Clip inputs and mixed-effects predictions to (upper, lower) or to selected quantiles to limit wild predictions outside the training set.
lmeWinsor(fixed, data, random, lower=NULL, upper=NULL, trim=0,
quantileType=7, correlation, weights, subset, method,
na.action, control, contrasts = NULL, keep.data=TRUE,
...)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 in 'data', untransformed.
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.
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'. If absent, these limit(s) will be inferred from quantile(..., prob=c(trim, 1-trim), na.rm=TRUE, type=quantileType).
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 singularity and retry.
(2) trim = 0.5 should NOT be used except to check the algorithm, because it trims everything to the median, thereby providing zero leverage for estimating a regression.
(3) The current algorithm does does NOT adjust any of the variance parameter estimates to account for predictions outside 'lower' and 'upper'. This will have no effect for trim = 0 or trim otherwise so small that there are not predictions outside 'lower' and 'upper'. However, for more substantive trimming, this could be an issue. This is different from lmWinsor.
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'.
an optional correlation structure, as described with lme.
an optional heteroscedasticity structure, as described with lme.
an optional vector specifying a subset of observations to be used in the fitting process, as described with lme.
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"'.
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.
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.ok' as documented in glsControl may also work, but should be used with caution.
an optional list. See the 'contrasts.arg' of 'model.matrix.default'.
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.
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.
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.
# NOT RUN {
fm1w <- lmeWinsor(distance ~ age, data = Orthodont,
random=~age|Subject)
fm1w.1 <- lmeWinsor(distance ~ age, data = Orthodont,
random=~age|Subject, trim=0.1)
# }
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