Nothing
R/mse.R
In baycn: Bayesian Inference for Causal Networks
# toPES
#
# Converts a posterior probablity adjacency matrix into a posterior probability
# edge state matrix. It only outputs the posterior for the edges in the
# adjacency matrix used as input.
#
# @param pm A posterior probability adjacency matrix.
#
# @param adjMatrix An adjacency matrix indicating which edges will be converted
# to the posterior probability edge state matrix. To consider all possible
# edges use a fully connected adjacency matrix as the input.
#
# @return A matrix with three columns, one for each edge state, and the number
# of rows will be the number of edges in the adjacency matrix used as the
# input.
#
toPES <- function (pm,
adjMatrix) {
# Get the position of the edges present in the adjacency matrix.
position <- coordinates(adjMatrix)
# Extract the number of rows and columns in the probability adjacency matrix.
# This should be a square matrix.
dims <- dim(pm)
# The output from partition and order MCMC is not a numeric matrix. Convert
# the input to a numeric matrix.
npm <- matrix(as.numeric(pm),
nrow = dims[1],
ncol = dims[2])
# Create an empty matrix to hold the posterior for each edge.
PES <- matrix(nrow = dim(position)[2],
ncol = 3)
# Name the columns according to the edge state.
colnames(PES) <- c('zero', 'one', 'two')
# Loop through each edge and only keep the values that correspond to the edges
# present in the adjacency matrix.
for(e in 1:dim(position)[2]) {
# Extract the posterior for the state 'zero'.
upper <- npm[position[1, e], position[2, e]]
# Extract the posterior for the state 'one'.
lower <- npm[position[2, e], position[1, e]]
# Fill in the posterior probability of the three edge states for the current
# edge.
PES[e, ] <- c(upper, lower, 1 - (upper + lower))
}
return (PES)
}
# innerMSE
#
# Calculates either the edge-wise or whole graph MSE. If more than one data set
# is input the mean and standard deviation of either the edge-wise or whole
# graph mse will be calculated.
#
# @param posterior A list of baycn objects or posterior probability adjacency
# matrices. For example, the output from the ep() function from the structmcmc
# package and the output from the edges.posterior() funciton from the BiDAG
# package are posterior probability adjacency matrices.
#
# @param expected The expected edge state probabilities for each edge. This
# must be a matrix with the expected probability for the three edge states
# across the columns and each edge down the rows.
#
# @param adjMatrix An adjacency matrix indicating which edges will be converted
# to the posterior probability edge state matrix. To consider all possible
# edges use a fully connected adjacency matrix as the input.
#
# @param type A character string indicating the type of MSE to be calculated.
# The two options are 'emse' - edge-wise MSE or 'gmse' - whole graph MSE.
#
innerMSE <- function (posterior,
expected,
adjMatrix,
type) {
# Extract the number of lists in the top level of posterior. First, check if
# the eth element in the posterior list is a single matrix. This step needs
# to be done because the length function will give the number of elements in
# the matrix if there is only one matrix in the first element of posterior.
# For example, if posterior[[1]] is *one* posterior probability adjacency
# matrix then the length function would give the number of elements in the
# matrix instead of one (for *one* matrix).
M <- ifelse(inherits(posterior, 'list'), length(posterior), 1)
# Determine if the object(s) in posterior are of class 'baycn' depending on
# whether M = 1.
is_baycn <- ifelse(M == 1,
inherits(posterior, 'baycn'),
inherits(posterior[[1]], 'baycn'))
# Extract the number of edges in the network
nEdges <- dim(expected)[1]
# Create a list for the squared error for each element in posterior.
sqd_error <- vector(mode = 'list',
length = M)
# Create a list for the edge-wise MSE for each edge in the network. Each
# element of this list will be a matrix of eMSEs.
emse <- vector(mode = 'list',
length = M)
