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(1.58,0.55,1.59,0.55,1.6,0.55,1.6,0.55,1.6,0.55,1.6,0.56,1.61,0.56,1.61,0.56,1.62,0.56,1.63,0.56,1.63,0.55,1.63,0.56,1.63,0.55,1.63,0.55,1.64,0.56,1.64,0.55,1.64,0.55,1.65,0.55,1.65,0.55,1.65,0.53,1.65,0.53,1.65,0.53,1.66,0.53,1.67,0.54,1.68,0.54,1.68,0.54,1.68,0.55,1.68,0.55,1.69,0.54,1.7,0.55,1.7,0.56,1.71,0.58,1.73,0.59,1.73,0.6,1.73,0.6,1.74,0.6,1.74,0.59,1.74,0.6,1.75,0.6,1.78,0.62,1.82,0.65,1.83,0.68,1.84,0.73,1.85,0.78,1.86,0.78,1.86,0.82,1.87,0.82,1.87,0.81,1.87,0.83,1.87,0.85,1.87,0.86,1.87,0.85,1.87,0.85,1.88,0.82,1.88,0.8,1.87,0.81,1.87,0.8,1.87,0.8,1.87,0.8,1.87,0.8,1.87,0.79),dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Y','X'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = '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
1 1.58 0.55 1 0 0 0 0 0 0 0 0 0 0
2 1.59 0.55 0 1 0 0 0 0 0 0 0 0 0
3 1.60 0.55 0 0 1 0 0 0 0 0 0 0 0
4 1.60 0.55 0 0 0 1 0 0 0 0 0 0 0
5 1.60 0.55 0 0 0 0 1 0 0 0 0 0 0
6 1.60 0.56 0 0 0 0 0 1 0 0 0 0 0
7 1.61 0.56 0 0 0 0 0 0 1 0 0 0 0
8 1.61 0.56 0 0 0 0 0 0 0 1 0 0 0
9 1.62 0.56 0 0 0 0 0 0 0 0 1 0 0
10 1.63 0.56 0 0 0 0 0 0 0 0 0 1 0
11 1.63 0.55 0 0 0 0 0 0 0 0 0 0 1
12 1.63 0.56 0 0 0 0 0 0 0 0 0 0 0
13 1.63 0.55 1 0 0 0 0 0 0 0 0 0 0
14 1.63 0.55 0 1 0 0 0 0 0 0 0 0 0
15 1.64 0.56 0 0 1 0 0 0 0 0 0 0 0
16 1.64 0.55 0 0 0 1 0 0 0 0 0 0 0
17 1.64 0.55 0 0 0 0 1 0 0 0 0 0 0
18 1.65 0.55 0 0 0 0 0 1 0 0 0 0 0
19 1.65 0.55 0 0 0 0 0 0 1 0 0 0 0
20 1.65 0.53 0 0 0 0 0 0 0 1 0 0 0
21 1.65 0.53 0 0 0 0 0 0 0 0 1 0 0
22 1.65 0.53 0 0 0 0 0 0 0 0 0 1 0
23 1.66 0.53 0 0 0 0 0 0 0 0 0 0 1
24 1.67 0.54 0 0 0 0 0 0 0 0 0 0 0
25 1.68 0.54 1 0 0 0 0 0 0 0 0 0 0
26 1.68 0.54 0 1 0 0 0 0 0 0 0 0 0
27 1.68 0.55 0 0 1 0 0 0 0 0 0 0 0
28 1.68 0.55 0 0 0 1 0 0 0 0 0 0 0
29 1.69 0.54 0 0 0 0 1 0 0 0 0 0 0
30 1.70 0.55 0 0 0 0 0 1 0 0 0 0 0
31 1.70 0.56 0 0 0 0 0 0 1 0 0 0 0
32 1.71 0.58 0 0 0 0 0 0 0 1 0 0 0
33 1.73 0.59 0 0 0 0 0 0 0 0 1 0 0
34 1.73 0.60 0 0 0 0 0 0 0 0 0 1 0
35 1.73 0.60 0 0 0 0 0 0 0 0 0 0 1
36 1.74 0.60 0 0 0 0 0 0 0 0 0 0 0
37 1.74 0.59 1 0 0 0 0 0 0 0 0 0 0
38 1.74 0.60 0 1 0 0 0 0 0 0 0 0 0
39 1.75 0.60 0 0 1 0 0 0 0 0 0 0 0
40 1.78 0.62 0 0 0 1 0 0 0 0 0 0 0
41 1.82 0.65 0 0 0 0 1 0 0 0 0 0 0
42 1.83 0.68 0 0 0 0 0 1 0 0 0 0 0
43 1.84 0.73 0 0 0 0 0 0 1 0 0 0 0
44 1.85 0.78 0 0 0 0 0 0 0 1 0 0 0
45 1.86 0.78 0 0 0 0 0 0 0 0 1 0 0
46 1.86 0.82 0 0 0 0 0 0 0 0 0 1 0
47 1.87 0.82 0 0 0 0 0 0 0 0 0 0 1
48 1.87 0.81 0 0 0 0 0 0 0 0 0 0 0
49 1.87 0.83 1 0 0 0 0 0 0 0 0 0 0
50 1.87 0.85 0 1 0 0 0 0 0 0 0 0 0
51 1.87 0.86 0 0 1 0 0 0 0 0 0 0 0
52 1.87 0.85 0 0 0 1 0 0 0 0 0 0 0
53 1.87 0.85 0 0 0 0 1 0 0 0 0 0 0
54 1.88 0.82 0 0 0 0 0 1 0 0 0 0 0
55 1.88 0.80 0 0 0 0 0 0 1 0 0 0 0
56 1.87 0.81 0 0 0 0 0 0 0 1 0 0 0
