R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.
> x <- array(list(7.55,42.97,7.55,42.98,7.59,43.01,7.59,43.09,7.59,43.14,7.57,43.39,7.57,43.46,7.59,43.54,7.6,43.62,7.64,44.01,7.64,44.5,7.76,44.73,7.76,44.89,7.76,45.09,7.77,45.17,7.83,45.24,7.94,45.42,7.94,45.67,7.94,45.68,8.09,46.56,8.18,46.72,8.26,47.01,8.28,47.26,8.28,47.49,8.28,47.51,8.29,47.52,8.3,47.66,8.3,47.71,8.31,47.87,8.33,48,8.33,48,8.34,48.05,8.48,48.25,8.59,48.72,8.67,48.94,8.67,49.16,8.67,49.18,8.71,49.25,8.72,49.34,8.72,49.49,8.72,49.57,8.74,49.63,8.74,49.67,8.74,49.7,8.74,49.8,8.79,50.09,8.85,50.49,8.86,50.73,8.87,51.12,8.92,51.15,8.96,51.41,8.97,51.61,8.99,52.06,8.98,52.17,8.98,52.18,9.01,52.19,9.01,52.74,9.03,53.05,9.05,53.38,9.05,53.78),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
1 7.55 42.97
2 7.55 42.98
3 7.59 43.01
4 7.59 43.09
5 7.59 43.14
6 7.57 43.39
7 7.57 43.46
8 7.59 43.54
9 7.60 43.62
10 7.64 44.01
11 7.64 44.50
12 7.76 44.73
13 7.76 44.89
14 7.76 45.09
15 7.77 45.17
16 7.83 45.24
17 7.94 45.42
18 7.94 45.67
19 7.94 45.68
20 8.09 46.56
21 8.18 46.72
22 8.26 47.01
23 8.28 47.26
24 8.28 47.49
25 8.28 47.51
26 8.29 47.52
27 8.30 47.66
28 8.30 47.71
29 8.31 47.87
30 8.33 48.00
31 8.33 48.00
32 8.34 48.05
33 8.48 48.25
34 8.59 48.72
35 8.67 48.94
36 8.67 49.16
37 8.67 49.18
38 8.71 49.25
39 8.72 49.34
40 8.72 49.49
41 8.72 49.57
42 8.74 49.63
43 8.74 49.67
44 8.74 49.70
45 8.74 49.80
46 8.79 50.09
47 8.85 50.49
48 8.86 50.73
49 8.87 51.12
50 8.92 51.15
51 8.96 51.41
52 8.97 51.61
53 8.99 52.06
54 8.98 52.17
55 8.98 52.18
56 9.01 52.19
57 9.01 52.74
58 9.03 53.05
59 9.05 53.38
60 9.05 53.78
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X
0.4621 0.1646
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.262340 -0.040446 0.001123 0.065745 0.154151
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.462073 0.178599 2.587 0.0122 *
X 0.164564 0.003719 44.254 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08843 on 58 degrees of freedom
Multiple R-squared: 0.9712, Adjusted R-squared: 0.9707
F-statistic: 1958 on 1 and 58 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,] 7.088882e-03 1.417776e-02 9.929111e-01
[2,] 5.382416e-03 1.076483e-02 9.946176e-01
[3,] 1.176589e-03 2.353179e-03 9.988234e-01
[4,] 2.425289e-04 4.850579e-04 9.997575e-01
[5,] 5.265156e-05 1.053031e-04 9.999473e-01
[6,] 2.307036e-05 4.614071e-05 9.999769e-01
[7,] 5.669553e-06 1.133911e-05 9.999943e-01
[8,] 2.145122e-04 4.290244e-04 9.997855e-01
[9,] 1.389755e-04 2.779510e-04 9.998610e-01
[10,] 6.131066e-05 1.226213e-04 9.999387e-01
[11,] 3.175934e-05 6.351868e-05 9.999682e-01
[12,] 6.481191e-05 1.296238e-04 9.999352e-01
[13,] 2.744966e-03 5.489932e-03 9.972550e-01
[14,] 4.438253e-03 8.876505e-03 9.955617e-01
[15,] 5.590415e-03 1.118083e-02 9.944096e-01
[16,] 9.370187e-03 1.874037e-02 9.906298e-01
[17,] 2.638608e-02 5.277215e-02 9.736139e-01
[18,] 5.513768e-02 1.102754e-01 9.448623e-01
[19,] 5.715501e-02 1.143100e-01 9.428450e-01
[20,] 4.673076e-02 9.346152e-02 9.532692e-01
[21,] 3.907274e-02 7.814548e-02 9.609273e-01
[22,] 3.359258e-02 6.718517e-02 9.664074e-01
[23,] 3.143040e-02 6.286080e-02 9.685696e-01
[24,] 3.502047e-02 7.004094e-02 9.649795e-01
[25,] 5.437108e-02 1.087422e-01 9.456289e-01