# Loop through each element of the posterior list and calculate the eMSE.
for (e in 1:M) {
# Determine if the current object is from baycn.
if (is_baycn) {
# If the posterior object only has one element it doesn't need to be
# subsetted to calculate the squared error.
if (M == 1) {
# Calculate the squared error for each edge in the network.
sqd_error[[e]] <- (expected - posterior@posteriorES[, 2:4])^2
# If the posterior object has more than one element it needs to be
# subsetted in order to calculate the squared error.
} else {
# Calculate the squared error for each edge in the network.
sqd_error[[e]] <- (expected - posterior[[e]]@posteriorES[, 2:4])^2
}
} else {
# If the posterior object only has one element it doesn't need to be
# subsetted in order to convert it to a posterior edge state matrix.
if (M == 1) {
# Convert the probability adjacency matrix to an edge state matrix.
pamToES <- toPES(pm = posterior,
adjMatrix = adjMatrix)
# If the posterior object only has more than one element it needs to be
# subsetted in order to convert it to a posterior edge state matrix.
} else {
# Convert the probability adjacency matrix to an edge state matrix.
pamToES <- toPES(pm = posterior[[e]],
adjMatrix = adjMatrix)
}
# Calcualte the squared error for each edge state matrix.
sqd_error[[e]] <- as.matrix((expected - pamToES)^2)
}
# Create a matrix to hold the edge-wise MSE for each edge in the network.
emse[[e]] <- matrix(nrow = nEdges,
ncol = 1)
for (v in 1:nEdges) {
# Calculate the eMSE for each edge by averaging the MSE across the three
# edge states for each edge.
emse[[e]][v, ] <- mean(as.numeric(sqd_error[[e]][v, ]))
}
}
# Create a character vector with mean and sd as the elements. This will be
# used to name the emse or gmse matrices when M > 1.
mean_sd <- c('mean', 'sd')
# Create a character vector with eMSE as the element. This will be used to
# name the emse or gmse matrices when M = 1.
emse_char <- 'eMSE'
# Check if M is greater than one. This will determine the structure of the
# matrix that is output for either emse or gmse.
if (M > 1) {
# Calculate the mean and standard deviation of the eMSE for each edge.
mean_emse <- apply(simplify2array(emse), 1, mean)
sd_emse <- apply(simplify2array(emse), 1, sd)
}
switch(type,
# edge-wise mse ------------------------
'emse' = {
if (M > 1) {
# Create a matrix with nEdges rows and N*2 columns to hold the mean
# and standard deviation of the MSE for each edge in the network.
emse_mat <- matrix(0,
nrow = nEdges,
ncol = 2)
# Name the columns of emse_mat.
colnames(emse_mat) <- mean_sd
# Fill in the emse matrix with the mean and sd of the eMSE for each
# edge in the network.
emse_mat[, 1] <- mean_emse
emse_mat[, 2] <- sd_emse
# If only one data set was analyzed return the MSE for each edge.
} else {
# Create a matrix with nEdges rows (for the edge-wise MSE) and N
# columns.
emse_mat <- matrix(0,
nrow = nEdges,
ncol = 1)
# Name the column of emse_mat.
colnames(emse_mat) <- emse_char
# Fill in the eMSE for each edge in the network.
emse_mat[, 1] <- emse[[1]]
}
return (emse_mat)
},
# graph-wise mse -----------------------
'gmse' = {
if (M > 1) {
# Create a matrix with one row (for the whole graph MSE) and N * 2
# columns. The columns will contain the mean and standard deviation
# of the whole graph MSE for the output from baycn for the
# different combinations of N, ss, and topology used as input.
gmse_mat <- matrix(1,
nrow = 1,
ncol = 2)
# Fill in the gmse matrix with the mean and sd of the MSE of the
# whole graph.
gmse_mat[, 1:2] <- c(mean(unlist(emse)),
sd(unlist(emse)))
# If only one data set was analyzed return the MSE for the whole
# graph.
} else {
# Create a matrix with one row (for the whole graph MSE) and N
# columns. The columns will contain the whole graph MSE for the
# output from baycn for the different combinations of N, ss, and
# topology used as input.
gmse_mat <- matrix(1,
nrow = 1,
ncol = 1)
# Calculate the whole graph MSE.
gmse_mat[1, 1] <- mean(emse[[1]])
}
return (gmse_mat)
})
}
#' mse
#'
#' Calculates either the edge-wise or whole graph MSE. If more than one data set
#' is input the mean and standard deviation of the MSE will be calculated.
#'
#' @param posterior A list of baycn objects or posterior probability adjacency
#' matrices. Each element in posterior can also be a list. For example, if
#' multiple data sets were generated under the same simulation scheme and baycn
#' was run on each data set, the input list could be a list containing the
#' output from each run of baycn. In this scenario the mean and standard
#' deviation of of the edgewise or whole graph MSE would be output.
