DNA Methylation Data for Normal Tissue Types

Description

Illumina GoldenGate DNA methylation data for 217 normal tissues. 100 most variable CpG sites.

Usage

IlluminaMethylation

Format

a 217 x 100 matrix containing Illumina Avg Beta values (IllumBeta), and a corresponding factor vector of 217 tissue types (tissue).

References

Christensen BC, Houseman EA, et al. 2009 Aging and Environmental Exposures Alter Tissue-Specific DNA Methylation Dependent upon CpG Island Context. PLoS Genet 5(8): e1000602.

Beta Distribution Maximum Likelihood Estimator

Description

Estimates a beta distribution via Maximum Likelihood

Usage

betaEst(y, w, weights)

Arguments

Details

Typically not be called by user.

Value

(a,b) parameters

Beta Maximum Likelihood on a Matrix

Description

Maximum likelihood estimator for beta model on matrix of values (columns having different, independent beta distributions)

Usage

betaEstMultiple(Y, weights = NULL)

Arguments

Value

A list of beta parameters and BIC

Beta Maximum Likelihood Objective Function

Description

Objective function for fitting a beta model using maximum likelihood

Usage

betaObjf(logab, ydata, wdata, weights)

Arguments

Details

Typically not be called by user.

Value

negative log-likelihood

Beta Latent Class Model

Description

Fits a beta mixture model for any number of classes

Usage

blc(Y, w, maxiter = 25, tol = 1e-06, weights = NULL, verbose = TRUE)

Arguments

Details

Typically not be called by user.

Value

A list of parameters representing mixture model fit, including posterior weights and log-likelihood

Initialize Gaussian Latent Class via Mean Dichotomization

Description

Creates a function for initializing latent class model by dichotomizing via mean over all responses

Usage

blcInitializeSplitDichotomizeUsingMean(threshold = 0.5, fuzz = 0.95)

Arguments

Details

Creates a function f(x) that will take a data matrix x and initialize a weight matrix for a two-class latent class model. Here, a simple threshold will be applied to the mean over all item responses. See blcTree for example of using “blcInitializeSplit...” to create starting values.

Value

A function f(x) (see Details.)

See Also

glcInitializeSplitFanny, glcInitializeSplitHClust

Initialize Gaussian Latent Class via Eigendecomposition

Description

Creates a function for initializing latent class model based on Eigendecomposition

Usage

blcInitializeSplitEigen(eigendim = 1, 
    assignmentf = function(s) (rank(s) - 0.5)/length(s))

Arguments

Details

Creates a function f(x) that will take a data matrix x and initialize a weight matrix for a two-class latent class model. Here, the initialized classes will be based on eigendecomposition of the variance of x. See blcTree for example of using “blcSplitCriterion...” to control split.

Value

A function f(x) (see Details.)

See Also

blcInitializeSplitDichotomizeUsingMean, glcInitializeSplitFanny, glcInitializeSplitHClust

Initialize Beta Latent Class via Fanny

Description

Creates a function for initializing latent class model using the fanny algorithm

Usage

blcInitializeSplitFanny(nu = 2, nufac = 0.875, metric = "euclidean")

Arguments

Details

Creates a function f(x) that will take a data matrix x and initialize a weight matrix for a two-class latent class model. Here, the “fanny” algorithm will be used. See blcTree for example of using “blcSplitCriterion...” to control split.

Value

A function f(x) (see Details.)

See Also

blcInitializeSplitDichotomizeUsingMean, blcInitializeSplitEigen, blcInitializeSplitHClust

Initialize Beta Latent Class via Hierarchical Clustering

Description

Creates a function for initializing latent class model using hierarchical clustering.

Usage

blcInitializeSplitHClust(metric = "manhattan", method = "ward")

Arguments

Details

Creates a function f(x) that will take a data matrix x and initialize a weight matrix for a two-class latent class model. Here, a two-branch split from hierarchical clustering will be used. See blcTree for example of using “blcSplitCriterion...” to control split.

Value

A function f(x) (see Details.)

See Also

blcInitializeSplitDichotomizeUsingMean, blcInitializeSplitEigen, blcInitializeSplitFanny

Beta Latent Class Splitter

Description

Splits a data set into two via a beta mixture model

Usage

blcSplit(x, initFunctions, weight = NULL, index = NULL, level = NULL, 
    wthresh = 1e-09, verbose = TRUE, nthresh = 5, 
    splitCriterion = NULL)

Arguments

Details

Should not be called by user.