[26,] 1.107109e-01 2.214217e-01 8.892891e-01
[27,] 3.296471e-01 6.592941e-01 6.703529e-01
[28,] 9.269022e-01 1.461956e-01 7.309778e-02
[29,] 9.961225e-01 7.754992e-03 3.877496e-03
[30,] 9.993817e-01 1.236622e-03 6.183108e-04
[31,] 9.996953e-01 6.094173e-04 3.047086e-04
[32,] 9.997422e-01 5.156956e-04 2.578478e-04
[33,] 9.997876e-01 4.248153e-04 2.124076e-04
[34,] 9.997140e-01 5.720154e-04 2.860077e-04
[35,] 9.995414e-01 9.171485e-04 4.585743e-04
[36,] 9.992368e-01 1.526364e-03 7.631819e-04
[37,] 9.989316e-01 2.136722e-03 1.068361e-03
[38,] 9.981610e-01 3.678092e-03 1.839046e-03
[39,] 9.972533e-01 5.493448e-03 2.746724e-03
[40,] 9.968555e-01 6.288922e-03 3.144461e-03
[41,] 9.987487e-01 2.502572e-03 1.251286e-03
[42,] 9.991975e-01 1.605079e-03 8.025394e-04
[43,] 9.985427e-01 2.914682e-03 1.457341e-03
[44,] 9.986279e-01 2.744269e-03 1.372135e-03
[45,] 9.999864e-01 2.718210e-05 1.359105e-05
[46,] 9.999937e-01 1.253140e-05 6.265698e-06
[47,] 9.999627e-01 7.467368e-05 3.733684e-05
[48,] 9.998000e-01 4.000655e-04 2.000327e-04
[49,] 9.991440e-01 1.711919e-03 8.559595e-04
[50,] 9.974836e-01 5.032705e-03 2.516353e-03
[51,] 9.965136e-01 6.972743e-03 3.486371e-03
> postscript(file="/var/www/html/rcomp/tmp/1nzy91258557981.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2tpfk1258557981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/37z8r1258557981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/46pso1258557981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5y2ow1258557981.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
0.0165996549 0.0149540120 0.0500170832 0.0368519397 0.0286237251
6 7 8 9 10
-0.0325173482 -0.0440368487 -0.0372019922 -0.0403671356 -0.0645472100
11 12 13 14 15
-0.1451837137 -0.0630335011 -0.0893637880 -0.1222766466 -0.1254417901
16 17 18 19 20
-0.0769612906 0.0034171366 -0.0377239367 -0.0393695796 -0.0341861576
21 22 23 24 25
0.0294835555 0.0617599104 0.0406188372 0.0027690497 -0.0005222361
26 27 28 29 30
0.0078321209 -0.0052068801 -0.0134350948 -0.0297653817 -0.0311587398
31 32 33 34 35
-0.0311587398 -0.0293869545 0.0777001869 0.1103549691 0.1541508246
36 37 38 39 40
0.1179466801 0.1146553942 0.1431358937 0.1383251073 0.1136404633
41 42 43 44 45
0.1004753199 0.1106014623 0.1040188906 0.0990819618 0.0826255325
46 47 48 49 50
0.0849018874 0.0790761702 0.0495807398 -0.0045993346 0.0404637367
51 52 53 54 55
0.0376770204 0.0147641618 -0.0392897701 -0.0673918424 -0.0690374853
56 57 58 59 60
-0.0406831283 -0.1311934895 -0.1622084204 -0.1965146372 -0.2623403544
> postscript(file="/var/www/html/rcomp/tmp/64r2l1258557981.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 0.0165996549 NA
1 0.0149540120 0.0165996549
2 0.0500170832 0.0149540120
3 0.0368519397 0.0500170832
4 0.0286237251 0.0368519397
5 -0.0325173482 0.0286237251
6 -0.0440368487 -0.0325173482
7 -0.0372019922 -0.0440368487
8 -0.0403671356 -0.0372019922
9 -0.0645472100 -0.0403671356
10 -0.1451837137 -0.0645472100
11 -0.0630335011 -0.1451837137
12 -0.0893637880 -0.0630335011
13 -0.1222766466 -0.0893637880
14 -0.1254417901 -0.1222766466
15 -0.0769612906 -0.1254417901
16 0.0034171366 -0.0769612906
17 -0.0377239367 0.0034171366
18 -0.0393695796 -0.0377239367
19 -0.0341861576 -0.0393695796
