R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(7.59,43.14,7.59,7.57,43.39,7.59,7.57,43.46,7.57,7.59,43.54,7.57,7.6,43.62,7.59,7.64,44.01,7.6,7.64,44.5,7.64,7.76,44.73,7.64,7.76,44.89,7.76,7.76,45.09,7.76,7.77,45.17,7.76,7.83,45.24,7.77,7.94,45.42,7.83,7.94,45.67,7.94,7.94,45.68,7.94,8.09,46.56,7.94,8.18,46.72,8.09,8.26,47.01,8.18,8.28,47.26,8.26,8.28,47.49,8.28,8.28,47.51,8.28,8.29,47.52,8.28,8.3,47.66,8.29,8.3,47.71,8.3,8.31,47.87,8.3,8.33,48,8.31,8.33,48,8.33,8.34,48.05,8.33,8.48,48.25,8.34,8.59,48.72,8.48,8.67,48.94,8.59,8.67,49.16,8.67,8.67,49.18,8.67,8.71,49.25,8.67,8.72,49.34,8.71,8.72,49.49,8.72,8.72,49.57,8.72,8.74,49.63,8.72,8.74,49.67,8.74,8.74,49.7,8.74,8.74,49.8,8.74,8.79,50.09,8.74,8.85,50.49,8.79,8.86,50.73,8.85,8.87,51.12,8.86,8.92,51.15,8.87,8.96,51.41,8.92,8.97,51.61,8.96,8.99,52.06,8.97,8.98,52.17,8.99,8.98,52.18,8.98,9.01,52.19,8.98,9.01,52.74,9.01,9.03,53.05,9.01,9.05,53.38,9.03,9.05,53.78,9.05),dim=c(3,56),dimnames=list(c('Y','X','Y1'),1:56))
> y <- array(NA,dim=c(3,56),dimnames=list(c('Y','X','Y1'),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 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 7.59 43.14 7.59 1 0 0 0 0 0 0 0 0 0 0 1
2 7.57 43.39 7.59 0 1 0 0 0 0 0 0 0 0 0 2
3 7.57 43.46 7.57 0 0 1 0 0 0 0 0 0 0 0 3
4 7.59 43.54 7.57 0 0 0 1 0 0 0 0 0 0 0 4
5 7.60 43.62 7.59 0 0 0 0 1 0 0 0 0 0 0 5
6 7.64 44.01 7.60 0 0 0 0 0 1 0 0 0 0 0 6
7 7.64 44.50 7.64 0 0 0 0 0 0 1 0 0 0 0 7
8 7.76 44.73 7.64 0 0 0 0 0 0 0 1 0 0 0 8
9 7.76 44.89 7.76 0 0 0 0 0 0 0 0 1 0 0 9
10 7.76 45.09 7.76 0 0 0 0 0 0 0 0 0 1 0 10
11 7.77 45.17 7.76 0 0 0 0 0 0 0 0 0 0 1 11
12 7.83 45.24 7.77 0 0 0 0 0 0 0 0 0 0 0 12
13 7.94 45.42 7.83 1 0 0 0 0 0 0 0 0 0 0 13
14 7.94 45.67 7.94 0 1 0 0 0 0 0 0 0 0 0 14
15 7.94 45.68 7.94 0 0 1 0 0 0 0 0 0 0 0 15
16 8.09 46.56 7.94 0 0 0 1 0 0 0 0 0 0 0 16
17 8.18 46.72 8.09 0 0 0 0 1 0 0 0 0 0 0 17
18 8.26 47.01 8.18 0 0 0 0 0 1 0 0 0 0 0 18
19 8.28 47.26 8.26 0 0 0 0 0 0 1 0 0 0 0 19
20 8.28 47.49 8.28 0 0 0 0 0 0 0 1 0 0 0 20
21 8.28 47.51 8.28 0 0 0 0 0 0 0 0 1 0 0 21
22 8.29 47.52 8.28 0 0 0 0 0 0 0 0 0 1 0 22
23 8.30 47.66 8.29 0 0 0 0 0 0 0 0 0 0 1 23
24 8.30 47.71 8.30 0 0 0 0 0 0 0 0 0 0 0 24
25 8.31 47.87 8.30 1 0 0 0 0 0 0 0 0 0 0 25
26 8.33 48.00 8.31 0 1 0 0 0 0 0 0 0 0 0 26
27 8.33 48.00 8.33 0 0 1 0 0 0 0 0 0 0 0 27
28 8.34 48.05 8.33 0 0 0 1 0 0 0 0 0 0 0 28
29 8.48 48.25 8.34 0 0 0 0 1 0 0 0 0 0 0 29
30 8.59 48.72 8.48 0 0 0 0 0 1 0 0 0 0 0 30
31 8.67 48.94 8.59 0 0 0 0 0 0 1 0 0 0 0 31
32 8.67 49.16 8.67 0 0 0 0 0 0 0 1 0 0 0 32
33 8.67 49.18 8.67 0 0 0 0 0 0 0 0 1 0 0 33
34 8.71 49.25 8.67 0 0 0 0 0 0 0 0 0 1 0 34
35 8.72 49.34 8.71 0 0 0 0 0 0 0 0 0 0 1 35
36 8.72 49.49 8.72 0 0 0 0 0 0 0 0 0 0 0 36
