lev2bool: Conversion of factor to 0/1 variable

In spaMM: Mixed-Effect Models, with or without Spatial Random Effects

Conversion of factor to 0/1 variable

Description

It may be straightforward to add columns of indicator variables for each level of a factor to the data, by
<data> <- cbind(<data>, model.matrix( ~ <factor> - 1, data = <data>)). Alternatively, indicator variables can be created on the fly for given levels, using the lev2bool function.

Usage

lev2bool(fac, lev)

Arguments

fac	An object coercible to factor.
lev	The level of fac to be converted to 1.

Value

A one-column matrix.

Examples

## Elementary bivariate-response model

# Data preparation
#
fam <- rep(c(1,2),rep(6,2)) # define two biological 'families'
ID <- gl(6,2) # define 6 'individuals'
resp <- as.factor(rep(c("x","y"),6)) # distinguishes two responses per individual
set.seed(123)
toymv <- data.frame(
  fam = factor(fam), ID = ID, resp = resp, 
  y = 1 + (resp=="x") + rnorm(4)[2*(resp=="x")+fam] + rnorm(12)[6*(resp=="x")+as.integer(ID)]
)
toymv <- cbind(toymv, model.matrix( ~ resp - 1, data = toymv)) 

# fit response-specific variances of random effect and residuals: 
#
(fitme(y ~ resp+ (0+respx|fam)+ (0+respy|fam), 
        resid.model = ~ 0+resp ,data=toymv))

# Same result by different syntaxes:

#  * by the lev2bool() specifier:
 (fitme(y ~ resp+ (0+lev2bool(resp,"x")|fam)+ (0+lev2bool(resp,"y")|fam), 
        resid.model = ~ 0+resp ,data=toymv))

#  * or by random-coefficient model using 'resp' factor:    
(fitme(y ~ resp+ (0+resp|fam), resid.model = ~ 0+resp ,data=toymv, 
       fixed=list(ranCoefs=list("1"=c(NA,0,NA)))))
       
#  * or by the dummy() specifier from lme4:
# (fitme(y ~ resp+ (0+dummy(resp,"x")|fam)+ (0+dummy(resp,"y")|fam), 
#        resid.model = ~ 0+resp ,data=toymv))

spaMM documentation built on Aug. 30, 2023, 1:07 a.m.