testingParams: Get or set parameters for model test and selection

Description Usage Arguments Details Value See Also Examples

Description

With this methods the user can personalize the criteria by which INSPEcT selects a rate to be variable or constant. In particular, the model selection criteria can be selected between log-likelihood ratio test and Akaike's information criterion. In case log-likelihood ratio test is selected, the thresholds of chi-squared and Brown's method can be set (see Details section).

Usage

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modelSelection(object)

modelSelection(object) <- value

thresholds(object)

thresholds(object) <- value

llrtests(object)

llrtests(object) <- value

## S4 method for signature 'INSPEcT'
modelSelection(object)

## S4 replacement method for signature 'INSPEcT'
modelSelection(object) <- value

## S4 method for signature 'INSPEcT_model'
modelSelection(object)

## S4 replacement method for signature 'INSPEcT_model'
modelSelection(object) <- value

## S4 method for signature 'INSPEcT'
thresholds(object)

## S4 replacement method for signature 'INSPEcT'
thresholds(object) <- value

## S4 method for signature 'INSPEcT_model'
thresholds(object)

## S4 replacement method for signature 'INSPEcT_model'
thresholds(object) <- value

## S4 method for signature 'INSPEcT'
llrtests(object)

## S4 replacement method for signature 'INSPEcT'
llrtests(object) <- value

## S4 method for signature 'INSPEcT_model'
llrtests(object)

## S4 replacement method for signature 'INSPEcT_model'
llrtests(object) <- value

Arguments

object

An object of class INSPEcT or INSPEcT_model

value

A list or a character that will substitute the set of parameters

  • modelSelection: A character, either "llr" to test whether a rate is varying using log-likelihood testing framework or "aic" to choose the best model via Akaike Information Criterion (Default: "llr").

  • thresholds: A named list containing the threshold that is used to consider a model as accepted in terms of the chi-squared test and three thresholds (one per each rate) that are used to consider a rate as variable using the Brown's method on the log-likelihood ratio tests

  • llrtests: A list of three elements that represent, for each rate, the pairs of models that will be compared via log likelihood ratio test to assess whether the rate is variable or not

Details

When log-likelihood is chosen as a criterion for model selection, different nested models can be compared to assess wheter a single rate is varying or constant. For example, in case we want to establish whether synthesis rate is constant or not we can test the null hypothesis "all the rates are constant" against the alternative hypothesis "synthesis rate is changing". The null hypothesis is a special case of the alternative hypothesis, therefore the models are nested. We can also assess whether synthesis rate is constant or not by comparing the null hypothesis "degradation rate is changing" against the alternative hypothesis "degradation and synthesis are changing". The method llrtests set the models that are compared to assess the variability of eache rate. Different comparisons will be combined using Brown's method for combinig p-values. Models are named with a short notation where synthesis is "a", degradation is "b" and processing is "c". "0" is the model where all genes are kept constant and "ab", for example is the model where synthesis rate and degradation rate are changing. The user can also set the thresholds for Brown's p-value and chi-suqared p-value. While the former set the threshold to assess whether a rate is variable or not over time, the latter set the chi-squared threshold for a pair of model to be used via the log-likelihood ratio test. In order for a pair to be used, at least one model of the pair should have a chi-squared p-value (goodness of fit) below the threshold. The construction of a synthetic data-set can help in the choice of the correct parameters for the test (makeSimModel, makeSimDataset).

Value

See "value"

See Also

makeSimModel, makeSimDataset

Examples

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data('nascentInspObj10', package='INSPEcT')
modelSelection(nascentInspObj10)
modelSelection(nascentInspObj10) <- 'aic'
data('nascentInspObj10', package='INSPEcT')
thresholds(nascentInspObj10)
thresholds(nascentInspObj10)$chisquare <- 1e-3
thresholds(nascentInspObj10)$brown['synthesis'] <- 1e-3
data('nascentInspObj10', package='INSPEcT')
llrtests(nascentInspObj10)
llrtests(nascentInspObj10)$synthesis <- list(c('0','a'), c('b','ab'))

ste-depo/INSPEcT documentation built on July 30, 2018, 12:04 p.m.