37 8.72 49.57 8.72 1 0 0 0 0 0 0 0 0 0 0 37
38 8.74 49.63 8.72 0 1 0 0 0 0 0 0 0 0 0 38
39 8.74 49.67 8.74 0 0 1 0 0 0 0 0 0 0 0 39
40 8.74 49.70 8.74 0 0 0 1 0 0 0 0 0 0 0 40
41 8.74 49.80 8.74 0 0 0 0 1 0 0 0 0 0 0 41
42 8.79 50.09 8.74 0 0 0 0 0 1 0 0 0 0 0 42
43 8.85 50.49 8.79 0 0 0 0 0 0 1 0 0 0 0 43
44 8.86 50.73 8.85 0 0 0 0 0 0 0 1 0 0 0 44
45 8.87 51.12 8.86 0 0 0 0 0 0 0 0 1 0 0 45
46 8.92 51.15 8.87 0 0 0 0 0 0 0 0 0 1 0 46
47 8.96 51.41 8.92 0 0 0 0 0 0 0 0 0 0 1 47
48 8.97 51.61 8.96 0 0 0 0 0 0 0 0 0 0 0 48
49 8.99 52.06 8.97 1 0 0 0 0 0 0 0 0 0 0 49
50 8.98 52.17 8.99 0 1 0 0 0 0 0 0 0 0 0 50
51 8.98 52.18 8.98 0 0 1 0 0 0 0 0 0 0 0 51
52 9.01 52.19 8.98 0 0 0 1 0 0 0 0 0 0 0 52
53 9.01 52.74 9.01 0 0 0 0 1 0 0 0 0 0 0 53
54 9.03 53.05 9.01 0 0 0 0 0 1 0 0 0 0 0 54
55 9.05 53.38 9.03 0 0 0 0 0 0 1 0 0 0 0 55
56 9.05 53.78 9.05 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 M1 M2 M3
-0.561884 0.021662 0.954028 0.008923 -0.016468 -0.016153
M4 M5 M6 M7 M8 M9
0.024085 0.030081 0.039492 0.013716 0.002439 -0.018119
M10 M11 t
0.005604 -0.001046 -0.002787
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.048739 -0.023581 -0.006973 0.013636 0.090837
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.561884 0.832539 -0.675 0.504
X 0.021662 0.018110 1.196 0.239
Y1 0.954028 0.064319 14.833 <2e-16 ***
M1 0.008923 0.026233 0.340 0.735
M2 -0.016468 0.026221 -0.628 0.533
M3 -0.016153 0.026455 -0.611 0.545
M4 0.024085 0.026581 0.906 0.370
M5 0.030081 0.026429 1.138 0.262
M6 0.039492 0.026322 1.500 0.141
M7 0.013716 0.026509 0.517 0.608
M8 0.002439 0.026793 0.091 0.928
M9 -0.018119 0.027897 -0.649 0.520
M10 0.005604 0.027659 0.203 0.840
M11 -0.001046 0.027620 -0.038 0.970
t -0.002787 0.003377 -0.825 0.414
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.03902 on 41 degrees of freedom
Multiple R-squared: 0.9953, Adjusted R-squared: 0.9936
F-statistic: 614.4 on 14 and 41 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,] 0.09294438 0.185888752 0.907055624
[2,] 0.13621576 0.272431528 0.863784236
[3,] 0.55092628 0.898147433 0.449073716
[4,] 0.41571121 0.831422421 0.584288789
[5,] 0.44114431 0.882288620 0.558855690
[6,] 0.37523114 0.750462289 0.624768855
[7,] 0.34110014 0.682200278 0.658899861
[8,] 0.57512139 0.849757224 0.424878612
[9,] 0.49581040 0.991620791 0.504189604
[10,] 0.50793780 0.984124390 0.492062195
[11,] 0.89756685 0.204866305 0.102433152
[12,] 0.96456883 0.070862334 0.035431167
[13,] 0.98688697 0.026226061 0.013113031
[14,] 0.99922249 0.001555026 0.000777513
[15,] 0.99822839 0.003543227 0.001771613
[16,] 0.99596583 0.008068341 0.004034170
[17,] 0.99440042 0.011199163 0.005599582
[18,] 0.98446294 0.031074129 0.015537064
[19,] 0.96404341 0.071913190 0.035956595
