Description Usage Arguments Value Author(s) Examples

This function models the missing data mechanism and uses an EM algorithm to impute the non-detect values in qPCR data.

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`object` |
a qPCRset |

`dj` |
normalization values. If NULL, features with "control" in featureType(object) are used to normalize the data. If no control features are found, the data are not normalized. |

`pyfit` |
initial estimate of the relationship between the probability of a non-detect and average expression. If NULL, this relationship is estimated from the data. |

`groupVars` |
which columns in pData(object) should be used to determine replicate samples. If NULL, all columns are used. |

`batch` |
amatrix with control samples for each batch, if NULL, batch effect is not taken into account. |

`tol` |
likelihood convergence criterion of the EM algorithm. |

`iterMax` |
maximimum number of iterations of the EM algorithm. |

`outform` |
the form of the output requested.If "Single" performes a single imputation of missing values. If "Param" returnes estimated model parameters: mean and variance. If "Multy" performes a multiple imputation of missing values, and creats multiple data sets with imputed values. |

`vary_fit` |
if outform="Multy", includes the model uncertainty due to the logit of the probability of being missing. The default value is "TRUE". |

`vary_model` |
if outform="Multy", includes the model uncertainty due to the estimating mean of the data. The default value is "TRUE". |

`add_noise` |
if outform="Multy", introduses the variance component due to the random noise. The default value is "TRUE". |

`formula` |
specifies the model. |

`numsam` |
number of the datasets to be created if outform="Multy". The default value is 5. |

`linkglm` |
a link used for estimation of the missing data mechanism. |

The function returns a qPCRset object with non-detects replaced by their imputed values.

Valeriia Sherina

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