R: Fit multiple Artificial Neural Network models on "tabular"...

ann_tab_cv_best {glmnetr}

R Documentation

Fit multiple Artificial Neural Network models on "tabular" provided as a matrix, and keep the best one.

Description

Fit an multiple Artificial Neural Network models for analysis of "tabular" data using ann_tab_cv() and select the best fitting model according to cross validaiton.

Usage

ann_tab_cv_best(
  myxs,
  mystart = NULL,
  myy,
  myevent = NULL,
  myoffset = NULL,
  family = "binomial",
  fold_n = 5,
  epochs = 200,
  eppr = 40,
  lenz1 = 32,
  lenz2 = 8,
  actv = 1,
  drpot = 0,
  mylr = 0.005,
  wd = 0,
  l1 = 0,
  lasso = 0,
  lscale = 5,
  scale = 1,
  resetlw = 1,
  minloss = 1,
  gotoend = 0,
  bestof = 10,
  seed = NULL,
  foldid = NULL
)

Arguments

`myxs`	predictor input - an n by p matrix, where n (rows) is sample size, and p (columns) the number of predictors. Must be in matrix form for complete data, no NA's, no Inf's, etc., and not a data frame.
`mystart`	an optional vector of start times in case of a Cox model. Class numeric of length same as number of patients (n)
`myy`	dependent variable as a vector: time, or stop time for Cox model, Y_ 0 or 1 for binomial (logistic), numeric for gaussian. Must be a vector of length same as number of sample size.
`myevent`	event indicator, 1 for event, 0 for census, Cox model only. Must be a numeric vector of length same as sample size.
`myoffset`	an offset term to be ues when fitting the ANN. Not yet implemented.
`family`	model family, "cox", "binomial" or "gaussian" (default)
`fold_n`	number of folds for each level of cross validation
`epochs`	number of epochs to run when tuning on number of epochs for fitting final model number of epochs informed by cross validation
`eppr`	for EPoch PRint. print summry info every eppr epochs. 0 will print first and last epochs, -1 nothing.
`lenz1`	length of the first hidden layer in the neural network, default 16
`lenz2`	length of the second hidden layer in the neural network, default 16
`actv`	for ACTiVation function. Activation function between layers, 1 for relu, 2 for gelu, 3 for sigmoid.
`drpot`	fraction of weights to randomly zero out. NOT YET implemented.
`mylr`	learning rate for the optimization step in teh neural network model fit
`wd`	weight decay for the model fit.
`l1`	a possible L1 penalty weight for the model fit, default 0 for not considered
`lasso`	1 to indicate the first column of the input matrix is an offset term, often derived from a lasso model
`lscale`	Scale used to allow ReLU to extend +/- lscale before capping the inputted linear estimated
`scale`	Scale used to transform the initial random parameter assingments by dividing by scale
`resetlw`	1 as default to re-adjust weights to account for the offset every epoch. This is only used in case lasso is set to 1
`minloss`	default of 1 for minimizing loss, else maximizing agreement (concordance for Cox and Binomial, R-square for Gaussian), as function of epochs by cross validation
`gotoend`	fit to the end of epochs. Good for plotting and exploration
`bestof`	how many models to run, from which the best fitting model will be selected.
`seed`	an optional a numerical/integer vector of length 2, for R and torch random generators, default NULL to generate these. Integers should be positive and not more than 2147483647.
`foldid`	a vector of integers to associate each record to a fold. Should be integers from 1 and fold_n.

Value

an artificial neural network model fit

Author(s)