# Fits a multilevel random regression to n simulated data frames. Performs multilevel random regressions to objects created with the function Sim.MultiRR. ```r Anal.MultiRR(x) ``` ## Arguments - `x`: Object created with the function sim.MultiRR. ## Returns A list of results from the multi-level random regression for n simulated data sets. ## 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. ## Author(s) Yimen Araya ## See Also `Sim.MultiRR` ## Examples ```r #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 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) ```

Anal.MultiRR function