plmm_format: PLMM format: a function to format the output of a model...

In plmmr: Penalized Linear Mixed Models for Correlated Data

PLMM format: a function to format the output of a model constructed with plmm_fit

Description

PLMM format: a function to format the output of a model constructed with plmm_fit

Usage

plmm_format(fit, p, std_X_details, fbm_flag, plink_flag)

Arguments

fit	A list of parameters describing the output of a model constructed with plmm_fit
p	The number of features in the original data (including constant features)
std_X_details	A list with 3 items: * 'center': the centering values for the columns of X * 'scale': the scaling values for the non-singular columns of X * 'ns': indicesof nonsingular columns in std_X
fbm_flag	Logical: is the corresponding design matrix filebacked? Passed from plmm().
plink_flag	Logical: did these data come from PLINK files? Note: This flag matters because of how non-genomic features are handled for PLINK files – in data from PLINK files, unpenalized columns are not counted in the p argument. For delimited files, p does include unpenalized columns. This difference has implications for how the untransform() function determines the appropriate dimensions for the estimated coefficient matrix it returns.

Value

A list with the components:

• beta_vals: the matrix of estimated coefficients on the original scale. Rows are predictors, columns are values of lambda

• lambda: a numeric vector of the lasso tuning parameter values used in model fitting.

• eta: a number (double) between 0 and 1 representing the estimated proportion of the variance in the outcome attributable to population/correlation structure.

• s: a vectof of the eigenvalues of relatedness matrix K; see relatedness_mat() for details.

• U: a matrix of the eigenvalues of relatedness matrix K

• rot_y: the vector of outcome values on the rotated scale. This is the scale on which the model was fit.

• linear_predictors: the matrix resulting from the product of stdrot_X and the estimated coefficients on the ~rotated~ scale.

• penalty: character string indicating the penalty with which the model was fit (e.g., 'MCP')

• gamma: numeric value indicating the tuning parameter used for the SCAD or lasso penalties was used. Not relevant for lasso models.

• alpha: numeric value indicating the elastic net tuning parameter.

• loss: vector with the numeric values of the loss at each value of lambda (calculated on the ~rotated~ scale)

• penalty_factor: vector of indicators corresponding to each predictor, where 1 = predictor was penalized.

• ns_idx: vector with the indices of predictors which were nonsingular features (i.e., had variation).

• iter: numeric vector with the number of iterations needed in model fitting for each value of lambda

• converged: vector of logical values indicating whether the model fitting converged at each value of lambda

plmmr documentation built on April 4, 2025, 12:19 a.m.