doc: miscellaneous minor edits

Author: ctoney

R/calc_crown_overlay.R

@@ -29,6 +29,9 @@
 #' tree list is assumed to be filtered to the specific set of live trees for
 #' one subplot or microplot with the given `sample_radius`.
 #'
+#' @seealso
+#' [calc_tcc_metrics()]
+#'
 #' @examples
 #' # subplot 1 of the `plantation` plot
 #' trees <- within(plantation, CRWIDTH <- calc_crwidth(plantation))

R/calc_crwidth.R

@@ -1,24 +1,23 @@
-#' Compute predicted tree crown width using species-specific equations
+#' Predict tree crown widths using species-specific equations
 #'
 #' `calc_crwidth()` computes tree crown width using species-specific
 #' regression equations from the literature.
 #'
 #' @details
 #' Crown width is predicted from tree diameter using coefficients provided in
-#' the lookup table [cw_coef] (see `?cw_coef` for detailed documentation and
-#' references). The method also incorporates adjustment factors used to derive
-#' crown width estimates for FIA "saplings", i.e., trees less than 5.0 in.
-#' (12.7 cm) diameter but greater than or equal to 1.0 in. (2.54 cm) diameter.
-#' Details are described in the documentation for the lookup table
-#' [cw_sapling_adj].
+#' the lookup table [cw_coef] (see `?cw_coef`) The method also incorporates
+#' adjustment factors used to derive crown width estimates for FIA "saplings",
+#' i.e., trees less than 5.0 in. (12.7 cm) diameter but greater than or equal
+#' to 1.0 in. (2.54 cm) diameter. Details are described in the documentation
+#' for the lookup table [cw_sapling_adj].
 #'
 #' Large diameter trees in the temperate rain forests of the Pacific Northwest
 #' region can far exceed the range of diameters in the broadly applicable
-#' datasets that have been used to develop crown width prediction equations. To
-#' avoid extrapolation beyond the range of the model fitting data in those
-#' cases, `calc_crwidth()` makes use of the "old growth" equation presented
-#' by Gill et al. (2000) to estimate crown width for nine tree species when
-#' their diameter is greater than 50 in. (127 cm).
+#' datasets that have been used to develop crown width prediction equations
+#' (Bechold 2003, 2004). To avoid extrapolation beyond the range of the model
+#' fitting data in those cases, `calc_crwidth()` makes use of the "old growth"
+#' equation presented by Gill et al. (2000) to estimate crown width for nine
+#' tree species when their diameter is greater than 50 in. (127 cm).
 #'
 #' @param tree_list A data frame containing tree records. Must have columns
 #' `SPCD` (FIA integer species code), `STATUSCD` (FIA integer tree status code,
@@ -30,10 +29,21 @@
 #' feet for live trees. `NA` is returned for trees with `STATUSCD != 1`.
 #'
 #' @references
+#' Bechtold, W.A. 2003. Crown-Diameter Prediction Models for 87 Species of
+#' Stand-Grown Trees in the Eastern United States. _Southern Journal of Applied
+#' Forestry_, 27(4): 269-278.
+#'
+#' Bechtold, W.A. 2004. Largest-Crown-Width Prediction Models for 53 Species in
+#' the Western United States. _Western Journal of Applied Forestry_, 19(4):
+#' 245-251.
+#'
 #' Gill, S.J., G.S. Biging, E.C. Murphy. 2000. Modeling conifer tree crown
 #' radius and estimating canopy cover. _Forest Ecology and Management_, 126(3):
 #' 405-416.
 #'
+#' @seealso
+#' [cw_coef], [cw_sapling_adj]
+#'
 #' @examples
 #' calc_crwidth(plantation)
 #' @export

