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authorJohannes Ranke <jranke@uni-bremen.de>2018-11-26 11:44:27 +0100
committerJohannes Ranke <jranke@uni-bremen.de>2018-11-26 11:44:27 +0100
commitfbfffc0e47171cac03797340fde697a75a5afd61 (patch)
treeb893e08e0f28673c2407c4bd1354b4bc23099b18 /man/logLik.mkinfit.Rd
parent282f88f9e28e683f524d5e05d65d8b18ab856a8d (diff)
Add AIC method for mmkin column objects
Static documentation rebuilt by pkgdown
Diffstat (limited to 'man/logLik.mkinfit.Rd')
-rw-r--r--man/logLik.mkinfit.Rd12
1 files changed, 8 insertions, 4 deletions
diff --git a/man/logLik.mkinfit.Rd b/man/logLik.mkinfit.Rd
index 8080f3db..fe517955 100644
--- a/man/logLik.mkinfit.Rd
+++ b/man/logLik.mkinfit.Rd
@@ -7,8 +7,8 @@
This function simply calculates the product of the likelihood densities
calculated using \code{\link{dnorm}}, i.e. assuming normal distribution.
- The total number of estimated parameters returned with the value
- of the likelihood is calculated as the sum of fitted degradation
+ 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.
For the case of unweighted least squares fitting, we calculate one
@@ -17,7 +17,7 @@
For the case of manual weighting, we use the weight given for each
observation as standard deviation in calculating its likelihood
- and the total number of estimated parameters is equal to the
+ and the total number of estimated parameters is equal to the
number of fitted degradation model parameters.
In the case of iterative reweighting, the variances obtained by this
@@ -28,7 +28,7 @@
reweighting method is "tc".
}
\usage{
-\method{logLik}{mkinfit}(object, ...)
+ \method{logLik}{mkinfit}(object, ...)
}
\arguments{
\item{object}{
@@ -43,6 +43,10 @@
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{
sfo_sfo <- mkinmod(
parent = mkinsub("SFO", to = "m1"),

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