R version 2.9.0 (2009-04-17)
Copyright (C) 2009 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(19,0,18,0,19,0,19,0,22,0,23,0,20,0,14,0,14,0,14,0,15,0,11,0,17,0,16,0,20,0,24,0,23,0,20,0,21,0,19,0,23,0,23,0,23,0,23,0,27,0,26,0,17,0,24,0,26,0,24,0,27,0,27,0,26,0,24,0,23,0,23,0,24,1,17,1,21,1,19,1,22,1,22,1,18,1,16,1,14,1,12,1,14,1,16,1,8,1,3,1,0,1,5,1,1,1,1,1,3,1,6,1,7,1,8,1,14,1,14,1,13,1,15,1),dim=c(2,62),dimnames=list(c('consumentenvertrouwen','financiële_crisis'),1:62))
> y <- array(NA,dim=c(2,62),dimnames=list(c('consumentenvertrouwen','financiële_crisis'),1:62))
> 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
consumentenvertrouwen financi\353le_crisis M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 19 0 1 0 0 0 0 0 0 0 0 0
2 18 0 0 1 0 0 0 0 0 0 0 0
3 19 0 0 0 1 0 0 0 0 0 0 0
4 19 0 0 0 0 1 0 0 0 0 0 0
5 22 0 0 0 0 0 1 0 0 0 0 0
6 23 0 0 0 0 0 0 1 0 0 0 0
7 20 0 0 0 0 0 0 0 1 0 0 0
8 14 0 0 0 0 0 0 0 0 1 0 0
9 14 0 0 0 0 0 0 0 0 0 1 0
10 14 0 0 0 0 0 0 0 0 0 0 1
11 15 0 0 0 0 0 0 0 0 0 0 0
12 11 0 0 0 0 0 0 0 0 0 0 0
13 17 0 1 0 0 0 0 0 0 0 0 0
14 16 0 0 1 0 0 0 0 0 0 0 0
15 20 0 0 0 1 0 0 0 0 0 0 0
16 24 0 0 0 0 1 0 0 0 0 0 0
17 23 0 0 0 0 0 1 0 0 0 0 0
18 20 0 0 0 0 0 0 1 0 0 0 0
19 21 0 0 0 0 0 0 0 1 0 0 0
20 19 0 0 0 0 0 0 0 0 1 0 0
21 23 0 0 0 0 0 0 0 0 0 1 0
22 23 0 0 0 0 0 0 0 0 0 0 1
23 23 0 0 0 0 0 0 0 0 0 0 0
24 23 0 0 0 0 0 0 0 0 0 0 0
25 27 0 1 0 0 0 0 0 0 0 0 0
26 26 0 0 1 0 0 0 0 0 0 0 0
27 17 0 0 0 1 0 0 0 0 0 0 0
28 24 0 0 0 0 1 0 0 0 0 0 0
29 26 0 0 0 0 0 1 0 0 0 0 0
30 24 0 0 0 0 0 0 1 0 0 0 0
31 27 0 0 0 0 0 0 0 1 0 0 0
32 27 0 0 0 0 0 0 0 0 1 0 0
33 26 0 0 0 0 0 0 0 0 0 1 0
34 24 0 0 0 0 0 0 0 0 0 0 1
35 23 0 0 0 0 0 0 0 0 0 0 0
36 23 0 0 0 0 0 0 0 0 0 0 0
37 24 1 1 0 0 0 0 0 0 0 0 0
38 17 1 0 1 0 0 0 0 0 0 0 0
39 21 1 0 0 1 0 0 0 0 0 0 0
40 19 1 0 0 0 1 0 0 0 0 0 0
41 22 1 0 0 0 0 1 0 0 0 0 0
42 22 1 0 0 0 0 0 1 0 0 0 0
43 18 1 0 0 0 0 0 0 1 0 0 0
44 16 1 0 0 0 0 0 0 0 1 0 0
45 14 1 0 0 0 0 0 0 0 0 1 0
46 12 1 0 0 0 0 0 0 0 0 0 1
47 14 1 0 0 0 0 0 0 0 0 0 0
48 16 1 0 0 0 0 0 0 0 0 0 0
49 8 1 1 0 0 0 0 0 0 0 0 0
50 3 1 0 1 0 0 0 0 0 0 0 0
51 0 1 0 0 1 0 0 0 0 0 0 0
52 5 1 0 0 0 1 0 0 0 0 0 0
53 1 1 0 0 0 0 1 0 0 0 0 0
54 1 1 0 0 0 0 0 1 0 0 0 0
55 3 1 0 0 0 0 0 0 1 0 0 0
56 6 1 0 0 0 0 0 0 0 1 0 0
57 7 1 0 0 0 0 0 0 0 0 1 0
58 8 1 0 0 0 0 0 0 0 0 0 1
59 14 1 0 0 0 0 0 0 0 0 0 0
60 14 1 0 0 0 0 0 0 0 0 0 0
61 13 1 1 0 0 0 0 0 0 0 0 0
62 15 1 0 1 0 0 0 0 0 0 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
57 0 57
58 0 58
59 1 59
60 0 60
61 0 61
62 0 62
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `financi\353le_crisis` M1
19.91667 -10.34524 1.85972
M2 M3 M4
-0.35198 -1.59464 1.16032
M5 M6 M7
1.71528 0.87024 0.62520
M8 M9 M10
-0.81984 -0.46488 -1.10992
M11 t
