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(106.60,106.80,107.00,107.10,107.30,107.40,107.60,107.70,107.90,108.20,108.30,108.50,108.92,109.23,109.41,109.65,109.91,110.01,110.20,110.49,110.57,110.72,110.94,111.09,111.28,111.41,111.62,111.76,111.89,112.04,112.12,112.30,112.47,112.59,112.78,112.73,112.99,113.10,113.33,113.38,113.68,113.65,113.81,113.88,114.02,114.25,114.28,114.38,114.73,114.97,115.05,115.29,115.37,115.54,115.76,115.92,116.02,116.21,116.26,116.51),dim=c(1,60),dimnames=list(c('Y'),1:60))
> y <- array(NA,dim=c(1,60),dimnames=list(c('Y'),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
Y M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 106.60 1 0 0 0 0 0 0 0 0 0 0 1
2 106.80 0 1 0 0 0 0 0 0 0 0 0 2
3 107.00 0 0 1 0 0 0 0 0 0 0 0 3
4 107.10 0 0 0 1 0 0 0 0 0 0 0 4
5 107.30 0 0 0 0 1 0 0 0 0 0 0 5
6 107.40 0 0 0 0 0 1 0 0 0 0 0 6
7 107.60 0 0 0 0 0 0 1 0 0 0 0 7
8 107.70 0 0 0 0 0 0 0 1 0 0 0 8
9 107.90 0 0 0 0 0 0 0 0 1 0 0 9
10 108.20 0 0 0 0 0 0 0 0 0 1 0 10
11 108.30 0 0 0 0 0 0 0 0 0 0 1 11
12 108.50 0 0 0 0 0 0 0 0 0 0 0 12
13 108.92 1 0 0 0 0 0 0 0 0 0 0 13
14 109.23 0 1 0 0 0 0 0 0 0 0 0 14
15 109.41 0 0 1 0 0 0 0 0 0 0 0 15
16 109.65 0 0 0 1 0 0 0 0 0 0 0 16
17 109.91 0 0 0 0 1 0 0 0 0 0 0 17
18 110.01 0 0 0 0 0 1 0 0 0 0 0 18
19 110.20 0 0 0 0 0 0 1 0 0 0 0 19
20 110.49 0 0 0 0 0 0 0 1 0 0 0 20
21 110.57 0 0 0 0 0 0 0 0 1 0 0 21
22 110.72 0 0 0 0 0 0 0 0 0 1 0 22
23 110.94 0 0 0 0 0 0 0 0 0 0 1 23
24 111.09 0 0 0 0 0 0 0 0 0 0 0 24
25 111.28 1 0 0 0 0 0 0 0 0 0 0 25
26 111.41 0 1 0 0 0 0 0 0 0 0 0 26
27 111.62 0 0 1 0 0 0 0 0 0 0 0 27
28 111.76 0 0 0 1 0 0 0 0 0 0 0 28
29 111.89 0 0 0 0 1 0 0 0 0 0 0 29
30 112.04 0 0 0 0 0 1 0 0 0 0 0 30
31 112.12 0 0 0 0 0 0 1 0 0 0 0 31
32 112.30 0 0 0 0 0 0 0 1 0 0 0 32
33 112.47 0 0 0 0 0 0 0 0 1 0 0 33
34 112.59 0 0 0 0 0 0 0 0 0 1 0 34
35 112.78 0 0 0 0 0 0 0 0 0 0 1 35
36 112.73 0 0 0 0 0 0 0 0 0 0 0 36
37 112.99 1 0 0 0 0 0 0 0 0 0 0 37
38 113.10 0 1 0 0 0 0 0 0 0 0 0 38
39 113.33 0 0 1 0 0 0 0 0 0 0 0 39
40 113.38 0 0 0 1 0 0 0 0 0 0 0 40
41 113.68 0 0 0 0 1 0 0 0 0 0 0 41
42 113.65 0 0 0 0 0 1 0 0 0 0 0 42
43 113.81 0 0 0 0 0 0 1 0 0 0 0 43
44 113.88 0 0 0 0 0 0 0 1 0 0 0 44
45 114.02 0 0 0 0 0 0 0 0 1 0 0 45
46 114.25 0 0 0 0 0 0 0 0 0 1 0 46
47 114.28 0 0 0 0 0 0 0 0 0 0 1 47
48 114.38 0 0 0 0 0 0 0 0 0 0 0 48
49 114.73 1 0 0 0 0 0 0 0 0 0 0 49
50 114.97 0 1 0 0 0 0 0 0 0 0 0 50
51 115.05 0 0 1 0 0 0 0 0 0 0 0 51
52 115.29 0 0 0 1 0 0 0 0 0 0 0 52
53 115.37 0 0 0 0 1 0 0 0 0 0 0 53
54 115.54 0 0 0 0 0 1 0 0 0 0 0 54
55 115.76 0 0 0 0 0 0 1 0 0 0 0 55
56 115.92 0 0 0 0 0 0 0 1 0 0 0 56
57 116.02 0 0 0 0 0 0 0 0 1 0 0 57
58 116.21 0 0 0 0 0 0 0 0 0 1 0 58
59 116.26 0 0 0 0 0 0 0 0 0 0 1 59
60 116.51 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) M1 M2 M3 M4 M5
106.69025 0.08059 0.11326 0.12794 0.11661 0.14528
M6 M7 M8 M9 M10 M11
0.07796 0.08263 0.07731 0.04998 0.08265 0.03533
t
0.16533
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.39017 -0.20385 -0.06692 0.26098 0.43192
