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.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(8.7,4.7,9.3,9.3,8.2,4.3,8.7,9.3,8.3,3.9,8.2,8.7,8.5,4,8.3,8.2,8.6,4.3,8.5,8.3,8.5,4.8,8.6,8.5,8.2,4.4,8.5,8.6,8.1,4.3,8.2,8.5,7.9,4.7,8.1,8.2,8.6,4.7,7.9,8.1,8.7,4.9,8.6,7.9,8.7,5,8.7,8.6,8.5,4.2,8.7,8.7,8.4,4.3,8.5,8.7,8.5,4.8,8.4,8.5,8.7,4.8,8.5,8.4,8.7,4.8,8.7,8.5,8.6,4.2,8.7,8.7,8.5,4.6,8.6,8.7,8.3,4.8,8.5,8.6,8,4.5,8.3,8.5,8.2,4.4,8,8.3,8.1,4.3,8.2,8,8.1,3.9,8.1,8.2,8,3.7,8.1,8.1,7.9,4,8,8.1,7.9,4.1,7.9,8,8,3.7,7.9,7.9,8,3.8,8,7.9,7.9,3.8,8,8,8,3.8,7.9,8,7.7,3.3,8,7.9,7.2,3.3,7.7,8,7.5,3.3,7.2,7.7,7.3,3.2,7.5,7.2,7,3.4,7.3,7.5,7,4.2,7,7.3,7,4.9,7,7,7.2,5.1,7,7,7.3,5.5,7.2,7,7.1,5.6,7.3,7.2,6.8,6.4,7.1,7.3,6.4,6.1,6.8,7.1,6.1,7.1,6.4,6.8,6.5,7.8,6.1,6.4,7.7,7.9,6.5,6.1,7.9,7.4,7.7,6.5,7.5,7.5,7.9,7.7,6.9,6.8,7.5,7.9,6.6,5.2,6.9,7.5,6.9,4.7,6.6,6.9,7.7,4.1,6.9,6.6,8,3.9,7.7,6.9,8,2.6,8,7.7,7.7,2.7,8,8,7.3,1.8,7.7,8,7.4,1,7.3,7.7,8.1,0.3,7.4,7.3),dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> y <- array(NA,dim=c(4,58),dimnames=list(c('Y','X','Y1','Y2'),1:58))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X Y1 Y2 M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 8.7 4.7 9.3 9.3 1 0 0 0 0 0 0 0 0 0 0 1
2 8.2 4.3 8.7 9.3 0 1 0 0 0 0 0 0 0 0 0 2
3 8.3 3.9 8.2 8.7 0 0 1 0 0 0 0 0 0 0 0 3
4 8.5 4.0 8.3 8.2 0 0 0 1 0 0 0 0 0 0 0 4
5 8.6 4.3 8.5 8.3 0 0 0 0 1 0 0 0 0 0 0 5
6 8.5 4.8 8.6 8.5 0 0 0 0 0 1 0 0 0 0 0 6
7 8.2 4.4 8.5 8.6 0 0 0 0 0 0 1 0 0 0 0 7
8 8.1 4.3 8.2 8.5 0 0 0 0 0 0 0 1 0 0 0 8
9 7.9 4.7 8.1 8.2 0 0 0 0 0 0 0 0 1 0 0 9
10 8.6 4.7 7.9 8.1 0 0 0 0 0 0 0 0 0 1 0 10
11 8.7 4.9 8.6 7.9 0 0 0 0 0 0 0 0 0 0 1 11
12 8.7 5.0 8.7 8.6 0 0 0 0 0 0 0 0 0 0 0 12
13 8.5 4.2 8.7 8.7 1 0 0 0 0 0 0 0 0 0 0 13
14 8.4 4.3 8.5 8.7 0 1 0 0 0 0 0 0 0 0 0 14
15 8.5 4.8 8.4 8.5 0 0 1 0 0 0 0 0 0 0 0 15
16 8.7 4.8 8.5 8.4 0 0 0 1 0 0 0 0 0 0 0 16
17 8.7 4.8 8.7 8.5 0 0 0 0 1 0 0 0 0 0 0 17
18 8.6 4.2 8.7 8.7 0 0 0 0 0 1 0 0 0 0 0 18
19 8.5 4.6 8.6 8.7 0 0 0 0 0 0 1 0 0 0 0 19
20 8.3 4.8 8.5 8.6 0 0 0 0 0 0 0 1 0 0 0 20
21 8.0 4.5 8.3 8.5 0 0 0 0 0 0 0 0 1 0 0 21
