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authorJohannes Ranke <jranke@uni-bremen.de>2019-07-08 18:12:21 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2019-07-09 09:10:02 +0200
commite0bef15657df1d6cade99cc3f6d8b07fa35792fe (patch)
tree7d050ac3b25e37a952a1f4efa46263980fbe502b /man
parent67d032fb345f8e0afc7628d7525da5b7f0a98f79 (diff)
Adaptations for gmkin
Address winbuilder check problems, update check log, update of static docs
Diffstat (limited to 'man')
-rw-r--r--man/AIC.mmkin.Rd2
-rw-r--r--man/mkinfit.Rd11
2 files changed, 10 insertions, 3 deletions
diff --git a/man/AIC.mmkin.Rd b/man/AIC.mmkin.Rd
index 08e4cc57..ca3fcf20 100644
--- a/man/AIC.mmkin.Rd
+++ b/man/AIC.mmkin.Rd
@@ -26,6 +26,7 @@
there are several fits in the column).
}
\examples{
+ \dontrun{ # skip, as it takes > 10 s on winbuilder
f <- mmkin(c("SFO", "FOMC", "DFOP"),
list("FOCUS A" = FOCUS_2006_A,
"FOCUS C" = FOCUS_2006_C), cores = 1, quiet = TRUE)
@@ -38,6 +39,7 @@
# For FOCUS C, the more complex models fit better
AIC(f[, "FOCUS C"])
+ }
}
\author{
Johannes Ranke
diff --git a/man/mkinfit.Rd b/man/mkinfit.Rd
index e7d35e4d..f7dd7009 100644
--- a/man/mkinfit.Rd
+++ b/man/mkinfit.Rd
@@ -31,7 +31,8 @@ mkinfit(mkinmod, observed,
quiet = FALSE,
atol = 1e-8, rtol = 1e-10, n.outtimes = 100,
error_model = c("const", "obs", "tc"),
- error_model_algorithm = c("d_3", "direct", "twostep", "threestep", "fourstep", "IRLS"),
+ error_model_algorithm = c("d_3", "direct", "twostep", "threestep", "fourstep", "IRLS",
+ "OLS"),
reweight.tol = 1e-8, reweight.max.iter = 10,
trace_parms = FALSE, ...)
}
@@ -200,10 +201,14 @@ mkinfit(mkinmod, observed,
with fixed error model parameters, and finally minimizes the negative
log-likelihood with free degradation and error model parameters.
- The algorithm "IRLS" starts with unweighted least squares,
- and then iterates optimization of the error model parameters and subsequent
+ The algorithm "IRLS" (Iteratively Reweighted Least Squares) starts with
+ unweighted least squares, and then iterates optimization of the error model
+ parameters and subsequent
optimization of the degradation model using those error model parameters,
until the error model parameters converge.
+
+ The algorithm "OLS" (Ordinary Least Squares) is automatically selected when
+ the error model is "const" and results in an unweighted least squares fit.
}
\item{reweight.tol}{
Tolerance for the convergence criterion calculated from the error model

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