0.44504 0.04504
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.674 -3.915 1.194 3.167 11.267
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 19.91667 3.51673 5.663 8.16e-07 ***
`financi\353le_crisis` -10.34524 3.19263 -3.240 0.00217 **
M1 1.85972 3.80747 0.488 0.62746
M2 -0.35198 3.79170 -0.093 0.92642
M3 -1.59464 3.98226 -0.400 0.69061
M4 1.16032 3.96544 0.293 0.77108
M5 1.71528 3.95054 0.434 0.66610
M6 0.87024 3.93758 0.221 0.82602
M7 0.62520 3.92657 0.159 0.87416
M8 -0.81984 3.91755 -0.209 0.83512
M9 -0.46488 3.91052 -0.119 0.90587
M10 -1.10992 3.90549 -0.284 0.77748
M11 0.44504 3.90247 0.114 0.90968
t 0.04504 0.08868 0.508 0.61387
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.169 on 48 degrees of freedom
Multiple R-squared: 0.4108, Adjusted R-squared: 0.2513
F-statistic: 2.575 on 13 and 48 DF, p-value: 0.00874
> postscript(file="/var/www/html/rcomp/tmp/1j29x1260983523.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/28twy1260983523.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3iyx41260983523.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/45o5a1260983523.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5q5pe1260983523.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 62
Frequency = 1
1 2 3 4 5 6
-2.8214286 -1.6547619 0.5428571 -2.2571429 0.1428571 1.9428571
7 8 9 10 11 12
-0.8571429 -5.4571429 -5.8571429 -5.2571429 -5.8571429 -9.4571429
13 14 15 16 17 18
-5.3619048 -4.1952381 1.0023810 2.2023810 0.6023810 -1.5976190
19 20 21 22 23 24
-0.3976190 -0.9976190 2.6023810 3.2023810 1.6023810 2.0023810
25 26 27 28 29 30
4.0976190 5.2642857 -2.5380952 1.6619048 3.0619048 1.8619048
31 32 33 34 35 36
5.0619048 6.4619048 5.0619048 3.6619048 1.0619048 1.4619048
37 38 39 40 41 42
10.9023810 6.0690476 11.2666667 6.4666667 8.8666667 9.6666667
43 44 45 46 47 48
5.8666667 5.2666667 2.8666667 1.4666667 1.8666667 4.2666667
49 50 51 52 53 54
-5.6380952 -8.4714286 -10.2738095 -8.0738095 -12.6738095 -11.8738095
55 56 57 58 59 60
-9.6738095 -5.2738095 -4.6738095 -3.0738095 1.3261905 1.7261905
61 62
-1.1785714 2.9880952
> postscript(file="/var/www/html/rcomp/tmp/6e46b1260983523.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 = 62
Frequency = 1
lag(myerror, k = 1) myerror
0 -2.8214286 NA
1 -1.6547619 -2.8214286
2 0.5428571 -1.6547619
3 -2.2571429 0.5428571
4 0.1428571 -2.2571429
5 1.9428571 0.1428571
6 -0.8571429 1.9428571
7 -5.4571429 -0.8571429
8 -5.8571429 -5.4571429
9 -5.2571429 -5.8571429
10 -5.8571429 -5.2571429
11 -9.4571429 -5.8571429
12 -5.3619048 -9.4571429
13 -4.1952381 -5.3619048
14 1.0023810 -4.1952381
15 2.2023810 1.0023810
16 0.6023810 2.2023810
17 -1.5976190 0.6023810
18 -0.3976190 -1.5976190
19 -0.9976190 -0.3976190
20 2.6023810 -0.9976190
21 3.2023810 2.6023810
22 1.6023810 3.2023810
