IN-vivo reSPonsE Classification of Tumours

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Documentation for package ‘INSPECTumours’ version 0.1.0

Help Pages

aggregate_study_info create a table with aggregated data: each row contains information about control and treatments of a single study
animal_info_classification Generate table representing number of animals in classification groups
assess_efficacy Credible interval (or say “Bayesian confidence interval”) of the mean difference between two groups (treatment and reference) is used to assess the efficacy. If 0 falls outside the interval, the drug was considered significantly effective
below_min_points makes df with data to be excluded
calc_gr Function to return rate of growth (e.g. the slope after a log transformation of the tumour data against time)
calc_probability Calculate probability of categories
calc_survived Calculate percentage of survived animals
change_time_multi Get an array with change_time for studies from the population-level effects, multiple studies
change_time_single Get a change time from the population-level effects, single study
classify_data_point Classify individual data points as Responders or Non-responders
classify_subcategories Make predictions for subcategories
classify_type_responder Classify tumour based on the growth rate and the p_value for a two-sided T test Tumour will be considered as "Non-responder", "Modest responder", "Stable responder" or "Regressing responder"
clean_string function to remove hyphens, underscores, spaces and transform to lowercase
control_growth_plot Function to plot a control growth profile
example_data Tumour volume data over time for in-vivo studies
exclude_data Filter rows to exclude from the analysis
expand_palette Function to expand a vector of colors if needed
f_start Calculate coefficients for a nonlinear model
get_responder Classify tumour based on response status of individuals
guess_match function to search for the possible critical columns in a data.frame
hide_outliers Function to hide outliers in boxplots with jitterdodge as suggested
load_data function to read data from users (.csv or .xlsx files)
make_terms Create a character vector with the names of terms from model, for which predictions should be displayed Specific values are specified in square brackets
model_control Build model and make predictions
notify_error_and_reset_input Display a popup message and reset fileInput
ordered_regression Fit model (Bayesian ordered logistic regression)
plotly_volume Create volume plot for one-batch data
plot_animal_info Plot representing number of animals in classification groups
plot_class_gr Function to plot classification over growth rate
plot_class_tv Function to plot classification over tumour volume
plot_proportions Plot estimated proportions
plot_waterfall Function to plot waterfall
predict_lm Make predictions, linear model
predict_nlm_multi Make predictions based on non-linear model, multiple studies
predict_nlm_single Make predictions based on non-linear model, single study
predict_regr_model Make predictions
run_app Run the Shiny Application
run_nl_model Fit nonlinear model - continuous hinge function
set_waiter Set up a waiting screen