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(627,0,696,0,825,0,677,0,656,0,785,0,412,0,352,0,839,0,729,0,696,0,641,0,695,0,638,0,762,0,635,0,721,0,854,0,418,0,367,0,824,0,687,0,601,0,676,0,740,0,691,0,683,0,594,0,729,0,731,0,386,0,331,0,707,0,715,0,657,0,653,0,642,0,643,0,718,0,654,0,632,0,731,0,392,0,344,0,792,0,852,0,649,0,629,0,685,1,617,1,715,1,715,1,629,1,916,1,531,1,357,1,917,1,828,1,708,1,858,1),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 627 0 1 0 0 0 0 0 0 0 0 0 0 1
2 696 0 0 1 0 0 0 0 0 0 0 0 0 2
3 825 0 0 0 1 0 0 0 0 0 0 0 0 3
4 677 0 0 0 0 1 0 0 0 0 0 0 0 4
5 656 0 0 0 0 0 1 0 0 0 0 0 0 5
6 785 0 0 0 0 0 0 1 0 0 0 0 0 6
7 412 0 0 0 0 0 0 0 1 0 0 0 0 7
8 352 0 0 0 0 0 0 0 0 1 0 0 0 8
9 839 0 0 0 0 0 0 0 0 0 1 0 0 9
10 729 0 0 0 0 0 0 0 0 0 0 1 0 10
11 696 0 0 0 0 0 0 0 0 0 0 0 1 11
12 641 0 0 0 0 0 0 0 0 0 0 0 0 12
13 695 0 1 0 0 0 0 0 0 0 0 0 0 13
14 638 0 0 1 0 0 0 0 0 0 0 0 0 14
15 762 0 0 0 1 0 0 0 0 0 0 0 0 15
16 635 0 0 0 0 1 0 0 0 0 0 0 0 16
17 721 0 0 0 0 0 1 0 0 0 0 0 0 17
18 854 0 0 0 0 0 0 1 0 0 0 0 0 18
19 418 0 0 0 0 0 0 0 1 0 0 0 0 19
20 367 0 0 0 0 0 0 0 0 1 0 0 0 20
21 824 0 0 0 0 0 0 0 0 0 1 0 0 21
22 687 0 0 0 0 0 0 0 0 0 0 1 0 22
23 601 0 0 0 0 0 0 0 0 0 0 0 1 23
24 676 0 0 0 0 0 0 0 0 0 0 0 0 24
25 740 0 1 0 0 0 0 0 0 0 0 0 0 25
26 691 0 0 1 0 0 0 0 0 0 0 0 0 26
27 683 0 0 0 1 0 0 0 0 0 0 0 0 27
28 594 0 0 0 0 1 0 0 0 0 0 0 0 28
29 729 0 0 0 0 0 1 0 0 0 0 0 0 29
30 731 0 0 0 0 0 0 1 0 0 0 0 0 30
31 386 0 0 0 0 0 0 0 1 0 0 0 0 31
32 331 0 0 0 0 0 0 0 0 1 0 0 0 32
33 707 0 0 0 0 0 0 0 0 0 1 0 0 33
34 715 0 0 0 0 0 0 0 0 0 0 1 0 34
35 657 0 0 0 0 0 0 0 0 0 0 0 1 35
36 653 0 0 0 0 0 0 0 0 0 0 0 0 36
37 642 0 1 0 0 0 0 0 0 0 0 0 0 37
38 643 0 0 1 0 0 0 0 0 0 0 0 0 38
39 718 0 0 0 1 0 0 0 0 0 0 0 0 39
40 654 0 0 0 0 1 0 0 0 0 0 0 0 40
41 632 0 0 0 0 0 1 0 0 0 0 0 0 41
42 731 0 0 0 0 0 0 1 0 0 0 0 0 42
43 392 0 0 0 0 0 0 0 1 0 0 0 0 43
44 344 0 0 0 0 0 0 0 0 1 0 0 0 44
45 792 0 0 0 0 0 0 0 0 0 1 0 0 45
46 852 0 0 0 0 0 0 0 0 0 0 1 0 46
47 649 0 0 0 0 0 0 0 0 0 0 0 1 47
48 629 0 0 0 0 0 0 0 0 0 0 0 0 48
49 685 1 1 0 0 0 0 0 0 0 0 0 0 49
50 617 1 0 1 0 0 0 0 0 0 0 0 0 50
51 715 1 0 0 1 0 0 0 0 0 0 0 0 51
52 715 1 0 0 0 1 0 0 0 0 0 0 0 52
53 629 1 0 0 0 0 1 0 0 0 0 0 0 53
54 916 1 0 0 0 0 0 1 0 0 0 0 0 54
55 531 1 0 0 0 0 0 0 1 0 0 0 0 55
56 357 1 0 0 0 0 0 0 0 1 0 0 0 56
57 917 1 0 0 0 0 0 0 0 0 1 0 0 57
58 828 1 0 0 0 0 0 0 0 0 0 1 0 58
59 708 1 0 0 0 0 0 0 0 0 0 0 1 59
60 858 1 0 0 0 0 0 0 0 0 0 0 0 60
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