23 2.0023810 1.6023810
24 4.0976190 2.0023810
25 5.2642857 4.0976190
26 -2.5380952 5.2642857
27 1.6619048 -2.5380952
28 3.0619048 1.6619048
29 1.8619048 3.0619048
30 5.0619048 1.8619048
31 6.4619048 5.0619048
32 5.0619048 6.4619048
33 3.6619048 5.0619048
34 1.0619048 3.6619048
35 1.4619048 1.0619048
36 10.9023810 1.4619048
37 6.0690476 10.9023810
38 11.2666667 6.0690476
39 6.4666667 11.2666667
40 8.8666667 6.4666667
41 9.6666667 8.8666667
42 5.8666667 9.6666667
43 5.2666667 5.8666667
44 2.8666667 5.2666667
45 1.4666667 2.8666667
46 1.8666667 1.4666667
47 4.2666667 1.8666667
48 -5.6380952 4.2666667
49 -8.4714286 -5.6380952
50 -10.2738095 -8.4714286
51 -8.0738095 -10.2738095
52 -12.6738095 -8.0738095
53 -11.8738095 -12.6738095
54 -9.6738095 -11.8738095
55 -5.2738095 -9.6738095
56 -4.6738095 -5.2738095
57 -3.0738095 -4.6738095
58 1.3261905 -3.0738095
59 1.7261905 1.3261905
60 -1.1785714 1.7261905
61 2.9880952 -1.1785714
62 NA 2.9880952
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.6547619 -2.8214286
[2,] 0.5428571 -1.6547619
[3,] -2.2571429 0.5428571
[4,] 0.1428571 -2.2571429
[5,] 1.9428571 0.1428571
[6,] -0.8571429 1.9428571
[7,] -5.4571429 -0.8571429
[8,] -5.8571429 -5.4571429
[9,] -5.2571429 -5.8571429
[10,] -5.8571429 -5.2571429
[11,] -9.4571429 -5.8571429
[12,] -5.3619048 -9.4571429
[13,] -4.1952381 -5.3619048
[14,] 1.0023810 -4.1952381
[15,] 2.2023810 1.0023810
[16,] 0.6023810 2.2023810
[17,] -1.5976190 0.6023810
[18,] -0.3976190 -1.5976190
[19,] -0.9976190 -0.3976190
[20,] 2.6023810 -0.9976190
[21,] 3.2023810 2.6023810
[22,] 1.6023810 3.2023810
[23,] 2.0023810 1.6023810
[24,] 4.0976190 2.0023810
[25,] 5.2642857 4.0976190
[26,] -2.5380952 5.2642857
[27,] 1.6619048 -2.5380952
[28,] 3.0619048 1.6619048
[29,] 1.8619048 3.0619048
[30,] 5.0619048 1.8619048
[31,] 6.4619048 5.0619048
[32,] 5.0619048 6.4619048
[33,] 3.6619048 5.0619048
[34,] 1.0619048 3.6619048
[35,] 1.4619048 1.0619048
[36,] 10.9023810 1.4619048
[37,] 6.0690476 10.9023810
[38,] 11.2666667 6.0690476
[39,] 6.4666667 11.2666667
[40,] 8.8666667 6.4666667
[41,] 9.6666667 8.8666667
[42,] 5.8666667 9.6666667
[43,] 5.2666667 5.8666667
[44,] 2.8666667 5.2666667
[45,] 1.4666667 2.8666667
[46,] 1.8666667 1.4666667
[47,] 4.2666667 1.8666667
[48,] -5.6380952 4.2666667
[49,] -8.4714286 -5.6380952
[50,] -10.2738095 -8.4714286
[51,] -8.0738095 -10.2738095
[52,] -12.6738095 -8.0738095
[53,] -11.8738095 -12.6738095
[54,] -9.6738095 -11.8738095
[55,] -5.2738095 -9.6738095
[56,] -4.6738095 -5.2738095
[57,] -3.0738095 -4.6738095
[58,] 1.3261905 -3.0738095
[59,] 1.7261905 1.3261905
[60,] -1.1785714 1.7261905
[61,] 2.9880952 -1.1785714
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.6547619 -2.8214286
2 0.5428571 -1.6547619
3 -2.2571429 0.5428571
