Subtypes of HypothesisTest can have optional fields :tail and :alpha

.travis.yml

@@ -5,7 +5,7 @@ os:
   - osx
 julia:
   - 0.5
-  - nightly
+#  - nightly    # The REQUIRE file states that only julia 0.5 is supported
 notifications:
   email: false
 # uncomment the following lines to override the default test script

doc/api/confint.rst

@@ -3,11 +3,12 @@
 Confidence Interval
 ==============================================
 
-.. function:: confint(test::HypothesisTest, alpha=0.05; tail=:both)
+.. function:: confint(test::HypothesisTest, alpha=alpha(test); tail=tail(test))
 
-    Compute a confidence interval C with coverage 1-``alpha``.
+    Compute a confidence interval C with coverage 1-``alpha``
+    (``alpha=0.05`` is the default for all tests).
 
-    If ``tail`` is ``:both`` (default), then a two-sided confidence
+    If ``tail`` is ``:both`` (default for most tests), then a two-sided confidence
     interval is returned. If ``tail`` is ``:left`` or
     ``:right``, then a one-sided confidence interval is returned
 

doc/api/pvalue.rst

@@ -3,11 +3,11 @@
 p-value
 ==============================================
 
-.. function:: pvalue(test::HypothesisTest; tail=:both)
+.. function:: pvalue(test::HypothesisTest; tail=tail(test))
 
     Compute the p-value for a given significance test.
 
-    If ``tail`` is ``:both`` (default), then the p-value for the
+    If ``tail`` is ``:both`` (default for most hypothesis tests), then the p-value for the
     two-sided test is returned. If ``tail`` is ``:left`` or
     ``:right``, then a one-sided test is performed.
 

doc/parametric/test_t.rst

@@ -1,45 +1,57 @@
 T-test
 =============================================
 
-.. function:: OneSampleTTest(v::AbstractVector{T<:Real}, mu0::Real=0)
+.. function:: OneSampleTTest(v::AbstractVector{T<:Real}, mu0::Real=0; tail::Symbol=:both, alpha::Real=0.05)
 
     Perform a one sample t-test of the null hypothesis that the data
     in vector ``v`` comes from a distribution with mean ``mu0`` against
     the alternative hypothesis that the distribution does not have mean
     ``mu0``.
+
+    ``tail`` and ``alpha`` specify the defaults for the application of 
+    :ref:`pvalue<pvalue>` and :ref:`confint<confint>`.
 
     Implements: :ref:`pvalue<pvalue>`, :ref:`confint<confint>`
 
-.. function:: OneSampleTTest(xbar::Real, stdev::Real, n::Int, mu0::Real=0)
+.. function:: OneSampleTTest(xbar::Real, stdev::Real, n::Int, mu0::Real=0; tail::Symbol=:both, alpha::Real=0.05)
 
     Perform a one sample t-test of the null hypothesis that ``n``
     values with mean ``xbar`` and sample standard deviation
     ``stdev``  come from a distribution with ``mu0`` against
     the alternative hypothesis that the distribution does not have mean
     ``mu0``.
+
+    ``tail`` and ``alpha`` specify the defaults for the application of 
+    :ref:`pvalue<pvalue>` and :ref:`confint<confint>`.
 
     Implements: :ref:`pvalue<pvalue>`, :ref:`confint<confint>`
 
-.. function:: OneSampleTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real}, mu0::Real=0)
+.. function:: OneSampleTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real}, mu0::Real=0; tail::Symbol=:both, alpha::Real=0.05)
 
     Perform a paired sample t-test of the null hypothesis that
     the differences between pairs of values in vectors ``x`` and
     ``y`` come from a distribution with ``mu0`` against the
     alternative hypothesis that the distribution does not have mean
     ``mu0``.
+
+    ``tail`` and ``alpha`` specify the defaults for the application of 
+    :ref:`pvalue<pvalue>` and :ref:`confint<confint>`.
 
     Implements: :ref:`pvalue<pvalue>`, :ref:`confint<confint>`
 
-.. function:: EqualVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real})
+.. function:: EqualVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real}; tail::Symbol=:both, alpha::Real=0.05)
 
     Perform a two-sample t-test of the null hypothesis that
     ``x`` and ``y`` come from a distributions with the same mean
     and equal variances against the alternative hypothesis that the
     distributions have different means and but equal variances.
+
+    ``tail`` and ``alpha`` specify the defaults for the application of 
+    :ref:`pvalue<pvalue>` and :ref:`confint<confint>`.
 
