Confidence Intervals for LNRE Model Parameters (zipfR)
Compute bootstrapped confidence intervals for LNRE model parameters. The supplied model must contain a sufficient number of bootstrapping replicates.
## S3 method for class 'lnre'
confint(object, parm, level=0.95, method=c("mad", "normal", "empirical"),
plot=FALSE, breaks="Sturges", ...)object |
an LNRE model (i.e. an object belonging to a subclass of |
parm |
model parameter(s) for which confidence intervals are desired. If unspecified, all parameters as well as population diversity S and goodness-of-fit statistic X^2 are shown. |
level |
desired confidence level (two-sided) |
method |
type of confidence interval to be estimated (see |
plot |
if |
breaks |
breakpoints for histogram shown with |
... |
all other arguments are ignored |
A data frame with one numeric column for each selected model parameter (labelled with the parameter name) and four rows:
the lower boundary of the confidence interval (labelled with the corresponding quantile, e.g. 2.5%)
the upper boundary of the confidence interval (labelled with the corresponding quantile, e.g. 97.5%)
an estimate of central tendency (labelled center)
an estimate of spread on a scale comparable to standard deviaton (labelled spread)
lnre for estimating LNRE models with bootstrap replicates,
lnre.bootstrap for the underlying parameteric bootstrapping code, and
bootstrap.confint for the different methods of estimating confidence intervals.
model <- lnre("fzm", spc=BrownAdj.spc, bootstrap=20)
confint(model, "alpha") # Zipf slope
confint(model, "S") # population diversity
confint(model, "S", method="normal") # Gaussian approx works well in this case
confint(model) # overview
confint(model, "alpha", plot=TRUE) # visualize bootstrap distributionPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.