haplo.ccs | R Documentation |

'haplo.ccs' estimates haplotype and covariate relative risks in case-control data by weighted logistic regression. Diplotype probabilities, which are estimated by EM computation with progressive insertion of loci, are utilized as weights. The model is specified by a symbolic description of the linear predictor, which includes specification of an allele matrix, inheritance mode, and preferences for rare haplotypes using 'haplo'. Note that use of this function requires installation of the 'haplo.stats' and 'survival' packages. See 'haplo.em' for a description of EM computation of diplotype probabilities. Currently missing genotype information is not allowed.

haplo.ccs(formula, data=NULL, ...) haplo.ccs.fit(y, x, int, geno, inherit.mode, group.rare, rare.freq, referent, names.x, names.int, ...)

`formula` |
a symbolic description of the model to be fit, which requires specification of an allele matrix and inheritance mode using 'haplo'. Note that 'additive' is the default inheritance mode for 'haplo'. Preferences for grouping rare haplotypes are also specified using 'haplo'. Note that by default 'haplo' groups haplotypes with an estimated population frequency less than 0.02. More details on model formulae are given below. |

`data` |
an optional data frame, list, or environment (or object coercible by 'as.data.frame' to a data frame) containing the variables in the model. If not found in 'data', the variables are taken from 'environment(formula)', typically the environment from which 'haplo.ccs' is called. |

`referent` |
a character string representing the haplotype to be used as the referent. The haplotype with the highest estimated population frequency is the default referent. |

`...` |
optional model-fitting arguments to be passed to 'glm'. |

`y` |
a vector of observations. |

`x` |
the design matrix for environmental covariates. |

`int` |
the design matrix for haplotype-environment interaction. |

`geno` |
the allele matrix. |

`inherit.mode` |
the inheritance mode specified by 'haplo'. |

`group.rare` |
a logical value indicating whether rare haplotypes should be grouped, specified by 'haplo'. |

`rare.freq` |
the population haplotype frequency used to define the rare haplotypes, specified by 'haplo'. |

`names.x` |
the column names of the design matrix for covariates. |

`names.int` |
the column names of the design matrix for haplotype-environment interaction. |

A formula has the form 'y ~ terms' where 'y' is a numeric vector indicating case-control status and 'terms' is a series of terms which specifies a linear predictor for 'y'. A terms specification of the form 'first + second' indicates all the terms in 'first' together with all the terms in 'second' with duplicates removed. The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on. The specification 'first*second' indicates the cross of 'first' and 'second'.

Note that 'haplo.ccs.fit' is the workhorse function. The inputs 'y', 'x', 'geno', and 'int' represent case-control status, the matrix of covariates, the matrix of alleles, and the matrix of terms that have interaction with the haplotypes to be estimated from the alleles. The argument 'inherit.mode' corresponds to the inheritance mode specified by 'haplo', and the arguments 'group.rare' and 'rare.freq' correspond to the preferences for grouping rare haplotypes specified by 'haplo'. 'names.x' and 'names.int' correspond to the column names of 'x' and 'int', respectively. The background functions 'one', 'count.haps', and 'return.haps' are used in specifying the model terms and neatly packaging the results.

'haplo.ccs' returns an object of class inheriting from '"haplo.ccs"'. More details appear later in this section. The function 'summary' (i.e., 'summary.haplo.ccs') obtains or prints a summary of the results, which include haplotype and covariate relative risks, robust standard error estimates, and estimated haplotype frequencies. The generic accessory functions 'coefficients', 'fitted.values', and 'residuals' extract corresponding features of the object returned by 'haplo.ccs'. The function 'vcov' (i.e., 'vcov.haplo.ccs') returns sandwich variance-covariance estimates. The function 'haplo.freq' extracts information returned by the EM computation of haplotype frequencies. Note that if rare haplotypes are grouped, then their individual estimated frequencies are summed. An object of class '"haplo.ccs"' is a list containing at least the following components:

`formula` |
the formula supplied. |

`call` |
the matched call. |

`coefficients` |
a named vector of coefficients. |

`covariance` |
a named matrix of sandwich variance-covariance estimates, computed using 'sandcov'. |

`residuals` |
the working residuals, i.e., the residuals from the final iteration of the IWLS fit. |

`fitted.values` |
the fitted mean values, obtained by transforming the linear predictors by the expit function. |

`linear.predictors` |
the linear fit on the logit scale. |

`df` |
the model degrees of freedom. |

`rank` |
the numeric rank of the fitted model. |

`family` |
the family object, in this case, quasibinomial. |

`iter` |
the number of iterations of IWLS used. |

`weights` |
the working weights, i.e., the weights from the final iteration of the IWLS fit. |

`prior.weights` |
the weights initially supplied, in this case, the diplotype probabilities estimated by the EM computation. |

`y` |
a vector indicating case-control status, expanded for each subject by the number of plausible diplotypes for that subject. |

`id` |
the numeric vector used to identify subjects, expanded for each subject by the number of plausible diplotypes for that subject. |

`converged` |
a logical indicating whether the IWLS fit converged. |

`boundary` |
a logical indicating whether the fitted values are on the boundary of the attainable values. |

`model` |
the model matrix used. |

`terms` |
the terms object used. |

`offset` |
the offset vector used. |

`contrasts` |
the contrasts used. |

`xlevels` |
a record of the levels of the factors used in fitting. |

`inheritance.mode` |
the method of inheritance. |

`rare.freq` |
the value used to define the rare haplotypes. |

`em.lnlike` |
the value of the log likelihood at the last EM iteration. |

`em.lr` |
the likelihood ratio statistic used to test the assumed model against the model that assumes complete linkage equilibrium among all loci. |

`em.df.lr` |
the degrees of freedom for the likelihood ratio statistic. |

`em.nreps` |
the count of haplotype pairs that map to each subject's marker genotypes. |

`hap1` |
character strings representing the possible first haplotype for each subject. |

`hap2` |
character strings representing the possible second haplotype for each subject. |

`hap.names` |
character strings representing the unique haplotypes. |

`hap.probs` |
the estimated frequency of each unique haplotype. Note that if rare haplotypes are grouped, then their individual estimated frequencies are summed. |

`em.converged` |
a logical indicating whether the EM computation converged. |

`em.nreps` |
the number of haplotype pairs that map to the marker genotypes for each subject. |

`em.max.pairs` |
the maximum number of pairs of haplotypes per subject that are consistent with their marker data. |

`em.control` |
a list of control parameters for the EM computation. |

The functions 'anova', 'logLik', and 'AIC' are not appropriate for models of class '"haplo.ccs"', because 'haplo.ccs' does not fit by maximum likelihood. Accordingly, model and null deviance are not reported.

Benjamin French and Thomas Lumley, University of Washington

French B, Lumley T, Monks SA, Rice KM, Hindorff LA, Reiner AP, Psaty BM. Simple estimates of haplotype relative risks in case-control data. Genetic Epidemiology 2006; 30(6):485-494.

The help files for 'glm', 'haplo.em', and 'haplo.glm' were instrumental in creating this help file.

`glm`

,
`haplo`

,
`haplo.em`

,
`haplo.glm`

,
`sandcov`

data(renin) ## Fit a model for haplotype effects. haplo.ccs(case ~ haplo(geno)) ## Fit a model for haplotype and covariate effects. haplo.ccs(case ~ gender + age + factor(race) + haplo(geno)) ## Fit a model for haplotype interaction with gender. haplo.ccs(case ~ age + factor(race) + gender*haplo(geno))

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