701.2500 79.7500 -21.4833 -41.5667 42.7500 -42.1333
M5 M6 M7 M8 M9 M10
-23.0167 107.7000 -267.1833 -344.0667 122.2500 69.3667
M11 t
-29.9167 -0.7167
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-92.850 -35.300 -1.625 24.250 120.000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 701.2500 29.9766 23.393 < 2e-16 ***
X 79.7500 24.6406 3.237 0.002246 **
M1 -21.4833 34.7356 -0.618 0.539308
M2 -41.5667 34.6334 -1.200 0.236211
M3 42.7500 34.5408 1.238 0.222120
M4 -42.1333 34.4577 -1.223 0.227650
M5 -23.0167 34.3842 -0.669 0.506589
M6 107.7000 34.3204 3.138 0.002966 **
M7 -267.1833 34.2663 -7.797 5.89e-10 ***
M8 -344.0667 34.2219 -10.054 3.43e-13 ***
M9 122.2500 34.1874 3.576 0.000834 ***
M10 69.3667 34.1628 2.030 0.048113 *
M11 -29.9167 34.1479 -0.876 0.385533
t -0.7167 0.5808 -1.234 0.223484
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 53.98 on 46 degrees of freedom
Multiple R-squared: 0.8916, Adjusted R-squared: 0.861
F-statistic: 29.11 on 13 and 46 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,] 0.51224342 0.97551316 0.4877566
[2,] 0.46846025 0.93692050 0.5315397
[3,] 0.31657413 0.63314825 0.6834259
[4,] 0.20914920 0.41829839 0.7908508
[5,] 0.13815548 0.27631097 0.8618445
[6,] 0.10524146 0.21048291 0.8947585
[7,] 0.13111348 0.26222695 0.8688865
[8,] 0.08998322 0.17996645 0.9100168
[9,] 0.13628121 0.27256242 0.8637188
[10,] 0.12373544 0.24747088 0.8762646
[11,] 0.21162349 0.42324697 0.7883765
[12,] 0.17476247 0.34952494 0.8252375
[13,] 0.31937442 0.63874883 0.6806256
[14,] 0.30795938 0.61591876 0.6920406
[15,] 0.22621296 0.45242593 0.7737870
[16,] 0.19137261 0.38274522 0.8086274
[17,] 0.24521712 0.49043424 0.7547829
[18,] 0.20488314 0.40976627 0.7951169
[19,] 0.16222544 0.32445088 0.8377746
[20,] 0.10627213 0.21254425 0.8937279
[21,] 0.06758219 0.13516438 0.9324178
[22,] 0.06458896 0.12917792 0.9354110
[23,] 0.05558601 0.11117201 0.9444140
[24,] 0.03535284 0.07070568 0.9646472
[25,] 0.03961026 0.07922052 0.9603897
[26,] 0.03634207 0.07268415 0.9636579
[27,] 0.02024666 0.04049332 0.9797533
> postscript(file="/var/www/html/rcomp/tmp/1yuwj1259320413.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/294l31259320413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3od071259320413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/43lel1259320413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/58oqb1259320413.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 7 8 9 10 11
-52.05 37.75 83.15 20.75 -18.65 -19.65 -17.05 0.55 21.95 -34.45 32.55
