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(8.4,0,8.4,0,8.4,0,8.6,0,8.9,0,8.8,0,8.3,0,7.5,0,7.2,0,7.5,0,8.8,0,9.3,0,9.3,0,8.7,0,8.2,0,8.3,0,8.5,0,8.6,0,8.6,0,8.2,0,8.1,0,8,0,8.6,1,8.7,1,8.8,1,8.5,1,8.4,1,8.5,1,8.7,1,8.7,1,8.6,1,8.5,1,8.3,1,8.1,1,8.2,1,8.1,1,8.1,1,7.9,1,7.9,1,7.9,1,8,1,8,1,7.9,1,8,1,7.7,1,7.2,1,7.5,1,7.3,1,7,1,7,1,7,1,7.2,1,7.3,1,7.1,1,6.8,1,6.6,1,6.2,1,6.2,1,6.8,1,6.9,1),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 8.4 0 1 0 0 0 0 0 0 0 0 0 0 1
2 8.4 0 0 1 0 0 0 0 0 0 0 0 0 2
3 8.4 0 0 0 1 0 0 0 0 0 0 0 0 3
4 8.6 0 0 0 0 1 0 0 0 0 0 0 0 4
5 8.9 0 0 0 0 0 1 0 0 0 0 0 0 5
6 8.8 0 0 0 0 0 0 1 0 0 0 0 0 6
7 8.3 0 0 0 0 0 0 0 1 0 0 0 0 7
8 7.5 0 0 0 0 0 0 0 0 1 0 0 0 8
9 7.2 0 0 0 0 0 0 0 0 0 1 0 0 9
10 7.5 0 0 0 0 0 0 0 0 0 0 1 0 10
11 8.8 0 0 0 0 0 0 0 0 0 0 0 1 11
12 9.3 0 0 0 0 0 0 0 0 0 0 0 0 12
13 9.3 0 1 0 0 0 0 0 0 0 0 0 0 13
14 8.7 0 0 1 0 0 0 0 0 0 0 0 0 14
15 8.2 0 0 0 1 0 0 0 0 0 0 0 0 15
16 8.3 0 0 0 0 1 0 0 0 0 0 0 0 16
17 8.5 0 0 0 0 0 1 0 0 0 0 0 0 17
18 8.6 0 0 0 0 0 0 1 0 0 0 0 0 18
19 8.6 0 0 0 0 0 0 0 1 0 0 0 0 19
20 8.2 0 0 0 0 0 0 0 0 1 0 0 0 20
21 8.1 0 0 0 0 0 0 0 0 0 1 0 0 21
22 8.0 0 0 0 0 0 0 0 0 0 0 1 0 22
23 8.6 1 0 0 0 0 0 0 0 0 0 0 1 23
24 8.7 1 0 0 0 0 0 0 0 0 0 0 0 24
25 8.8 1 1 0 0 0 0 0 0 0 0 0 0 25
26 8.5 1 0 1 0 0 0 0 0 0 0 0 0 26
27 8.4 1 0 0 1 0 0 0 0 0 0 0 0 27
28 8.5 1 0 0 0 1 0 0 0 0 0 0 0 28
29 8.7 1 0 0 0 0 1 0 0 0 0 0 0 29
30 8.7 1 0 0 0 0 0 1 0 0 0 0 0 30
31 8.6 1 0 0 0 0 0 0 1 0 0 0 0 31
32 8.5 1 0 0 0 0 0 0 0 1 0 0 0 32
33 8.3 1 0 0 0 0 0 0 0 0 1 0 0 33
34 8.1 1 0 0 0 0 0 0 0 0 0 1 0 34
35 8.2 1 0 0 0 0 0 0 0 0 0 0 1 35
36 8.1 1 0 0 0 0 0 0 0 0 0 0 0 36
37 8.1 1 1 0 0 0 0 0 0 0 0 0 0 37
38 7.9 1 0 1 0 0 0 0 0 0 0 0 0 38
39 7.9 1 0 0 1 0 0 0 0 0 0 0 0 39
40 7.9 1 0 0 0 1 0 0 0 0 0 0 0 40
41 8.0 1 0 0 0 0 1 0 0 0 0 0 0 41
42 8.0 1 0 0 0 0 0 1 0 0 0 0 0 42
43 7.9 1 0 0 0 0 0 0 1 0 0 0 0 43
44 8.0 1 0 0 0 0 0 0 0 1 0 0 0 44
45 7.7 1 0 0 0 0 0 0 0 0 1 0 0 45
46 7.2 1 0 0 0 0 0 0 0 0 0 1 0 46
47 7.5 1 0 0 0 0 0 0 0 0 0 0 1 47
48 7.3 1 0 0 0 0 0 0 0 0 0 0 0 48
49 7.0 1 1 0 0 0 0 0 0 0 0 0 0 49
50 7.0 1 0 1 0 0 0 0 0 0 0 0 0 50
51 7.0 1 0 0 1 0 0 0 0 0 0 0 0 51
52 7.2 1 0 0 0 1 0 0 0 0 0 0 0 52
53 7.3 1 0 0 0 0 1 0 0 0 0 0 0 53
54 7.1 1 0 0 0 0 0 1 0 0 0 0 0 54
55 6.8 1 0 0 0 0 0 0 1 0 0 0 0 55
56 6.6 1 0 0 0 0 0 0 0 1 0 0 0 56
57 6.2 1 0 0 0 0 0 0 0 0 1 0 0 57
58 6.2 1 0 0 0 0 0 0 0 0 0 1 0 58
59 6.8 1 0 0 0 0 0 0 0 0 0 0 1 59
60 6.9 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
9.21361 0.87227 -0.13126 -0.29983 -0.36840 -0.19697
M5 M6 M7 M8 M9 M10
0.03445 0.04588 -0.10269 -0.33126 -0.53983 -0.58840
M11 t
-0.13143 -0.05143
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.01092 -0.21185 0.04689 0.19395 0.88622
