weightLine

Vector of estimated weights, as outputted by <code><a rd-options="" href="/link/DearBegg?package=selectMeta&version=1.0.8" data-mini-rdoc="selectMeta::DearBegg">DearBegg</a></code> or <code><a rd-options="" href="/link/DearBeggMonotone?package=selectMeta&version=1.0.8" data-mini-rdoc="selectMeta::DearBeggMonotone">DearBeggMonotone</a></code>.

col0

lwd0

lty0

Should weights be drawn versus original $p$-values (<code>type == "pval"</code>) or versus the empirical distribution of the 
 $p$-values (<code>type === "empirical"</code>).

type

This function faclitiates plotting of estimated weight functions according to the method in Dear and Begg (1992) or
its non-increasing version described in Rufibach (2010).

distribution

htest

nonparametric

Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).

Kaspar Rufibach

selectMeta

Estimation of Weight Functions in Meta Analysis

weightLine function

<dl> <dt>p</dt>
<dd>Vector of $p$-values.</dd> <dt>w</dt>
<dd>Vector of estimated weights, as outputted by <code>DearBegg</code> or <code>DearBeggMonotone</code>.</dd> <dt>col0</dt>
<dd>Color of line that is drawn.</dd> <dt>lwd0</dt>
<dd>Line width.</dd> <dt>lty0</dt>
<dd>Line type.</dd> <dt>type</dt>
<dd>Should weights be drawn versus original $p$-values (<code>type == "pval"</code>) or versus the empirical distribution of the 
 $p$-values (<code>type === "empirical"</code>).</dd></dl>

Arguments

Kaspar Rufibach (maintainer), <a href="/link/kaspar.rufibach%40gmail.com?package=selectMeta&version=1.0.8" data-mini-rdoc="selectMeta::kaspar.rufibach@gmail.com">kaspar.rufibach@gmail.com</a>, <a href="http://www.kasparrufibach.ch">http://www.kasparrufibach.ch</a>

Author

Function to plot estimated weight functions — weightLine

<dl>

 <dt>p</dt>
<dd>Vector of $p$-values.</dd>

 <dt>w</dt>
<dd>Vector of estimated weights, as outputted by <code>DearBegg</code> or <code>DearBeggMonotone</code>.</dd>

 <dt>col0</dt>
<dd>Color of line that is drawn.</dd>

 <dt>lwd0</dt>
<dd>Line width.</dd>

 <dt>lty0</dt>
<dd>Line type.</dd>

 <dt>type</dt>
<dd>Should weights be drawn versus original $p$-values (<code>type == "pval"</code>) or versus the empirical distribution of the 
 $p$-values (<code>type === "empirical"</code>).</dd>

</dl>

Kaspar Rufibach (maintainer), <a href='mailto:kaspar.rufibach@gmail.com'>kaspar.rufibach@gmail.com</a>, <a href='http://www.kasparrufibach.ch'>http://www.kasparrufibach.ch</a>

weightLine: Function to plot estimated weight functions

Description

Usage

Arguments

Author

References

See Also

Examples