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(111.4,0,87.4,0,96.8,0,114.1,0,110.3,0,103.9,0,101.6,0,94.6,0,95.9,0,104.7,0,102.8,0,98.1,0,113.9,0,80.9,0,95.7,0,113.2,0,105.9,0,108.8,0,102.3,0,99,0,100.7,0,115.5,0,100.7,0,109.9,0,114.6,0,85.4,0,100.5,0,114.8,0,116.5,0,112.9,0,102,0,106,0,105.3,0,118.8,0,106.1,0,109.3,0,117.2,0,92.5,0,104.2,0,112.5,0,122.4,0,113.3,0,100,0,110.7,0,112.8,0,109.8,0,117.3,0,109.1,0,115.9,0,96,0,99.8,0,116.8,1,115.7,1,99.4,1,94.3,1,91,1,93.2,1,103.1,1,94.1,1,91.8,1,102.7,1),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 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 111.4 0 1 0 0 0 0 0 0 0 0 0 0 1
2 87.4 0 0 1 0 0 0 0 0 0 0 0 0 2
3 96.8 0 0 0 1 0 0 0 0 0 0 0 0 3
4 114.1 0 0 0 0 1 0 0 0 0 0 0 0 4
5 110.3 0 0 0 0 0 1 0 0 0 0 0 0 5
6 103.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 101.6 0 0 0 0 0 0 0 1 0 0 0 0 7
8 94.6 0 0 0 0 0 0 0 0 1 0 0 0 8
9 95.9 0 0 0 0 0 0 0 0 0 1 0 0 9
10 104.7 0 0 0 0 0 0 0 0 0 0 1 0 10
11 102.8 0 0 0 0 0 0 0 0 0 0 0 1 11
12 98.1 0 0 0 0 0 0 0 0 0 0 0 0 12
13 113.9 0 1 0 0 0 0 0 0 0 0 0 0 13
14 80.9 0 0 1 0 0 0 0 0 0 0 0 0 14
15 95.7 0 0 0 1 0 0 0 0 0 0 0 0 15
16 113.2 0 0 0 0 1 0 0 0 0 0 0 0 16
17 105.9 0 0 0 0 0 1 0 0 0 0 0 0 17
18 108.8 0 0 0 0 0 0 1 0 0 0 0 0 18
19 102.3 0 0 0 0 0 0 0 1 0 0 0 0 19
20 99.0 0 0 0 0 0 0 0 0 1 0 0 0 20
21 100.7 0 0 0 0 0 0 0 0 0 1 0 0 21
22 115.5 0 0 0 0 0 0 0 0 0 0 1 0 22
23 100.7 0 0 0 0 0 0 0 0 0 0 0 1 23
24 109.9 0 0 0 0 0 0 0 0 0 0 0 0 24
25 114.6 0 1 0 0 0 0 0 0 0 0 0 0 25
26 85.4 0 0 1 0 0 0 0 0 0 0 0 0 26
27 100.5 0 0 0 1 0 0 0 0 0 0 0 0 27
28 114.8 0 0 0 0 1 0 0 0 0 0 0 0 28
29 116.5 0 0 0 0 0 1 0 0 0 0 0 0 29
30 112.9 0 0 0 0 0 0 1 0 0 0 0 0 30
31 102.0 0 0 0 0 0 0 0 1 0 0 0 0 31
32 106.0 0 0 0 0 0 0 0 0 1 0 0 0 32
33 105.3 0 0 0 0 0 0 0 0 0 1 0 0 33
34 118.8 0 0 0 0 0 0 0 0 0 0 1 0 34
35 106.1 0 0 0 0 0 0 0 0 0 0 0 1 35
36 109.3 0 0 0 0 0 0 0 0 0 0 0 0 36
37 117.2 0 1 0 0 0 0 0 0 0 0 0 0 37
38 92.5 0 0 1 0 0 0 0 0 0 0 0 0 38
39 104.2 0 0 0 1 0 0 0 0 0 0 0 0 39
40 112.5 0 0 0 0 1 0 0 0 0 0 0 0 40
41 122.4 0 0 0 0 0 1 0 0 0 0 0 0 41
42 113.3 0 0 0 0 0 0 1 0 0 0 0 0 42
43 100.0 0 0 0 0 0 0 0 1 0 0 0 0 43
44 110.7 0 0 0 0 0 0 0 0 1 0 0 0 44
45 112.8 0 0 0 0 0 0 0 0 0 1 0 0 45
46 109.8 0 0 0 0 0 0 0 0 0 0 1 0 46
47 117.3 0 0 0 0 0 0 0 0 0 0 0 1 47
48 109.1 0 0 0 0 0 0 0 0 0 0 0 0 48
49 115.9 0 1 0 0 0 0 0 0 0 0 0 0 49
50 96.0 0 0 1 0 0 0 0 0 0 0 0 0 50
51 99.8 0 0 0 1 0 0 0 0 0 0 0 0 51
52 116.8 1 0 0 0 1 0 0 0 0 0 0 0 52
53 115.7 1 0 0 0 0 1 0 0 0 0 0 0 53
54 99.4 1 0 0 0 0 0 1 0 0 0 0 0 54
55 94.3 1 0 0 0 0 0 0 1 0 0 0 0 55
56 91.0 1 0 0 0 0 0 0 0 1 0 0 0 56
57 93.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 103.1 1 0 0 0 0 0 0 0 0 0 1 0 58
59 94.1 1 0 0 0 0 0 0 0 0 0 0 1 59
60 91.8 1 0 0 0 0 0 0 0 0 0 0 0 60
61 102.7 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
98.8506 -14.9166 9.5590 -16.0242 -5.2801 12.3673
M5 M6 M7 M8 M9 M10
12.0314 5.3155 -2.5205 -2.5164 -1.4123 7.1718
M11 t
0.7759 0.2159
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.6524 -3.0400 -0.1142 2.5818 9.2715
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 98.85060 2.30862 42.818 < 2e-16 ***
X -14.91664 1.97849 -7.539 1.25e-09 ***
