## Statistical data analysis - Christoph Scherber

**Last updates: 24th July 2017**

- Workshops &

Courses - Introduction to Statistics

and R - R scripts

& Code - YouTube

Channel

**October 2014**

PhD course "**Linear statistical models with R**", Göttingen; Room L318, Grisebachstrasse 6, 6th-10th October

**June 2014**

Workshop with John Fox on structural equation modelling on **6th June 2014**. John Fox is an eminent expert in linear statistical models and he´s written books such as An R Companion to Applied Regression.

**April 2014**

Workshop by Christoph Scherber "**Structural equation models in the Soil Sciences**", Deutsche Bodenkundliche Gesellschaft

**February 2014 **

Workshop "**Structural Equation models**" organized by C. Scherber. Guest speakers and lecturers:

James B. Grace (USGS)

W. Daniel Kissling (Denmark & The Netherlands)

Niels J. Blunch (Denmark)

**Statistical graphics**

Using R in combination with Adobe Illustrator CS6 for professional graphics outputpublished in **Software Developer´s Magazine** 4/2012.

**General introductions to R and to statistical data analysis**

An Introduction to Statistical Data analysis

**Linear models**

An introduction to mixed effects models

Basic model formulae for mixed-effects models in R

Non-linear mixed-effects models in R

An introduction to generalized linear models

Linear models in matrix notation

**Log-transformation for data assuming negative values:**

log.modulus=function(x,k)sign(x)*log(abs(x)+k)

# k is an arbitrary constant to avoid taking logs of zero

**Christoph´s mixed R functions**

This is a continuously updated file containing useful snippets of R code.

General collection of useful R functions, Version 2016-09-13

**Multinomial models**

When fitting **multinomial models** with the nnet package (multinom() function), it is sometimes desirable to** increase the number of weights** (especially when there is a large number of response categories). This may happen for example in the analysis of **next-generation sequencing data**. The Anova() function from John Fox´s package "car" can not deal with the MaxNWts argument and hence cannot be used for multinomial models with **user-specified maximum number of weights**. Below, I provide a function called **Anova.multinom2**, which allows MaxNWts to be set to any desirded number.

Anova for models with user-specified weights, fitted with the multinom() function

**Nonlinear regression**

In nonlinear regression situations, one often wishes to use power law functions of the form **y=a+b*x^c**. The following code allows starting estimates for this function to be estimated automatically.

Self-starting non-linear power law function in R

**Model selection**

StepAICc function for linear, generalized linear and mixed models

selMod function for model selection in mixed models

**Contrast matrices**

Working with orthogonal contrasts in R

Test for orthogonality of a contrast matrix

**Linear mixed models**

Extract lme ANOVAs from multiple models

Extract lme summaries from multiple models

**Generalized linear mixed models**

**Time series analysis**

A short introduction to time-series analysis in R

**Graphics**

Creating publication-quality R Graphics

**Ecological diversity**

Calculate Shannon´s diversity index

**Seed bank data analysis**

Analyzing data from a seed bank study using R

Please contact me if you feel there are things that would need to be corrected. R is open-source and new libraries are published every other day, and so it is always a challenging task to keep up with all new developments.

*Thanks to the R Core Development Team for making R possible, and also to Mick Crawley for introducing me to R. For downloading and installing R, please visit the R Project website.*