From 194659fcaccdd1ee37851725b8c72e99daa3a8cf Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 10 Apr 2019 10:17:35 +0200 Subject: Adapt tests, vignettes and examples - Write the NEWS - Static documentation rebuilt by pkgdown - Adapt mkinerrmin - Fix (hopefully all) remaining problems in mkinfit --- docs/reference/logLik.mkinfit.html | 51 ++++++++------------------------------ 1 file changed, 11 insertions(+), 40 deletions(-) (limited to 'docs/reference/logLik.mkinfit.html') diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index fc4193cb..0184d573 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -33,23 +33,12 @@ + model parameters and the fitted error model parameters." /> @@ -80,7 +69,7 @@ In the case of iterative reweighting, the variances obtained by this mkin - 0.9.48.1 + 0.9.49.4 @@ -146,23 +135,12 @@ In the case of iterative reweighting, the variances obtained by this

This function simply calculates the product of the likelihood densities - calculated using dnorm, i.e. assuming normal distribution.

+ calculated using 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 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".

@@ -199,17 +177,10 @@ In the case of iterative reweighting, the variances obtained by this parent = mkinsub("SFO", to = "m1"), m1 = mkinsub("SFO") )
#> Successfully compiled differential equation model from auto-generated C code.
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)
#> df AIC -#> f_nw 5 204.4619 + f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)
#> Warning: Observations with value of zero were removed from the data
f_obs <- mkinfit(sfo_sfo, d_t, error_model = "obs", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
f_tc <- mkinfit(sfo_sfo, d_t, error_model = "tc", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
AIC(f_nw, f_obs, f_tc)
#> df AIC +#> f_nw 5 204.4486 #> f_obs 6 205.8727 -#> f_tc 6 143.8773 -#> f_man 4 291.8000
+#> f_tc 6 141.9656