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.067e+02 1.467e-01 727.141 <2e-16 ***
M1 8.059e-02 1.785e-01 0.451 0.654
M2 1.133e-01 1.782e-01 0.635 0.528
M3 1.279e-01 1.780e-01 0.719 0.476
M4 1.166e-01 1.778e-01 0.656 0.515
M5 1.453e-01 1.776e-01 0.818 0.417
M6 7.796e-02 1.774e-01 0.439 0.662
M7 8.263e-02 1.773e-01 0.466 0.643
M8 7.731e-02 1.772e-01 0.436 0.665
M9 4.998e-02 1.771e-01 0.282 0.779
M10 8.265e-02 1.770e-01 0.467 0.643
M11 3.533e-02 1.770e-01 0.200 0.843
t 1.653e-01 2.128e-03 77.674 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2798 on 47 degrees of freedom
Multiple R-squared: 0.9925, Adjusted R-squared: 0.9906
F-statistic: 521.5 on 12 and 47 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/1br1k1227442069.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/283k21227442069.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3g4tz1227442069.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/42ukg1227442069.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/52vq71227442069.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-3.361667e-01 -3.341667e-01 -3.141667e-01 -3.681667e-01 -3.621667e-01
6 7 8 9 10
-3.601667e-01 -3.301667e-01 -3.901667e-01 -3.281667e-01 -2.261667e-01
11 12 13 14 15
-2.441667e-01 -1.741667e-01 -8.333333e-05 1.119167e-01 1.119167e-01
16 17 18 19 20
1.979167e-01 2.639167e-01 2.659167e-01 2.859167e-01 4.159167e-01
21 22 23 24 25
3.579167e-01 3.099167e-01 4.119167e-01 4.319167e-01 3.760000e-01
26 27 28 29 30
3.080000e-01 3.380000e-01 3.240000e-01 2.600000e-01 3.120000e-01
31 32 33 34 35
2.220000e-01 2.420000e-01 2.740000e-01 1.960000e-01 2.680000e-01
36 37 38 39 40
8.800000e-02 1.020833e-01 1.408333e-02 6.408333e-02 -3.991667e-02
41 42 43 44 45
6.608333e-02 -6.191667e-02 -7.191667e-02 -1.619167e-01 -1.599167e-01
46 47 48 49 50
-1.279167e-01 -2.159167e-01 -2.459167e-01 -1.418333e-01 -9.983333e-02
51 52 53 54 55
-1.998333e-01 -1.138333e-01 -2.278333e-01 -1.558333e-01 -1.058333e-01
56 57 58 59 60
-1.058333e-01 -1.438333e-01 -1.518333e-01 -2.198333e-01 -9.983333e-02
> postscript(file="/var/www/html/rcomp/tmp/6j8a31227442069.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 -3.361667e-01 NA
1 -3.341667e-01 -3.361667e-01
2 -3.141667e-01 -3.341667e-01
3 -3.681667e-01 -3.141667e-01
4 -3.621667e-01 -3.681667e-01
5 -3.601667e-01 -3.621667e-01
6 -3.301667e-01 -3.601667e-01
7 -3.901667e-01 -3.301667e-01
8 -3.281667e-01 -3.901667e-01
9 -2.261667e-01 -3.281667e-01
10 -2.441667e-01 -2.261667e-01
11 -1.741667e-01 -2.441667e-01
12 -8.333333e-05 -1.741667e-01
13 1.119167e-01 -8.333333e-05
14 1.119167e-01 1.119167e-01
15 1.979167e-01 1.119167e-01
16 2.639167e-01 1.979167e-01
17 2.659167e-01 2.639167e-01
18 2.859167e-01 2.659167e-01
19 4.159167e-01 2.859167e-01
20 3.579167e-01 4.159167e-01
21 3.099167e-01 3.579167e-01
22 4.119167e-01 3.099167e-01
23 4.319167e-01 4.119167e-01
24 3.760000e-01 4.319167e-01
25 3.080000e-01 3.760000e-01
26 3.380000e-01 3.080000e-01
