parameter.reestimation: uses simulation to determine robustness of parameter...
In EvoRAG: Evolutionary Rates Across Gradients

Description Usage Arguments Details Value Author(s) Examples

uses simulation to determine robustness of parameter estimates under a model

1	parameter.reestimation(GRAD, TIME, model, PARAMETERS, N, REP = 1)

`GRAD`	vector of gradient values (i.e. any continuous variable) for sister pair dataset
`TIME`	vector of evolutionary ages (i.e. node ages ) for sister pair dataset
`model`	any model implemented in EvoRAG
`PARAMETERS`	A vector listing the model parameters under which to simulate. Model parameters must be in the same order as described in sisterContinuous.
`REP`	How many replicated datasets of TIME and GRAD to use. Default = 1. Example: REP=3 generates a dataset with each element in TIME and GRAD repeated 3 times. This option will be used primarily for calculating statistical power as a function of increasing number of sister pairs
`N`	The number of simulations to perform

Simulates data under a model, and re-estimates model parameters using the same model. A model performs well if the parameters it is simulated under are similar to those it re-estimates.

Returns a matrix showing the mean, median, range, several percentiles and the standard error for each model parameter.

Jason T. Weir

## Not run: 
  ###simulate data
  set.seed(seed = 3)
  TIME = runif(n=300, min = 0, max = 10)
  GRAD = runif(n=300, min = 0, max = 60)
  DATA1 <- sim.sisters(TIME = TIME, GRAD=GRAD, parameters = c(2, -0.03), 
     model=c("BM_linear"))

  ###run parameter.reestimation
  model = c("BM_linear")
  parameter.reestimation(GRAD, TIME, model=model, PARAMETERS=c(2, -0.03), 
     N=100, REP = 1)
 
## End(Not run)#end dontrun