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(461,463,462,456,455,456,472,472,471,465,459,465,468,467,463,460,462,461,476,476,471,453,443,442,444,438,427,424,416,406,431,434,418,412,404,409,412,406,398,397,385,390,413,413,401,397,397,409,419,424,428,430,424,433,456,459,446,441,439,454,460),dim=c(1,61),dimnames=list(c('HPC'),1:61))
> y <- array(NA,dim=c(1,61),dimnames=list(c('HPC'),1:61))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
HPC M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 461 1 0 0 0 0 0 0 0 0 0 0 1
2 463 0 1 0 0 0 0 0 0 0 0 0 2
3 462 0 0 1 0 0 0 0 0 0 0 0 3
4 456 0 0 0 1 0 0 0 0 0 0 0 4
5 455 0 0 0 0 1 0 0 0 0 0 0 5
6 456 0 0 0 0 0 1 0 0 0 0 0 6
7 472 0 0 0 0 0 0 1 0 0 0 0 7
8 472 0 0 0 0 0 0 0 1 0 0 0 8
9 471 0 0 0 0 0 0 0 0 1 0 0 9
10 465 0 0 0 0 0 0 0 0 0 1 0 10
11 459 0 0 0 0 0 0 0 0 0 0 1 11
12 465 0 0 0 0 0 0 0 0 0 0 0 12
13 468 1 0 0 0 0 0 0 0 0 0 0 13
14 467 0 1 0 0 0 0 0 0 0 0 0 14
15 463 0 0 1 0 0 0 0 0 0 0 0 15
16 460 0 0 0 1 0 0 0 0 0 0 0 16
17 462 0 0 0 0 1 0 0 0 0 0 0 17
18 461 0 0 0 0 0 1 0 0 0 0 0 18
19 476 0 0 0 0 0 0 1 0 0 0 0 19
20 476 0 0 0 0 0 0 0 1 0 0 0 20
21 471 0 0 0 0 0 0 0 0 1 0 0 21
22 453 0 0 0 0 0 0 0 0 0 1 0 22
23 443 0 0 0 0 0 0 0 0 0 0 1 23
24 442 0 0 0 0 0 0 0 0 0 0 0 24
25 444 1 0 0 0 0 0 0 0 0 0 0 25
26 438 0 1 0 0 0 0 0 0 0 0 0 26
27 427 0 0 1 0 0 0 0 0 0 0 0 27
28 424 0 0 0 1 0 0 0 0 0 0 0 28
29 416 0 0 0 0 1 0 0 0 0 0 0 29
30 406 0 0 0 0 0 1 0 0 0 0 0 30
31 431 0 0 0 0 0 0 1 0 0 0 0 31
32 434 0 0 0 0 0 0 0 1 0 0 0 32
33 418 0 0 0 0 0 0 0 0 1 0 0 33
34 412 0 0 0 0 0 0 0 0 0 1 0 34
35 404 0 0 0 0 0 0 0 0 0 0 1 35
36 409 0 0 0 0 0 0 0 0 0 0 0 36
37 412 1 0 0 0 0 0 0 0 0 0 0 37
38 406 0 1 0 0 0 0 0 0 0 0 0 38
39 398 0 0 1 0 0 0 0 0 0 0 0 39
40 397 0 0 0 1 0 0 0 0 0 0 0 40
41 385 0 0 0 0 1 0 0 0 0 0 0 41
42 390 0 0 0 0 0 1 0 0 0 0 0 42
43 413 0 0 0 0 0 0 1 0 0 0 0 43
44 413 0 0 0 0 0 0 0 1 0 0 0 44
45 401 0 0 0 0 0 0 0 0 1 0 0 45
46 397 0 0 0 0 0 0 0 0 0 1 0 46
47 397 0 0 0 0 0 0 0 0 0 0 1 47
48 409 0 0 0 0 0 0 0 0 0 0 0 48
49 419 1 0 0 0 0 0 0 0 0 0 0 49
50 424 0 1 0 0 0 0 0 0 0 0 0 50
51 428 0 0 1 0 0 0 0 0 0 0 0 51
52 430 0 0 0 1 0 0 0 0 0 0 0 52
53 424 0 0 0 0 1 0 0 0 0 0 0 53
54 433 0 0 0 0 0 1 0 0 0 0 0 54
55 456 0 0 0 0 0 0 1 0 0 0 0 55
56 459 0 0 0 0 0 0 0 1 0 0 0 56
57 446 0 0 0 0 0 0 0 0 1 0 0 57
58 441 0 0 0 0 0 0 0 0 0 1 0 58
59 439 0 0 0 0 0 0 0 0 0 0 1 59
60 454 0 0 0 0 0 0 0 0 0 0 0 60
61 460 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) M1 M2 M3 M4 M5
466.0118 4.0039 -4.5922 -7.7529 -9.1137 -13.2745
M6 M7 M8 M9 M10 M11
-11.6353 9.6039 11.6431 3.0824 -3.8784 -8.2392
t
-0.8392
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-33.329 -21.329 3.259 16.329 41.176
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 466.0118 11.7107 39.794 < 2e-16 ***
M1 4.0039 13.6574 0.293 0.771
M2 -4.5922 14.3349 -0.320 0.750
M3 -7.7529 14.3166 -0.542 0.591
M4 -9.1137 14.3002 -0.637 0.527
M5 -13.2745 14.2857 -0.929 0.357
M6 -11.6353 14.2731 -0.815 0.419
M7 9.6039 14.2625 0.673 0.504
M8 11.6431 14.2538 0.817 0.418
