Sim.MultiRR: Simulate data setes to be analyzed by a multi-level random regression.

Description

Simulate n data sets to be analyzed with a multi-level random regression.

Usage

Sim.MultiRR(n.ind, SeriesPerInd, ObsPerLevel, EnvGradient, PopInt, 
PopSlope, VCVInd, VCVSeries, ResVar, n.sim, unbalanced = FALSE, 
prop.ind, complete.observations = TRUE, n.obs)

Arguments

n.ind

A vector consisting of the total individuals sampled.

SeriesPerInd

A vector consisting of the number of series sampled for each individual.

ObsPerLevel

The number of observations per series in each level of the environment.

EnvGradient

A vector consisting of the levels in the environmental gradient.

PopInt

Population level intercept.

PopSlope

Population level slope.

VCVInd

A positive definite variance covariance matrix of dimensions 2 X 2, defining the among-individual variance in intercepts and slopes in the diagonals and their covariance in the off diagonals.

VCVSeries

A positive definite variance covariance matrix of dimensions 2 X 2, defining the among-series variance in intercepts and slopes in the diagonals and their covariance in the off diagonals.

ResVar

Residual variance

n.sim

Number of data sets to simulate.

unbalanced

Optional argument determining whether not all the individuals were assayed the same number of series. The default is "FALSE".

prop.ind

When unbalanced = "TRUE", A vector that has the same length as the number of series per individual, with the proportion of individuals measured n times. All individuals should have been measured once (1,.,.,.).

complete.observations

Optional argument determining whether all the levels were assayed the same number of times. The default is "TRUE".

n.obs

The total number of observations, if complete.observartions = "FALSE".

Value

A list of data sets to be analyzed by Anal.MultiRR.

References

Araya-Ajoy Y.G., Mathot, K. J., Dingemanse N. J. (2015) An approach to estimate short-term, long-term, and reaction norm repeatability. Methods in Ecology and Evolution.

Examples

Run this code

#Example 1: Balanced sampling design.
#Define sample sizes.
n.ind <-c(40, 50) ##Numbers of individuals to simulate.
SeriesPerInd <- c(4, 5) ##Number of series per individual to simulate.
ObsPerLevel <- 2 ##Number of observations per level in the environmental gradient.
 
#Number of simulated data sets, use at least 10.
n.sim=3

#Define the environmetal gradient.
EnvGradient <- c(-0.5, 0.5)
 
#Define the population level parameters.
PopInt <- 0 ##Population level intercept.
PopSlope <- -0.5 ##Population level slope.
 
#Define individual level parameters
VCVInd <-matrix(c(0.3, 0.15, 0.15, 0.3),2,2) ##Creates a variance-covariance matrix.
 
#Define series level parameters
VCVSeries <-matrix(c(0.3, 0.15, 0.15, 0.3),2,2) ##Creates a variance-covariance matrix.
 
#Define the residual variance.
ResVar <- 0.4
 
#Simulate the data sets.
sim.data <- Sim.MultiRR(n.ind=n.ind, SeriesPerInd=SeriesPerInd, 
ObsPerLevel=ObsPerLevel, EnvGradient=EnvGradient, PopInt=PopInt, PopSlope=PopSlope, 
VCVInd=VCVInd, VCVSeries=VCVSeries, ResVar=ResVar, n.sim=3)
 
#Analyze the simulated data sets. This may take a while.
ressim <- Anal.MultiRR(sim.data)

#Summarize the results of the multi-level random regressions. 
Summary(ressim) 
 
#Estimate bias.
Bias(ressim)
 
#Estiamte imprecision.
Imprecision(ressim)
 
#Estimate power.
Power(ressim)

#Example 2: Unbalanced sampling desing.
#Define sample sizes.
n.ind <-40 ##Numbers of individuals to simulate.
SeriesPerInd <- 4 ##Number of series per individual to simulate.
ObsPerLevel <- 2 ##Number of observations per level in the environmental gradient.

#Define the proportion of individuals that were sampled in all the series.
#All individuals were assayed at least once, 0.9 of individuals twice...

prop.ind<-c(1, 0.9, 0.8, 0.7)

#Define the total number of observations
n.obs=300

#Number of simulated data sets, use at least 10.
n.sim=3

#Define the environmetal gradient.
EnvGradient <- c(-0.5, 0.5)
 
#Define the population level parameters.
PopInt <- 0 ##Population level intercept.
PopSlope <- -0.5 ##Population level slope.

#Define the individual level parameters.
VCVInd <-matrix(c(0.3, 0.15, 0.15, 0.3),2,2) ##Creates a variance-covariance matrix.
 
#Define the series level parameters.
VCVSeries <-matrix(c(0.3, 0.15, 0.15, 0.3),2,2) ##Creates a variance-covariance matrix.
 
#Define the residual variance.
ResVar <- 0.4
 
#Simulate the data.
sim.data <- Sim.MultiRR(n.ind=n.ind, SeriesPerInd=SeriesPerInd, ObsPerLevel=ObsPerLevel,
EnvGradient=EnvGradient, PopInt=PopInt, PopSlope=PopSlope, VCVInd= VCVInd, VCVSeries=VCVSeries,
ResVar=ResVar, n.sim=n.sim, unbalanced=TRUE, prop.ind=c(1, 0.9, 0.8, 0.7), 
complete.observations=FALSE, n.obs=n.obs)
 
#Analyze simulated data sets. This may take a while.
ressim <- Anal.MultiRR(sim.data)
 
#Summarize the results of the multi-level random regressions.
Summary(ressim)
 
#Estimate bias.
Bias(ressim)
 
#Estiamte imprecision.
Imprecision(ressim)
 
#Estimate power.
Power(ressim)