R version 2.7.0 (2008-04-22)
Copyright (C) 2008 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(2.2,0,2.3,0,2.1,0,2.8,0,3.1,0,2.9,0,2.6,0,2.7,0,2.3,0,2.3,0,2.1,0,2.2,0,2.9,0,2.6,0,2.7,0,1.8,1,1.3,1,0.9,1,1.3,1,1.3,1,1.3,1,1.3,1,1.1,1,1.4,1,1.2,1,1.7,1,1.8,1,1.5,1,1,1,1.6,1,1.5,1,1.8,1,1.8,1,1.6,1,1.9,1,1.7,1,1.6,1,1.3,1,1.1,1,1.9,0,2.6,0,2.3,0,2.4,0,2.2,0,2,0,2.9,0,2.6,0,2.3,0,2.3,0,2.6,0,3.1,0,2.8,0,2.5,0,2.9,0,3.1,0,3.1,0,3.2,0,2.5,0,2.6,0,2.9,0),dim=c(2,60),dimnames=list(c('Consumptieprijsindex','Dumivariabele'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Consumptieprijsindex','Dumivariabele'),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)
> 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
Consumptieprijsindex Dumivariabele M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 2.2 0 1 0 0 0 0 0 0 0 0 0 0 1
2 2.3 0 0 1 0 0 0 0 0 0 0 0 0 2
3 2.1 0 0 0 1 0 0 0 0 0 0 0 0 3
4 2.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 3.1 0 0 0 0 0 1 0 0 0 0 0 0 5
6 2.9 0 0 0 0 0 0 1 0 0 0 0 0 6
7 2.6 0 0 0 0 0 0 0 1 0 0 0 0 7
8 2.7 0 0 0 0 0 0 0 0 1 0 0 0 8
9 2.3 0 0 0 0 0 0 0 0 0 1 0 0 9
10 2.3 0 0 0 0 0 0 0 0 0 0 1 0 10
11 2.1 0 0 0 0 0 0 0 0 0 0 0 1 11
12 2.2 0 0 0 0 0 0 0 0 0 0 0 0 12
13 2.9 0 1 0 0 0 0 0 0 0 0 0 0 13
14 2.6 0 0 1 0 0 0 0 0 0 0 0 0 14
15 2.7 0 0 0 1 0 0 0 0 0 0 0 0 15
16 1.8 1 0 0 0 1 0 0 0 0 0 0 0 16
17 1.3 1 0 0 0 0 1 0 0 0 0 0 0 17
18 0.9 1 0 0 0 0 0 1 0 0 0 0 0 18
19 1.3 1 0 0 0 0 0 0 1 0 0 0 0 19
20 1.3 1 0 0 0 0 0 0 0 1 0 0 0 20
21 1.3 1 0 0 0 0 0 0 0 0 1 0 0 21
22 1.3 1 0 0 0 0 0 0 0 0 0 1 0 22
23 1.1 1 0 0 0 0 0 0 0 0 0 0 1 23
24 1.4 1 0 0 0 0 0 0 0 0 0 0 0 24
25 1.2 1 1 0 0 0 0 0 0 0 0 0 0 25
26 1.7 1 0 1 0 0 0 0 0 0 0 0 0 26
27 1.8 1 0 0 1 0 0 0 0 0 0 0 0 27
28 1.5 1 0 0 0 1 0 0 0 0 0 0 0 28
29 1.0 1 0 0 0 0 1 0 0 0 0 0 0 29
30 1.6 1 0 0 0 0 0 1 0 0 0 0 0 30
31 1.5 1 0 0 0 0 0 0 1 0 0 0 0 31
32 1.8 1 0 0 0 0 0 0 0 1 0 0 0 32
33 1.8 1 0 0 0 0 0 0 0 0 1 0 0 33
34 1.6 1 0 0 0 0 0 0 0 0 0 1 0 34
35 1.9 1 0 0 0 0 0 0 0 0 0 0 1 35
36 1.7 1 0 0 0 0 0 0 0 0 0 0 0 36
37 1.6 1 1 0 0 0 0 0 0 0 0 0 0 37
38 1.3 1 0 1 0 0 0 0 0 0 0 0 0 38
39 1.1 1 0 0 1 0 0 0 0 0 0 0 0 39
40 1.9 0 0 0 0 1 0 0 0 0 0 0 0 40
41 2.6 0 0 0 0 0 1 0 0 0 0 0 0 41
42 2.3 0 0 0 0 0 0 1 0 0 0 0 0 42
43 2.4 0 0 0 0 0 0 0 1 0 0 0 0 43
44 2.2 0 0 0 0 0 0 0 0 1 0 0 0 44
45 2.0 0 0 0 0 0 0 0 0 0 1 0 0 45
46 2.9 0 0 0 0 0 0 0 0 0 0 1 0 46
47 2.6 0 0 0 0 0 0 0 0 0 0 0 1 47
48 2.3 0 0 0 0 0 0 0 0 0 0 0 0 48
49 2.3 0 1 0 0 0 0 0 0 0 0 0 0 49
50 2.6 0 0 1 0 0 0 0 0 0 0 0 0 50
51 3.1 0 0 0 1 0 0 0 0 0 0 0 0 51
52 2.8 0 0 0 0 1 0 0 0 0 0 0 0 52
53 2.5 0 0 0 0 0 1 0 0 0 0 0 0 53
54 2.9 0 0 0 0 0 0 1 0 0 0 0 0 54
55 3.1 0 0 0 0 0 0 0 1 0 0 0 0 55
56 3.1 0 0 0 0 0 0 0 0 1 0 0 0 56
57 3.2 0 0 0 0 0 0 0 0 0 1 0 0 57
58 2.5 0 0 0 0 0 0 0 0 0 0 1 0 58
59 2.6 0 0 0 0 0 0 0 0 0 0 0 1 59
60 2.9 0 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) Dumivariabele M1 M2 M3
2.371915 -1.098936 -0.008771 0.046572 0.101915
M4 M5 M6 M7 M8
0.097258 0.032600 0.047943 0.103286 0.138629
M9 M10 M11 t