[20,] 0.92859115 0.142817692 0.071408846
[21,] 0.89637678 0.207246447 0.103623223
> postscript(file="/var/www/html/rcomp/tmp/1rppp1258561897.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2pcbw1258561897.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/31rxn1258561897.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4jlxl1258561897.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52n4w1258561897.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
-2.982599e-02 -2.706278e-02 -7.027289e-03 -2.621123e-02 -4.023325e-02
6 7 8 9 10
-2.484666e-02 -4.505872e-02 8.402280e-02 -1.058197e-02 -3.585050e-02
11 12 13 14 15
-1.814592e-02 3.253824e-02 7.526087e-02 -6.919028e-03 -4.664379e-03
16 17 18 19 20
8.882206e-02 2.904342e-02 1.027395e-02 -2.290035e-02 -3.289939e-02
21 22 23 24 25
-9.988100e-03 -2.114084e-02 -1.427626e-02 -2.315887e-02 -2.276131e-02
26 27 28 29 30
1.306106e-02 -3.548236e-03 -3.208231e-02 9.083651e-02 5.046647e-02
31 32 33 34 35
4.932118e-02 -1.770293e-02 5.208359e-03 2.275589e-02 2.082728e-03
36 37 38 39 40
-8.966076e-03 -1.683556e-02 3.004343e-02 1.256766e-02 -2.553318e-02
41 42 43 44 45
-3.090787e-02 6.185201e-03 3.838244e-02 5.649046e-06 1.536171e-02
46 47 48 49 50
3.423545e-02 3.033945e-02 -4.132990e-04 -5.838011e-03 -9.122685e-03
51 52 53 54 55
2.672246e-03 -4.995349e-03 -4.873880e-02 -4.207897e-02 -1.974454e-02
56
-3.342613e-02
> postscript(file="/var/www/html/rcomp/tmp/6z41q1258561897.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 -2.982599e-02 NA
1 -2.706278e-02 -2.982599e-02
2 -7.027289e-03 -2.706278e-02
3 -2.621123e-02 -7.027289e-03
4 -4.023325e-02 -2.621123e-02
5 -2.484666e-02 -4.023325e-02
6 -4.505872e-02 -2.484666e-02
7 8.402280e-02 -4.505872e-02
8 -1.058197e-02 8.402280e-02
9 -3.585050e-02 -1.058197e-02
10 -1.814592e-02 -3.585050e-02
11 3.253824e-02 -1.814592e-02
12 7.526087e-02 3.253824e-02
13 -6.919028e-03 7.526087e-02
14 -4.664379e-03 -6.919028e-03
15 8.882206e-02 -4.664379e-03
16 2.904342e-02 8.882206e-02
17 1.027395e-02 2.904342e-02
18 -2.290035e-02 1.027395e-02
19 -3.289939e-02 -2.290035e-02
20 -9.988100e-03 -3.289939e-02
21 -2.114084e-02 -9.988100e-03
22 -1.427626e-02 -2.114084e-02
23 -2.315887e-02 -1.427626e-02
24 -2.276131e-02 -2.315887e-02
25 1.306106e-02 -2.276131e-02
26 -3.548236e-03 1.306106e-02
27 -3.208231e-02 -3.548236e-03
28 9.083651e-02 -3.208231e-02
29 5.046647e-02 9.083651e-02
30 4.932118e-02 5.046647e-02
31 -1.770293e-02 4.932118e-02
32 5.208359e-03 -1.770293e-02
33 2.275589e-02 5.208359e-03
34 2.082728e-03 2.275589e-02
35 -8.966076e-03 2.082728e-03
36 -1.683556e-02 -8.966076e-03
37 3.004343e-02 -1.683556e-02
38 1.256766e-02 3.004343e-02
39 -2.553318e-02 1.256766e-02
40 -3.090787e-02 -2.553318e-02
41 6.185201e-03 -3.090787e-02
