set.par.ps defines the model coefficients of a geostatistical linear model with preferentially sampled locations.
The output of this function can be used to: 1) define the parameters of the importance sampling distribution in lm.ps.MCML; 2) the starting values of the optimization algorithm in lm.ps.MCML.
set.par.ps(p = 1, q = 1, intensity, response, preferentiality.par)number of covariates used in the response variable model, including the intercept. Default is p=1.
number of covariates used in the log-Guassian Cox process model, including the intercept. Default is q=1.
a vector of parameters of the log-Gaussian Cox process model. These must be provided in the following order: regression coefficients of the explanatory variables; variance and scale of the spatial correlation for the isotropic Gaussian process. In the case of a model with a mix of preferentially and non-preferentially sampled locations, the order of the regression coefficients should be the following: regression coefficients for the linear predictor with preferential sampling; regression coefficients for the linear predictor with non-preferential samples.
a vector of parameters of the response variable model. These must be provided in the following order: regression coefficients of the explanatory variables; variance and scale of the spatial correlation for the isotropic Gaussian process; and variance of the nugget effect.
value of the preferentiality paramter.
a list of coefficients of class coef.PrevMap.ps.