predict_outcome

Processing to predict response for various actual vs expected plots

One of the main advantages of using Generalised Linear Models
is their interpretability.  The goal of 'prettyglm' is to provide a
set of functions which easily create beautiful coefficient summaries
which can readily be shared and explained. 'prettyglm' helps users
create coefficient summaries which include categorical base levels,
variable importance and type III p.values. 'prettyglm' also creates
beautiful relativity plots for categorical, continuous and splined
coefficients.

Jared Fowler

prettyglm

Pretty Summaries of Generalized Linear Model Coefficients

predict_outcome function

<dl><dt>target</dt>
<dd>String of target variable name.</dd>
<dt>model_object</dt>
<dd>Model object. prettyglm currently supports</dd>
<dt>dataset</dt>
<dd>This is used to plot the number in each class as a barchart if plotly is TRUE.</dd>
<dt>prediction_type</dt>
<dd>type of prediction to be passed to the model object. For ...GLM defaults to ....</dd>
<dt>weights</dt>
<dd>weightings to be provided to predictions if required.</dd></dl>

Arguments

Author

predict_outcome — predict_outcome

<dl>

<dt>target</dt>
<dd>String of target variable name.</dd>


<dt>model_object</dt>
<dd>Model object. prettyglm currently supports</dd>


<dt>dataset</dt>
<dd>This is used to plot the number in each class as a barchart if plotly is TRUE.</dd>


<dt>prediction_type</dt>
<dd>type of prediction to be passed to the model object. For ...GLM defaults to ....</dd>


<dt>weights</dt>
<dd>weightings to be provided to predictions if required.</dd>

</dl>

predict_outcome: predict_outcome

Description

Usage

Value

Arguments

Author

See Also