kFquant: Quantile regression modelling of kinetic field data

In kineticF: Framework for the Analysis of Kinetic Visual Field Data

Description

Fits quantile regression models to kinetic field data and displays predicted isopter values for selected quantiles. Used to generate normative/control isopter values.

Usage

kFquant(inf = NULL, is.octopus = FALSE, range.sex = NULL, 
        range.age = NULL, range.qual = NULL, plot.iso = "III4e", 
		    show.raw = FALSE, tau = c(0.025, 0.25, 0.5, 0.75, 0.975))

Arguments

inf	character, name of the demographics matrix
is.octopus	logical, TRUE if Octopus perimeter data
range.sex	character, either NULL (use all data) or "Male" or "Female"
range.age	numeric, either NULL (use all data) or single value or a vector of length 2 specifying a closed age range
range.qual	character, either NULL (use all data) or a single value from "Good witness", "Fair witness", "Poor witness"
plot.iso	character, "III4e", "I4e", or "I2e"
show.raw	logical, superimpose raw data points on grid? Default is FALSE.
tau	numeric, vector of quantiles to be fitted. Default is 5%, 25%, 50%, 75% and 95%.

Value

Graphical output

Author(s)

Dipesh E Patel & Mario Cortina-Borja

References

Geraci, M and Bottai, M. (2014) Linear quantile mixed models. Statistics and Computing, 24(3), 461-479. doi: 10.1007/s11222-013-9381-9.

Examples

## This requires sufficient data to generate robust models

kf.sort()
kFquant(range.qual="Good witness", range.age= 8:400,
        plot.iso="III4e", show.raw=FALSE)

kineticF documentation built on May 2, 2019, 2:45 p.m.