spatstat (version 1.64-1)

coef.mppm: Coefficients of Point Process Model Fitted to Multiple Point Patterns


Given a point process model fitted to a list of point patterns, extract the coefficients of the fitted model. A method for coef.


# S3 method for mppm
coef(object, …)



The fitted point process model (an object of class "mppm")



Either a vector containing the fitted coefficients, or a data frame containing the fitted coefficients for each point pattern.


This function is a method for the generic function coef.

The argument object must be a fitted point process model (object of class "mppm") produced by the fitting algorithm mppm). This represents a point process model that has been fitted to a list of several point pattern datasets. See mppm for information.

This function extracts the vector of coefficients of the fitted model. This is the estimate of the parameter vector \(\theta\) such that the conditional intensity of the model is of the form $$ \lambda(u,x) = \exp(\theta S(u,x)) $$ where \(S(u,x)\) is a (vector-valued) statistic.

For example, if the model object is the uniform Poisson process, then coef(object) will yield a single value (named "(Intercept)") which is the logarithm of the fitted intensity of the Poisson process.

If the fitted model includes random effects (i.e. if the argument random was specified in the call to mppm), then the fitted coefficients are different for each point pattern in the original data, so coef(object) is a data frame with one row for each point pattern, and one column for each parameter. Use fixef.mppm to extract the vector of fixed effect coefficients, and ranef.mppm to extract the random effect coefficients at each level.

Use print.mppm to print a more useful description of the fitted model.


Baddeley, A., Rubak, E. and Turner, R. (2015) Spatial Point Patterns: Methodology and Applications with R. London: Chapman and Hall/CRC Press.

See Also

fixef.mppm and ranef.mppm for the fixed and random effect coefficients in a model that includes random effects.

print.mppm, mppm


    H <- hyperframe(X=waterstriders)

    fit.Poisson <- mppm(X ~ 1, H)

    # The single entry "(Intercept)" 
    # is the log of the fitted intensity of the Poisson process

    fit.Strauss <- mppm(X~1, H, Strauss(7))

    # The two entries "(Intercept)" and "Interaction"
    # are respectively log(beta) and log(gamma)
    # in the usual notation for Strauss(beta, gamma, r)

    # Tweak data to exaggerate differences
    H$X[[1]] <- rthin(H$X[[1]], 0.3)
    # Model with random effects
    fitran <- mppm(X ~ 1, H, random=~1|id)
# }