Moved trimcibt() to unmaintained status.

README.md

@@ -268,25 +268,6 @@ Compute a (1-α) confidence interval for a trimmed mean by bootstrap methods.
      p value:              0.086000
 
 
-
-
-####30. `trimcibt()`
-Compute a (1-α) confidence interval for the trimmed mean using a bootstrap percentile t method. The default amount of trimming is tr=.2. side=true,  indicates the symmetric two-sided method. side=false yields an equal-tailed confidence interval. NOTE: p.value is reported when side=true only.
-
-    julia> trimcibt(x, nboot=5000, plotit=true)
-    Bootstrap .95 confidence interval for the trimmed mean
-    using a bootstrap percentile t method
-
-     Estimate:             1.292180
-     Statistic:            3.469611
-     Confidence interval:  0.292162       2.292199
-     p value:              0.022600
-
-![plot](http://imageshack.us/a/img844/1970/34o.png)
-
-
-
-
 #### `pcorb`
 Compute a .95 confidence interval for Pearson's correlation coefficient. This function uses an adjusted percentile bootstrap method that gives good results when the error term is heteroscedastic.
 

UNMAINTAINED.md

@@ -128,3 +128,16 @@ Two examples are shown below to demonstrate the two ways of specifying data:
     Degrees of freedom:   2.00   8.11
     Statistic:            28.990510
     p value:              0.000202
+
+
+#### `trimcibt`
+Compute a (1-α) confidence interval for the trimmed mean using a bootstrap percentile t method. The default amount of trimming is tr=.2. side=true,  indicates the symmetric two-sided method. side=false yields an equal-tailed confidence interval. NOTE: p.value is reported when side=true only.
+
+    julia> trimcibt(x, nboot=5000)
+    Bootstrap .95 confidence interval for the trimmed mean
+    using a bootstrap percentile t method
+
+     Estimate:             1.292180
+     Statistic:            3.469611
+     Confidence interval:  0.292162       2.292199
+     p value:              0.022600

src/RobustStats.jl

@@ -39,9 +39,6 @@ export
     trimpb,
     pcorb,
     yuend,
-
-    trimcibt,
-
     bisquareWM,
     huberWM,
     trimean,

src/functions.jl

@@ -685,70 +685,6 @@ end
 
 
 
-#  Compute a (1-α) confidence interval for the trimmed mean
-#  using a bootstrap percentile t method.
-#
-#  The default amount of trimming is tr=.2
-#  side=T, for true,  indicates the symmetric two-sided method
-#
-#
-#  Side=F yields an equal-tailed confidence interval
-#
-#
-#  NOTE: p.value is reported when side=T only.
-#
-
-function trimcibt{S <: Real}(x::AbstractArray{S}; tr::Real=0.2, alpha::Real=0.05, nboot::Integer=2000, side::Bool=true,
-                             nullvalue::Real=0, seed=2, method::Bool=true)
-    if isa(seed, Bool)
-        if seed
-            srand(2)
-        end
-    else
-        srand(seed)
-    end
-    n=length(x)
-    test=(tmean(x, tr=tr)-nullval)./trimse(x, tr=tr)
-    randid=rand(1:n, n*nboot)
-    tempout=trimcibt_loop(x, n, nboot, tr, side, randid, test)
-    if side
-        tval=tempout[1]
-        pval=tempout[2]
-    end
-    icrit=floor((1-alpha)*nboot+0.5)
-    ibot=round(alpha*nboot/2)+1
-    itop=nboot-ibot-1
-    ci=zeros(2)
-    if method && side
-        METHOD="Bootstrap (1-α) confidence interval for the trimmed mean\nusing a bootstrap percentile t method\n"
-    elseif method && !side
-        METHOD="Bootstrap (1-α) confidence interval for the trimmed mean\nusing a bootstrap percentile t method\n[NOTE: p value is computed only when side=true]\n"
-    else
-        METHOD=nothing
-    end
-    if !side
-        ci[1]=tmean(x, tr=tr)-tempout[itop]*trimse(x, tr=tr)
-        ci[2]=tmean(x, tr=tr)-tempout[ibot]*trimse(x, tr=tr)
-        output = testOutput()
-        output.method = METHOD
-        output.estimate = tmean(x, tr=tr)
-        output.ci = ci
-        output.statistic = test
-        return output
-    else
-        ci[1]=tmean(x, tr=tr)-tval[icrit]*trimse(x, tr=tr)
-        ci[2]=tmean(x, tr=tr)+tval[icrit]*trimse(x, tr=tr)
-        output = testOutput()
-        output.method = METHOD
-        output.estimate = tmean(x, tr=tr)
-        output.ci = ci
-        output.statistic = test
-        output.p = pval
-        return output
-    end
-end
-
-
 """`procb(x, y; seed=2)`
 
 Compute a (1-α) confidence interval for Pearson's correlation coefficient.