#'
#' @param expected The expected edge state probabilities for each edge. This
#' must be a matrix with the expected probability for the three edge states
#' across the columns and each edge down the rows.
#'
#' @param adjMatrix This is only required if the input to posterior is a
#' posterior probability adjacency matrix (for example, output from the BiDAG or
#' structmcmc pacakges). This matrix will be used to convert the posterior
#' probability adjacency matrix to an edge state matrix. Only the edges in
#' adjMatrix will be converted to the edge state matrix. To consider all
#' possible edges use a fully connected adjacency matrix.
#'
#' @param type A character string indicating the type of MSE to be calculated.
#' The two options are 'emse' - edge-wise MSE or 'gmse' - whole graph MSE.
#'
#' @examples
#'
#' set.seed(3)
#'
#' # Generate data from topology M2.
#' data_m2 <- simdata(graph = 'm2_ge',
#' N = 200,
#' b0 = 0,
#' ss = 1,
#' s = 1)
#'
#' # Adjacency matrix for topology M2
#' am_m2 <- matrix(c(0, 1, 1,
#' 0, 0, 1,
#' 0, 0, 0),
#' byrow = TRUE,
#' nrow = 3)
#'
#' # Run baycn on the data from topology M2.
#' baycn_m2 <- mhEdge(data = data_m2,
#' adjMatrix = am_m2,
#' prior = c(0.05,
#' 0.05,
#' 0.9),
#' nCPh = 0,
#' nGV = 0,
#' pmr = FALSE,
#' iterations = 1000,
#' burnIn = 0.2,
#' thinTo = 500,
#' progress = FALSE)
#'
#' # Expected probabilities for topology M2.
#' ep_m2 <- matrix(c(1, 0, 0,
#' 0, 0, 1,
#' 0, 1, 0),
#' byrow = TRUE,
#' ncol = 3)
#'
#' # Calculate the edgewise MSE.
#' emse_m2 <- mse(posterior = list(baycn_m2),
#' expected = ep_m2,
#' type = 'emse')
#'
#' \donttest{
#' # When a list with two levels is passed posterior the mean and standard
#' # deviation of the MSE is returned. Below is an example demonstrating this
#' # feature.
#'
#' # Adjacency matrix for topology M2
#' am_m2 <- matrix(c(0, 1, 1,
#' 0, 0, 1,
#' 0, 0, 0),
#' byrow = TRUE,
#' nrow = 3)
#'
#' # Create a list to hold multiple M2 data sets.
#' data_m2 <- vector(mode = 'list',
#' length = 5)
#'
#' # Create a list to hold the output from baycn.
#' baycn_m2 <- vector(mode = 'list',
#' length = 5)
#'
#' for (e in 1:5) {
#'
#' # Generate data for topology M2.
#' data_m2[[e]] <- simdata(graph = 'm2_ge',
#' N = 200,
#' b0 = 0,
#' ss = 1,
#' s = 1)
#'
#' # Run baycn on the data simulated for topology M2.
#' baycn_m2[[e]] <- mhEdge(data = data_m2[[e]],
#' adjMatrix = am_m2,
#' prior = c(0.05,
#' 0.05,
#' 0.9),
#' nCPh = 0,
#' nGV = 0,
#' pmr = FALSE,
#' iterations = 1000,
#' burnIn = 0.2,
#' thinTo = 500,
#' progress = FALSE)
#' }
#'
#' # Expected probabilities for topology M2.
#' ep_m2 <- matrix(c(1, 0, 0,
#' 0, 0, 1,
#' 0, 1, 0),
#' byrow = TRUE,
#' ncol = 3)
#'
#' # Calculate the edgewise MSE
#' emse_m2 <- mse(posterior = list(baycn_m2),
#' expected = ep_m2,
#' type = 'emse')
#'
#' }
#'
#' @export
#'
mse <- function (posterior,
expected,
adjMatrix,
type = 'emse') {
# Get the number of posterior probability lists passed to mse.
N <- length(posterior)
# Create a matrix to hold the output from innerMSE.
mse_list <- vector(mode = 'list',
length = N)
# Loop through the elements of the posterior object.
for (e in 1:N) {
mse_list[[e]] <- innerMSE(posterior = posterior[[e]],
expected = expected,
adjMatrix = adjMatrix,
type = type)
}
# Combine the matrices in mse_list columnwise.
return (do.call(cbind, mse_list))
}
Try the baycn package in your browser
Any scripts or data that you put into this service are public.