57 1.87 0.80 0 0 0 0 0 0 0 0 1 0 0
58 1.87 0.80 0 0 0 0 0 0 0 0 0 1 0
59 1.87 0.80 0 0 0 0 0 0 0 0 0 0 1
60 1.87 0.80 0 0 0 0 0 0 0 0 0 0 0
61 1.87 0.79 1 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
1.2129473 0.8203213 -0.0109868 -0.0179059 -0.0168278 -0.0108278
M5 M6 M7 M8 M9 M10
-0.0041091 0.0006096 -0.0019529 -0.0097968 -0.0017968 -0.0080000
M11
-0.0023594
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.073137 -0.023137 0.002592 0.030171 0.077953
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.2129473 0.0351047 34.552 <2e-16 ***
X 0.8203213 0.0451226 18.180 <2e-16 ***
M1 -0.0109868 0.0249855 -0.440 0.662
M2 -0.0179059 0.0261544 -0.685 0.497
M3 -0.0168278 0.0261352 -0.644 0.523
M4 -0.0108278 0.0261352 -0.414 0.681
M5 -0.0041091 0.0261240 -0.157 0.876
M6 0.0006096 0.0261140 0.023 0.981
M7 -0.0019529 0.0260978 -0.075 0.941
M8 -0.0097968 0.0260828 -0.376 0.709
M9 -0.0017968 0.0260828 -0.069 0.945
M10 -0.0080000 0.0260789 -0.307 0.760
M11 -0.0023594 0.0260791 -0.090 0.928
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.04123 on 48 degrees of freedom
Multiple R-squared: 0.8764, Adjusted R-squared: 0.8455
F-statistic: 28.37 on 12 and 48 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,] 0.7191026 5.617947e-01 2.808974e-01
[2,] 0.7514211 4.971577e-01 2.485789e-01
[3,] 0.8938311 2.123377e-01 1.061689e-01
[4,] 0.9064136 1.871729e-01 9.358645e-02
[5,] 0.8669728 2.660544e-01 1.330272e-01
[6,] 0.8483511 3.032979e-01 1.516489e-01
[7,] 0.8206408 3.587185e-01 1.793592e-01
[8,] 0.7782275 4.435450e-01 2.217725e-01
[9,] 0.7564804 4.870392e-01 2.435196e-01
[10,] 0.8853273 2.293454e-01 1.146727e-01
[11,] 0.9164454 1.671093e-01 8.355463e-02
[12,] 0.9332278 1.335444e-01 6.677220e-02
[13,] 0.9619569 7.608628e-02 3.804314e-02
[14,] 0.9716673 5.666543e-02 2.833272e-02
[15,] 0.9871793 2.564132e-02 1.282066e-02
[16,] 0.9969273 6.145419e-03 3.072710e-03
[17,] 0.9995700 8.599794e-04 4.299897e-04
[18,] 0.9998273 3.453927e-04 1.726964e-04
[19,] 0.9998331 3.338852e-04 1.669426e-04
[20,] 0.9998958 2.083643e-04 1.041821e-04
[21,] 0.9999416 1.167999e-04 5.839994e-05
[22,] 0.9999823 3.533952e-05 1.766976e-05
[23,] 0.9999880 2.402027e-05 1.201013e-05
[24,] 0.9999907 1.857238e-05 9.286188e-06
[25,] 0.9999791 4.182830e-05 2.091415e-05
[26,] 0.9999471 1.057694e-04 5.288469e-05
[27,] 0.9997040 5.920868e-04 2.960434e-04
[28,] 0.9996303 7.394518e-04 3.697259e-04
[29,] 0.9996800 6.400208e-04 3.200104e-04
[30,] 0.9991966 1.606719e-03 8.033594e-04
> postscript(file="/var/www/html/rcomp/tmp/10ytq1258717154.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2t25y1258717154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3fkl81258717154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4mhej1258717154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5enz81258717154.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 = 61