22 8.2 4.4 8.0 8.3 0 0 0 0 0 0 0 0 0 1 0 22
23 8.1 4.3 8.2 8.0 0 0 0 0 0 0 0 0 0 0 1 23
24 8.1 3.9 8.1 8.2 0 0 0 0 0 0 0 0 0 0 0 24
25 8.0 3.7 8.1 8.1 1 0 0 0 0 0 0 0 0 0 0 25
26 7.9 4.0 8.0 8.1 0 1 0 0 0 0 0 0 0 0 0 26
27 7.9 4.1 7.9 8.0 0 0 1 0 0 0 0 0 0 0 0 27
28 8.0 3.7 7.9 7.9 0 0 0 1 0 0 0 0 0 0 0 28
29 8.0 3.8 8.0 7.9 0 0 0 0 1 0 0 0 0 0 0 29
30 7.9 3.8 8.0 8.0 0 0 0 0 0 1 0 0 0 0 0 30
31 8.0 3.8 7.9 8.0 0 0 0 0 0 0 1 0 0 0 0 31
32 7.7 3.3 8.0 7.9 0 0 0 0 0 0 0 1 0 0 0 32
33 7.2 3.3 7.7 8.0 0 0 0 0 0 0 0 0 1 0 0 33
34 7.5 3.3 7.2 7.7 0 0 0 0 0 0 0 0 0 1 0 34
35 7.3 3.2 7.5 7.2 0 0 0 0 0 0 0 0 0 0 1 35
36 7.0 3.4 7.3 7.5 0 0 0 0 0 0 0 0 0 0 0 36
37 7.0 4.2 7.0 7.3 1 0 0 0 0 0 0 0 0 0 0 37
38 7.0 4.9 7.0 7.0 0 1 0 0 0 0 0 0 0 0 0 38
39 7.2 5.1 7.0 7.0 0 0 1 0 0 0 0 0 0 0 0 39
40 7.3 5.5 7.2 7.0 0 0 0 1 0 0 0 0 0 0 0 40
41 7.1 5.6 7.3 7.2 0 0 0 0 1 0 0 0 0 0 0 41
42 6.8 6.4 7.1 7.3 0 0 0 0 0 1 0 0 0 0 0 42
43 6.4 6.1 6.8 7.1 0 0 0 0 0 0 1 0 0 0 0 43
44 6.1 7.1 6.4 6.8 0 0 0 0 0 0 0 1 0 0 0 44
45 6.5 7.8 6.1 6.4 0 0 0 0 0 0 0 0 1 0 0 45
46 7.7 7.9 6.5 6.1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.9 7.4 7.7 6.5 0 0 0 0 0 0 0 0 0 0 1 47
48 7.5 7.5 7.9 7.7 0 0 0 0 0 0 0 0 0 0 0 48
49 6.9 6.8 7.5 7.9 1 0 0 0 0 0 0 0 0 0 0 49
50 6.6 5.2 6.9 7.5 0 1 0 0 0 0 0 0 0 0 0 50
51 6.9 4.7 6.6 6.9 0 0 1 0 0 0 0 0 0 0 0 51
52 7.7 4.1 6.9 6.6 0 0 0 1 0 0 0 0 0 0 0 52
53 8.0 3.9 7.7 6.9 0 0 0 0 1 0 0 0 0 0 0 53
54 8.0 2.6 8.0 7.7 0 0 0 0 0 1 0 0 0 0 0 54
55 7.7 2.7 8.0 8.0 0 0 0 0 0 0 1 0 0 0 0 55
56 7.3 1.8 7.7 8.0 0 0 0 0 0 0 0 1 0 0 0 56
57 7.4 1.0 7.3 7.7 0 0 0 0 0 0 0 0 1 0 0 57
58 8.1 0.3 7.4 7.3 0 0 0 0 0 0 0 0 0 1 0 58
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X Y1 Y2 M1 M2
2.686712 -0.043180 1.389498 -0.689862 -0.043151 0.077514
M3 M4 M5 M6 M7 M8
0.295761 0.247109 0.005390 0.025958 0.026734 -0.032572
M9 M10 M11 t
0.098894 0.680603 -0.247086 -0.008169
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.35379 -0.13862 -0.00392 0.12784 0.35797
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.686712 0.789666 3.402 0.00148 **
X -0.043180 0.021873 -1.974 0.05497 .
