ternaryFit-class | R Documentation |

This is a class representation of the output of the ternary
network fitting algorithm implemented in the function `tnetfit`

.

While one can create their own objects using the function
`ternaryFit()`

, this is highly discouraged. Typically this class
is created by running the `tnetfit`

function.

`perturbationObj`

:a matrix of perturbation experiments. Rows are genes and columns are experiments.

`steadyStateObj`

:a matrix of steady gene expression observations from a perturbation experiment. Rows are genes and columns are experiments.

`geneNames`

:a vector of gene names corresponding to the rows of the perturbationObj and steadyStateObj.

`experimentNames`

:a vector of experiment names corresponding to the columns of the perturbationObj and steadyStateObj.

`degreeObjMin`

:a vector containing the in-degree of each node in the fit achieving the minimum score

`graphObjMin`

:a matrix containing the parents of each node in the fit achieving the minimum score

`tableObjMin`

:a matrix containing the table in the fit achieving the minimum score

`newScore`

:the most recent score

`minScore`

:the minimum score

`finalTemperature`

:the final value of the temperature parameter

`traces`

:a dataframe contain the traces for 4 parameters

`stageCount`

:the number of stages

`xSeed`

:the random seed.

`inputParams`

:the ternaryFitParameters object used.

All named elements can be accessed and set in the standard way
(e.g. `xSeed(object)`

and `xSeed(object)<-`

).

Matthew N. McCall and Anthony Almudevar

`tnetpost`

, `ternaryFitParameters-class`

, `ternaryPost-class`

.
Almudevar A, McCall MN, McMurray H, Land H (2011). Fitting
Boolean Networks from Steady State Perturbation Data, Statistical
Applications in Genetics and Molecular Biology, 10(1): Article 47.

```
ssObj <- matrix(c(1,1,1,0,1,1,0,0,1),nrow=3)
pObj <- matrix(c(1,0,0,0,1,0,0,0,1),nrow=3)
tnfitObj <- tnetfit(ssObj, pObj)
class(tnfitObj)
```

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