20 0.0294835555 -0.0341861576
21 0.0617599104 0.0294835555
22 0.0406188372 0.0617599104
23 0.0027690497 0.0406188372
24 -0.0005222361 0.0027690497
25 0.0078321209 -0.0005222361
26 -0.0052068801 0.0078321209
27 -0.0134350948 -0.0052068801
28 -0.0297653817 -0.0134350948
29 -0.0311587398 -0.0297653817
30 -0.0311587398 -0.0311587398
31 -0.0293869545 -0.0311587398
32 0.0777001869 -0.0293869545
33 0.1103549691 0.0777001869
34 0.1541508246 0.1103549691
35 0.1179466801 0.1541508246
36 0.1146553942 0.1179466801
37 0.1431358937 0.1146553942
38 0.1383251073 0.1431358937
39 0.1136404633 0.1383251073
40 0.1004753199 0.1136404633
41 0.1106014623 0.1004753199
42 0.1040188906 0.1106014623
43 0.0990819618 0.1040188906
44 0.0826255325 0.0990819618
45 0.0849018874 0.0826255325
46 0.0790761702 0.0849018874
47 0.0495807398 0.0790761702
48 -0.0045993346 0.0495807398
49 0.0404637367 -0.0045993346
50 0.0376770204 0.0404637367
51 0.0147641618 0.0376770204
52 -0.0392897701 0.0147641618
53 -0.0673918424 -0.0392897701
54 -0.0690374853 -0.0673918424
55 -0.0406831283 -0.0690374853
56 -0.1311934895 -0.0406831283
57 -0.1622084204 -0.1311934895
58 -0.1965146372 -0.1622084204
59 -0.2623403544 -0.1965146372
60 NA -0.2623403544
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0149540120 0.0165996549
[2,] 0.0500170832 0.0149540120
[3,] 0.0368519397 0.0500170832
[4,] 0.0286237251 0.0368519397
[5,] -0.0325173482 0.0286237251
[6,] -0.0440368487 -0.0325173482
[7,] -0.0372019922 -0.0440368487
[8,] -0.0403671356 -0.0372019922
[9,] -0.0645472100 -0.0403671356
[10,] -0.1451837137 -0.0645472100
[11,] -0.0630335011 -0.1451837137
[12,] -0.0893637880 -0.0630335011
[13,] -0.1222766466 -0.0893637880
[14,] -0.1254417901 -0.1222766466
[15,] -0.0769612906 -0.1254417901
[16,] 0.0034171366 -0.0769612906
[17,] -0.0377239367 0.0034171366
[18,] -0.0393695796 -0.0377239367
[19,] -0.0341861576 -0.0393695796
[20,] 0.0294835555 -0.0341861576
[21,] 0.0617599104 0.0294835555
[22,] 0.0406188372 0.0617599104
[23,] 0.0027690497 0.0406188372
[24,] -0.0005222361 0.0027690497
[25,] 0.0078321209 -0.0005222361
[26,] -0.0052068801 0.0078321209
[27,] -0.0134350948 -0.0052068801
[28,] -0.0297653817 -0.0134350948
[29,] -0.0311587398 -0.0297653817
[30,] -0.0311587398 -0.0311587398
[31,] -0.0293869545 -0.0311587398
[32,] 0.0777001869 -0.0293869545
[33,] 0.1103549691 0.0777001869
[34,] 0.1541508246 0.1103549691
[35,] 0.1179466801 0.1541508246
[36,] 0.1146553942 0.1179466801
[37,] 0.1431358937 0.1146553942
[38,] 0.1383251073 0.1431358937
[39,] 0.1136404633 0.1383251073
[40,] 0.1004753199 0.1136404633
[41,] 0.1106014623 0.1004753199
[42,] 0.1040188906 0.1106014623
[43,] 0.0990819618 0.1040188906
[44,] 0.0826255325 0.0990819618
[45,] 0.0849018874 0.0826255325
[46,] 0.0790761702 0.0849018874
[47,] 0.0495807398 0.0790761702
[48,] -0.0045993346 0.0495807398
[49,] 0.0404637367 -0.0045993346
[50,] 0.0376770204 0.0404637367
[51,] 0.0147641618 0.0376770204
[52,] -0.0392897701 0.0147641618
[53,] -0.0673918424 -0.0392897701
[54,] -0.0690374853 -0.0673918424
[55,] -0.0406831283 -0.0690374853
[56,] -0.1311934895 -0.0406831283
[57,] -0.1622084204 -0.1311934895
[58,] -0.1965146372 -0.1622084204
[59,] -0.2623403544 -0.1965146372
