nn: Neural Networks using nnet

In radiant-rstats/radiant.model: Model Menu for Radiant: Business Analytics using R and Shiny

Neural Networks using nnet

Description

Neural Networks using nnet

Usage

nn(
  dataset,
  rvar,
  evar,
  type = "classification",
  lev = "",
  size = 1,
  decay = 0.5,
  wts = "None",
  seed = NA,
  check = "standardize",
  form,
  data_filter = "",
  arr = "",
  rows = NULL,
  envir = parent.frame()
)

Arguments

dataset	Dataset
rvar	The response variable in the model
evar	Explanatory variables in the model
type	Model type (i.e., "classification" or "regression")
lev	The level in the response variable defined as _success_
size	Number of units (nodes) in the hidden layer
decay	Parameter decay
wts	Weights to use in estimation
seed	Random seed to use as the starting point
check	Optional estimation parameters ("standardize" is the default)
form	Optional formula to use instead of rvar and evar
data_filter	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
arr	Expression to arrange (sort) the data on (e.g., "color, desc(price)")
rows	Rows to select from the specified dataset
envir	Environment to extract data from

Details

See https://radiant-rstats.github.io/docs/model/nn.html for an example in Radiant

Value

A list with all variables defined in nn as an object of class nn

See Also

summary.nn to summarize results

plot.nn to plot results

predict.nn for prediction

Examples

nn(titanic, "survived", c("pclass", "sex"), lev = "Yes") %>% summary()
nn(titanic, "survived", c("pclass", "sex")) %>% str()
nn(diamonds, "price", c("carat", "clarity"), type = "regression") %>% summary()

radiant-rstats/radiant.model documentation built on Nov. 29, 2023, 5:59 a.m.