27 3.240000e-01 3.380000e-01
28 2.600000e-01 3.240000e-01
29 3.120000e-01 2.600000e-01
30 2.220000e-01 3.120000e-01
31 2.420000e-01 2.220000e-01
32 2.740000e-01 2.420000e-01
33 1.960000e-01 2.740000e-01
34 2.680000e-01 1.960000e-01
35 8.800000e-02 2.680000e-01
36 1.020833e-01 8.800000e-02
37 1.408333e-02 1.020833e-01
38 6.408333e-02 1.408333e-02
39 -3.991667e-02 6.408333e-02
40 6.608333e-02 -3.991667e-02
41 -6.191667e-02 6.608333e-02
42 -7.191667e-02 -6.191667e-02
43 -1.619167e-01 -7.191667e-02
44 -1.599167e-01 -1.619167e-01
45 -1.279167e-01 -1.599167e-01
46 -2.159167e-01 -1.279167e-01
47 -2.459167e-01 -2.159167e-01
48 -1.418333e-01 -2.459167e-01
49 -9.983333e-02 -1.418333e-01
50 -1.998333e-01 -9.983333e-02
51 -1.138333e-01 -1.998333e-01
52 -2.278333e-01 -1.138333e-01
53 -1.558333e-01 -2.278333e-01
54 -1.058333e-01 -1.558333e-01
55 -1.058333e-01 -1.058333e-01
56 -1.438333e-01 -1.058333e-01
57 -1.518333e-01 -1.438333e-01
58 -2.198333e-01 -1.518333e-01
59 -9.983333e-02 -2.198333e-01
60 NA -9.983333e-02
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.341667e-01 -3.361667e-01
[2,] -3.141667e-01 -3.341667e-01
[3,] -3.681667e-01 -3.141667e-01
[4,] -3.621667e-01 -3.681667e-01
[5,] -3.601667e-01 -3.621667e-01
[6,] -3.301667e-01 -3.601667e-01
[7,] -3.901667e-01 -3.301667e-01
[8,] -3.281667e-01 -3.901667e-01
[9,] -2.261667e-01 -3.281667e-01
[10,] -2.441667e-01 -2.261667e-01
[11,] -1.741667e-01 -2.441667e-01
[12,] -8.333333e-05 -1.741667e-01
[13,] 1.119167e-01 -8.333333e-05
[14,] 1.119167e-01 1.119167e-01
[15,] 1.979167e-01 1.119167e-01
[16,] 2.639167e-01 1.979167e-01
[17,] 2.659167e-01 2.639167e-01
[18,] 2.859167e-01 2.659167e-01
[19,] 4.159167e-01 2.859167e-01
[20,] 3.579167e-01 4.159167e-01
[21,] 3.099167e-01 3.579167e-01
[22,] 4.119167e-01 3.099167e-01
[23,] 4.319167e-01 4.119167e-01
[24,] 3.760000e-01 4.319167e-01
[25,] 3.080000e-01 3.760000e-01
[26,] 3.380000e-01 3.080000e-01
[27,] 3.240000e-01 3.380000e-01
[28,] 2.600000e-01 3.240000e-01
[29,] 3.120000e-01 2.600000e-01
[30,] 2.220000e-01 3.120000e-01
[31,] 2.420000e-01 2.220000e-01
[32,] 2.740000e-01 2.420000e-01
[33,] 1.960000e-01 2.740000e-01
[34,] 2.680000e-01 1.960000e-01
[35,] 8.800000e-02 2.680000e-01
[36,] 1.020833e-01 8.800000e-02
[37,] 1.408333e-02 1.020833e-01
[38,] 6.408333e-02 1.408333e-02
[39,] -3.991667e-02 6.408333e-02
[40,] 6.608333e-02 -3.991667e-02
[41,] -6.191667e-02 6.608333e-02
[42,] -7.191667e-02 -6.191667e-02
[43,] -1.619167e-01 -7.191667e-02
[44,] -1.599167e-01 -1.619167e-01
[45,] -1.279167e-01 -1.599167e-01
[46,] -2.159167e-01 -1.279167e-01
[47,] -2.459167e-01 -2.159167e-01
[48,] -1.418333e-01 -2.459167e-01
[49,] -9.983333e-02 -1.418333e-01
[50,] -1.998333e-01 -9.983333e-02
[51,] -1.138333e-01 -1.998333e-01
[52,] -2.278333e-01 -1.138333e-01
[53,] -1.558333e-01 -2.278333e-01
[54,] -1.058333e-01 -1.558333e-01
[55,] -1.058333e-01 -1.058333e-01
[56,] -1.438333e-01 -1.058333e-01
[57,] -1.518333e-01 -1.438333e-01
[58,] -2.198333e-01 -1.518333e-01
