From 91a5834dd701211f929fd25419dc34561ce3b4e7 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 14 Feb 2025 09:15:20 +0100 Subject: Initialize dev docs --- docs/dev/reference/logLik.mkinfit.html | 160 +++++++++++++++++++++++++++++++++ 1 file changed, 160 insertions(+) create mode 100644 docs/dev/reference/logLik.mkinfit.html (limited to 'docs/dev/reference/logLik.mkinfit.html') diff --git a/docs/dev/reference/logLik.mkinfit.html b/docs/dev/reference/logLik.mkinfit.html new file mode 100644 index 00000000..bc2847cc --- /dev/null +++ b/docs/dev/reference/logLik.mkinfit.html @@ -0,0 +1,160 @@ + +Calculated the log-likelihood of a fitted mkinfit object — logLik.mkinfit • mkin + Skip to contents + + +
+
+
+ +

Calculated the log-likelihood of a fitted mkinfit object

+ Source: R/logLik.mkinfit.R +
logLik.mkinfit.Rd
+
+ +
+

This function returns the product of the likelihood densities of each +observed value, as calculated as part of the fitting procedure using +dnorm, i.e. assuming normal distribution, and with the means +predicted by the degradation model, and the standard deviations predicted by +the error model.

+
+ +
+

Usage

+ 
# S3 method for class 'mkinfit'
+logLik(object, ...)
+
+ +
+

Arguments

+ + +
object
+

An object of class mkinfit.

+ + +
...
+

For compatibility with the generic method

+ +
+
+

Value

+

An object of class logLik with the number of estimated +parameters (degradation model parameters plus variance model parameters) +as attribute.

+
+
+

Details

+

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.

+
+
+

See also

+

Compare the AIC of columns of mmkin objects using +AIC.mmkin.

+
+
+

Author

+

Johannes Ranke

+
+ +
+

Examples

+ 

+  # \dontrun{
+  sfo_sfo <- mkinmod(
+    parent = mkinsub("SFO", to = "m1"),
+    m1 = mkinsub("SFO")
+  )
+#> Temporary DLL for differentials generated and loaded
+  d_t <- subset(FOCUS_2006_D, value != 0)
+  f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)
+  f_obs <- update(f_nw, error_model = "obs")
+  f_tc <- update(f_nw, error_model = "tc")
+  AIC(f_nw, f_obs, f_tc)
+#>       df      AIC
+#> f_nw   5 204.4486
+#> f_obs  6 205.8727
+#> f_tc   6 141.9656
+  # }
+
+
+
+
+ + +
+ + + + + + + -- cgit v1.2.1