Description Usage Arguments Examples
Plot the sampled profile likelihood estimations (PLE). The table is made of two columns: ObjVal | Parameter
1 2 | plot_sampled_ple(df99, cl66_objval, cl95_objval, cl99_objval, plots_dir, model,
logspace = TRUE, scientific_notation = TRUE)
|
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 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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)
|
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