Y1 1.389498 0.118923 11.684 8.83e-15 ***
Y2 -0.689862 0.131892 -5.230 5.02e-06 ***
M1 -0.043151 0.126666 -0.341 0.73506
M2 0.077514 0.130730 0.593 0.55640
M3 0.295761 0.131503 2.249 0.02981 *
M4 0.247109 0.133171 1.856 0.07054 .
M5 0.005390 0.132226 0.041 0.96768
M6 0.025958 0.126118 0.206 0.83792
M7 0.026734 0.126659 0.211 0.83385
M8 -0.032572 0.128552 -0.253 0.80121
M9 0.098894 0.132076 0.749 0.45817
M10 0.680603 0.133804 5.087 8.03e-06 ***
M11 -0.247086 0.154883 -1.595 0.11814
t -0.008169 0.002965 -2.755 0.00865 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1871 on 42 degrees of freedom
Multiple R-squared: 0.9419, Adjusted R-squared: 0.9212
F-statistic: 45.39 on 15 and 42 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,] 0.08880604 0.17761209 0.9111940
[2,] 0.04092657 0.08185315 0.9590734
[3,] 0.01455646 0.02911291 0.9854435
[4,] 0.16265039 0.32530079 0.8373496
[5,] 0.14955953 0.29911907 0.8504405
[6,] 0.15369197 0.30738393 0.8463080
[7,] 0.09766160 0.19532320 0.9023384
[8,] 0.08281254 0.16562507 0.9171875
[9,] 0.09845914 0.19691828 0.9015409
[10,] 0.06666794 0.13333588 0.9333321
[11,] 0.06675918 0.13351836 0.9332408
[12,] 0.05128469 0.10256937 0.9487153
[13,] 0.39475751 0.78951503 0.6052425
[14,] 0.46027225 0.92054450 0.5397278
[15,] 0.42829449 0.85658898 0.5717055
[16,] 0.45605809 0.91211617 0.5439419
[17,] 0.47049485 0.94098970 0.5295051
[18,] 0.50198804 0.99602393 0.4980120
[19,] 0.65835075 0.68329850 0.3416492
[20,] 0.61087439 0.77825122 0.3891256
[21,] 0.85670121 0.28659759 0.1432988
> postscript(file="/var/www/html/rcomp/tmp/1fjx91261060853.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/22qfl1261060853.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3md5y1261060853.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4nxxb1261060853.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5oliv1261060853.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 = 58
Frequency = 1
1 2 3 4 5
-0.2390633572 -0.0351329623 0.1183494126 -0.1043930960 0.0495355838
6 7 8 9 10
-0.0422513919 -0.1441945971 0.1668252355 -0.2072084778 0.1281644679
11 12 13 14 15
0.0620368584 0.1713912560 0.0571523595 0.1268736985 0.0393638657
16 17 18 19 20
0.0882480036 0.1292225623 0.1288869569 0.1925019030 0.1385762278
21 22 23 24 25
-0.0887616103 -0.1877430622 0.1589381078 0.1796710739 0.0533680763
26 27 28 29 30
-0.0072243184 -0.1430201410 -0.0724583502 0.0427978916 -0.0006156432
31 32 33 34 35
0.2457271413 -0.2163244454 -0.3537860045 -0.1395359588 -0.1697769467
36 37 38 39 40
-0.2151997521 0.1495409258 -0.1396876370 -0.1411290620 -0.2449364054
41 42 43 44 45
-0.1917078202 -0.1226774030 -0.2493614540 -0.0898657061 0.3579681589
46 47 48 49 50
0.2259879148 -0.0511980195 -0.1358625778 -0.0209980044 0.0551712192
51 52 53 54 55
0.1264359247 0.3335398480 -0.0298482175 0.0366574812 -0.0446729932
56 57 58
0.0007886881 0.2917879336 -0.0268733618
> postscript(file="/var/www/html/rcomp/tmp/69z001261060853.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 = 58
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.2390633572 NA
1 -0.0351329623 -0.2390633572
2 0.1183494126 -0.0351329623
3 -0.1043930960 0.1183494126
4 0.0495355838 -0.1043930960
5 -0.0422513919 0.0495355838
6 -0.1441945971 -0.0422513919
7 0.1668252355 -0.1441945971
8 -0.2072084778 0.1668252355
9 0.1281644679 -0.2072084778
10 0.0620368584 0.1281644679
