Update documentation

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: poppr
 Type: Package
 Title: Genetic Analysis of Populations with Mixed Reproduction
-Version: 2.7.1.99-79
-Date: 2018-05-12
+Version: 2.7.1.99-85
+Date: 2018-05-16
 Authors@R: c(person(c("Zhian", "N."), "Kamvar", role = c("cre", "aut"),
     email = "[email protected]", comment = c(ORCID = "0000-0003-1458-7108")),
     person(c("Javier", "F."), "Tabima", role = "aut",
@@ -61,4 +61,4 @@ Suggests:
     cowplot
 License: GPL-2 | GPL-3
 VignetteBuilder: knitr
-RoxygenNote: 6.0.1
+RoxygenNote: 6.0.1.9000

R/filter_stats.R

@@ -89,9 +89,23 @@
 #'   
 #' @author Zhian N. Kamvar, Jonah C. Brooks
 #' @examples
+#' 
+#' # Basic usage example: Bruvo's Distance --------------------------------
 #' data(Pinf)
 #' pinfreps <- fix_replen(Pinf, c(2, 2, 6, 2, 2, 2, 2, 2, 3, 3, 2))
-#' filter_stats(Pinf, distance = bruvo.dist, replen = pinfreps, plot = TRUE, threads = 1L)
+#' bres <- filter_stats(Pinf, distance = bruvo.dist, replen = pinfreps, plot = TRUE, threads = 1L)
+#' print(bres) # shows all of the statistics
+#' 
+#' # Use these results with cutoff_filter()
+#' print(thresh <- cutoff_predictor(bres$farthest$THRESHOLDS))
+#' mlg.filter(Pinf, distance = bruvo.dist, replen = pinfreps) <- thresh
+#' Pinf 
+#' 
+#' # Different distances will give different results -----------------------
+#' nres <- filter_stats(Pinf, distance = nei.dist, plot = TRUE, threads = 1L, missing = "mean")
+#' print(thresh <- cutoff_predictor(nres$farthest$THRESHOLDS))
+#' mlg.filter(Pinf, distance = nei.dist, missing = "mean") <- thresh
+#' Pinf 
 #==============================================================================#
 filter_stats <- function(x, distance = bitwise.dist,
                          threshold = 1e6 + .Machine$double.eps^0.5, 

R/internal.r

@@ -315,7 +315,7 @@ mlg.matrix <- function(x){
 #
 # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! #
 #==============================================================================#
-
+#' @noRd
 .PA.Ia.Rd <- function(pop, missing=NULL){
 	vard.vector <- NULL
 	numLoci     <- ncol(pop@tab)

vignettes/mlg.Rmd

@@ -301,11 +301,11 @@ Notice how the information you see has changed. You can see that it's printed
 out that you have 5 **contracted** multilocus genotypes, but you have some
 cryptic code underneath:
 
-| symbol | meaning |
-|:----:|:----|
-| **[t]** | threshold |
+| symbol  | meaning                              |
+|:-------:|:-------------------------------------|
+| **[t]** | threshold                            |
 | **[d]** | distance (can be matrix or function) |
-| **[a]** | algorithm (see the next section) |
+| **[a]** | algorithm (see the next section)     |
 
 Genclone and snpclone objects will always remember what parameters were used for
 filtering multilocus genotypes, but the only catch is that, if you use your own
@@ -415,7 +415,7 @@ This initial peak likely represents clones differentiated by a small set of
 random mutations. You can see this in the figure above at a threshold of ~0.11
 for the "farthest neighbor" algorithm.
 
-However, if this peak is not obvious, then another method is to look for the largest gap between all putative thresholds. For this, you can use the `cutoff_predictor()` function with the output of `filter_stats()`. It should be noted, however, that this method is not a perfect solution. If we take the results from above, we can find the threshold for each algorithm:
+However, if this peak is not obvious, then another method is to look for the largest gap between all putative thresholds. For this, you can use the `cutoff_predictor()` function with the output of `filter_stats()`. It should be noted that this method is not a perfect solution. If we take the results from above, we can find the threshold for each algorithm:
 
 ```{r cutof_predictor}
 print(farthest_thresh <- cutoff_predictor(pinf_filtered$farthest$THRESHOLDS))
@@ -425,9 +425,11 @@ print(nearest_thresh  <- cutoff_predictor(pinf_filtered$nearest$THRESHOLDS))
 
 Now we can define multilocus lineages for *P. infestans* with the following criteria:
 
- - Genetic Distance: Bruvo's Distance
- - Filtering Algorithm: Farthest neighbor
- - Threshold: `r signif(farthest_thresh, 3)`
+|   |   |   |
+|:-------:|:----------|:----------|
+| **[t]** | threshold | `r signif(farthest_thresh, 3)` |
+| **[d]** | distance  | Bruvo's Distance |
+| **[a]** | algorithm | Farthest neighbor |
 
 ```{r pinf_thresh}
 mlg.filter(Pinf, distance = bruvo.dist, replen = pinfreps, algorithm = "f") <- farthest_thresh