NullModel-class: Class 'NullModel'

Description Objects Slots Details Methods Accessors Subsetting Author(s) References See Also Examples

Description

S4 class for storing null models for later usage with the assocTest method

Objects

Objects of this class are created by calling nullModel.

Slots

The following slots are defined for NullModel objects:

type:

type of model

residuals:

residuals of linear model; for type “bernoulli”, this is simply the trait vector (see nullModel-methods for details)

model.matrix:

model matrix of the (generalized) linear model trained for the covariates (if any)

inv.matrix:

pre-computed inverse of some matrix needed for computing the null distribution; only used for types “logistic” and “linear”

P0sqrt:

pre-computed square root of matrix P_0 (see Subsections 9.1 and 9.5 of the package vignette); needed for computing the null distribution in case the small sample correction is used for a logistic model; computed only if nullModel is called with adjExact=TRUE.

coefficients:

coefficients of (generalized) linear model trained for the covariates (if any)

na.omit:

indices of samples omitted from (generalized) linear model because of missing values in target or covariates

n.cases:

for binary traits (types “logistic” and “bernoulli”), the number of cases, i.e. the number of 1's in the trait vector

variance:

for continuous traits (type “linear”), this is a single numeric value with the variance of residuals of the linear model; for logistic models with binary traits (type “logistic”), this is a vector with variances of the per-sample Bernoulli distributions; for later use of the exact mixture-of-Bernoulli test (type “bernoulli”), this is the variance of the Bernoulli distribution

prob:

for logistic models with binary traits (type “logistic”), this is a vector with probabilities of the per-sample Bernoulli distributions; for later use of the exact mixture-of-Bernoulli test (type “bernoulli”), this is the probability of the Bernoulli distribution

type.resampling:

which resampling algorithm was used

res.resampling:

matrix with residuals sampled under the null hypothesis (if any)

res.resampling.adj:

matrix with residuals sampled under the null hypothesis for the purpose of higher moment correction (if any; only used for logistic models with small sample correction)

call:

the matched call with which the object was created

Details

This class serves as the general interface for storing the necessary phenotype information for a later association test. Objects of this class should only be created by the nullModel function. Direct modification of object slots is strongly discouraged!

Methods

show

signature(object="NullModel"): displays basic information about the null model, such as, the type of the model and the numbers of covariates.

Accessors

residuals

signature(object="NullModel"): returns the residuals slot.

names

signature(object="NullModel"): returns the names of samples in the null model.

coefficients

signature(object="NullModel"): returns the coefficients slot.

length

signature(x="NullModel"): returns the number of samples that was used to train the null model.

Subsetting

For a NullModel object x and an index vector i that is a permutation of 1:length(x), x[i] returns a new NullModel object in which the samples have been rearranged according to the permutation i. This is meant for applications in which the order of the samples in a subsequent association test is different from the order of the samples when the null model was trained/created.

Author(s)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

References

http://www.bioinf.jku.at/software/podkat

See Also

nullModel

Examples

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## read phenotype data from CSV file (continuous trait + covariates)
phenoFile <- system.file("examples/example1lin.csv", package="podkat")
pheno <-read.table(phenoFile, header=TRUE, sep=",")

## train null model with all covariates in data frame 'pheno'
model <- nullModel(y ~ ., pheno)
model
length(model)
residuals(model)

## read phenotype data from CSV file (binary trait + covariates)
phenoFile <- system.file("examples/example1log.csv", package="podkat")
pheno <-read.table(phenoFile, header=TRUE, sep=",")

## train null model with all covariates in data frame 'pheno'
model <- nullModel(y ~ ., pheno)
model
length(model)
residuals(model)

## "train" simple Bernoulli model on a subset of 100 samples
model <- nullModel(y ~ 0, pheno[1:100, ])
model
length(model)
residuals(model)

UBod/podkat documentation built on June 18, 2021, 10:01 a.m.