npdr.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/npdr.R
\name{npdr}
\alias{npdr}
\title{npdr}
\usage{
npdr(
  outcome,
  dataset,
  regression.type = "binomial",
  attr.diff.type = "numeric-abs",
  nbd.method = "multisurf",
  nbd.metric = "manhattan",
  knn = 0,
  msurf.sd.frac = 0.5,
  covars = "none",
  covar.diff.type = "match-mismatch",
  padj.method = "bonferroni",
  verbose = FALSE,
  use.glmnet = FALSE,
  glmnet.alpha = 1,
  glmnet.lower = 0,
  glmnet.lam = "lambda.1se",
  rm.attr.from.dist = c(),
  neighbor.sampling = "none",
  separate.hitmiss.nbds = FALSE,
  corr.attr.names = NULL,
  fast.reg = FALSE,
  fast.dist = FALSE,
  external.dist = NULL,
  dopar.nn = FALSE,
  dopar.reg = FALSE,
  unique.dof = FALSE
)
}
\arguments{
\item{outcome}{character name or length-m numeric outcome vector for linear regression, factor for logistic regression}

\item{dataset}{m x p matrix of m instances and p attributes, May also include outcome vector but then outcome should be name. Include attr names as colnames.}

\item{regression.type}{(\code{"lm"} or \code{"binomial"})}

\item{attr.diff.type}{diff type for attributes (\code{"numeric-abs"} or \code{"numeric-sqr"} for numeric, \code{"allele-sharing"} or \code{"match-mismatch"} for SNP). Phenotype diff uses same numeric diff as attr.diff.type when lm regression. For glm-binomial, phenotype diff is \code{"match-mismatch"} For correlation data (e.g., rs-fMRI), use \code{"correlation-data"}; diffs between two variables (e.g., ROIs) are taken across all their pairs of correlations and the attribute importances are given for the overall variable (e.g,. brain ROI), not individual pairs.}

\item{nbd.method}{neighborhood method \code{"multisurf"} or \code{"surf"} (no k) or \code{"relieff"} (specify k). Used by nearestNeighbors().}

\item{nbd.metric}{used in npdrDistances for distance matrix between instances, default: \code{"manhattan"} (numeric). Used by nearestNeighbors(). For \code{"precomputed"}, must specify external.dist matrix.}

\item{knn}{number of constant nearest hits/misses for \code{"relieff"} (fixed-k). Used by nearestNeighbors().
The default knn=0 means use the expected SURF theoretical k with msurf.sd.frac (.5 by default)}

\item{msurf.sd.frac}{multiplier of the standard deviation from the mean distances; subtracted from mean for SURF or multiSURF.
The multiSURF default is msurf.sd.frac=0.5: mean - sd/2. Used by nearestNeighbors().}

\item{covars}{optional vector or matrix of covariate columns for correction. Or separate data matrix of covariates.}

\item{covar.diff.type}{string (or string vector) specifying diff type(s) for covariate(s) (\code{"numeric-abs"} for numeric or \code{"match-mismatch"} for categorical).}

\item{padj.method}{for p.adjust (\code{"fdr"}, \code{"bonferroni"}, ...)}

\item{verbose}{logical, whether to print out intermediate steps}

\item{use.glmnet}{logical, whether glmnet is employed}

\item{glmnet.alpha}{penalty mixture for npdrNET: default alpha=1 (lasso, L1) alpha=0 (ridge, L2)}

\item{glmnet.lower}{lower limit for coefficients for npdrNET: lower.limits=0 npdrNET default}

\item{glmnet.lam}{lambda for penalized feature selection. Options: \code{"lambda.1se"} (default),  \code{"lambda.min"} or numeric.}

\item{rm.attr.from.dist}{attributes for removal (possible confounders) from the distance matrix calculation. Argument for nearestNeighbors. None by default c()}

\item{neighbor.sampling}{"none" or \code{"unique"} if you want to use only unique neighbor pairs (used in nearestNeighbors)}

\item{separate.hitmiss.nbds}{for case/control data, find neighbors for same (hit) and opposite (miss) classes separately (TRUE) or find nearest neighborhoods before assigning hit/miss groups (FALSE). Uses nearestNeighborsSeparateHitMiss function}

\item{corr.attr.names}{character vector of p variable names that correspond to the variables used to create the p(p-1) correlation-data predictors. If not specified, integer (1...p) labels used.}

\item{fast.reg}{logical, whether regression is run with speedlm or speedglm, default as F}

\item{fast.dist}{whether or not distance is computed by faster algorithm in wordspace, default as F}

\item{external.dist}{optional input distance matrix between samples. Used in conjunction with nbd.metric = \code{"precomputed"}.}

\item{dopar.nn}{logical, whether or not neighborhood is computed in parallel, default as F}

\item{dopar.reg}{logical, whether or not regression is run in parallel, default as F}

\item{unique.dof}{use unique neighbor pairs for degrees of freedom. FALSE lets R stats determine regression degrees of freedom}
}
\value{
npdr.stats.df: npdr fdr-corrected p-value for each attribute ($pval.adj \link{1}), raw p-value ($pval.attr \link{2}), and regression coefficient (beta.attr \link{3})
}
\description{
Nearest-Neighbor Projected-Distance Regression (npdr)
generalized linear model (GLM) extension of STatistical Inference Relief (STIR)
Computes attribute statistical signficance with logistic for case/control and linear model for quantitative outcomes.
NPDR allows for categorical (SNP) or numeric (expession) predictor data types.
NPDR allows for covariate correction.
Observations in the model are projected-distance differences between neighbors.
}
\examples{
# Data interface options.
# Specify name ("qtrait") of outcome and dataset,
# which is a data frame including the outcome column.
# ReliefF fixed-k neighborhood, uses surf theoretical default (with msurf.sd.frac=.5)
# if you do not specify k or let k=0.
npdr.results.df <- npdr(
  "qtrait", qtrait.3sets$train,
  regression.type = "lm", nbd.method = "relieff", nbd.metric = "manhattan",
  attr.diff.type = "manhattan", covar.diff.type = "manhattan",
  msurf.sd.frac = 0.5, padj.method = "bonferroni")

# Specify column index (101) of outcome and dataset,
# which is a data frame including the outcome column.
# ReliefF fixed-k nbd, choose a k (knn = 10). Or choose msurf.sd.frac
npdr.results.df <- npdr(
  101, case.control.3sets$train,
  regression.type = "binomial", nbd.method = "relieff", nbd.metric = "manhattan",
  attr.diff.type = "manhattan", covar.diff.type = "manhattan",
  knn = 10, padj.method = "bonferroni")

# if outcome vector (pheno.vec) is separate from attribute matrix
# multisurf
pheno.vec <- case.control.3sets$train$class
npdr.results.df <- npdr(
 pheno.vec, predictors.mat,
 regression.type = "binomial", nbd.method = "multisurf", nbd.metric = "manhattan",
 attr.diff.type = "manhattan", covar.diff.type = "manhattan",
 msurf.sd.frac = 0.5, padj.method = "bonferroni"
 )
# attributes with npdr adjusted p-value less than .05
npdr.positives <- row.names(npdr.results.df[npdr.results.df$pva.adj < .05, ])
}