11 0.1713912560 0.0620368584
12 0.0571523595 0.1713912560
13 0.1268736985 0.0571523595
14 0.0393638657 0.1268736985
15 0.0882480036 0.0393638657
16 0.1292225623 0.0882480036
17 0.1288869569 0.1292225623
18 0.1925019030 0.1288869569
19 0.1385762278 0.1925019030
20 -0.0887616103 0.1385762278
21 -0.1877430622 -0.0887616103
22 0.1589381078 -0.1877430622
23 0.1796710739 0.1589381078
24 0.0533680763 0.1796710739
25 -0.0072243184 0.0533680763
26 -0.1430201410 -0.0072243184
27 -0.0724583502 -0.1430201410
28 0.0427978916 -0.0724583502
29 -0.0006156432 0.0427978916
30 0.2457271413 -0.0006156432
31 -0.2163244454 0.2457271413
32 -0.3537860045 -0.2163244454
33 -0.1395359588 -0.3537860045
34 -0.1697769467 -0.1395359588
35 -0.2151997521 -0.1697769467
36 0.1495409258 -0.2151997521
37 -0.1396876370 0.1495409258
38 -0.1411290620 -0.1396876370
39 -0.2449364054 -0.1411290620
40 -0.1917078202 -0.2449364054
41 -0.1226774030 -0.1917078202
42 -0.2493614540 -0.1226774030
43 -0.0898657061 -0.2493614540
44 0.3579681589 -0.0898657061
45 0.2259879148 0.3579681589
46 -0.0511980195 0.2259879148
47 -0.1358625778 -0.0511980195
48 -0.0209980044 -0.1358625778
49 0.0551712192 -0.0209980044
50 0.1264359247 0.0551712192
51 0.3335398480 0.1264359247
52 -0.0298482175 0.3335398480
53 0.0366574812 -0.0298482175
54 -0.0446729932 0.0366574812
55 0.0007886881 -0.0446729932
56 0.2917879336 0.0007886881
57 -0.0268733618 0.2917879336
58 NA -0.0268733618
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.0351329623 -0.2390633572
[2,] 0.1183494126 -0.0351329623
[3,] -0.1043930960 0.1183494126
[4,] 0.0495355838 -0.1043930960
[5,] -0.0422513919 0.0495355838
[6,] -0.1441945971 -0.0422513919
[7,] 0.1668252355 -0.1441945971
[8,] -0.2072084778 0.1668252355
[9,] 0.1281644679 -0.2072084778
[10,] 0.0620368584 0.1281644679
[11,] 0.1713912560 0.0620368584
[12,] 0.0571523595 0.1713912560
[13,] 0.1268736985 0.0571523595
[14,] 0.0393638657 0.1268736985
[15,] 0.0882480036 0.0393638657
[16,] 0.1292225623 0.0882480036
[17,] 0.1288869569 0.1292225623
[18,] 0.1925019030 0.1288869569
[19,] 0.1385762278 0.1925019030
[20,] -0.0887616103 0.1385762278
[21,] -0.1877430622 -0.0887616103
[22,] 0.1589381078 -0.1877430622
[23,] 0.1796710739 0.1589381078
[24,] 0.0533680763 0.1796710739
[25,] -0.0072243184 0.0533680763
[26,] -0.1430201410 -0.0072243184
[27,] -0.0724583502 -0.1430201410
[28,] 0.0427978916 -0.0724583502
[29,] -0.0006156432 0.0427978916
[30,] 0.2457271413 -0.0006156432
[31,] -0.2163244454 0.2457271413
[32,] -0.3537860045 -0.2163244454
[33,] -0.1395359588 -0.3537860045
[34,] -0.1697769467 -0.1395359588
[35,] -0.2151997521 -0.1697769467
[36,] 0.1495409258 -0.2151997521
[37,] -0.1396876370 0.1495409258
[38,] -0.1411290620 -0.1396876370
[39,] -0.2449364054 -0.1411290620
[40,] -0.1917078202 -0.2449364054
[41,] -0.1226774030 -0.1917078202
[42,] -0.2493614540 -0.1226774030
[43,] -0.0898657061 -0.2493614540
[44,] 0.3579681589 -0.0898657061
[45,] 0.2259879148 0.3579681589
[46,] -0.0511980195 0.2259879148
[47,] -0.1358625778 -0.0511980195
[48,] -0.0209980044 -0.1358625778
[49,] 0.0551712192 -0.0209980044
[50,] 0.1264359247 0.0551712192
[51,] 0.3335398480 0.1264359247
[52,] -0.0298482175 0.3335398480
[53,] 0.0366574812 -0.0298482175
[54,] -0.0446729932 0.0366574812
[55,] 0.0007886881 -0.0446729932
[56,] 0.2917879336 0.0007886881
[57,] -0.0268733618 0.2917879336
