R version 2.8.0 (2008-10-20)
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(41.1,0,58,0,63,0,53.8,0,54.7,0,55.5,0,56.1,0,69.6,0,69.4,0,57.2,0,68,0,53.3,0,47.9,0,60.8,0,61.7,0,57.8,0,51.4,0,50.5,0,48.1,0,58.7,0,54,0,56.1,0,60.4,0,51.2,0,50.7,0,56.4,0,53.3,0,52.6,0,47.7,0,49.5,0,48.5,0,55.3,0,49.8,0,57.4,0,64.6,0,53,0,41.5,0,55.9,0,58.4,0,53.5,0,50.6,0,58.5,1,49.1,1,61.1,1,52.3,1,58.4,1,65.5,1,61.7,1,45.1,1,52.1,1,59.3,1,57.9,1,45,1,64.9,1,63.8,1,69.4,1,71.1,1,62.9,1,73.5,1,62.6,1),dim=c(2,60),dimnames=list(c('Tabakproductie','rookverbod'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Tabakproductie','rookverbod'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> ylab = ''
> xlab = ''
> main = ''
> #'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
Tabakproductie rookverbod M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 41.1 0 1 0 0 0 0 0 0 0 0 0 0 1
2 58.0 0 0 1 0 0 0 0 0 0 0 0 0 2
3 63.0 0 0 0 1 0 0 0 0 0 0 0 0 3
4 53.8 0 0 0 0 1 0 0 0 0 0 0 0 4
5 54.7 0 0 0 0 0 1 0 0 0 0 0 0 5
6 55.5 0 0 0 0 0 0 1 0 0 0 0 0 6
7 56.1 0 0 0 0 0 0 0 1 0 0 0 0 7
8 69.6 0 0 0 0 0 0 0 0 1 0 0 0 8
9 69.4 0 0 0 0 0 0 0 0 0 1 0 0 9
10 57.2 0 0 0 0 0 0 0 0 0 0 1 0 10
11 68.0 0 0 0 0 0 0 0 0 0 0 0 1 11
12 53.3 0 0 0 0 0 0 0 0 0 0 0 0 12
13 47.9 0 1 0 0 0 0 0 0 0 0 0 0 13
14 60.8 0 0 1 0 0 0 0 0 0 0 0 0 14
15 61.7 0 0 0 1 0 0 0 0 0 0 0 0 15
16 57.8 0 0 0 0 1 0 0 0 0 0 0 0 16
17 51.4 0 0 0 0 0 1 0 0 0 0 0 0 17
18 50.5 0 0 0 0 0 0 1 0 0 0 0 0 18
19 48.1 0 0 0 0 0 0 0 1 0 0 0 0 19
20 58.7 0 0 0 0 0 0 0 0 1 0 0 0 20
21 54.0 0 0 0 0 0 0 0 0 0 1 0 0 21
22 56.1 0 0 0 0 0 0 0 0 0 0 1 0 22
23 60.4 0 0 0 0 0 0 0 0 0 0 0 1 23
24 51.2 0 0 0 0 0 0 0 0 0 0 0 0 24
25 50.7 0 1 0 0 0 0 0 0 0 0 0 0 25
26 56.4 0 0 1 0 0 0 0 0 0 0 0 0 26
27 53.3 0 0 0 1 0 0 0 0 0 0 0 0 27
28 52.6 0 0 0 0 1 0 0 0 0 0 0 0 28
29 47.7 0 0 0 0 0 1 0 0 0 0 0 0 29
30 49.5 0 0 0 0 0 0 1 0 0 0 0 0 30
31 48.5 0 0 0 0 0 0 0 1 0 0 0 0 31
32 55.3 0 0 0 0 0 0 0 0 1 0 0 0 32
33 49.8 0 0 0 0 0 0 0 0 0 1 0 0 33
34 57.4 0 0 0 0 0 0 0 0 0 0 1 0 34
35 64.6 0 0 0 0 0 0 0 0 0 0 0 1 35
36 53.0 0 0 0 0 0 0 0 0 0 0 0 0 36
37 41.5 0 1 0 0 0 0 0 0 0 0 0 0 37
38 55.9 0 0 1 0 0 0 0 0 0 0 0 0 38
39 58.4 0 0 0 1 0 0 0 0 0 0 0 0 39
40 53.5 0 0 0 0 1 0 0 0 0 0 0 0 40
41 50.6 0 0 0 0 0 1 0 0 0 0 0 0 41
42 58.5 1 0 0 0 0 0 1 0 0 0 0 0 42
43 49.1 1 0 0 0 0 0 0 1 0 0 0 0 43
44 61.1 1 0 0 0 0 0 0 0 1 0 0 0 44
45 52.3 1 0 0 0 0 0 0 0 0 1 0 0 45
46 58.4 1 0 0 0 0 0 0 0 0 0 1 0 46
47 65.5 1 0 0 0 0 0 0 0 0 0 0 1 47
48 61.7 1 0 0 0 0 0 0 0 0 0 0 0 48
49 45.1 1 1 0 0 0 0 0 0 0 0 0 0 49
50 52.1 1 0 1 0 0 0 0 0 0 0 0 0 50
51 59.3 1 0 0 1 0 0 0 0 0 0 0 0 51
52 57.9 1 0 0 0 1 0 0 0 0 0 0 0 52
53 45.0 1 0 0 0 0 1 0 0 0 0 0 0 53
54 64.9 1 0 0 0 0 0 1 0 0 0 0 0 54
55 63.8 1 0 0 0 0 0 0 1 0 0 0 0 55
56 69.4 1 0 0 0 0 0 0 0 1 0 0 0 56
57 71.1 1 0 0 0 0 0 0 0 0 1 0 0 57
58 62.9 1 0 0 0 0 0 0 0 0 0 1 0 58
