sraAutoregTimeseries: Expected dynamics for different SRA models (internal...

In sra: Selection Response Analysis

Expected dynamics for different SRA models (internal functions).

Description

Each genetic architecture model leads to an expected dynamics for the mean and the variance of the population, given a selection pressure. These functions provide the expectation of the models, which are used (i) to fit the model by maximum-likelihood, and (ii) to provide the theoretical dynamics of the best model.

Usage

sraAutoregTimeseries(beta, delta=rep(0, length(beta)), mu0=0, logvarA0=0, logvarE0=0, 
	relativekA0=0, kA1=1, kA2=0, kA3=0, 
	relativekE0=0, kE1=1, kE2=0, kE3=0, threshold=1e-10, 
	logrelativekA0=NULL, logrelativekE0=NULL, 
	logkA1=NULL, logkE1=NULL, logkA2=NULL, logkE2=NULL, logkA3=NULL, logkE3=NULL)
sraAutoregHeritTimeseries(beta, delta=rep(0, length(beta)), mu0=0, logith20=0, logvarP0=0,
	relativekA0=0, kA1=1, kA2=0, kA3=0, 
	relativekE0=0, kE1=1, kE2=0, kE3=0, threshold=1e-10, 
	logrelativekA0=NULL, logrelativekE0=NULL, 
	logkA1=NULL, logkE1=NULL, logkA2=NULL, logkE2=NULL, logkA3=NULL, logkE3=NULL)
sraAutoregEvolvTimeseries(beta, delta=rep(0, length(beta)), mu0=0, logIA0=0, logIE0=0, 
	relativekA0=0, kA1=1, kA2=0, kA3=0, 
	relativekE0=0, kE1=1, kE2=0, kE3=0, threshold=1e-10, 
	logrelativekA0=NULL, logrelativekE0=NULL, 
	logkA1=NULL, logkE1=NULL, logkA2=NULL, logkE2=NULL, logkA3=NULL, logkE3=NULL)
sraTimeseries(beta, delta=rep(0, length(beta)), mu0=0, logvarA0=0, logvarE0=0, 
	logNe=log(100), logn=log(1e+10), logvarM=log(1e-20), kc=0, kg=0, o=mu0, s=0)
sraEpiTimeseries(beta, delta=rep(0, length(beta)), mu0=0, logvarA0=0, logvarE0=0, 
	logNe=log(1000), logvarM=log(1.e-20), 
	logepsilon=0, logminusepsilon=-99, logvarepsilon=0)

Arguments

beta	The vector of the selection gradients for all generations.
delta	The vector of the relative selection strenght on variance.
mu0	See sraAutoreg for the description of the model parameters.
logvarA0	See sraAutoreg for the description of the model parameters.
logvarE0	See sraAutoreg for the description of the model parameters.
relativekA0	See sraAutoreg for the description of the model parameters.
kA1	See sraAutoreg for the description of the model parameters.
kA2	See sraAutoreg for the description of the model parameters.
kA3	See sraAutoreg for the description of the model parameters.
relativekE0	See sraAutoreg for the description of the model parameters.
kE1	See sraAutoreg for the description of the model parameters.
kE2	See sraAutoreg for the description of the model parameters.
kE3	See sraAutoreg for the description of the model parameters.
logrelativekA0	See sraAutoreg for the description of the model parameters.
logkA1	See sraAutoreg for the description of the model parameters.
logkA2	See sraAutoreg for the description of the model parameters.
logkA3	See sraAutoreg for the description of the model parameters.
logrelativekE0	See sraAutoreg for the description of the model parameters.
logkE1	See sraAutoreg for the description of the model parameters.
logkE2	See sraAutoreg for the description of the model parameters.
logkE3	See sraAutoreg for the description of the model parameters.
logNe	See sraCstvar for the description of the model parameters.
logn	See sraCstvar for the description of the model parameters.
logvarM	See sraCstvar for the description of the model parameters.
kc	See sraCstvar for the description of the model parameters.
kg	See sraCstvar for the description of the model parameters.
o	See sraCstvar for the description of the model parameters.
logs2	See sraCstvar for the description of the model parameters.
logepsilon	See sraCstvar for the description of the model parameters.
logminusepsilon	See sraCstvar for the description of the model parameters.
logvarepsilon	See sraCstvar for the description of the model parameters.

Details

In sraEpitimeseries, the value of the directionality of epistasis (epsilon) should be provided either by logepsilon when epsilon is positive, or by logminusepsilon when epsilon is negative. One of them should therefore be NA.

Value

The functions return a list of vectors: means for the phenotypic average, varA, varE and varP for the additive, residual, and phenotypic variances respectively.

Note

These functions are not designed to be called directly by the end user. The models implemented in the time series functions are described in sraAutoreg and sraCstvar.

See Also

sraAutoreg, sraCstvar

sra documentation built on March 31, 2023, 9:31 p.m.