hapassoc (version 0.1)

EM: Use the EM algorithm to fit maximum likelihood estimates of trait associations with SNP haplotypes

Description

This function takes a data set of haplotypes in which rows for individuals of uncertain phase have been augmented by "pseudo-individuals" carrying consistent multilocus genotypes. The function uses the EM algorithm to find MLE's for trait associations with covariates in generalized linear models.

Usage

EM(form,haplos.list,baseline = "missing" ,family = binomial(),
gamma = FALSE, maxit = 50, tol = 0.001, ...)

Arguments

form

model equation in usual R format

haplos.list

list of haplotype data from CheckHaplos

baseline

optional, haplotype to be used for baseline coding. Default is the most frequent haplotype.

family

binomial, poisson, gaussian or gamma are supported, default=binomial

gamma

initial values for haplotype frequencies, default values are calculated in CheckHaplos using the standard haplotype-counting EM algorithm without adjustment for non-haplotype covariates

maxit

maximum iterations of the EM loop, default=50

tol

convergence tolerance in terms of the maximum difference in parameter estimates between interations; default=0.001

...

additional arguments to be passed to the glm function such as starting values

Value

• it
  number of iterations
• beta
  estimated regression coefficients
• gamma
  estimated haplotype frequencies
• fits
  fitted values of the trait
• wts
  final weights calculated in last iteration of the EM loop. These are estimates of the conditional probabilities of each multilocus genotype given the observed single-locus genotypes.
• var
  joint variance-covariance matrix of the estimated regression coefficients and the estimated haplotype frequencies
• dispersionML
  maximum likelihood estimate of dispersion parameter
• family
  family of the generalized linear model fit (e.g. binomial, gaussian, etc.)
• response
  trait value

See Also

RecodeHaplos,CheckHaplos,summary.EM,glm,family.

Examples

data(hypoDat)
example.haplos<-RecodeHaplos(hypoDat,2)
example.newhaplos<-CheckHaplos(example.haplos, 2)

# Logistic regression, baseline group: '10/10'

example.regr <- EM(affected ~ gender + age + BMI + n00+ n01 + n11,
                     example.newhaplos, family=binomial())