M1 9.55899 2.61324 3.658 0.000641 ***
M2 -16.02424 2.74509 -5.837 4.74e-07 ***
M3 -5.28014 2.74298 -1.925 0.060298 .
M4 12.36727 2.74355 4.508 4.35e-05 ***
M5 12.03136 2.73886 4.393 6.33e-05 ***
M6 5.31546 2.73479 1.944 0.057940 .
M7 -2.52045 2.73135 -0.923 0.360831
M8 -2.51636 2.72852 -0.922 0.361112
M9 -1.41227 2.72633 -0.518 0.606879
M10 7.17182 2.72475 2.632 0.011445 *
M11 0.77591 2.72381 0.285 0.777002
t 0.21591 0.04139 5.217 4.02e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 4.306 on 47 degrees of freedom
Multiple R-squared: 0.8305, Adjusted R-squared: 0.7836
F-statistic: 17.71 on 13 and 47 DF, p-value: 7.074e-14
> 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,] 0.253278762 0.50655752 0.7467212
[2,] 0.298159055 0.59631811 0.7018409
[3,] 0.182101150 0.36420230 0.8178988
[4,] 0.162175030 0.32435006 0.8378250
[5,] 0.140275499 0.28055100 0.8597245
[6,] 0.281099139 0.56219828 0.7189009
[7,] 0.242114362 0.48422872 0.7578856
[8,] 0.375554373 0.75110875 0.6244456
[9,] 0.280854085 0.56170817 0.7191459
[10,] 0.233311476 0.46662295 0.7666885
[11,] 0.163883874 0.32776775 0.8361161
[12,] 0.123389280 0.24677856 0.8766107
[13,] 0.155473287 0.31094657 0.8445267
[14,] 0.113796345 0.22759269 0.8862037
[15,] 0.083140979 0.16628196 0.9168590
[16,] 0.074333300 0.14866660 0.9256667
[17,] 0.057373848 0.11474770 0.9426262
[18,] 0.054256599 0.10851320 0.9457434
[19,] 0.043834653 0.08766931 0.9561653
[20,] 0.025426372 0.05085274 0.9745736
[21,] 0.014304335 0.02860867 0.9856957
[22,] 0.009835917 0.01967183 0.9901641
[23,] 0.004655055 0.00931011 0.9953449
[24,] 0.081651435 0.16330287 0.9183486
[25,] 0.116169749 0.23233950 0.8838303
[26,] 0.066198057 0.13239611 0.9338019
[27,] 0.177083641 0.35416728 0.8229164
[28,] 0.126005659 0.25201132 0.8739943
> postscript(file="/var/www/html/rcomp/tmp/1sstz1258735502.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2m3331258735502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3k9vv1258735502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4xw041258735502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5sp801258735502.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 6
2.77450331 4.14182119 2.58182119 2.01849338 -1.66150662 -1.56150662
7 8 9 10 11 12
3.75849338 -3.46150662 -3.48150662 -3.48150662 0.79849338 -3.34150662
13 14 15 16 17 18
2.68359272 -4.94908940 -1.10908940 -1.47241722 -8.65241722 0.74758278
19 20 21 22 23 24
1.86758278 -1.65241722 -1.27241722 4.72758278 -3.89241722 5.86758278
25 26 27 28 29 30
0.79268212 -3.04000000 1.10000000 -2.46332781 -0.64332781 2.25667219
31 32 33 34 35 36
-1.02332781 2.75667219 0.73667219 5.43667219 -1.08332781 2.67667219
37 38 39 40 41 42
0.80177152 1.46908940 2.20908940 -7.35423841 2.66576159 0.06576159
43 44 45 46 47 48
-5.61423841 4.86576159 5.64576159 -6.15423841 7.52576159 -0.11423841
49 50 51 52 53 54
-3.08913907 2.37817881 -4.78182119 9.27149007 8.29149007 -1.50850993
55 56 57 58 59 60
1.01149007 -2.50850993 -1.62850993 -0.52850993 -3.34850993 -5.08850993
61
-3.96341060
> postscript(file="/var/www/html/rcomp/tmp/6ngb61258735502.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 2.77450331 NA
1 4.14182119 2.77450331