R/calc_ht_metrics.R

@@ -1,28 +1,28 @@
 #' Calculate stand height metrics
 #'
 #' `calc_ht_metrics()` computes several stand height metrics for a given tree
-#' list (see Details).
+#' list.
 #'
 #' @details
 #' Stand height metrics are based on live trees (`STATUSCD == 1`), and are
 #' are assigned `0` by definition if no live trees are present. Height metrics
 #' are returned in a named list with the following elements:
-#' * `numTrees`: number of live trees `>= 5.0` in. (`12.7` cm) diameter
-#' * `meanTreeHt`: mean height of trees `>= 5.0` in. (`12.7` cm) diameter
-#' * `meanTreeHtBAW`: basal-area weighted mean height of trees `>= 5.0` in.
+#' * `$numTrees`: number of live trees `>= 5.0` in. (`12.7` cm) diameter
+#' * `$meanTreeHt`: mean height of trees `>= 5.0` in. (`12.7` cm) diameter
+#' * `$meanTreeHtBAW`: basal-area weighted mean height of trees `>= 5.0` in.
 #'   (`12.7` cm) diameter
-#' * `meanTreeHtDom`: mean height of canopy dominant/co-dominant trees
+#' * `$meanTreeHtDom`: mean height of canopy dominant/co-dominant trees
 #'   `>= 5.0` in. (`12.7` cm) diameter
-#' * `meanTreeHtDomBAW`: basal-area weighted mean height of canopy
+#' * `$meanTreeHtDomBAW`: basal-area weighted mean height of canopy
 #'   dominant/co-dominant trees `>= 5.0` in. (`12.7` cm) diameter
-#' * `maxTreeHt`: height of the tallest tree `>= 5.0` in. (`12.7` cm) diameter
-#' * `predomTreeHt`: predominant tree height, as the mean height of the tallest
+#' * `$maxTreeHt`: height of the tallest tree `>= 5.0` in. (`12.7` cm) diameter
+#' * `$predomTreeHt`: predominant tree height, as the mean height of the tallest
 #'   trees `>= 5.0` in. (`12.7` cm) diameter comprising up to `16` trees per
 #'   acre (`39.5` trees per hectare)
-#' * `numSaplings`: number of live saplings (trees `>= 1.0` in. but `< 5.0` in.
+#' * `$numSaplings`: number of live saplings (trees `>= 1.0` in. but `< 5.0` in.
 #'   diameter, i.e., `>= 2.54` cm but `< 12.7` cm)
-#' * `meanSapHt`: mean height of saplings
-#' * `maxSapHt`: height of the tallest sapling
+#' * `$meanSapHt`: mean height of saplings
+#' * `$maxSapHt`: height of the tallest sapling
 #'
 #' For the purpose of height calculations, metrics based on
 #' "canopy dominant/co-dominant" include open grown trees, i.e., include trees

