Table output for analyzing the dependent variable from the clusters. This will either be an ANOVA for a quantitative dependent variable or a chi-squared test for qualitative dependent variable.
Usage
n_cluster_message(id)
cluster_method_input(id)
n_clusters_input(
id,
label = "Number of clusters:",
min = 2,
max = 10,
value = 4
)
cluster_variable_input(id)
n_cluster_plot_output(id)
cluster_size_bar_plot_output(id, ...)
profile_plot_output(id, ...)
cluster_pairs_plot_output(id, ...)
bivariate_cluster_plot_output(id, ...)
discriminant_projection_plot_output(id, ...)
dependent_variable_input(id)
dependent_variable_plot_output(id, ...)
dependent_variable_table_output(id)
dependent_null_hypothesis_output(id)
optimal_clusters_plot_output(id)
cluster_valdiation_plot_output(id)
cluster_validation_distribution_plot_output(id)
cluster_module(
id,
data,
default_vars = names(data())[sapply(data(), function(x) {
is.numeric(x)
})],
default_dependent_variable = NULL,
se_factor = 1
)Arguments
- id
An ID string that corresponds with the ID used to call the module's UI function.
- label
label for the slider input.
- min
The minimum value (inclusive) that can be selected.
- max
The maximum value (inclusive) that can be selected.
- value
The initial value of the slider.
- ...
other parmaeters passed to
shiny::plotOutput().- data
a function to return the data (probably a reactive function).
- default_vars
character list for the variables to include by default.
- default_dependent_variable
the name of the dependent variable, or NULL for none.
- se_factor
how many standard errors to plot.