Package 'maptree'

Title: Mapping, Pruning, and Graphing Tree Models
Description: Functions with example data for graphing, pruning, and mapping models from hierarchical clustering, and classification and regression trees.
Authors: Denis White, Robert B. Gramacy <[email protected]>
Maintainer: Robert B. Gramacy <[email protected]>
License: Unlimited
Version: 1.4-8
Built: 2024-11-14 06:25:23 UTC
Source: CRAN

Help Index

Prunes a Hierarchical Cluster Tree

Description

Reduces a hierarchical cluster tree to a smaller tree either by pruning until a given number of observation groups remain, or by pruning tree splits below a given height.

Usage

clip.clust (cluster, data=NULL, k=NULL, h=NULL)

Arguments

cluster

object of class hclust or twins.
data

clustered dataset for hclust application.
k

desired number of groups.
h

height at which to prune for grouping.

At least one of k or h must be specified; k takes precedence if both are given.

Details

Used with draw.clust. See example.

Value

Pruned cluster object of class hclust.

Author(s)

Denis White

See Also

hclust, twins.object, cutree, draw.clust

Examples

library (cluster)
  data (oregon.bird.dist)

  draw.clust (clip.clust (agnes (oregon.bird.dist), k=6))

Prunes an Rpart Classification or Regression Tree

Description

Reduces a prediction tree produced by rpart to a smaller tree by specifying either a cost-complexity parameter, or a number of nodes to which to prune.

Usage

clip.rpart (tree, cp=NULL, best=NULL)

Arguments

tree

object of class rpart.
cp

cost-complexity parameter.
best

number of nodes to which to prune.

If both cp and best are not NULL, then cp is used.

Details

A minor enhancement of the existing prune.rpart to incorporate the parameter best as it is used in the (now defunct) prune.tree function in the old tree package. See example.

Value

Pruned tree object of class rpart.

Author(s)

Denis White

See Also

rpart, prune.rpart

Examples

library (rpart)
  data (oregon.env.vars, oregon.border, oregon.grid)

  draw.tree (clip.rpart (rpart (oregon.env.vars), best=7), 
    nodeinfo=TRUE, units="species", cases="cells", digits=0)

  group <- group.tree (clip.rpart (rpart (oregon.env.vars), best=7))
  names(group) <- row.names(oregon.env.vars)
  map.groups (oregon.grid, group)
  lines (oregon.border)
  map.key (0.05, 0.65, labels=as.character(seq(6)), 
    size=1, new=FALSE, sep=0.5, pch=19, head="node")

Graph a Hierarchical Cluster Tree

Description

Graph a hierarchical cluster tree of class twins or hclust using colored symbols at observations.

Usage

draw.clust (cluster, data=NULL, cex=par("cex"), pch=par("pch"), size=2.5*cex, 
      col=NULL, nodeinfo=FALSE, cases="obs", new=TRUE)

Arguments

cluster

object of class hclust or twins.
data

clustered dataset for hclust application.
cex

size of text, par parameter.
pch

shape of symbol at leaves, par parameter.
size

size in cex units of symbol at leaves.
col

vector of colors from hsv, rgb, etc, or if NULL, then use rainbow.
nodeinfo

if TRUE, add a line at each node with number of observations included in each leaf.
cases

label for type of observations.
new

if TRUE, call plot.new.

Details

An alternative to pltree and plot.hclust.

Value

The vector of colors supplied or generated.

Author(s)

Denis White

See Also

agnes, diana, hclust, draw.tree, map.groups

Examples

library (cluster)
  data (oregon.bird.dist)

  draw.clust (clip.clust (agnes (oregon.bird.dist), k=6))

Graph a Classification or Regression Tree

Description

Graph a classification or regression tree with a hierarchical tree diagram, optionally including colored symbols at leaves and additional info at intermediate nodes.

