| write_popu {riskyr} | R Documentation |
Write a population table (data) from a riskyr scenario (description).
Description
write_popu computes (or expands) a table popu
(as an R data frame) from a riskyr scenario (description),
using its 4 essential frequencies.
Usage
write_popu(x = NULL, ...)
Arguments
x |
A |
... |
Additional parameters (text labels, passed to |
Format
An object of class data.frame
with N rows and 3 columns
(e.g., "X/truth/cd", "Y/test/dc", "SDT/cell/class").
Details
write_popu expects a riskyr scenario as input
and passes its 4 essential frequencies (rounded to integers)
to comp_popu.
By default, write_popu uses the text settings
contained in txt, but labels can be changed
by passing arguments to comp_popu (via ...).
Value
A data frame popu
containing N rows (individual cases)
and 3 columns (e.g., "X/truth/cd", "Y/test/dc", "SDT/cell/class").
encoded as ordered factors (with 2, 2, and 4 levels, respectively).
See Also
comp_popu creates data (as df) from description (frequencies);
read_popu creates a scenario (description) from data (as df);
popu for data format;
txt for current text settings;
riskyr initializes a riskyr scenario.
Other functions converting data/descriptions:
comp_popu(),
read_popu()
Examples
# Define scenarios (by description):
s1 <- riskyr(prev = .5, sens = .5, spec = .5, N = 10) # s1: define by 3 prob & N
s2 <- riskyr(hi = 2, mi = 3, fa = 2, cr = 3) # s2: same scenario by 4 freq
# Create data (from descriptions):
write_popu(s1) # data from (prob) description
write_popu(s2, # data from (freq) description & change labels:
cond_lbl = "Disease (X)",
cond_true_lbl = "sick", cond_false_lbl = "healthy",
dec_lbl = "Test (Y)")
# Rounding:
s3 <- riskyr(prev = 1/3, sens = 2/3, spec = 6/7, N = 10, round = FALSE) # s3: w/o rounding
write_popu(s3, cond_lbl = "X", dec_lbl = "Y", sdt_lbl = "class") # rounded to nearest integers
# Sampling:
s4 <- riskyr(prev = 1/3, sens = 2/3, spec = 6/7, N = 10, sample = TRUE) # s4: with sampling
write_popu(s4, cond_lbl = "X", dec_lbl = "Y", sdt_lbl = "class") # data from sampling