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(1.4,2,1.2,2,1,2,1.7,2,2.4,2,2,2,2.1,2,2,2,1.8,2,2.7,2,2.3,2,1.9,2,2,2,2.3,2,2.8,2,2.4,2,2.3,2,2.7,2,2.7,2,2.9,2,3,2,2.2,2,2.3,2,2.8,2.21,2.8,2.25,2.8,2.25,2.2,2.45,2.6,2.5,2.8,2.5,2.5,2.64,2.4,2.75,2.3,2.93,1.9,3,1.7,3.17,2,3.25,2.1,3.39,1.7,3.5,1.8,3.5,1.8,3.65,1.8,3.75,1.3,3.75,1.3,3.9,1.3,4,1.2,4,1.4,4,2.2,4,2.9,4,3.1,4,3.5,4,3.6,4,4.4,4,4.1,4,5.1,4,5.8,4,5.9,4.18,5.4,4.25,5.5,4.25,4.8,3.97,3.2,3.42,2.7,2.75),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> 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 1.4 2.00 1 0 0 0 0 0 0 0 0 0 0 1
2 1.2 2.00 0 1 0 0 0 0 0 0 0 0 0 2
3 1.0 2.00 0 0 1 0 0 0 0 0 0 0 0 3
4 1.7 2.00 0 0 0 1 0 0 0 0 0 0 0 4
5 2.4 2.00 0 0 0 0 1 0 0 0 0 0 0 5
6 2.0 2.00 0 0 0 0 0 1 0 0 0 0 0 6
7 2.1 2.00 0 0 0 0 0 0 1 0 0 0 0 7
8 2.0 2.00 0 0 0 0 0 0 0 1 0 0 0 8
9 1.8 2.00 0 0 0 0 0 0 0 0 1 0 0 9
10 2.7 2.00 0 0 0 0 0 0 0 0 0 1 0 10
11 2.3 2.00 0 0 0 0 0 0 0 0 0 0 1 11
12 1.9 2.00 0 0 0 0 0 0 0 0 0 0 0 12
13 2.0 2.00 1 0 0 0 0 0 0 0 0 0 0 13
14 2.3 2.00 0 1 0 0 0 0 0 0 0 0 0 14
15 2.8 2.00 0 0 1 0 0 0 0 0 0 0 0 15
16 2.4 2.00 0 0 0 1 0 0 0 0 0 0 0 16
17 2.3 2.00 0 0 0 0 1 0 0 0 0 0 0 17
18 2.7 2.00 0 0 0 0 0 1 0 0 0 0 0 18
19 2.7 2.00 0 0 0 0 0 0 1 0 0 0 0 19
20 2.9 2.00 0 0 0 0 0 0 0 1 0 0 0 20
21 3.0 2.00 0 0 0 0 0 0 0 0 1 0 0 21
22 2.2 2.00 0 0 0 0 0 0 0 0 0 1 0 22
23 2.3 2.00 0 0 0 0 0 0 0 0 0 0 1 23
24 2.8 2.21 0 0 0 0 0 0 0 0 0 0 0 24
25 2.8 2.25 1 0 0 0 0 0 0 0 0 0 0 25
26 2.8 2.25 0 1 0 0 0 0 0 0 0 0 0 26
27 2.2 2.45 0 0 1 0 0 0 0 0 0 0 0 27
28 2.6 2.50 0 0 0 1 0 0 0 0 0 0 0 28
29 2.8 2.50 0 0 0 0 1 0 0 0 0 0 0 29
30 2.5 2.64 0 0 0 0 0 1 0 0 0 0 0 30
31 2.4 2.75 0 0 0 0 0 0 1 0 0 0 0 31
32 2.3 2.93 0 0 0 0 0 0 0 1 0 0 0 32
33 1.9 3.00 0 0 0 0 0 0 0 0 1 0 0 33
34 1.7 3.17 0 0 0 0 0 0 0 0 0 1 0 34
35 2.0 3.25 0 0 0 0 0 0 0 0 0 0 1 35
36 2.1 3.39 0 0 0 0 0 0 0 0 0 0 0 36
37 1.7 3.50 1 0 0 0 0 0 0 0 0 0 0 37
38 1.8 3.50 0 1 0 0 0 0 0 0 0 0 0 38
39 1.8 3.65 0 0 1 0 0 0 0 0 0 0 0 39
40 1.8 3.75 0 0 0 1 0 0 0 0 0 0 0 40
41 1.3 3.75 0 0 0 0 1 0 0 0 0 0 0 41
42 1.3 3.90 0 0 0 0 0 1 0 0 0 0 0 42
43 1.3 4.00 0 0 0 0 0 0 1 0 0 0 0 43
44 1.2 4.00 0 0 0 0 0 0 0 1 0 0 0 44
45 1.4 4.00 0 0 0 0 0 0 0 0 1 0 0 45
46 2.2 4.00 0 0 0 0 0 0 0 0 0 1 0 46
47 2.9 4.00 0 0 0 0 0 0 0 0 0 0 1 47
48 3.1 4.00 0 0 0 0 0 0 0 0 0 0 0 48
49 3.5 4.00 1 0 0 0 0 0 0 0 0 0 0 49
50 3.6 4.00 0 1 0 0 0 0 0 0 0 0 0 50
51 4.4 4.00 0 0 1 0 0 0 0 0 0 0 0 51
52 4.1 4.00 0 0 0 1 0 0 0 0 0 0 0 52
53 5.1 4.00 0 0 0 0 1 0 0 0 0 0 0 53
54 5.8 4.00 0 0 0 0 0 1 0 0 0 0 0 54
55 5.9 4.18 0 0 0 0 0 0 1 0 0 0 0 55
56 5.4 4.25 0 0 0 0 0 0 0 1 0 0 0 56
57 5.5 4.25 0 0 0 0 0 0 0 0 1 0 0 57
58 4.8 3.97 0 0 0 0 0 0 0 0 0 1 0 58
59 3.2 3.42 0 0 0 0 0 0 0 0 0 0 1 59
60 2.7 2.75 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
2.07837 -0.77631 0.48256 0.46841 0.54859 0.57772
M5 M6 M7 M8 M9 M10
0.76357 0.81444 0.82083 0.66549 0.56220 0.47097
M11 t
0.14384 0.07416
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.70152 -0.58986 0.05108 0.45906 2.16716
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.07837 0.70286 2.957 0.00489 **
X -0.77631 0.37901 -2.048 0.04627 *
