poLCA_tidiers {broom} | R Documentation

## Tidiers for poLCA objects

### Description

Tidiers for poLCA latent class regression models. Summarize the probabilities of each outcome for each variable within each class with `tidy`, add predictions to the data with `augment`, or find the log-likelihood/AIC/BIC with `glance`.

### Usage

    ## S3 method for class 'poLCA'
    tidy(x, ...)

    ## S3 method for class 'poLCA'
    augment(x, data, ...)

    ## S3 method for class 'poLCA'
    glance(x, ...)

### Arguments

`x` | A poLCA object
`...` | Extra arguments, not used
`data` | For `augment`, the original dataset used to fit the latent class model. If not given, uses manifest variables in `x$y` and, if applicable, covariates in `x$x`

### Value

All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.

`tidy` returns a data frame with one row per variable-class-outcome combination, with columns:

variable

  Manifest variable

class

  Latent class ID, an integer

outcome

  Outcome of manifest variable

estimate

  Estimated class-conditional response probability

std.error

  Standard error of estimated probability

`augment` returns a data frame with one row for each original observation, augmented with the following columns:

.class

  Predicted class, using modal assignment

.probability

  Posterior probability of predicted class

If the `data` argument is given, those columns are included in the output (only rows for which predictions could be made). Otherwise, the `y` element of the poLCA object, which contains the manifest variables used to fit the model, are used, along with any covariates, if present, in `x`.

Note that while the probability of all the classes (not just the predicted modal class) can be found in the `posterior` element, these are not included in the augmented output, since it would result in potentially many additional columns, which augment tends to avoid.

`glance` returns a one-row data frame with the following columns:

logLik

  the data's log-likelihood under the model

AIC

  the Akaike Information Criterion

BIC

  the Bayesian Information Criterion

g.squared

  The likelihood ratio/deviance statistic

chi.squared

  The Pearson Chi-Square goodness of fit statistic for multiway tables

df

  Number of parameters estimated, and therefore degrees of freedom used

df.residual

  Number of residual degrees of freedom left

### Examples

    if (require("poLCA", quietly = TRUE)) {
      library(poLCA)
      library(dplyr)

      data(values)
      f <- cbind(A, B, C, D)~1
      M1 <- poLCA(f, values, nclass = 2, verbose = FALSE)

      M1
      tidy(M1)
      head(augment(M1))
      glance(M1)

      library(ggplot2)

      ggplot(tidy(M1), aes(factor(class), estimate, fill = factor(outcome))) +
        geom_bar(stat = "identity", width = 1) +
        facet_wrap(~ variable)

      set.seed(2016)
      # compare multiple
      mods <- data_frame(nclass = 1:3) %>%
        group_by(nclass) %>%
        do(mod = poLCA(f, values, nclass = .$nclass, verbose = FALSE))

      # compare log-likelihood and/or AIC, BIC
      mods %>%
        glance(mod)

      ## Three-class model with a single covariate.

      data(election)
      f2a <- cbind(MORALG,CARESG,KNOWG,LEADG,DISHONG,INTELG,
                   MORALB,CARESB,KNOWB,LEADB,DISHONB,INTELB)~PARTY
      nes2a <- poLCA(f2a, election, nclass = 3, nrep = 5, verbose = FALSE)

      td <- tidy(nes2a)
      head(td)

      # show

      ggplot(td, aes(outcome, estimate, color = factor(class), group = class)) +
        geom_line() +
        facet_wrap(~ variable, nrow = 2) +
        theme(axis.text.x = element_text(angle = 90, hjust = 1))

      au <- augment(nes2a)
      head(au)
      au %>%
        count(.class)

      # if the original data is provided, it leads to NAs in new columns
      # for rows that weren't predicted
      au2 <- augment(nes2a, data = election)
      head(au2)
      dim(au2)
    }

[Package broom version 0.4.2 Index]