Value

A list of objects representing split.

Beta RPMM Split Criterion: Use BIC

Description

Split criterion function: compare BICs to determine split.

Usage

blcSplitCriterionBIC(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. See blcTree for example of using “blcSplitCriterion...” to control split.

Value

See Also

blcSplitCriterionBIC, blcSplitCriterionJustRecordEverything, blcSplitCriterionLevelWtdBIC, blcSplitCriterionLRT

Beta RPMM Split Criterion: Use ICL-BIC

Description

Split criterion function: compare ICL-BICs to determine split (i.e. include entropy term in comparison).

Usage

blcSplitCriterionBICICL(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. See blcTree for example of using “blcSplitCriterion...” to control split.

Value

See Also

blcSplitCriterionBICICL, blcSplitCriterionJustRecordEverything, blcSplitCriterionLevelWtdBIC, blcSplitCriterionLRT

Beta RPMM Split Criterion: Always Split and Record Everything

Description

Split criterion function: always split, but record everything as you go.

Usage

blcSplitCriterionJustRecordEverything(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. This function ALWAYS returns split=TRUE. Useful for gathering information. It is recommended that you set the maxlev argument in the main function to something less than infinity (say, 3 or 4). See blcTree for example of using “blcSplitCriterion...” to control split.

Value

See Also

blcSplitCriterionBIC, blcSplitCriterionBICICL, blcSplitCriterionLevelWtdBIC, blcSplitCriterionLRT

Beta RPMM Split Criterion: use likelihood ratio test p value

Description

Split criterion function: Use likelihood ratio test p value to determine split.

Usage

blcSplitCriterionLRT(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “blcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. See blcTree for example of using “blcSplitCriterion...” to control split.

Value

See Also

blcSplitCriterionBIC, blcSplitCriterionBICICL, blcSplitCriterionJustRecordEverything, blcSplitCriterionLevelWtdBIC

Beta RPMM Split Criterion: Level-Weighted BIC

Description

Split criterion function: use a level-weighted version of BIC to determine split; there is an additional penalty incorporated for deep recursion.

Usage

blcSplitCriterionLevelWtdBIC(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. See blcTree for example of using “blcSplitCriterion...” to control split.

Value

See Also

blcSplitCriterionBIC, blcSplitCriterionBICICL, blcSplitCriterionJustRecordEverything, blcSplitCriterionLRT

Beta Subtree

Description

Subsets a “blcTree” object, i.e. considers the tree whose root is a given node.

Usage

blcSubTree(tr, node)

Arguments

Details

Typically not be called by user.

Value

A “blcTree” object whose root is the given node of tr

Beta RPMM Tree

Description

Performs beta latent class modeling using recursively-partitioned mixture model

Usage

blcTree(x, initFunctions = list(blcInitializeSplitFanny()), 
   weight = NULL, index = NULL, wthresh = 1e-08, nodename = "root",
   maxlevel = Inf, verbose = 2, nthresh = 5, level = 0, env = NULL, 
   unsplit = NULL, splitCriterion = blcSplitCriterionBIC)

Arguments

Details

This function is called recursively by itself. Upon each recursion, certain arguments (e.g. nodename) are reset. Do not attempt to set these arguments yourself.

Value

An object of class “blcTree”. This is an environment, each of whose component objects represents a node in the tree.

Note

The class “blcTree” is currently implemented as an environment object with nodes represented flatly, with name indicating positition in hierarchy (e.g. “rLLR” = “right child of left child of left child of root”) This implementation is to make certain plotting and update functions simpler than would be required if the data were stored in a more natural “list of list” format.

The following error may appear during the course of the algorithm:

      Error in optim(logab, betaObjf, ydata = y, wdata = w, weights = weights,  : 
           non-finite value supplied by optim
      

This is merely an indication that the node being split is too small, in which case the splitting will terminate at that node; in other words, it is nothing to worry about.

Author(s)

E. Andres Houseman

References

Houseman et al., Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics 9:365, 2008.