M1 0.48256 0.67532 0.715 0.47849
M2 0.46841 0.67064 0.698 0.48841
M3 0.54859 0.67155 0.817 0.41819
M4 0.57772 0.66946 0.863 0.39263
M5 0.76357 0.66542 1.147 0.25711
M6 0.81444 0.66557 1.224 0.22731
M7 0.82083 0.66731 1.230 0.22493
M8 0.66549 0.66713 0.998 0.32372
M9 0.56220 0.66442 0.846 0.40184
M10 0.47097 0.65991 0.714 0.47903
M11 0.14384 0.65419 0.220 0.82694
t 0.07416 0.01942 3.819 0.00040 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.032 on 46 degrees of freedom
Multiple R-squared: 0.3901, Adjusted R-squared: 0.2177
F-statistic: 2.263 on 13 and 46 DF, p-value: 0.02116
> 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,] 1.260075e-01 2.520151e-01 0.8739925
[2,] 4.786747e-02 9.573494e-02 0.9521325
[3,] 1.682423e-02 3.364846e-02 0.9831758
[4,] 5.493324e-03 1.098665e-02 0.9945067
[5,] 2.128855e-03 4.257710e-03 0.9978711
[6,] 4.252879e-03 8.505759e-03 0.9957471
[7,] 2.393005e-03 4.786009e-03 0.9976070
[8,] 1.216210e-03 2.432420e-03 0.9987838
[9,] 4.768569e-04 9.537138e-04 0.9995231
[10,] 1.866821e-04 3.733642e-04 0.9998133
[11,] 1.088856e-04 2.177711e-04 0.9998911
[12,] 4.143839e-05 8.287678e-05 0.9999586
[13,] 1.727798e-05 3.455596e-05 0.9999827
[14,] 6.556905e-06 1.311381e-05 0.9999934
[15,] 2.502589e-06 5.005179e-06 0.9999975
[16,] 1.367653e-06 2.735307e-06 0.9999986
[17,] 9.789828e-07 1.957966e-06 0.9999990
[18,] 8.310817e-07 1.662163e-06 0.9999992
[19,] 4.755373e-06 9.510746e-06 0.9999952
[20,] 6.075903e-05 1.215181e-04 0.9999392
[21,] 1.332117e-04 2.664235e-04 0.9998668
[22,] 1.694840e-03 3.389680e-03 0.9983052
[23,] 4.790877e-03 9.581755e-03 0.9952091
[24,] 5.270314e-02 1.054063e-01 0.9472969
[25,] 4.995681e-02 9.991363e-02 0.9500432
[26,] 6.979865e-02 1.395973e-01 0.9302014
[27,] 1.130314e-01 2.260627e-01 0.8869686
> postscript(file="/var/www/html/rcomp/tmp/1xmdq1258721604.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2pwhz1258721604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35twd1258721604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fao31258721604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5v7re1258721604.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 = 60
Frequency = 1
1 2 3 4 5 6
0.31752246 0.05752246 -0.29681892 0.29989191 0.73989191 0.21486619
7 8 9 10 11 12
0.23431436 0.21549909 0.04463081 0.96170953 0.81468225 0.48436580
13 14 15 16 17 18
0.02764667 0.26764667 0.61330528 0.11001612 -0.24998388 0.02499040
19 20 21 22 23 24
-0.05556143 0.22562330 0.35475502 -0.42816626 -0.07519354 0.65751416
25 26 27 28 29 30
0.13184725 0.07184725 -0.52723303 -0.19170692 -0.25170692 -0.56804987
31 32 33 34 35 36
-0.66320810 -0.54228838 -0.85881527 -0.90976462 -0.29468746 -0.01632114
37 38 39 40 41 42
-0.88764667 -0.84764667 -0.88554223 -0.91120084 -1.67120084 -1.67978074
43 44 45 46 47 48
-1.68270202 -1.70151729 -1.47238557 -0.65530685 0.29766587 0.56734942
49 50 51 52 53 54
0.41063029 0.45063029 1.09628890 0.69299974 1.43299974 2.00797402
55 56 57 58 59 60
2.16715718 1.80268329 1.93181501 1.03152820 -0.74246711 -1.69290824
> postscript(file="/var/www/html/rcomp/tmp/6q4pr1258721604.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 = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 0.31752246 NA
