Description Usage Arguments Details Value See Also Examples
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 loglikelihood ratio test and Akaike's information criterion. In case loglikelihood ratio test is selected, the thresholds of chisquared and Brown's method can be set (see Details section).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  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

object 
An object of class INSPEcT or INSPEcT_model 
value 
A list or a character that will substitute the set of parameters

When loglikelihood 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 pvalues.
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 pvalue and chisuqared pvalue.
While the former set the threshold to assess whether a rate is variable or not over time,
the latter set the chisquared threshold for a pair of model to be used via the
loglikelihood ratio test. In order for a pair to be used, at least one model of the
pair should have a chisquared pvalue (goodness of fit) below the threshold.
The construction of a synthetic dataset can help in the choice of the correct
parameters for the test (makeSimModel
, makeSimDataset
).
See "value"
1 2 3 4 5 6 7 8 9 10  data('nascentInspObj10', package='INSPEcT')
modelSelection(nascentInspObj10)
modelSelection(nascentInspObj10) < 'aic'
data('nascentInspObj10', package='INSPEcT')
thresholds(nascentInspObj10)
thresholds(nascentInspObj10)$chisquare < 1e3
thresholds(nascentInspObj10)$brown['synthesis'] < 1e3
data('nascentInspObj10', package='INSPEcT')
llrtests(nascentInspObj10)
llrtests(nascentInspObj10)$synthesis < list(c('0','a'), c('b','ab'))

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