M9 3.0824 14.2470 0.216 0.830
M10 -3.8784 14.2421 -0.272 0.787
M11 -8.2392 14.2392 -0.579 0.566
t -0.8392 0.1661 -5.051 6.77e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22.51 on 48 degrees of freedom
Multiple R-squared: 0.4007, Adjusted R-squared: 0.2508
F-statistic: 2.674 on 12 and 48 DF, p-value: 0.007811
> 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,] 4.441894e-04 8.883788e-04 9.995558e-01
[2,] 5.492899e-05 1.098580e-04 9.999451e-01
[3,] 4.097210e-06 8.194420e-06 9.999959e-01
[4,] 2.984752e-07 5.969504e-07 9.999997e-01
[5,] 2.211833e-08 4.423667e-08 1.000000e+00
[6,] 1.724627e-08 3.449253e-08 1.000000e+00
[7,] 6.631493e-06 1.326299e-05 9.999934e-01
[8,] 6.939718e-05 1.387944e-04 9.999306e-01
[9,] 5.871586e-04 1.174317e-03 9.994128e-01
[10,] 1.311690e-03 2.623380e-03 9.986883e-01
[11,] 4.391683e-03 8.783366e-03 9.956083e-01
[12,] 1.651575e-02 3.303150e-02 9.834843e-01
[13,] 3.105246e-02 6.210492e-02 9.689475e-01
[14,] 8.898201e-02 1.779640e-01 9.110180e-01
[15,] 1.899035e-01 3.798070e-01 8.100965e-01
[16,] 2.387641e-01 4.775283e-01 7.612359e-01
[17,] 3.095357e-01 6.190715e-01 6.904643e-01
[18,] 4.860655e-01 9.721309e-01 5.139345e-01
[19,] 6.808554e-01 6.382891e-01 3.191446e-01
[20,] 8.512862e-01 2.974276e-01 1.487138e-01
[21,] 9.480148e-01 1.039704e-01 5.198522e-02
[22,] 9.966276e-01 6.744786e-03 3.372393e-03
[23,] 9.999090e-01 1.819480e-04 9.097399e-05
[24,] 9.999831e-01 3.377348e-05 1.688674e-05
[25,] 9.999993e-01 1.490439e-06 7.452193e-07
[26,] 9.999994e-01 1.102344e-06 5.511722e-07
[27,] 9.999943e-01 1.138214e-05 5.691068e-06
[28,] 9.999452e-01 1.096719e-04 5.483593e-05
[29,] 9.997407e-01 5.186741e-04 2.593371e-04
[30,] 9.982695e-01 3.460980e-03 1.730490e-03
> postscript(file="/var/www/html/rcomp/tmp/18dn41292587192.ps",horizontal=F,onefile=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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/25pbv1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/35pbv1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45pbv1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/55pbv1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6 7
-8.176471 3.258824 6.258824 2.458824 6.458824 6.658824 2.258824
8 9 10 11 12 13 14
1.058824 9.458824 11.258824 10.458824 9.058824 8.894118 17.329412
15 16 17 18 19 20 21
17.329412 16.529412 23.529412 21.729412 16.329412 15.129412 19.529412
22 23 24 25 26 27 28
9.329412 4.529412 -3.870588 -5.035294 -1.600000 -8.600000 -9.400000
29 30 31 32 33 34 35
-12.400000 -23.200000 -18.600000 -16.800000 -23.400000 -21.600000 -24.400000
36 37 38 39 40 41 42
-26.800000 -26.964706 -23.529412 -27.529412 -26.329412 -33.329412 -29.129412
43 44 45 46 47 48 49
-26.529412 -27.729412 -30.329412 -26.529412 -21.329412 -16.729412 -9.894118
50 51 52 53 54 55 56
4.541176 12.541176 16.741176 15.741176 23.941176 26.541176 28.341176
57 58 59 60 61
24.741176 27.541176 30.741176 38.341176 41.176471
> postscript(file="/var/www/html/rcomp/tmp/6yzsg1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 -8.176471 NA
