Package index • riskyr

Initialization

Creating riskyr scenarios and corresponding parameter collections.

FFTrees_riskyr(): Convert from FFTrees to riskyr objects.

plot(<riskyr>): Plot a riskyr scenario.

print(<summary.riskyr>): Print summary information of a riskyr scenario.

riskyr(): Create a riskyr scenario.

riskyr.guide(): Opens the riskyr package guides

summary(<riskyr>): Summarize a riskyr scenario.

df_scenarios: A collection of riskyr scenarios from various sources (as df).

scenarios: A collection of riskyr scenarios from various sources (as list).

init_num(): Initialize basic numeric variables.

init_pal(): Initialize basic color information.

init_txt(): Initialize basic text elements.

num: List current values of basic numeric variables.

popu: A table of cases (in the current population).

read_popu(): Read population data (from df) into a riskyr scenario (description).

pal: List current values of scenario color palette.

pal_bw: Alternative color palette for black-and-white (greyscale) graphs.

pal_bwp: Alternative color palette for black-and-white graphs (for printing purposes).

pal_crisk: Color palette for cumulative risk curve.

pal_kn: Alternative color palette for kn.

pal_mbw: Modern and reduced color palette (in green/blue/bw).

pal_mod: Modern color palette (in green/blue/orange).

pal_org: Original color palette.

pal_rgb: Alternative color palette for graphs (with RGB colors).

pal_unikn: Alternative color palette for unikn.

pal_vir: Alternative color palette using viridis colors.

txt: Basic text elements.

txt_TF: Alternative text labels (TP, FN, FP, TN).

txt_org: List of original values of text elements.

Visualization

Functions for creating graphical representations of riskyr scenarios.

plot(<box>): Plot a frequency box object

plot(<riskyr>): Plot a riskyr scenario.

plot_area(): Plot an area diagram of probabilities or frequencies.

plot_bar(): Plot bar charts of population frequencies.

plot_cbar(): Plot cumulative risk dynamics (as bar chart)

plot_crisk(): Plot a cumulative risk curve.

plot_curve(): Plot curves of selected values (e.g., PPV or NPV) as a function of prevalence.

plot_fnet(): Plot frequency net diagram of frequencies and probabilities.

plot_icons(): Plot an icon array of a population.

plot_mosaic(): Plot a mosaic plot of population frequencies.

plot_plane(): Plot a plane of selected values (e.g., PPV or NPV) as a function of sensitivity and specificity.

plot_prism(): Plot prism diagram of frequencies and probabilities.

plot_tab(): Plot a 2 x 2 contingency table of population frequencies.

plot_tree(): Plot a tree diagram of frequencies and probabilities.

Probabilities

Lists and objects representing probability information.

prob: List current probability information.

prev: The prevalence (baseline probability) of a condition.

sens: The sensitivity (or hit rate) of a decision process or diagnostic procedure.

mirt: The miss rate of a decision process or diagnostic procedure.

spec: The specificity of a decision process or diagnostic procedure.

fart: The false alarm rate (or false positive rate) of a decision process or diagnostic procedure.

ppod: The proportion (or baseline) of a positive decision (aka. bias).

PPV: The positive predictive value of a decision process or diagnostic procedure.

NPV: The negative predictive value of a decision process or diagnostic procedure.

FDR: The false detection rate of a decision process or diagnostic procedure.

FOR: The false omission rate (FOR) of a decision process or diagnostic procedure.

acc: Accuracy (acc) is the probability of a correct decision.

accu: A list containing current accuracy information.

err: Error rate (err) as the probability of an incorrect decision.

Frequencies

Lists and objects representing frequencies (i.e., various subsets of a population).

freq: List current frequency information.

N: Number of individuals in the population.

cond_false: Number of individuals for which the condition is false.

cond_true: Number of individuals for which the condition is true.

dec_cor: Number of individuals for which the decision is correct.

dec_err: Number of individuals for which the decision is erroneous.

dec_neg: Number of individuals for which the decision is negative.

dec_pos: Number of individuals for which the decision is positive.

hi: Frequency of hits or true positives (TP).

mi: Frequency of misses or false negatives (FN).

fa: Frequency of false alarms or false positives (FP).

cr: Frequency of correct rejections or true negatives (TN).

