Provides diagnositic graphs and score tests to evaluate linear model assumptions
of normality, constant variance and linearity. Follows best practices and uses
many functions from car package.
Usage
modelAssumptions(Model, Type = "NORMAL", ID=row.names(Model$model), one.page = TRUE)
Arguments
Model
a linear model produced by lm.
Type
Type =c('NORMAL', 'CONSTANT', 'LINEAR') for normally distributed residuals
with constant variance, and linear (e.g., mean of residuals 0 for all Y')
ID
Use to identify points. Default = row.names(model$model). NULL = no identification
one.page
logical; display all graphs on one page if TRUE (Default).
References
Fox, J. (1991).
Regression diagnostics. SAGE Series (79)
Quantitative Applictions in the Social Science.
# NOT RUN {data(BAC)
m = lm(FPS~BAC+TA, data=BAC)
modelAssumptions(m,'NORMAL')
modelAssumptions(m,'CONSTANT')
modelAssumptions(m,'LINEAR', ID=NULL)
# }