59 73.5 1 0 0 0 0 0 0 0 0 0 0 1 59
60 62.6 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) rookverbod M1 M2 M3 M4
57.0990 6.2173 -10.8422 0.6274 3.2170 -0.7134
M5 M6 M7 M8 M9 M10
-5.8638 -1.1176 -3.6880 6.1016 2.6912 1.8608
M11 t
9.9504 -0.0896
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9.675066 -3.568397 0.006546 2.866176 10.416319
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 57.09898 2.82405 20.219 < 2e-16 ***
rookverbod 6.21727 2.43205 2.556 0.01394 *
M1 -10.84223 3.25275 -3.333 0.00170 **
M2 0.62738 3.24687 0.193 0.84763
M3 3.21698 3.24229 0.992 0.32629
M4 -0.71341 3.23902 -0.220 0.82665
M5 -5.86380 3.23705 -1.811 0.07660 .
M6 -1.11765 3.24704 -0.344 0.73226
M7 -3.68804 3.23984 -1.138 0.26087
M8 6.10157 3.23394 1.887 0.06552 .
M9 2.69118 3.22935 0.833 0.40895
M10 1.86078 3.22606 0.577 0.56689
M11 9.95039 3.22408 3.086 0.00343 **
t -0.08961 0.06515 -1.375 0.17566
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5.097 on 46 degrees of freedom
Multiple R-squared: 0.6147, Adjusted R-squared: 0.5058
F-statistic: 5.645 on 13 and 46 DF, p-value: 5.224e-06
> postscript(file="/var/www/html/freestat/rcomp/tmp/1nl9p1228122659.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/freestat/rcomp/tmp/20p371228122659.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3xjqd1228122659.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4y26h1228122659.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5wvpq1228122659.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 6
-5.06713472 0.45286528 2.95286528 -2.22713472 3.91286528 0.05631879
7 8 9 10 11 12
3.31631879 7.11631879 10.41631879 -0.86368121 1.93631879 -2.72368121
13 14 15 16 17 18
2.80815939 4.32815939 2.72815939 2.84815939 1.68815939 -3.86838710
19 20 21 22 23 24
-3.60838710 -2.70838710 -3.90838710 -0.88838710 -4.58838710 -3.74838710
25 26 27 28 29 30
6.68345351 1.00345351 -4.59654649 -1.27654649 -0.93654649 -3.79309298
31 32 33 34 35 36
-2.13309298 -5.03309298 -7.03309298 1.48690702 0.68690702 -0.87309298
37 38 39 40 41 42
-1.44125237 1.57874763 1.57874763 0.69874763 3.03874763 0.06493359
43 44 45 46 47 48
-6.67506641 -4.37506641 -9.67506641 -2.65506641 -3.55506641 2.68493359
49 50 51 52 53 54
-2.98322581 -7.36322581 -2.66322581 -0.04322581 -7.70322581 7.54022770
55 56 57 58 59 60
9.10022770 5.00022770 10.20022770 2.92022770 5.52022770 4.66022770
> postscript(file="/var/www/html/freestat/rcomp/tmp/6bvxx1228122659.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 -5.06713472 NA
1 0.45286528 -5.06713472
2 2.95286528 0.45286528
3 -2.22713472 2.95286528
4 3.91286528 -2.22713472
5 0.05631879 3.91286528
6 3.31631879 0.05631879
7 7.11631879 3.31631879
8 10.41631879 7.11631879
9 -0.86368121 10.41631879
10 1.93631879 -0.86368121
11 -2.72368121 1.93631879
12 2.80815939 -2.72368121
13 4.32815939 2.80815939
14 2.72815939 4.32815939
15 2.84815939 2.72815939
