plot_Blend

plot the identified varying effects

Our recently developed fully Bayesian semiparametric quantile mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'.

Kun Fan

Blend

Bayesian Longitudinal Regularized Semiparametric Quantile Mixed
Models

Cen Wu

plot_Blend function

<dl><dt>x</dt>
<dd>Blend object.</dd>
<dt>sparse</dt>
<dd>sparsity.</dd>
<dt>prob</dt>
<dd>probability for credible interval, between 0 and 1. e.g. prob=0.95 leads to 95% credible interval</dd></dl>

Arguments

plot a Blend object — plot_Blend

<dl>

<dt>x</dt>
<dd>Blend object.</dd>


<dt>sparse</dt>
<dd>sparsity.</dd>


<dt>prob</dt>
<dd>probability for credible interval, between 0 and 1. e.g. prob=0.95 leads to 95% credible interval</dd>

</dl>

plot_Blend: plot a Blend object

Description

Usage

Value

Arguments

See Also

Examples