1 0.05752246 0.31752246
2 -0.29681892 0.05752246
3 0.29989191 -0.29681892
4 0.73989191 0.29989191
5 0.21486619 0.73989191
6 0.23431436 0.21486619
7 0.21549909 0.23431436
8 0.04463081 0.21549909
9 0.96170953 0.04463081
10 0.81468225 0.96170953
11 0.48436580 0.81468225
12 0.02764667 0.48436580
13 0.26764667 0.02764667
14 0.61330528 0.26764667
15 0.11001612 0.61330528
16 -0.24998388 0.11001612
17 0.02499040 -0.24998388
18 -0.05556143 0.02499040
19 0.22562330 -0.05556143
20 0.35475502 0.22562330
21 -0.42816626 0.35475502
22 -0.07519354 -0.42816626
23 0.65751416 -0.07519354
24 0.13184725 0.65751416
25 0.07184725 0.13184725
26 -0.52723303 0.07184725
27 -0.19170692 -0.52723303
28 -0.25170692 -0.19170692
29 -0.56804987 -0.25170692
30 -0.66320810 -0.56804987
31 -0.54228838 -0.66320810
32 -0.85881527 -0.54228838
33 -0.90976462 -0.85881527
34 -0.29468746 -0.90976462
35 -0.01632114 -0.29468746
36 -0.88764667 -0.01632114
37 -0.84764667 -0.88764667
38 -0.88554223 -0.84764667
39 -0.91120084 -0.88554223
40 -1.67120084 -0.91120084
41 -1.67978074 -1.67120084
42 -1.68270202 -1.67978074
43 -1.70151729 -1.68270202
44 -1.47238557 -1.70151729
45 -0.65530685 -1.47238557
46 0.29766587 -0.65530685
47 0.56734942 0.29766587
48 0.41063029 0.56734942
49 0.45063029 0.41063029
50 1.09628890 0.45063029
51 0.69299974 1.09628890
52 1.43299974 0.69299974
53 2.00797402 1.43299974
54 2.16715718 2.00797402
55 1.80268329 2.16715718
56 1.93181501 1.80268329
57 1.03152820 1.93181501
58 -0.74246711 1.03152820
59 -1.69290824 -0.74246711
60 NA -1.69290824
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.05752246 0.31752246
[2,] -0.29681892 0.05752246
[3,] 0.29989191 -0.29681892
[4,] 0.73989191 0.29989191
[5,] 0.21486619 0.73989191
[6,] 0.23431436 0.21486619
[7,] 0.21549909 0.23431436
[8,] 0.04463081 0.21549909
[9,] 0.96170953 0.04463081
[10,] 0.81468225 0.96170953
[11,] 0.48436580 0.81468225
[12,] 0.02764667 0.48436580
[13,] 0.26764667 0.02764667
[14,] 0.61330528 0.26764667
[15,] 0.11001612 0.61330528
[16,] -0.24998388 0.11001612
[17,] 0.02499040 -0.24998388
[18,] -0.05556143 0.02499040
[19,] 0.22562330 -0.05556143
[20,] 0.35475502 0.22562330
[21,] -0.42816626 0.35475502
[22,] -0.07519354 -0.42816626
[23,] 0.65751416 -0.07519354
[24,] 0.13184725 0.65751416
[25,] 0.07184725 0.13184725
[26,] -0.52723303 0.07184725
[27,] -0.19170692 -0.52723303
[28,] -0.25170692 -0.19170692
[29,] -0.56804987 -0.25170692
[30,] -0.66320810 -0.56804987
[31,] -0.54228838 -0.66320810
[32,] -0.85881527 -0.54228838
[33,] -0.90976462 -0.85881527
[34,] -0.29468746 -0.90976462
[35,] -0.01632114 -0.29468746
[36,] -0.88764667 -0.01632114
[37,] -0.84764667 -0.88764667
[38,] -0.88554223 -0.84764667
[39,] -0.91120084 -0.88554223
[40,] -1.67120084 -0.91120084
[41,] -1.67978074 -1.67120084
[42,] -1.68270202 -1.67978074
[43,] -1.70151729 -1.68270202
[44,] -1.47238557 -1.70151729
[45,] -0.65530685 -1.47238557
[46,] 0.29766587 -0.65530685
[47,] 0.56734942 0.29766587
[48,] 0.41063029 0.56734942
[49,] 0.45063029 0.41063029
[50,] 1.09628890 0.45063029
[51,] 0.69299974 1.09628890
[52,] 1.43299974 0.69299974
[53,] 2.00797402 1.43299974
[54,] 2.16715718 2.00797402
