ranblock {statmod} | R Documentation |
Fits a mixed linear model by REML. The linear model must contain only one random factor apart from the unit errors.
randomizedBlock(formula, random, weights=NULL, fixed.estimates=TRUE, data=list(), subset=NULL, contrasts=NULL) randomizedBlockFit(y,X,Z,w=NULL,fixed.estimates=TRUE)
The arguments formula
, weights
, data
, subset
and contrasts
have the same meaning as in lm
.
The arguments y
, X
and w
have the same meaning as in lm.wfit
.
formula |
formula specifying the fixed model. |
random |
vector or factor specifying the blocks corresponding to random effects. |
weights |
optional vector of prior weights. |
fixed.estimates |
should the fixed effect coefficients be returned? |
data |
an optional data frame containing the variables in the model. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
contrasts |
an optional list. See the contrasts.arg of model.matrix.default . |
y |
response vector. |
X |
design matrix for fixed model. |
Z |
design matrix for random effects. |
w |
optional vector of prior weights. |
This function fits the model y=Xb+Zu+e where b is a vector of fixed coefficients and u is a vector of random effects. Write n for the length of y and q for the length of u. The random effect vector u is assumed to be normal, mean zero, with covariance matrix σ^2_uI_q while e is normal, mean zero, with covariance matrix σ^2I_n. If Z is an indicator matrix, then this model corresponds to a randomized block experiment. The model is fitted using an eigenvalue decomposition which transforms the problem into a Gamma generalized linear model.
This function is essentially equivalent to lme(fixed=formula,random=~1|random)
but is more accurate and is much faster for small to moderate size data sets.
Missing values in the data are not allowed.
A list with the components.
If fixed.estimates=TRUE
then the components from "lm.fit"
are also returned.
sigmasquared |
vector of length two containing the residual and block components of variance. |
se.sigmasquared |
standard errors for the components of variance. |
Gordon Smyth
# Compare with first data example from Venable and Ripley (2002), Chapter 10, "Linear Models" library(MASS) data(petrol) out <- randomizedBlock(Y~SG+VP+V10+EP, random=No, data=petrol) cbind(sigmasquared=out$sigmasquared,se=out$se.sigmasquared)