The {squidSim} R package
Why use {squidSim}?
Using the vignette
Installation
Issues and bugs
simulate_population()
function
Terminology and notation
Mathematical Notation
General rules
Notation for a linear mixed model
Distributions
Interactions / Random regression
Multi-response
1
Simulating from linear models
1.1
Simple Linear Model
1.1.1
Adding more information about the predictors
1.2
Correlated predictors
1.3
Interactions and non-linear effects
1.3.1
Interactions
1.3.2
Non-linear effects
1.4
Transformations
1.5
Known Predictors
1.6
Non-Gaussian phenotypes
1.7
Model equations
1.8
Simulating multiple populations
1.9
Parameter list summary
2
Hierarchical structure
2.1
Making a hierarchical structure
2.1.1
Single Factor
2.1.2
Nested factors
2.1.3
Crossed factors
2.1.4
Temporal structure
2.1.5
Naming factor levels
2.2
Factors
2.2.1
Fixed Factor Interactions
2.3
Simulating predictors at different hierarchical levels
2.3.1
Simulating ‘random’ effects
2.3.2
Incorporating existing data structures
2.4
Random Regression
2.5
Among- and within-group effects
3
Multi-response Models
3.1
Predictors affecting multiple responses
3.2
One response repeatedly measured, the other not
3.3
Different distributions
3.4
Multivariate Random Slopes
4
Genetic effects
4.1
Additive genetics effects
4.2
Multivariate genetic effects
4.3
Sex specific genetic variance and inter-sexual genetic correlations
4.4
Indirect Genetic Effects
4.5
GxE
4.6
Dominance
4.7
Inbreeding depression
4.8
Genetic Groups
5
Phylogenetic Effects
6
Temporal and Spatial Effects
6.1
Simple Temporal Effects
6.2
Cyclical Temporal Effects
6.3
Temporal Autocorrelation
6.4
Spatial Autocorrelation
7
Sampling
7.1
Nested
7.1.1
Worked example 1
7.2
Missing data
7.2.1
MCAR
7.2.2
MAR
7.2.3
MNAR
7.3
Temporal Sampling
8
Other model Types
8.1
Double hierarchical models (DHGLMs)
8.1.1
DHGLM
8.1.2
Bivariate DHGLM
8.2
Zero-inflated Poisson
The {squidSim} R Package Vignette
6.4
Spatial Autocorrelation