     Implements: :ref:`pvalue<pvalue>`, :ref:`confint<confint>`
 
-.. function:: UnequalVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real})
+.. function:: UnequalVarianceTTest(x::AbstractVector{T<:Real}, y::AbstractVector{T<:Real}; tail::Symbol=:both, alpha::Real=0.05)
 
     Perform an unequal variance two-sample t-test of the null
     hypothesis that ``x`` and ``y`` come from a distributions with
@@ -54,5 +66,8 @@ T-test
     .. math::
         \nu_{\chi'} \approx \frac{\left(\sum_{i=1}^n k_i s_i^2\right)^2}
                                  {\sum_{i=1}^n \frac{(k_i s_i^2)^2}{\nu_i}}
-
+
+    ``tail`` and ``alpha`` specify the defaults for the application of 
+    :ref:`pvalue<pvalue>` and :ref:`confint<confint>`.
+
     Implements: :ref:`pvalue<pvalue>`, :ref:`confint<confint>`

src/HypothesisTests.jl

@@ -72,32 +72,35 @@ end
 function Base.show{T<:HypothesisTest}(io::IO, test::T)
     println(io, testname(test))
     println(io, repeat("-", length(testname(test))))
+
+    # utilities for pretty-printing
+    conf_string = string(floor((1 - alpha(test)) * 100, 6)) # limit to 6 decimals in %
+    prettify_detail(label::String, value::Any, len::Int) = # len is max length of label
+        "    " * label * " "^max(len - length(label), 0) * string(value)
 
     # population details
     has_ci = applicable(StatsBase.confint, test)
     (param_name, param_under_h0, param_estimate) = population_param_of_interest(test)
     println(io, "Population details:")
-    println(io, "    parameter of interest:   $param_name")
-    println(io, "    value under h_0:         $param_under_h0")
-    println(io, "    point estimate:          $param_estimate")
+    println(io, prettify_detail("parameter of interest:", param_name, 32))
+    println(io, prettify_detail("value under h_0:", param_under_h0, 32))
+    println(io, prettify_detail("point estimate:", param_estimate, 32))
     if has_ci
-        println(io, "    95% confidence interval: $(StatsBase.confint(test))")
+        println(io, prettify_detail(conf_string*"% confidence interval:", StatsBase.confint(test), 32))
     end
     println(io)
 
     # test summary
-    p = pvalue(test)
-    outcome = if p > 0.05 "fail to reject" else "reject" end
-    tail = default_tail(test)
+    tail = HypothesisTests.tail(test)
+    p = pvalue(test) # obeys value of HypothesisTests.tail(test) if applicable
+    outcome = if p > alpha(test) "fail to reject" else "reject" end
+    tailvalue =
+        if     tail == :both "two-sided p-value:"
+        elseif tail == :left || tail == :right "one-sided p-value ($(string(tail)) tail):"
+        else   "p-value:" end
     println(io, "Test summary:")
-    println(io, "    outcome with 95% confidence: $outcome h_0")
-    if tail == :both
-        println(io, "    two-sided p-value:           $p")
-    elseif tail == :left || tail == :right
-        println(io, "    one-sided p-value:           $p")
-    else
-        println(io, "    p-value:                     $p")
-    end
+    println(io, prettify_detail("outcome with "*conf_string*"% confidence:", outcome*" h_0", 36))
+    println(io, prettify_detail(tailvalue, p, 36))
     println(io)
 
     # further details
@@ -108,8 +111,9 @@ end
 # parameter of interest: name, value under h0, point estimate
 population_param_of_interest{T<:HypothesisTest}(test::T) = ("not implemented yet", NaN, NaN)
 
-# is the test one- or two-sided
-default_tail(test::HypothesisTest) = :undefined
+# is the test one- or two-sided?
+tail(test::HypothesisTest)  = :undefined    # overloaded for defaults or field access
+alpha(test::HypothesisTest) = 0.05
 
 function show_params{T<:HypothesisTest}(io::IO, test::T, ident="")
     fieldidx = find(Bool[t<:Number for t in T.types])

src/anderson_darling.jl

@@ -35,7 +35,7 @@ function OneSampleADTest{T<:Real}(x::AbstractVector{T}, d::UnivariateDistributio
 end
 
 testname(::OneSampleADTest) = "One sample Anderson-Darling test"
-default_tail(test::OneSampleADTest) = :right
+tail(test::OneSampleADTest) = :right
 
 function show_params(io::IO, x::OneSampleADTest, ident="")
     println(io, ident, "number of observations:   $(x.n)")
@@ -78,7 +78,7 @@ function KSampleADTest{T<:Real}(xs::AbstractVector{T}...; modified=true)
 end
 
 testname(::KSampleADTest) = "k-sample Anderson-Darling test"
-default_tail(test::KSampleADTest) = :right
+tail(test::KSampleADTest) = :right
 
