spaMM (version 4.4.16)

lev2bool: 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)

Value

A one-column matrix.

Arguments

fac

An object coercible to factor.

lev

The level of fac to be converted to 1.

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))