Description Usage Arguments Details Value Author(s)

Fit an error model to reduced replicate data, see
`reduceReplicates`

.

1 2 3 4 5 6 7 8 9 |

`data` |
Reduced replicate data, see |

`factors` |
data is pooled with respect to the columns named here, see Details. |

`errorModel` |
Character vector defining the error model in terms of the variance.
Use |

`par` |
Inital values for the parameters of the error model. |

`plotting` |
If TRUE, a plot of the pooled variance together with the fit of the error model is shown. |

`blather` |
If TRUE, additional information is returned, such as fit parameters and sigmaLS (original sigma given in input data). |

`...` |
Parameters handed to the optimizer |

The variance estimator using *n-1* data points is *chi^2*
distributed with *n-1* degrees of freedom. Given replicates for
consecutive time points, the sample variance can be assumed a function of
the sample mean. By defining an error model which must hold for all time
points, a maximum likelihood estimator for the parameters of the error
model can be derived. The parameter errorModel takes the error
model as a character vector, where the mean (independent variable) is
refered to as `x`.

It is desireable to estimate the variance from many replicates. The parameter data must provide one or more columns which define the pooling of data. In case more than one column is announced by factors, all combinations are constructed. If, e.g., factors = c("condition", "name") is used, where "condition" is "a", "b", "c" and repeating and "name" is "d", "e" and repeating, the effective conditions used for pooling are "a d", "b e", "c d", "a e", "b d", and "c e".

By default, a plot of the pooled data, sigma and its confidence bound at 68% and 95% is shown.

Returned by default is a data frame with columns as in data, but with the sigma values replaced by the derived values, obtained by evaluating the error model with the fit parameters.

If the blather = TRUE option is chosen, fit values of the parameters of the error model are appended, with the column names equal to the parameter names. The error model is appended as the attribute "errorModel". Confidence bounds for sigma at confidence level 68% and 95% are calculated, their values come next in the returned data frame. Finally, the effective conditions are appended to easily check how the pooling was done.

Wolfgang Mader, Wolfgang.Mader@fdm.uni-freiburg.de

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.