Q_theta: Likelihood function

In sahirbhatnagar/eclustProject: High Dimensional Interactions with an Environment Variable

Description

calculates likelihood function. Used to assess convergence of fitting algorithm. This corresponds to the Q(theta) function in the paper

Usage

Q_theta(x, y, beta, gamma, weights, lambda.beta, lambda.gamma,
  main.effect.names, interaction.names)

Arguments

x	Design matrix of dimension n x q, where n is the number of subjects and q is the total number of variables; each row is an observation vector. This must include all main effects and interactions as well, with column names corresponding to the names of the main effects (e.g. x1, x2, E) and their interactions (e.g. x1:E, x2:E). All columns should be scaled to have mean 0 and variance 1; this is done internally by the shim function.
y	response variable (matrix form) of dimension n x 1
beta	p x 1 matrix of main effect estimates
gamma	p*(p-1)/2 x 1 matrix of gamma estimates
weights	adaptive weights calculated by ridge_weights function with rownames corresponding to column names of x
lambda.beta	a single tuning parameter for main effects
lambda.gamma	a single tuning parameter for gammas
main.effect.names	character vector of main effects names
interaction.names	character vector of interaction names. must be separated by a colon (e.g. x1:E)

Value

value of likelihood function

Note

you dont use the intercept in the calculation of the Q function because its not being penalized

sahirbhatnagar/eclustProject documentation built on May 29, 2019, 12:58 p.m.