version 1.0.1

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: HOasso
 Type: Package
 Title: Higher Order Assortativity for Complex Networks
-Version: 1.0.0
-Date: 2023-07-21
+Version: 1.0.1
+Date: 2023-09-07
 Authors@R: c(person("Alberto", "Arcagni", role = c("aut", "cre"), email = "alberto.arcagni@uniroma1.it"), person("Roy", "Cerqueti", role = c("aut")), person("Rosanna", "Grassi", role = c("aut")))
 Author: Alberto Arcagni [aut, cre],
   Roy Cerqueti [aut],
@@ -13,7 +13,7 @@ RdMacros: Rdpack
 License: GPL (>= 2)
 Encoding: UTF-8
 NeedsCompilation: no
-Packaged: 2023-08-10 10:04:48 UTC; Alberto
+Packaged: 2023-09-07 08:23:50 UTC; alber
 Maintainer: Alberto Arcagni <alberto.arcagni@uniroma1.it>
 Repository: CRAN
-Date/Publication: 2023-08-11 08:10:02 UTC
+Date/Publication: 2023-09-07 08:40:10 UTC

MD5

@@ -1,6 +1,8 @@
-53cef403a3435cf508be10b7bf061a4f *DESCRIPTION
-740a27e00bcbc56143f5897b0f897cf2 *NAMESPACE
-cb5dde28544d0c2c5b72b3589eb9fb28 *R/HOasso.R
-d24568214203b863f387622b7c2db5f7 *build/partial.rdb
+7d5e7c35a9744b8dc2167418fa443f59 *DESCRIPTION
+fc420ee82fc67c21481a1d5e84d9346b *NAMESPACE
+dfaafb7176f3ba787f1e11c28ac807e4 *R/HOasso.R
+2db4ede97bb98e8a886821db996b0e8c *R/plot.assortativity.R
+379de7059a1e55b33f3fc2e51ac8f070 *R/print.assortativity.R
+0940d37adb75b7d4e453e90fb64f3258 *build/partial.rdb
 88faf3510a569a1df58dd341d105b5c1 *inst/REFERENCES.bib
-9a60794edffbdbc41b2de921d45c8479 *man/HOasso.Rd
+b30d056cca79f67167e87c9ae402914f *man/HOasso.Rd

NAMESPACE

@@ -1,3 +1,6 @@
 exportPattern("^[[:alpha:]]+")
 import("igraph")
 import("Rdpack")
+importFrom("graphics", "plot.default")
+S3method(plot, assortativity)
+S3method(print, assortativity)

R/HOasso.R

@@ -91,5 +91,7 @@ HOasso <- function(
     warning(paste0("Error of approximation to zero of the covariance or a variance, ", sum(sel), "results forced to NaN"))
   }
 
+  class(res) <- c("assortativity", "numeric")
+
   return(res)
 }

R/plot.assortativity.R

@@ -0,0 +1,22 @@
+plot.assortativity <- function(x,
+  type = "h",
+  ylim = c(-1, 1),
+  xlab = "Orders",
+  ylab = "Assortativity",
+  ...
+  ) {
+
+  h <- length(x)
+
+  plot.default(
+    x = 1:h,
+    y = as.numeric(x),
+    type = type,
+    ylim = ylim,
+    xlab = xlab,
+    ylab = ylab,
+    ...
+  )
+
+  invisible(NULL)
+}

R/print.assortativity.R

@@ -0,0 +1,7 @@
+print.assortativity <- function(x, ...) {
+  h <- length(x)
+  x <- matrix(x, dimnames = list(order = 1:h, "assortativity"))
+  print(x)
+
+  invisible(NULL)
+}

man/HOasso.Rd

@@ -1,5 +1,7 @@
 \name{HOasso}
 \alias{HOasso}
+\alias{plot.assortativity}
+\alias{print.assortativity}
 \title{Evalutes Higer Order Assortativity of complex networks}
 \usage{
 HOasso(
@@ -9,13 +11,29 @@ HOasso(
     x = c("sout", "dout", "lout", "sin", "din", "lin"),
     y = c("sin", "din", "lin", "sout", "dout", "lout")
 )
+
+  \method{plot}{assortativity}(x,
+    type = "h",
+    ylim = c(-1, 1),
+    xlab = "Orders",
+    ylab = "Assortativity",
+    ...
+  )
+
+  \method{print}{assortativity}(x, ...)
 }
+
 \arguments{
 \item{g}{an object of class \code{igraph} with two columns, listing the dominances, by rows.}
 \item{h}{an integer value, the function will evaluates the assortativity from the order 1 to the order \code{h}.}
 \item{weighted}{\code{logical}, if to use the weighted matrix to create the trasnition probabilities.}
-\item{x}{The first centrarlity measure, out-strength by default, see \code{details}.}
+\item{x}{In case of the \code{HOasso} function the first centrarlity measure, out-strength by default, see \code{details}. An object of class \code{assortativity} in case of the \code{print} and \code{plot} functions}
 \item{y}{The second centrarlity measure, in-strength by default, see \code{details}.}
+\item{type}{Type of plot, histogram-like vertical lines by default.}
+\item{xlab}{A label for the x axis, \code{Orders} by default.}
+\item{ylab}{A label for the x axis, \code{Assortativity} by default.}
+\item{ylim}{The y limits of the plot, the assortativity index can assume only values between -1 and 1.}
+\item{...}{Other arguments of the \code{plot.default} or the \code{print.default}  functions.}
 }
 \value{A vector \code{h} long containing the assortativity measures from the order 1 to the order \code{h}.}
 \description{
@@ -25,6 +43,12 @@ The function evalutes Higer Order Assortativity of complex networks represented
 Arguments \code{x} and {y} are \code{character} objects and can assume values   \code{"sout"}, \code{"dout"}, \code{"lout"}, \code{"sin"}, \code{"din"}, \code{"lin"} representing the out-strength, out-degree, out-log-strength, in-strength, in-degree, and in-log-strength respectively.
 
 In case of undirected graphs in- and out- centrality measures are equal. In case of unweighted graphs the strength is equal to the degree.
+
+The function returns an object of class \code{assortativity} subclass of a \code{numeric} vector.
+
+\code{plot.assortativity} is identical to \code{plot.default} but with different defaults in order to get a plot coherent with the assortativity index.
+
+\code{print.assortativity} is a method to show the assortativity values and the order side by syde.
 }
 \references{
 \insertRef{arcagni2017higher}{HOasso}
@@ -40,8 +64,7 @@ In case of undirected graphs in- and out- centrality measures are equal. In case
     weight = c( 10,  5,   2,   3,   2 )
   ))
   E(g)$label <- E(g)$weight
-  plot(g, layout = layout_in_circle(g))
   a <- HOasso(g, h = 10)
-  a
-  plot(a, type = "b", ylim = c(-1, 1), panel.first = abline(h = 0, lty = 2))
+  print(a)
+  plot(a, lwd = 3, panel.first = abline(h = 0, lty = 2))
 }