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update
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@PharmCat
PharmCat committed Feb 12, 2023
1 parent 3190272 commit 50299b8
Showing 1 changed file with 133 additions and 129 deletions.
262 changes: 133 additions & 129 deletions src/descriptive.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -213,147 +213,151 @@ function descriptives(data::DataSet{T}; kwargs...) where T <: ObsData
cicalk = false
q = NaN
end

#
ds = Vector{Descriptives}(undef, length(data))
i = 1
for d in data
# skipmissing
if kwargs[:skipmissing]
vec = skipnanormissing(d.obs)
else
vec = d.obs
end
n_ = length(vec)
if cicalk
if n_ > 1 q = quantile(TDist(n_ - 1), 1 - (1-kwargs[:level])/2) end
end
# skipnonpositive
logstats = makelogvec #calk logstats
if makelogvec
if kwargs[:skipnonpositive]
logvec = log.(skipnonpositive(d.obs))
ds[i] = Descriptives(d, descriptives_(d.obs, kwargs, makelogvec, cicalk))
i += 1
end
DataSet(identity.(ds))

end

function descriptives_(obsvec, kwargs, makelogvec, cicalk)
# skipmissing
if kwargs[:skipmissing]
vec = skipnanormissing(obsvec)
else
if kwargs[:skipmissing]
itr = skipnanormissing(d.obs)
if any(!ispositive, itr)
logvec = Float64[]
logstats = false
else
logvec = log.(itr)
end
vec = obsvec
end
n_ = length(vec)
if cicalk
if n_ > 1 q = quantile(TDist(n_ - 1), 1 - (1-kwargs[:level])/2) end
end
# skipnonpositive
logstats = makelogvec #calk logstats
if makelogvec
if kwargs[:skipnonpositive]
logvec = log.(skipnonpositive(obsvec))
else
if any(!ispositive, d.obs)
logvec = Float64[]
logstats = false
if kwargs[:skipmissing]
itr = skipnanormissing(obsvec)
if any(!ispositive, itr)
logvec = Float64[]
logstats = false
else
logvec = log.(itr)
end
else
logvec = log.(d.obs)
if any(!ispositive, obsvec)
logvec = Float64[]
logstats = false
else
logvec = log.(obsvec)
end
end
end
end
end
logn_ = length(skipnonpositive(d.obs))

result = Dict{Symbol, Float64}()

for s in kwargs[:stats]

if s == :n
result[s] = n_
elseif s == :posn
result[s] = logn_
elseif !(n_ > 0)
result[s] = NaN
continue
elseif s == :mean
result[s] = sum(vec) / n_
elseif s == :sd
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = std(vec; corrected = kwargs[:corrected], mean = result[:mean])
elseif s == :var
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = var(vec; corrected = kwargs[:corrected], mean = result[:mean])
elseif s == :bvar
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = var(vec; corrected = false, mean = result[:mean])
elseif s == :se
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
result[s] = result[:sd] / sqrt(n_)
elseif s == :cv
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
result[s] = abs(result[:sd] / result[:mean] * 100)
elseif s == :uci
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
result[s] = result[:mean] + q*result[:sd]
elseif s == :lci
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
result[s] = result[:mean] - q*result[:sd]
elseif s == :umeanci
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
haskey(result, :se) || begin result[:se] = result[:sd] / sqrt(n_) end
result[s] = result[:mean] + q*result[:se]
elseif s == :lmeanci
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
haskey(result, :se) || begin result[:se] = result[:sd] / sqrt(n_) end
result[s] = result[:mean] - q*result[:se]
elseif s == :median
result[s] = median(vec)
elseif s == :min
result[s] = minimum(vec)
elseif s == :max
result[s] = maximum(vec)
elseif s == :q1
result[s] = quantile(vec, 0.25)
elseif s == :q3
result[s] = quantile(vec, 0.75)
elseif s == :iqr
result[s] = abs(quantile(vec, 0.75) - quantile(vec, 0.25))
elseif s == :range
result[s] = abs(maximum(vec) - minimum(vec))
elseif s == :kurt
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = kurtosis_(vec, result[:mean])
elseif s == :skew
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = skewness_(vec, result[:mean])
elseif s == :harmmean
result[s] = harmmean(vec)
elseif s == :ses
result[s] = sesvec(vec)
elseif s == :sek
result[s] = sekvec(vec)
elseif s == :sum
result[s] = sum(vec)
elseif !logstats
result[s] = NaN
continue
elseif s == :logmean
result[s] = sum(logvec) / logn_
elseif s == :geom || s == :geomean
haskey(result, :logmean) || begin result[:logmean] = sum(logvec) / logn_ end
result[s] = exp(result[:logmean])
elseif s == :logvar
haskey(result, :logmean) || begin result[:logmean] = sum(logvec) / logn_ end
result[s] = var(logvec; corrected = kwargs[:corrected], mean = result[:logmean])
elseif s == :geocv
haskey(result, :logmean) || begin result[:logmean] = sum(logvec) / logn_ end
haskey(result, :logvar) || begin result[:logvar] = var(logvec; corrected = kwargs[:corrected], mean = result[:logmean]) end
result[s] = sqrt(exp(result[:logvar])-1)*100
logn_ = length(skipnonpositive(obsvec))