Usage

draw.tree (tree, cex=par("cex"), pch=par("pch"), size=2.5*cex, 
      col=NULL, nodeinfo=FALSE, units="", cases="obs", 
      digits=getOption("digits"), print.levels=TRUE, 
      new=TRUE)

Arguments

tree

object of class rpart or tree.
cex

size of text, par parameter.
pch

shape of symbol at leaves, par parameter.
size

if size=0, draw terminal symbol at leaves else a symbol of size in cex units.
col

vector of colors from hsv, rgb, etc, or if NULL, then use rainbow.
nodeinfo

if TRUE, add a line at each node with mean value of response, number of observations, and percent deviance explained (or classified correct).
units

label for units of mean value of response, if regression tree.
cases

label for type of observations.
digits

number of digits to round mean value of response, if regression tree.
print.levels

if TRUE, print levels of factors at splits, otherwise only the factor name.
new

if TRUE, call plot.new.

Details

As in plot.rpart(,uniform=TRUE), each level has constant depth. Specifying nodeinfo=TRUE, shows the deviance explained or the classification rate at each node.

A split is shown, for numerical variables, as variable <> value when the cases with lower values go left, or as variable >< value when the cases with lower values go right. When the splitting variable is a factor, and print.levels=TRUE, the split is shown as levels = factor = levels with the cases on the left having factor levels equal to those on the left of the factor name, and correspondingly for the right.

Value

The vector of colors supplied or generated.

Author(s)

Denis White

See Also

rpart, draw.clust, map.groups

Examples

library (rpart)
  data (oregon.env.vars)

  draw.tree (clip.rpart (rpart (oregon.env.vars), best=7), 
      nodeinfo=TRUE, units="species", cases="cells", digits=0)

Observation Groups for a Hierarchical Cluster Tree

Description

Alternative to cutree that orders pruned groups from left to right in draw order.

Usage

group.clust (cluster, k=NULL, h=NULL)

Arguments

cluster

object of class hclust or twins.
k

desired number of groups.
h

height at which to prune for grouping.

At least one of k or h must be specified; k takes precedence if both are given.

Details

Normally used with map.groups. See example.

Value

Vector of pruned cluster membership

Author(s)

Denis White

See Also

hclust, twins.object, cutree, map.groups

Examples

data (oregon.bird.dist, oregon.grid)

  group <- group.clust (hclust (dist (oregon.bird.dist)), k=6)
  names(group) <- row.names(oregon.bird.dist)
  map.groups (oregon.grid, group)

Observation Groups for Classification or Regression Tree

Description

Alternative to tree[["where"]] that orders groups from left to right in draw order.

Usage

group.tree (tree)

Arguments

tree

object of class rpart or tree.

Details

Normally used with map.groups. See example.

Value

Vector of rearranged tree[["where"]]

Author(s)

Denis White

See Also

rpart, map.groups

Examples

library (rpart)
  data (oregon.env.vars, oregon.grid)

  group <- group.tree (clip.rpart (rpart (oregon.env.vars), best=7))
  names(group) <- row.names(oregon.env.vars)
  map.groups (oregon.grid, group=group)

KGS Measure for Pruning Hierarchical Clusters

Description

Computes the Kelley-Gardner-Sutcliffe penalty function for a hierarchical cluster tree.

Usage

kgs (cluster, diss, alpha=1, maxclust=NULL)

Arguments

cluster

object of class hclust or twins.
diss

object of class dissimilarity or dist.
alpha

weight for number of clusters.
maxclust

maximum number of clusters for which to compute measure.

Details

Kelley et al. (see reference) proposed a method that can help decide where to prune a hierarchical cluster tree. At any level of the tree the mean across all clusters of the mean within clusters of the dissimilarity measure is calculated. After normalizing, the number of clusters times alpha is added. The minimum of this function corresponds to the suggested pruning size.

The current implementation has complexity O(n*n*maxclust), thus very slow with large n. For improvements, at least it should only calculate the spread for clusters that are split at each level, rather than over again for all.

