Create a riskyr scenario.

Source: R/riskyr_class.R

riskyr creates a scenario of class "riskyr", which can be visualized by the plot method plot.riskyr and summarized by the summary method summary.riskyr.

riskyr(
  scen_lbl = txt$scen_lbl,
  popu_lbl = txt$popu_lbl,
  N_lbl = txt$N_lbl,
  cond_lbl = txt$cond_lbl,
  cond_true_lbl = txt$cond_true_lbl,
  cond_false_lbl = txt$cond_false_lbl,
  dec_lbl = txt$dec_lbl,
  dec_pos_lbl = txt$dec_pos_lbl,
  dec_neg_lbl = txt$dec_neg_lbl,
  acc_lbl = txt$acc_lbl,
  dec_cor_lbl = txt$dec_cor_lbl,
  dec_err_lbl = txt$dec_err_lbl,
  sdt_lbl = txt$sdt_lbl,
  hi_lbl = txt$hi_lbl,
  mi_lbl = txt$mi_lbl,
  fa_lbl = txt$fa_lbl,
  cr_lbl = txt$cr_lbl,
  prev = NA,
  sens = NA,
  spec = NA,
  fart = NA,
  N = NA,
  hi = NA,
  mi = NA,
  fa = NA,
  cr = NA,
  scen_lng = txt$scen_lng,
  scen_txt = txt$scen_txt,
  scen_src = txt$scen_src,
  scen_apa = txt$scen_apa,
  round = TRUE,
  sample = FALSE
)

Format

A riskyr object describing a risk-related scenario (with textual and numeric information).

Arguments

scen_lbl

The current scenario title (sometimes in Title Caps).

popu_lbl

A brief description of the current population or sample.

N_lbl

A label for the current population popu or sample.

cond_lbl

A label for the condition or feature (e.g., some disease) currently considered.

cond_true_lbl

A label for the presence of the current condition or cond_true cases (the condition's true state of TRUE).

cond_false_lbl

A label for the absence of the current condition or cond_false cases (the condition's true state of FALSE).

dec_lbl

A label for the decision or judgment (e.g., some diagnostic test) currently made.

dec_pos_lbl

A label for positive decisions or dec_pos cases (e.g., predicting the presence of the condition).

dec_neg_lbl

A label for negative decisions or dec_neg cases (e.g., predicting the absence of the condition).

acc_lbl

A label for accuracy (i.e., correspondence between condition and decision or judgment).

dec_cor_lbl

A label for correct (or accurate) decisions or judgments.

dec_err_lbl

A label for incorrect (or erroneous) decisions or judgments.

sdt_lbl

A label for the combination of condition and decision currently made.

hi_lbl

A label for hits or true positives hi (i.e., correct decisions of the presence of the condition, when the condition is actually present).

mi_lbl

A label for misses or false negatives mi (i.e., incorrect decisions of the absence of the condition when the condition is actually present).

fa_lbl

A label for false alarms or false positives fa (i.e., incorrect decisions of the presence of the condition when the condition is actually absent).

cr_lbl

A label for correct rejections or true negatives cr (i.e., a correct decision of the absence of the condition, when the condition is actually absent).

Essential probabilities:

prev

The condition's prevalence prev (i.e., the probability of condition being TRUE).

sens

The decision's sensitivity sens (i.e., the conditional probability of a positive decision provided that the condition is TRUE). sens is optional when its complement mirt is provided.

spec

The decision's specificity value spec (i.e., the conditional probability of a negative decision provided that the condition is FALSE). spec is optional when its complement fart is provided.

fart

The decision's false alarm rate fart (i.e., the conditional probability of a positive decision provided that the condition is FALSE). fart is optional when its complement spec is provided.

Essential frequencies:

N

The number of individuals in the scenario's population. A suitable value of N is computed, if not provided.

hi

The number of hits hi (or true positives).

mi

The number of misses mi (or false negatives).

fa

The number of false alarms fa (or false positives).

cr

The number of correct rejections cr (or true negatives).

Details and source information:

scen_lng

Language of the current scenario (as character code). Options: "en" for English, "de" for German.

scen_txt

A longer text description of the current scenario (which may extend over several lines).

scen_src

Source information for the current scenario.

scen_apa

Source information for the current scenario according to the American Psychological Association (APA style).

round

Boolean value that determines whether frequency values are rounded to the nearest integer. Default: round = TRUE.

Note: Only rounding when using comp_freq_prob (i.e., computing freq from prob description).

sample

Boolean value that determines whether frequency values are sampled from N, given the probability values of prev, sens, and spec. Default: sample = FALSE.