16 1.68815939 2.84815939
17 -3.86838710 1.68815939
18 -3.60838710 -3.86838710
19 -2.70838710 -3.60838710
20 -3.90838710 -2.70838710
21 -0.88838710 -3.90838710
22 -4.58838710 -0.88838710
23 -3.74838710 -4.58838710
24 6.68345351 -3.74838710
25 1.00345351 6.68345351
26 -4.59654649 1.00345351
27 -1.27654649 -4.59654649
28 -0.93654649 -1.27654649
29 -3.79309298 -0.93654649
30 -2.13309298 -3.79309298
31 -5.03309298 -2.13309298
32 -7.03309298 -5.03309298
33 1.48690702 -7.03309298
34 0.68690702 1.48690702
35 -0.87309298 0.68690702
36 -1.44125237 -0.87309298
37 1.57874763 -1.44125237
38 1.57874763 1.57874763
39 0.69874763 1.57874763
40 3.03874763 0.69874763
41 0.06493359 3.03874763
42 -6.67506641 0.06493359
43 -4.37506641 -6.67506641
44 -9.67506641 -4.37506641
45 -2.65506641 -9.67506641
46 -3.55506641 -2.65506641
47 2.68493359 -3.55506641
48 -2.98322581 2.68493359
49 -7.36322581 -2.98322581
50 -2.66322581 -7.36322581
51 -0.04322581 -2.66322581
52 -7.70322581 -0.04322581
53 7.54022770 -7.70322581
54 9.10022770 7.54022770
55 5.00022770 9.10022770
56 10.20022770 5.00022770
57 2.92022770 10.20022770
58 5.52022770 2.92022770
59 4.66022770 5.52022770
60 NA 4.66022770
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.45286528 -5.06713472
[2,] 2.95286528 0.45286528
[3,] -2.22713472 2.95286528
[4,] 3.91286528 -2.22713472
[5,] 0.05631879 3.91286528
[6,] 3.31631879 0.05631879
[7,] 7.11631879 3.31631879
[8,] 10.41631879 7.11631879
[9,] -0.86368121 10.41631879
[10,] 1.93631879 -0.86368121
[11,] -2.72368121 1.93631879
[12,] 2.80815939 -2.72368121
[13,] 4.32815939 2.80815939
[14,] 2.72815939 4.32815939
[15,] 2.84815939 2.72815939
[16,] 1.68815939 2.84815939
[17,] -3.86838710 1.68815939
[18,] -3.60838710 -3.86838710
[19,] -2.70838710 -3.60838710
[20,] -3.90838710 -2.70838710
[21,] -0.88838710 -3.90838710
[22,] -4.58838710 -0.88838710
[23,] -3.74838710 -4.58838710
[24,] 6.68345351 -3.74838710
[25,] 1.00345351 6.68345351
[26,] -4.59654649 1.00345351
[27,] -1.27654649 -4.59654649
[28,] -0.93654649 -1.27654649
[29,] -3.79309298 -0.93654649
[30,] -2.13309298 -3.79309298
[31,] -5.03309298 -2.13309298
[32,] -7.03309298 -5.03309298
[33,] 1.48690702 -7.03309298
[34,] 0.68690702 1.48690702
[35,] -0.87309298 0.68690702
[36,] -1.44125237 -0.87309298
[37,] 1.57874763 -1.44125237
[38,] 1.57874763 1.57874763
[39,] 0.69874763 1.57874763
[40,] 3.03874763 0.69874763
[41,] 0.06493359 3.03874763
[42,] -6.67506641 0.06493359
[43,] -4.37506641 -6.67506641
[44,] -9.67506641 -4.37506641
[45,] -2.65506641 -9.67506641
[46,] -3.55506641 -2.65506641
[47,] 2.68493359 -3.55506641
[48,] -2.98322581 2.68493359
[49,] -7.36322581 -2.98322581
[50,] -2.66322581 -7.36322581
[51,] -0.04322581 -2.66322581
[52,] -7.70322581 -0.04322581
[53,] 7.54022770 -7.70322581
[54,] 9.10022770 7.54022770
[55,] 5.00022770 9.10022770
[56,] 10.20022770 5.00022770
[57,] 2.92022770 10.20022770