1 3.258824 -8.176471
2 6.258824 3.258824
3 2.458824 6.258824
4 6.458824 2.458824
5 6.658824 6.458824
6 2.258824 6.658824
7 1.058824 2.258824
8 9.458824 1.058824
9 11.258824 9.458824
10 10.458824 11.258824
11 9.058824 10.458824
12 8.894118 9.058824
13 17.329412 8.894118
14 17.329412 17.329412
15 16.529412 17.329412
16 23.529412 16.529412
17 21.729412 23.529412
18 16.329412 21.729412
19 15.129412 16.329412
20 19.529412 15.129412
21 9.329412 19.529412
22 4.529412 9.329412
23 -3.870588 4.529412
24 -5.035294 -3.870588
25 -1.600000 -5.035294
26 -8.600000 -1.600000
27 -9.400000 -8.600000
28 -12.400000 -9.400000
29 -23.200000 -12.400000
30 -18.600000 -23.200000
31 -16.800000 -18.600000
32 -23.400000 -16.800000
33 -21.600000 -23.400000
34 -24.400000 -21.600000
35 -26.800000 -24.400000
36 -26.964706 -26.800000
37 -23.529412 -26.964706
38 -27.529412 -23.529412
39 -26.329412 -27.529412
40 -33.329412 -26.329412
41 -29.129412 -33.329412
42 -26.529412 -29.129412
43 -27.729412 -26.529412
44 -30.329412 -27.729412
45 -26.529412 -30.329412
46 -21.329412 -26.529412
47 -16.729412 -21.329412
48 -9.894118 -16.729412
49 4.541176 -9.894118
50 12.541176 4.541176
51 16.741176 12.541176
52 15.741176 16.741176
53 23.941176 15.741176
54 26.541176 23.941176
55 28.341176 26.541176
56 24.741176 28.341176
57 27.541176 24.741176
58 30.741176 27.541176
59 38.341176 30.741176
60 41.176471 38.341176
61 NA 41.176471
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3.258824 -8.176471
[2,] 6.258824 3.258824
[3,] 2.458824 6.258824
[4,] 6.458824 2.458824
[5,] 6.658824 6.458824
[6,] 2.258824 6.658824
[7,] 1.058824 2.258824
[8,] 9.458824 1.058824
[9,] 11.258824 9.458824
[10,] 10.458824 11.258824
[11,] 9.058824 10.458824
[12,] 8.894118 9.058824
[13,] 17.329412 8.894118
[14,] 17.329412 17.329412
[15,] 16.529412 17.329412
[16,] 23.529412 16.529412
[17,] 21.729412 23.529412
[18,] 16.329412 21.729412
[19,] 15.129412 16.329412
[20,] 19.529412 15.129412
[21,] 9.329412 19.529412
[22,] 4.529412 9.329412
[23,] -3.870588 4.529412
[24,] -5.035294 -3.870588
[25,] -1.600000 -5.035294
[26,] -8.600000 -1.600000
[27,] -9.400000 -8.600000
[28,] -12.400000 -9.400000
[29,] -23.200000 -12.400000
[30,] -18.600000 -23.200000
[31,] -16.800000 -18.600000
[32,] -23.400000 -16.800000
[33,] -21.600000 -23.400000
[34,] -24.400000 -21.600000
[35,] -26.800000 -24.400000
[36,] -26.964706 -26.800000
[37,] -23.529412 -26.964706
[38,] -27.529412 -23.529412
[39,] -26.329412 -27.529412
[40,] -33.329412 -26.329412
[41,] -29.129412 -33.329412
[42,] -26.529412 -29.129412
[43,] -27.729412 -26.529412
[44,] -30.329412 -27.729412
[45,] -26.529412 -30.329412
[46,] -21.329412 -26.529412
[47,] -16.729412 -21.329412
[48,] -9.894118 -16.729412
[49,] 4.541176 -9.894118
[50,] 12.541176 4.541176
[51,] 16.741176 12.541176
[52,] 15.741176 16.741176
[53,] 23.941176 15.741176
[54,] 26.541176 23.941176
[55,] 28.341176 26.541176
[56,] 24.741176 28.341176
[57,] 27.541176 24.741176
[58,] 30.741176 27.541176
[59,] 38.341176 30.741176
[60,] 41.176471 38.341176
