updated documentation

Laboratorio-de-Pedometria · Jul 31, 2015 · 5d8a8b7 · 5d8a8b7
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diff --git a/man-roxygen/spSANN_doc.R b/man-roxygen/spSANN_doc.R
@@ -1,7 +1,7 @@
 #  Template documentation for spatial simulated annealing
 ################################################################################
 #' @param iterations Integer. The maximum number of iterations that should be
-#' used for the optimization. Defaults to \code{iterations = 100}. IS 100 A GOOD NUMBER? I WOULD THINK IT IS TOO LITTLE.
+#' used for the optimization. Defaults to \code{iterations = 100}.
 #' 
 #' @param acceptance List with two named sub-arguments: \code{initial} -- 
 #' numeric value between 0 and 1 defining the initial acceptance probability, 
@@ -21,7 +21,7 @@
 #' are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.
 #' 
 #' @param boundary SpatialPolygon. The boundary of the spatial domain. 
-#' If missing, it is calculated as the bounding box of \code{candi} using \code{\link[sp]{bbox}}.
+#' If missing, it is estimated from \code{candi}.
 #' 
 #' @param progress Logical for printing a progress bar. Defaults to 
 #' \code{progress = TRUE}.

diff --git a/man/optimACDC.Rd b/man/optimACDC.Rd
@@ -58,7 +58,7 @@ the projected x- and y-coordinates. If missing, they are estimated from
 \item{acceptance}{List with two named sub-arguments: \code{initial} --
 numeric value between 0 and 1 defining the initial acceptance probability,
 and \code{cooling} -- a numeric value defining the exponential factor by
-witch the acceptance probability decreases at each iteration. Defaults to
+which the acceptance probability decreases at each iteration. Defaults to
 \code{acceptance = list(initial = 0.99, cooling = iterations / 10)}.}
 
 \item{stopping}{List with one named sub-argument: \code{max.count} --
@@ -74,7 +74,7 @@ are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.}
 
 \item{track}{Logical value. Should the evolution of the energy state and
 acceptance probability be recorded and returned with the result? If
-\code{track = FALSE} (the default), only the starting and ending enery state
+\code{track = FALSE} (the default), only the starting and ending energy state
 values are returned with the result.}
 
 \item{boundary}{SpatialPolygon. The boundary of the spatial domain.
@@ -252,9 +252,9 @@ The \emph{correlation} between two numeric covariates is measured using the
 Pearson's r, a descriptive statistic that ranges from $-1$ to $+1$.
 This statistic is also known as the linear correlation coefficient.
 
-When the set of covariates includes factor covariates, any numeric covariate
-is transformed into a factor covariate. The factor levels are defined
-using the marginal sampling strata created using one of the two methods
+When the set of covariates includes factor covariates, all numeric covariates
+are transformed into factor covariates. The factor levels are defined
+using the marginal sampling strata created from one of the two methods
 available (equal-area or equal-range strata).
 
 The \emph{association} between two factor covariates is measured using the
@@ -270,7 +270,7 @@ of association (weak or strong).
 
 Reproducing the marginal distribution of the numeric covariates depends upon
 the definition of marginal sampling strata. These marginal sampling strata
-are also used to define the factor levels of any numeric covariate when they
+are also used to define the factor levels of all numeric covariates that
 are passed together with factor covariates.
 
 Two types of marginal sampling strata can be used. \emph{Equal-area}
@@ -285,7 +285,7 @@ relatively high frequency in the population of covariate values. The number
 of repeated break points increases with the number of marginal sampling
 strata. Only unique break points are used to create marginal sampling strata.
 
