summary.scalescape: Summarizing scalescape model fits

In benjaminiuliano/scalescape: Distance-Weighted Landscape Analysis

Summarizing scalescape model fits

Description

summary method for classes scalescape and scalescape.boot

Usage

## S3 method for class 'scalescape'
summary(object, ...)

Arguments

object	an object of class scalescape or scalescape.boot
...	additional arguments affecting the summary produced

Details

Note that although summary.scalescape displays p-values for the regression coefficients (given by the underlying lm(),glm, lmer, glmer, lme, or gls functions, p-values for landscape effets are conditional on the estimate of the range parameter, and consequently they will likely have inflated type I error rates. The dist_weight_boot() function uses a bootstrap likelihood ratio test to generate a single p-value for the landscape predictor variable(s) in the model. By bootstrapping, it accounts for the co-dependence of regression coefficient and range parameter. Therefore, p-values reported for landscape predictor(s) should come from dist_weight_boot rather than dist_weight.

Value

For objects of class scalescape, summary.scalescape returns a list of summary statistics of the fitted model given in object, including:

• the estimated range parameter, in meters

• metrics of model fit (log-likelihood, AIC, BIC), and the number of parameters

• the call for the local (no landscape variables) model

• the call for the distance-weighted landscape model

• residuals

• coefficients, standard errors, t-statistics and corresponding p-values

For objects of class scalescape.boot, summary.scalescape returns a list of summary statistics of the bootstrap likelihood ratio test comparing the full and reduced models, including:

• the number of bootstrapped datasets successfully refit

• the call for the full (e.g., landscape) model

• the call for the reduced (e.g., local) model

• the observed deviance, bootstrap deviance, standard deviation, and corresponding p-value

benjaminiuliano/scalescape documentation built on April 4, 2022, 1:51 p.m.