Note: Only sampling when using comp_freq_prob (i.e., computing freq from prob description).

Value

A riskyr object describing a risk-related scenario.

Scenario-specific titles and text labels (see txt).

Details

Beyond basic scenario information (i.e., text elements describing a scenario) only the population size N and the essential probabilities prev, sens, spec, and fart are used and returned.

Note:

  • Basic text information and some numeric parameters (see num and init_num) are integral parts of a riskyr scenario.

  • By contrast, basic color information (see pal and init_pal) is not an integral part, but independently defined.

  • The names of probabilities (see prob) are currently not an integral part of txt and riskyr scenarios (but defined in prob_lbl_def and label_prob).

See also

init_num and num for basic numeric parameters; init_txt and txt for current text settings; init_pal and pal for current color settings.

Other riskyr scenario functions: plot.riskyr(), summary.riskyr()

Other functions initializing scenario information: init_num(), init_pal(), init_txt()

Examples

# Defining scenarios: -----
# (a) minimal information:
hustosis <- riskyr(scen_lbl = "Screening for hustosis",
                   N = 1000, prev = .04, sens = .80, spec = .95)

# (2) detailed information:
scen_reoffend <- riskyr(scen_lbl = "Identify reoffenders",
                        cond_lbl = "being a reoffender",
                        popu_lbl = "Prisoners",
                        cond_true_lbl = "has reoffended",
                        cond_false_lbl = "has not reoffended",
                        dec_lbl = "test result",
                        dec_pos_lbl = "will reoffend",
                        dec_neg_lbl = "will not reoffend",
                        sdt_lbl = "combination",
                        hi_lbl = "reoffender found", mi_lbl = "reoffender missed",
                        fa_lbl = "false accusation", cr_lbl = "correct release",
                        prev = .45,  # prevalence of being a reoffender.
                        sens = .98,
                        spec = .46, fart = NA,  # (provide 1 of 2)
                        N = 753,
                        scen_src = "Example scenario")

# Using scenarios: -----
summary(hustosis)
#> Scenario:  Screening for hustosis 
#> 
#> Condition:  True condition 
#> Decision:  Outcome 
#> Population:  Population 
#> N =  1000 
#> Source:  Source information 
#> 
#> Probabilities:
#> 
#>  Essential probabilities:
#> prev sens mirt spec fart 
#> 0.04 0.80 0.20 0.95 0.05 
#> 
#>  Other probabilities:
#>  ppod   PPV   NPV   FDR   FOR   acc 
#> 0.080 0.400 0.991 0.600 0.009 0.944 
#> 
#> Frequencies:
#> 
#>  by conditions:
#>  cond_true cond_false 
#>         40        960 
#> 
#>  by decision:
#> dec_pos dec_neg 
#>      80     920 
#> 
#>  by correspondence (of decision to condition):
#> dec_cor dec_err 
#>     944      56 
#> 
#>  4 essential (SDT) frequencies:
#>  hi  mi  fa  cr 
#>  32   8  48 912 
#> 
#> Accuracy:
#> 
#>  acc:
#> 0.944
plot(hustosis)


summary(scen_reoffend)
#> Scenario:  Identify reoffenders 
#> 
#> Condition:  being a reoffender 
#> Decision:  test result 
#> Population:  Prisoners 
#> N =  753 
#> Source:  Example scenario 
#> 
#> Probabilities:
#> 
#>  Essential probabilities:
#> prev sens mirt spec fart 
#> 0.45 0.98 0.02 0.46 0.54 
#> 
#>  Other probabilities:
#>  ppod   PPV   NPV   FDR   FOR   acc 
#> 0.738 0.598 0.966 0.402 0.034 0.694 
#> 
#> Frequencies:
#> 
#>  by conditions:
#>  cond_true cond_false 
#>        339        414 
#> 
#>  by decision:
#> dec_pos dec_neg 
#>     556     197 
#> 
#>  by correspondence (of decision to condition):
#> dec_cor dec_err 
#>     522     231 
#> 
#>  4 essential (SDT) frequencies:
#>  hi  mi  fa  cr 
#> 332   7 224 190 
#> 
#> Accuracy:
#> 
#>  acc:
#> 0.694
plot(scen_reoffend)


# 2 ways of defining the same scenario:
s1 <- riskyr(prev = .5, sens = .5, spec = .5, N = 100)  # s1: define by 3 prob & N
s2 <- riskyr(hi = 25, mi = 25, fa = 25, cr = 25)        # s2: same scenario by 4 freq
all.equal(s1, s2)  # should be TRUE
#> [1] TRUE

