Create a kalisBackwardTable

Allocates the memory for and initializes a backward table.

Usage

MakeBackwardTable(pars, from_recipient = 1, to_recipient = Inf)

Arguments

pars

a kalisParameters object specifying the genetics parameters to be associated with this backward table. These parameters can be set up by using the Parameters() function.

from_recipient

first recipient haplotype included if creating a partial backward table. By default all are included from the first recipient haplotype. Haplotypes are indexed from 1.

to_recipient

last recipient haplotype included if creating a partial backward table. By default all are included upto the last recipient haplotype. Haplotypes are indexed from 1.

Value

A specialized list of class kalisBackwardTable. The elements of the backward table list are:

l

denotes the current variant position. 2147483647 indicates a newly created backward table which has not yet been propagated to any variant.

beta

is a matrix of rescaled backward probabilities under the Li and Stephens HMM. Each column of beta corresponds to an independent HMM such that \(\beta^l_{ji}\) is proportional to the probability of observing haplotype \(i\) from variant \(l+1\) up through variant \(L\) given that haplotype \(j\) is copied by haplotype \(i\) at variant \(l\).

beta.g

is a vector containing scaling constants needed to continue propagating the HMM (TODO: add ref please see kalis paper for details).

beta.theta

boolean indicator for whether the matrix beta is currently in so-called "beta-theta space" or not. See Backward() for details about "beta-theta space" which is specified during the run.

A kalisBackwardTable also carries with it a checksum key for the parameters pars it was provided. If one attempts to interact a kalisBackwardTable with a kalisForwardTable with mismatched parameters, an error will be thrown.

Details

MakeBackwardTable initializes a kalisBackwardTable object that will store the rescaled backward probabilities for each recipient haplotype. Note: since all other haplotypes loaded by CacheHaplotypes() into the kalis cache are taken as potential donor haplotypes, this table will be of size (total haplotypes in cache)x(number of recipients). Also note that this table object is linked to a given set of Li and Stephens hidden Markov model parameters, created by Parameters(), to ensure consistency for any given HMM run.

The returned kalisBackwardTable is ready to be propagated to a given target variant with the function Backward().

Since each column corresponds to an independent Li and Stephens hidden Markov model (ie for each recipient), it is possible to create a partial backward table object which corresponds to a subset of recipients using the from_recipient and to_recipient arguments.

See also

Backward() to propagate the newly created kalisBackwardTable; MakeForwardTable() to create a corresponding kalisForwardTable.

Examples

# Load the toy haplotype example and set toy parameters
data("SmallHaps")
data("SmallMap")

CacheHaplotypes(SmallHaps)
#> Warning: haplotypes already cached ... overwriting existing cache.

rho <- CalcRho(diff(SmallMap))
pars <- Parameters(rho)

# Create the forward table we want to propagate
bck <- MakeBackwardTable(pars)
bck
#> Full Backward Table object for 300 haplotypes, in rescaled probability space. 
#>   Newly created table, currently uninitialised to any variant (ready for Backward function next).
#>   Memory consumed: 724.13 kB 

# Now ready to run the HMM backward recursions with Backward() ...