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(2.05,1.00,2.11,1.00,2.09,1.00,2.05,1.00,2.08,1.00,2.06,1.00,2.06,1.00,2.08,1.00,2.07,1.00,2.06,1.00,2.07,1.00,2.06,1.00,2.09,1.00,2.07,1.00,2.09,1.00,2.28,1.25,2.33,1.25,2.35,1.25,2.52,1.50,2.63,1.50,2.58,1.50,2.70,1.75,2.81,1.75,2.97,2.00,3.04,2.00,3.28,2.25,3.33,2.25,3.50,2.50,3.56,2.50,3.57,2.50,3.69,2.75,3.82,2.75,3.79,2.75,3.96,3.00,4.06,3.00,4.05,3.00,4.03,3.00,3.94,3.00,4.02,3.00,3.88,3.00,4.02,3.00,4.03,3.00,4.09,3.00,3.99,3.00,4.01,3.00,4.01,3.00,4.19,3.25,4.30,3.25,4.27,3.25,3.82,3.25,3.15,2.75,2.49,2.00,1.81,1.00,1.26,1.00,1.06,0.50,0.84,0.25,0.78,0.25,0.70,0.25,0.36,0.25,0.35,0.25),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 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 2.05 1.00 1 0 0 0 0 0 0 0 0 0 0
2 2.11 1.00 0 1 0 0 0 0 0 0 0 0 0
3 2.09 1.00 0 0 1 0 0 0 0 0 0 0 0
4 2.05 1.00 0 0 0 1 0 0 0 0 0 0 0
5 2.08 1.00 0 0 0 0 1 0 0 0 0 0 0
6 2.06 1.00 0 0 0 0 0 1 0 0 0 0 0
7 2.06 1.00 0 0 0 0 0 0 1 0 0 0 0
8 2.08 1.00 0 0 0 0 0 0 0 1 0 0 0
9 2.07 1.00 0 0 0 0 0 0 0 0 1 0 0
10 2.06 1.00 0 0 0 0 0 0 0 0 0 1 0
11 2.07 1.00 0 0 0 0 0 0 0 0 0 0 1
12 2.06 1.00 0 0 0 0 0 0 0 0 0 0 0
13 2.09 1.00 1 0 0 0 0 0 0 0 0 0 0
14 2.07 1.00 0 1 0 0 0 0 0 0 0 0 0
15 2.09 1.00 0 0 1 0 0 0 0 0 0 0 0
16 2.28 1.25 0 0 0 1 0 0 0 0 0 0 0
17 2.33 1.25 0 0 0 0 1 0 0 0 0 0 0
18 2.35 1.25 0 0 0 0 0 1 0 0 0 0 0
19 2.52 1.50 0 0 0 0 0 0 1 0 0 0 0
20 2.63 1.50 0 0 0 0 0 0 0 1 0 0 0
21 2.58 1.50 0 0 0 0 0 0 0 0 1 0 0
22 2.70 1.75 0 0 0 0 0 0 0 0 0 1 0
23 2.81 1.75 0 0 0 0 0 0 0 0 0 0 1
24 2.97 2.00 0 0 0 0 0 0 0 0 0 0 0
25 3.04 2.00 1 0 0 0 0 0 0 0 0 0 0
26 3.28 2.25 0 1 0 0 0 0 0 0 0 0 0
27 3.33 2.25 0 0 1 0 0 0 0 0 0 0 0
28 3.50 2.50 0 0 0 1 0 0 0 0 0 0 0
29 3.56 2.50 0 0 0 0 1 0 0 0 0 0 0
30 3.57 2.50 0 0 0 0 0 1 0 0 0 0 0
31 3.69 2.75 0 0 0 0 0 0 1 0 0 0 0
32 3.82 2.75 0 0 0 0 0 0 0 1 0 0 0
33 3.79 2.75 0 0 0 0 0 0 0 0 1 0 0
34 3.96 3.00 0 0 0 0 0 0 0 0 0 1 0
35 4.06 3.00 0 0 0 0 0 0 0 0 0 0 1
36 4.05 3.00 0 0 0 0 0 0 0 0 0 0 0
37 4.03 3.00 1 0 0 0 0 0 0 0 0 0 0
38 3.94 3.00 0 1 0 0 0 0 0 0 0 0 0
39 4.02 3.00 0 0 1 0 0 0 0 0 0 0 0
40 3.88 3.00 0 0 0 1 0 0 0 0 0 0 0
41 4.02 3.00 0 0 0 0 1 0 0 0 0 0 0
42 4.03 3.00 0 0 0 0 0 1 0 0 0 0 0
43 4.09 3.00 0 0 0 0 0 0 1 0 0 0 0
44 3.99 3.00 0 0 0 0 0 0 0 1 0 0 0
45 4.01 3.00 0 0 0 0 0 0 0 0 1 0 0
46 4.01 3.00 0 0 0 0 0 0 0 0 0 1 0
47 4.19 3.25 0 0 0 0 0 0 0 0 0 0 1
48 4.30 3.25 0 0 0 0 0 0 0 0 0 0 0
49 4.27 3.25 1 0 0 0 0 0 0 0 0 0 0
50 3.82 3.25 0 1 0 0 0 0 0 0 0 0 0
51 3.15 2.75 0 0 1 0 0 0 0 0 0 0 0
52 2.49 2.00 0 0 0 1 0 0 0 0 0 0 0
53 1.81 1.00 0 0 0 0 1 0 0 0 0 0 0
54 1.26 1.00 0 0 0 0 0 1 0 0 0 0 0
55 1.06 0.50 0 0 0 0 0 0 1 0 0 0 0
56 0.84 0.25 0 0 0 0 0 0 0 1 0 0 0
57 0.78 0.25 0 0 0 0 0 0 0 0 1 0 0
58 0.70 0.25 0 0 0 0 0 0 0 0 0 1 0
59 0.36 0.25 0 0 0 0 0 0 0 0 0 0 1
60 0.35 0.25 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
0.697387 1.078218 0.188267 0.082356 0.082178 0.040089
M5 M6 M7 M8 M9 M10
0.175733 0.069733 0.099733 0.141644 0.115644 0.047822
M11
0.005911
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.61694 -0.08471 0.06745 0.16548 0.28848
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.697387 0.134558 5.183 4.52e-06 ***
X 1.078218 0.034679 31.091 < 2e-16 ***
M1 0.188267 0.165999 1.134 0.262
