log: Log transformation of parameter estimates and their...

In ctmm: Continuous-Time Movement Modeling

Log transformation of parameter estimates and their uncertainties

Description

Methods for log transforming individual parameter estimates and their uncertainty estimates for use in meta-analytic regression, and then back-transforming mean-log parameter estimates back to mean parameter estimates.

Usage

Log(x,debias=TRUE,...)

Exp(est,VAR.est=0,VAR=0,VAR.VAR=0,variable="area",debias=TRUE,level=0.95,units=TRUE,...)

Arguments

x	A list of UD objects, UD summary objects, or speed objects.
debias	Apply \logχ^2 and \logχ bias corrections if TRUE.
...	Further arguments passed.
est	Point estimate of the mean log-parameter.
VAR.est	Uncertainty in the mean log-parameter estimate (square standard error).
VAR	Variance in the log-parameters.
VAR.VAR	Uncertainty in the log-paramter variance estimate (square standard error).
variable	Variable being back-transformed. Can be "area" or "speed".
level	Confidence level for parameter estimates.
units	Convert result to natural units.

Value

Log returns a list with two slots, log and VAR.log, corresponding to the point estimates and variance estimates of the logged variables.

Exp returns a confidence intervals for the back-transformed mean parameter estimate.

Author(s)

C. H. Fleming.

See Also

meta, mean.

Examples

# load package and data
library(ctmm)
data(buffalo)

# fit movement models
FITS <- AKDES <- list()
for(i in 1:length(buffalo))
{
  GUESS <- ctmm.guess(buffalo[[i]],interactive=FALSE)
  # use ctmm.select unless you are certain that the selected model is OUF
  FITS[[i]] <- ctmm.fit(buffalo[[i]],GUESS)
}

# calculate AKDES on a consistent grid
AKDES <- akde(buffalo,FITS)

# extract 95% areas
AREAS <- lapply(AKDES,summary)

# log transform for further meta-analysis
LOG <- Log(AREAS)

LOG

ctmm documentation built on Nov. 4, 2022, 5:06 p.m.