-\emph{Equal-range} sampling strata are defined breaking the range of
+\emph{Equal-range} sampling strata are defined by breaking the range of
 covariate values into pieces of equal size. This method usually creates
 break points that do not occur in the population of existing covariate
 values. Such break points are replaced by the nearest existing covariate
@@ -302,7 +302,7 @@ some of them with different area/size. Because the goal is to have a sample
 that reproduces the marginal distribution of the covariate, each marginal
 sampling strata will have a different number of sample points. The wanted
 distribution of the number of sample points per marginal strata is estimated
-empirically computing the proportion of points of the population of existing
+empirically as the proportion of points in the population of existing
 covariate values that fall in each marginal sampling strata.
 }
 \examples{

diff --git a/man/optimCORR.Rd b/man/optimCORR.Rd
@@ -55,7 +55,7 @@ the projected x- and y-coordinates. If missing, they are estimated from
 \item{acceptance}{List with two named sub-arguments: \code{initial} --
 numeric value between 0 and 1 defining the initial acceptance probability,
 and \code{cooling} -- a numeric value defining the exponential factor by
-witch the acceptance probability decreases at each iteration. Defaults to
+which the acceptance probability decreases at each iteration. Defaults to
 \code{acceptance = list(initial = 0.99, cooling = iterations / 10)}.}
 
 \item{stopping}{List with one named sub-argument: \code{max.count} --
@@ -71,7 +71,7 @@ are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.}
 
 \item{track}{Logical value. Should the evolution of the energy state and
 acceptance probability be recorded and returned with the result? If
-\code{track = FALSE} (the default), only the starting and ending enery state
+\code{track = FALSE} (the default), only the starting and ending energy state
 values are returned with the result.}
 
 \item{boundary}{SpatialPolygon. The boundary of the spatial domain.
@@ -240,9 +240,9 @@ The \emph{correlation} between two numeric covariates is measured using the
 Pearson's r, a descriptive statistic that ranges from $-1$ to $+1$.
 This statistic is also known as the linear correlation coefficient.
 
-When the set of covariates includes factor covariates, any numeric covariate
-is transformed into a factor covariate. The factor levels are defined
-using the marginal sampling strata created using one of the two methods
+When the set of covariates includes factor covariates, all numeric covariates
+are transformed into factor covariates. The factor levels are defined
+using the marginal sampling strata created from one of the two methods
 available (equal-area or equal-range strata).
 
 The \emph{association} between two factor covariates is measured using the

diff --git a/man/optimDIST.Rd b/man/optimDIST.Rd
@@ -55,7 +55,7 @@ the projected x- and y-coordinates. If missing, they are estimated from
 \item{acceptance}{List with two named sub-arguments: \code{initial} --
 numeric value between 0 and 1 defining the initial acceptance probability,
 and \code{cooling} -- a numeric value defining the exponential factor by
-witch the acceptance probability decreases at each iteration. Defaults to
+which the acceptance probability decreases at each iteration. Defaults to
 \code{acceptance = list(initial = 0.99, cooling = iterations / 10)}.}
 
 \item{stopping}{List with one named sub-argument: \code{max.count} --
@@ -71,7 +71,7 @@ are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.}
 
 \item{track}{Logical value. Should the evolution of the energy state and
 acceptance probability be recorded and returned with the result? If
-\code{track = FALSE} (the default), only the starting and ending enery state
+\code{track = FALSE} (the default), only the starting and ending energy state
 values are returned with the result.}
 
 \item{boundary}{SpatialPolygon. The boundary of the spatial domain.
@@ -238,7 +238,7 @@ function B.
 
 Reproducing the marginal distribution of the numeric covariates depends upon
 the definition of marginal sampling strata. These marginal sampling strata
-are also used to define the factor levels of any numeric covariate when they
+are also used to define the factor levels of all numeric covariates that
 are passed together with factor covariates.
 
 Two types of marginal sampling strata can be used. \emph{Equal-area}
@@ -253,7 +253,7 @@ relatively high frequency in the population of covariate values. The number
 of repeated break points increases with the number of marginal sampling
 strata. Only unique break points are used to create marginal sampling strata.
 
