modnets (version 0.9.0)

modSelect: Select a model based on output from resample

Description

Creates the necessary input for fitNetwork when selecting variables based on the resample function. The purpose of making this function available to the user is to that different decisions can be made about how exactly to use the resample output to select a model, as sometimes there is more than one option for choosing a final model.

Usage

modSelect(
  obj,
  data = NULL,
  fit = FALSE,
  select = "select",
  thresh = NULL,
  ascall = TRUE,
  type = "gaussian",
  ...
)

Arguments

obj

resample output

data

The dataframe used to create the resample object. Necessary if ascall = TRUE or fit = TRUE.

fit

Logical. Determines whether to fit the selected model to the data or just return the model specifications. Must supply a dataset in the data argument as well.

select

Character string, referring to which variable of the output should be used as the basis for selecting variables. If the resampling method was either "bootstrap" or "split", then setting select = "select" will select variables based on the aggregated p-values being below a pre-specified threshold. Setting select = "select_ci", however, will use the adjusted confidence intervals rather than p-values to select variables. Alternatively, if select = "freq" then the thresh argument can be used to indicate the minimum selection frequency across iterations. In this case, variables are selected based on how frequently they were selected in the resampling procedure. This also works if select is simply set a numeric value (this value will serve as the value for thresh).

When the resampling method was "stability", the default option of select = "select" chooses variables based on the original threshold provided to the resample function, and relies on the simultaneous selection proportion (the "freq" column in the "results" element). Alternatively, if select is a numeric value, or a value for thresh is provided, that new frequency selection threshold will determine the choice of variables. Alternatively, one can specify select = "split1" or select = "split2" to base the threshold on the selection frequency in one of the two splits rather than on the simultaneous selection frequency which is likely to be the most conservative.

For all types of resample objects, when select = "Pvalue" then thresh can be set to a numeric value in order to select variables based on aggregated p-values. For the "bootstrapping" and "split" methods this allows one to override the original threshold (set as part of resample) if desired.

thresh

Numeric value. If select = "Pvalue", then this value will be the p-value threshold. Otherwise, this value will determine the minimum frequency selection threshold.

ascall

Logical. Determines whether to return a list with arguments necessary for fitting the model with do.call to fitNetwork. Only possible if a dataset is supplied.

type

Should just leave as-is. Automatically taken from the resample object.

...

Additional arguments.

Value

A call ready for fitNetwork, a fitted network model, or a list of selected variables for each node along with relevant attributes. Essentially, the output is either the selected model itself or a list of the necessary parameters to fit it.

See Also

resample

Examples

# NOT RUN {
res1 <- resample(ggmDat, m = 'M', niter = 10)
mods1 <- modSelect(res1)
fit1 <- fitNetwork(ggmDat, morderators = 'M', type = mods1)

res2 <- resample(ggmDat, m = 'M', sampMethod = 'stability')
fit2 <- modSelect(res2, data = ggmDat, fit = TRUE, thresh = .7)
# }