fitAndEvalDataset {TPP2D} | R Documentation

## Fit H0 and H1 model to 2D thermal profiles of proteins and compute F statistic

### Description

Fit H0 and H1 model to 2D thermal profiles of proteins and compute F statistic

### Usage

    fitAndEvalDataset(df, maxit = 500, optim_fun_h0 = .min_RSS_h0,
      optim_fun_h1 = .min_RSS_h1_slope_pEC50, optim_fun_h1_2 = NULL,
      gr_fun_h0 = NULL, gr_fun_h1 = NULL, gr_fun_h1_2 = NULL,
      ec50_lower_limit = NULL, ec50_upper_limit = NULL, slopEC50 = TRUE)

### Arguments

`df` | tidy data_frame retrieved after import of a 2D-TPP dataset, potential filtering and addition of a column "nObs" containing the number of observations per protein
`maxit` | maximal number of iterations the optimization should be given, default is set to 500
`optim_fun_h0` | optimization function that should be used for fitting the H0 model
`optim_fun_h1` | optimization function that should be used for fitting the H1 model
`optim_fun_h1_2` | optional additional optimization function that will be run with paramters retrieved from optim_fun_h1 and should be used for fitting the H1 model with the trimmed sum model, default is NULL
`gr_fun_h0` | optional gradient function for optim_fun_h0, default is NULL
`gr_fun_h1` | optional gradient function for optim_fun_h1, default is NULL
`gr_fun_h1_2` | optional gradient function for optim_fun_h1_2, default is NULL
`ec50_lower_limit` | lower limit of ec50 parameter
`ec50_upper_limit` | lower limit of ec50 parameter
`slopEC50` | logical flag indicating whether the h1 model is fitted with a linear model describing the shift od the pEC50 over temperatures

### Value

data frame with H0 and H1 model characteristics for each protein and respectively computed F statistics

### Examples

    data("simulated_cell_extract_df")
    temp_df <- simulated_cell_extract_df %>%
      group_by(representative) %>%
      mutate(nObs = n()) %>%
      ungroup
    fitAndEvalDataset(temp_df)

[Package TPP2D version 1.4.1 Index]