baycn documentation built on Aug. 1, 2020, 1:07 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
# toPES
#
# Converts a posterior probablity adjacency matrix into a posterior probability
# edge state matrix. It only outputs the posterior for the edges in the
# adjacency matrix used as input.
#
# @param pm A posterior probability adjacency matrix.
#
# @param adjMatrix An adjacency matrix indicating which edges will be converted
# to the posterior probability edge state matrix. To consider all possible
# edges use a fully connected adjacency matrix as the input.
#
# @return A matrix with three columns, one for each edge state, and the number
# of rows will be the number of edges in the adjacency matrix used as the
# input.
#
toPES <- function (pm,
adjMatrix) {
# Get the position of the edges present in the adjacency matrix.
position <- coordinates(adjMatrix)
# Extract the number of rows and columns in the probability adjacency matrix.
# This should be a square matrix.
dims <- dim(pm)
# The output from partition and order MCMC is not a numeric matrix. Convert
# the input to a numeric matrix.
npm <- matrix(as.numeric(pm),
nrow = dims[1],
ncol = dims[2])
# Create an empty matrix to hold the posterior for each edge.
PES <- matrix(nrow = dim(position)[2],
ncol = 3)
# Name the columns according to the edge state.
colnames(PES) <- c('zero', 'one', 'two')
# Loop through each edge and only keep the values that correspond to the edges
# present in the adjacency matrix.
for(e in 1:dim(position)[2]) {
# Extract the posterior for the state 'zero'.
upper <- npm[position[1, e], position[2, e]]
# Extract the posterior for the state 'one'.
lower <- npm[position[2, e], position[1, e]]
# Fill in the posterior probability of the three edge states for the current
# edge.
PES[e, ] <- c(upper, lower, 1 - (upper + lower))
}
return (PES)
}
# innerMSE
#
# Calculates either the edge-wise or whole graph MSE. If more than one data set
# is input the mean and standard deviation of either the edge-wise or whole
# graph mse will be calculated.
#
# @param posterior A list of baycn objects or posterior probability adjacency
# matrices. For example, the output from the ep() function from the structmcmc
# package and the output from the edges.posterior() funciton from the BiDAG
# package are posterior probability adjacency matrices.
#
# @param expected The expected edge state probabilities for each edge. This
# must be a matrix with the expected probability for the three edge states
# across the columns and each edge down the rows.
#
# @param adjMatrix An adjacency matrix indicating which edges will be converted
# to the posterior probability edge state matrix. To consider all possible
# edges use a fully connected adjacency matrix as the input.
#
# @param type A character string indicating the type of MSE to be calculated.
# The two options are 'emse' - edge-wise MSE or 'gmse' - whole graph MSE.
#
innerMSE <- function (posterior,
expected,
adjMatrix,
type) {
# Extract the number of lists in the top level of posterior. First, check if
# the eth element in the posterior list is a single matrix. This step needs
# to be done because the length function will give the number of elements in
# the matrix if there is only one matrix in the first element of posterior.
# For example, if posterior[[1]] is *one* posterior probability adjacency
# matrix then the length function would give the number of elements in the
# matrix instead of one (for *one* matrix).
M <- ifelse(inherits(posterior, 'list'), length(posterior), 1)
# Determine if the object(s) in posterior are of class 'baycn' depending on
# whether M = 1.
is_baycn <- ifelse(M == 1,
inherits(posterior, 'baycn'),
inherits(posterior[[1]], 'baycn'))
# Extract the number of edges in the network
nEdges <- dim(expected)[1]
# Create a list for the squared error for each element in posterior.
sqd_error <- vector(mode = 'list',
length = M)
# Create a list for the edge-wise MSE for each edge in the network. Each
# element of this list will be a matrix of eMSEs.
emse <- vector(mode = 'list',
length = M)
