R version 2.6.1 (2007-11-26)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
R is free software and comes with ABSOLUTELY NO WARRANTY.
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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
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Type 'q()' to quit R.
> x <- array(list(1.0014,0,1.0183,0,1.0622,0,1.0773,0,1.0807,0,1.0848,0,1.1582,0,1.1663,0,1.1372,0,1.1139,0,1.1222,0,1.1692,0,1.1702,0,1.2286,0,1.2613,0,1.2646,0,1.2262,0,1.1985,0,1.2007,0,1.2138,0,1.2266,0,1.2176,0,1.2218,0,1.249,0,1.2991,0,1.3408,0,1.3119,0,1.3014,0,1.3201,0,1.2938,0,1.2694,0,1.2165,0,1.2037,0,1.2292,0,1.2256,0,1.2015,0,1.1786,0,1.1856,0,1.2103,0,1.1938,0,1.202,0,1.2271,0,1.277,0,1.265,0,1.2684,0,1.2811,0,1.2727,0,1.2611,0,1.2881,0,1.3213,0,1.2999,0,1.3074,1,1.3242,1,1.3516,1,1.3511,1,1.3419,1,1.3716,1,1.3622,1,1.3896,1,1.4227,1),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)
> 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 1.0014 0 1 0 0 0 0 0 0 0 0 0 0 1
2 1.0183 0 0 1 0 0 0 0 0 0 0 0 0 2
3 1.0622 0 0 0 1 0 0 0 0 0 0 0 0 3
4 1.0773 0 0 0 0 1 0 0 0 0 0 0 0 4
5 1.0807 0 0 0 0 0 1 0 0 0 0 0 0 5
6 1.0848 0 0 0 0 0 0 1 0 0 0 0 0 6
7 1.1582 0 0 0 0 0 0 0 1 0 0 0 0 7
8 1.1663 0 0 0 0 0 0 0 0 1 0 0 0 8
9 1.1372 0 0 0 0 0 0 0 0 0 1 0 0 9
10 1.1139 0 0 0 0 0 0 0 0 0 0 1 0 10
11 1.1222 0 0 0 0 0 0 0 0 0 0 0 1 11
12 1.1692 0 0 0 0 0 0 0 0 0 0 0 0 12
13 1.1702 0 1 0 0 0 0 0 0 0 0 0 0 13
14 1.2286 0 0 1 0 0 0 0 0 0 0 0 0 14
15 1.2613 0 0 0 1 0 0 0 0 0 0 0 0 15
16 1.2646 0 0 0 0 1 0 0 0 0 0 0 0 16
17 1.2262 0 0 0 0 0 1 0 0 0 0 0 0 17
18 1.1985 0 0 0 0 0 0 1 0 0 0 0 0 18
19 1.2007 0 0 0 0 0 0 0 1 0 0 0 0 19
20 1.2138 0 0 0 0 0 0 0 0 1 0 0 0 20
21 1.2266 0 0 0 0 0 0 0 0 0 1 0 0 21
22 1.2176 0 0 0 0 0 0 0 0 0 0 1 0 22
23 1.2218 0 0 0 0 0 0 0 0 0 0 0 1 23
24 1.2490 0 0 0 0 0 0 0 0 0 0 0 0 24
25 1.2991 0 1 0 0 0 0 0 0 0 0 0 0 25
26 1.3408 0 0 1 0 0 0 0 0 0 0 0 0 26
27 1.3119 0 0 0 1 0 0 0 0 0 0 0 0 27
28 1.3014 0 0 0 0 1 0 0 0 0 0 0 0 28
29 1.3201 0 0 0 0 0 1 0 0 0 0 0 0 29
30 1.2938 0 0 0 0 0 0 1 0 0 0 0 0 30
31 1.2694 0 0 0 0 0 0 0 1 0 0 0 0 31
32 1.2165 0 0 0 0 0 0 0 0 1 0 0 0 32
33 1.2037 0 0 0 0 0 0 0 0 0 1 0 0 33
34 1.2292 0 0 0 0 0 0 0 0 0 0 1 0 34
35 1.2256 0 0 0 0 0 0 0 0 0 0 0 1 35
36 1.2015 0 0 0 0 0 0 0 0 0 0 0 0 36
37 1.1786 0 1 0 0 0 0 0 0 0 0 0 0 37
38 1.1856 0 0 1 0 0 0 0 0 0 0 0 0 38
39 1.2103 0 0 0 1 0 0 0 0 0 0 0 0 39
40 1.1938 0 0 0 0 1 0 0 0 0 0 0 0 40
41 1.2020 0 0 0 0 0 1 0 0 0 0 0 0 41
42 1.2271 0 0 0 0 0 0 1 0 0 0 0 0 42
43 1.2770 0 0 0 0 0 0 0 1 0 0 0 0 43
44 1.2650 0 0 0 0 0 0 0 0 1 0 0 0 44
45 1.2684 0 0 0 0 0 0 0 0 0 1 0 0 45
46 1.2811 0 0 0 0 0 0 0 0 0 0 1 0 46
47 1.2727 0 0 0 0 0 0 0 0 0 0 0 1 47
48 1.2611 0 0 0 0 0 0 0 0 0 0 0 0 48
49 1.2881 0 1 0 0 0 0 0 0 0 0 0 0 49
50 1.3213 0 0 1 0 0 0 0 0 0 0 0 0 50
51 1.2999 0 0 0 1 0 0 0 0 0 0 0 0 51
52 1.3074 1 0 0 0 1 0 0 0 0 0 0 0 52
53 1.3242 1 0 0 0 0 1 0 0 0 0 0 0 53
54 1.3516 1 0 0 0 0 0 1 0 0 0 0 0 54
55 1.3511 1 0 0 0 0 0 0 1 0 0 0 0 55
56 1.3419 1 0 0 0 0 0 0 0 1 0 0 0 56