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.213613 0.218311 42.204 < 2e-16 ***
x 0.872269 0.208785 4.178 0.00013 ***
M1 -0.131261 0.265577 -0.494 0.62348
M2 -0.299832 0.264995 -1.131 0.26372
M3 -0.368403 0.264541 -1.393 0.17043
M4 -0.196975 0.264217 -0.746 0.45976
M5 0.034454 0.264022 0.130 0.89674
M6 0.045882 0.263957 0.174 0.86277
M7 -0.102689 0.264022 -0.389 0.69911
M8 -0.331261 0.264217 -1.254 0.21627
M9 -0.539832 0.264541 -2.041 0.04705 *
M10 -0.588403 0.264995 -2.220 0.03136 *
M11 -0.131429 0.263057 -0.500 0.61972
t -0.051429 0.005857 -8.780 2.15e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4158 on 46 degrees of freedom
Multiple R-squared: 0.7517, Adjusted R-squared: 0.6816
F-statistic: 10.71 on 13 and 46 DF, p-value: 5.989e-10
> postscript(file="/var/www/html/rcomp/tmp/1kpvj1227564440.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/2o8161227564440.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3cxjg1227564440.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4f8a21227564440.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5s6ce1227564440.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-0.63092437 -0.41092437 -0.29092437 -0.21092437 -0.09092437 -0.15092437
7 8 9 10 11 12
-0.45092437 -0.97092437 -1.01092437 -0.61092437 0.28352941 0.70352941
13 14 15 16 17 18
0.88621849 0.50621849 0.12621849 0.10621849 0.12621849 0.26621849
19 20 21 22 23 24
0.46621849 0.34621849 0.50621849 0.50621849 -0.17159664 -0.15159664
25 26 27 28 29 30
0.13109244 0.05109244 0.07109244 0.05109244 0.07109244 0.11109244
31 32 33 34 35 36
0.21109244 0.39109244 0.45109244 0.35109244 0.04554622 -0.13445378
37 38 39 40 41 42
0.04823529 0.06823529 0.18823529 0.06823529 -0.01176471 0.02823529
43 44 45 46 47 48
0.12823529 0.50823529 0.46823529 0.06823529 -0.03731092 -0.31731092
49 50 51 52 53 54
-0.43462185 -0.21462185 -0.09462185 -0.01462185 -0.09462185 -0.25462185
55 56 57 58 59 60
-0.35462185 -0.27462185 -0.41462185 -0.31462185 -0.12016807 -0.10016807
> postscript(file="/var/www/html/rcomp/tmp/6mvtj1227564440.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 -0.63092437 NA
1 -0.41092437 -0.63092437
2 -0.29092437 -0.41092437
3 -0.21092437 -0.29092437
4 -0.09092437 -0.21092437
5 -0.15092437 -0.09092437
6 -0.45092437 -0.15092437
7 -0.97092437 -0.45092437
8 -1.01092437 -0.97092437
9 -0.61092437 -1.01092437
10 0.28352941 -0.61092437
11 0.70352941 0.28352941
12 0.88621849 0.70352941
13 0.50621849 0.88621849
14 0.12621849 0.50621849
15 0.10621849 0.12621849
16 0.12621849 0.10621849
17 0.26621849 0.12621849
18 0.46621849 0.26621849
19 0.34621849 0.46621849
20 0.50621849 0.34621849
21 0.50621849 0.50621849
22 -0.17159664 0.50621849
