fda (version 2.4.0)

lmeWinsor: Winsorized Regression with mixed effects

Description

Clip inputs and mixed-effects predictions to (upper, lower) or to selected quantiles to limit wild predictions outside the training set.

Usage

lmeWinsor(fixed, data, random, lower=NULL, upper=NULL, trim=0,
        quantileType=7, correlation, weights, subset, method,
        na.action, control, contrasts = NULL, keep.data=TRUE,
        ...)

Arguments

fixed

a two-sided linear formula object describing the fixed-effects part of the model, with the response on the left of a '~' operator and the terms, separated by '+' operators, on the right. The left hand side of 'formula' must be a single vector

data

an optional data frame containing the variables named in 'fixed', 'random', 'correlation', 'weights', and 'subset'. By default the variables are taken from the environment from which lme is called.

random

a random- / mixed-effects specification, as described with lme.

NOTE: Unlike lme, 'random' must be provided; it can not be inferred from 'data'.

lower, upper

optional numeric vectors with names matching columns of 'data' giving limits on the ranges of predictors and predictions: If present, values below 'lower' will be increased to 'lower', and values above 'upper' will be decreased to 'upper'. I

trim

the fraction (0 to 0.5) of observations to be considered outside the range of the data in determining limits not specified in 'lower' and 'upper'.

NOTES:

(1) trim>0 with a singular fit may give an error. In such cases, fix the singu

quantileType

an integer between 1 and 9 selecting one of the nine quantile algorithms to be used with 'trim' to determine limits not provided with 'lower' and 'upper'.

correlation

an optional correlation structure, as described with lme.

weights

an optional heteroscedasticity structure, as described with lme.

subset

an optional vector specifying a subset of observations to be used in the fitting process, as described with lme.

method

a character string. If '"REML"' the model is fit by maximizing the restricted log-likelihood. If '"ML"' the log-likelihood is maximized. Defaults to '"REML"'.

na.action

a function that indicates what should happen when the data contain 'NA's. The default action ('na.fail') causes 'lme' to print an error message and terminate if there are any incomplete observations.

control

a list of control values for the estimation algorithm to replace the default values returned by the function lmeControl. Defaults to an empty list.

NOTE: Other control parameters such as 'singular.o

contrasts

an optional list. See the 'contrasts.arg' of 'model.matrix.default'.

keep.data

logical: should the 'data' argument (if supplied and a data frame) be saved as part of the model object?

...

additional arguments to be passed to the low level regression fitting functions; see lme.

Value

• an object of class c('lmeWinsor', 'lme') with 'lower', 'upper', and 'message' components in addition to the standard 'lm' components. The 'message' is a list with its first component being either 'all predictions inside limits' or 'predictions outside limits'. In the latter case, there rest of the list summarizes how many and which points have predictions outside limits.

Details

1. Identify inputs and outputs as follows:

1.1. mdly <- mdlx <- fixed; mdly[[3]] <- NULL; mdlx[[2]] <- NULL;

1.2. xNames <- c(all.vars(mdlx), all.vars(random)).

1.3. yNames <- all.vars(mdly). Give an error if as.character(mdly[[2]]) != yNames.

2. Do 'lower' and 'upper' contain limits for all numeric columns of 'data? Create limits to fill any missing.

3. clipData = data with all xNames clipped to (lower, upper).

4. fit0 <- lme(...)

5. Add components lower and upper to fit0 and convert it to class c('lmeWinsor', 'lme').

6. Clip any stored predictions at the Winsor limits for 'y'.

NOTE: This is different from lmWinsor, which uses quadratic programming with predictions outside limits, transferring extreme points one at a time to constraints that force the unWinsorized predictions for those points to be at least as extreme as the limits.

See Also

lmWinsor predict.lmeWinsor lme quantile

Examples

fm1w <- lmeWinsor(distance ~ age, data = Orthodont,
                 random=~age|Subject)
fm1w.1 <- lmeWinsor(distance ~ age, data = Orthodont,
                 random=~age|Subject, trim=0.1)