57 1.3716 1 0 0 0 0 0 0 0 0 1 0 0 57
58 1.3622 1 0 0 0 0 0 0 0 0 0 1 0 58
59 1.3896 1 0 0 0 0 0 0 0 0 0 0 1 59
60 1.4227 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
1.118260 0.032600 -0.025169 0.002496 0.008920 -0.001596
M5 M6 M7 M8 M9 M10
-0.003631 -0.006887 0.009458 -0.004898 -0.007873 -0.012349
M11 t
-0.010544 0.003776
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.110007 -0.033008 -0.001917 0.027442 0.121880
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.1182600 0.0312353 35.801 < 2e-16 ***
x 0.0326000 0.0273951 1.190 0.240
M1 -0.0251689 0.0371143 -0.678 0.501
M2 0.0024956 0.0370763 0.067 0.947
M3 0.0089200 0.0370468 0.241 0.811
M4 -0.0015956 0.0370257 -0.043 0.966
M5 -0.0036311 0.0369623 -0.098 0.922
M6 -0.0068867 0.0369072 -0.187 0.853
M7 0.0094578 0.0368606 0.257 0.799
M8 -0.0048978 0.0368224 -0.133 0.895
M9 -0.0078733 0.0367927 -0.214 0.831
M10 -0.0123489 0.0367714 -0.336 0.739
M11 -0.0105444 0.0367587 -0.287 0.776
t 0.0037756 0.0005592 6.752 2.15e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.05811 on 46 degrees of freedom
Multiple R-Squared: 0.6773, Adjusted R-squared: 0.5861
F-statistic: 7.426 on 13 and 46 DF, p-value: 1.456e-07
> postscript(file="/var/www/html/rcomp/tmp/1z4db1197539816.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/2cj5u1197539816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3qpxa1197539816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4fe101197539816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5lgkh1197539816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.095466667 -0.110006667 -0.076306667 -0.054466667 -0.052806667 -0.049226667
7 8 9 10 11 12
0.004053333 0.022733333 -0.007166667 -0.029766667 -0.027046667 0.005633333
13 14 15 16 17 18
0.028026667 0.054986667 0.077486667 0.087526667 0.047386667 0.019166667
19 20 21 22 23 24
0.001246667 0.024926667 0.036926667 0.028626667 0.027246667 0.040126667
25 26 27 28 29 30
0.111620000 0.121880000 0.082780000 0.079020000 0.095980000 0.069160000
31 32 33 34 35 36
0.024640000 -0.017680000 -0.031280000 -0.005080000 -0.014260000 -0.052680000
37 38 39 40 41 42
-0.054186667 -0.078626667 -0.064126667 -0.073886667 -0.067426667 -0.042846667
43 44 45 46 47 48
-0.013066667 -0.014486667 -0.011886667 0.001513333 -0.012466667 -0.038386667
49 50 51 52 53 54
0.010006667 0.011766667 -0.019833333 -0.038193333 -0.023133333 0.003746667
55 56 57 58 59 60
-0.016873333 -0.015493333 0.013406667 0.004706667 0.026526667 0.045306667
> postscript(file="/var/www/html/rcomp/tmp/6rht21197539816.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 -0.095466667 NA
1 -0.110006667 -0.095466667
2 -0.076306667 -0.110006667
3 -0.054466667 -0.076306667
4 -0.052806667 -0.054466667
5 -0.049226667 -0.052806667
6 0.004053333 -0.049226667
7 0.022733333 0.004053333
8 -0.007166667 0.022733333
9 -0.029766667 -0.007166667
10 -0.027046667 -0.029766667
11 0.005633333 -0.027046667
12 0.028026667 0.005633333
13 0.054986667 0.028026667
14 0.077486667 0.054986667
15 0.087526667 0.077486667
16 0.047386667 0.087526667
17 0.019166667 0.047386667
18 0.001246667 0.019166667
19 0.024926667 0.001246667
20 0.036926667 0.024926667
21 0.028626667 0.036926667
22 0.027246667 0.028626667
23 0.040126667 0.027246667
24 0.111620000 0.040126667
25 0.121880000 0.111620000
26 0.082780000 0.121880000
27 0.079020000 0.082780000
28 0.095980000 0.079020000
29 0.069160000 0.095980000
30 0.024640000 0.069160000
31 -0.017680000 0.024640000
32 -0.031280000 -0.017680000
33 -0.005080000 -0.031280000
34 -0.014260000 -0.005080000
35 -0.052680000 -0.014260000
36 -0.054186667 -0.052680000
37 -0.078626667 -0.054186667
38 -0.064126667 -0.078626667
39 -0.073886667 -0.064126667
40 -0.067426667 -0.073886667
41 -0.042846667 -0.067426667
42 -0.013066667 -0.042846667
43 -0.014486667 -0.013066667
44 -0.011886667 -0.014486667
45 0.001513333 -0.011886667
46 -0.012466667 0.001513333
47 -0.038386667 -0.012466667
48 0.010006667 -0.038386667
49 0.011766667 0.010006667
50 -0.019833333 0.011766667
51 -0.038193333 -0.019833333
52 -0.023133333 -0.038193333
53 0.003746667 -0.023133333
54 -0.016873333 0.003746667
55 -0.015493333 -0.016873333
56 0.013406667 -0.015493333
57 0.004706667 0.013406667
58 0.026526667 0.004706667
59 0.045306667 0.026526667
60 NA 0.045306667
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.110006667 -0.095466667
[2,] -0.076306667 -0.110006667
[3,] -0.054466667 -0.076306667
[4,] -0.052806667 -0.054466667
[5,] -0.049226667 -0.052806667
[6,] 0.004053333 -0.049226667
[7,] 0.022733333 0.004053333
[8,] -0.007166667 0.022733333
[9,] -0.029766667 -0.007166667
[10,] -0.027046667 -0.029766667
[11,] 0.005633333 -0.027046667
[12,] 0.028026667 0.005633333
[13,] 0.054986667 0.028026667
[14,] 0.077486667 0.054986667
[15,] 0.087526667 0.077486667
[16,] 0.047386667 0.087526667
[17,] 0.019166667 0.047386667
[18,] 0.001246667 0.019166667
[19,] 0.024926667 0.001246667
[20,] 0.036926667 0.024926667
[21,] 0.028626667 0.036926667
[22,] 0.027246667 0.028626667
[23,] 0.040126667 0.027246667
[24,] 0.111620000 0.040126667
[25,] 0.121880000 0.111620000
[26,] 0.082780000 0.121880000
[27,] 0.079020000 0.082780000
[28,] 0.095980000 0.079020000
[29,] 0.069160000 0.095980000
[30,] 0.024640000 0.069160000
[31,] -0.017680000 0.024640000
[32,] -0.031280000 -0.017680000
[33,] -0.005080000 -0.031280000
[34,] -0.014260000 -0.005080000
[35,] -0.052680000 -0.014260000
[36,] -0.054186667 -0.052680000
[37,] -0.078626667 -0.054186667
[38,] -0.064126667 -0.078626667
[39,] -0.073886667 -0.064126667
[40,] -0.067426667 -0.073886667
[41,] -0.042846667 -0.067426667
[42,] -0.013066667 -0.042846667
[43,] -0.014486667 -0.013066667
[44,] -0.011886667 -0.014486667
[45,] 0.001513333 -0.011886667
[46,] -0.012466667 0.001513333
[47,] -0.038386667 -0.012466667
[48,] 0.010006667 -0.038386667
[49,] 0.011766667 0.010006667
[50,] -0.019833333 0.011766667
[51,] -0.038193333 -0.019833333
[52,] -0.023133333 -0.038193333
[53,] 0.003746667 -0.023133333
[54,] -0.016873333 0.003746667
[55,] -0.015493333 -0.016873333
[56,] 0.013406667 -0.015493333