Value

Vector of the penalty function for trees of size 2:maxclust. The names of vector elements are the respective numbers of clusters.

Author(s)

Denis White

References

Kelley, L.A., Gardner, S.P., Sutcliffe, M.J. (1996) An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally-related subfamilies, Protein Engineering, 9, 1063-1065.

See Also

twins.object, dissimilarity.object, hclust, dist, clip.clust,

Examples

library (cluster)
  data (votes.repub)

  a <- agnes (votes.repub, method="ward")
  b <- kgs (a, a$diss, maxclust=20)
  plot (names (b), b, xlab="# clusters", ylab="penalty")

Map Groups of Observations

Description

Draws maps of groups of observations created by clustering, classification or regression trees, or some other type of classification.

Usage

map.groups (pts, group, pch=par("pch"), size=2, col=NULL, 
      border=NULL, new=TRUE)

Arguments

pts

matrix or data frame with components "x", and "y" for each observation (see details).
group

vector of integer class numbers corresponding to pts (see details), and indexing colors in col.
pch

symbol number from par("pch") if < 100, otherwise parameter n for ngon.
size

size in cex units of point symbol.
col

vector of fill colors from hsv, rgb, etc, or if NULL, then use rainbow.
border

vector of border colors from hsv, rgb, etc, or if NULL, then use rainbow.
new

if TRUE, call plot.new.

Details

If the number of rows of pts is not equal to the length of group, then (1) pts are assumed to represent polygons and polygon is used, (2) the identifiers in group are matched to the polygons in pts through names(group) and pts$x[is.na(pts$y)], and (3) these identifiers are mapped to dense integers to reference colours. Otherwise, group is assumed to parallel pts, and, if pch < 100, then points is used, otherwise ngon, to draw shaded polygon symbols for each observation in pts.

Value

The vector of fill colors supplied or generated.

Author(s)

Denis White

See Also

ngon, polygon, group.clust, group.tree, map.key

Examples

data (oregon.bird.names, oregon.env.vars, oregon.bird.dist)
  data (oregon.border, oregon.grid)

  # range map for American Avocet
  spp <- match ("American avocet", oregon.bird.names[["common.name"]])
  group <- oregon.bird.dist[,spp] + 1
  names(group) <- row.names(oregon.bird.dist)
  kol <- gray (seq(0.8,0.2,length.out=length (table (group))))
  map.groups (oregon.grid, group=group, col=kol)
  lines (oregon.border)

  # distribution of January temperatures
  cuts <- quantile (oregon.env.vars[["jan.temp"]], probs=seq(0,1,1/5))
  group <- cut (oregon.env.vars[["jan.temp"]], cuts, labels=FALSE, 
    include.lowest=TRUE)
  names(group) <- row.names(oregon.env.vars)
  kol <- gray (seq(0.8,0.2,length.out=length (table (group))))
  map.groups (oregon.grid, group=group, col=kol)
  lines (oregon.border)

  # January temperatures using point symbols rather than polygons
  map.groups (oregon.env.vars, group, col=kol, pch=19)
  lines (oregon.border)

Draw Key to accompany Map of Groups

Description

Draws legends for maps of groups of observations.

Usage

map.key (x, y, labels=NULL, cex=par("cex"), pch=par("pch"),
      size=2.5*cex, col=NULL, head="", sep=0.25*cex, new=FALSE)

Arguments

x, y

coordinates of lower left position of key in proportional units (0-1) of plot.
labels

vector of labels for classes, or if NULL, then integers 1:length(col), or 1.
size

size in cex units of shaded key symbol.
pch

symbol number for par if < 100, otherwise parameter n for ngon.
cex

pointsize of text, par parameter.
head

text heading for key.
sep

separation in cex units between adjacent symbols in key. If sep=0, assume a continuous scale, use square symbols, and put labels at breaks between squares.
col

vector of colors from hsv, rgb, etc, or if NULL, then use rainbow.
new

if TRUE, call plot.

