CNVassoc: Association analysis between a CNV and phenotype

In CNVassoc: Association Analysis of CNV Data and Imputed SNPs

Description

This function performs an association analysis between a CNV and a dependent variable (phenotype) using a latent class model that incorporates the uncertainty arising from calling procedure. The phenotype may be quantitative or categorical. In the second case (e.g. case-control studies) this variable must be coded as 1 (for cases) and 0 (for controls). The association can be adjusted for other covariates (e.g. clinical covariates, stratification, ...)

Usage

  CNVassoc(formula, data, subset, na.action, model = "multiplicative", 
           family = "binomial", tol = 1e-06, max.iter = 30, emsteps = 0, 
           verbose = FALSE, coef.start, sigma.start, alpha.start=1)

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. Right side of ~ should have an object of class 'cnv'.
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)'.
subset	an optional vector specifying a subset of observations to be used in the fitting process.
na.action	a function which indicates what should happen when the data contain 'NA's. The default is set by the 'na.action' setting of 'options', and is 'na.fail' if that is unset. The 'factory-fresh' default is 'na.omit'. Another possible value is 'NULL', no action. Value 'na.exclude' can be useful.
model	Genetic model to be tested. Possible values are "multiplicative" (model free, e.g. co-dominant) or "additive", partial matching allowed. Default value is "multiplicative".
family	a description of the error distribution and link function to be used in the model. This must be a character string naming a family function. Possible values are "binomial", "gaussian", "poisson" or "weibull". Default value is "binomial"
tol	Tolerance for convergence in fitting model. Default value is 1e-06.
max.iter	Maximum number of iterations in fitting model. Default value is 30.
emsteps	Number of iterations using Expectation Maximization (EM) alghorithm to set initial values before using Newton-Rapson (NR) in fitting model. Default value is zero, that means that EM step is not performed
verbose	logical. If TRUE parameter values for each iteration are shown in the console. Default value is FALSE
coef.start	initial values for coefficients in NR procedure
sigma.start	initial values for scale parameter (only for "gaussian") in NR procedure
alpha.start	initial values for shape parameter (only for "weibull") in NR procedure

Value

An object of class 'CNVassoc'.

'print' returns model parameter estimates

'summary' returns a summary table similar to summary.glm

'anova' performs a Likelihood Ratio Test comparing two nested models fitted using CNVassoc

'logLik' returns the log-likelihood of a model fitted using CNVassoc

See examples for further illustration about all previous issues.

References

Gonzalez JR, Subirana I, Escaramis G, Peraza S, Caceres A, Estivill X and Armengol L. Accounting for uncertainty when assessing association between copy number and disease: a latent class model. BMC Bioinformatics, 2009;10:172.

See Also

cnv,CNVtest

Examples

data(dataMLPA)
CNV  <-  cnv(x  =  dataMLPA$Gene2,  threshold.0  =  0.01,  mix.method  =  "mixdist")
modmul  <-  CNVassoc(casco  ~  CNV,  data  =  dataMLPA,  model  =  "mul")
modmul
summary(modmul)
anova(modmul,update(modmul,model="add"))
logLik(modmul)

CNVassoc documentation built on May 30, 2017, 12:50 a.m.