0072960374 0.0204564105
27 -0.0271599656 -0.0072960374
28 0.0985232505 -0.0271599656
29 0.0217890100 0.0985232505
30 0.0513373415 0.0217890100
31 -0.0317411396 0.0513373415
32 0.0173373159 -0.0317411396
33 0.0177275844 0.0173373159
34 0.0033326831 0.0177275844
35 -0.0023436330 0.0033326831
36 -0.0153984795 -0.0023436330
37 0.0377847365 -0.0153984795
38 0.0052776247 0.0377847365
39 -0.0220812458 0.0052776247
40 -0.0238203848 -0.0220812458
41 0.0108780050 -0.0238203848
42 0.0355844745 0.0108780050
43 -0.0082418096 0.0355844745
44 0.0216946025 -0.0082418096
45 0.0218227489 0.0216946025
46 0.0222918285 0.0218227489
47 -0.0080245594 0.0222918285
48 -0.0070635428 -0.0080245594
49 -0.0106066638 -0.0070635428
50 0.0041194788 -0.0106066638
51 -0.0088167468 0.0041194788
52 -0.0448892175 -0.0088167468
53 -0.0314328472 -0.0448892175
54 -0.0153303942 -0.0314328472
55 -0.0309658659 -0.0153303942
56 NA -0.0309658659
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0215541346 -0.0206719310
[2,] -0.0014668364 -0.0215541346
[3,] -0.0263086951 -0.0014668364
[4,] -0.0335960915 -0.0263086951
[5,] -0.0190020770 -0.0335960915
[6,] -0.0434420882 -0.0190020770
[7,] 0.0930093869 -0.0434420882
[8,] -0.0416306926 0.0930093869
[9,] -0.0231805598 -0.0416306926
[10,] -0.0241550322 -0.0231805598
[11,] 0.0287066449 -0.0241550322
[12,] 0.0589665649 0.0287066449
[13,] -0.0260803486 0.0589665649
[14,] -0.0006342297 -0.0260803486
[15,] 0.0843666533 -0.0006342297
[16,] 0.0037824433 0.0843666533
[17,] 0.0177679093 0.0037824433
[18,] -0.0281493336 0.0177679093
[19,] -0.0220605719 -0.0281493336
[20,] 0.0025987743 -0.0220605719
[21,] -0.0163697735 0.0025987743
[22,] -0.0014694793 -0.0163697735
[23,] -0.0183384526 -0.0014694793
[24,] -0.0158326116 -0.0183384526
[25,] 0.0204564105 -0.0158326116
[26,] -0.0072960374 0.0204564105
[27,] -0.0271599656 -0.0072960374
[28,] 0.0985232505 -0.0271599656
[29,] 0.0217890100 0.0985232505
[30,] 0.0513373415 0.0217890100
[31,] -0.0317411396 0.0513373415
[32,] 0.0173373159 -0.0317411396
[33,] 0.0177275844 0.0173373159
[34,] 0.0033326831 0.0177275844
[35,] -0.0023436330 0.0033326831
[36,] -0.0153984795 -0.0023436330
[37,] 0.0377847365 -0.0153984795
[38,] 0.0052776247 0.0377847365
[39,] -0.0220812458 0.0052776247
[40,] -0.0238203848 -0.0220812458
[41,] 0.0108780050 -0.0238203848
[42,] 0.0355844745 0.0108780050
[43,] -0.0082418096 0.0355844745
[44,] 0.0216946025 -0.0082418096
[45,] 0.0218227489 0.0216946025
[46,] 0.0222918285 0.0218227489
[47,] -0.0080245594 0.0222918285
[48,] -0.0070635428 -0.0080245594
[49,] -0.0106066638 -0.0070635428
[50,] 0.0041194788 -0.0106066638
[51,] -0.0088167468 0.0041194788
[52,] -0.0448892175 -0.0088167468
[53,] -0.0314328472 -0.0448892175
[54,] -0.0153303942 -0.0314328472
[55,] -0.0309658659 -0.0153303942
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0215541346 -0.0206719310
2 -0.0014668364 -0.0215541346
3 -0.0263086951 -0.0014668364
4 -0.0335960915 -0.0263086951
5 -0.0190020770 -0.0335960915
6 -0.0434420882 -0.0190020770
7 0.0930093869 -0.0434420882
8 -0.0416306926 0.0930093869
9 -0.0231805598 -0.0416306926
10 -0.0241550322 -0.0231805598
11 0.0287066449 -0.0241550322
12 0.0589665649 0.0287066449
13 -0.0260803486 0.0589665649
14 -0.0006342297 -0.0260803486
15 0.0843666533 -0.0006342297
16 0.0037824433 0.0843666533
17 0.0177679093 0.0037824433
18 -0.0281493336 0.0177679093
19 -0.0220605719 -0.0281493336
20 0.0025987743 -0.0220605719
21 -0.0163697735 0.0025987743
22 -0.0014694793 -0.0163697735
23 -0.0183384526 -0.0014694793
24 -0.0158326116 -0.0183384526
25 0.0204564105 -0.0158326116
26 -0.0072960374 0.0204564105
27 -0.0271599656 -0.0072960374
28 0.0985232505 -0.0271599656
29 0.0217890100 0.0985232505
30 0.0513373415 0.0217890100
31 -0.0317411396 0.0513373415
32 0.0173373159 -0.0317411396
33 0.0177275844 0.0173373159
34 0.0033326831 0.0177275844
35 -0.0023436330 0.0033326831
36 -0.0153984795 -0.0023436330
37 0.0377847365 -0.0153984795
38 0.0052776247 0.0377847365
39 -0.0220812458 0.0052776247
40 -0.0238203848 -0.0220812458
41 0.0108780050 -0.0238203848
42 0.0355844745 0.0108780050
43 -0.0082418096 0.0355844745
44 0.0216946025 -0.0082418096
45 0.0218227489 0.0216946025
46 0.0222918285 0.0218227489
47 -0.0080245594 0.0222918285
48 -0.0070635428 -0.0080245594
49 -0.0106066638 -0.0070635428
50 0.0041194788 -0.0106066638
51 -0.0088167468 0.0041194788
52 -0.0448892175 -0.0088167468
53 -0.0314328472 -0.0448892175
54 -0.0153303942 -0.0314328472
55 -0.0309658659 -0.0153303942
> 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/7u4kt1258561579.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/878ic1258561579.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/944vb1258561579.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/104n381258561579.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11u7441258561579.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/12n30w1258561579.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/13hohs1258561579.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/14qnae1258561579.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/15471u1258561579.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/16t7p61258561579.tab")
+ }
>
> system("convert tmp/1qm801258561579.ps tmp/1qm801258561579.png")
> system("convert tmp/2u4fx1258561579.ps tmp/2u4fx1258561579.png")
> system("convert tmp/379wh1258561579.ps tmp/379wh1258561579.png")
> system("convert tmp/4s9zd1258561579.ps tmp/4s9zd1258561579.png")
> system("convert tmp/5izct1258561579.ps tmp/5izct1258561579.png")
> system("convert tmp/6v03o1258561579.ps tmp/6v03o1258561579.png")
> system("convert tmp/7u4kt1258561579.ps tmp/7u4kt1258561579.png")
> system("convert tmp/878ic1258561579.ps tmp/878ic1258561579.png")
> system("convert tmp/944vb1258561579.ps tmp/944vb1258561579.png")
> system("convert tmp/104n381258561579.ps tmp/104n381258561579.png")
>
>
> proc.time()
user system elapsed
2.273 1.498 2.743