42 3.838244e-02 6.185201e-03
43 5.649046e-06 3.838244e-02
44 1.536171e-02 5.649046e-06
45 3.423545e-02 1.536171e-02
46 3.033945e-02 3.423545e-02
47 -4.132990e-04 3.033945e-02
48 -5.838011e-03 -4.132990e-04
49 -9.122685e-03 -5.838011e-03
50 2.672246e-03 -9.122685e-03
51 -4.995349e-03 2.672246e-03
52 -4.873880e-02 -4.995349e-03
53 -4.207897e-02 -4.873880e-02
54 -1.974454e-02 -4.207897e-02
55 -3.342613e-02 -1.974454e-02
56 NA -3.342613e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.706278e-02 -2.982599e-02
[2,] -7.027289e-03 -2.706278e-02
[3,] -2.621123e-02 -7.027289e-03
[4,] -4.023325e-02 -2.621123e-02
[5,] -2.484666e-02 -4.023325e-02
[6,] -4.505872e-02 -2.484666e-02
[7,] 8.402280e-02 -4.505872e-02
[8,] -1.058197e-02 8.402280e-02
[9,] -3.585050e-02 -1.058197e-02
[10,] -1.814592e-02 -3.585050e-02
[11,] 3.253824e-02 -1.814592e-02
[12,] 7.526087e-02 3.253824e-02
[13,] -6.919028e-03 7.526087e-02
[14,] -4.664379e-03 -6.919028e-03
[15,] 8.882206e-02 -4.664379e-03
[16,] 2.904342e-02 8.882206e-02
[17,] 1.027395e-02 2.904342e-02
[18,] -2.290035e-02 1.027395e-02
[19,] -3.289939e-02 -2.290035e-02
[20,] -9.988100e-03 -3.289939e-02
[21,] -2.114084e-02 -9.988100e-03
[22,] -1.427626e-02 -2.114084e-02
[23,] -2.315887e-02 -1.427626e-02
[24,] -2.276131e-02 -2.315887e-02
[25,] 1.306106e-02 -2.276131e-02
[26,] -3.548236e-03 1.306106e-02
[27,] -3.208231e-02 -3.548236e-03
[28,] 9.083651e-02 -3.208231e-02
[29,] 5.046647e-02 9.083651e-02
[30,] 4.932118e-02 5.046647e-02
[31,] -1.770293e-02 4.932118e-02
[32,] 5.208359e-03 -1.770293e-02
[33,] 2.275589e-02 5.208359e-03
[34,] 2.082728e-03 2.275589e-02
[35,] -8.966076e-03 2.082728e-03
[36,] -1.683556e-02 -8.966076e-03
[37,] 3.004343e-02 -1.683556e-02
[38,] 1.256766e-02 3.004343e-02
[39,] -2.553318e-02 1.256766e-02
[40,] -3.090787e-02 -2.553318e-02
[41,] 6.185201e-03 -3.090787e-02
[42,] 3.838244e-02 6.185201e-03
[43,] 5.649046e-06 3.838244e-02
[44,] 1.536171e-02 5.649046e-06
[45,] 3.423545e-02 1.536171e-02
[46,] 3.033945e-02 3.423545e-02
[47,] -4.132990e-04 3.033945e-02
[48,] -5.838011e-03 -4.132990e-04
[49,] -9.122685e-03 -5.838011e-03
[50,] 2.672246e-03 -9.122685e-03
[51,] -4.995349e-03 2.672246e-03
[52,] -4.873880e-02 -4.995349e-03
[53,] -4.207897e-02 -4.873880e-02
[54,] -1.974454e-02 -4.207897e-02
[55,] -3.342613e-02 -1.974454e-02
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.706278e-02 -2.982599e-02
2 -7.027289e-03 -2.706278e-02
3 -2.621123e-02 -7.027289e-03
4 -4.023325e-02 -2.621123e-02
5 -2.484666e-02 -4.023325e-02
6 -4.505872e-02 -2.484666e-02
7 8.402280e-02 -4.505872e-02
8 -1.058197e-02 8.402280e-02
9 -3.585050e-02 -1.058197e-02
10 -1.814592e-02 -3.585050e-02
11 3.253824e-02 -1.814592e-02
12 7.526087e-02 3.253824e-02