23 -0.15159664 -0.17159664
24 0.13109244 -0.15159664
25 0.05109244 0.13109244
26 0.07109244 0.05109244
27 0.05109244 0.07109244
28 0.07109244 0.05109244
29 0.11109244 0.07109244
30 0.21109244 0.11109244
31 0.39109244 0.21109244
32 0.45109244 0.39109244
33 0.35109244 0.45109244
34 0.04554622 0.35109244
35 -0.13445378 0.04554622
36 0.04823529 -0.13445378
37 0.06823529 0.04823529
38 0.18823529 0.06823529
39 0.06823529 0.18823529
40 -0.01176471 0.06823529
41 0.02823529 -0.01176471
42 0.12823529 0.02823529
43 0.50823529 0.12823529
44 0.46823529 0.50823529
45 0.06823529 0.46823529
46 -0.03731092 0.06823529
47 -0.31731092 -0.03731092
48 -0.43462185 -0.31731092
49 -0.21462185 -0.43462185
50 -0.09462185 -0.21462185
51 -0.01462185 -0.09462185
52 -0.09462185 -0.01462185
53 -0.25462185 -0.09462185
54 -0.35462185 -0.25462185
55 -0.27462185 -0.35462185
56 -0.41462185 -0.27462185
57 -0.31462185 -0.41462185
58 -0.12016807 -0.31462185
59 -0.10016807 -0.12016807
60 NA -0.10016807
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.41092437 -0.63092437
[2,] -0.29092437 -0.41092437
[3,] -0.21092437 -0.29092437
[4,] -0.09092437 -0.21092437
[5,] -0.15092437 -0.09092437
[6,] -0.45092437 -0.15092437
[7,] -0.97092437 -0.45092437
[8,] -1.01092437 -0.97092437
[9,] -0.61092437 -1.01092437
[10,] 0.28352941 -0.61092437
[11,] 0.70352941 0.28352941
[12,] 0.88621849 0.70352941
[13,] 0.50621849 0.88621849
[14,] 0.12621849 0.50621849
[15,] 0.10621849 0.12621849
[16,] 0.12621849 0.10621849
[17,] 0.26621849 0.12621849
[18,] 0.46621849 0.26621849
[19,] 0.34621849 0.46621849
[20,] 0.50621849 0.34621849
[21,] 0.50621849 0.50621849
[22,] -0.17159664 0.50621849
[23,] -0.15159664 -0.17159664
[24,] 0.13109244 -0.15159664
[25,] 0.05109244 0.13109244
[26,] 0.07109244 0.05109244
[27,] 0.05109244 0.07109244
[28,] 0.07109244 0.05109244
[29,] 0.11109244 0.07109244
[30,] 0.21109244 0.11109244
[31,] 0.39109244 0.21109244
[32,] 0.45109244 0.39109244
[33,] 0.35109244 0.45109244
[34,] 0.04554622 0.35109244
[35,] -0.13445378 0.04554622
[36,] 0.04823529 -0.13445378
[37,] 0.06823529 0.04823529
[38,] 0.18823529 0.06823529
[39,] 0.06823529 0.18823529
[40,] -0.01176471 0.06823529
[41,] 0.02823529 -0.01176471
[42,] 0.12823529 0.02823529
[43,] 0.50823529 0.12823529
[44,] 0.46823529 0.50823529
[45,] 0.06823529 0.46823529
[46,] -0.03731092 0.06823529
[47,] -0.31731092 -0.03731092
[48,] -0.43462185 -0.31731092
[49,] -0.21462185 -0.43462185
[50,] -0.09462185 -0.21462185
[51,] -0.01462185 -0.09462185
[52,] -0.09462185 -0.01462185
[53,] -0.25462185 -0.09462185
[54,] -0.35462185 -0.25462185
[55,] -0.27462185 -0.35462185
[56,] -0.41462185 -0.27462185
[57,] -0.31462185 -0.41462185
[58,] -0.12016807 -0.31462185
[59,] -0.10016807 -0.12016807
