diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2019-07-08 18:12:21 +0200 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2019-07-09 09:10:02 +0200 |
| commit | e0bef15657df1d6cade99cc3f6d8b07fa35792fe (patch) | |
| tree | 7d050ac3b25e37a952a1f4efa46263980fbe502b /man | |
| parent | 67d032fb345f8e0afc7628d7525da5b7f0a98f79 (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.Rd | 2 | ||||
| -rw-r--r-- | man/mkinfit.Rd | 11 |
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 |