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(10.413,0,10.709,0,10.662,0,10.570,0,10.297,0,10.635,0,10.872,0,10.296,0,10.383,0,10.431,0,10.574,0,10.653,0,10.805,0,10.872,0,10.625,0,10.407,0,10.463,0,10.556,0,10.646,0,10.702,0,11.353,1,11.346,1,11.451,1,11.964,1,12.574,1,13.031,1,13.812,1,14.544,1,14.931,1,14.886,1,16.005,1,17.064,1,15.168,1,16.050,1,15.839,1,15.137,1,14.954,1,15.648,1,15.305,1,15.579,1,16.348,1,15.928,1,16.171,1,15.937,1,15.713,1,15.594,1,15.683,1,16.438,1,17.032,1,17.696,1,17.745,1,19.394,1,20.148,1,20.108,1,18.584,1,18.441,1,18.391,1,19.178,1,18.079,1,18.483,1,19.644,1),dim=c(2,61),dimnames=list(c('Goudkoers','DrasticChange'),1:61))
> y <- array(NA,dim=c(2,61),dimnames=list(c('Goudkoers','DrasticChange'),1:61))
> 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
Goudkoers DrasticChange M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 10.413 0 1 0 0 0 0 0 0 0 0 0 0 1
2 10.709 0 0 1 0 0 0 0 0 0 0 0 0 2
3 10.662 0 0 0 1 0 0 0 0 0 0 0 0 3
4 10.570 0 0 0 0 1 0 0 0 0 0 0 0 4
5 10.297 0 0 0 0 0 1 0 0 0 0 0 0 5
6 10.635 0 0 0 0 0 0 1 0 0 0 0 0 6
7 10.872 0 0 0 0 0 0 0 1 0 0 0 0 7
8 10.296 0 0 0 0 0 0 0 0 1 0 0 0 8
9 10.383 0 0 0 0 0 0 0 0 0 1 0 0 9
10 10.431 0 0 0 0 0 0 0 0 0 0 1 0 10
11 10.574 0 0 0 0 0 0 0 0 0 0 0 1 11
12 10.653 0 0 0 0 0 0 0 0 0 0 0 0 12
13 10.805 0 1 0 0 0 0 0 0 0 0 0 0 13
14 10.872 0 0 1 0 0 0 0 0 0 0 0 0 14
15 10.625 0 0 0 1 0 0 0 0 0 0 0 0 15
16 10.407 0 0 0 0 1 0 0 0 0 0 0 0 16
17 10.463 0 0 0 0 0 1 0 0 0 0 0 0 17
18 10.556 0 0 0 0 0 0 1 0 0 0 0 0 18
19 10.646 0 0 0 0 0 0 0 1 0 0 0 0 19
20 10.702 0 0 0 0 0 0 0 0 1 0 0 0 20
21 11.353 1 0 0 0 0 0 0 0 0 1 0 0 21
22 11.346 1 0 0 0 0 0 0 0 0 0 1 0 22
23 11.451 1 0 0 0 0 0 0 0 0 0 0 1 23
24 11.964 1 0 0 0 0 0 0 0 0 0 0 0 24
25 12.574 1 1 0 0 0 0 0 0 0 0 0 0 25
26 13.031 1 0 1 0 0 0 0 0 0 0 0 0 26
27 13.812 1 0 0 1 0 0 0 0 0 0 0 0 27
28 14.544 1 0 0 0 1 0 0 0 0 0 0 0 28
29 14.931 1 0 0 0 0 1 0 0 0 0 0 0 29
30 14.886 1 0 0 0 0 0 1 0 0 0 0 0 30
31 16.005 1 0 0 0 0 0 0 1 0 0 0 0 31
32 17.064 1 0 0 0 0 0 0 0 1 0 0 0 32
33 15.168 1 0 0 0 0 0 0 0 0 1 0 0 33
34 16.050 1 0 0 0 0 0 0 0 0 0 1 0 34
35 15.839 1 0 0 0 0 0 0 0 0 0 0 1 35
36 15.137 1 0 0 0 0 0 0 0 0 0 0 0 36
37 14.954 1 1 0 0 0 0 0 0 0 0 0 0 37
38 15.648 1 0 1 0 0 0 0 0 0 0 0 0 38
39 15.305 1 0 0 1 0 0 0 0 0 0 0 0 39
40 15.579 1 0 0 0 1 0 0 0 0 0 0 0 40
41 16.348 1 0 0 0 0 1 0 0 0 0 0 0 41
42 15.928 1 0 0 0 0 0 1 0 0 0 0 0 42
43 16.171 1 0 0 0 0 0 0 1 0 0 0 0 43
44 15.937 1 0 0 0 0 0 0 0 1 0 0 0 44
45 15.713 1 0 0 0 0 0 0 0 0 1 0 0 45
46 15.594 1 0 0 0 0 0 0 0 0 0 1 0 46
47 15.683 1 0 0 0 0 0 0 0 0 0 0 1 47
48 16.438 1 0 0 0 0 0 0 0 0 0 0 0 48
49 17.032 1 1 0 0 0 0 0 0 0 0 0 0 49
50 17.696 1 0 1 0 0 0 0 0 0 0 0 0 50
51 17.745 1 0 0 1 0 0 0 0 0 0 0 0 51
52 19.394 1 0 0 0 1 0 0 0 0 0 0 0 52
53 20.148 1 0 0 0 0 1 0 0 0 0 0 0 53
54 20.108 1 0 0 0 0 0 1 0 0 0 0 0 54
55 18.584 1 0 0 0 0 0 0 1 0 0 0 0 55
56 18.441 1 0 0 0 0 0 0 0 1 0 0 0 56
