lms.bcn {VGAM}R Documentation

LMS Quantile Regression with a Box-Cox Transformation to Normality

Description

LMS quantile regression with the Box-Cox transformation to normality.

Usage

lms.bcn(percentiles = c(25, 50, 75), zero = NULL, 
        link.mu="identity", link.sigma = "loge",
        emu=list(), esigma=list(),
        dfmu.init=4, dfsigma.init=2,
        init.lambda = 1, init.sigma = NULL)

Arguments

In the following, n is the number of (independent) observations.

percentiles A numerical vector containing values between 0 and 100, which are the quantiles. They will be returned as `fitted values'.
zero An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The values must be from the set {1,2,3}. The default value, NULL, means they all are functions of the covariates.
link.mu Parameter link function applied to the second linear/additive predictor. See Links for more choices.
link.sigma Parameter link function applied to the third linear/additive predictor. See Links for more choices.
emu, esigma List. Extra argument for each of the links. See earg in Links for general information.
dfmu.init Degrees of freedom for the cubic smoothing spline fit applied to get an initial estimate of mu. See vsmooth.spline.
dfsigma.init Degrees of freedom for the cubic smoothing spline fit applied to get an initial estimate of sigma. See vsmooth.spline. This argument may be assigned NULL to get an initial value using some other algorithm.
init.lambda Initial value for lambda. If necessary, it is recycled to be a vector of length n.
init.sigma Optional initial value for sigma. If necessary, it is recycled to be a vector of length n. The default value, NULL, means an initial value is computed in the @initialize slot of the family function.

Details

Given a value of the covariate, this function applies a Box-Cox transformation to the response to best obtain normality. The parameters chosen to do this are estimated by maximum likelihood or penalized maximum likelihood.

Value

An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.

Warning

The computations are not simple, therefore convergence may fail. In that case, try different starting values. Also, the estimate may diverge quickly near the solution, in which case try prematurely stopping the iterations by assigning maxits to be the iteration number corresponding to the highest likelihood value.

Note

The response must be positive because the Box-Cox transformation cannot handle negative values. The LMS-Yeo-Johnson-normal method can handle both positive and negative values.

In general, the lambda and sigma functions should be more smoother than the mean function. Often setting zero=1 or zero=3 or zero=c(1,3) is a good idea. See the example below.

While it is usual to regress the response against a single covariate, it is possible to add other explanatory variables, e.g., sex. See http://www.stat.auckland.ac.nz/~yee for further information and examples about this feature.

Author(s)

Thomas W. Yee

References

Cole, T. J. and Green, P. J. (1992) Smoothing Reference Centile Curves: The LMS Method and Penalized Likelihood. Statistics in Medicine, 11, 1305–1319.

Yee, T. W. (2004) Quantile regression via vector generalized additive models. Statistics in Medicine, 23, 2295–2315.

Documentation accompanying the VGAM package at http://www.stat.auckland.ac.nz/~yee contains further information and examples.

See Also

lms.bcg, lms.yjn, qtplot.lmscreg, deplot.lmscreg, cdf.lmscreg, bminz, alaplace1, alsqreg.

Examples

data(bminz)
fit = vgam(BMI ~ s(age, df=c(4,2)), fam=lms.bcn(zero=1), data=bminz, tr=TRUE)
predict(fit)[1:3,]
fitted(fit)[1:3,]
bminz[1:3,]
# Person 1 is near the lower quartile of BMI amongst people his age
cdf(fit)[1:3]

## Not run: 
# Quantile plot
par(bty="l", mar=c(5,4,4,3)+0.1, xpd=TRUE)
qtplot(fit, percentiles=c(5,50,90,99), main="Quantiles",
       xlim=c(15,90), las=1, ylab="BMI", lwd=2, lcol=4)

# Density plot
ygrid = seq(15, 43, len=100)  # BMI ranges
par(mfrow=c(1,1), lwd=2)
a = deplot(fit, x0=20, y=ygrid, xlab="BMI", col="black",
    main="Density functions at Age = 20 (black), 42 (red) and 55 (blue)")
a
a = deplot(fit, x0=42, y=ygrid, add=TRUE, llty=2, col="red")
a = deplot(fit, x0=55, y=ygrid, add=TRUE, llty=4, col="blue", Attach=TRUE)
a@post$deplot  # Contains density function values
## End(Not run)

[Package VGAM version 0.7-7 Index]