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0149540120 0.0165996549
2 0.0500170832 0.0149540120
3 0.0368519397 0.0500170832
4 0.0286237251 0.0368519397
5 -0.0325173482 0.0286237251
6 -0.0440368487 -0.0325173482
7 -0.0372019922 -0.0440368487
8 -0.0403671356 -0.0372019922
9 -0.0645472100 -0.0403671356
10 -0.1451837137 -0.0645472100
11 -0.0630335011 -0.1451837137
12 -0.0893637880 -0.0630335011
13 -0.1222766466 -0.0893637880
14 -0.1254417901 -0.1222766466
15 -0.0769612906 -0.1254417901
16 0.0034171366 -0.0769612906
17 -0.0377239367 0.0034171366
18 -0.0393695796 -0.0377239367
19 -0.0341861576 -0.0393695796
20 0.0294835555 -0.0341861576
21 0.0617599104 0.0294835555
22 0.0406188372 0.0617599104
23 0.0027690497 0.0406188372
24 -0.0005222361 0.0027690497
25 0.0078321209 -0.0005222361
26 -0.0052068801 0.0078321209
27 -0.0134350948 -0.0052068801
28 -0.0297653817 -0.0134350948
29 -0.0311587398 -0.0297653817
30 -0.0311587398 -0.0311587398
31 -0.0293869545 -0.0311587398
32 0.0777001869 -0.0293869545
33 0.1103549691 0.0777001869
34 0.1541508246 0.1103549691
35 0.1179466801 0.1541508246
36 0.1146553942 0.1179466801
37 0.1431358937 0.1146553942
38 0.1383251073 0.1431358937
39 0.1136404633 0.1383251073
40 0.1004753199 0.1136404633
41 0.1106014623 0.1004753199
42 0.1040188906 0.1106014623
43 0.0990819618 0.1040188906
44 0.0826255325 0.0990819618
45 0.0849018874 0.0826255325
46 0.0790761702 0.0849018874
47 0.0495807398 0.0790761702
48 -0.0045993346 0.0495807398
49 0.0404637367 -0.0045993346
50 0.0376770204 0.0404637367
51 0.0147641618 0.0376770204
52 -0.0392897701 0.0147641618
53 -0.0673918424 -0.0392897701
54 -0.0690374853 -0.0673918424
55 -0.0406831283 -0.0690374853
56 -0.1311934895 -0.0406831283
57 -0.1622084204 -0.1311934895
58 -0.1965146372 -0.1622084204
59 -0.2623403544 -0.1965146372
> 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/79kov1258557981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88gi81258557981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ewni1258557981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10q5231258557981.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11t7gg1258557981.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/124i6z1258557981.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/13gply1258557981.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/14yhjl1258557981.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/15k7ru1258557981.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/16f5ry1258557981.tab")
+ }
>
> system("convert tmp/1nzy91258557981.ps tmp/1nzy91258557981.png")
> system("convert tmp/2tpfk1258557981.ps tmp/2tpfk1258557981.png")
> system("convert tmp/37z8r1258557981.ps tmp/37z8r1258557981.png")
> system("convert tmp/46pso1258557981.ps tmp/46pso1258557981.png")
> system("convert tmp/5y2ow1258557981.ps tmp/5y2ow1258557981.png")
> system("convert tmp/64r2l1258557981.ps tmp/64r2l1258557981.png")
> system("convert tmp/79kov1258557981.ps tmp/79kov1258557981.png")
> system("convert tmp/88gi81258557981.ps tmp/88gi81258557981.png")
> system("convert tmp/9ewni1258557981.ps tmp/9ewni1258557981.png")
> system("convert tmp/10q5231258557981.ps tmp/10q5231258557981.png")
>
>
> proc.time()
user system elapsed
2.527 1.633 5.041