[55,] 1.80268329 2.16715718
[56,] 1.93181501 1.80268329
[57,] 1.03152820 1.93181501
[58,] -0.74246711 1.03152820
[59,] -1.69290824 -0.74246711
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.05752246 0.31752246
2 -0.29681892 0.05752246
3 0.29989191 -0.29681892
4 0.73989191 0.29989191
5 0.21486619 0.73989191
6 0.23431436 0.21486619
7 0.21549909 0.23431436
8 0.04463081 0.21549909
9 0.96170953 0.04463081
10 0.81468225 0.96170953
11 0.48436580 0.81468225
12 0.02764667 0.48436580
13 0.26764667 0.02764667
14 0.61330528 0.26764667
15 0.11001612 0.61330528
16 -0.24998388 0.11001612
17 0.02499040 -0.24998388
18 -0.05556143 0.02499040
19 0.22562330 -0.05556143
20 0.35475502 0.22562330
21 -0.42816626 0.35475502
22 -0.07519354 -0.42816626
23 0.65751416 -0.07519354
24 0.13184725 0.65751416
25 0.07184725 0.13184725
26 -0.52723303 0.07184725
27 -0.19170692 -0.52723303
28 -0.25170692 -0.19170692
29 -0.56804987 -0.25170692
30 -0.66320810 -0.56804987
31 -0.54228838 -0.66320810
32 -0.85881527 -0.54228838
33 -0.90976462 -0.85881527
34 -0.29468746 -0.90976462
35 -0.01632114 -0.29468746
36 -0.88764667 -0.01632114
37 -0.84764667 -0.88764667
38 -0.88554223 -0.84764667
39 -0.91120084 -0.88554223
40 -1.67120084 -0.91120084
41 -1.67978074 -1.67120084
42 -1.68270202 -1.67978074
43 -1.70151729 -1.68270202
44 -1.47238557 -1.70151729
45 -0.65530685 -1.47238557
46 0.29766587 -0.65530685
47 0.56734942 0.29766587
48 0.41063029 0.56734942
49 0.45063029 0.41063029
50 1.09628890 0.45063029
51 0.69299974 1.09628890
52 1.43299974 0.69299974
53 2.00797402 1.43299974
54 2.16715718 2.00797402
55 1.80268329 2.16715718
56 1.93181501 1.80268329
57 1.03152820 1.93181501
58 -0.74246711 1.03152820
59 -1.69290824 -0.74246711
> 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/71cgl1258721604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84sbs1258721604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9y3s01258721604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10dxmr1258721604.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ta1h1258721604.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/125z921258721604.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/13trh51258721604.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/14afo41258721604.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/15un9i1258721604.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/16pds21258721604.tab")
+ }
>
> system("convert tmp/1xmdq1258721604.ps tmp/1xmdq1258721604.png")
> system("convert tmp/2pwhz1258721604.ps tmp/2pwhz1258721604.png")
> system("convert tmp/35twd1258721604.ps tmp/35twd1258721604.png")
> system("convert tmp/4fao31258721604.ps tmp/4fao31258721604.png")
> system("convert tmp/5v7re1258721604.ps tmp/5v7re1258721604.png")
> system("convert tmp/6q4pr1258721604.ps tmp/6q4pr1258721604.png")
> system("convert tmp/71cgl1258721604.ps tmp/71cgl1258721604.png")
> system("convert tmp/84sbs1258721604.ps tmp/84sbs1258721604.png")
> system("convert tmp/9y3s01258721604.ps tmp/9y3s01258721604.png")
> system("convert tmp/10dxmr1258721604.ps tmp/10dxmr1258721604.png")
>
>
> proc.time()
user system elapsed
2.381 1.571 2.765