Estimate the type-specific probabilities for a multivariate Poisson point process with independent component processes of each type.
phat(gpts, pts, marks, h)matrix containing the x,y-coordinates of the
point locations at which type-specific probabilities are estimated.
matrix containing the x,y-coordinates of the data points.
numeric/character vector of the types of the point in the data.
numeric value of the bandwidth used in the kernel regression.
A list with components
matrix of the type-specific probabilities for all types, with the type marks as the matrix row names.
copy of the arguments pts, dpts, marks, h.
The type-specific probabilities for data \((x_i, m_i)\), where \(x_i\) are the spatial point locations and \(m_i\) are the categorical mark sequence numbers, \(m_i=1,2,\ldots\), are estimated using the kernel smoothing methodology \(\hat p_k(x)=\sum_{i=1}^nw_{ik}(x)I(m_i=k)\), where \(w_{ik}(x)=w_k(x-x_i)/\sum_{j=1}^n w_k(x-x_j)\), \(w_k(.)\) is the kernel function with bandwidth \(h_k>0\), \(w_k(x)=w_0(x/h_k)/h_k^2\), and \(w_0(\cdot)\) is the standardised form of the kernel function.
The default kernel is the Gaussian. Different kernels can be
selected by calling setkernel.
Diggle, P. J. and Zheng, P. and Durr, P. A. (2005) Nonparametric estimation of spatial segregation in a multivariate point process: bovine tuberculosis in Cornwall, UK. J. R. Stat. Soc. C, 54, 3, 645--658.
cvloglk, mcseg.test, and
setkernel