13 -6.919028e-03 7.526087e-02
14 -4.664379e-03 -6.919028e-03
15 8.882206e-02 -4.664379e-03
16 2.904342e-02 8.882206e-02
17 1.027395e-02 2.904342e-02
18 -2.290035e-02 1.027395e-02
19 -3.289939e-02 -2.290035e-02
20 -9.988100e-03 -3.289939e-02
21 -2.114084e-02 -9.988100e-03
22 -1.427626e-02 -2.114084e-02
23 -2.315887e-02 -1.427626e-02
24 -2.276131e-02 -2.315887e-02
25 1.306106e-02 -2.276131e-02
26 -3.548236e-03 1.306106e-02
27 -3.208231e-02 -3.548236e-03
28 9.083651e-02 -3.208231e-02
29 5.046647e-02 9.083651e-02
30 4.932118e-02 5.046647e-02
31 -1.770293e-02 4.932118e-02
32 5.208359e-03 -1.770293e-02
33 2.275589e-02 5.208359e-03
34 2.082728e-03 2.275589e-02
35 -8.966076e-03 2.082728e-03
36 -1.683556e-02 -8.966076e-03
37 3.004343e-02 -1.683556e-02
38 1.256766e-02 3.004343e-02
39 -2.553318e-02 1.256766e-02
40 -3.090787e-02 -2.553318e-02
41 6.185201e-03 -3.090787e-02
42 3.838244e-02 6.185201e-03
43 5.649046e-06 3.838244e-02
44 1.536171e-02 5.649046e-06
45 3.423545e-02 1.536171e-02
46 3.033945e-02 3.423545e-02
47 -4.132990e-04 3.033945e-02
48 -5.838011e-03 -4.132990e-04
49 -9.122685e-03 -5.838011e-03
50 2.672246e-03 -9.122685e-03
51 -4.995349e-03 2.672246e-03
52 -4.873880e-02 -4.995349e-03
53 -4.207897e-02 -4.873880e-02
54 -1.974454e-02 -4.207897e-02
55 -3.342613e-02 -1.974454e-02
> 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/7hexs1258561897.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xkiq1258561897.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9qa6h1258561897.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101wwz1258561897.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11l8pw1258561897.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/12tlkq1258561897.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/134e8j1258561897.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/14rwwq1258561897.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/15gq4i1258561897.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/1685ca1258561897.tab")
+ }
>
> system("convert tmp/1rppp1258561897.ps tmp/1rppp1258561897.png")
> system("convert tmp/2pcbw1258561897.ps tmp/2pcbw1258561897.png")
> system("convert tmp/31rxn1258561897.ps tmp/31rxn1258561897.png")
> system("convert tmp/4jlxl1258561897.ps tmp/4jlxl1258561897.png")
> system("convert tmp/52n4w1258561897.ps tmp/52n4w1258561897.png")
> system("convert tmp/6z41q1258561897.ps tmp/6z41q1258561897.png")
> system("convert tmp/7hexs1258561897.ps tmp/7hexs1258561897.png")
> system("convert tmp/8xkiq1258561897.ps tmp/8xkiq1258561897.png")
> system("convert tmp/9qa6h1258561897.ps tmp/9qa6h1258561897.png")
> system("convert tmp/101wwz1258561897.ps tmp/101wwz1258561897.png")
>
>
> proc.time()
user system elapsed
2.358 1.561 2.733