12 13 14 15 16 17 18 19 20 21 22
-51.65 24.55 -11.65 28.75 -12.65 54.95 57.95 -2.45 24.15 15.55 -67.85
23 24 25 26 27 28 29 30 31 32 33
-53.85 -8.05 78.15 49.95 -41.65 -45.05 71.55 -56.45 -25.85 -3.25 -92.85
34 35 36 37 38 39 40 41 42 43 44
-31.25 10.75 -22.45 -11.25 10.55 1.95 23.55 -16.85 -47.85 -11.25 18.35
45 46 47 48 49 50 51 52 53 54 55
0.75 114.35 11.35 -37.85 -39.40 -86.60 -72.20 13.40 -91.00 66.00 56.60
56 57 58 59 60
-39.80 54.60 19.20 -0.80 120.00
> postscript(file="/var/www/html/rcomp/tmp/6923e1259320413.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 -52.05 NA
1 37.75 -52.05
2 83.15 37.75
3 20.75 83.15
4 -18.65 20.75
5 -19.65 -18.65
6 -17.05 -19.65
7 0.55 -17.05
8 21.95 0.55
9 -34.45 21.95
10 32.55 -34.45
11 -51.65 32.55
12 24.55 -51.65
13 -11.65 24.55
14 28.75 -11.65
15 -12.65 28.75
16 54.95 -12.65
17 57.95 54.95
18 -2.45 57.95
19 24.15 -2.45
20 15.55 24.15
21 -67.85 15.55
22 -53.85 -67.85
23 -8.05 -53.85
24 78.15 -8.05
25 49.95 78.15
26 -41.65 49.95
27 -45.05 -41.65
28 71.55 -45.05
29 -56.45 71.55
30 -25.85 -56.45
31 -3.25 -25.85
32 -92.85 -3.25
33 -31.25 -92.85
34 10.75 -31.25
35 -22.45 10.75
36 -11.25 -22.45
37 10.55 -11.25
38 1.95 10.55
39 23.55 1.95
40 -16.85 23.55
41 -47.85 -16.85
42 -11.25 -47.85
43 18.35 -11.25
44 0.75 18.35
45 114.35 0.75
46 11.35 114.35
47 -37.85 11.35
48 -39.40 -37.85
49 -86.60 -39.40
50 -72.20 -86.60
51 13.40 -72.20
52 -91.00 13.40
53 66.00 -91.00
54 56.60 66.00
55 -39.80 56.60
56 54.60 -39.80
57 19.20 54.60
58 -0.80 19.20
59 120.00 -0.80
60 NA 120.00
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 37.75 -52.05
[2,] 83.15 37.75
[3,] 20.75 83.15
[4,] -18.65 20.75
[5,] -19.65 -18.65
[6,] -17.05 -19.65
[7,] 0.55 -17.05
[8,] 21.95 0.55
[9,] -34.45 21.95
[10,] 32.55 -34.45
[11,] -51.65 32.55
[12,] 24.55 -51.65
[13,] -11.65 24.55
[14,] 28.75 -11.65
[15,] -12.65 28.75
[16,] 54.95 -12.65
[17,] 57.95 54.95
[18,] -2.45 57.95
[19,] 24.15 -2.45
[20,] 15.55 24.15
[21,] -67.85 15.55
[22,] -53.85 -67.85
[23,] -8.05 -53.85
[24,] 78.15 -8.05
[25,] 49.95 78.15
[26,] -41.65 49.95
[27,] -45.05 -41.65
[28,] 71.55 -45.05
[29,] -56.45 71.55
[30,] -25.85 -56.45
[31,] -3.25 -25.85
[32,] -92.85 -3.25
[33,] -31.25 -92.85
[34,] 10.75 -31.25
[35,] -22.45 10.75
[36,] -11.25 -22.45
[37,] 10.55 -11.25
[38,] 1.95 10.55
[39,] 23.55 1.95
[40,] -16.85 23.55
[41,] -47.85 -16.85
[42,] -11.25 -47.85
[43,] 18.35 -11.25
[44,] 0.75 18.35
[45,] 114.35 0.75
[46,] 11.35 114.35
[47,] -37.85 11.35
[48,] -39.40 -37.85
[49,] -86.60 -39.40
[50,] -72.20 -86.60
[51,] 13.40 -72.20
[52,] -91.00 13.40
