make_rejection

Provides tools for simulating spatially dependent predictors (continuous or binary),
which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous
predictors are generated using traditional multivariate normal distributions or Gauss Markov random
fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288>
and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using
a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors
exhibiting spatial clustering can also be easily specified by the user.

Justin Leach

sim2Dpredictr

Simulate Outcomes Using Spatially Dependent Design Matrices

make_rejection function

<dl><dt>B</dt>
<dd>A vector of "true" parameter values. For inference purposes, this
can be a vector of actual parameter values, or a binary vector indicating
non-zero status.</dd>
<dt>reject.threshold</dt>
<dd>A list whose first element is the rejection 
criteria, e.g., the minimum t-statistic or maximum p-value for which to
reject the null hypothesis. The second element is one of 
<code>c("greater", "less", "2-tailed")</code>, which tell the function to reject
when the values in <code>test.statistic</code> are greater than or less than the
threshold, the test is a 2-tailed, respectively. In the latter case the 
function internally calculates the upper or lower threshold needed for the
2-tailed test.</dd>
<dt>test.statistic</dt>
<dd>A vector of test statistics; e.g., t-statistics or
p-values that are used to determine whether or not to reject the null
hypothesis.</dd></dl>

Arguments

Determine rejections — make_rejection

<dl>

<dt>B</dt>
<dd>A vector of "true" parameter values. For inference purposes, this
can be a vector of actual parameter values, or a binary vector indicating
non-zero status.</dd>


<dt>reject.threshold</dt>
<dd>A list whose first element is the rejection 
criteria, e.g., the minimum t-statistic or maximum p-value for which to
reject the null hypothesis. The second element is one of 
<code>c("greater", "less", "2-tailed")</code>, which tell the function to reject
when the values in <code>test.statistic</code> are greater than or less than the
threshold, the test is a 2-tailed, respectively. In the latter case the 
function internally calculates the upper or lower threshold needed for the
2-tailed test.</dd>


<dt>test.statistic</dt>
<dd>A vector of test statistics; e.g., t-statistics or
p-values that are used to determine whether or not to reject the null
hypothesis.</dd>

</dl>

make_rejection: Determine rejections

Description

Usage

Value

Arguments