R/calc_tcc_metrics.R

@@ -1,9 +1,9 @@
-#' Compute predicted canopy cover from individual tree measurements
+#' Predict plot-level canopy cover from individual tree measurements
 #'
 #' `calc_tcc_metrics()` computes predicted plot-level tree canopy cover (TCC)
 #' from standard field inventory measurements. By default, a full set of stand
-#' structure metrics used to derive the plot-level TCC value are included in the
-#' output (see Details).
+#' structure variables used to derive the plot-level TCC value are included in
+#' the output (see Details).
 #'
 #' @details
 #' This function provides two methods for predicting plot-level TCC.
@@ -17,7 +17,7 @@
 #' modeling of the understory sapling contribution to total canopy cover
 #' (Toney et al. 2009). The empirical model for the sapling component also uses
 #' the spatial point pattern of overstory trees as a predictor variable (using
-#' a square root transformation of Ripley's edge-corrected K function, Ripley
+#' a square root transformation of Ripley's edge-corrected K-function, Ripley
 #' 1977, Stoyan and Penttinen 2000).
 #'
 #' Alternatively, TCC can be predicted using a simplified approach that does not
@@ -44,20 +44,20 @@
 #' variables. Specific elements of the returned list include some or all of the
 #' following, conditionally:
 #'
-#' * `model_tcc`: plot-level predicted canopy cover of trees `>= 1` inch
+#' * `$model_tcc`: plot-level predicted canopy cover of trees `>= 1` inch
 #' (`2.54` cm)  diameter, derived by one of the two methods described above
 #' depending on the value given for argument `stem_map = TRUE|FALSE`
 #'
 #' If the stem-map method is used, then TCC values derived by crown overlay
 #' on the individual subplot and microplot boundaries are included, along with
 #' means of the four subplot/microplot values:
 #'
-#' * `subpN_crown_overlay`: estimated canopy cover of trees `>= 5-in.`
+#' * `$subpN_crown_overlay`: estimated canopy cover of trees `>= 5-in.`
 #' (12.7 cm) diameter in subplot `N` based on crown overlay (`N = 1:4`)
-#' * `subp_overlay_mean`: mean of the four subplot crown overlays
-#' * `micrN_crown_overlay`: estimated canopy cover of saplings in the microplot
+#' * `$subp_overlay_mean`: mean of the four subplot crown overlays
+#' * `$micrN_crown_overlay`: estimated canopy cover of saplings in the microplot
 #' of subplot `N` based on crown overlay (`N = 1:4`)
-#' * `micr_overlay_mean`: mean of the four microplot crown overlays
+#' * `$micr_overlay_mean`: mean of the four microplot crown overlays
 #'
 #' A set of spatial point pattern statistics is also included when the stem-map
 #' method is used. A square root transformation of Ripley's K function using
@@ -67,7 +67,8 @@
 #' regression model used to estimate the sapling contribution to total tree
 #' canopy cover:
 #'
-#' * `L_rft`: Ripley’s L function at `r` feet (`r` = `6`, `8`, `10`, and `12`)
+#' * `$L_rft`: estimates of the L-function at `r` feet (`r` = `6`, `8`, `10`,
+#' and `12`)
 #'
 #' If the argument `full_output = TRUE` (the default), then the output will
 #' also include all of the the named elements from the output of
@@ -134,7 +135,7 @@
 #' # using the spatially explicit "stem-map model" by default
 #' calc_tcc_metrics(plantation)
 #'
-#' # return only the predicted TCC
+#' # return only the predicted TCC value (`$model_tcc`)
 #' calc_tcc_metrics(plantation, full_output = FALSE)
 #'
 #' # using the "FVS method" which assumes random tree locations

R/data.R

@@ -127,7 +127,7 @@ DEFAULT_TREE_COLUMNS <- c("PLT_CN", "SUBP", "TREE", "AZIMUTH", "DIST",
 "cw_sapling_adj"
 
 
-#' Tree list for a plot in a loblolly pine plantation
+#' Pine plantation tree list
 #'
 #' An example tree list for an FIA plot in a loblolly pine plantation.
 #'

R/plot_crowns.R

@@ -37,6 +37,9 @@
 #' @return
 #' The input, invisibly.
 #'
+#' @seealso
+#' [calc_crwidth()], [calc_crown_overlay()]
+#'
 #' @examples
 #' trees <- within(plantation, CRWIDTH <- calc_crwidth(plantation))
 #'

R/spatstat_helpers.R

@@ -1,15 +1,15 @@
 #' Analyze the spatial pattern of trees on an FIA plot
 #'
-#' Functions that facilitate point pattern analysis of FIA tree data using the
+#' These functions facilitate point pattern analysis of FIA tree data using the
 #' \pkg{spatstat} library.
 #'
 #' @name spatstat_helpers
 #' @details
 #' `create_fia_owin()` returns an object of class `"owin"` from the
 #' \pkg{spatstat} library. This object represents the generic 2-D observation
 #' window for the nationally standard FIA plot design which is a four-point
-#' cluster of subplots. Used when creating a \pkg{spatstat} point pattern object
-#' for an FIA tree list.
+#' cluster of circular subplots. Used when creating a \pkg{spatstat} point
+#' pattern object for an FIA tree list.
 #'
 #' `create_fia_ppp()` returns an object of class `"ppp"` representing the point
 #' pattern of an FIA tree list in the 2-D plane. A point pattern object defines