Package 'manta'

Title: Multivariate Asymptotic Non-Parametric Test of Association
Description: The Multivariate Asymptotic Non-parametric Test of Association (MANTA) enables non-parametric, asymptotic P-value computation for multivariate linear models. MANTA relies on the asymptotic null distribution of the PERMANOVA test statistic. P-values are computed using a highly accurate approximation of the corresponding cumulative distribution function. Garrido-Martín et al. (2022) <doi:10.1101/2022.06.06.493041>.
Authors: Diego Garrido-Martín [aut, cre], Ferran Reverter [aut], Miquel Calvo [aut], Roderic Guigó [aut]
Maintainer: Diego Garrido-Martín <[email protected]>
License: GPL-3
Version: 1.0.1
Built: 2025-03-07 04:03:30 UTC
Source: https://github.com/dgarrimar/manta

Help Index

Simulated Measurements of Five Disease Biomarkers

Description

A simulated dataset containing the levels of 5 biomarkers, measured in 100 individuals, with different scales. Missing observations appear as NA.

Usage

data(biomarkers)

Format

A matrix with 100 rows and 5 numerical variables:

biomarker1

levels of biomarker1

biomarker2

levels of biomarker2

...

Author(s)

Diego Garrido-Martín

Non-parametric, Asymptotic P-values for Multivariate Linear Models

Description

Fits a multivariate linear model and computes test statistics and asymptotic P-values for predictors in a non-parametric manner.

Usage

manta(
  formula,
  data,
  transform = "none",
  type = "II",
  contrasts = NULL,
  subset = NULL,
  fit = FALSE
)

Arguments

formula

object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which manta is called.
transform

transformation of the response variables: "none", "sqrt" or "log". Default is "none".
type

type of sum of squares: "I", "II" or "III". Default is "II".
contrasts

an optional list. See contrasts.arg in model.matrix.default. Default is "contr.sum" for ordered factors and "contr.poly" for unordered factors. Note that this is different from the default setting in options("contrasts").
subset

subset of predictors for which summary statistics will be reported. Note that this is different from the "subset" argument in lm.
fit

logical. If TRUE the multivariate fit on transformed and centered responses is returned.

Details

A Y matrix is obtained after transforming (optionally) and centering the original response variables. Then, the multivariate fit obtained by lm can be used to compute sums of squares (type-I, type-II or type-III), pseudo-F statistics and asymptotic P-values for the terms specified by the formula in a non-parametric manner. The designations "type-II" and "type-III" correspond exactly to those used in Anova. "type-I" refers to sequential sums of squares.

Value

manta returns an object of class "manta", a list containing:

call

the matched call.
aov.tab

ANOVA table with Df, Sum Sq, Mean Sq, F values, partial R-squared and P-values.
type

the type of sum of squares ("I", "II" or "III").
precision

the precision in P-value computation.
transform

the transformation applied to the response variables.
na.omit

incomplete cases removed (see na.omit).
fit

if fit = TRUE the multivariate fit done on the transformed and centered response variables is also returned.

Author(s)

Diego Garrido-Martín

See Also

lm, Anova

Simulated Metadata for 100 Patients

Description

A simulated dataset containing the age, gender and disease status of 100 individuals. Missing observations appear as NA.

Usage

data(patients)

Format

A matrix with 100 rows and 3 variables:

age

Age of the patient (numerical)

gender

Gender of the patient (factor with levels: "male" and "female")

status

Disease status of the patient (ordered factor with levels: "healthy", "mild" and "severe")

Author(s)

Diego Garrido-Martín