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.41092437 -0.63092437
2 -0.29092437 -0.41092437
3 -0.21092437 -0.29092437
4 -0.09092437 -0.21092437
5 -0.15092437 -0.09092437
6 -0.45092437 -0.15092437
7 -0.97092437 -0.45092437
8 -1.01092437 -0.97092437
9 -0.61092437 -1.01092437
10 0.28352941 -0.61092437
11 0.70352941 0.28352941
12 0.88621849 0.70352941
13 0.50621849 0.88621849
14 0.12621849 0.50621849
15 0.10621849 0.12621849
16 0.12621849 0.10621849
17 0.26621849 0.12621849
18 0.46621849 0.26621849
19 0.34621849 0.46621849
20 0.50621849 0.34621849
21 0.50621849 0.50621849
22 -0.17159664 0.50621849
23 -0.15159664 -0.17159664
24 0.13109244 -0.15159664
25 0.05109244 0.13109244
26 0.07109244 0.05109244
27 0.05109244 0.07109244
28 0.07109244 0.05109244
29 0.11109244 0.07109244
30 0.21109244 0.11109244
31 0.39109244 0.21109244
32 0.45109244 0.39109244
33 0.35109244 0.45109244
34 0.04554622 0.35109244
35 -0.13445378 0.04554622
36 0.04823529 -0.13445378
37 0.06823529 0.04823529
38 0.18823529 0.06823529
39 0.06823529 0.18823529
40 -0.01176471 0.06823529
41 0.02823529 -0.01176471
42 0.12823529 0.02823529
43 0.50823529 0.12823529
44 0.46823529 0.50823529
45 0.06823529 0.46823529
46 -0.03731092 0.06823529
47 -0.31731092 -0.03731092
48 -0.43462185 -0.31731092
49 -0.21462185 -0.43462185
50 -0.09462185 -0.21462185
51 -0.01462185 -0.09462185
52 -0.09462185 -0.01462185
53 -0.25462185 -0.09462185
54 -0.35462185 -0.25462185
55 -0.27462185 -0.35462185
56 -0.41462185 -0.27462185
57 -0.31462185 -0.41462185
58 -0.12016807 -0.31462185
59 -0.10016807 -0.12016807
> 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/706q21227564440.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/84zg21227564440.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9tnt81227564440.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10rl8p1227564440.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/11k2x31227564440.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/12fvkk1227564440.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/13xcqx1227564440.tab")
>
> system("convert tmp/1kpvj1227564440.ps tmp/1kpvj1227564440.png")
> system("convert tmp/2o8161227564440.ps tmp/2o8161227564440.png")
> system("convert tmp/3cxjg1227564440.ps tmp/3cxjg1227564440.png")
> system("convert tmp/4f8a21227564440.ps tmp/4f8a21227564440.png")
> system("convert tmp/5s6ce1227564440.ps tmp/5s6ce1227564440.png")
> system("convert tmp/6mvtj1227564440.ps tmp/6mvtj1227564440.png")
> system("convert tmp/706q21227564440.ps tmp/706q21227564440.png")
> system("convert tmp/84zg21227564440.ps tmp/84zg21227564440.png")
> system("convert tmp/9tnt81227564440.ps tmp/9tnt81227564440.png")
>
>
> proc.time()
user system elapsed
1.941 1.394 2.261