-\emph{Equal-range} sampling strata are defined breaking the range of
+\emph{Equal-range} sampling strata are defined by breaking the range of
 covariate values into pieces of equal size. This method usually creates
 break points that do not occur in the population of existing covariate
 values. Such break points are replaced by the nearest existing covariate
@@ -270,7 +270,7 @@ some of them with different area/size. Because the goal is to have a sample
 that reproduces the marginal distribution of the covariate, each marginal
 sampling strata will have a different number of sample points. The wanted
 distribution of the number of sample points per marginal strata is estimated
-empirically computing the proportion of points of the population of existing
+empirically as the proportion of points in the population of existing
 covariate values that fall in each marginal sampling strata.
 }
 \examples{

diff --git a/man/optimMKV.Rd b/man/optimMKV.Rd
@@ -63,7 +63,7 @@ the projected x- and y-coordinates. If missing, they are estimated from
 \item{acceptance}{List with two named sub-arguments: \code{initial} --
 numeric value between 0 and 1 defining the initial acceptance probability,
 and \code{cooling} -- a numeric value defining the exponential factor by
-witch the acceptance probability decreases at each iteration. Defaults to
+which the acceptance probability decreases at each iteration. Defaults to
 \code{acceptance = list(initial = 0.99, cooling = iterations / 10)}.}
 
 \item{stopping}{List with one named sub-argument: \code{max.count} --
@@ -79,7 +79,7 @@ are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.}
 
 \item{track}{Logical value. Should the evolution of the energy state and
 acceptance probability be recorded and returned with the result? If
-\code{track = FALSE} (the default), only the starting and ending enery state
+\code{track = FALSE} (the default), only the starting and ending energy state
 values are returned with the result.}
 
 \item{boundary}{SpatialPolygon. The boundary of the spatial domain.

diff --git a/man/optimMSSD.Rd b/man/optimMSSD.Rd
@@ -43,7 +43,7 @@ the projected x- and y-coordinates. If missing, they are estimated from
 \item{acceptance}{List with two named sub-arguments: \code{initial} --
 numeric value between 0 and 1 defining the initial acceptance probability,
 and \code{cooling} -- a numeric value defining the exponential factor by
-witch the acceptance probability decreases at each iteration. Defaults to
+which the acceptance probability decreases at each iteration. Defaults to
 \code{acceptance = list(initial = 0.99, cooling = iterations / 10)}.}
 
 \item{stopping}{List with one named sub-argument: \code{max.count} --
@@ -59,7 +59,7 @@ are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.}
 
 \item{track}{Logical value. Should the evolution of the energy state and
 acceptance probability be recorded and returned with the result? If
-\code{track = FALSE} (the default), only the starting and ending enery state
+\code{track = FALSE} (the default), only the starting and ending energy state
 values are returned with the result.}
 
 \item{boundary}{SpatialPolygon. The boundary of the spatial domain.

diff --git a/man/optimPPL.Rd b/man/optimPPL.Rd
@@ -81,7 +81,7 @@ the projected x- and y-coordinates. If missing, they are estimated from
 \item{acceptance}{List with two named sub-arguments: \code{initial} --
 numeric value between 0 and 1 defining the initial acceptance probability,
 and \code{cooling} -- a numeric value defining the exponential factor by
-witch the acceptance probability decreases at each iteration. Defaults to
+which the acceptance probability decreases at each iteration. Defaults to
 \code{acceptance = list(initial = 0.99, cooling = iterations / 10)}.}
 
 \item{stopping}{List with one named sub-argument: \code{max.count} --
@@ -97,7 +97,7 @@ are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.}
 
 \item{track}{Logical value. Should the evolution of the energy state and
 acceptance probability be recorded and returned with the result? If
-\code{track = FALSE} (the default), only the starting and ending enery state
+\code{track = FALSE} (the default), only the starting and ending energy state
 values are returned with the result.}
 
 \item{boundary}{SpatialPolygon. The boundary of the spatial domain.

