Description Usage Arguments Value Author(s) References See Also Examples

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

1 2 3 | ```
Sim.MultiRR(n.ind, SeriesPerInd, ObsPerLevel, EnvGradient, PopInt,
PopSlope, VCVInd, VCVSeries, ResVar, n.sim, unbalanced = FALSE,
prop.ind, complete.observations = TRUE, n.obs)
``` |

`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". |

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

Yimen Araya

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | ```
#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)
``` |

MultiRR documentation built on May 29, 2017, 11:30 a.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.