# residuals.stackedsdm: Calculate residuals from a stackedsdm object In ecoCopula: Graphical Modelling and Ordination using Copulas

## Description

Calculate residuals from a stackedsdm object

## Usage

 ```1 2``` ```## S3 method for class 'stackedsdm' residuals(object, type = "dunnsmyth", seed = NULL, ...) ```

## Arguments

 `object` An object of class `stackedsdm`; `type` Determined what type of residuals to calculate. The current options include Dunn-Smyth residuals (default; "dunnsmyth"), raw response residuals ("response") or probability integral transform residuals ("PIT"); `seed` For Dunn-Smyth and PIT residuals applied to discrete responses, random jittering is added, and the seed can be used to seed to jittering. `...` not used

## Value

A matrix of residuals

## Details

Calculated the residuals from `stackedsdm` object.

## Author(s)

Francis K.C. Hui <francis.hui@anu.edu.au>.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```X <- as.data.frame(spider\$x) abund <- spider\$abund # Example 1: Simple example myfamily <- "negative.binomial" # Example 1: Funkier example where Species are assumed to have different distributions # Fit models including all covariates are linear terms, but exclude for bare sand fit0 <- stackedsdm(abund, formula_X = ~. -bare.sand, data = X, family = myfamily, ncores=2) residuals(fit0) # Example 2: Funkier example where Species are assumed to have different distributions abund[,1:3] <- (abund[,1:3]>0)*1 # First three columns for presence absence myfamily <- c(rep(c("binomial"), 3), rep(c("negative.binomial"), (ncol(abund)-3))) fit0 <- stackedsdm(abund, formula_X = ~ bare.sand, data = X, family = myfamily, ncores=2) residuals(fit0) ```

ecoCopula documentation built on Nov. 10, 2020, 3:50 p.m.