0.033972 0.029314 -0.035343 0.004657
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.755461 -0.198582 -0.005993 0.251330 0.672199
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.371915 0.190751 12.435 2.58e-16 ***
Dumivariabele -1.098936 0.093299 -11.779 1.74e-15 ***
M1 -0.008771 0.223545 -0.039 0.9689
M2 0.046572 0.223204 0.209 0.8356
M3 0.101915 0.222895 0.457 0.6497
M4 0.097258 0.222619 0.437 0.6642
M5 0.032600 0.222374 0.147 0.8841
M6 0.047943 0.222162 0.216 0.8301
M7 0.103286 0.221982 0.465 0.6439
M8 0.138629 0.221835 0.625 0.5351
M9 0.033972 0.221721 0.153 0.8789
M10 0.029314 0.221639 0.132 0.8954
M11 -0.035343 0.221590 -0.159 0.8740
t 0.004657 0.002693 1.729 0.0905 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3503 on 46 degrees of freedom
Multiple R-squared: 0.7675, Adjusted R-squared: 0.7018
F-statistic: 11.68 on 13 and 46 DF, p-value: 1.468e-10
> postscript(file="/var/www/html/rcomp/tmp/1sij01226786058.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/22kvr1226786058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3mxou1226786058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/48ge21226786058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/53qsg1226786058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5
-0.1678014184 -0.1278014184 -0.3878014184 0.3121985816 0.6721985816
6 7 8 9 10
0.4521985816 0.0921985816 0.1521985816 -0.1478014184 -0.1478014184
11 12 13 14 15
-0.2878014184 -0.2278014184 0.4763120567 0.1163120567 0.1563120567
16 17 18 19 20
0.3552482270 -0.0847517730 -0.5047517730 -0.1647517730 -0.2047517730
21 22 23 24 25
-0.1047517730 -0.1047517730 -0.2447517730 0.0152482270 -0.1806382979
26 27 28 29 30
0.2593617021 0.2993617021 -0.0006382979 -0.4406382979 0.1393617021
31 32 33 34 35
-0.0206382979 0.2393617021 0.3393617021 0.1393617021 0.4993617021
36 37 38 39 40
0.2593617021 0.1634751773 -0.1965248227 -0.4565248227 -0.7554609929
41 42 43 44 45
0.0045390071 -0.3154609929 -0.2754609929 -0.5154609929 -0.6154609929
46 47 48 49 50
0.2845390071 0.0445390071 -0.2954609929 -0.2913475177 -0.0513475177
51 52 53 54 55
0.3886524823 0.0886524823 -0.1513475177 0.2286524823 0.3686524823
56 57 58 59 60
0.3286524823 0.5286524823 -0.1713475177 -0.0113475177 0.2486524823
> postscript(file="/var/www/html/rcomp/tmp/6uuvg1226786058.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 -0.1678014184 NA
1 -0.1278014184 -0.1678014184
2 -0.3878014184 -0.1278014184
3 0.3121985816 -0.3878014184
4 0.6721985816 0.3121985816
5 0.4521985816 0.6721985816
6 0.0921985816 0.4521985816
7 0.1521985816 0.0921985816
8 -0.1478014184 0.1521985816
9 -0.1478014184 -0.1478014184
10 -0.2878014184 -0.1478014184
11 -0.2278014184 -0.2878014184
12 0.4763120567 -0.2278014184
13 0.1163120567 0.4763120567
14 0.1563120567 0.1163120567
15 0.3552482270 0.1563120567
16 -0.0847517730 0.3552482270
17 -0.5047517730 -0.0847517730
18 -0.1647517730 -0.5047517730
19 -0.2047517730 -0.1647517730
20 -0.1047517730 -0.2047517730
21 -0.1047517730 -0.1047517730
22 -0.2447517730 -0.1047517730
