From 6e5630a0df7e857697ff2ce4730a5f7f45b67377 Mon Sep 17 00:00:00 2001
From: Johannes Ranke Confidence intervals for parameters of mkinfit objects An A vector of names of the parameters which are to be given
+confidence intervals. If missing, all parameters are considered. The confidence level required The allowed error probability, overrides 'level' if specified. The 'profile' method searches the parameter space for the
+cutoff of the confidence intervals by means of a likelihood ratio test.
+The 'quadratic' method approximates the likelihood function at the
+optimised parameters using the second term of the Taylor expansion, using
+a second derivative (hessian) contained in the object. If the quadratic approximation is used, should it be
+applied to the likelihood based on the transformed parameters? If we approximate the likelihood in terms of the
+transformed parameters, should we backtransform the parameters with
+their confidence intervals? For the quadratic approximation, should we use
+the student t distribution or assume normal distribution for
+the parameter estimate Should we suppress messages? Not used A matrix with columns giving lower and upper confidence limits for
+ each parameter. Pawitan Y (2013) In all likelihood - Statistical modelling and
+ inference using likelihood. Clarendon Press, Oxford.Confidence intervals for parameters of mkinfit objects
+
+ confint.mkinfit.Rd
# S3 method for mkinfit
+confint(object, parm, level = 0.95, alpha = 1 -
+ level, method = c("profile", "quadratic"), transformed = TRUE,
+ backtransform = TRUE, distribution = c("student_t", "normal"),
+ quiet = FALSE, ...)
+
+ Arguments
+
+
+
+
+
+ object
+
+ mkinfit
object
+
+ parm
+
+
+
+ level
+
+
+
+ alpha
+
+
+
+ method
+
+
+
+ transformed
+
+
+
+ backtransform
+
+
+
+ distribution
+
+
+
+ quiet
+
+
+
+ ...
+
+ Value
+
+ References
+
+ Examples
+
+
Plot the observed data and the fitted model of an mkinfit object
Extract model parameters from mkinfit models
Confidence intervals for parameters of mkinfit objects
Summary method for class "mkinfit"
Calculated the log-likelihood of a fitted mkinfit object
Calculate the minimum error to assume in order to pass the variance test
Calculated the log-likelihood of a fitted mkinfit object
parms.Rd
This function always returns degradation model parameters as well as error +model parameters, in order to avoid working with a fitted model without +considering the error structure that was assumed for the fit.
+parms(object, ...) + +# S3 method for mkinfit +parms(object, transformed = FALSE, ...)+ +
object | +A fitted model object |
+
---|---|
... | +Not used |
+
transformed | +Should the parameters be returned +as used internally during the optimisation? |
+
A numeric vector of fitted model parameters
+ +