Adapt tests, vignettes and examples

- Write the NEWS
- Static documentation rebuilt by pkgdown
- Adapt mkinerrmin
- Fix (hopefully all) remaining problems in mkinfit

1 files changed, 6 insertions, 23 deletions

diff --git a/man/logLik.mkinfit.Rd b/man/logLik.mkinfit.Rd
index 736ccd1e..5e910c2e 100644
--- a/man/logLik.mkinfit.Rd
+++ b/man/logLik.mkinfit.Rd
@@ -5,27 +5,13 @@
 }
 \description{
   This function simply calculates the product of the likelihood densities
-  calculated using \code{\link{dnorm}}, i.e. assuming normal distribution.
+  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.
-
-  For the case of unweighted least squares fitting, we calculate one
-  constant standard deviation from the residuals using \code{\link{sd}}
-  and add one to the number of fitted degradation model parameters.
-
-  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
-  number of fitted degradation model parameters.
-
-  In the case of iterative reweighting, the variances obtained by this
-  procedure are used in the likelihood calculations, and the number of
-  estimated parameters is obtained by the number of degradation model
-  parameters plus the number of variance model parameters, i.e. the number of
-  observed variables if the reweighting method is "obs", and two if the
-  reweighting method is "tc".
 }
 \usage{
   \method{logLik}{mkinfit}(object, ...)
@@ -54,13 +40,10 @@
     m1 = mkinsub("SFO")
   )
   d_t <- FOCUS_2006_D
-  d_t[23:24, "value"] <- c(NA, NA) # can't cope with zero values at the moment
   f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)
-  f_obs <- mkinfit(sfo_sfo, d_t, reweight.method = "obs", quiet = TRUE)
-  f_tc <- mkinfit(sfo_sfo, d_t, reweight.method = "tc", quiet = TRUE)
-  d_t$err <- d_t$value # Manual weighting assuming sigma ~ y
-  f_man <- mkinfit(sfo_sfo, d_t, err = "err", quiet = TRUE)
-  AIC(f_nw, f_obs, f_tc, f_man)
+  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{