Log | R Documentation |
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.
Log(x,debias=TRUE,...) Exp(est,VAR.est=0,VAR=0,VAR.VAR=0,variable="area",debias=TRUE,level=0.95,units=TRUE,...)
x |
A list of |
debias |
Apply \logχ^2 and \logχ bias corrections if |
... |
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 |
level |
Confidence level for parameter estimates. |
units |
Convert result to natural units. |
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.
C. H. Fleming.
meta
, mean
.
# 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
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