Frequency = 1
1 2 3 4 5
-0.0731372119 -0.0562181499 -0.0472962219 -0.0532962219 -0.0600149366
6 7 8 9 10
-0.0729368646 -0.0603742940 -0.0525304381 -0.0505304381 -0.0343272248
11 12 13 14 15
-0.0317646542 -0.0423272248 -0.0231372119 -0.0162181499 -0.0154994352
16 17 18 19 20
-0.0132962219 -0.0200149366 -0.0147336513 -0.0121710807 0.0120792017
21 22 23 24 25
0.0040792017 0.0102824149 0.0146417723 0.0140792017 0.0350660014
26 27 28 29 30
0.0419850634 0.0327037781 0.0267037781 0.0381882766 0.0352663487
31 32 33 34 35
0.0296257060 0.0310631354 0.0348599222 0.0328599222 0.0272192795
36 37 38 39 40
0.0348599222 0.0540499351 0.0527657839 0.0616877118 0.0692812853
41 42 43 44 45
0.0779529308 0.0586245764 0.0301710807 0.0069988704 0.0089988704
46 47 48 49 50
-0.0176107694 -0.0132514120 -0.0074075561 -0.0128271829 -0.0223145475
51 52 53 54 55
-0.0315958328 -0.0293926195 -0.0361113342 -0.0062204091 0.0127485880
56 57 58 59 60
0.0023892306 0.0025924439 0.0087956571 0.0031550145 0.0007956571
61
0.0199856701
> postscript(file="/var/www/html/rcomp/tmp/6zo1w1258717154.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0731372119 NA
1 -0.0562181499 -0.0731372119
2 -0.0472962219 -0.0562181499
3 -0.0532962219 -0.0472962219
4 -0.0600149366 -0.0532962219
5 -0.0729368646 -0.0600149366
6 -0.0603742940 -0.0729368646
7 -0.0525304381 -0.0603742940
8 -0.0505304381 -0.0525304381
9 -0.0343272248 -0.0505304381
10 -0.0317646542 -0.0343272248
11 -0.0423272248 -0.0317646542
12 -0.0231372119 -0.0423272248
13 -0.0162181499 -0.0231372119
14 -0.0154994352 -0.0162181499
15 -0.0132962219 -0.0154994352
16 -0.0200149366 -0.0132962219
17 -0.0147336513 -0.0200149366
18 -0.0121710807 -0.0147336513
19 0.0120792017 -0.0121710807
20 0.0040792017 0.0120792017
21 0.0102824149 0.0040792017
22 0.0146417723 0.0102824149
23 0.0140792017 0.0146417723
24 0.0350660014 0.0140792017
25 0.0419850634 0.0350660014
26 0.0327037781 0.0419850634
27 0.0267037781 0.0327037781
28 0.0381882766 0.0267037781
29 0.0352663487 0.0381882766
30 0.0296257060 0.0352663487
31 0.0310631354 0.0296257060
32 0.0348599222 0.0310631354
33 0.0328599222 0.0348599222
34 0.0272192795 0.0328599222
35 0.0348599222 0.0272192795
36 0.0540499351 0.0348599222
37 0.0527657839 0.0540499351
38 0.0616877118 0.0527657839
39 0.0692812853 0.0616877118
40 0.0779529308 0.0692812853
41 0.0586245764 0.0779529308
42 0.0301710807 0.0586245764
43 0.0069988704 0.0301710807
44 0.0089988704 0.0069988704
45 -0.0176107694 0.0089988704
46 -0.0132514120 -0.0176107694
47 -0.0074075561 -0.0132514120
48 -0.0128271829 -0.0074075561
49 -0.0223145475 -0.0128271829
50 -0.0315958328 -0.0223145475
51 -0.0293926195 -0.0315958328
52 -0.0361113342 -0.0293926195
