sbpiper (version 1.9.0)

plot_sampled_ple: Plot the sampled profile likelihood estimations (PLE). The table is made of two columns: ObjVal | Parameter

Description

Plot the sampled profile likelihood estimations (PLE). The table is made of two columns: ObjVal | Parameter

Usage

plot_sampled_ple(df99, cl66_objval, cl95_objval, cl99_objval, plots_dir, model,
  logspace = TRUE, scientific_notation = TRUE)

Arguments

df99

the data set including the fits within 99% confidence level

cl66_objval

the objective value at 66% confidence level

cl95_objval

the objective value at 95% confidence level

cl99_objval

the objective value at 99% confidence level

plots_dir

the directory to save the generated plots

model

the model name

logspace

true if parameters should be plotted in logspace

scientific_notation

true if the axis labels should be plotted in scientific notation

Examples

# NOT RUN {
dir.create(file.path("pe_datasets"))
dir.create(file.path("pe_plots"))
data(insulin_receptor_all_fits)
write.table(insulin_receptor_all_fits, 
            file=file.path("pe_datasets", "all_fits.csv"), 
            row.names=FALSE)
# generate the global statistics for the parameter estimation
pe_ds_preproc(filename=file.path("pe_datasets", "all_fits.csv"), 
              param.names=c('k1', 'k2', 'k3'), 
              logspace=TRUE, 
              all.fits=TRUE, 
              data_point_num=33, 
              fileout_param_estim_summary=file.path("pe_datasets", "param_estim_summary.csv"))
# load the fits for this parameter
df <- as.data.frame(data.table::fread(file.path("pe_datasets", "all_fits_log10.csv"), 
                                      select=c("ObjVal", "k2")))
# load the global statistics for the parameter estimation
dt.stats <- data.table::fread(file.path("pe_datasets", "param_estim_summary.csv"), 
                              select=c("MinObjVal", "CL66ObjVal", "CL95ObjVal", "CL99ObjVal"))
df99 <- df[df[ ,"ObjVal"] <= dt.stats$CL99ObjVal, ]
# compute the stats for parameter k2. 
plot_sampled_ple(df99=df99, 
                 cl66_objval=dt.stats$CL66ObjVal, 
                 cl95_objval=dt.stats$CL95ObjVal, 
                 cl99_objval=dt.stats$CL99ObjVal, 
                 plots_dir="pe_plots",
                 model="ir_beta",
                 logspace=TRUE)
# }