 function show_params(io::IO, x::KSampleADTest, ident="")
     println(io, ident, "number of samples:        $(x.k)")

src/augmented_dickey_fuller.jl

@@ -207,6 +207,7 @@ end
 testname(::ADFTest) = "Augmented Dickey-Fuller unit root test"
 population_param_of_interest(x::ADFTest) =
     ("coefficient on lagged non-differenced variable", 0, x.coef)
+tail(test::ADFTest) = :left
 
 function show_params(io::IO, x::ADFTest, ident)
     println(io, ident, "sample size in regression:          ", x.n)

src/binomial.jl

@@ -46,7 +46,7 @@ Returns the string value. E.g. "Binomial test", "Sign Test"
 """
 testname(::BinomialTest) = "Binomial test"
 population_param_of_interest(x::BinomialTest) = ("Probability of success", x.p, x.x/x.n) # parameter of interest: name, value under h0, point estimate
-default_tail(test::BinomialTest) = :both
+tail(test::BinomialTest) = :both
 
 function show_params(io::IO, x::BinomialTest, ident="")
     println(io, ident, "number of observations: $(x.n)")
@@ -157,7 +157,7 @@ SignTest{T<:Real, S<:Real}(x::AbstractVector{T}, y::AbstractVector{S}) = SignTes
 
 testname(::SignTest) = "Sign Test"
 population_param_of_interest(x::SignTest) = ("Median", x.median, median(x.data)) # parameter of interest: name, value under h0, point estimate
-default_tail(test::SignTest) = :both
+tail(test::SignTest) = :both
 
 function show_params(io::IO, x::SignTest, ident="")
     text1 = "number of observations:"

src/box_test.jl

@@ -62,7 +62,7 @@ end
 testname(::BoxPierceTest) = "Box-Pierce autocorrelation test"
 population_param_of_interest(x::BoxPierceTest) = ("autocorrelations up to lag k",
     "all zero", NaN)
-default_tail(test::BoxPierceTest) = :right
+tail(test::BoxPierceTest) = :right
 
 function show_params(io::IO, x::BoxPierceTest, ident)
     println(io, ident, "number of observations:         ", x.n)
@@ -111,7 +111,7 @@ end
 testname(::LjungBoxTest) = "Ljung-Box autocorrelation test"
 population_param_of_interest(x::LjungBoxTest) = ("autocorrelations up to lag k",
     "all zero", NaN)
-default_tail(test::LjungBoxTest) = :right
+tail(test::LjungBoxTest) = :right
 
 function show_params(io::IO, x::LjungBoxTest, ident)
     println(io, ident, "number of observations:         ", x.n)

src/breusch_godfrey.jl

@@ -68,7 +68,7 @@ end
 testname(::BreuschGodfreyTest) = "Breusch-Godfrey autocorrelation test"
 population_param_of_interest(x::BreuschGodfreyTest) =
     ("coefficients on lagged residuals up to lag p", "all zero", NaN)
-default_tail(test::BreuschGodfreyTest) = :right
+tail(test::BreuschGodfreyTest) = :right
 
 function show_params(io::IO, x::BreuschGodfreyTest, ident)
     println(io, ident, "number of observations:         ", x.n)

src/circular.jl

@@ -53,7 +53,7 @@ end
 
 testname(::RayleighTest) = "Rayleigh test"
 population_param_of_interest(x::RayleighTest) = ("Mean resultant length", 0, x.Rbar) # parameter of interest: name, value under h0, point estimate
-default_tail(test::RayleighTest) = :both
+tail(test::RayleighTest) = :both
 
 function show_params(io::IO, x::RayleighTest, ident="")
     println(io, ident, "number of observations: $(x.n)")
@@ -99,7 +99,7 @@ FisherTLinearAssociation{S <: Real, T <: Real}(theta::Vector{S},
 testname(::FisherTLinearAssociation) =
     "T-linear test of circular-circular association"
 population_param_of_interest(x::FisherTLinearAssociation) = ("Circular correlation coefficient", 0, x.rho_t) # parameter of interest: name, value under h0, point estimate
-default_tail(test::FisherTLinearAssociation) = :both
+tail(test::FisherTLinearAssociation) = :both
 
 function show_params(io::IO, x::FisherTLinearAssociation, ident="")
     println(io, ident, "number of observations: [$(length(x.theta)),$(length(x.phi))]")
@@ -212,7 +212,7 @@ end
 
 testname(::JammalamadakaCircularCorrelation) = "Jammalamadaka circular correlation"
 population_param_of_interest(x::JammalamadakaCircularCorrelation) = ("Circular-circular correlation coefficient", 0, x.r) # parameter of interest: name, value under h0, point estimate
-default_tail(test::JammalamadakaCircularCorrelation) = :both
+tail(test::JammalamadakaCircularCorrelation) = :both
 
