fads: Factor Analysis for data on a sphere (high or low...

In fad: Factor Analysis for Data

Factor Analysis for data on a sphere (high or low dimensional).

Description

Perform fast matrix-free maximum-likelihood factor analysis on data on sphere, works if number of variables is more than number of observations.

Usage

fads(
  inputs,
  q,
  ii = 123,
  M = NULL,
  L = NULL,
  D = NULL,
  gamma = NA,
  maxiter = 10000,
  epsi = 1e-04
)

Arguments

inputs	A numeric matrix or an object that can be coerced to a numeric matrix.
q	The number of factors to be fitted.
ii	The random seeds for initialization. Default 123 if no initial values of parameters are imported.
M	The initial values of mean.
L	The initial values of loading matrix.
D	The initial values of uniquenesses.
gamma	The common constant used in the eBIC formula. Default 'NA'.
maxiter	The maximum iterations. Default 10,000
epsi	The absolute difference between final data log-likelihood values on consecutive step. Default 0.0001.

Value

An object of class "fads" with components

mu	The estimate mean.
loadings	A matrix of loadings on the correlation scale, one column for each factor. The factors are ordered in decreasing order of sums of squares of loadings, and given the sign that will make the sum of the loadings positive.This is of class "loadings"
uniquenesses	The uniquenesses computed on the correlation scale.
sd	The estimated standard deviations.
iter	The number of iterations
gerr	the difference between the gradients on consecutive step.
loglik,eBIC	The maximum log-likelihood the extended Bayesian Information Criteria (Chen and Chen,2008).

fad documentation built on May 1, 2022, 5:08 p.m.