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.0351329623 -0.2390633572
2 0.1183494126 -0.0351329623
3 -0.1043930960 0.1183494126
4 0.0495355838 -0.1043930960
5 -0.0422513919 0.0495355838
6 -0.1441945971 -0.0422513919
7 0.1668252355 -0.1441945971
8 -0.2072084778 0.1668252355
9 0.1281644679 -0.2072084778
10 0.0620368584 0.1281644679
11 0.1713912560 0.0620368584
12 0.0571523595 0.1713912560
13 0.1268736985 0.0571523595
14 0.0393638657 0.1268736985
15 0.0882480036 0.0393638657
16 0.1292225623 0.0882480036
17 0.1288869569 0.1292225623
18 0.1925019030 0.1288869569
19 0.1385762278 0.1925019030
20 -0.0887616103 0.1385762278
21 -0.1877430622 -0.0887616103
22 0.1589381078 -0.1877430622
23 0.1796710739 0.1589381078
24 0.0533680763 0.1796710739
25 -0.0072243184 0.0533680763
26 -0.1430201410 -0.0072243184
27 -0.0724583502 -0.1430201410
28 0.0427978916 -0.0724583502
29 -0.0006156432 0.0427978916
30 0.2457271413 -0.0006156432
31 -0.2163244454 0.2457271413
32 -0.3537860045 -0.2163244454
33 -0.1395359588 -0.3537860045
34 -0.1697769467 -0.1395359588
35 -0.2151997521 -0.1697769467
36 0.1495409258 -0.2151997521
37 -0.1396876370 0.1495409258
38 -0.1411290620 -0.1396876370
39 -0.2449364054 -0.1411290620
40 -0.1917078202 -0.2449364054
41 -0.1226774030 -0.1917078202
42 -0.2493614540 -0.1226774030
43 -0.0898657061 -0.2493614540
44 0.3579681589 -0.0898657061
45 0.2259879148 0.3579681589
46 -0.0511980195 0.2259879148
47 -0.1358625778 -0.0511980195
48 -0.0209980044 -0.1358625778
49 0.0551712192 -0.0209980044
50 0.1264359247 0.0551712192
51 0.3335398480 0.1264359247
52 -0.0298482175 0.3335398480
53 0.0366574812 -0.0298482175
54 -0.0446729932 0.0366574812
55 0.0007886881 -0.0446729932
56 0.2917879336 0.0007886881
57 -0.0268733618 0.2917879336
> 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/7afpl1261060853.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8vgk61261060853.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ctdv1261060853.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10kszw1261060853.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11hldv1261060853.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/12k2w81261060853.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/130gjl1261060853.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/144hkz1261060853.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/15yhc71261060853.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/16jcbj1261060853.tab")
+ }
>
> try(system("convert tmp/1fjx91261060853.ps tmp/1fjx91261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/22qfl1261060853.ps tmp/22qfl1261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/3md5y1261060853.ps tmp/3md5y1261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/4nxxb1261060853.ps tmp/4nxxb1261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/5oliv1261060853.ps tmp/5oliv1261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/69z001261060853.ps tmp/69z001261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/7afpl1261060853.ps tmp/7afpl1261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/8vgk61261060853.ps tmp/8vgk61261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ctdv1261060853.ps tmp/9ctdv1261060853.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kszw1261060853.ps tmp/10kszw1261060853.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.417 1.596 2.944