update

Project.toml

@@ -21,6 +21,7 @@ CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 MetidaBase = "075456b7-4006-432f-9324-2f8453996c49"
+StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [targets]
-test = [ "Test", "CSV", "DataFrames", "MetidaBase"]
+test = [ "Test", "CSV", "DataFrames", "MetidaBase", "StatsBase"]

test/runtests.jl

@@ -1,5 +1,6 @@
 using MetidaStats
 using MetidaBase
+using StatsBase
 using Test
 using DataFrames, CSV
 
@@ -24,4 +25,57 @@ io       = IOBuffer();
     @test des[:, :mean] == des2[:, :mean]
 
     show(io, des)
+
+    sort!(des2, [:col, :row, :Variable])
+
+    @test des2[:, :n]  == [24
+    22
+    44
+    44
+    20
+    20
+    83
+    83]
+
+    @test des2[:, :mean]  ≈ [51.84339580144830000
+    -3.2427526832130800
+    58.06263961606340000
+    1.8800364385656400
+    51.84107641126520000
+    0.4353627340913350
+    47.25778648204360000
+    -3.2515999935069800]
+
+    @test des2[:, :sd]  ≈ [30.67890817616400000
+    26.7334282254075000
+    26.95184403646440000
+    28.9591749375126000
+    25.29979111399270000
+    27.5437061096263000
+    27.16599075359280000
+    28.8185876098308000]
+
+    @test des2[:, :se]  ≈ [6.26230590810839000
+    5.6995860482981700
+    4.06314336714229000
+    4.3657598866360500
+    5.65720527474328000
+    6.1589599213400700
+    2.98185487195489000
+    3.1632509429411400]
+
+    @test des2[1, :geom]  ≈ 39.7551197516893
+    @test des2[3, :geom]  ≈ 48.4385401710072
+    @test des2[5, :harmmean] ≈ 38.7909224529887
+    @test des2[7, :harmmean] ≈ 15.394283582287837
+
+    di  = MetidaStats.dataimport(ds, vars = [:var1, :var2], sort = [:col, :row])
+    sort!(di, [:col, :row, :Variable])
+
+
+    des2[1, :skew] ≈ skewness(di[1].obs)
+
+
+    des2[1, :kurt] ≈ kurtosis(di[1].obs)
+
 end