diff --git a/man/optimSPAN.Rd b/man/optimSPAN.Rd
@@ -92,7 +92,7 @@ the projected x- and y-coordinates. If missing, they are estimated from
 \item{acceptance}{List with two named sub-arguments: \code{initial} --
 numeric value between 0 and 1 defining the initial acceptance probability,
 and \code{cooling} -- a numeric value defining the exponential factor by
-witch the acceptance probability decreases at each iteration. Defaults to
+which the acceptance probability decreases at each iteration. Defaults to
 \code{acceptance = list(initial = 0.99, cooling = iterations / 10)}.}
 
 \item{stopping}{List with one named sub-argument: \code{max.count} --
@@ -108,7 +108,7 @@ are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.}
 
 \item{track}{Logical value. Should the evolution of the energy state and
 acceptance probability be recorded and returned with the result? If
-\code{track = FALSE} (the default), only the starting and ending enery state
+\code{track = FALSE} (the default), only the starting and ending energy state
 values are returned with the result.}
 
 \item{boundary}{SpatialPolygon. The boundary of the spatial domain.
@@ -280,9 +280,9 @@ The \emph{correlation} between two numeric covariates is measured using the
 Pearson's r, a descriptive statistic that ranges from $-1$ to $+1$.
 This statistic is also known as the linear correlation coefficient.
 
-When the set of covariates includes factor covariates, any numeric covariate
-is transformed into a factor covariate. The factor levels are defined
-using the marginal sampling strata created using one of the two methods
+When the set of covariates includes factor covariates, all numeric covariates
+are transformed into factor covariates. The factor levels are defined
+using the marginal sampling strata created from one of the two methods
 available (equal-area or equal-range strata).
 
 The \emph{association} between two factor covariates is measured using the
@@ -298,7 +298,7 @@ of association (weak or strong).
 
 Reproducing the marginal distribution of the numeric covariates depends upon
 the definition of marginal sampling strata. These marginal sampling strata
-are also used to define the factor levels of any numeric covariate when they
+are also used to define the factor levels of all numeric covariates that
 are passed together with factor covariates.
 
 Two types of marginal sampling strata can be used. \emph{Equal-area}
@@ -313,7 +313,7 @@ relatively high frequency in the population of covariate values. The number
 of repeated break points increases with the number of marginal sampling
 strata. Only unique break points are used to create marginal sampling strata.
 
-\emph{Equal-range} sampling strata are defined breaking the range of
+\emph{Equal-range} sampling strata are defined by breaking the range of
 covariate values into pieces of equal size. This method usually creates
 break points that do not occur in the population of existing covariate
 values. Such break points are replaced by the nearest existing covariate
@@ -330,7 +330,7 @@ some of them with different area/size. Because the goal is to have a sample
 that reproduces the marginal distribution of the covariate, each marginal
 sampling strata will have a different number of sample points. The wanted
 distribution of the number of sample points per marginal strata is estimated
-empirically computing the proportion of points of the population of existing
+empirically as the proportion of points in the population of existing
 covariate values that fall in each marginal sampling strata.
 }
 

diff --git a/man/optimUSER.Rd b/man/optimUSER.Rd
@@ -47,7 +47,7 @@ the projected x- and y-coordinates. If missing, they are estimated from
 \item{acceptance}{List with two named sub-arguments: \code{initial} --
 numeric value between 0 and 1 defining the initial acceptance probability,
 and \code{cooling} -- a numeric value defining the exponential factor by
-witch the acceptance probability decreases at each iteration. Defaults to
+which the acceptance probability decreases at each iteration. Defaults to
 \code{acceptance = list(initial = 0.99, cooling = iterations / 10)}.}
 
 \item{stopping}{List with one named sub-argument: \code{max.count} --
@@ -63,7 +63,7 @@ are updated at each 10 iterations. Defaults to \code{plotit = FALSE}.}
 
 \item{track}{Logical value. Should the evolution of the energy state and
 acceptance probability be recorded and returned with the result? If
-\code{track = FALSE} (the default), only the starting and ending enery state
+\code{track = FALSE} (the default), only the starting and ending energy state
 values are returned with the result.}
 
 \item{boundary}{SpatialPolygon. The boundary of the spatial domain.