[59,] -9.983333e-02 -2.198333e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.341667e-01 -3.361667e-01
2 -3.141667e-01 -3.341667e-01
3 -3.681667e-01 -3.141667e-01
4 -3.621667e-01 -3.681667e-01
5 -3.601667e-01 -3.621667e-01
6 -3.301667e-01 -3.601667e-01
7 -3.901667e-01 -3.301667e-01
8 -3.281667e-01 -3.901667e-01
9 -2.261667e-01 -3.281667e-01
10 -2.441667e-01 -2.261667e-01
11 -1.741667e-01 -2.441667e-01
12 -8.333333e-05 -1.741667e-01
13 1.119167e-01 -8.333333e-05
14 1.119167e-01 1.119167e-01
15 1.979167e-01 1.119167e-01
16 2.639167e-01 1.979167e-01
17 2.659167e-01 2.639167e-01
18 2.859167e-01 2.659167e-01
19 4.159167e-01 2.859167e-01
20 3.579167e-01 4.159167e-01
21 3.099167e-01 3.579167e-01
22 4.119167e-01 3.099167e-01
23 4.319167e-01 4.119167e-01
24 3.760000e-01 4.319167e-01
25 3.080000e-01 3.760000e-01
26 3.380000e-01 3.080000e-01
27 3.240000e-01 3.380000e-01
28 2.600000e-01 3.240000e-01
29 3.120000e-01 2.600000e-01
30 2.220000e-01 3.120000e-01
31 2.420000e-01 2.220000e-01
32 2.740000e-01 2.420000e-01
33 1.960000e-01 2.740000e-01
34 2.680000e-01 1.960000e-01
35 8.800000e-02 2.680000e-01
36 1.020833e-01 8.800000e-02
37 1.408333e-02 1.020833e-01
38 6.408333e-02 1.408333e-02
39 -3.991667e-02 6.408333e-02
40 6.608333e-02 -3.991667e-02
41 -6.191667e-02 6.608333e-02
42 -7.191667e-02 -6.191667e-02
43 -1.619167e-01 -7.191667e-02
44 -1.599167e-01 -1.619167e-01
45 -1.279167e-01 -1.599167e-01
46 -2.159167e-01 -1.279167e-01
47 -2.459167e-01 -2.159167e-01
48 -1.418333e-01 -2.459167e-01
49 -9.983333e-02 -1.418333e-01
50 -1.998333e-01 -9.983333e-02
51 -1.138333e-01 -1.998333e-01
52 -2.278333e-01 -1.138333e-01
53 -1.558333e-01 -2.278333e-01
54 -1.058333e-01 -1.558333e-01
55 -1.058333e-01 -1.058333e-01
56 -1.438333e-01 -1.058333e-01
57 -1.518333e-01 -1.438333e-01
58 -2.198333e-01 -1.518333e-01
59 -9.983333e-02 -2.198333e-01
> 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/7t3ba1227442069.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8we861227442069.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/92u8m1227442069.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10z0o71227442069.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/11lbrl1227442069.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/12jd5c1227442069.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/13kukc1227442069.tab")
>
> system("convert tmp/1br1k1227442069.ps tmp/1br1k1227442069.png")
> system("convert tmp/283k21227442069.ps tmp/283k21227442069.png")
> system("convert tmp/3g4tz1227442069.ps tmp/3g4tz1227442069.png")
> system("convert tmp/42ukg1227442069.ps tmp/42ukg1227442069.png")
> system("convert tmp/52vq71227442069.ps tmp/52vq71227442069.png")
> system("convert tmp/6j8a31227442069.ps tmp/6j8a31227442069.png")
> system("convert tmp/7t3ba1227442069.ps tmp/7t3ba1227442069.png")
> system("convert tmp/8we861227442069.ps tmp/8we861227442069.png")
> system("convert tmp/92u8m1227442069.ps tmp/92u8m1227442069.png")
>
>
> proc.time()
user system elapsed
3.958 2.444 4.312