4 0.1428571 -2.2571429
5 1.9428571 0.1428571
6 -0.8571429 1.9428571
7 -5.4571429 -0.8571429
8 -5.8571429 -5.4571429
9 -5.2571429 -5.8571429
10 -5.8571429 -5.2571429
11 -9.4571429 -5.8571429
12 -5.3619048 -9.4571429
13 -4.1952381 -5.3619048
14 1.0023810 -4.1952381
15 2.2023810 1.0023810
16 0.6023810 2.2023810
17 -1.5976190 0.6023810
18 -0.3976190 -1.5976190
19 -0.9976190 -0.3976190
20 2.6023810 -0.9976190
21 3.2023810 2.6023810
22 1.6023810 3.2023810
23 2.0023810 1.6023810
24 4.0976190 2.0023810
25 5.2642857 4.0976190
26 -2.5380952 5.2642857
27 1.6619048 -2.5380952
28 3.0619048 1.6619048
29 1.8619048 3.0619048
30 5.0619048 1.8619048
31 6.4619048 5.0619048
32 5.0619048 6.4619048
33 3.6619048 5.0619048
34 1.0619048 3.6619048
35 1.4619048 1.0619048
36 10.9023810 1.4619048
37 6.0690476 10.9023810
38 11.2666667 6.0690476
39 6.4666667 11.2666667
40 8.8666667 6.4666667
41 9.6666667 8.8666667
42 5.8666667 9.6666667
43 5.2666667 5.8666667
44 2.8666667 5.2666667
45 1.4666667 2.8666667
46 1.8666667 1.4666667
47 4.2666667 1.8666667
48 -5.6380952 4.2666667
49 -8.4714286 -5.6380952
50 -10.2738095 -8.4714286
51 -8.0738095 -10.2738095
52 -12.6738095 -8.0738095
53 -11.8738095 -12.6738095
54 -9.6738095 -11.8738095
55 -5.2738095 -9.6738095
56 -4.6738095 -5.2738095
57 -3.0738095 -4.6738095
58 1.3261905 -3.0738095
59 1.7261905 1.3261905
60 -1.1785714 1.7261905
61 2.9880952 -1.1785714
> 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/7nycx1260983523.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ij841260983523.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/95xdw1260983523.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10x5hf1260983523.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/11783f1260983523.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/127yge1260983523.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/134go81260983523.tab")
> try(system("convert tmp/1j29x1260983523.ps tmp/1j29x1260983523.png",intern=TRUE))
character(0)
> try(system("convert tmp/28twy1260983523.ps tmp/28twy1260983523.png",intern=TRUE))
character(0)
> try(system("convert tmp/3iyx41260983523.ps tmp/3iyx41260983523.png",intern=TRUE))
character(0)
> try(system("convert tmp/45o5a1260983523.ps tmp/45o5a1260983523.png",intern=TRUE))
character(0)
> try(system("convert tmp/5q5pe1260983523.ps tmp/5q5pe1260983523.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e46b1260983523.ps tmp/6e46b1260983523.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nycx1260983523.ps tmp/7nycx1260983523.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ij841260983523.ps tmp/8ij841260983523.png",intern=TRUE))
character(0)
> try(system("convert tmp/95xdw1260983523.ps tmp/95xdw1260983523.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
1.980 1.433 3.621