M2 0.082356 0.166063 0.496 0.622
M3 0.082178 0.165954 0.495 0.623
M4 0.040089 0.165927 0.242 0.810
M5 0.175733 0.165999 1.059 0.295
M6 0.069733 0.165999 0.420 0.676
M7 0.099733 0.165999 0.601 0.551
M8 0.141644 0.166063 0.853 0.398
M9 0.115644 0.166063 0.696 0.490
M10 0.047822 0.165954 0.288 0.774
M11 0.005911 0.165927 0.036 0.972
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2623 on 47 degrees of freedom
Multiple R-squared: 0.9545, Adjusted R-squared: 0.9429
F-statistic: 82.18 on 12 and 47 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,] 1.302931e-03 2.605861e-03 0.9986971
[2,] 1.227360e-04 2.454720e-04 0.9998773
[3,] 3.204956e-05 6.409911e-05 0.9999680
[4,] 5.801845e-06 1.160369e-05 0.9999942
[5,] 2.369442e-06 4.738884e-06 0.9999976
[6,] 3.069463e-07 6.138925e-07 0.9999997
[7,] 7.487816e-07 1.497563e-06 0.9999993
[8,] 2.057742e-07 4.115484e-07 0.9999998
[9,] 6.082292e-08 1.216458e-07 0.9999999
[10,] 1.238746e-08 2.477492e-08 1.0000000
[11,] 4.734779e-09 9.469558e-09 1.0000000
[12,] 7.003625e-09 1.400725e-08 1.0000000
[13,] 2.410424e-09 4.820847e-09 1.0000000
[14,] 4.816629e-10 9.633258e-10 1.0000000
[15,] 2.659027e-10 5.318053e-10 1.0000000
[16,] 1.498832e-10 2.997664e-10 1.0000000
[17,] 2.449245e-11 4.898490e-11 1.0000000
[18,] 3.714835e-12 7.429670e-12 1.0000000
[19,] 5.812880e-13 1.162576e-12 1.0000000
[20,] 3.719157e-13 7.438314e-13 1.0000000
[21,] 7.037871e-13 1.407574e-12 1.0000000
[22,] 1.037315e-13 2.074630e-13 1.0000000
[23,] 4.421674e-12 8.843349e-12 1.0000000
[24,] 1.088854e-09 2.177708e-09 1.0000000
[25,] 8.258541e-09 1.651708e-08 1.0000000
[26,] 1.958988e-08 3.917976e-08 1.0000000
[27,] 3.845837e-07 7.691674e-07 0.9999996
[28,] 1.544542e-06 3.089084e-06 0.9999985
[29,] 3.917271e-05 7.834542e-05 0.9999608
> postscript(file="/var/www/html/rcomp/tmp/1k8mz1258736750.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2egh01258736750.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3yb2n1258736750.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ay061258736750.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5omrt1258736750.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
0.086128440 0.252039318 0.232217562 0.234306684 0.128663172 0.214663172
7 8 9 10 11 12
0.184663172 0.162752294 0.178752294 0.236574050 0.288484928 0.284395806
13 14 15 16 17 18
0.126128440 0.212039318 0.232217562 0.194752294 0.109108781 0.235108781
19 20 21 22 23 24
0.105554391 0.173643512 0.149643512 0.067910878 0.219821756 0.116178244
25 26 27 28 29 30
-0.002089122 0.074267366 0.124445609 0.066980341 -0.008663172 0.107336828
31 32 33 34 35 36
-0.072217562 0.015871560 0.011871560 -0.019861075 0.122049803 0.117960682
37 38 39 40 41 42
-0.090306684 -0.074395806 0.005782438 -0.092128440 -0.087771953 0.028228047
43 44 45 46 47 48
0.058228047 -0.083682831 -0.037682831 0.030138925 -0.017504587 0.098406291
49 50 51 52 53 54
-0.119861075 -0.463950197 -0.594663172 -0.403910878 -0.141336828 -0.585336828
55 56 57 58 59 60
-0.276228047 -0.268584535 -0.302584535 -0.314762779 -0.612851900 -0.616941022
> postscript(file="/var/www/html/rcomp/tmp/6pw5c1258736750.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 0.086128440 NA
1 0.252039318 0.086128440
2 0.232217562 0.252039318
3 0.234306684 0.232217562