# Loop through each element of the posterior list and calculate the eMSE.
for (e in 1:M) {
# Determine if the current object is from baycn.
if (is_baycn) {
# If the posterior object only has one element it doesn't need to be
# subsetted to calculate the squared error.
if (M == 1) {
# Calculate the squared error for each edge in the network.
sqd_error[[e]] <- (expected - posterior@posteriorES[, 2:4])^2
# If the posterior object has more than one element it needs to be
# subsetted in order to calculate the squared error.
} else {
# Calculate the squared error for each edge in the network.
sqd_error[[e]] <- (expected - posterior[[e]]@posteriorES[, 2:4])^2
}
} else {
# If the posterior object only has one element it doesn't need to be
# subsetted in order to convert it to a posterior edge state matrix.
if (M == 1) {
# Convert the probability adjacency matrix to an edge state matrix.
pamToES <- toPES(pm = posterior,
adjMatrix = adjMatrix)
# If the posterior object only has more than one element it needs to be
# subsetted in order to convert it to a posterior edge state matrix.
} else {
# Convert the probability adjacency matrix to an edge state matrix.
pamToES <- toPES(pm = posterior[[e]],
adjMatrix = adjMatrix)
}
# Calcualte the squared error for each edge state matrix.
sqd_error[[e]] <- as.matrix((expected - pamToES)^2)
}
# Create a matrix to hold the edge-wise MSE for each edge in the network.
emse[[e]] <- matrix(nrow = nEdges,
ncol = 1)
for (v in 1:nEdges) {
# Calculate the eMSE for each edge by averaging the MSE across the three
# edge states for each edge.
emse[[e]][v, ] <- mean(as.numeric(sqd_error[[e]][v, ]))
}
}
# Create a character vector with mean and sd as the elements. This will be
# used to name the emse or gmse matrices when M > 1.
mean_sd <- c('mean', 'sd')
# Create a character vector with eMSE as the element. This will be used to
# name the emse or gmse matrices when M = 1.
emse_char <- 'eMSE'
# Check if M is greater than one. This will determine the structure of the
# matrix that is output for either emse or gmse.
if (M > 1) {
# Calculate the mean and standard deviation of the eMSE for each edge.
mean_emse <- apply(simplify2array(emse), 1, mean)
sd_emse <- apply(simplify2array(emse), 1, sd)
}
switch(type,
# edge-wise mse ------------------------
'emse' = {
if (M > 1) {
# Create a matrix with nEdges rows and N*2 columns to hold the mean
# and standard deviation of the MSE for each edge in the network.
emse_mat <- matrix(0,
nrow = nEdges,
ncol = 2)
# Name the columns of emse_mat.
colnames(emse_mat) <- mean_sd
# Fill in the emse matrix with the mean and sd of the eMSE for each
# edge in the network.
emse_mat[, 1] <- mean_emse
emse_mat[, 2] <- sd_emse
# If only one data set was analyzed return the MSE for each edge.
} else {
# Create a matrix with nEdges rows (for the edge-wise MSE) and N
# columns.
emse_mat <- matrix(0,
nrow = nEdges,
ncol = 1)
# Name the column of emse_mat.
colnames(emse_mat) <- emse_char
# Fill in the eMSE for each edge in the network.
emse_mat[, 1] <- emse[[1]]
}
return (emse_mat)
},
# graph-wise mse -----------------------
'gmse' = {
if (M > 1) {
# Create a matrix with one row (for the whole graph MSE) and N * 2
# columns. The columns will contain the mean and standard deviation
# of the whole graph MSE for the output from baycn for the
# different combinations of N, ss, and topology used as input.
gmse_mat <- matrix(1,
nrow = 1,
ncol = 2)
# Fill in the gmse matrix with the mean and sd of the MSE of the
# whole graph.
gmse_mat[, 1:2] <- c(mean(unlist(emse)),
sd(unlist(emse)))
# If only one data set was analyzed return the MSE for the whole
# graph.
} else {
# Create a matrix with one row (for the whole graph MSE) and N
# columns. The columns will contain the whole graph MSE for the
# output from baycn for the different combinations of N, ss, and
# topology used as input.
gmse_mat <- matrix(1,
nrow = 1,
ncol = 1)
# Calculate the whole graph MSE.
gmse_mat[1, 1] <- mean(emse[[1]])
}
return (gmse_mat)
})
}
#' mse
#'
#' Calculates either the edge-wise or whole graph MSE. If more than one data set
#' is input the mean and standard deviation of the MSE will be calculated.
#'
#' @param posterior A list of baycn objects or posterior probability adjacency
#' matrices. Each element in posterior can also be a list. For example, if
#' multiple data sets were generated under the same simulation scheme and baycn
#' was run on each data set, the input list could be a list containing the
#' output from each run of baycn. In this scenario the mean and standard
#' deviation of of the edgewise or whole graph MSE would be output.
