This function performs iterative conditional modes to obtain
maximum a posteriori estimates for $\beta$ (covariate coefficients),
$w$ (latent abundances), and $P$ (the precision matrix).
Usage
estimate(mfit)
Arguments
mfit
- a MInt model object.
Value
A MInt model object with the following attributes:
optim
List containing optimization details
optim$lambda
Value of the L1 penalty used during optimization
data
List containing the raw data
data$design
File path of the design matrix
data$response
File path of the response matrix
data$fmla
Formula used to model each response in terms of the design variables
data$y
Raw numerical data for the response matrix
data$xd
Design matrix in categorical form
data$x
Design matrix in numerical form
param
List containing parameter estimates
param$beta
p-covariates x o-responses matrix of regression coefficients
param$w
n-samples x o-responses matrix of latent abundances
x <- system.file("extdata", "x.txt", package="MInt");
y <- system.file("extdata", "y.txt", package="MInt");
m <- mint(y,x,fmla = ~feature1 + feature2)
m <- estimate(m)