using MetidaFreq, CSV, DataFrames;
df = CSV.File(joinpath(dirname(pathof(MetidaFreq)), "..", "test", "csv", "ft.csv")) |> DataFrame
ct = MetidaFreq.contab(df, :row, :col)
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 20 24 44
d 83 44 127
--- ---- ---- -------
MetidaFreq.propci(ct, method = :cp)
2-element Vector{Tuple{Float64, Float64}}:
(0.3039071135156737, 0.6115278775270854)
(0.5640069557739404, 0.7357376131143176)
MetidaFreq.diffci(ct)
(-0.36019484734063234, -0.029845370337608354)
MetidaFreq.orci(ct)
(0.2208465177513969, 0.8835988210482012)
MetidaFreq.rrci(ct)
(0.47558217064492814, 0.951044895777462)
ct = MetidaFreq.contab(df, :row, :col; sort = :s1)
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 0 2 2
d 65 34 99
--- ---- ---- -------
ID: s1 => f;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 20 22 42
d 18 10 28
--- ---- ---- -------
ID: s1 => e;
ct = MetidaFreq.contab(df, :row, :col; sort = [:s1, :s2])
Contingency table:
--- --- ---- -------
b a Total
--- --- ---- -------
c 0 0 0
d 3 10 13
--- --- ---- -------
ID: s1 => f; s2 => g;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 14 10 24
d 0 0 0
--- ---- ---- -------
ID: s1 => e; s2 => g;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 0 0 0
d 41 20 61
--- ---- ---- -------
ID: s1 => f; s2 => h;
Contingency table:
--- --- --- -------
b a Total
--- --- --- -------
c 0 0 0
d 0 0 0
--- --- --- -------
ID: s1 => e; s2 => h;
Contingency table:
--- ---- --- -------
b a Total
--- ---- --- -------
c 0 2 2
d 21 4 25
--- ---- --- -------
ID: s1 => f; s2 => i;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 6 12 18
d 18 10 28
--- ---- ---- -------
ID: s1 => e; s2 => i;
MetidaFreq.dropzeros!(ct)
Contingency table:
--- --- ---- -------
b a Total
--- --- ---- -------
c 0 0 0
d 3 10 13
--- --- ---- -------
ID: s1 => f; s2 => g;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 14 10 24
d 0 0 0
--- ---- ---- -------
ID: s1 => e; s2 => g;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 0 0 0
d 41 20 61
--- ---- ---- -------
ID: s1 => f; s2 => h;
Contingency table:
--- ---- --- -------
b a Total
--- ---- --- -------
c 0 2 2
d 21 4 25
--- ---- --- -------
ID: s1 => f; s2 => i;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 6 12 18
d 18 10 28
--- ---- ---- -------
ID: s1 => e; s2 => i;
MetidaFreq.dropzeros!(ct)
Contingency table:
--- --- ---- -------
b a Total
--- --- ---- -------
c 0 0 0
d 3 10 13
--- --- ---- -------
ID: s1 => f; s2 => g;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 14 10 24
d 0 0 0
--- ---- ---- -------
ID: s1 => e; s2 => g;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 0 0 0
d 41 20 61
--- ---- ---- -------
ID: s1 => f; s2 => h;
Contingency table:
--- ---- --- -------
b a Total
--- ---- --- -------
c 0 2 2
d 21 4 25
--- ---- --- -------
ID: s1 => f; s2 => i;
Contingency table:
--- ---- ---- -------
b a Total
--- ---- ---- -------
c 6 12 18
d 18 10 28
--- ---- ---- -------
ID: s1 => e; s2 => i;
pf1 = MetidaFreq.contab([15 8; 5 14])
pf2 = MetidaFreq.contab([45 72; 23 95])
mds = MetidaFreq.DataSet([pf1, pf2])
Contingency table:
--- ---- ---- -------
Total
--- ---- ---- -------
15 8 23
5 14 19
--- ---- ---- -------
Contingency table:
--- ---- ---- -------
Total
--- ---- ---- -------
45 72 117
23 95 118
--- ---- ---- -------
mp = MetidaFreq.metaprop(mds, :rr)
Meta-proportion:
Tables: 2
Metric: rr
Metric vector: [0.9075570519054006, 0.6796789635090787]
Metric variance: [0.17055682684973306, 0.04867889827359245]
mpf = MetidaFreq.metapropfixed(mp; weights = :mh)
Meta-proportion fixed-effect result:
Weights (%): [19.297, 80.703]
Estimate: 0.727886
Variance (Std. error): 0.0378135 (0.194457)
Exp(Estimate): 2.0707
Chi²: 14.3193
Q: 0.237012
mpf = MetidaFreq.metaproprandom(mp; tau = :dl)
Meta-proportion random-effect result:
Weights (%): [22.204, 77.796]
Estimate: 0.730277
Variance (Std. error): 0.0378703 (0.194603)
Exp(Estimate): 2.07566
Chi²: 14.3193
Q: 0.236861
I²: 0.0
τ²: 0.0