mselect_adproclus_low_dim {adproclus} | R Documentation |
Model selection helper for low dimensional ADPROCLUS
Description
Performs low dimensional ADPROCLUS for the number of clusters from
min_nclusters
to max_nclusters
and the number of components
from min_ncomponents
to max_ncomponents
.
This replaces the need to manually estimate multiple models to select the best
number of clusters and components and returns the results in a format compatible with
plot_scree_adpc
to obtain a scree plot / multiple scree plots.
Output is also compatible with select_by_CHull
to
automatically select a suitable number of components for each number of clusters.
The compatibility with both functions is only given if
return_models = FALSE
.
Usage
mselect_adproclus_low_dim(
data,
min_nclusters,
max_nclusters,
min_ncomponents,
max_ncomponents,
return_models = FALSE,
unexplvar = TRUE,
start_allocation = NULL,
nrandomstart = 1,
nsemirandomstart = 1,
save_all_starts = FALSE,
seed = NULL
)
Arguments
data |
Object-by-variable data matrix of class |
min_nclusters |
Minimum number of clusters to estimate. |
max_nclusters |
Maximum number of clusters to estimate. |
min_ncomponents |
Minimum number of components to estimate.
Must be smaller or equal than |
max_ncomponents |
Maximum number of components to estimate.
Must be smaller or equal than |
return_models |
Boolean. If |
unexplvar |
Boolean. If |
start_allocation |
Optional starting cluster membership matrix to be
passed to the low dimensional ADPROCLUS procedure.
See |
nrandomstart |
Number of random starts computed for each model. |
nsemirandomstart |
Number of semi-random starts computed for each model. |
save_all_starts |
Logical. If |
seed |
Integer. Seed for the random number generator. Default: NULL, meaning no reproducibility. |
Value
Number of clusters by number of components matrix
where the values are SSE or unexplained variance scores for all estimated
models. Row names are the value of the cluster parameter for the relevant
model. Column names contain the value of the components parameter.
Depends on the choice of return_models
.
If TRUE
a list of estimated models is returned.
See Also
adproclus_low_dim
for the actual low dimensional ADPROCLUS procedure
plot_scree_adpc
for plotting the model fits
select_by_CHull
for automatic model selection via CHull method
Examples
# Loading a test dataset into the global environment
x <- stackloss
# Estimating models with cluster parameter values ranging from 1 to 4
# and component parameter values also ranging from 1 to 4
model_fits <- mselect_adproclus_low_dim(data = x, 1, 4, 1, 4, seed = 1)
# Plot the results as a scree plot to select the appropriate number of clusters
plot_scree_adpc(model_fits)