Computation and conversion

Computing parameters and translating between representational formats and data/descriptions.

comp_FDR(): Compute a decision's false detection rate (FDR) from probabilities.

comp_FOR(): Compute a decision's false omission rate (FOR) from probabilities.

comp_NPV(): Compute a decision's negative predictive value (NPV) from probabilities.

comp_PPV(): Compute a decision's positive predictive value (PPV) from probabilities.

comp_acc(): Compute overall accuracy (acc) from probabilities.

comp_accu_freq(): Compute accuracy metrics of current classification results.

comp_accu_prob(): Compute exact accuracy metrics based on probabilities.

comp_comp_pair(): Compute a probability's (missing) complement and return both.

comp_complement(): Compute a probability's complement probability.

comp_complete_prob_set(): Compute a complete set of probabilities from valid probability inputs.

comp_err(): Compute overall error rate (err) from probabilities.

comp_fart(): Compute a decision's false alarm rate from its specificity.

comp_freq(): Compute frequencies from (3 essential) probabilities.

comp_freq_freq(): Compute frequencies from (4 essential) frequencies.

comp_freq_prob(): Compute frequencies from (3 essential) probabilities.

comp_min_N(): Compute a suitable minimum population size value N.

comp_mirt(): Compute a decision's miss rate from its sensitivity.

comp_popu(): Compute a population table (data) from frequencies (description).

comp_ppod(): Compute the proportion of positive decisions (ppod) from probabilities.

comp_prev(): Compute the condition's prevalence (baseline probability) from frequencies.

comp_prob(): Compute probabilities from (3 essential) probabilities.

comp_prob_freq(): Compute probabilities from (4 essential) frequencies.

comp_prob_prob(): Compute probabilities from (3 essential) probabilities.

comp_sens(): Compute a decision's sensitivity from its miss rate.

comp_spec(): Compute a decision's specificity from its false alarm rate.

read_popu(): Read population data (from df) into a riskyr scenario (description).

write_popu(): Write a population table (data) from a riskyr scenario (description).

Datasets

Data used in examples and illustrations.

df_scenarios: A collection of riskyr scenarios from various sources (as df).

BRCA1: Cumulative risk of breast cancer in women with the BRCA1 mutation.

BRCA1_mam: Cumulative risk of breast cancer in women with the BRCA1 mutation.

BRCA1_ova: Cumulative risk of ovarian cancer in women with the BRCA1 mutation.

BRCA2: Cumulative risk of breast cancer in women with the BRCA2 mutation.

BRCA2_mam: Cumulative risk of breast cancer in women with the BRCA2 mutation.

BRCA2_ova: Cumulative risk of ovarian cancer in women with the BRCA2 mutation.

t_A: Cumulative risk curve A (main/transfer task A).

t_B: Cumulative risk curve B (main/transfer task B).

t_I: Cumulative risk curve I (introductory task).

Validation and formatting

Verifying and displaying representational formats.

is_complement(): Verify that two numbers are complements.

is_extreme_prob_set(): Verify that a set of probabilities describes an extreme case.

is_freq(): Verify that input is a frequency (positive integer value).

is_integer(): Test for inters (i.e., whole numbers).

is_matrix(): Verify a 2x2 matrix as a numeric contingency table.

is_perc(): Verify that input is a percentage (numeric value from 0 to 100).

is_prob(): Verify that input is a probability (numeric value from 0 to 1).

is_suff_prob_set(): Verify a sufficient set of probability inputs.

is_valid_prob_pair(): Verify that a pair of probability inputs can be a pair of complementary probabilities.

is_valid_prob_set(): Verify that a set of probability inputs is valid.

is_valid_prob_triple(): Verify that a triple of essential probability inputs is valid.

as_pb(): Display a percentage as a (numeric and rounded) probability.

as_pc(): Display a probability as a (numeric and rounded) percentage.

Extensions

Extensions and links to other packages.

FFTrees_riskyr(): Convert from FFTrees to riskyr objects.