23 0.0152482270 -0.2447517730
24 -0.1806382979 0.0152482270
25 0.2593617021 -0.1806382979
26 0.2993617021 0.2593617021
27 -0.0006382979 0.2993617021
28 -0.4406382979 -0.0006382979
29 0.1393617021 -0.4406382979
30 -0.0206382979 0.1393617021
31 0.2393617021 -0.0206382979
32 0.3393617021 0.2393617021
33 0.1393617021 0.3393617021
34 0.4993617021 0.1393617021
35 0.2593617021 0.4993617021
36 0.1634751773 0.2593617021
37 -0.1965248227 0.1634751773
38 -0.4565248227 -0.1965248227
39 -0.7554609929 -0.4565248227
40 0.0045390071 -0.7554609929
41 -0.3154609929 0.0045390071
42 -0.2754609929 -0.3154609929
43 -0.5154609929 -0.2754609929
44 -0.6154609929 -0.5154609929
45 0.2845390071 -0.6154609929
46 0.0445390071 0.2845390071
47 -0.2954609929 0.0445390071
48 -0.2913475177 -0.2954609929
49 -0.0513475177 -0.2913475177
50 0.3886524823 -0.0513475177
51 0.0886524823 0.3886524823
52 -0.1513475177 0.0886524823
53 0.2286524823 -0.1513475177
54 0.3686524823 0.2286524823
55 0.3286524823 0.3686524823
56 0.5286524823 0.3286524823
57 -0.1713475177 0.5286524823
58 -0.0113475177 -0.1713475177
59 0.2486524823 -0.0113475177
60 NA 0.2486524823
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.1278014184 -0.1678014184
[2,] -0.3878014184 -0.1278014184
[3,] 0.3121985816 -0.3878014184
[4,] 0.6721985816 0.3121985816
[5,] 0.4521985816 0.6721985816
[6,] 0.0921985816 0.4521985816
[7,] 0.1521985816 0.0921985816
[8,] -0.1478014184 0.1521985816
[9,] -0.1478014184 -0.1478014184
[10,] -0.2878014184 -0.1478014184
[11,] -0.2278014184 -0.2878014184
[12,] 0.4763120567 -0.2278014184
[13,] 0.1163120567 0.4763120567
[14,] 0.1563120567 0.1163120567
[15,] 0.3552482270 0.1563120567
[16,] -0.0847517730 0.3552482270
[17,] -0.5047517730 -0.0847517730
[18,] -0.1647517730 -0.5047517730
[19,] -0.2047517730 -0.1647517730
[20,] -0.1047517730 -0.2047517730
[21,] -0.1047517730 -0.1047517730
[22,] -0.2447517730 -0.1047517730
[23,] 0.0152482270 -0.2447517730
[24,] -0.1806382979 0.0152482270
[25,] 0.2593617021 -0.1806382979
[26,] 0.2993617021 0.2593617021
[27,] -0.0006382979 0.2993617021
[28,] -0.4406382979 -0.0006382979
[29,] 0.1393617021 -0.4406382979
[30,] -0.0206382979 0.1393617021
[31,] 0.2393617021 -0.0206382979
[32,] 0.3393617021 0.2393617021
[33,] 0.1393617021 0.3393617021
[34,] 0.4993617021 0.1393617021
[35,] 0.2593617021 0.4993617021
[36,] 0.1634751773 0.2593617021
[37,] -0.1965248227 0.1634751773
[38,] -0.4565248227 -0.1965248227
[39,] -0.7554609929 -0.4565248227
[40,] 0.0045390071 -0.7554609929
[41,] -0.3154609929 0.0045390071
[42,] -0.2754609929 -0.3154609929
[43,] -0.5154609929 -0.2754609929
[44,] -0.6154609929 -0.5154609929
[45,] 0.2845390071 -0.6154609929
[46,] 0.0445390071 0.2845390071
[47,] -0.2954609929 0.0445390071
[48,] -0.2913475177 -0.2954609929
[49,] -0.0513475177 -0.2913475177
[50,] 0.3886524823 -0.0513475177
[51,] 0.0886524823 0.3886524823
[52,] -0.1513475177 0.0886524823
[53,] 0.2286524823 -0.1513475177
[54,] 0.3686524823 0.2286524823
[55,] 0.3286524823 0.3686524823
[56,] 0.5286524823 0.3286524823
[57,] -0.1713475177 0.5286524823