[53,] 66.00 -91.00
[54,] 56.60 66.00
[55,] -39.80 56.60
[56,] 54.60 -39.80
[57,] 19.20 54.60
[58,] -0.80 19.20
[59,] 120.00 -0.80
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 37.75 -52.05
2 83.15 37.75
3 20.75 83.15
4 -18.65 20.75
5 -19.65 -18.65
6 -17.05 -19.65
7 0.55 -17.05
8 21.95 0.55
9 -34.45 21.95
10 32.55 -34.45
11 -51.65 32.55
12 24.55 -51.65
13 -11.65 24.55
14 28.75 -11.65
15 -12.65 28.75
16 54.95 -12.65
17 57.95 54.95
18 -2.45 57.95
19 24.15 -2.45
20 15.55 24.15
21 -67.85 15.55
22 -53.85 -67.85
23 -8.05 -53.85
24 78.15 -8.05
25 49.95 78.15
26 -41.65 49.95
27 -45.05 -41.65
28 71.55 -45.05
29 -56.45 71.55
30 -25.85 -56.45
31 -3.25 -25.85
32 -92.85 -3.25
33 -31.25 -92.85
34 10.75 -31.25
35 -22.45 10.75
36 -11.25 -22.45
37 10.55 -11.25
38 1.95 10.55
39 23.55 1.95
40 -16.85 23.55
41 -47.85 -16.85
42 -11.25 -47.85
43 18.35 -11.25
44 0.75 18.35
45 114.35 0.75
46 11.35 114.35
47 -37.85 11.35
48 -39.40 -37.85
49 -86.60 -39.40
50 -72.20 -86.60
51 13.40 -72.20
52 -91.00 13.40
53 66.00 -91.00
54 56.60 66.00
55 -39.80 56.60
56 54.60 -39.80
57 19.20 54.60
58 -0.80 19.20
59 120.00 -0.80
> 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/7fqpe1259320413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ntf61259320413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9dc361259320413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10l9qn1259320413.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/119h131259320413.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/12dgnr1259320413.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/13ang21259320413.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/14z51v1259320413.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/15wkxz1259320413.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/16njnu1259320413.tab")
+ }
>
> system("convert tmp/1yuwj1259320413.ps tmp/1yuwj1259320413.png")
> system("convert tmp/294l31259320413.ps tmp/294l31259320413.png")
> system("convert tmp/3od071259320413.ps tmp/3od071259320413.png")
> system("convert tmp/43lel1259320413.ps tmp/43lel1259320413.png")
> system("convert tmp/58oqb1259320413.ps tmp/58oqb1259320413.png")
> system("convert tmp/6923e1259320413.ps tmp/6923e1259320413.png")
> system("convert tmp/7fqpe1259320413.ps tmp/7fqpe1259320413.png")
> system("convert tmp/8ntf61259320413.ps tmp/8ntf61259320413.png")
> system("convert tmp/9dc361259320413.ps tmp/9dc361259320413.png")
> system("convert tmp/10l9qn1259320413.ps tmp/10l9qn1259320413.png")
>
>
> proc.time()
user system elapsed
2.409 1.555 3.208