modelTrim: Trim off non-significant variables from a model

View source: R/modelTrim.R

modelTrimR Documentation

Trim off non-significant variables from a model

Description

This function performs a stepwise removal of non-significant variables from a model, following Crawley (2005, 2007). See stepwise for a newer, more complete function that can be used instead.

Usage

modelTrim(model, method = "summary", alpha = 0.05)

Arguments

model

a model object.

method

the method for getting the individual p-values. Can be either "summary" for the p-values of the coefficient estimates, or "anova" for the p-values of the variables themselves (see Details).

alpha

the p-value above which a variable is removed.

Details

Stepwise variable selection is a common procedure for simplifying models. It maximizes predictive efficiency in an objective and reproducible way, and is useful when the individual importance of the predictors is not known a priori (Hosmer & Lemeshow, 2000). The step R function performs such procedure using an information criterion (AIC) to select the variables, but it often leaves variables that are not significant in the model. Such variables can be subsequently removed with a manual stepwise procedure (e.g. Crawley 2005, p. 208; Crawley 2007, p. 442 and 601; Barbosa & Real 2010, 2012; Estrada & Arroyo 2012). The 'modelTrim' function performs such removal automatically until all remaining variables are significant. It can also be applied to a full model (i.e., without previous use of the 'step' function), as it serves as a backward stepwise selection procedure based on the significance of the coefficients (if method = "summary", the default) or on the significance of the variables (if method = "anova", better when there are categorical variables in the model). See stepwise for a more complete stepwise selection procedure.

Value

The input model object after removal of non-significant variables.

Author(s)

A. Marcia Barbosa

References

Barbosa A.M. & Real R. (2010) Favourable areas for expansion and reintroduction of Iberian lynx accounting for distribution trends and genetic diversity of the European rabbit. Wildlife Biology in Practice 6: 34-47

Barbosa A.M. & Real R. (2012) Applying fuzzy logic to comparative distribution modelling: a case study with two sympatric amphibians. The Scientific World Journal, Article ID 428206

Crawley, M.J. (2005) Statistics: An introdution using R. John Wiley & Sons, Ltd.

Crawley, M.J. (2007) The R Book. John Wiley & Sons, Ltd.

Estrada A. & Arroyo B. (2012) Occurrence vs abundance models: Differences between species with varying aggregation patterns. Biological Conservation, 152: 37-45

Hosmer D. W. & Lemeshow S. (2000) Applied Logistic Regression (2nd ed). John Wiley and Sons, New York

See Also

step

Examples

# load sample data:

data(rotif.env)

names(rotif.env)


# build a stepwise model of a species' occurrence based on 
# some of the variables:

mod <- with(rotif.env, step(glm(Abrigh ~ Area + Altitude + AltitudeRange +
HabitatDiversity + HumanPopulation, family = binomial)))


# examine the model:

summary(mod)  # contains non-significant variables


# use modelTrim to get rid of non-significan effects:

mod <- modelTrim(mod)

summary(mod)  # only significant variables now


fuzzySim documentation built on Oct. 31, 2022, 1:07 a.m.