Plot.Sim {MultiRR}

Density plots for each variance component.

Description

Plots distributions of the estimated parameters from the simulations.

Usage

Plot.Sim(x)

Arguments

x

Object created with Anal.MultiRR.

Note

Better use with only one combination of parameters (i.e., number of individuals and series per individual.

Author(s)

Yimen Araya

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.

See Also

Sim.MultiRR, Anal.MultiRR, Summary

Examples

#Example: Unbalanced sampling design.
#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)  

#Plot the simulations
Plot.Sim(ressim)