Search results for 'mahal ericapo'

manschmi/blmr: Bayesian Linear Regression

of a data point to a multivariate normal
distribution object of class mvnd.
Usage

Mahal: Compute Mahalanobis Distance for Multiple Regression

BetterReg: Better Statistics for OLS and Binomial Logistic Regression

Distance for Multiple Regression
Description
Compute Mahalanobis Distance for Multiple Regression

mahal: Mahalanobis model

RFORGE

negative numbers.
Usage
mahal(x, p, ...)

mahal: Mahalanobis model

rspatial/dismo: Species Distribution Modeling

be large negative numbers.
Usage
mahal(x, p, ...)

mahal: Mahalanobis Distance Matrix for Optimal Matching

DOS: Design of Observational Studies

. The method is discussed in Chapter 8 of Design of Observational Studies (2010).
Usage
mahal(z, X)

mahal: Flags outliers based on Mahalanobis distance matrix for all

specleanr: Detecting Environmental Outliers in Data Analysis Pipelines

for all records.
Usage
mahal(

Mahal: Compute Mahalanobis Distance for Multiple Regression

chrisaberson/BetterReg: Better Statistics for OLS and Binomial Logistic Regression

for Multiple Regression
Description
Compute Mahalanobis Distance for Multiple Regression

mahal: Mahalanobis Distance

manschmi/blm: Bayesian Linear Regression

of a data point to a multivariate normal
distribution object of class mvnd.
Usage

mahal: Mahalanobis model

be large negative numbers.
Usage
mahal(x, p, ...)

mahal: Mahalanobis Distance Matrix for Optimal Matching

DOS2: Design of Observational Studies, Companion to the Second Edition

", second edition.
Usage
mahal(z, X)

mahal: Classify using Mahalanobis distance

emuR: Main Package of the EMU Speech Database Management System

Description
Classifies using Mahalanobis distance
Usage

PSIMEX: SIMEX Algorithm on Pedigree Structures

PSIMEX: SIMEX Algorithm on Pedigree Structures

: no
Packaged: 2017-08-28 08:19:00 UTC; ericapo
Repository: CRAN

R/Mahalanobis.R:

rspatial/dismo: Species Distribution Modeling

= function(object)	{
return(TRUE)
if (!isGeneric("mahal")) {

R/Mahalanobis.R:

RFORGE

validity = function(object)	{
return(TRUE)
if (!isGeneric("mahal")) {

R/Mahalanobis.R:

validity = function(object)	{
return(TRUE)
if (!isGeneric("mahal")) {

RaJIVE: Robust Angle Based Joint and Individual Variation Explained

RaJIVE: Robust Angle Based Joint and Individual Variation Explained

: 7.1.1
NeedsCompilation: no
Packaged: 2021-01-28 12:53:49 UTC; ericapo

R/extractFromRasterSnips.R:

environmentalinformatics-marburg/satelliteTools: What the package does (short line)

#' @param mahal Compute mahalanobis distance mean, standard deviation and
#' variance.
#'

R/BLAnalyzePost.r:

RFORGE

BLCOP: Black-Litterman and copula-opinion pooling frameworks

as described by the papaer
mahal <- mahalanobis(result@posteriorMean, result@priorMean, cov = result@priorCovar

R/Mahal.R:

chrisaberson/BetterReg: Better Statistics for OLS and Binomial Logistic Regression

of predictors
#'@param values number of Mahal values to print (highest values). Default is 10
#'

R/BLAnalyzePost.r: