1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
|
\name{logLik.mkinfit}
\alias{logLik.mkinfit}
\title{
Calculated the log-likelihood of a fitted mkinfit object
}
\description{
This function simply calculates the product of the likelihood densities
calculated using \code{\link{dnorm}}, i.e. assuming normal distribution,
with of the mean predicted by the degradation model, and the
standard deviation predicted by the error model.
The total number of estimated parameters returned with the value
of the likelihood is calculated as the sum of fitted degradation
model parameters and the fitted error model parameters.
}
\usage{
\method{logLik}{mkinfit}(object, ...)
}
\arguments{
\item{object}{
An object of class \code{\link{mkinfit}}.
}
\item{\dots}{
For compatibility with the generic method
}
}
\value{
An object of class \code{\link{logLik}} with the number of
estimated parameters (degradation model parameters plus variance
model parameters) as attribute.
}
\seealso{
Compare the AIC of columns of \code{\link{mmkin}} objects using
\code{\link{AIC.mmkin}}.
}
\examples{
\dontrun{
sfo_sfo <- mkinmod(
parent = mkinsub("SFO", to = "m1"),
m1 = mkinsub("SFO")
)
d_t <- FOCUS_2006_D
f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)
f_obs <- mkinfit(sfo_sfo, d_t, error_model = "obs", quiet = TRUE)
f_tc <- mkinfit(sfo_sfo, d_t, error_model = "tc", quiet = TRUE)
AIC(f_nw, f_obs, f_tc)
}
}
\author{
Johannes Ranke
}
|