See Also

glcTree

Examples

## Not run: 
data(IlluminaMethylation)

heatmap(IllumBeta, scale="n",
  col=colorRampPalette(c("yellow","black","blue"),space="Lab")(128))

# Fit Gaussian RPMM
rpmm <- blcTree(IllumBeta, verbose=0)
rpmm

# Get weight matrix and show first few rows
rpmmWeightMatrix <- blcTreeLeafMatrix(rpmm)
rpmmWeightMatrix[1:3,]

# Get class assignments and compare with tissue
rpmmClass <- blcTreeLeafClasses(rpmm)
table(rpmmClass,tissue)

# Plot fit
par(mfrow=c(2,2))
plot(rpmm) ; title("Image of RPMM Profile")
plotTree.blcTree(rpmm) ; title("Dendrogram with Labels")
plotTree.blcTree(rpmm, 
  labelFunction=function(u,digits) table(as.character(tissue[u$index])))
title("Dendrogram with Tissue Counts")

# Alternate initialization
rpmm2 <- blcTree(IllumBeta, verbose=0, 
  initFunctions=list(blcInitializeSplitEigen(),
                     blcInitializeSplitFanny(nu=2.5)))
rpmm2

# Alternate split criterion
rpmm3 <- blcTree(IllumBeta, verbose=0, maxlev=3,
  splitCriterion=blcSplitCriterionLevelWtdBIC)
rpmm3

rpmm4 <- blcTree(IllumBeta, verbose=0, maxlev=3,
  splitCriterion=blcSplitCriterionJustRecordEverything)
rpmm4$rLL$splitInfo$llike1
rpmm4$rLL$splitInfo$llike2

## End(Not run)

Recursive Apply Function for Beta RPMM Objects

Description

Recursively applies a function down the nodes of a Gaussian RPMM tree.

Usage

blcTreeApply(tr, f, start = "root", terminalOnly = FALSE, asObject = TRUE, ...)

Arguments

. In the latter case, f should be defined as f <- function(nn,tree){...}.

Value

A list of results; names of elements are names of nodes.

Posterior Class Assignments for Beta RPMM

Description

Gets a vector of posterior class membership assignments for terminal nodes.

Usage

blcTreeLeafClasses(tr)

Arguments

Details

See blcTree for example.

Value

Vector of class assignments

See Also

blcTreeLeafMatrix

Posterior Weight Matrix for Beta RPMM

Description

Gets a matrix of posterior class membership weights for terminal nodes.

Usage

blcTreeLeafMatrix(tr, rounding = 3)

Arguments

Details

See blcTree for example.

Value

N x K matrix of posterior weights

See Also

blcTreeLeafClasses

Overall BIC for Entire RPMM Tree (Beta version)

Description

Computes the BIC for the latent class model represented by terminal nodes

Usage

blcTreeOverallBIC(tr, ICL = FALSE)

Arguments

Value

BIC or BIC-ICL.

Empirical Bayes predictions for a specific RPMM model

Description

Empirical Bayes predictions for a specific RPMM model

Usage

ebayes(rpmm, x, type, nodelist=NULL)

Arguments

Details

Typically not be called by user.

Value

Matrix of empirical bayes predictions corresponding to x.

Gaussian Maximum Likelihood on a Matrix

Description

Maximum likelihood estimator for Gaussian model on matrix of values (columns having different, independent Gaussian distributions)

Usage

gaussEstMultiple(Y, weights = NULL)

Arguments

Value

A list of beta parameters and BIC

Gaussian Finite Mixture Model

Description

Fits a Gaussian mixture model for any number of classes

Usage

glc(Y, w, maxiter = 100, tol = 1e-06, weights = NULL, verbose = TRUE)

Arguments

Details

Typically not be called by user.

Value

A list of parameters representing mixture model fit, including posterior weights and log-likelihood

Initialize Gaussian Latent Class via Eigendecomposition

Description

Creates a function for initializing latent class model based on Eigendecomposition

Usage

glcInitializeSplitEigen(eigendim = 1, 
   assignmentf = function(s) (rank(s) - 0.5)/length(s))

Arguments

Details

Creates a function f(x) that will take a data matrix x and initialize a weight matrix for a two-class latent class model. Here, the initialized classes will be based on eigendecomposition of the variance of x. See glcTree for example of using “glcInitializeSplit...” to create starting values.

Value

A function f(x) (see Details.)