57 18.391 1 0 0 0 0 0 0 0 0 1 0 0 57
58 19.178 1 0 0 0 0 0 0 0 0 0 1 0 58
59 18.079 1 0 0 0 0 0 0 0 0 0 0 1 59
60 18.483 1 0 0 0 0 0 0 0 0 0 0 0 60
61 19.644 1 1 0 0 0 0 0 0 0 0 0 0 61
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) DrasticChange M1 M2 M3
8.21336 0.77259 0.59717 0.79504 0.67521
M4 M5 M6 M7 M8
0.98578 1.16594 0.99271 0.86728 0.74125
M9 M10 M11 t
0.14190 0.30166 -0.05137 0.15843
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.609656 -0.697906 -0.008894 0.601850 2.266962
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.21336 0.53259 15.422 < 2e-16 ***
DrasticChange 0.77259 0.48843 1.582 0.1204
M1 0.59717 0.62111 0.961 0.3412
M2 0.79504 0.65183 1.220 0.2287
M3 0.67521 0.65096 1.037 0.3049
M4 0.98578 0.65035 1.516 0.1363
M5 1.16594 0.64999 1.794 0.0793 .
M6 0.99271 0.64989 1.528 0.1333
M7 0.86728 0.65005 1.334 0.1886
M8 0.74125 0.65047 1.140 0.2602
M9 0.14190 0.64856 0.219 0.8278
M10 0.30166 0.64791 0.466 0.6437
M11 -0.05137 0.64752 -0.079 0.9371
t 0.15843 0.01296 12.222 3.37e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.024 on 47 degrees of freedom
Multiple R-squared: 0.9206, Adjusted R-squared: 0.8987
F-statistic: 41.95 on 13 and 47 DF, p-value: < 2.2e-16
> postscript(file="/var/www/html/rcomp/tmp/13d0o1227120780.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/20sm01227120780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/35mnp1227120780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4svii1227120780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5niv71227120780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 61
Frequency = 1
1 2 3 4 5 6
1.44403125 1.38373125 1.29813125 0.73713125 0.12553125 0.47833125
7 8 9 10 11 12
0.68233125 0.07393125 0.60185000 0.33165000 0.66925000 0.53845000
13 14 15 16 17 18
-0.06515625 -0.35445625 -0.64005625 -1.32705625 -1.60965625 -1.50185625
19 20 21 22 23 24
-1.44485625 -1.42125625 -1.10193125 -1.42713125 -1.12753125 -0.82433125
25 26 27 28 29 30
-0.96993750 -0.86923750 -0.12683750 0.13616250 0.18456250 0.15436250
31 32 33 34 35 36
1.24036250 2.26696250 0.81188125 1.37568125 1.35928125 0.44748125
37 38 39 40 41 42
-0.49112500 -0.15342500 -0.53502500 -0.73002500 -0.29962500 -0.70482500
43 44 45 46 47 48
-0.49482500 -0.76122500 -0.54430625 -0.98150625 -0.69790625 -0.15270625
49 50 51 52 53 54
-0.31431250 -0.00661250 0.00378750 1.18378750 1.59918750 1.57398750
55 56 57 58 59 60
0.01698750 -0.15841250 0.23250625 0.70130625 -0.20309375 -0.00889375
61
0.39650000
> postscript(file="/var/www/html/rcomp/tmp/6al2l1227120780.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 = 61
Frequency = 1
lag(myerror, k = 1) myerror
0 1.44403125 NA
1 1.38373125 1.44403125
2 1.29813125 1.38373125
3 0.73713125 1.29813125
4 0.12553125 0.73713125
5 0.47833125 0.12553125
6 0.68233125 0.47833125
7 0.07393125 0.68233125
8 0.60185000 0.07393125
9 0.33165000 0.60185000
10 0.66925000 0.33165000
11 0.53845000 0.66925000
