R: Estimating Parameters of 'slca' Object

estimate {slca}

R Documentation

Estimating Parameters of `slca` Object

Description

Estimate the parameters of model constructed using the slca function.

Usage

estimate(x, ...)

## S3 method for class 'slca'
estimate(x, data,
    method = c("em", "hybrid", "nlm"),
    fix2zero = NULL,
    control = slcaControl(), ...)

Arguments

`x`	an `slca` object defining SLCM model to be estimated.
`...`	additional arguments.
`data`	a `data.frame` object containing observed categorical variables incorporated in the model.
`method`	estimation method for SLCM parameters. The default is `"em"`, which employs expecation-maximization (EM) algorithm for estimation; the alternative `"nlm"`, utilizes `nlm` function for Newton-Raphson algorithm. The `"hybrid"` method begins with the EM algorithm and concludes with the `nlm` function for refined estimation.
`fix2zero`	a `vector` of parameters to be restricted to zero. The details of restriction is given under 'Details'
`control`	a `list` of control for the estimation procedure. Used to modify default values in slcaControl.

Details

To constrain certain parameters to zero, use the fix2zero argument. Each parameter is associated with a unique index. You can identify the index of a specific parameter by invoking the param function with the index = TRUE argument. To apply these constraints, include the relevant parameter indices in the fix2zero argument.

Value

An object of class slca and estimated with an following elements:

`model`	a `list` describing of the model.
`method`	the method used for estimation
`arg`	the brief model description used during the estimation.
`mf`	the data.frame used for estimation.
`par`	the log of the estimated paramters.
`logit`	the log-odds of the estimated parameters.
`score`	the score function for the estimated parameters.
`posterior`	the `list` of posterior probablities for each latent class variable.
`convergence`	a logical indicator of whether convergence was achieved.
`loglikelihood`	the loglikelihood of the estimated model.
`control`	the control values used during the estimation process.

This returned object can be further processed using the param functions to extract the estimated parameters or their respective standard errors. Additionally, the regress function enables logistic regression analysis using three-step approach to evaluate the effect of external variables on latent class variables.