[58,] -0.0113475177 -0.1713475177
[59,] 0.2486524823 -0.0113475177
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.1278014184 -0.1678014184
2 -0.3878014184 -0.1278014184
3 0.3121985816 -0.3878014184
4 0.6721985816 0.3121985816
5 0.4521985816 0.6721985816
6 0.0921985816 0.4521985816
7 0.1521985816 0.0921985816
8 -0.1478014184 0.1521985816
9 -0.1478014184 -0.1478014184
10 -0.2878014184 -0.1478014184
11 -0.2278014184 -0.2878014184
12 0.4763120567 -0.2278014184
13 0.1163120567 0.4763120567
14 0.1563120567 0.1163120567
15 0.3552482270 0.1563120567
16 -0.0847517730 0.3552482270
17 -0.5047517730 -0.0847517730
18 -0.1647517730 -0.5047517730
19 -0.2047517730 -0.1647517730
20 -0.1047517730 -0.2047517730
21 -0.1047517730 -0.1047517730
22 -0.2447517730 -0.1047517730
23 0.0152482270 -0.2447517730
24 -0.1806382979 0.0152482270
25 0.2593617021 -0.1806382979
26 0.2993617021 0.2593617021
27 -0.0006382979 0.2993617021
28 -0.4406382979 -0.0006382979
29 0.1393617021 -0.4406382979
30 -0.0206382979 0.1393617021
31 0.2393617021 -0.0206382979
32 0.3393617021 0.2393617021
33 0.1393617021 0.3393617021
34 0.4993617021 0.1393617021
35 0.2593617021 0.4993617021
36 0.1634751773 0.2593617021
37 -0.1965248227 0.1634751773
38 -0.4565248227 -0.1965248227
39 -0.7554609929 -0.4565248227
40 0.0045390071 -0.7554609929
41 -0.3154609929 0.0045390071
42 -0.2754609929 -0.3154609929
43 -0.5154609929 -0.2754609929
44 -0.6154609929 -0.5154609929
45 0.2845390071 -0.6154609929
46 0.0445390071 0.2845390071
47 -0.2954609929 0.0445390071
48 -0.2913475177 -0.2954609929
49 -0.0513475177 -0.2913475177
50 0.3886524823 -0.0513475177
51 0.0886524823 0.3886524823
52 -0.1513475177 0.0886524823
53 0.2286524823 -0.1513475177
54 0.3686524823 0.2286524823
55 0.3286524823 0.3686524823
56 0.5286524823 0.3286524823
57 -0.1713475177 0.5286524823
58 -0.0113475177 -0.1713475177
59 0.2486524823 -0.0113475177
> 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/78ymu1226786058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hglt1226786058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/98ph41226786058.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10flvi1226786058.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/11s4jo1226786059.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/124mf51226786059.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/13ld5j1226786059.tab")
>
> system("convert tmp/1sij01226786058.ps tmp/1sij01226786058.png")
> system("convert tmp/22kvr1226786058.ps tmp/22kvr1226786058.png")
> system("convert tmp/3mxou1226786058.ps tmp/3mxou1226786058.png")
> system("convert tmp/48ge21226786058.ps tmp/48ge21226786058.png")
> system("convert tmp/53qsg1226786058.ps tmp/53qsg1226786058.png")
> system("convert tmp/6uuvg1226786058.ps tmp/6uuvg1226786058.png")
> system("convert tmp/78ymu1226786058.ps tmp/78ymu1226786058.png")
> system("convert tmp/8hglt1226786058.ps tmp/8hglt1226786058.png")
> system("convert tmp/98ph41226786058.ps tmp/98ph41226786058.png")
>
>
> proc.time()
user system elapsed
4.029 2.498 4.390