12 -0.06515625 0.53845000
13 -0.35445625 -0.06515625
14 -0.64005625 -0.35445625
15 -1.32705625 -0.64005625
16 -1.60965625 -1.32705625
17 -1.50185625 -1.60965625
18 -1.44485625 -1.50185625
19 -1.42125625 -1.44485625
20 -1.10193125 -1.42125625
21 -1.42713125 -1.10193125
22 -1.12753125 -1.42713125
23 -0.82433125 -1.12753125
24 -0.96993750 -0.82433125
25 -0.86923750 -0.96993750
26 -0.12683750 -0.86923750
27 0.13616250 -0.12683750
28 0.18456250 0.13616250
29 0.15436250 0.18456250
30 1.24036250 0.15436250
31 2.26696250 1.24036250
32 0.81188125 2.26696250
33 1.37568125 0.81188125
34 1.35928125 1.37568125
35 0.44748125 1.35928125
36 -0.49112500 0.44748125
37 -0.15342500 -0.49112500
38 -0.53502500 -0.15342500
39 -0.73002500 -0.53502500
40 -0.29962500 -0.73002500
41 -0.70482500 -0.29962500
42 -0.49482500 -0.70482500
43 -0.76122500 -0.49482500
44 -0.54430625 -0.76122500
45 -0.98150625 -0.54430625
46 -0.69790625 -0.98150625
47 -0.15270625 -0.69790625
48 -0.31431250 -0.15270625
49 -0.00661250 -0.31431250
50 0.00378750 -0.00661250
51 1.18378750 0.00378750
52 1.59918750 1.18378750
53 1.57398750 1.59918750
54 0.01698750 1.57398750
55 -0.15841250 0.01698750
56 0.23250625 -0.15841250
57 0.70130625 0.23250625
58 -0.20309375 0.70130625
59 -0.00889375 -0.20309375
60 0.39650000 -0.00889375
61 NA 0.39650000
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.38373125 1.44403125
[2,] 1.29813125 1.38373125
[3,] 0.73713125 1.29813125
[4,] 0.12553125 0.73713125
[5,] 0.47833125 0.12553125
[6,] 0.68233125 0.47833125
[7,] 0.07393125 0.68233125
[8,] 0.60185000 0.07393125
[9,] 0.33165000 0.60185000
[10,] 0.66925000 0.33165000
[11,] 0.53845000 0.66925000
[12,] -0.06515625 0.53845000
[13,] -0.35445625 -0.06515625
[14,] -0.64005625 -0.35445625
[15,] -1.32705625 -0.64005625
[16,] -1.60965625 -1.32705625
[17,] -1.50185625 -1.60965625
[18,] -1.44485625 -1.50185625
[19,] -1.42125625 -1.44485625
[20,] -1.10193125 -1.42125625
[21,] -1.42713125 -1.10193125
[22,] -1.12753125 -1.42713125
[23,] -0.82433125 -1.12753125
[24,] -0.96993750 -0.82433125
[25,] -0.86923750 -0.96993750
[26,] -0.12683750 -0.86923750
[27,] 0.13616250 -0.12683750
[28,] 0.18456250 0.13616250
[29,] 0.15436250 0.18456250
[30,] 1.24036250 0.15436250
[31,] 2.26696250 1.24036250
[32,] 0.81188125 2.26696250
[33,] 1.37568125 0.81188125
[34,] 1.35928125 1.37568125
[35,] 0.44748125 1.35928125
[36,] -0.49112500 0.44748125
[37,] -0.15342500 -0.49112500
[38,] -0.53502500 -0.15342500
[39,] -0.73002500 -0.53502500
[40,] -0.29962500 -0.73002500
[41,] -0.70482500 -0.29962500
[42,] -0.49482500 -0.70482500
[43,] -0.76122500 -0.49482500
[44,] -0.54430625 -0.76122500
[45,] -0.98150625 -0.54430625
[46,] -0.69790625 -0.98150625
[47,] -0.15270625 -0.69790625
[48,] -0.31431250 -0.15270625
[49,] -0.00661250 -0.31431250
[50,] 0.00378750 -0.00661250
[51,] 1.18378750 0.00378750
[52,] 1.59918750 1.18378750
[53,] 1.57398750 1.59918750
[54,] 0.01698750 1.57398750
