Description Usage Arguments Details Value Author(s) References Examples
logrr
computes the log ratio of spatial density
functions for cases and controls. The numerator in this
ratio is related to the "cases" and the denominator to
the "controls". If nsim > 0
, then pointwise
tolerance intervals are used to assess potential
clustering of cases and controls relative to each other.
1 2 3 4 5  logrr(x, sigma = NULL, sigmacon = NULL, case = 2, nsim = 0,
level = 0.9, alternative = "two.sided", ..., bwargs = list(),
weights = NULL, edge = TRUE, varcov = NULL, at = "pixels",
leaveoneout = TRUE, adjust = 1, diggle = FALSE, kernel = "gaussian",
scalekernel = is.character(kernel), positive = FALSE, verbose = TRUE)

x 
Point pattern (object of class 
sigma 
Standard deviation of isotropic smoothing
kernel for cases. Either a numerical value, or a function that
computes an appropriate value of 
sigmacon 
Standard deviation of isotropic smoothing
kernel for controls. Default is the same as

case 
The position of the name of the "case" group
in 
nsim 
The number of simulated data sets from which to construct the tolerance intervals under the random labeling hypothesis. Default is 0 (i.e., no intervals). 
level 
The tolerance level used for the pointwise tolerance intervals. 
alternative 
The direction of the significance test
to identify potential clusters using a Monte Carlo test
based on the pointwise tolerance intervals. Default is

... 
Additional arguments passed to 
bwargs 
A list of arguments for the bandwidth
function supplied to 
weights 
Optional weights to be attached to the points.
A numeric vector, numeric matrix, an 
edge 
Logical value indicating whether to apply edge correction. 
varcov 
Variancecovariance matrix of anisotropic smoothing kernel.
Incompatible with 
at 
String specifying whether to compute the intensity values
at a grid of pixel locations ( 
leaveoneout 
Logical value indicating whether to compute a leaveoneout
estimator. Applicable only when 
adjust 
Optional. Adjustment factor for the smoothing parameter. 
diggle 
Logical. If 
kernel 
The smoothing kernel.
A character string specifying the smoothing kernel
(current options are 
scalekernel 
Logical value.
If 
positive 
Logical value indicating whether to force all density values to
be positive numbers. Default is 
verbose 
Logical value indicating whether to issue warnings about numerical problems and conditions. 
The plot
function makes it easy to visualize the
log ratio of spatial densities (if nsim = 0
) or
the regions where the log ratio deviates farther from
than what is expected under the random labeling
hypothesis (i.e., the locations of potential clustering).
The shaded regions indicate the locations of potential
clustering.
The two.sided
alternative test assesses
whether the observed ratio of log densities deviates
more than what is expected under the random labeling
hypothesis. When the test is significant, this
suggests that the cases and controls are clustered,
relative to the other. The greater
alternative
assesses whehter the cases are more clustered than
the controls. The less
alternative
assesses whether the controls are more clustered than
the cases. If the estimated density of the case or
control group becomes too small, this function may
produce warnings due to numerical underflow. Increasing
the bandwidth (sigma) may help.
The function produces an object of type
logrrenv
. Its components are similar to those
returned by the density.ppp
function from the
spatstat
package, with the intensity values
replaced by the log ratio of spatial densities of f and
g. Includes an array simr
of dimension c(nx,
ny, nsim + 1), where nx and ny are the number of x and
y grid points used to estimate the spatial density.
simr[,,1]
is the log ratio of spatial densities
for the observed data, and the remaining nsim
elements in the third dimension of the array are the
log ratios of spatial densities from a new ppp
simulated under the random labeling hypothesis.
Joshua French (and a small chunk by the authors
of the density.ppp
) function
for consistency with the default behavior of that
function)
Waller, L.A. and Gotway, C.A. (2005). Applied Spatial Statistics for Public Health Data. Hoboken, NJ: Wiley.
Kelsall, Julia E., and Peter J. Diggle. "Kernel estimation of relative risk." Bernoulli (1995): 316.
Kelsall, Julia E., and Peter J. Diggle. "Nonparametric estimation of spatial variation in relative risk." Statistics in Medicine 14.2122 (1995): 23352342.
1 2 3 4 5 6 7 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.