 function show_params(io::IO, x::JammalamadakaCircularCorrelation, ident="")
     println(io, ident, "test statistic: $(x.Z)")

src/deprecated.jl

@@ -1,3 +1,4 @@
 using Base: @deprecate
 
 @deprecate ci(args...) confint(args...)
+@deprecate default_tail(test::HypothesisTest) tail(test)

src/fisher.jl

@@ -45,7 +45,7 @@ end
 
 testname(::FisherExactTest) = "Fisher's exact test"
 population_param_of_interest(x::FisherExactTest) = ("Odds ratio", 1.0, x.ω) # parameter of interest: name, value under h0, point estimate
-default_tail(test::FisherExactTest) = :both
+tail(test::FisherExactTest) = :both
 
 # The sizing argument to print_matrix was removed during the 0.5 dev period
 if VERSION < v"0.5.0-dev+1936"

src/jarque_bera.jl

@@ -77,7 +77,7 @@ end
 testname(::JarqueBeraTest) = "Jarque-Bera normality test"
 population_param_of_interest(x::JarqueBeraTest) =
     ("skewness and kurtosis", "0 and 3", "$(x.skew) and $(x.kurt)")
-default_tail(test::JarqueBeraTest) = :right
+tail(test::JarqueBeraTest) = :right
 
 function show_params(io::IO, x::JarqueBeraTest, ident)
     println(io, ident, "number of observations:         ", x.n)

src/kolmogorov_smirnov.jl

@@ -31,7 +31,7 @@ export
 @compat abstract type ExactKSTest <: KSTest end
 
 population_param_of_interest(x::KSTest) = ("Supremum of CDF differences", 0.0, x.δ) # parameter of interest: name, value under h0, point estimate
-default_tail(test::KSTest) = :both
+tail(test::KSTest) = :both
 
 ## ONE SAMPLE KS-TEST
 

src/kruskal_wallis.jl

@@ -43,7 +43,7 @@ end
 
 testname(::KruskalWallisTest) = "Kruskal-Wallis rank sum test (chi-square approximation)"
 population_param_of_interest(x::KruskalWallisTest) = ("Location parameters", "all equal", NaN) # parameter of interest: name, value under h0, point estimate
-default_tail(test::KruskalWallisTest) = :right
+tail(test::KruskalWallisTest) = :right
 
 function show_params(io::IO, x::KruskalWallisTest, ident)
     println(io, ident, "number of observation in each group: ", x.n_i)

src/mann_whitney.jl

@@ -66,7 +66,7 @@ ExactMannWhitneyUTest{S<:Real,T<:Real}(x::AbstractVector{S}, y::AbstractVector{T
 
 testname(::ExactMannWhitneyUTest) = "Exact Mann-Whitney U test"
 population_param_of_interest(x::ExactMannWhitneyUTest) = ("Location parameter (pseudomedian)", 0, x.median) # parameter of interest: name, value under h0, point estimate
-default_tail(test::ExactMannWhitneyUTest) = :both
+tail(test::ExactMannWhitneyUTest) = :both
 
 function show_params(io::IO, x::ExactMannWhitneyUTest, ident)
     println(io, ident, "number of observations in each group: ", [x.nx, x.ny])
@@ -153,7 +153,7 @@ ApproximateMannWhitneyUTest{S<:Real,T<:Real}(x::AbstractVector{S}, y::AbstractVe
 
 testname(::ApproximateMannWhitneyUTest) = "Approximate Mann-Whitney U test"
 population_param_of_interest(x::ApproximateMannWhitneyUTest) = ("Location parameter (pseudomedian)", 0, x.median) # parameter of interest: name, value under h0, point estimate
-default_tail(test::ApproximateMannWhitneyUTest) = :both
+tail(test::ApproximateMannWhitneyUTest) = :both
 
 function show_params(io::IO, x::ApproximateMannWhitneyUTest, ident)
     println(io, ident, "number of observations in each group: ", [x.nx, x.ny])

src/power_divergence.jl

@@ -40,7 +40,7 @@ end
 
 # parameter of interest: name, value under h0, point estimate
 population_param_of_interest(x::PowerDivergenceTest) = ("Multinomial Probabilities", x.theta0, x.thetahat)
-default_tail(test::PowerDivergenceTest) = :right
+tail(test::PowerDivergenceTest) = :right
 
 pvalue(x::PowerDivergenceTest; tail=:right) = pvalue(Chisq(x.df),x.stat; tail=tail)