lmr: Rank Based Fixed Effect Regression

In rlme: Rank-Based Estimation and Prediction in Random Effects Nested Models

Description

Computes rank based regression estimates for fixed effect models.

Usage

lmr(f, data, se = FALSE, method = "L-BFGS-B")

Arguments

f	A model formula
data	Data to use for model fitting
se	Boolean indicating whether or not to calculate standard errors for intercept and slope estimates
method	Optimization method to use. Will accept any method usable by optim, e.g. one of c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN", "Brent"). "BFGS" or "L-BFGS-B" are reccomended. "L-BFGS-B" should be used for large datasets to conserve memory.

Value

fixed.effects	Fixed effect estimates
ehat	Residuals from model

Author(s)

Herb Susmann

See Also

rlme, optim

Examples

# load schools data
data(schools)

# Fit fixed effects model with lmr
lmr.fit = lmr(y ~ age + sex, data=schools)

summary(lmr.fit)

# Fit with lmr and calculate standard errors
lmr.fit = lmr(y ~ age + sex, data=schools, se=TRUE)

summary(lmr.fit)

rlme documentation built on May 2, 2019, 3:47 p.m.