stat.glm: Permuted Generalized Linear Model
In SebastianSosa/ant: Animal Network Toolkit Software

stat.glm

R Documentation

Permuted Generalized Linear Model

Description

Performs Generalized Linear Models tests and computes permuted p-values

Usage

stat.glm(
  ant,
  oda,
  formula,
  family = "gaussian",
  progress = TRUE,
  start = NULL,
  control = list(...),
  model = TRUE,
  method = "glm.fit",
  x = FALSE,
  y = TRUE,
  contrasts = NULL,
  ...
)

Arguments

`ant`	an output of ANT function `perm.net.nl` without any random factor declared, or output of ANT 'met' category functions in which output of ANT functions `perm.ds.focal`, `perm.ds.grp` or `perm.net.lk` where single matrices were used.
`oda`	the original data frame of associations when argument ant is obtained with `perm.ds.focal` or `perm.ds.grp` ANT functions.
`formula`	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under ‘Details’.
`family`	a description of the error distribution and link function to be used in the model. For glm this can be a character string naming a family function, a family function or the result of a call to a family function. For glm.fit only the third option is supported, see `glm` and `family`.
`progress`	a boolean indicating the visualization of the permutation process.
`start`	starting values for the parameters in the linear predictor.
`control`	a list of parameters for controlling the fitting process.
`model`	a logical value indicating whether model frame should be included as a component of the returned value.
`method`	the method to be used in fitting the model. The default method "glm.fit" uses iteratively reweighted least squares (IWLS): the alternative "model.frame" returns the model frame and does no fitting.
`x, y`	For glm: logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.
`contrasts`	an optional list. See the contrasts.arg of model.matrix.default.
`...`	Extra arguments for `glm` function only.

Details

This function is the first step in the process to create a t-test in permuted data. For more details on t-tests, see R documentation.

Value

Returns a list of 3 elements :

An object of class inheriting from "glm" which inherits from the class "lm".
A data frame if the estimates of the permuted models.
A vector of integers indicating the permutations that returned model errors or warnings (e.g. model convergence issues) and for which new permutations were done.

Author(s)

Sebastian Sosa, Ivan Puga-Gonzalez.

References

Dobson, A. J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.

Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. New York: Springer.

Examples

t=met.degree(sim.m, sym = TRUE,sim.df,1) # Computing network metric
t=perm.net.nl(t,labels='age',rf=NULL,nperm=10,progress=FALSE) # Node label permutations
r.glm=stat.glm(ant = t,formula = degree ~ age,progress=FALSE) # Permuted GLM

SebastianSosa/ant documentation built on Sept. 23, 2023, 7:06 a.m.