Estimate an Auxiliary Model using the Pseudo-Class Method

Source: R/mixture-pseudo_class.R

Estimate an auxiliary model based on multiple datasets, randomly drawing latent class values based on the estimated probability of belonging to each class. The pseudo class variable is treated as an observed variable within each dataset, and results are pooled across datasets to account for classification uncertainty.

pseudo_class(x, model, df_complete = NULL, ...)

# S3 method for MxModel
pseudo_class(x, model, df_complete = NULL, data = NULL, m = 20, ...)

Arguments

x

An object for which a method exists, typically either a fitted mx_mixture model or class_draws object.

model

Either an expression to execute on every generated dataset, or a function that performs the analysis on every generated dataset, or a character that can be interpreted as a structural equation model using as_ram. This model can explicitly refer to data.

df_complete

Integer. Degrees of freedom of the complete-data analysis.

...

Additional arguments passed to other functions.

data

A data.frame on which the auxiliary model can be evaluated. Note that the row order must be identical to that of the data used to fit x, as these data will be augmented with a pseudo-class draw for that specific individual.

m

Integer. Number of datasets to generate. Default is 20.

Value

An object of class data.frame containing pooled estimates.

References

Pseudo-class technique: Wang C-P, Brown CH, Bandeen-Roche K (2005). Residual Diagnostics for Growth Mixture Models: Examining the Impact of a Preventive Intervention on Multiple Trajectories of Aggressive Behavior. Journal of the American Statistical Association 100(3):1054-1076. doi:10.1198/016214505000000501

Pooling results across samples: Van Buuren, S. 2018. Flexible Imputation of Missing Data. Second Edition. Boca Raton, FL: Chapman & Hall/CRC. doi:10.1201/9780429492259

Examples

set.seed(2)
dat <- iris[c(1:5, 50:55, 100:105), 1:4]
colnames(dat) <- c("SL", "SW", "PL", "PW")
fit <- suppressWarnings(mx_profiles(data = dat, classes = 3))
#> Running mix3 with 18 parameters
#> Running mix3 with 18 parameters

pct_mx <- pseudo_class(x = fit,
                       model = "SL ~ class",
                       data = dat,
                       m = 2)
#> The degrees of freedom are assumed to be equal to the total number of observations used in the model ( 17 ) minus the number of parameters estimated ( 5 ). This may not be correct. If necessary, provide a better value via the 'df_complete' argument

pct_lm <- pseudo_class(x = fit,
             model = lm( SL ~ class, data = data),
             data = dat,
             m = 2)


pcte <- pseudo_class(x = fit,
                     model = lm(SL ~ class, data = data),
                     data = dat,
                     m = 2)

pct_func <- pseudo_class(x = fit,
                         model = function(data){lm(SL ~ class, data = data)},
                         data = dat,
                         m = 2)