Blog Archives

Plotting regression curves with confidence intervals for LM, GLM and GLMM in R

October 8, 2015
By
Plotting regression curves with confidence intervals for LM, GLM and GLMM in R

Once models have been fitted and checked and re-checked comes the time to interpret them. The easiest way to do so is to plot the response variable versus the explanatory variables (I call them predictors) adding to this plot the fitted regression curve together (if you are feeling fancy) with a confidence interval around it.

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Plotting regression curves with confidence intervals for LM, GLM and GLMM in R

October 8, 2015
By
Plotting regression curves with confidence intervals for LM, GLM and GLMM in R

Once models have been fitted and checked and re-checked comes the time to interpret them. The easiest way to do so is to plot the response variable versus the explanatory variables (I call them predictors) adding to this plot the fitted regression curve together (if you are feeling fancy) with a confidence interval around it.

Read more »

Two little annoying stats detail

August 31, 2015
By
Two little annoying stats detail

UPDATED: Thanks to Ben and Florian comments I’ve updated the first part of the post   A very brief post at the end of the field season on two little “details” that are annoying me in paper/analysis that I see being done (sometimes) around me. The first one concern mixed effect models where the models

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Two little annoying stats detail

August 31, 2015
By
Two little annoying stats detail

A very brief post at the end of the field season on two little “details” that are annoying me in paper/analysis that I see being done (sometimes) around me. The first one concern mixed effect models where the models built in the contain a grouping factor (say month or season) that is fitted as both

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Count data: To Log or Not To Log

July 22, 2015
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Count data: To Log or Not To Log

Count data are widely collected in ecology, for example when one count the number of birds or the number of flowers. These data follow naturally a Poisson or negative binomial distribution and are therefore sometime tricky to fit with standard LMs. A traditional approach has been to log-transform such data and then fit LMs to

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Count data: To Log or Not To Log

July 22, 2015
By
Count data: To Log or Not To Log

Count data are widely collected in ecology, for example when one count the number of birds or the number of flowers. These data follow naturally a Poisson or negative binomial distribution and are therefore sometime tricky to fit with standard LMs. A traditional approach has been to log-transform such data and then fit LMs to

Read more »

Confidence Intervals for prediction in GLMMs

June 17, 2015
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Confidence Intervals for prediction in GLMMs

With LM and GLM the predict function can return the standard error for the predicted values on either the observed data or on new data. This is then used to draw confidence or prediction intervals around the fitted regression lines. The confidence intervals (CI) focus on the regression lines and can be interpreted as (assuming

Read more »

Confidence Intervals for prediction in GLMMs

June 17, 2015
By
Confidence Intervals for prediction in GLMMs

With LM and GLM the predict function can return the standard error for the predicted values on either the observed data or on new data. This is then used to draw confidence or prediction intervals around the fitted regression lines. The confidence intervals (CI) focus on the regression lines and can be interpreted as (assuming

Read more »

Exploration of Functional Diversity indices using Shiny

April 27, 2015
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Exploration of Functional Diversity indices using Shiny

Biological diversity (or biodiversity) is a complex concept with many different aspects in it, like species richness, evenness or functional redundancy. My field of research focus on understanding the effect of changing plant diversity on higher trophic levels communities but also ecosystem function. Even if the founding papers of this area of research already hypothesized

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Exploration of Functional Diversity indices using Shiny

April 27, 2015
By
Exploration of Functional Diversity indices using Shiny

Biological diversity (or biodiversity) is a complex concept with many different aspects in it, like species richness, evenness or functional redundancy. My field of research focus on understanding the effect of changing plant diversity on higher trophic levels communities but also ecosystem function. Even if the founding papers of this area of research already hypothesized

Read more »

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