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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ich_predict.R
+\name{ich_predict}
+\alias{ich_predict}
+\title{Predict ICH Images}
+\usage{
+ich_predict(
+  df,
+  nim,
+  model = c("rf", "logistic", "big_rf"),
+  verbose = TRUE,
+  native = TRUE,
+  native_img = NULL,
+  transformlist = NULL,
+  interpolator = NULL,
+  native_thresh = 0.5,
+  shiny = FALSE,
+  model_list = NULL,
+  smoothed_cutoffs = NULL,
+  outfile = NULL,
+  ...
+)
+}
+\arguments{
+\item{df}{\code{\link{data.frame}} of predictors.  If \code{multiplier}
+column does not exist, then \code{\link{ich_candidate_voxels}} will
+be called}
+
+\item{nim}{object of class \code{\link{nifti}}, from
+\code{\link{make_predictors}}}
+
+\item{model}{model to use for prediction,
+either the random forest (rf) or logistic}
+
+\item{verbose}{Print diagnostic output}
+
+\item{native}{Should native-space predictions be given?}
+
+\item{native_img}{object of class \code{\link{nifti}}, which
+is the dimensions of the native image}
+
+\item{transformlist}{Transforms list for the transformations back to native space.
+NOTE: these will be inverted.}
+
+\item{interpolator}{Interpolator for the transformation back to native space}
+
+\item{native_thresh}{Threshold for re-thresholding binary mask after
+interpolation}
+
+\item{shiny}{Should shiny progress be called?}
+
+\item{model_list}{list of model objects, used mainly for retraining
+but only expert use.}
+
+\item{smoothed_cutoffs}{A list with an element
+\code{mod.dice.coef}, only expert use.}
+
+\item{outfile}{filename for output file.
+We write the smoothed, thresholded image.  If \code{native = TRUE},
+then the file will be native space, otherwise in registered
+space}
+
+\item{...}{Additional options passsed to \code{\link{ich_preprocess}}}
+}
+\value{
+List of output registered and native space
+prediction/probability images
+}
+\description{
+This function will take the data.frame of predictors and
+predict the ICH voxels from the model chosen.
+}
+\seealso{
+\code{\link{ich_candidate_voxels}}
+}