[55,] -0.15841250 0.01698750
[56,] 0.23250625 -0.15841250
[57,] 0.70130625 0.23250625
[58,] -0.20309375 0.70130625
[59,] -0.00889375 -0.20309375
[60,] 0.39650000 -0.00889375
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.38373125 1.44403125
2 1.29813125 1.38373125
3 0.73713125 1.29813125
4 0.12553125 0.73713125
5 0.47833125 0.12553125
6 0.68233125 0.47833125
7 0.07393125 0.68233125
8 0.60185000 0.07393125
9 0.33165000 0.60185000
10 0.66925000 0.33165000
11 0.53845000 0.66925000
12 -0.06515625 0.53845000
13 -0.35445625 -0.06515625
14 -0.64005625 -0.35445625
15 -1.32705625 -0.64005625
16 -1.60965625 -1.32705625
17 -1.50185625 -1.60965625
18 -1.44485625 -1.50185625
19 -1.42125625 -1.44485625
20 -1.10193125 -1.42125625
21 -1.42713125 -1.10193125
22 -1.12753125 -1.42713125
23 -0.82433125 -1.12753125
24 -0.96993750 -0.82433125
25 -0.86923750 -0.96993750
26 -0.12683750 -0.86923750
27 0.13616250 -0.12683750
28 0.18456250 0.13616250
29 0.15436250 0.18456250
30 1.24036250 0.15436250
31 2.26696250 1.24036250
32 0.81188125 2.26696250
33 1.37568125 0.81188125
34 1.35928125 1.37568125
35 0.44748125 1.35928125
36 -0.49112500 0.44748125
37 -0.15342500 -0.49112500
38 -0.53502500 -0.15342500
39 -0.73002500 -0.53502500
40 -0.29962500 -0.73002500
41 -0.70482500 -0.29962500
42 -0.49482500 -0.70482500
43 -0.76122500 -0.49482500
44 -0.54430625 -0.76122500
45 -0.98150625 -0.54430625
46 -0.69790625 -0.98150625
47 -0.15270625 -0.69790625
48 -0.31431250 -0.15270625
49 -0.00661250 -0.31431250
50 0.00378750 -0.00661250
51 1.18378750 0.00378750
52 1.59918750 1.18378750
53 1.57398750 1.59918750
54 0.01698750 1.57398750
55 -0.15841250 0.01698750
56 0.23250625 -0.15841250
57 0.70130625 0.23250625
58 -0.20309375 0.70130625
59 -0.00889375 -0.20309375
60 0.39650000 -0.00889375
> 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/7c7ce1227120780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/88x0y1227120780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9j7uh1227120780.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/10qstx1227120780.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/11jjq81227120781.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/125ytp1227120781.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/13px281227120781.tab")
>
> system("convert tmp/13d0o1227120780.ps tmp/13d0o1227120780.png")
> system("convert tmp/20sm01227120780.ps tmp/20sm01227120780.png")
> system("convert tmp/35mnp1227120780.ps tmp/35mnp1227120780.png")
> system("convert tmp/4svii1227120780.ps tmp/4svii1227120780.png")
> system("convert tmp/5niv71227120780.ps tmp/5niv71227120780.png")
> system("convert tmp/6al2l1227120780.ps tmp/6al2l1227120780.png")
> system("convert tmp/7c7ce1227120780.ps tmp/7c7ce1227120780.png")
> system("convert tmp/88x0y1227120780.ps tmp/88x0y1227120780.png")
> system("convert tmp/9j7uh1227120780.ps tmp/9j7uh1227120780.png")
>
>
> proc.time()
user system elapsed
3.986 2.457 4.300