2 2.58182119 4.14182119
3 2.01849338 2.58182119
4 -1.66150662 2.01849338
5 -1.56150662 -1.66150662
6 3.75849338 -1.56150662
7 -3.46150662 3.75849338
8 -3.48150662 -3.46150662
9 -3.48150662 -3.48150662
10 0.79849338 -3.48150662
11 -3.34150662 0.79849338
12 2.68359272 -3.34150662
13 -4.94908940 2.68359272
14 -1.10908940 -4.94908940
15 -1.47241722 -1.10908940
16 -8.65241722 -1.47241722
17 0.74758278 -8.65241722
18 1.86758278 0.74758278
19 -1.65241722 1.86758278
20 -1.27241722 -1.65241722
21 4.72758278 -1.27241722
22 -3.89241722 4.72758278
23 5.86758278 -3.89241722
24 0.79268212 5.86758278
25 -3.04000000 0.79268212
26 1.10000000 -3.04000000
27 -2.46332781 1.10000000
28 -0.64332781 -2.46332781
29 2.25667219 -0.64332781
30 -1.02332781 2.25667219
31 2.75667219 -1.02332781
32 0.73667219 2.75667219
33 5.43667219 0.73667219
34 -1.08332781 5.43667219
35 2.67667219 -1.08332781
36 0.80177152 2.67667219
37 1.46908940 0.80177152
38 2.20908940 1.46908940
39 -7.35423841 2.20908940
40 2.66576159 -7.35423841
41 0.06576159 2.66576159
42 -5.61423841 0.06576159
43 4.86576159 -5.61423841
44 5.64576159 4.86576159
45 -6.15423841 5.64576159
46 7.52576159 -6.15423841
47 -0.11423841 7.52576159
48 -3.08913907 -0.11423841
49 2.37817881 -3.08913907
50 -4.78182119 2.37817881
51 9.27149007 -4.78182119
52 8.29149007 9.27149007
53 -1.50850993 8.29149007
54 1.01149007 -1.50850993
55 -2.50850993 1.01149007
56 -1.62850993 -2.50850993
57 -0.52850993 -1.62850993
58 -3.34850993 -0.52850993
59 -5.08850993 -3.34850993
60 -3.96341060 -5.08850993
61 NA -3.96341060
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 4.14182119 2.77450331
[2,] 2.58182119 4.14182119
[3,] 2.01849338 2.58182119
[4,] -1.66150662 2.01849338
[5,] -1.56150662 -1.66150662
[6,] 3.75849338 -1.56150662
[7,] -3.46150662 3.75849338
[8,] -3.48150662 -3.46150662
[9,] -3.48150662 -3.48150662
[10,] 0.79849338 -3.48150662
[11,] -3.34150662 0.79849338
[12,] 2.68359272 -3.34150662
[13,] -4.94908940 2.68359272
[14,] -1.10908940 -4.94908940
[15,] -1.47241722 -1.10908940
[16,] -8.65241722 -1.47241722
[17,] 0.74758278 -8.65241722
[18,] 1.86758278 0.74758278
[19,] -1.65241722 1.86758278
[20,] -1.27241722 -1.65241722
[21,] 4.72758278 -1.27241722
[22,] -3.89241722 4.72758278
[23,] 5.86758278 -3.89241722
[24,] 0.79268212 5.86758278
[25,] -3.04000000 0.79268212
[26,] 1.10000000 -3.04000000
[27,] -2.46332781 1.10000000
[28,] -0.64332781 -2.46332781
[29,] 2.25667219 -0.64332781
[30,] -1.02332781 2.25667219
[31,] 2.75667219 -1.02332781
[32,] 0.73667219 2.75667219
[33,] 5.43667219 0.73667219
[34,] -1.08332781 5.43667219
[35,] 2.67667219 -1.08332781
[36,] 0.80177152 2.67667219
[37,] 1.46908940 0.80177152
[38,] 2.20908940 1.46908940
[39,] -7.35423841 2.20908940
[40,] 2.66576159 -7.35423841
[41,] 0.06576159 2.66576159
[42,] -5.61423841 0.06576159
[43,] 4.86576159 -5.61423841
[44,] 5.64576159 4.86576159
[45,] -6.15423841 5.64576159
[46,] 7.52576159 -6.15423841
[47,] -0.11423841 7.52576159
[48,] -3.08913907 -0.11423841
[49,] 2.37817881 -3.08913907
[50,] -4.78182119 2.37817881
[51,] 9.27149007 -4.78182119
[52,] 8.29149007 9.27149007
[53,] -1.50850993 8.29149007
[54,] 1.01149007 -1.50850993
[55,] -2.50850993 1.01149007
