runNPARC {NPARC} | R Documentation

## Non-parametric analysis of response curves

### Description

Wrapper function for melting curve fitting and hypothesis testing.

### Usage

    runNPARC(x, y, id, groupsNull = NULL, groupsAlt,
      BPPARAM = BiocParallel::SerialParam(progressbar = TRUE),
      dfType = c("theoretical", "empirical"), control = getParams())

### Arguments

`x` | numeric vector of the independent variables (typically temperature)
`y` | numeric vector of the dependent variables (typically relative abundance measurements)
`id` | character vector with the protein ID to which each each data point belongs.
`groupsNull` | one or more vectors with grouping variables for the null models. See details.
`groupsAlt` | one or more vectors with grouping variables for the alternative models. See details.
`BPPARAM` | BiocParallel parameter object to invoke curve fitting in parallel. Default: BiocParallel::SerialParam()
`dfType` | character value indicating the method for degrees of freedom computation for the F-test. Theoretical yields the text-book solution. Empirical yields estimates derived from the distribution moments of the RSS.
`control` | list of parameters used to control specific parts of the analyse

### Details

`groupsNull` or `groupsAlt` can either be a single vector each, or data.frames of the same length as `x` and `y` with one column per factor

### Value

data frame with fitted model parameters and additional columns listing e.g. residuals sum of squares of null and alterantive model

### Examples

    data(stauro_TPP_data_tidy)
    df <- dplyr::filter(stauro_TPP_data_tidy, grepl("CDK|GTP|CRK", uniqueID))
    testResults <- runNPARC(x = df$temperature,
                         y = df$relAbundance,
                         id = df$uniqueID,
                         groupsAlt = df$compoundConcentration,
                         dfType = "empirical")

[Package NPARC version 1.4.0 Index]