[57,] 0.004706667 0.013406667
[58,] 0.026526667 0.004706667
[59,] 0.045306667 0.026526667
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.110006667 -0.095466667
2 -0.076306667 -0.110006667
3 -0.054466667 -0.076306667
4 -0.052806667 -0.054466667
5 -0.049226667 -0.052806667
6 0.004053333 -0.049226667
7 0.022733333 0.004053333
8 -0.007166667 0.022733333
9 -0.029766667 -0.007166667
10 -0.027046667 -0.029766667
11 0.005633333 -0.027046667
12 0.028026667 0.005633333
13 0.054986667 0.028026667
14 0.077486667 0.054986667
15 0.087526667 0.077486667
16 0.047386667 0.087526667
17 0.019166667 0.047386667
18 0.001246667 0.019166667
19 0.024926667 0.001246667
20 0.036926667 0.024926667
21 0.028626667 0.036926667
22 0.027246667 0.028626667
23 0.040126667 0.027246667
24 0.111620000 0.040126667
25 0.121880000 0.111620000
26 0.082780000 0.121880000
27 0.079020000 0.082780000
28 0.095980000 0.079020000
29 0.069160000 0.095980000
30 0.024640000 0.069160000
31 -0.017680000 0.024640000
32 -0.031280000 -0.017680000
33 -0.005080000 -0.031280000
34 -0.014260000 -0.005080000
35 -0.052680000 -0.014260000
36 -0.054186667 -0.052680000
37 -0.078626667 -0.054186667
38 -0.064126667 -0.078626667
39 -0.073886667 -0.064126667
40 -0.067426667 -0.073886667
41 -0.042846667 -0.067426667
42 -0.013066667 -0.042846667
43 -0.014486667 -0.013066667
44 -0.011886667 -0.014486667
45 0.001513333 -0.011886667
46 -0.012466667 0.001513333
47 -0.038386667 -0.012466667
48 0.010006667 -0.038386667
49 0.011766667 0.010006667
50 -0.019833333 0.011766667
51 -0.038193333 -0.019833333
52 -0.023133333 -0.038193333
53 0.003746667 -0.023133333
54 -0.016873333 0.003746667
55 -0.015493333 -0.016873333
56 0.013406667 -0.015493333
57 0.004706667 0.013406667
58 0.026526667 0.004706667
59 0.045306667 0.026526667
> 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/7gngr1197539816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8z7631197539816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9eumh1197539816.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> load(file='/var/www/html/rcomp/createtable')
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10807r1197539816.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/111xy91197539816.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/12bvxn1197539816.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/13lazn1197539816.tab")
>
> system("convert tmp/1z4db1197539816.ps tmp/1z4db1197539816.png")
> system("convert tmp/2cj5u1197539816.ps tmp/2cj5u1197539816.png")
> system("convert tmp/3qpxa1197539816.ps tmp/3qpxa1197539816.png")
> system("convert tmp/4fe101197539816.ps tmp/4fe101197539816.png")
> system("convert tmp/5lgkh1197539816.ps tmp/5lgkh1197539816.png")
> system("convert tmp/6rht21197539816.ps tmp/6rht21197539816.png")
> system("convert tmp/7gngr1197539816.ps tmp/7gngr1197539816.png")
> system("convert tmp/8z7631197539816.ps tmp/8z7631197539816.png")
> system("convert tmp/9eumh1197539816.ps tmp/9eumh1197539816.png")
>
>
> proc.time()
user system elapsed
3.992 2.450 4.322