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3.258824 -8.176471
2 6.258824 3.258824
3 2.458824 6.258824
4 6.458824 2.458824
5 6.658824 6.458824
6 2.258824 6.658824
7 1.058824 2.258824
8 9.458824 1.058824
9 11.258824 9.458824
10 10.458824 11.258824
11 9.058824 10.458824
12 8.894118 9.058824
13 17.329412 8.894118
14 17.329412 17.329412
15 16.529412 17.329412
16 23.529412 16.529412
17 21.729412 23.529412
18 16.329412 21.729412
19 15.129412 16.329412
20 19.529412 15.129412
21 9.329412 19.529412
22 4.529412 9.329412
23 -3.870588 4.529412
24 -5.035294 -3.870588
25 -1.600000 -5.035294
26 -8.600000 -1.600000
27 -9.400000 -8.600000
28 -12.400000 -9.400000
29 -23.200000 -12.400000
30 -18.600000 -23.200000
31 -16.800000 -18.600000
32 -23.400000 -16.800000
33 -21.600000 -23.400000
34 -24.400000 -21.600000
35 -26.800000 -24.400000
36 -26.964706 -26.800000
37 -23.529412 -26.964706
38 -27.529412 -23.529412
39 -26.329412 -27.529412
40 -33.329412 -26.329412
41 -29.129412 -33.329412
42 -26.529412 -29.129412
43 -27.729412 -26.529412
44 -30.329412 -27.729412
45 -26.529412 -30.329412
46 -21.329412 -26.529412
47 -16.729412 -21.329412
48 -9.894118 -16.729412
49 4.541176 -9.894118
50 12.541176 4.541176
51 16.741176 12.541176
52 15.741176 16.741176
53 23.941176 15.741176
54 26.541176 23.941176
55 28.341176 26.541176
56 24.741176 28.341176
57 27.541176 24.741176
58 30.741176 27.541176
59 38.341176 30.741176
60 41.176471 38.341176
> 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/74n3v1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84n3v1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9ewky1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10ewky1292587192.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ix031292587192.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/123xhr1292587192.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/13z7xi1292587192.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/14lpdo1292587192.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/1568uu1292587192.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/16a9ai1292587192.tab")
+ }
> try(system("convert tmp/18dn41292587192.ps tmp/18dn41292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/25pbv1292587192.ps tmp/25pbv1292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/35pbv1292587192.ps tmp/35pbv1292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/45pbv1292587192.ps tmp/45pbv1292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/55pbv1292587192.ps tmp/55pbv1292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/6yzsg1292587192.ps tmp/6yzsg1292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/74n3v1292587192.ps tmp/74n3v1292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/84n3v1292587192.ps tmp/84n3v1292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ewky1292587192.ps tmp/9ewky1292587192.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ewky1292587192.ps tmp/10ewky1292587192.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.434 1.621 6.017