competeModels {TPP2D} | R Documentation |
Compete H0 and H1 models per protein and obtain F statistic
competeModels(df, fcThres = 1.5, independentFiltering = FALSE, minObs = 20, 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, maxit = 750)
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 |
fcThres |
numeric value of minimal fold change (or inverse fold change) a protein has to show to be kept upon independent filtering |
independentFiltering |
boolean flag indicating whether independent filtering should be performed based on minimal fold changes per protein profile |
minObs |
numeric value of minimal number of observations that should be required per protein |
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 |
maxit |
maximal number of iterations the optimization should be given, default is set to 500 |
data frame summarising the fit characteristics of H0 and H1 models and therof resulting computed F statistics per protein
data("simulated_cell_extract_df") temp_df <- simulated_cell_extract_df %>% filter(clustername %in% paste0("protein", 1:10)) %>% group_by(representative) %>% mutate(nObs = n()) %>% ungroup competeModels(temp_df)