result = Dict{Symbol, Float64}()

for s in kwargs[:stats]

if s == :n
result[s] = n_
elseif s == :posn
result[s] = logn_
elseif !(n_ > 0)
result[s] = NaN
continue
elseif s == :mean
result[s] = sum(vec) / n_
elseif s == :sd
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = std(vec; corrected = kwargs[:corrected], mean = result[:mean])
elseif s == :var
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = var(vec; corrected = kwargs[:corrected], mean = result[:mean])
elseif s == :bvar
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = var(vec; corrected = false, mean = result[:mean])
elseif s == :se
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
result[s] = result[:sd] / sqrt(n_)
elseif s == :cv
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
result[s] = abs(result[:sd] / result[:mean] * 100)
elseif s == :uci
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
result[s] = result[:mean] + q*result[:sd]
elseif s == :lci
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
result[s] = result[:mean] - q*result[:sd]
elseif s == :umeanci
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
haskey(result, :se) || begin result[:se] = result[:sd] / sqrt(n_) end
result[s] = result[:mean] + q*result[:se]
elseif s == :lmeanci
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
haskey(result, :sd) || begin result[:sd] = std(vec; corrected = kwargs[:corrected], mean = result[:mean]) end
haskey(result, :se) || begin result[:se] = result[:sd] / sqrt(n_) end
result[s] = result[:mean] - q*result[:se]
elseif s == :median
result[s] = median(vec)
elseif s == :min
result[s] = minimum(vec)
elseif s == :max
result[s] = maximum(vec)
elseif s == :q1
result[s] = quantile(vec, 0.25)
elseif s == :q3
result[s] = quantile(vec, 0.75)
elseif s == :iqr
result[s] = abs(quantile(vec, 0.75) - quantile(vec, 0.25))
elseif s == :range
result[s] = abs(maximum(vec) - minimum(vec))
elseif s == :kurt
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = kurtosis_(vec, result[:mean])
elseif s == :skew
haskey(result, :mean) || begin result[:mean] = sum(vec) / n_ end
result[s] = skewness_(vec, result[:mean])
elseif s == :harmmean
result[s] = harmmean(vec)
elseif s == :ses
result[s] = sesvec(vec)
elseif s == :sek
result[s] = sekvec(vec)
elseif s == :sum
result[s] = sum(vec)
elseif !logstats
result[s] = NaN
continue
elseif s == :logmean
result[s] = sum(logvec) / logn_
elseif s == :geom || s == :geomean
haskey(result, :logmean) || begin result[:logmean] = sum(logvec) / logn_ end
result[s] = exp(result[:logmean])
elseif s == :logvar
haskey(result, :logmean) || begin result[:logmean] = sum(logvec) / logn_ end
result[s] = var(logvec; corrected = kwargs[:corrected], mean = result[:logmean])
elseif s == :geocv
haskey(result, :logmean) || begin result[:logmean] = sum(logvec) / logn_ end
haskey(result, :logvar) || begin result[:logvar] = var(logvec; corrected = kwargs[:corrected], mean = result[:logmean]) end
result[s] = sqrt(exp(result[:logvar]) - 1)*100
end
end
end
filter!(x -> x.first in kwargs[:stats], result)
ds[i] = Descriptives(d, result)
i += 1
end
DataSet(identity.(ds))
filter!(x -> x.first in kwargs[:stats], result)
result
end


function sesvec(vec)
ses(length(vec))
end
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