#'
#' @param expected The expected edge state probabilities for each edge. This
#' must be a matrix with the expected probability for the three edge states
#' across the columns and each edge down the rows.
#'
#' @param adjMatrix This is only required if the input to posterior is a
#' posterior probability adjacency matrix (for example, output from the BiDAG or
#' structmcmc pacakges). This matrix will be used to convert the posterior
#' probability adjacency matrix to an edge state matrix. Only the edges in
#' adjMatrix will be converted to the edge state matrix. To consider all
#' possible edges use a fully connected adjacency matrix.
#'
#' @param type A character string indicating the type of MSE to be calculated.
#' The two options are 'emse' - edge-wise MSE or 'gmse' - whole graph MSE.
#'
#' @examples
#'
#' set.seed(3)
#'
#' # Generate data from topology M2.
#' data_m2 <- simdata(graph = 'm2_ge',
#' N = 200,
#' b0 = 0,
#' ss = 1,
#' s = 1)
#'
#' # Adjacency matrix for topology M2
#' am_m2 <- matrix(c(0, 1, 1,
#' 0, 0, 1,
#' 0, 0, 0),
#' byrow = TRUE,
#' nrow = 3)
#'
#' # Run baycn on the data from topology M2.
#' baycn_m2 <- mhEdge(data = data_m2,
#' adjMatrix = am_m2,
#' prior = c(0.05,
#' 0.05,
#' 0.9),
#' nCPh = 0,
#' nGV = 0,
#' pmr = FALSE,
#' iterations = 1000,
#' burnIn = 0.2,
#' thinTo = 500,
#' progress = FALSE)
#'
#' # Expected probabilities for topology M2.
#' ep_m2 <- matrix(c(1, 0, 0,
#' 0, 0, 1,
#' 0, 1, 0),
#' byrow = TRUE,
#' ncol = 3)
#'
#' # Calculate the edgewise MSE.
#' emse_m2 <- mse(posterior = list(baycn_m2),
#' expected = ep_m2,
#' type = 'emse')
#'
#' \donttest{
#' # When a list with two levels is passed posterior the mean and standard
#' # deviation of the MSE is returned. Below is an example demonstrating this
#' # feature.
#'
#' # Adjacency matrix for topology M2
#' am_m2 <- matrix(c(0, 1, 1,
#' 0, 0, 1,
#' 0, 0, 0),
#' byrow = TRUE,
#' nrow = 3)
#'
#' # Create a list to hold multiple M2 data sets.
#' data_m2 <- vector(mode = 'list',
#' length = 5)
#'
#' # Create a list to hold the output from baycn.
#' baycn_m2 <- vector(mode = 'list',
#' length = 5)
#'
#' for (e in 1:5) {
#'
#' # Generate data for topology M2.
#' data_m2[[e]] <- simdata(graph = 'm2_ge',
#' N = 200,
#' b0 = 0,
#' ss = 1,
#' s = 1)
#'
#' # Run baycn on the data simulated for topology M2.
#' baycn_m2[[e]] <- mhEdge(data = data_m2[[e]],
#' adjMatrix = am_m2,
#' prior = c(0.05,
#' 0.05,
#' 0.9),
#' nCPh = 0,
#' nGV = 0,
#' pmr = FALSE,
#' iterations = 1000,
#' burnIn = 0.2,
#' thinTo = 500,
#' progress = FALSE)
#' }
#'
#' # Expected probabilities for topology M2.
#' ep_m2 <- matrix(c(1, 0, 0,
#' 0, 0, 1,
#' 0, 1, 0),
#' byrow = TRUE,
#' ncol = 3)
#'
#' # Calculate the edgewise MSE
#' emse_m2 <- mse(posterior = list(baycn_m2),
#' expected = ep_m2,
#' type = 'emse')
#'
#' }
#'
#' @export
#'
mse <- function (posterior,
expected,
adjMatrix,
type = 'emse') {
# Get the number of posterior probability lists passed to mse.
N <- length(posterior)
# Create a matrix to hold the output from innerMSE.
mse_list <- vector(mode = 'list',
length = N)
# Loop through the elements of the posterior object.
for (e in 1:N) {
mse_list[[e]] <- innerMSE(posterior = posterior[[e]],
expected = expected,
adjMatrix = adjMatrix,
type = type)
}
# Combine the matrices in mse_list columnwise.
return (do.call(cbind, mse_list))
}
Try the baycn package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.