This function fits a linear mixed effect model with generalized inference.
glme(fixed, data, random, correlation, weights, subset,
method, na.action, control, contrasts, keep.data)a linear model formula, with the response on the left of a operator and an expression involving parameters and covariates on the right.
an optional data frame containing the variables named in model, fixed, random, correlation, weights, subset, and naPattern. By default the variables are taken from the environment from which glme is called.
a two-sided linear formula of the form f1+...+fn ~ x1+...+xm, or a list of two-sided formulas of the form f1 ~ x1+...+xm, with possibly different models for different parameters. The f1,...,fn are the names of parameters included on the right hand side of model and the x1+...+xm expressions define linear models for these parameters. On the right hand side of the formula(s) indicates a single fixed effects for the corresponding parameter(s).
an optional corStruct object describing the within-group correlation structure
an optional varFunc object or one-sided formula describing the within-group heteroscedasticity structure.
an optional expression indicating the subset of the rows of data that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
a character string. If "GM" the model is fit by generalized inference. If "REML" the model is fit by maximizing the restricted log-likelihood. If "ML" the log-likelihood is maximized. Defaults to "GM".
a function that indicates what should happen when the data contain NAs.
a list of control values for the estimation algorithm to replace the default values returned.
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.
returns the coefficient estimations and model summary of the fixed part.
returns the standard deviation of random effects.
returns the coefficient estimations of the fixed and random part of the mixed model.
Yu, C.R., Kelly H.Z., Carlsson, M.O., and Weerahandi, S. (2015) Generalized Estimation of the BLUP in Mixed-Effects Models: A Comparison with ML and REML, Communications in Statistics - Simulation and Computation, 44:3, 694-704, https://doi.org/10.1080/03610918.2013.790445
Weerahandi, S. and Yu, CR. (2020) Exact distributions of statistics for making inferences on mixed models under the default covariance structure. Journal of Statistical Distributions and Applications, 7:4, https://doi.org/10.1186/s40488-020-00105-w
Gamage, J., Mathew, T., and Weerahandi, S. (2013) Generalized prediction intervals for BLUPs in mixed models, Journal of Multivariate Analysis, 120, 226 - 233, https://doi.org/10.1016/j.jmva.2013.05.011.
# NOT RUN {
library(nlme)
library(glme)
glme(distance ~ age + Sex, data = Orthodont, random = ~ age|Subject, method = "GM")
# }
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