surveillance (version 1.8-0)

twinstim_iafplot: Plot the spatial or temporal interaction function of a twimstim

Description

The function plots the fitted temporal or (isotropic) spatial interaction function of a twinstim object.

Usage

iafplot(object, which = c("siaf", "tiaf"), types = NULL,
        scaled = TRUE, truncated = FALSE, log="",
        conf.type = if (length(pars) > 1) "MC" else "parbounds",
        conf.level = 0.95, conf.B = 999, xgrid = 101,
        col.estimate = rainbow(length(types)), col.conf = col.estimate,
        alpha.B = 0.15, lwd = c(3,1), lty = c(1,2),
        verticals = FALSE, do.points = FALSE,
        add = FALSE, xlim = NULL, ylim = NULL, xlab = NULL, ylab = NULL,
        legend = !add && (length(types) > 1), ...)

Arguments

object

object of class "twinstim" containing the fitted model.

which

argument indicating which of the two interaction functions to plot. Possible values are "siaf" (default) for the spatial interaction function and "tiaf" for the temporal interaction function.

types

integer vector indicating for which event types the interaction function should be plotted in case of a marked twinstim. The default types=NULL checks if the interaction function is type-specific: if so, types=

scaled

logical indicating if the interaction function should be multiplied by the epidemic intercept $exp(\gamma_0)$. This is the default and required for the comparison of estimated interaction functions from different models.

truncated

logical indicating if the plotted interaction function should take the maximum range of interaction (eps.t/eps.s) into account, i.e., drop to zero at that point (if it is finite after all). If there is no common range

log

a character string passed to plot.default indicating which axes should be logarithmic. If add=TRUE, log is set according to par("xlog") and par(

conf.type

type of confidence interval to produce. If conf.type="MC" (or "bootstrap"), conf.B parameter vectors are sampled from the asymptotic (multivariate) normal distribution of the ML estimate of the interactio

conf.level

the confidence level required. For conf.type = "MC" it may also be specified as NA, in which case all conf.B sampled functions will be plotted with transparency value given by alpha.B.

conf.B

number of samples for the "MC" (Monte Carlo) confidence interval.

xgrid

either a numeric vector of x-values (distances from the host) where to evaluate which, or a scalar representing the desired number of evaluation points in the interval c(0,xlim[2]). If the interaction function is a step f

col.estimate

vector of colours to use for the function point estimates of the different types.

col.conf

vector of colours to use for the confidence intervals of the different types.

alpha.B

alpha transparency value (as relative opacity) used for the conf.B sampled interaction functions in case conf.level = NA

lwd, lty

numeric vectors of length two specifying the line width and type of point estimates (first element) and confidence limits (second element), respectively.

verticals,do.points

graphical settings for step function kernels. These can be logical (as in plot.stepfun) or lists of graphical parameters.

add

add to an existing plot?

xlim, ylim

vectors of length two containing the x- and y-axis limit of the plot. The default y-axis range (ylim=NULL) is from 0 to 1 (or to $exp(\gamma_0)$, if scaled). The default x-axis (xlim=NULL) starts at 0, an

xlab, ylab

labels for the axes with NULL providing sensible defaults.

legend

logical indicating if a legend for the types should be added. It can also be a list of arguments passed to legend to tweak the default settings.

...

additional arguments passed to the default plot method.

Value

• A plot is created -- see e.g. Figure 3(b) in Meyer et al. (2012). The function invisibly returns a matrix of the plotted values of the interaction function (evaluated on xgrid, by type). The first column of the matrix contains the distance $x$, and the remaining length(types) columns contain the (scaled) function values for each type. The pointwise confidence intervals of the interaction functions are returned in similar matrices as attributes: if length(types)==1, there is a single attribute "CI", whereas for multiple types, the attributes are named paste0("CI.",typeNames) (where the typeNames are retrieved from object$qmatrix).

encoding

latin1

References

Meyer, S., Elias, J. and H�{oe}hle, M. (2012): A space-time conditional intensity model for invasive meningococcal disease occurrence. Biometrics, 68, 607-616. DOI-Link: http://dx.doi.org/10.1111/j.1541-0420.2011.01684.x

See Also

plot.twinstim, which calls this function.

Examples

data("imdepifit")

iafplot(imdepifit, "tiaf", scaled=FALSE)   # tiaf.constant(), not very exciting
iafplot(imdepifit, "siaf", scaled=FALSE)

# scaled version uses a Monte-Carlo-CI
set.seed(1)  # result depends on .Random.seed
iafplot(imdepifit, "siaf", scaled=TRUE, conf.type="MC", conf.B=199,
        col.conf=gray(0.4), conf.level=NA)  # show MC samples