Blog Archives

Dynamic occupancy models in Stan

November 14, 2014
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Dynamic occupancy models in Stan

Occupancy modeling is possible in Stan as shown here, despite the lack of support for integer parameters. In many Bayesian applications of occupancy modeling, the true occupancy states (0 or 1) are directly modeled, but this can be avoided by marginalizing out the true occupancy state. The Stan manual (pg. 96) gives an example of this kind...

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Dynamic occupancy models in Stan

November 14, 2014
By
Dynamic occupancy models in Stan

Occupancy modeling is possible in Stan as shown here, despite the lack of support for integer parameters. In many Bayesian applications of occupancy modeling, the true occupancy states (0 or 1) are directly modeled, but this can be avoided by marginalizing out the true occupancy state. The Stan manual (pg. 96) gives an example of this kind of marginalization...

Read more »

Spatial data extraction around buffered points in R

November 8, 2014
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Quantifying spatial data (e.g. land cover) around points can be done in a variety of ways, some of which require considerable amounts of patience, clicking around, and/or cash for a license. Here’s a bit of code that I cobbled together to quickly extract land cover data from the National Land Cover Database for buffered regions around points (e.g....

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Spatial data extraction around buffered points in R

November 8, 2014
By

Quantifying spatial data (e.g. land cover) around points can be done in a variety of ways, some of which require considerable amounts of patience, clicking around, and/or cash for a license. Here’s a bit of code that I cobbled together to quickly extract land cover data from the National Land Cover Database for buffered regions around points (e.g. small...

Read more »

Multilevel modeling of community composition with imperfect detection

June 19, 2014
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Multilevel modeling of community composition with imperfect detection

This is a guest post generously provided by Joe Mihaljevic. A common goal of community ecology is to understand how and why species composition shifts across space. Common techniques to determine which environmental covariates might lead to such shifts typically rely on ordination of community data to reduce the amount of data. These techniques include redundancy analysis (RDA), canonical...

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Multilevel modeling of community composition with imperfect detection

June 19, 2014
By
Multilevel modeling of community composition with imperfect detection

This is a guest post generously provided by Joe Mihaljevic. A common goal of community ecology is to understand how and why species composition shifts across space. Common techniques to determine which environmental covariates might lead to such shifts typically rely on ordination of community data to reduce the amount of data. These techniques include redundancy analysis (RDA), canonical...

Read more »

Shiny variance inflation factor sandbox

April 30, 2014
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In multiple regression, strong correlation among covariates increases the uncertainty or variance in estimated regression coefficients. Variance inflation factors (VIFs) are one tool that has been used as an indicator of problematic covariate collinearity. In teaching students about VIFs, it may be useful to have some interactive supplementary material so that they can manipulate factors affecting the uncertainty in...

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Shiny variance inflation factor sandbox

April 30, 2014
By

In multiple regression, strong correlations among covariates increases the uncertainty or variance in estimated regression coefficients. Variance inflation factors (VIFs) are one tool that has been used as an indicator of problematic covariate collinearity. In teaching students about VIFs, it may be useful to have some interactive supplementary material so that they can manipulate factors affecting the uncertainty in slope terms...

Read more »

Stochastic search variable selection in JAGS

March 22, 2014
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Stochastic search variable selection in JAGS

Stochastic search variable selection (SSVS) identifies promising subsets of multiple regression covariates via Gibbs sampling (George and McCulloch 1993). Here’s a short SSVS demo with JAGS and R. Assume we have a multiple regression problem: We suspect only a subset of the elements of $\boldsymbol{\beta}$ are non-zero, i.e. some of the covariates have no effect. Assume $\boldsymbol{\beta}$...

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Stochastic search variable selection in JAGS

March 22, 2014
By
Stochastic search variable selection in JAGS

Stochastic search variable selection (SSVS) identifies promising subsets of multiple regression covariates via Gibbs sampling (George and McCulloch 1993). Here’s a short SSVS demo with JAGS and R. Assume we have a multiple regression problem: We suspect only a subset of the elements of $boldsymbol{beta}$ are non-zero, i.e. some of the covariates have no effect. Assume $boldsymbol{beta}$ arises from...

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