4 0.128663172 0.234306684
5 0.214663172 0.128663172
6 0.184663172 0.214663172
7 0.162752294 0.184663172
8 0.178752294 0.162752294
9 0.236574050 0.178752294
10 0.288484928 0.236574050
11 0.284395806 0.288484928
12 0.126128440 0.284395806
13 0.212039318 0.126128440
14 0.232217562 0.212039318
15 0.194752294 0.232217562
16 0.109108781 0.194752294
17 0.235108781 0.109108781
18 0.105554391 0.235108781
19 0.173643512 0.105554391
20 0.149643512 0.173643512
21 0.067910878 0.149643512
22 0.219821756 0.067910878
23 0.116178244 0.219821756
24 -0.002089122 0.116178244
25 0.074267366 -0.002089122
26 0.124445609 0.074267366
27 0.066980341 0.124445609
28 -0.008663172 0.066980341
29 0.107336828 -0.008663172
30 -0.072217562 0.107336828
31 0.015871560 -0.072217562
32 0.011871560 0.015871560
33 -0.019861075 0.011871560
34 0.122049803 -0.019861075
35 0.117960682 0.122049803
36 -0.090306684 0.117960682
37 -0.074395806 -0.090306684
38 0.005782438 -0.074395806
39 -0.092128440 0.005782438
40 -0.087771953 -0.092128440
41 0.028228047 -0.087771953
42 0.058228047 0.028228047
43 -0.083682831 0.058228047
44 -0.037682831 -0.083682831
45 0.030138925 -0.037682831
46 -0.017504587 0.030138925
47 0.098406291 -0.017504587
48 -0.119861075 0.098406291
49 -0.463950197 -0.119861075
50 -0.594663172 -0.463950197
51 -0.403910878 -0.594663172
52 -0.141336828 -0.403910878
53 -0.585336828 -0.141336828
54 -0.276228047 -0.585336828
55 -0.268584535 -0.276228047
56 -0.302584535 -0.268584535
57 -0.314762779 -0.302584535
58 -0.612851900 -0.314762779
59 -0.616941022 -0.612851900
60 NA -0.616941022
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.252039318 0.086128440
[2,] 0.232217562 0.252039318
[3,] 0.234306684 0.232217562
[4,] 0.128663172 0.234306684
[5,] 0.214663172 0.128663172
[6,] 0.184663172 0.214663172
[7,] 0.162752294 0.184663172
[8,] 0.178752294 0.162752294
[9,] 0.236574050 0.178752294
[10,] 0.288484928 0.236574050
[11,] 0.284395806 0.288484928
[12,] 0.126128440 0.284395806
[13,] 0.212039318 0.126128440
[14,] 0.232217562 0.212039318
[15,] 0.194752294 0.232217562
[16,] 0.109108781 0.194752294
[17,] 0.235108781 0.109108781
[18,] 0.105554391 0.235108781
[19,] 0.173643512 0.105554391
[20,] 0.149643512 0.173643512
[21,] 0.067910878 0.149643512
[22,] 0.219821756 0.067910878
[23,] 0.116178244 0.219821756
[24,] -0.002089122 0.116178244
[25,] 0.074267366 -0.002089122
[26,] 0.124445609 0.074267366
[27,] 0.066980341 0.124445609
[28,] -0.008663172 0.066980341
[29,] 0.107336828 -0.008663172
[30,] -0.072217562 0.107336828
[31,] 0.015871560 -0.072217562
[32,] 0.011871560 0.015871560
[33,] -0.019861075 0.011871560
[34,] 0.122049803 -0.019861075
[35,] 0.117960682 0.122049803
[36,] -0.090306684 0.117960682
[37,] -0.074395806 -0.090306684
[38,] 0.005782438 -0.074395806
[39,] -0.092128440 0.005782438
[40,] -0.087771953 -0.092128440
[41,] 0.028228047 -0.087771953
[42,] 0.058228047 0.028228047
[43,] -0.083682831 0.058228047
[44,] -0.037682831 -0.083682831
[45,] 0.030138925 -0.037682831
[46,] -0.017504587 0.030138925
[47,] 0.098406291 -0.017504587
[48,] -0.119861075 0.098406291
[49,] -0.463950197 -0.119861075
[50,] -0.594663172 -0.463950197
[51,] -0.403910878 -0.594663172
[52,] -0.141336828 -0.403910878
[53,] -0.585336828 -0.141336828
[54,] -0.276228047 -0.585336828
