NullModel-class | R Documentation |

`NullModel`

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

method

Objects of this class are created by calling `nullModel`

.

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

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!

- show
`signature(object="NullModel")`

: displays basic information about the null model, such as, the type of the model and the numbers of covariates.

- 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.

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.

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

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

`nullModel`

## 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)

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