See Also

glcInitializeSplitFanny, glcInitializeSplitHClust

Initialize Gaussian Latent Class via Fanny

Description

Creates a function for initializing latent class model using the fanny algorithm

Usage

glcInitializeSplitFanny(nu = 2, nufac = 0.875, metric = "euclidean")

Arguments

Details

Creates a function f(x) that will take a data matrix x and initialize a weight matrix for a two-class latent class model. Here, the “fanny” algorithm will be used. See glcTree for example of using “glcInitializeSplit...” to create starting values.

Value

A function f(x) (see Details.)

See Also

glcInitializeSplitEigen, glcInitializeSplitHClust

Initialize Gaussian Latent Class via Hierarchical Clustering

Description

Creates a function for initializing latent class model using hierarchical clustering.

Usage

glcInitializeSplitHClust(metric = "manhattan", method = "ward")

Arguments

Details

Creates a function f(x) that will take a data matrix x and initialize a weight matrix for a two-class latent class model. Here, a two-branch split from hierarchical clustering will be used. See glcTree for example of using “glcInitializeSplit...” to create starting values.

Value

A function f(x) (see Details.)

See Also

glcInitializeSplitEigen, glcInitializeSplitFanny

Gaussian Latent Class Splitter

Description

Splits a data set into two via a Gaussian mixture models

Usage

glcSplit(x, initFunctions, weight = NULL, index = NULL, level =
                 0, wthresh = 1e-09, verbose = TRUE, nthresh = 5,
                 splitCriterion = glcSplitCriterionBIC)

Arguments

Details

Should not be called by user.

Value

A list of objects representing split.

Gaussian RPMM Split Criterion: Use BIC

Description

Split criterion function: compare BICs to determine split.

Usage

glcSplitCriterionBIC(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. See glcTree for example of using “glcSplitCriterion...” to control split.

Value

See Also

glcSplitCriterionBIC, glcSplitCriterionJustRecordEverything, glcSplitCriterionLevelWtdBIC, glcSplitCriterionLRT

Gaussian RPMM Split Criterion: Use ICL-BIC

Description

Split criterion function: compare ICL-BICs to determine split (i.e. include entropy term in comparison).

Usage

glcSplitCriterionBICICL(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. See glcTree for example of using “glcSplitCriterion...” to control split.

Value

See Also

glcSplitCriterionBICICL, glcSplitCriterionJustRecordEverything, glcSplitCriterionLevelWtdBIC, glcSplitCriterionLRT

Gaussian RPMM Split Criterion: Always Split and Record Everything

Description

Split criterion function: always split, but record everything as you go.

Usage

glcSplitCriterionJustRecordEverything(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. This function ALWAYS returns split=TRUE. Useful for gathering information. It is recommended that you set the maxlev argument in the main function to something less than infinity (say, 3 or 4). See glcTree for example of using “glcSplitCriterion...” to control split.

Value

See Also

glcSplitCriterionBIC, glcSplitCriterionBICICL, glcSplitCriterionLevelWtdBIC, glcSplitCriterionLRT

Gaussian RPMM Split Criterion: Use likelihood ratio test p value

Description

Split criterion function: use likelihood ratio test p value to determine split.

Usage

glcSplitCriterionLRT(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. See glcTree for example of using “glcSplitCriterion...” to control split.

Value

See Also

glcSplitCriterionBIC, glcSplitCriterionBICICL, glcSplitCriterionJustRecordEverything, glcSplitCriterionLevelWtdBIC

Gaussian RPMM Split Criterion: Level-Weighted BIC

Description

Split criterion function: use a level-weighted version of BIC to determine split; there is an additional penalty incorporated for deep recursion.

Usage

glcSplitCriterionLevelWtdBIC(llike1, llike2, weight, ww, J, level)

Arguments

Details

This is a function of the form “glcSplitCriterion...”, which is required to return a list with at least a boolean value split, along with supporting information. See glcTree for example of using “glcSplitCriterion...” to control split.

Value

See Also

glcSplitCriterionBIC, glcSplitCriterionBICICL, glcSplitCriterionJustRecordEverything, glcSplitCriterionLRT

Gaussian Subtree

Description

Subsets a “glcTree” object, i.e. considers the tree whose root is a given node.

Usage

glcSubTree(tr, node)

Arguments

Details

Typically not be called by user.

