`compare_solutions.Rd`

Takes an object of class 'tidyLPA', containing multiple latent profile models with different number of classes or model specifications, and helps select the optimal number of classes and model specification.

compare_solutions(x, statistics = "BIC")

x | An object of class 'tidyLPA'. |
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statistics | Character vector. Which statistics to examine for determining the optimal model. Defaults to 'BIC'. |

An object of class 'bestLPA' and 'list', containing a tibble of fits
'fits', a named vector 'best', indicating which model fit best according to
each fit index, a numeric vector 'AHP' indicating the best model according to
the `AHP`

, an object 'plot' of class 'ggplot', and a numeric
vector 'statistics' corresponding to argument of the same name.

iris_subset <- sample(nrow(iris), 20) # so examples execute quickly results <- iris %>% subset(select = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")) %>% estimate_profiles(1:3) %>% compare_solutions()#> Warning: The solution with the maximum number of classes under consideration was considered to be the best solution according to one or more fit indices. Examine your results with care and consider estimating more classes.