plmm_fitPLMM format: a function to format the output of a model constructed with plmm_fit
plmm_format(fit, p, std_X_details, fbm_flag, plink_flag)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
A list of parameters describing the output of a model constructed with plmm_fit
The number of features in the original data (including constant features)
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
Logical: is the corresponding design matrix filebacked? Passed from plmm().
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.