Value

A “glcTree” object whose root is the given node of tr

Gaussian RPMM Tree

Description

Performs Gaussian latent class modeling using recursively-partitioned mixture model

Usage

glcTree(x, initFunctions = list(glcInitializeSplitFanny(nu=1.5)), 
   weight = NULL, index = NULL, wthresh = 1e-08, 
   nodename = "root", maxlevel = Inf, verbose = 2, nthresh = 5, level = 0, 
   env = NULL, unsplit = NULL, splitCriterion = glcSplitCriterionBIC)

Arguments

Details

This function is called recursively by itself. Upon each recursion, certain arguments (e.g. nodename) are reset. Do not attempt to set these arguments yourself.

Value

An object of class “glcTree”. This is an environment, each of whose component objects represents a node in the tree.

Note

The class “glcTree” is currently implemented as an environment object with nodes represented flatly, with name indicating positition in hierarchy (e.g. “rLLR” = “right child of left child of left child of root”) This implementation is to make certain plotting and update functions simpler than would be required if the data were stored in a more natural “list of list” format.

The following error may appear during the course of the algorithm:

      Error in optim(logab, betaObjf, ydata = y, wdata = w, weights = weights,  : 
           non-finite value supplied by optim
      

This is merely an indication that the node being split is too small, in which case the splitting will terminate at that node; in other words, it is nothing to worry about.

Author(s)

E. Andres Houseman

References

Houseman et al., Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions. BMC Bioinformatics 9:365, 2008.

See Also

blcTree

Examples

data(IlluminaMethylation)

## Not run: 
heatmap(IllumBeta, scale="n",
  col=colorRampPalette(c("yellow","black","blue"),space="Lab")(128))

## End(Not run)

# Fit Gaussian RPMM
rpmm <- glcTree(IllumBeta, verbose=0)
rpmm

# Get weight matrix and show first few rows
rpmmWeightMatrix <- glcTreeLeafMatrix(rpmm)
rpmmWeightMatrix[1:3,]

# Get class assignments and compare with tissue
rpmmClass <- glcTreeLeafClasses(rpmm)
table(rpmmClass,tissue)

## Not run: 
# Plot fit
par(mfrow=c(2,2))
plot(rpmm) ; title("Image of RPMM Profile")
plotTree.glcTree(rpmm) ; title("Dendrogram with Labels")
plotTree.glcTree(rpmm, 
  labelFunction=function(u,digits) table(as.character(tissue[u$index])))
title("Dendrogram with Tissue Counts")

# Alternate initialization
rpmm2 <- glcTree(IllumBeta, verbose=0, 
  initFunctions=list(glcInitializeSplitEigen(),
                     glcInitializeSplitFanny(nu=2.5)))
rpmm2

# Alternate split criterion
rpmm3 <- glcTree(IllumBeta, verbose=0, maxlev=3,
  splitCriterion=glcSplitCriterionLevelWtdBIC)
rpmm3

rpmm4 <- glcTree(IllumBeta, verbose=0, maxlev=3,
  splitCriterion=glcSplitCriterionJustRecordEverything)
rpmm4$rLL$splitInfo$llike1
rpmm4$rLL$splitInfo$llike2

## End(Not run)

Recursive Apply Function for Gaussian RPMM Objects

Description

Recursively applies a function down the nodes of a Gaussian RPMM tree.

Usage

glcTreeApply(tr, f, start = "root", terminalOnly = FALSE, 
   asObject = TRUE, ...)

Arguments

. In the latter case, f should be defined as f <- function(nn,tree){...}.

Value

A list of results; names of elements are names of nodes.

Posterior Class Assignments for Gaussian RPMM

Description

Gets a vector of posterior class membership assignments for terminal nodes.

Usage

glcTreeLeafClasses(tr)

Arguments

Details

See glcTree for example.

Value

Vector of class assignments

See Also

glcTreeLeafMatrix

Posterior Weight Matrix for Gaussian RPMM

Description

Gets a matrix of posterior class membership weights for terminal nodes.

Usage

glcTreeLeafMatrix(tr, rounding = 3)

Arguments

Details

See glcTree for example.

Value

N x K matrix of posterior weights

See Also

glcTreeLeafClasses

Overall BIC for Entire RPMM Tree (Gaussian version)

Description

Computes the BIC for the latent class model represented by terminal nodes

Usage

glcTreeOverallBIC(tr, ICL = FALSE)

Arguments

Value

BIC or BIC-ICL.