# Rounding and sampling:
s3 <- riskyr(prev = 1/3, sens = 2/3, spec = 6/7, N = 100, round = FALSE)  # s3: w/o rounding
s4 <- riskyr(prev = 1/3, sens = 2/3, spec = 6/7, N = 100, sample = TRUE)  # s4: with sampling

# Note:
riskyr(prev = .5, sens = .5, spec = .5, hi = 25, mi = 25, fa = 25, cr = 25)  # works (consistent)
#> $scen_lbl
#> [1] "Scenario"
#> 
#> $popu_lbl
#> [1] "Population"
#> 
#> $N_lbl
#> [1] "N"
#> 
#> $cond_lbl
#> [1] "True condition"
#> 
#> $cond_true_lbl
#> [1] "present"
#> 
#> $cond_false_lbl
#> [1] "absent"
#> 
#> $dec_lbl
#> [1] "Outcome"
#> 
#> $dec_pos_lbl
#> [1] "positive"
#> 
#> $dec_neg_lbl
#> [1] "negative"
#> 
#> $acc_lbl
#> [1] "Accuracy"
#> 
#> $dec_cor_lbl
#> [1] "correct"
#> 
#> $dec_err_lbl
#> [1] "incorrect"
#> 
#> $sdt_lbl
#> [1] "Cases"
#> 
#> $hi_lbl
#> [1] "TP"
#> 
#> $mi_lbl
#> [1] "FN"
#> 
#> $fa_lbl
#> [1] "FP"
#> 
#> $cr_lbl
#> [1] "TN"
#> 
#> $prev
#> [1] 0.5
#> 
#> $sens
#> [1] 0.5
#> 
#> $spec
#> [1] 0.5
#> 
#> $fart
#> [1] 0.5
#> 
#> $N
#> [1] 100
#> 
#> $hi
#> [1] 25
#> 
#> $mi
#> [1] 25
#> 
#> $fa
#> [1] 25
#> 
#> $cr
#> [1] 25
#> 
#> $scen_lng
#> [1] "en/de"
#> 
#> $scen_txt
#> [1] "Description of the current scenario in a short paragraph of text. This description may include several sentences."
#> 
#> $scen_src
#> [1] "Source information"
#> 
#> $scen_apa
#> [1] "Source information in APA format"
#> 
#> attr(,"class")
#> [1] "riskyr"
riskyr(prev = .5, sens = .5, spec = .5, hi = 25, mi = 25, fa = 25)           # works (ignores freq)
#> $scen_lbl
#> [1] "Scenario"
#> 
#> $popu_lbl
#> [1] "Population"
#> 
#> $N_lbl
#> [1] "N"
#> 
#> $cond_lbl
#> [1] "True condition"
#> 
#> $cond_true_lbl
#> [1] "present"
#> 
#> $cond_false_lbl
#> [1] "absent"
#> 
#> $dec_lbl
#> [1] "Outcome"
#> 
#> $dec_pos_lbl
#> [1] "positive"
#> 
#> $dec_neg_lbl
#> [1] "negative"
#> 
#> $acc_lbl
#> [1] "Accuracy"
#> 
#> $dec_cor_lbl
#> [1] "correct"
#> 
#> $dec_err_lbl
#> [1] "incorrect"
#> 
#> $sdt_lbl
#> [1] "Cases"
#> 
#> $hi_lbl
#> [1] "TP"
#> 
#> $mi_lbl
#> [1] "FN"
#> 
#> $fa_lbl
#> [1] "FP"
#> 
#> $cr_lbl
#> [1] "TN"
#> 
#> $prev
#> [1] 0.5
#> 
#> $sens
#> [1] 0.5
#> 
#> $spec
#> [1] 0.5
#> 
#> $fart
#> [1] 0.5
#> 
#> $N
#> [1] 10
#> 
#> $hi
#> [1] 2
#> 
#> $mi
#> [1] 3
#> 
#> $fa
#> [1] 3
#> 
#> $cr
#> [1] 2
#> 
#> $scen_lng
#> [1] "en/de"
#> 
#> $scen_txt
#> [1] "Description of the current scenario in a short paragraph of text. This description may include several sentences."
#> 
#> $scen_src
#> [1] "Source information"
#> 
#> $scen_apa
#> [1] "Source information in APA format"
#> 
#> attr(,"class")
#> [1] "riskyr"

## Watch out for:
# riskyr(hi = 25, mi = 25, fa = 25, cr = 25, N = 101)  # warns, uses actual sum of freq
# riskyr(prev = .4, sens = .5, spec = .5, hi = 25, mi = 25, fa = 25, cr = 25)  # warns, uses freq