coef.RobSBoosting {GEInter}

Extract coefficients from a "RobSBoosting" object

Description

This function extracts coefficients from a RobSBoosting model, using the stored "RobSBoosting" object.

Usage

## S3 method for class 'RobSBoosting'
coef(object, ...)

Arguments

object

Fitted "RobSBoosting" model object.

...

Not used. Other arguments to get coefficients.

Value

intercept

The intercept estimate.

unique_variable

A matrix with two columns that represents the variables that are selected for the model after removing the duplicates, since the loop_time iterations of the method may produce variables that are repeatedly selected into the model. Here, the first and second columns correspond to the indexes of environmental (E) factors and genetic (G) factors. For example, (1, 0) represents that this variable is the first E factor, and (1,2) represents that the variable is the interaction between the first E factor and second G factor.

unique_coef

Coefficients corresponding to unique_variable. Here, the coefficients are simple regression coefficients for the linear effect (discrete E factor, G factor, and their interaction), and B spline coefficients for the nonlinear effect (continuous E factor, and corresponding G-E interaction).

unique_knots

A list of knots corresponding to unique_variable. Here, when the type of unique_variable is discrete E factor, G factor, or their interaction, knot will be NULL, and knots will be B spline otherwise.

unique_Boundary.knots

A list of boundary knots corresponding to unique_variable.

unique_vtype

A vector representing the variable type of unique_variable. Here, "EC" stands for continuous E effect, "ED" for discrete E effect, "G" for G effect, "EC-G" for the interaction between "EC" and "G", and "ED-G" for the interaction between "ED" and "G".

estimation_results

A list of estimation results for each variable. Here, the first q elemnets are for the E effects, the (q+1) element is for the first G effect and the (q+2) to (2q+1) elements are for the interactions corresponding to the first G factor, and so on.

References

Mengyun Wu and Shuangge Ma. Robust semiparametric gene-environment interaction analysis using sparse boosting. Statistics in Medicine, 38(23):4625-4641, 2019.

See Also

RobSBoosting, and predict, and plot methods.