Package index • vpc

All functions

add_noise(): Add noise / residual error to data
add_sim_index_number(): Add sim index number
add_stratification(): Adds stratification to data set
as_vpcdb(): Create a vpcdb object, and standardize parameter checking
auto_bin(): Calculate appropriate bin separators for vpc
bin_data(): Function to bin data based on a vector of bin separators, e.g. for use in VPC
calc_pred_corr_continuous(): Perform prediction-correction
calc_vpc_continuous(): Calculate aggregate statistics for simulated and observed VPC data
check_stratification_columns_available(): Check whether stratification columns are available
compute_kaplan(): Compute Kaplan-Meier statistics
compute_kmmc(): Compute KMMC statistics
create_vpc_theme(): Create new vpc theme
define_bins() define_bins_tte(): Define bins for many types of data
define_data_columns(): Define data column defaults for various softwares
define_loq(): Check and standardize limits of quantification
draw_params_mvr(): Draw parameters from multivariate distribution
filter_dv(): Remove values that are not observed values from data
format_vpc_input_data() format_vpc_input_data_tte(): Prepare VPC data for future calculations by standardizing column names and modifying the input data based on the limits of quantification, stratification, and logarithmic values.
ggplot.vpcdb_continuous() ggplot.vpcdb_categorical() ggplot.vpcdb_censored() ggplot.vpcdb_time_to_event(): Create a ggplot for each vpcdb type
ggplot2_label_variable(): Function copied from ggplot2:::label_variable, with addition of ` Copied here since CRAN does not like use of `:::`.
label_vpc_tte(): Custom ggplot2 labeller function.
loq_frac(): Calculate fraction of observations below lloq / above uloq
new_vpc_theme(): Create a customized VPC theme
pk_iv_1cmt(): Simulate PK data from a 1-compartment iv model
pk_oral_1cmt(): Simulate PK data from a 1-compartment oral model
plot_vpc(): VPC plotting function
pred_correction_core(): Core prediction correction function
quantile_cens(): Calculate quantiles respecting the censored data
read_table_nm(): NONMEM output table import function
read_vpc(): Read in VPC data
replace_list_elements(): Replace list elements by name
rtte_obs_nm: Simulated RTTE data (1x)
rtte_sim_nm: Simulated RTTE data (100x)
show_default: Defaults for show argument with various endpoint types
simple_data: A small rich dataset
standardize_column(): Confirm that a column is in the data, and rename data to prepare that column for later use.
theme_empty(): Empty ggplot2 theme
theme_plain(): A nicer default theme for ggplot2
triangle_to_full(): Lower to full triangle
-package vpc-package: VPC package
vpc() vpc_vpc(): VPC function
vpc_cat(): VPC function for categorical
vpc_cens(): VPC function for left- or right-censored data (e.g. BLOQ data)
geom_bin_sep() geom_hline_loq() geom_obs_cens_dat_tte() geom_obs_ci_continuous() geom_obs_ci_tte() geom_obs_dv_continuous() geom_obs_km() geom_obs_median_continuous() geom_sim_km() geom_sim_median_continuous() geom_sim_median_tte() geom_sim_median_ci_continuous() geom_sim_pi_as_area_continuous() geom_sim_pi_as_area_tte() geom_sim_pi_continuous() geom_sim_pi_ci_continuous() facet_continuous() facet_tte() guides_stratify_color() scale_x_log10_vpc() scale_y_log10_vpc(): A collection of internal ggplot helpers for VPC plotting
vpc_tte(): VPC function for time-to-event (survival) data