Solve Linear Mixed Models using REML.
Usage
LMMsolve(
fixed,
random = NULL,
spline = NULL,
group = NULL,
ginverse = NULL,
weights = NULL,
data,
residual = NULL,
family = gaussian(),
offset = 0,
tolerance = 1e-06,
trace = FALSE,
maxit = 250,
theta = NULL,
grpTheta = NULL
)Arguments
- fixed
A formula for the fixed part of the model. Should be of the form "response ~ pred"
- random
A formula for the random part of the model. Should be of the form "~ pred".
- spline
A formula for the spline part of the model. Should be of the form "~ spl1D()", ~ spl2D()" or "~spl3D()".
- group
A named list where each component is a numeric vector specifying contiguous fields in data that are to be considered as a single term.
- ginverse
A named list with each component a symmetric matrix, the precision matrix of a corresponding random term in the model. The row and column order of the precision matrices should match the order of the levels of the corresponding factor in the data.
- weights
A character string identifying the column of data to use as relative weights in the fit. Default value NULL, weights are all equal to one.
- data
A data.frame containing the modeling data.
- residual
A formula for the residual part of the model. Should be of the form "~ pred".
- family
An object of class family specifying the distribution and link function.
- offset
An optional numerical vector containing an a priori known component to be included in the linear predictor during fitting.
- tolerance
A numerical value. The convergence tolerance for the modified Henderson algorithm to estimate the variance components.
- trace
Should the progress of the algorithm be printed? Default
trace = FALSE.- maxit
A numerical value. The maximum number of iterations for the algorithm. Default
maxit = 250.- theta
initial values for penalty or precision parameters. Default
NULL, all precision parameters set equal to 1.- grpTheta
a vector to give components the same penalty. Default
NULL, all components have a separate penalty.
Value
An object of class LMMsolve representing the fitted model.
See LMMsolveObject for a full description of the components in
this object.
Details
A Linear Mixed Model (LMM) has the form $$y = X \beta + Z u + e, u ~ N(0,G), e ~ N(0,R)$$ where \(y\) is a vector of observations, \(\beta\) is a vector with the fixed effects, \(u\) is a vector with the random effects, and \(e\) a vector of random residuals. \(X\) and \(Z\) are design matrices.
LMMsolve can fit models where the matrices \(G^{-1}\) and \(R^{-1}\) are a linear combination of precision matrices \(Q_{G,i}\) and \(Q_{R,i}\): $$G^{-1} = \sum_{i} \psi_i Q_{G,i} \;, R^{-1} = \sum_{i} \phi_i Q_{R,i}$$ where the precision parameters \(\psi_i\) and \(\phi_i\) are estimated using REML. For most standard mixed models \(1/{\psi_i}\) are the variance components and \(1/{\phi_i}\) the residual variances. We use a formulation in terms of precision parameters to allow for non-standard mixed models using tensor product splines.
Examples
## Fit models on john.alpha data from agridat package.
data(john.alpha, package = "agridat")
## Fit simple model with only fixed effects.
LMM1 <- LMMsolve(fixed = yield ~ rep + gen,
data = john.alpha)
## Fit the same model with genotype as random effect.
LMM1_rand <- LMMsolve(fixed = yield ~ rep,
random = ~gen,
data = john.alpha)
## Fit the model with a 1-dimensional spline at the plot level.
LMM1_spline <- LMMsolve(fixed = yield ~ rep + gen,
spline = ~spl1D(x = plot, nseg = 20),
data = john.alpha)
## Fit models on multipop data included in the package.
data(multipop)
## The residual variances for the two populations can be different.
## Allow for heterogeneous residual variances using the residual argument.
LMM2 <- LMMsolve(fixed = pheno ~ cross,
residual = ~cross,
data = multipop)
## QTL-probabilities are defined by the columns pA, pB, pC.
## They can be included in the random part of the model by specifying the
## group argument and using grp() in the random part.
# Define groups by specifying columns in data corresponding to groups in a list.
# Name used in grp() should match names specified in list.
lGrp <- list(QTL = 3:5)
LMM2_group <- LMMsolve(fixed = pheno ~ cross,
group = lGrp,
random = ~grp(QTL),
residual = ~cross,
data = multipop)