fitAndEvalDataset {TPP2D} | R Documentation |
Fit H0 and H1 model to 2D thermal profiles of proteins and compute F statistic
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 )
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 |
data frame with H0 and H1 model characteristics for each protein and respectively computed F statistics
data("simulated_cell_extract_df") temp_df <- simulated_cell_extract_df %>% group_by(representative) %>% mutate(nObs = n()) %>% ungroup fitAndEvalDataset(temp_df)