phat {spatialkernel} | R Documentation |
Estimate the type-specific probabilities for a multivariate Poisson point process with independent component processes of each type.
phat(gpts, pts, marks, h)
gpts |
matrix containing the x,y -coordinates of the
point locations at which type-specific probabilities are estimated. |
pts |
matrix containing the x,y -coordinates of the data points. |
marks |
numeric/character vector of the types of the point in the data. |
h |
numeric value of the bandwidth used in the kernel regression. |
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,..., 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
.
A list with components
p |
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 . |
cvloglk
, mcseg.test
, and
setkernel
.