Expected time series dynamics | R Documentation |
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.
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)
beta |
The vector of the selection gradients for all generations. |
delta |
The vector of the relative selection strenght on variance. |
mu0 |
See |
logvarA0 |
See |
logvarE0 |
See |
relativekA0 |
See |
kA1 |
See |
kA2 |
See |
kA3 |
See |
relativekE0 |
See |
kE1 |
See |
kE2 |
See |
kE3 |
See |
logrelativekA0 |
See |
logkA1 |
See |
logkA2 |
See |
logkA3 |
See |
logrelativekE0 |
See |
logkE1 |
See |
logkE2 |
See |
logkE3 |
See |
logNe |
See |
logn |
See |
logvarM |
See |
kc |
See |
kg |
See |
o |
See |
logepsilon |
See |
logminusepsilon |
See |
logvarepsilon |
See |
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
.
The functions return a list of vectors: means
for the phenotypic average, varA
, varE
and varP
for the additive, residual, and phenotypic variances respectively.
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
.
sraAutoreg
, sraCstvar
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.