# estVC: Estimate Variance Component Parameters In QTLRel: Tools for Mapping of Quantitative Traits of Genetically Related Individuals and Calculating Identity Coefficients from Pedigrees

 estVC R Documentation

## Estimate Variance Component Parameters

### Description

Estimate model parameters for covariates, genetic variance components and residual effect.

### Usage

``````estVC(y, x, v = list(E=diag(length(y))), initpar, nit = 25,
method = c("ML", "REML"), control = list(), hessian = FALSE)
``````

### Arguments

 `y` A numeric vector or a numeric matrix of one column (representing a phenotype for instance). `x` A data frame or matrix, representing covariates if not missing. `v` A list of matrices representing variance components of interest. Note: `E` is reserved for residual (or environmental) variance and can be missed in `v`; it is considered to be an identify matrix if it is missing. `v` can be provided as a single matrix, representing a variance component other than `E`. `initpar` Optional initial parameter values. When provided, `optim` will be called for optimization, which may take time but is good for checking of the result (see details for more). `nit` Maximum number of iterations for optimization. Ignored if there are not more than two variance components. `method` Either maximum likelihood (ML) or restricted maximum likelihood (REML). `control` A list of control parameters to be passed to `optim`. `hessian` Logical. Should a numerically differentiated Hessian matrix be returned?

### Details

The optimization function `optim` is adopted in the above function to estimate the parameters and maximum likelihood. Several optimization methods are available for the optimization algorithm in `optim`, but we recommend "Nelder-Mead" for the sake of stability. Alternatively, one may choose other options, e.g., "BFGS" to initialize and speed up the estimation procedure and then the procedure will automatically turn to "Nelder-Mead" for final results. If there is only one variance component (other than `E`), `optimize` will be used for optimization unless `initpar` is provided.

Normality is assumed for the random effects. Input data should be free of missing values.

### Value

 `par` estimates of the model parameters. `value` log-likelihood of the model. `y` y used. `x` associated with x used. `v` variance component matrices v used. `...` other information.

### Note

Hessian matrix, if requested, pertains to -log-likelihood function.

`optim` and `rem`.

### Examples

``````data(miscEx)

## Not run:
# no sex effect
pheno<- pdatF8[!is.na(pdatF8\$bwt) & !is.na(pdatF8\$sex),]
ii<- match(rownames(pheno), rownames(gmF8\$AA))
v<- list(A=gmF8\$AA[ii,ii], D=gmF8\$DD[ii,ii])

o<- estVC(y=pheno\$bwt, v=v)
o

# sex as fixed effect
fo<- estVC(y=pheno\$bwt, x=pheno\$sex, v=v)
fo
2*(fo\$value-o\$value) # log-likelihood test statistic

# sex as random effect
SM<- rem(~sex, data=pheno)
ro<- estVC(y=pheno\$bwt, v=c(v,list(Sex=SM\$sex)))
ro
2*(ro\$value-o\$value) # log-likelihood test statistic

## End(Not run)
``````

QTLRel documentation built on Aug. 9, 2023, 1:07 a.m.