fitMarginals {BIGL} | R Documentation |

This function uses dose-response data for two compounds and estimates coefficients for monotherapy models of both of these compounds such that they share a common baseline. Currently, these coefficients are estimated by default using a non-linear least squares approximation. Although entire dose-response data can be provided, estimation will subset the part of data where at least one of the compounds is dosed at zero, i.e. on-axis data.

```
fitMarginals(
data,
transforms = NULL,
start = NULL,
constraints = NULL,
fixed = NULL,
method = c("nlslm", "nls", "optim"),
names = NULL,
...
)
```

`data` |
Dose-response dataframe. Marginal data will be extracted from it automatically. |

`transforms` |
Transformation functions. If non-null, |

`start` |
Starting parameter values. If not specified, they will be
obtained from |

`constraints` |
List of constraint matrix and vector which will be passed
to |

`fixed` |
This arguments provides a user-friendly alternative to impose a
fixed value for marginal parameters. It must be a named vector with names
contained in |

`method` |
Which estimation method should be used to obtain the estimates.
If |

`names` |
Compound names to be used on the plot labels. |

`...` |
Further arguments that are passed to the optimizer function,
such as |

Model formula is specified as `effect ~ fn(h1, h2, ...)`

where `fn`

is a hard-coded function which fits two 4-parameter log-logistic functions
simultaneously so that the baseline can be shared. If transformation
functions are provided, `fn`

is consequently adjusted to account for
them.

This function returns a `MarginalFit`

object with monotherapy
coefficient estimates and diverse information regarding monotherapy
estimation. `MarginalFit`

object is essentially a list with
appropriately named elements.

Among these list elements, `"coef"`

is a named vector with parameter
estimates. `h1`

and `h2`

are Hill's slope coefficients for each
of the compounds, `m1`

and `m2`

are their maximal response levels
whereas `b`

is the shared baseline. Lastly, `e1`

and `e2`

are log-transformed EC50 values.

`"sigma"`

is standard deviation of residuals for the estimated
monotherapy model and `"df"`

is the degrees of freedom for the
residuals. `"vcov"`

is the variance-covariance matrix of the estimated
parameters.

Return object also contains information regarding data, biological and power transformations used in this estimation as well as model construct and method of estimation.

```
data <- subset(directAntivirals, experiment == 1)
## Data must contain d1, d2 and effect columns
transforms <- getTransformations(data)
fitMarginals(data, transforms)
```

[Package *BIGL* version 1.6.6 Index]