[56,] -1.62850993 -2.50850993
[57,] -0.52850993 -1.62850993
[58,] -3.34850993 -0.52850993
[59,] -5.08850993 -3.34850993
[60,] -3.96341060 -5.08850993
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 4.14182119 2.77450331
2 2.58182119 4.14182119
3 2.01849338 2.58182119
4 -1.66150662 2.01849338
5 -1.56150662 -1.66150662
6 3.75849338 -1.56150662
7 -3.46150662 3.75849338
8 -3.48150662 -3.46150662
9 -3.48150662 -3.48150662
10 0.79849338 -3.48150662
11 -3.34150662 0.79849338
12 2.68359272 -3.34150662
13 -4.94908940 2.68359272
14 -1.10908940 -4.94908940
15 -1.47241722 -1.10908940
16 -8.65241722 -1.47241722
17 0.74758278 -8.65241722
18 1.86758278 0.74758278
19 -1.65241722 1.86758278
20 -1.27241722 -1.65241722
21 4.72758278 -1.27241722
22 -3.89241722 4.72758278
23 5.86758278 -3.89241722
24 0.79268212 5.86758278
25 -3.04000000 0.79268212
26 1.10000000 -3.04000000
27 -2.46332781 1.10000000
28 -0.64332781 -2.46332781
29 2.25667219 -0.64332781
30 -1.02332781 2.25667219
31 2.75667219 -1.02332781
32 0.73667219 2.75667219
33 5.43667219 0.73667219
34 -1.08332781 5.43667219
35 2.67667219 -1.08332781
36 0.80177152 2.67667219
37 1.46908940 0.80177152
38 2.20908940 1.46908940
39 -7.35423841 2.20908940
40 2.66576159 -7.35423841
41 0.06576159 2.66576159
42 -5.61423841 0.06576159
43 4.86576159 -5.61423841
44 5.64576159 4.86576159
45 -6.15423841 5.64576159
46 7.52576159 -6.15423841
47 -0.11423841 7.52576159
48 -3.08913907 -0.11423841
49 2.37817881 -3.08913907
50 -4.78182119 2.37817881
51 9.27149007 -4.78182119
52 8.29149007 9.27149007
53 -1.50850993 8.29149007
54 1.01149007 -1.50850993
55 -2.50850993 1.01149007
56 -1.62850993 -2.50850993
57 -0.52850993 -1.62850993
58 -3.34850993 -0.52850993
59 -5.08850993 -3.34850993
60 -3.96341060 -5.08850993
> 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/7knt41258735502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8v9by1258735502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9hc891258735502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/101x1b1258735502.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/113t1o1258735502.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/12r7m01258735502.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/133gnh1258735502.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/14kxms1258735502.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/15bty51258735502.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/16unqd1258735502.tab")
+ }
>
> system("convert tmp/1sstz1258735502.ps tmp/1sstz1258735502.png")
> system("convert tmp/2m3331258735502.ps tmp/2m3331258735502.png")
> system("convert tmp/3k9vv1258735502.ps tmp/3k9vv1258735502.png")
> system("convert tmp/4xw041258735502.ps tmp/4xw041258735502.png")
> system("convert tmp/5sp801258735502.ps tmp/5sp801258735502.png")
> system("convert tmp/6ngb61258735502.ps tmp/6ngb61258735502.png")
> system("convert tmp/7knt41258735502.ps tmp/7knt41258735502.png")
> system("convert tmp/8v9by1258735502.ps tmp/8v9by1258735502.png")
> system("convert tmp/9hc891258735502.ps tmp/9hc891258735502.png")
> system("convert tmp/101x1b1258735502.ps tmp/101x1b1258735502.png")
>
>
> proc.time()
user system elapsed
2.497 1.637 8.892