53 -0.0062204091 -0.0361113342
54 0.0127485880 -0.0062204091
55 0.0023892306 0.0127485880
56 0.0025924439 0.0023892306
57 0.0087956571 0.0025924439
58 0.0031550145 0.0087956571
59 0.0007956571 0.0031550145
60 0.0199856701 0.0007956571
61 NA 0.0199856701
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0562181499 -0.0731372119
[2,] -0.0472962219 -0.0562181499
[3,] -0.0532962219 -0.0472962219
[4,] -0.0600149366 -0.0532962219
[5,] -0.0729368646 -0.0600149366
[6,] -0.0603742940 -0.0729368646
[7,] -0.0525304381 -0.0603742940
[8,] -0.0505304381 -0.0525304381
[9,] -0.0343272248 -0.0505304381
[10,] -0.0317646542 -0.0343272248
[11,] -0.0423272248 -0.0317646542
[12,] -0.0231372119 -0.0423272248
[13,] -0.0162181499 -0.0231372119
[14,] -0.0154994352 -0.0162181499
[15,] -0.0132962219 -0.0154994352
[16,] -0.0200149366 -0.0132962219
[17,] -0.0147336513 -0.0200149366
[18,] -0.0121710807 -0.0147336513
[19,] 0.0120792017 -0.0121710807
[20,] 0.0040792017 0.0120792017
[21,] 0.0102824149 0.0040792017
[22,] 0.0146417723 0.0102824149
[23,] 0.0140792017 0.0146417723
[24,] 0.0350660014 0.0140792017
[25,] 0.0419850634 0.0350660014
[26,] 0.0327037781 0.0419850634
[27,] 0.0267037781 0.0327037781
[28,] 0.0381882766 0.0267037781
[29,] 0.0352663487 0.0381882766
[30,] 0.0296257060 0.0352663487
[31,] 0.0310631354 0.0296257060
[32,] 0.0348599222 0.0310631354
[33,] 0.0328599222 0.0348599222
[34,] 0.0272192795 0.0328599222
[35,] 0.0348599222 0.0272192795
[36,] 0.0540499351 0.0348599222
[37,] 0.0527657839 0.0540499351
[38,] 0.0616877118 0.0527657839
[39,] 0.0692812853 0.0616877118
[40,] 0.0779529308 0.0692812853
[41,] 0.0586245764 0.0779529308
[42,] 0.0301710807 0.0586245764
[43,] 0.0069988704 0.0301710807
[44,] 0.0089988704 0.0069988704
[45,] -0.0176107694 0.0089988704
[46,] -0.0132514120 -0.0176107694
[47,] -0.0074075561 -0.0132514120
[48,] -0.0128271829 -0.0074075561
[49,] -0.0223145475 -0.0128271829
[50,] -0.0315958328 -0.0223145475
[51,] -0.0293926195 -0.0315958328
[52,] -0.0361113342 -0.0293926195
[53,] -0.0062204091 -0.0361113342
[54,] 0.0127485880 -0.0062204091
[55,] 0.0023892306 0.0127485880
[56,] 0.0025924439 0.0023892306
[57,] 0.0087956571 0.0025924439
[58,] 0.0031550145 0.0087956571
[59,] 0.0007956571 0.0031550145
[60,] 0.0199856701 0.0007956571
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0562181499 -0.0731372119
2 -0.0472962219 -0.0562181499
3 -0.0532962219 -0.0472962219
4 -0.0600149366 -0.0532962219
5 -0.0729368646 -0.0600149366
6 -0.0603742940 -0.0729368646
7 -0.0525304381 -0.0603742940
8 -0.0505304381 -0.0525304381
9 -0.0343272248 -0.0505304381
10 -0.0317646542 -0.0343272248
11 -0.0423272248 -0.0317646542
12 -0.0231372119 -0.0423272248
13 -0.0162181499 -0.0231372119
14 -0.0154994352 -0.0162181499
15 -0.0132962219 -0.0154994352