[58,] 5.52022770 2.92022770
[59,] 4.66022770 5.52022770
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.45286528 -5.06713472
2 2.95286528 0.45286528
3 -2.22713472 2.95286528
4 3.91286528 -2.22713472
5 0.05631879 3.91286528
6 3.31631879 0.05631879
7 7.11631879 3.31631879
8 10.41631879 7.11631879
9 -0.86368121 10.41631879
10 1.93631879 -0.86368121
11 -2.72368121 1.93631879
12 2.80815939 -2.72368121
13 4.32815939 2.80815939
14 2.72815939 4.32815939
15 2.84815939 2.72815939
16 1.68815939 2.84815939
17 -3.86838710 1.68815939
18 -3.60838710 -3.86838710
19 -2.70838710 -3.60838710
20 -3.90838710 -2.70838710
21 -0.88838710 -3.90838710
22 -4.58838710 -0.88838710
23 -3.74838710 -4.58838710
24 6.68345351 -3.74838710
25 1.00345351 6.68345351
26 -4.59654649 1.00345351
27 -1.27654649 -4.59654649
28 -0.93654649 -1.27654649
29 -3.79309298 -0.93654649
30 -2.13309298 -3.79309298
31 -5.03309298 -2.13309298
32 -7.03309298 -5.03309298
33 1.48690702 -7.03309298
34 0.68690702 1.48690702
35 -0.87309298 0.68690702
36 -1.44125237 -0.87309298
37 1.57874763 -1.44125237
38 1.57874763 1.57874763
39 0.69874763 1.57874763
40 3.03874763 0.69874763
41 0.06493359 3.03874763
42 -6.67506641 0.06493359
43 -4.37506641 -6.67506641
44 -9.67506641 -4.37506641
45 -2.65506641 -9.67506641
46 -3.55506641 -2.65506641
47 2.68493359 -3.55506641
48 -2.98322581 2.68493359
49 -7.36322581 -2.98322581
50 -2.66322581 -7.36322581
51 -0.04322581 -2.66322581
52 -7.70322581 -0.04322581
53 7.54022770 -7.70322581
54 9.10022770 7.54022770
55 5.00022770 9.10022770
56 10.20022770 5.00022770
57 2.92022770 10.20022770
58 5.52022770 2.92022770
59 4.66022770 5.52022770
> 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/freestat/rcomp/tmp/7at0e1228122659.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/88lkp1228122659.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9kfh51228122659.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/freestat/rcomp/tmp/10y2qu1228122659.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/freestat/rcomp/tmp/115yyb1228122659.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/freestat/rcomp/tmp/12thcu1228122659.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/freestat/rcomp/tmp/13a0ee1228122659.tab")
>
> system("convert tmp/1nl9p1228122659.ps tmp/1nl9p1228122659.png")
> system("convert tmp/20p371228122659.ps tmp/20p371228122659.png")
> system("convert tmp/3xjqd1228122659.ps tmp/3xjqd1228122659.png")
> system("convert tmp/4y26h1228122659.ps tmp/4y26h1228122659.png")
> system("convert tmp/5wvpq1228122659.ps tmp/5wvpq1228122659.png")
> system("convert tmp/6bvxx1228122659.ps tmp/6bvxx1228122659.png")
> system("convert tmp/7at0e1228122659.ps tmp/7at0e1228122659.png")
> system("convert tmp/88lkp1228122659.ps tmp/88lkp1228122659.png")
> system("convert tmp/9kfh51228122659.ps tmp/9kfh51228122659.png")
>
>
> proc.time()
user system elapsed
2.994 2.252 3.328