findviews_to_predict_core: Views of a multidimensional dataset, ranked by their...
In findviews: A View Generator for Multidimensional Data

Description Usage Arguments Details Examples

View source: R/findviews_to_predict.R

findviews_to_predict_core detects groups of mutually dependent columns, and ranks them by their predictive power. It produces the same results as findviews_to_predict, but does not present them with a Shiny app.

1 2	findviews_to_predict_core(target, data, view_size_max = NULL, clust_method = "complete")

`target`	Name of the variable to be predicted.
`data`	Data frame or matrix to be processed
`view_size_max`	Maximum number of columns in the views. If set to `NULL`, findviews uses `log2(ncol(data))`, rounded upwards and capped at 5.
`clust_method`	Character describing a clustering method, used internally by `hclust`. Example values are "complete", "single" or "average".

The function findviews_to_predict_core takes a data set and a target variable as input. It detects clusters of statistically dependent columns in the data set - e.g., views - and ranks those groups according to how well they predict the target variable. See the documentation of findviews_to_predict for more details.

The difference between findviews_to_predict and findviews_to_predict_core is that the former presents its results with a Shiny app, while the latter simply outputs them as R stuctures.