sitar (version 1.0.1)

BICadj: Ways to compare SITAR models for fit

Description

BICadj and AICadj calculate the BIC and AIC for SITAR models, adjusting the likelihood for Box-Cox transformed y variables. varexp calculates the variance explained by SITAR models, compared to the corresponding fixed effect models.

Usage

BICadj(..., pattern)
AICadj(..., k = 2, pattern)
varexp(..., pattern)

Arguments

...

one or more SITAR models.

k

numeric, the penalty per parameter to be used; the default k = 2 is the classical AIC.

pattern

regular expression defining names of SITAR models.

Value

• For BICadj and AICadj a named vector of deviances in increasing order. For varexp a named vector of percentages in decreasing order.

Details

The deviance is adjusted if the y variable is power-transformed, using the formula $$adjusted deviance = deviance - 2n ( (\lambda-1) * log(gm) + log(abs(\lambda)) )$$ where lambda is the power transform, and n and gm are the length and geometric mean of y.

The variance explained is given by $$% explained = 100 * (1 - (\sigma2/\sigma1)^2)$$ where sigma1 is the fixed effects RSD and sigma2 the SITAR random effects RSD.

BICadj and AICadj accept non-sitar models with a logLik class. varexp ignores objects not of class sitar.

See Also

BIC, AIC

Examples

data(heights)
## fit sitar model for height
m1 <- sitar(x=age, y=height, id=id, data=heights, df=5)

## update it for log(height)
m2 <- update(m1, y=sqrt(height))

## compare variance explained in the two models
varexp(m1, m2)

## compare BIC adjusting for sqrt transform
## the pattern matches names starting with "m" followed by a digit
BICadj(pattern="^m[0-9]")