Remove DataFramesMeta from the docs (#515)

* fix reflink

* remove DataFramesMeta from the docs

docs/Project.toml

@@ -1,7 +1,6 @@
 [deps]
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
 DisplayAs = "0b91fe84-8a4c-11e9-3e1d-67c38462b6d6"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FreqTables = "da1fdf0e-e0ff-5433-a45f-9bb5ff651cb1"

docs/src/bootstrap.md

@@ -30,7 +30,6 @@ using DisplayAs
 
 ```@example Main
 using DataFrames
-using DataFramesMeta  # dplyr-like operations
 using Gadfly          # plotting package
 using MixedModels
 using Random
@@ -54,21 +53,26 @@ first(df, 10)
 Especially for those with a background in [`R`](https://www.R-project.org/) or [`pandas`](https://pandas.pydata.org),
 the simplest way of accessing the parameter estimates in the parametric bootstrap object is to create a `DataFrame` from the `allpars` property as shown above.
 
-The [`DataFramesMeta`](https://github.com/JuliaData/DataFramesMeta.jl) package provides macros for extracting rows or columns of a dataframe.
+We can use `filter` to filter out relevant rows of a dataframe.
 A density plot of the estimates of `σ`, the residual standard deviation, can be created as
 ```@example Main
-σres = @where(df, :type .== "σ", :group .== "residual").value
-plot(x = σres, Geom.density, Guide.xlabel("Parametric bootstrap estimates of σ"))
+σres = filter(df) do row # create a thunk that operates on rows
+    row.type == "σ" && row.group == "residual" # our filtering rule
+end
+
+plot(x = σres.value, Geom.density, Guide.xlabel("Parametric bootstrap estimates of σ"))
 ```
 For the estimates of the intercept parameter, the `getproperty` extractor must be used
 ```@example Main
-plot(@where(df, :type .== "β"), x = :value, Geom.density, Guide.xlabel("Parametric bootstrap estimates of β₁"))
+plot(filter(:type => ==("β"),  df), x = :value, Geom.density, Guide.xlabel("Parametric bootstrap estimates of β₁"))
 ```
 
 A density plot of the estimates of the standard deviation of the random effects is obtained as
 ```@example Main
-σbatch = @where(df, :type .== "σ", :group .== "batch").value
-plot(x = σbatch, Geom.density,
+σbatch = filter(df) do row # create a thunk that operates on rows
+    row.type == "σ" && row.group == "batch" # our filtering rule
+end
+plot(x = σbatch.value, Geom.density,
     Guide.xlabel("Parametric bootstrap estimates of σ₁"))
 ```
 
@@ -77,7 +81,7 @@ Although this mode appears to be diffuse, this is an artifact of the way that de
 In fact, it is a pulse, as can be seen from a histogram.
 
 ```@example Main
-plot(x = σbatch, Geom.histogram,
+plot(x = σbatch.value, Geom.histogram,
     Guide.xlabel("Parametric bootstrap estimates of σ₁"))
 ```
 
@@ -126,24 +130,28 @@ DataFrame(shortestcovint(samp2))
 
 A histogram of the estimated correlations from the bootstrap sample has a spike at `+1`.
 ```@example Main
-ρs = @where(df2, :type .== "ρ", :group .== "subj").value
-plot(x = ρs, Geom.histogram,
+ρs = filter(df2) do row
+    row.type == "ρ" && row.group == "subj"
+end
+plot(x = ρs.value, Geom.histogram,
     Guide.xlabel("Parametric bootstrap samples of correlation of random effects"))
 ```
 or, as a count,
 ```@example Main
-sum(ρs .≈ 1)
+count(ρs.value .≈ 1)
 ```
 
 Close examination of the histogram shows a few values of `-1`.
 ```@example Main
-sum(ρs .≈ -1)
+count(ρs.value .≈ -1)
 ```
 
 Furthermore there are even a few cases where the estimate of the standard deviation of the random effect for the intercept is zero.
 ```@example Main
-σs = @where(df2, :type .== "σ", :group .== "subj", :names .== "(Intercept)").value
-sum(σs .≈ 0)
+σs = filter(df2) do row
+    row.type == "σ" && row.group == "subj" && row.names == "(Intercept)"
+end
+count(σs.value .≈ 0)
 ```
 
 There is a general condition to check for singularity of an estimated covariance matrix or matrices in a bootstrap sample.
@@ -154,5 +162,5 @@ The `issingular` method for a `MixedModel` object that tests if a parameter vect
 
 This operation is encapsulated in a method for the `issingular` function.
 ```@example Main
-sum(issingular(samp2))
+count(issingular(samp2))
 ```

src/bootstrap.jl

@@ -62,7 +62,7 @@ bar is automatically disabled for non-interactive (i.e. logging) contexts.
     Julia- and BLAS-level threads in the future.
 
 !!! warning
-    The PRNG shared between threads is locked using [`Threads.SpinLock`](@ref), which
+    The PRNG shared between threads is locked using `Threads.SpinLock`, which
     should not be used recursively. Do not wrap `parametricbootstrap` in an outer `SpinLock`.
 """
 function parametricbootstrap(
@@ -261,7 +261,7 @@ end
 """
     shortestcovint(bsamp::MixedModelBootstrap, level = 0.95)
 
-Return the shortest interval containing `level` proportion for each parameter from [`bsamp.allpars`](@ref)
+Return the shortest interval containing `level` proportion for each parameter from `bsamp.allpars`
 """
 function shortestcovint(bsamp::MixedModelBootstrap{T}, level = 0.95) where {T}
     allpars = bsamp.allpars