src/unmaintained.jl

@@ -677,3 +677,98 @@ function t1waycore(args...)
     output.method = METHOD
     output
 end
+
+
+#  Compute a (1-α) confidence interval for the trimmed mean
+#  using a bootstrap percentile t method.
+#
+#  The default amount of trimming is tr=.2
+#  side=T, for true,  indicates the symmetric two-sided method
+#
+#
+#  Side=F yields an equal-tailed confidence interval
+#
+#
+#  NOTE: p.value is reported when side=T only.
+#
+
+function trimcibt{S <: Real}(x::AbstractArray{S}; tr::Real=0.2, alpha::Real=0.05, nboot::Integer=2000, side::Bool=true,
+                             nullvalue::Real=0, seed=2, method::Bool=true)
+    if isa(seed, Bool)
+        if seed
+            srand(2)
+        end
+    else
+        srand(seed)
+    end
+    n=length(x)
+    test=(tmean(x, tr=tr)-nullval)./trimse(x, tr=tr)
+    randid=rand(1:n, n*nboot)
+    tempout=trimcibt_loop(x, n, nboot, tr, side, randid, test)
+    if side
+        tval=tempout[1]
+        pval=tempout[2]
+    end
+    icrit=floor((1-alpha)*nboot+0.5)
+    ibot=round(alpha*nboot/2)+1
+    itop=nboot-ibot-1
+    ci=zeros(2)
+    if method && side
+        METHOD="Bootstrap (1-α) confidence interval for the trimmed mean\nusing a bootstrap percentile t method\n"
+    elseif method && !side
+        METHOD="Bootstrap (1-α) confidence interval for the trimmed mean\nusing a bootstrap percentile t method\n[NOTE: p value is computed only when side=true]\n"
+    else
+        METHOD=nothing
+    end
+    if !side
+        ci[1]=tmean(x, tr=tr)-tempout[itop]*trimse(x, tr=tr)
+        ci[2]=tmean(x, tr=tr)-tempout[ibot]*trimse(x, tr=tr)
+        output = testOutput()
+        output.method = METHOD
+        output.estimate = tmean(x, tr=tr)
+        output.ci = ci
+        output.statistic = test
+        return output
+    else
+        ci[1]=tmean(x, tr=tr)-tval[icrit]*trimse(x, tr=tr)
+        ci[2]=tmean(x, tr=tr)+tval[icrit]*trimse(x, tr=tr)
+        output = testOutput()
+        output.method = METHOD
+        output.estimate = tmean(x, tr=tr)
+        output.ci = ci
+        output.statistic = test
+        output.p = pval
+        return output
+    end
+end
+
+
+function trimcibt_loop(x, n, nboot, tr, side, randid, test)
+    xbar=tmean(x, tr=tr)
+    temp=zeros(n)
+    tval=zeros(nboot)
+    if side
+        test=abs(test)
+        pval=0.0
+        for i=1:(nboot*n)
+            if (i%n)!=0
+                temp[i%n]=x[randid[i]]-xbar
+            else
+                temp[n]=x[randid[i]]-xbar
+                tval[div(i, n)]=abs(tmean(temp, tr=tr)./trimse(temp, tr=tr))
+                pval += tval[div(i, n)]>=test? 1/nboot :0.0
+            end
+        end
+        return sort!(tval), pval
+    else
+        for i=1:(nboot*n)
+            if (i%n)!=0
+                temp[i%n]=x[randid[i]]-xbar
+            else
+                temp[n]=x[randid[i]]-xbar
+                tval[div(i, n)]=tmean(temp, tr=tr)./trimse(temp, tr=tr)
+            end
+        end
+        return sort!(tval)
+    end
+end

src/utils.jl

@@ -11,34 +11,3 @@ function duplicated(x::AbstractArray)
     end
     false
 end
-
-
-function trimcibt_loop(x, n, nboot, tr, side, randid, test)
-    xbar=tmean(x, tr=tr)
-    temp=zeros(n)
-    tval=zeros(nboot)
-    if side
-        test=abs(test)
-        pval=0.0
-        for i=1:(nboot*n)
-            if (i%n)!=0
-                temp[i%n]=x[randid[i]]-xbar
-            else
-                temp[n]=x[randid[i]]-xbar
-                tval[div(i, n)]=abs(tmean(temp, tr=tr)./trimse(temp, tr=tr))
-                pval += tval[div(i, n)]>=test? 1/nboot :0.0
-            end
-        end
-        return sort!(tval), pval
-    else
-        for i=1:(nboot*n)
-            if (i%n)!=0
-                temp[i%n]=x[randid[i]]-xbar
-            else
-                temp[n]=x[randid[i]]-xbar
-                tval[div(i, n)]=tmean(temp, tr=tr)./trimse(temp, tr=tr)
-            end
-        end
-        return sort!(tval)
-    end
-end