16 -0.0200149366 -0.0132962219
17 -0.0147336513 -0.0200149366
18 -0.0121710807 -0.0147336513
19 0.0120792017 -0.0121710807
20 0.0040792017 0.0120792017
21 0.0102824149 0.0040792017
22 0.0146417723 0.0102824149
23 0.0140792017 0.0146417723
24 0.0350660014 0.0140792017
25 0.0419850634 0.0350660014
26 0.0327037781 0.0419850634
27 0.0267037781 0.0327037781
28 0.0381882766 0.0267037781
29 0.0352663487 0.0381882766
30 0.0296257060 0.0352663487
31 0.0310631354 0.0296257060
32 0.0348599222 0.0310631354
33 0.0328599222 0.0348599222
34 0.0272192795 0.0328599222
35 0.0348599222 0.0272192795
36 0.0540499351 0.0348599222
37 0.0527657839 0.0540499351
38 0.0616877118 0.0527657839
39 0.0692812853 0.0616877118
40 0.0779529308 0.0692812853
41 0.0586245764 0.0779529308
42 0.0301710807 0.0586245764
43 0.0069988704 0.0301710807
44 0.0089988704 0.0069988704
45 -0.0176107694 0.0089988704
46 -0.0132514120 -0.0176107694
47 -0.0074075561 -0.0132514120
48 -0.0128271829 -0.0074075561
49 -0.0223145475 -0.0128271829
50 -0.0315958328 -0.0223145475
51 -0.0293926195 -0.0315958328
52 -0.0361113342 -0.0293926195
53 -0.0062204091 -0.0361113342
54 0.0127485880 -0.0062204091
55 0.0023892306 0.0127485880
56 0.0025924439 0.0023892306
57 0.0087956571 0.0025924439
58 0.0031550145 0.0087956571
59 0.0007956571 0.0031550145
60 0.0199856701 0.0007956571
> 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/7sfgu1258717154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/82lpd1258717154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9l2jq1258717154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ff051258717154.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/114epm1258717154.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/12hwzj1258717154.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/138sxj1258717154.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/14zw761258717154.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/1552gb1258717154.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/16oa3w1258717154.tab")
+ }
>
> system("convert tmp/10ytq1258717154.ps tmp/10ytq1258717154.png")
> system("convert tmp/2t25y1258717154.ps tmp/2t25y1258717154.png")
> system("convert tmp/3fkl81258717154.ps tmp/3fkl81258717154.png")
> system("convert tmp/4mhej1258717154.ps tmp/4mhej1258717154.png")
> system("convert tmp/5enz81258717154.ps tmp/5enz81258717154.png")
> system("convert tmp/6zo1w1258717154.ps tmp/6zo1w1258717154.png")
> system("convert tmp/7sfgu1258717154.ps tmp/7sfgu1258717154.png")
> system("convert tmp/82lpd1258717154.ps tmp/82lpd1258717154.png")
> system("convert tmp/9l2jq1258717154.ps tmp/9l2jq1258717154.png")
> system("convert tmp/10ff051258717154.ps tmp/10ff051258717154.png")
>
>
> proc.time()
user system elapsed
2.365 1.561 2.774