Description Usage Arguments Value Empirical Bayes variance regularization See Also Examples

View source: R/zeroinf.R View source: R/zeroinf.R

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'

1 2 3 |

`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 |

`parallel` |
If TRUE and |

`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, |

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.

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`

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
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)
``` |

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