[55,] -0.268584535 -0.276228047
[56,] -0.302584535 -0.268584535
[57,] -0.314762779 -0.302584535
[58,] -0.612851900 -0.314762779
[59,] -0.616941022 -0.612851900
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.252039318 0.086128440
2 0.232217562 0.252039318
3 0.234306684 0.232217562
4 0.128663172 0.234306684
5 0.214663172 0.128663172
6 0.184663172 0.214663172
7 0.162752294 0.184663172
8 0.178752294 0.162752294
9 0.236574050 0.178752294
10 0.288484928 0.236574050
11 0.284395806 0.288484928
12 0.126128440 0.284395806
13 0.212039318 0.126128440
14 0.232217562 0.212039318
15 0.194752294 0.232217562
16 0.109108781 0.194752294
17 0.235108781 0.109108781
18 0.105554391 0.235108781
19 0.173643512 0.105554391
20 0.149643512 0.173643512
21 0.067910878 0.149643512
22 0.219821756 0.067910878
23 0.116178244 0.219821756
24 -0.002089122 0.116178244
25 0.074267366 -0.002089122
26 0.124445609 0.074267366
27 0.066980341 0.124445609
28 -0.008663172 0.066980341
29 0.107336828 -0.008663172
30 -0.072217562 0.107336828
31 0.015871560 -0.072217562
32 0.011871560 0.015871560
33 -0.019861075 0.011871560
34 0.122049803 -0.019861075
35 0.117960682 0.122049803
36 -0.090306684 0.117960682
37 -0.074395806 -0.090306684
38 0.005782438 -0.074395806
39 -0.092128440 0.005782438
40 -0.087771953 -0.092128440
41 0.028228047 -0.087771953
42 0.058228047 0.028228047
43 -0.083682831 0.058228047
44 -0.037682831 -0.083682831
45 0.030138925 -0.037682831
46 -0.017504587 0.030138925
47 0.098406291 -0.017504587
48 -0.119861075 0.098406291
49 -0.463950197 -0.119861075
50 -0.594663172 -0.463950197
51 -0.403910878 -0.594663172
52 -0.141336828 -0.403910878
53 -0.585336828 -0.141336828
54 -0.276228047 -0.585336828
55 -0.268584535 -0.276228047
56 -0.302584535 -0.268584535
57 -0.314762779 -0.302584535
58 -0.612851900 -0.314762779
59 -0.616941022 -0.612851900
> 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/79cpt1258736750.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8y6jl1258736750.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9crku1258736750.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10exnv1258736750.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pit81258736750.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/1246po1258736750.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/13ltho1258736750.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/14nagn1258736750.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/159sf11258736750.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/160jgq1258736750.tab")
+ }
>
> system("convert tmp/1k8mz1258736750.ps tmp/1k8mz1258736750.png")
> system("convert tmp/2egh01258736750.ps tmp/2egh01258736750.png")
> system("convert tmp/3yb2n1258736750.ps tmp/3yb2n1258736750.png")
> system("convert tmp/4ay061258736750.ps tmp/4ay061258736750.png")
> system("convert tmp/5omrt1258736750.ps tmp/5omrt1258736750.png")
> system("convert tmp/6pw5c1258736750.ps tmp/6pw5c1258736750.png")
> system("convert tmp/79cpt1258736750.ps tmp/79cpt1258736750.png")
> system("convert tmp/8y6jl1258736750.ps tmp/8y6jl1258736750.png")
> system("convert tmp/9crku1258736750.ps tmp/9crku1258736750.png")
> system("convert tmp/10exnv1258736750.ps tmp/10exnv1258736750.png")
>
>
> proc.time()
user system elapsed
2.440 1.549 5.328