LongituRF (version 0.9)

REEMtree: (S)REEMtree algorithm

Description

(S)REEMtree is an adaptation of the random forest regression method to longitudinal data introduced by Sela and Simonoff. (2012) <doi:10.1007/s10994-011-5258-3>. The algorithm will estimate the parameters of the following semi-parametric stochastic mixed-effects model: $$Y_i(t)=f(X_i(t))+Z_i(t)\beta_i + \omega_i(t)+\epsilon_i$$ with \(Y_i(t)\) the output at time \(t\) for the \(i\)th individual; \(X_i(t)\) the input predictors (fixed effects) at time \(t\) for the \(i\)th individual; \(Z_i(t)\) are the random effects at time \(t\) for the \(i\)th individual; \(\omega_i(t)\) is the stochastic process at time \(t\) for the \(i\)th individual which model the serial correlations of the output measurements; \(\epsilon_i\) is the residual error.

Usage

REEMtree(X, Y, id, Z, iter = 10, time, sto, delta = 0.001)

Arguments

X

[matrix]: A Nxp matrix containing the p predictors of the fixed effects, column codes for a predictor.

Y

[vector]: A vector containing the output trajectories.

id

[vector]: Is the vector of the identifiers for the different trajectories.

Z

[matrix]: A Nxq matrix containing the q predictor of the random effects.

iter

[numeric]: Maximal number of iterations of the algorithm. The default is set to iter=100

time

[vector]: Is the vector of the measurement times associated with the trajectories in Y,Z and X.

sto

[character]: Defines the covariance function of the stochastic process, can be either "none" for no stochastic process, "BM" for Brownian motion, OrnUhl for standard Ornstein-Uhlenbeck process, BBridge for Brownian Bridge, fbm for Fractional Brownian motion; can also be a function defined by the user.

delta

[numeric]: The algorithm stops when the difference in log likelihood between two iterations is smaller than delta. The default value is set to O.O01

Value

A fitted (S)MERF model which is a list of the following elements:

• forest: Tree obtained at the last iteration.

• random_effects : Predictions of random effects for different trajectories.

• id_btilde: Identifiers of individuals associated with the predictions random_effects.

• var_random_effects: Estimation of the variance covariance matrix of random effects.

• sigma_sto: Estimation of the volatility parameter of the stochastic process.

• sigma: Estimation of the residual variance parameter.

• time: The vector of the measurement times associated with the trajectories in Y,Z and X.

• sto: Stochastic process used in the model.

• Vraisemblance: Log-likelihood of the different iterations.

• id: Vector of the identifiers for the different trajectories.

Examples

# NOT RUN {
set.seed(123)
data <- DataLongGenerator(n=20) # Generate the data composed by n=20 individuals.
# Train a SREEMtree model on the generated data.
# The data are generated with a Brownian motion,
# so we use the parameter sto="BM" to specify a Brownian motion as stochastic process
X.fixed.effects <- as.data.frame(data$X)
sreemt <- REEMtree(X=X.fixed.effects,Y=data$Y,Z=data$Z,id=data$id,time=data$time,
sto="BM", delta=0.0001)
sreemt$forest # is the fitted random forest (obtained at the last iteration).
sreemt$random_effects # are the predicted random effects for each individual.
sreemt$omega # are the predicted stochastic processes.
plot(sreemt$Vraisemblance) #evolution of the log-likelihood.

# }