Compute the significance of the filtering test

Usage

default_screener(
  x,
  y,
  weights = NULL,
  alternative = "two.sided",
  method = "pearson",
  conf.level = 0.95,
  resp.type = c("gaussian", "mgaussian", "binomial", "multinomial", "poisson", "cox"),
  observations = NULL,
  coef = NULL,
  adjust.method = "none",
  logger,
  multi = c("max", "raw", "average", "sum"),
  ...
)

Arguments

x

the input matrix, where rows are observations and columns are variables.

y

the response variable. Its number of rows must match the number of rows of x.

weights

priors of the observations

alternative

alternative hypotesis to use. Must be one of "two.sided" (default), "greater" or "less".

method

which correlation method to use. Currently, only "pearson" is supported.

conf.level

confidence levels used for the confidence intervals (where computed). A single number or a numeric vector with value for each observation. All values must be in the range of [0;1].

resp.type

the response type

observations

(optional) indices of observations to keep

coef

(optional) an integer indicating the response variable to consider in multi-response data when multi = "raw"

adjust.method

method used to adjust the p-values for multiple testing. Options, in increasing conservatism, include "none", "BH", "BY" and "holm"

logger

a Logger

multi

what to do when response has multiple output values

max

the max value of scores across multiple outputs is selected to get a single value for each observation

average

scores of multiple outputs are averaged to get a single value for each observation

sum

scores of multiple outputs are summed up to get a single value for each observation

raw

returns the scores for the multiple outputs

...

further arguments

Value

a Screened object

Author

Alessandro Barberis