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Better docstring for VarianceTests #254

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Better docstring for VarianceTests #254

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8a379cc
Improve docstring of EqualVarianceTTest
KronosTheLate Jan 4, 2022
33ac46d
Improve docstring of variance tests
KronosTheLate Jan 4, 2022
68cb302
Update t.jl
KronosTheLate Jan 4, 2022
ac4a2ed
Update src/z.jl
KronosTheLate Feb 6, 2022
f0b8758
Mention μ0 in docstring
KronosTheLate Feb 6, 2022
332d1ea
Mention μ0 in docstring
KronosTheLate Feb 6, 2022
20adf29
fron -> from
KronosTheLate Feb 6, 2022
1121adc
Update z.jl
KronosTheLate Feb 6, 2022
7728d74
Add newlines between unrelated information
KronosTheLate Feb 6, 2022
365c335
Make order of subtraction explicit
KronosTheLate Sep 25, 2022
8940d97
Update t.jl
nalimilan Oct 8, 2022
7b424f2
Update z.jl
nalimilan Oct 8, 2022
400a366
Use `Δμ0`, also update another docstring
nalimilan Oct 22, 2022
a8af75d
Use `Δμ0` in z-test
nalimilan Oct 22, 2022
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18 changes: 11 additions & 7 deletions src/t.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -159,12 +159,14 @@ function EqualVarianceTTest(nx::Int, ny::Int, mx::Real, my::Real, vx::Real, vy::
end

"""
EqualVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real})
EqualVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real}, μ0::Real=0)

Perform a two-sample t-test of the null hypothesis that `x` and `y` come from distributions
with equal means and variances against the alternative hypothesis that the distributions
have different means but equal variances.
with equal variances and mean difference μ0 (defaults to 0) against the alternative hypothesis
that the distributions have equal variances and a mean difference different from μ0.
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See also: [`VarianceFTest`](@ref) to test whether two datasets have equal variance.

Implements: [`pvalue`](@ref), [`confint`](@ref)
"""
function EqualVarianceTTest(x::AbstractVector{T}, y::AbstractVector{S}, μ0::Real=0) where {T<:Real,S<:Real}
Expand All @@ -190,11 +192,11 @@ end
testname(::UnequalVarianceTTest) = "Two sample t-test (unequal variance)"

"""
UnequalVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real})
UnequalVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real}, μ0::Real=0)

Perform an unequal variance two-sample t-test of the null hypothesis that `x` and `y` come
from distributions with equal means against the alternative hypothesis that the
distributions have different means.
Perform a two-sample t-test of the null hypothesis that `x` and `y` come from distributions
with different variances and mean difference μ0 (defaults to 0) against the alternative hypothesis
that the distributions have different variances and a mean difference different from μ0.

This test is sometimes known as Welch's t-test. It differs from the equal variance t-test in
that it computes the number of degrees of freedom of the test using the Welch-Satterthwaite
Expand All @@ -204,6 +206,8 @@ equation:
\\frac{(k_i s_i^2)^2}{ν_i}}
```

See also: [`VarianceFTest`](@ref) to test whether two datasets have equal variance.

Implements: [`pvalue`](@ref), [`confint`](@ref)
"""
function UnequalVarianceTTest(x::AbstractVector{T}, y::AbstractVector{S}, μ0::Real=0) where {T<:Real,S<:Real}
Expand Down
18 changes: 11 additions & 7 deletions src/z.jl
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Expand Up @@ -131,11 +131,13 @@ testname(::EqualVarianceZTest) = "Two sample z-test (equal variance)"
population_param_of_interest(x::TwoSampleZTest) = ("Mean difference", x.μ0, x.xbar) # parameter of interest: name, value under h0, point estimate

"""
EqualVarianceZTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real})
EqualVarianceZTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real}, μ0::Real=0)

Perform a two-sample z-test of the null hypothesis that `x` and `y` come from distributions
with equal means and variances against the alternative hypothesis that the distributions
have different means but equal variances.
with equal variances and mean difference μ0 (defaults to 0) against the alternative hypothesis
that the distributions have equal variances and a mean difference different from μ0.

See also: [`VarianceFTest`](@ref) to test whether two datasets have equal variance.

Implements: [`pvalue`](@ref), [`confint`](@ref)
"""
Expand Down Expand Up @@ -163,12 +165,14 @@ end
testname(::UnequalVarianceZTest) = "Two sample z-test (unequal variance)"

"""
UnequalVarianceZTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real})
UnequalVarianceZTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real}, μ0::Real=0)

Perform an unequal variance two-sample z-test of the null hypothesis that `x` and `y` come
from distributions with equal means against the alternative hypothesis that the
distributions have different means.
Perform a two-sample z-test of the null hypothesis that `x` and `y` come from distributions
with different variances and mean difference μ0 (defaults to 0) against the alternative hypothesis
that the distributions have different variances and a mean difference different from μ0.

See also: [`VarianceFTest`](@ref) to test whether two datasets have equal variance.

Implements: [`pvalue`](@ref), [`confint`](@ref)
"""
function UnequalVarianceZTest(x::AbstractVector{T}, y::AbstractVector{S}, μ0::Real=0) where {T<:Real,S<:Real}
Expand Down