calc_vpc_continuous

Calculate aggregate statistics for simulated and observed VPC data

Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.

Ron Keizer

Create Visual Predictive Checks

Bill Denney

calc_vpc_continuous function

<dl><dt>sim</dt>
<dd>this is usually a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm. However it can also be an object like a nlmixr or xpose object</dd>
<dt>obs</dt>
<dd>a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm</dd>
<dt>loq</dt>
<dd>The list output from <code>define_loq()</code></dd>
<dt>pi</dt>
<dd>simulated prediction interval to plot. Default is c(0.05, 0.95),</dd>
<dt>ci</dt>
<dd>confidence interval to plot. Default is (0.05, 0.95)</dd>
<dt>stratify</dt>
<dd>character vector of stratification variables.</dd>
<dt>bins</dt>
<dd>either "density", "time", or "data", "none", or one of the
approaches available in classInterval() such as "jenks" (default) or
"pretty", or a numeric vector specifying the bin separators.</dd>
<dt>bin_mid</dt>
<dd>either "mean" for the mean of all timepoints (default) or "middle" to use the average of the bin boundaries.</dd>
<dt>verbose</dt>
<dd>show debugging information (TRUE or FALSE)</dd></dl>

Arguments

Calculate aggregate statistics for simulated and observed VPC data — calc_vpc_continuous

<dl>

<dt>sim</dt>
<dd>this is usually a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm. However it can also be an object like a nlmixr or xpose object</dd>


<dt>obs</dt>
<dd>a data.frame with observed data, containing the independent and dependent variable, a column indicating the individual, and possibly covariates. E.g. load in from NONMEM using read_table_nm</dd>


<dt>loq</dt>
<dd>The list output from <code>define_loq()</code></dd>


<dt>pi</dt>
<dd>simulated prediction interval to plot. Default is c(0.05, 0.95),</dd>


<dt>ci</dt>
<dd>confidence interval to plot. Default is (0.05, 0.95)</dd>


<dt>stratify</dt>
<dd>character vector of stratification variables.</dd>


<dt>bins</dt>
<dd>either "density", "time", or "data", "none", or one of the
approaches available in classInterval() such as "jenks" (default) or
"pretty", or a numeric vector specifying the bin separators.</dd>


<dt>bin_mid</dt>
<dd>either "mean" for the mean of all timepoints (default) or "middle" to use the average of the bin boundaries.</dd>


<dt>verbose</dt>
<dd>show debugging information (TRUE or FALSE)</dd>

</dl>

calc_vpc_continuous: Calculate aggregate statistics for simulated and observed VPC data

Description

Usage

Value

Arguments