Weighted GLM for latent class covariates

Description

Wrapper for glm function to incorporate weights corresponding to latent classes

Usage

glmLC(y,W,family=quasibinomial(),eps=1E-8,Z=NULL)

Arguments

Details

This function is a wrapper for glm to incorporate weights corresponding to latent classes (e.g. from an RPMM prediction)

Value

a glm object

Data log-likelihood implied by a specific RPMM model

Description

Data log-likelihood implied by a specific RPMM model

Usage

llikeRPMMObject(o, x, type)

Arguments

Details

Typically not be called by user.

Value

Vector of loglikelihoods corresponding to rows of x.

Plot a Beta RPMM Tree Profile

Description

Plot method for objects of type “blcTree”. Plots profiles of terminal nodes in color. Method wrapper for plotImage.blcTree.

Usage

## S3 method for class 'blcTree'
plot(x,...)

Arguments

Details

See blcTree for example.

Plot a Gaussian RPMM Tree Profile

Description

Plot method for objects of type “glcTree”. Plots profiles of terminal nodes in color. Method wrapper for plotImage.glcTree.

Usage

## S3 method for class 'glcTree'
plot(x,...)

Arguments

Details

See glcTree for example.

Plot a Beta RPMM Tree Profile

Description

Plots profiles of terminal nodes in color.

Usage

plotImage.blcTree(env, 
   start = "r", method = "weight", 
   palette = colorRampPalette(c("yellow", "black", "blue"), space = "Lab")(128), 
   divcol = "red", xorder = NULL, dimensions = NULL, labelType = "LR")

Arguments

Details

See blcTree for example.

Value

Returns a vector of indices similar to the order function, representing the orrdering of items used in the plot. This is useful for replicating the order in another plot, or for axis labeling.

Plot a Gaussian RPMM Tree Profile

Description

Plots profiles of terminal nodes in color.

Usage

plotImage.glcTree(env, 
   start = "r", method = "weight", 
   palette = colorRampPalette(c("yellow", "black", "blue"), space = "Lab")(128), 
   divcol = "red", xorder = NULL, dimensions = NULL, labelType = "LR", muColorEps = 1e-08)

Arguments

Details

See glcTree for example.

Value

Returns a vector of indices similar to the order function, representing the orrdering of items used in the plot. This is useful for replicating the order in another plot, or for axis labeling.

Plot a Beta RPMM Tree Dendrogram

Description

Alternate plot function for objects of type blcTree: plots a dendrogram

Usage

plotTree.blcTree(env, start = "r", labelFunction = NULL, 
    buff = 4, cex = 0.9, square = TRUE, labelAllNodes = FALSE, labelDigits = 1, ...)

Arguments

Details

This plots a dendrogram based on RPMM tree, with labels constructed from summaries of tree object. See blcTree for example.

Plot a Gaussian RPMM Tree Dendrogram

Description

Alternate plot function for objects of type glcTree: plots a dendrogram

Usage

plotTree.glcTree(env, start = "r", labelFunction = NULL, 
    buff = 4, cex = 0.9, square = TRUE, labelAllNodes = FALSE, labelDigits = 1, ...)

Arguments

Details

This plots a dendrogram based on RPMM tree, with labels constructed from summaries of tree object. See glcTree for example.

Predict using a Beta RPMM object

Description

Prediction method for objects of type blcTree

Usage

## S3 method for class 'blcTree'
predict(object, newdata=NULL, nodelist=NULL, type="weight",...)

Arguments

Details

This function is similar to blcTreeLeafMatrix and blcTreeLeafClasses, except that it supports prediction on an external data set via the argument newdata.

See Also

blcTreeLeafMatrix

Predict using a Gaussian RPMM object

Description

Prediction method for objects of type glcTree

Usage

## S3 method for class 'glcTree'
predict(object, newdata=NULL, nodelist=NULL, type="weight",...)

Arguments

Details

This function is similar to glcTreeLeafMatrix and glcTreeLeafClasses, except that it supports prediction on an external data set via the argument newdata.

See Also

glcTreeLeafMatrix

Print a Beta RPMM object

Description

Print method for objects of type blcTree

Usage

## S3 method for class 'blcTree'
print(x,...)

Arguments

Details

See blcTree for example.

Print a Gaussian RPMM object

Description

Print method for objects of type blcTree

Usage

## S3 method for class 'glcTree'
print(x,...)

Arguments

Details

See glcTree for example.