Details

Uses points or ngon, depending on value of pch, to draw shaded polygon symbols for key.

Value

The vector of colors supplied or generated.

Author(s)

Denis White

See Also

ngon, map.groups

Examples

data (oregon.env.vars)

  # key for examples in help(map.groups)
  # range map for American Avocet
  kol <- gray (seq(0.8,0.2,length.out=2))
  map.key (0.2, 0.2, labels=c("absent","present"), pch=106, 
    col=kol, head="key", new=TRUE)
  # distribution of January temperatures
  cuts <- quantile (oregon.env.vars[["jan.temp"]], probs=seq(0,1,1/5))
  kol <- gray (seq(0.8,0.2,length.out=5))
  map.key (0.2, 0.2, labels=as.character(round(cuts,0)), 
    col=kol, sep=0, head="key", new=TRUE)

  # key for example in help file for group.tree
  map.key (0.2, 0.2, labels=as.character(seq(6)), 
    pch=19, head="node", new=TRUE)

Outline or Fill a Regular Polygon

Description

Draws a regular polygon at specified coordinates as an outline or shaded.

Usage

ngon (xydc, n=4, angle=0, type=1)

Arguments

xydc

four element vector with x and y coordinates of center, d diameter in mm, and c color.
n

number of sides for polygon (>8 => circle).
angle

rotation angle of figure, in degrees.
type

type=1 => interior filled, type=2 => edge, type=3 => both.

Details

Uses polygon to draw shaded polygons and lines for outline. If n is odd, there is a vertex at (0, d/2), otherwise the midpoint of a side is at (0, d/2).

Value

Invisible.

Author(s)

Denis White

See Also

polygon, lines, map.key, map.groups

Examples

plot (c(0,1), c(0,1), type="n")
  ngon (c(.5, .5, 10, "blue"), angle=30, n=3)
  apply (cbind (runif(8), runif(8), 6, 2), 1, ngon)

Presence/Absence of Bird Species in Oregon, USA

Description

Binary matrix (1 = present) for distributions of 248 native breeding bird species for 389 grid cells in Oregon, USA.

Usage

data (oregon.bird.dist)

Format

A data frame with 389 rows and 248 columns.

Details

Row names are hexagon identifiers from White et al. (1992). Column names are species element codes developed by The Nature Conservancy (TNC), the Oregon Natural Heritage Program (ONHP), and NatureServe.

Source

Denis White

References

Master, L. (1996) Predicting distributions for vertebrate species: some observations, Gap Analysis: A Landscape Approach to Biodiversity Planning, Scott, J.M., Tear, T.H., and Davis, F.W., editors, American Society for Photogrammetry and Remote Sensing, Bethesda, MD, pp. 171-176.

White, D., Preston, E.M., Freemark, K.E., Kiester, A.R. (1999) A hierarchical framework for conserving biodiversity, Landscape ecological analysis: issues and applications, Klopatek, J.M., Gardner, R.H., editors, Springer-Verlag, pp. 127-153.

White, D., Kimerling, A.J., Overton, W.S. (1992) Cartographic and geometric components of a global sampling design for environmental monitoring, Cartography and Geographic Information Systems, 19(1), 5-22.

TNC, https://www.nature.org/en-us/

ONHP, https://inr.oregonstate.edu/orbic/

NatureServe, https://www.natureserve.org/

See Also

oregon.env.vars, oregon.bird.names, oregon.grid, oregon.border

Names of Bird Species in Oregon, USA

Description

Scientific and common names for 248 native breeding bird species in Oregon, USA.

Usage

data (oregon.bird.names)

Format

A data frame with 248 rows and 2 columns.

Details

Row names are species element codes. Columns are "scientific.name" and "common.name". Data are provided by The Nature Conservancy (TNC), the Oregon Natural Heritage Program (ONHP), and NatureServe.

