zlm {MAST} | R Documentation

## Zero-inflated regression for SingleCellAssay

### Description

For each gene in sca, fits the hurdle model in `formula` (linear for et>0), logistic for et==0 vs et>0. Return an object of class `ZlmFit` containing slots giving the coefficients, variance-covariance matrices, etc. After each gene, optionally run the function on the fit named by 'hook'

### Usage

    zlm(formula, sca, method = "bayesglm", silent = TRUE, ebayes = TRUE,
      ebayesControl = NULL, force = FALSE, hook = NULL, parallel = TRUE,
      LMlike, onlyCoef = FALSE, ...)

### Arguments

`formula` | a formula with the measurement variable on the LHS and predictors present in colData on the RHS
`sca` | SingleCellAssay object
`method` | character vector, either 'glm', 'glmer' or 'bayesglm'
`silent` | Silence common problems with fitting some genes
`ebayes` | if TRUE, regularize variance using empirical bayes method
`ebayesControl` | list with parameters for empirical bayes procedure. See ebayes.
`force` | Should we continue testing genes even after many errors have occurred?
`hook` | a function called on the `fit` after each gene.
`parallel` | If TRUE and `option(mc.cores)>1` then multiple cores will be used in fitting.
`LMlike` | if provided, then the model defined in this object will be used, rather than following the formulas. This is intended for internal use.
`onlyCoef` | If TRUE then only an array of model coefficients will be returned (probably only useful for bootstrapping).
`...` | arguments passed to the S4 model object upon construction. For example, `fitArgsC` and `fitArgsD`, or `coefPrior`.

### Value

a object of class `ZlmFit` with methods to extract coefficients, etc. OR, if data is a `data.frame` just a list of the discrete and continuous fits.

### Empirical Bayes variance regularization

The empirical bayes regularization of the gene variance assumes that the precision (1/variance) is drawn from a gamma distribution with unknown parameters. These parameters are estimated by considering the distribution of sample variances over all genes. The procedure used for this is determined from `ebayesControl`, a named list with components 'method' (one of 'MOM' or 'MLE') and 'model' (one of 'H0' or 'H1') method MOM uses a method-of-moments estimator, while MLE using the marginal likelihood. H0 model estimates the precisions using the intercept alone in each gene, while H1 fits the full model specified by `formula`

### See Also

ZlmFit-class, ebayes, GLMlike-class, BayesGLMlike-class

### Examples

    data(vbetaFA)
    zlmVbeta <- zlm(~ Stim.Condition, subset(vbetaFA, ncells==1)[1:10,])
    slotNames(zlmVbeta)
    #A matrix of coefficients
    coef(zlmVbeta, 'D')['CCL2',]
    #An array of covariance matrices
    vcov(zlmVbeta, 'D')[,,'CCL2']
    waldTest(zlmVbeta, CoefficientHypothesis('Stim.ConditionUnstim'))

    ## Can also provide just a \code{data.frame} instead
    data<- data.frame(x=rnorm(500), z=rbinom(500, 1, .3))
    logit.y <- with(data, x*2 + z*2); mu.y <- with(data, 10+10*x+10*z + rnorm(500))
    y <- (runif(500)<exp(logit.y)/(1+exp(logit.y)))*1
    y[y>0] <- mu.y[y>0]
    data$y <- y
    fit <- zlm(y ~ x+z, data)
    summary.glm(fit$disc)
    summary.glm(fit$cont)

[Package MAST version 1.4.1 Index]