Source

Denis White

References

Master, L. (1996) Predicting distributions for vertebrate species: some observations, Gap Analysis: A Landscape Approach to Biodiversity Planning, Scott, J.M., Tear, T.H., and Davis, F.W., editors, American Society for Photogrammetry and Remote Sensing, Bethesda, MD, pp. 171-176.

TNC, https://www.nature.org/en-us/

ONHP, https://inr.oregonstate.edu/orbic/

NatureServe, https://www.natureserve.org/

See Also

oregon.bird.dist

Boundary of Oregon, USA

Description

The boundary of the state of Oregon, USA, in lines format.

Usage

data (oregon.border)

Format

A data frame with 485 rows and 2 columns (the components "x" and "y").

Details

The map projection for this boundary, as well as the point coordinates in oregon.env.vars, is the Lambert Conformal Conic with standard parallels at 33 and 45 degrees North latitude, with the longitude of the central meridian at 120 degrees, 30 minutes West longitude, and with the projection origin latitude at 41 degrees, 45 minutes North latitude.

Source

Denis White

Environmental Variables for Oregon, USA

Description

Distributions of 10 environmental variables for 389 grid cells in Oregon, USA.

Usage

data (oregon.env.vars)

Format

A data frame with 389 rows and 10 columns.

Details

Row names are hexagon identifiers from White et al. (1992). Variables (columns) are

bird.spp number of native breeding bird species
x x coordinate of center of grid cell
y y coordinate of center of grid cell
jan.temp mean minimum January temperature (C)
jul.temp mean maximum July temperature (C)
rng.temp mean difference between July and January temperatures (C)
ann.ppt mean annual precipitation (mm)
min.elev minimum elevation (m)
rng.elev range of elevation (m)
max.slope maximum slope (percent)

Source

Denis White

References

White, D., Preston, E.M., Freemark, K.E., Kiester, A.R. (1999) A hierarchical framework for conserving biodiversity, Landscape ecological analysis: issues and applications, Klopatek, J.M., Gardner, R.H., editors, Springer-Verlag, pp. 127-153.

White, D., Kimerling, A.J., Overton, W.S. (1992) Cartographic and geometric components of a global sampling design for environmental monitoring, Cartography and Geographic Information Systems, 19(1), 5-22.

See Also

oregon.bird.dist, oregon.grid, oregon.border

Hexagonal Grid Cell Polygons covering Oregon, USA

Description

Polygon borders for 389 hexagonal grid cells covering Oregon, USA, in polygon format.

Usage

data (oregon.grid)

Format

A data frame with 3112 rows and 2 columns (the components "x" and "y").

Details

The polygon format used for these grid cell boundaries is a slight variation from the standard R/S format. Each cell polygon is described by seven coordinate pairs, the last repeating the first. Prior to the first coordinate pair of each cell is a row containing NA in the "y" column and, in the "x" column, an identifier for the cell. The identifiers are the same as the row names in oregon.bird.dist and oregon.env.vars. See map.groups for how the linkage is made in mapping.

These grid cells are extracted from a larger set covering the conterminous United States and adjacent parts of Canada and Mexico, as described in White et al. (1992). Only cells with at least 50 percent of their area contained within the state of Oregon are included.

The map projection for the coordinates, as well as the point coordinates in oregon.env.vars, is the Lambert Conformal Conic with standard parallels at 33 and 45 degrees North latitude, with the longitude of the central meridian at 120 degrees, 30 minutes West longitude, and with the projection origin latitude at 41 degrees, 45 minutes North latitude.

Source

Denis White

References

White, D., Kimerling, A.J., Overton, W.S. (1992) Cartographic and geometric components of a global sampling design for environmental monitoring, Cartography and Geographic Information Systems, 19(1), 5-22.

Converts agnes or diana object to hclust object

Description

Alternative to as.hclust that retains cluster data.

Usage

twins.to.hclust (cluster)

Arguments

cluster

object of class twins.

Details

Used internally in with clip.clust and draw.clust.

Value

hclust object

Author(s)

Denis White

See Also

hclust, twins.object