Package 'IsingSampler' reference manual

Title:	Sampling Methods and Distribution Functions for the Ising Model
Description:	Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractability of the Ising model the distribution can only be computed up to roughly 10 nodes. The Blume-Capel model, an Ising model with an additional on-site quadratic (crystal-field) term, is also supported.
Authors:	Sacha Epskamp [aut, cre], Jesse Boot [ctb]
Maintainer:	Sacha Epskamp <[email protected]>
License:	GPL-2
Version:	0.4.0
Built:	2026-05-30 15:07:04 UTC
Source:	https://github.com/sachaepskamp/isingsampler

Sampling methods and distribution functions for the Ising model

Description

This package can be used to sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractability of the Ising model the distribution can only be computed up to ~10 nodes.

Author(s)

Sacha Epskamp

Maintainer: Sacha Epskamp <[email protected]>

Examples

## This code compares the different sampling algorithms to the expected
## distribution of states in a tractible number of nodes.

## In the end are examples on how to obtain the distribution.

# Input:
N <- 5 # Number of nodes
nSample <- 1000 # Number of samples

# Ising parameters:
Graph <- matrix(sample(0:1,N^2,TRUE,prob = c(0.7, 0.3)),N,N) * rnorm(N^2)
Graph <- pmax(Graph,t(Graph)) / N
diag(Graph) <- 0
Thresh <- -(rnorm(N)^2)
Beta <- 1

# Response options (0,1 or -1,1):
Resp <- c(0L,1L)

# All posible states:
AllStates <- do.call(expand.grid,lapply(1:N,function(x)Resp))

# Simulate with metropolis:
MetData <- IsingSampler(nSample, Graph, Thresh, Beta, 1000/N, 
  responses = Resp, method = "MH")

# Simulate exact samples (CFTP):
ExData <- IsingSampler(nSample, Graph, Thresh, Beta, 100,
  responses = Resp, method = "CFTP")

# Direct simulation:
DirectData <- IsingSampler(nSample, Graph, Thresh, Beta, method = "direct")

# State distributions:
MetDist <- apply(AllStates,1,function(x)sum(colSums(t(MetData) == x)==N))
ExDist <- apply(AllStates,1,function(x)sum(colSums(t(ExData) == x)==N))
DirectDist <- apply(AllStates,1,function(x)sum(colSums(t(DirectData) == x)==N))
ExpDist <- exp(- Beta * apply(AllStates,1,function(s)IsingSampler:::H(Graph,s,Thresh)))  
ExpDist <- ExpDist/sum(ExpDist) * nSample

## Plot to compare distributions:
plot(MetDist, type="l", col="blue", pch=16, xlab="State", ylab="Freq", 
  ylim=c(0,max(MetDist,DirectDist,ExDist)))
points(DirectDist,type="l",col="red",pch=16)
points(ExpDist,type="l",col="green",pch=16)
points(ExDist,type="l",col="purple",pch=16)
legend("topright", col=c("blue","red","purple","green"), 
  legend=c("Metropolis","Direct","Exact","Expected"),lty=1,bty='n')

## Likelihoods:

# Sumscores:
IsingSumLikelihood(Graph, Thresh, Beta, Resp)

# All states:
IsingLikelihood(Graph, Thresh, Beta, Resp)

# Single state:
IsingStateProb(rep(Resp[1],N),Graph, Thresh, Beta, Resp)
## This code compares the different sampling algorithms to the expected
## distribution of states in a tractible number of nodes.

## In the end are examples on how to obtain the distribution.

# Input:
N <- 5 # Number of nodes
nSample <- 1000 # Number of samples

# Ising parameters:
Graph <- matrix(sample(0:1,N^2,TRUE,prob = c(0.7, 0.3)),N,N) * rnorm(N^2)
Graph <- pmax(Graph,t(Graph)) / N
diag(Graph) <- 0
Thresh <- -(rnorm(N)^2)
Beta <- 1

# Response options (0,1 or -1,1):
Resp <- c(0L,1L)

# All posible states:
AllStates <- do.call(expand.grid,lapply(1:N,function(x)Resp))

# Simulate with metropolis:
MetData <- IsingSampler(nSample, Graph, Thresh, Beta, 1000/N, 
  responses = Resp, method = "MH")

# Simulate exact samples (CFTP):
ExData <- IsingSampler(nSample, Graph, Thresh, Beta, 100,
  responses = Resp, method = "CFTP")

# Direct simulation:
DirectData <- IsingSampler(nSample, Graph, Thresh, Beta, method = "direct")

# State distributions:
MetDist <- apply(AllStates,1,function(x)sum(colSums(t(MetData) == x)==N))
ExDist <- apply(AllStates,1,function(x)sum(colSums(t(ExData) == x)==N))
DirectDist <- apply(AllStates,1,function(x)sum(colSums(t(DirectData) == x)==N))
ExpDist <- exp(- Beta * apply(AllStates,1,function(s)IsingSampler:::H(Graph,s,Thresh)))  
ExpDist <- ExpDist/sum(ExpDist) * nSample

## Plot to compare distributions:
plot(MetDist, type="l", col="blue", pch=16, xlab="State", ylab="Freq", 
  ylim=c(0,max(MetDist,DirectDist,ExDist)))
points(DirectDist,type="l",col="red",pch=16)
points(ExpDist,type="l",col="green",pch=16)
points(ExDist,type="l",col="purple",pch=16)
legend("topright", col=c("blue","red","purple","green"), 
  legend=c("Metropolis","Direct","Exact","Expected"),lty=1,bty='n')

## Likelihoods:

# Sumscores:
IsingSumLikelihood(Graph, Thresh, Beta, Resp)

# All states:
IsingLikelihood(Graph, Thresh, Beta, Resp)

# Single state:
IsingStateProb(rep(Resp[1],N),Graph, Thresh, Beta, Resp)

Sample states from the Blume-Capel model

Description

Samples states from the Blume-Capel model, an Ising model with an additional on-site quadratic ("single-ion anisotropy" or "crystal-field") term. This is a thin wrapper around IsingSampler that sets ordered (spin-1) response options by default and requires the quadratic parameter delta.

Usage

BlumeCapelSampler(n, graph, thresholds, delta, beta = 1, nIter = 100,
    responses = c(-1L, 0L, 1L), method = c("MH", "direct"),
    CFTPretry = 10, constrain)
BlumeCapelSampler(n, graph, thresholds, delta, beta = 1, nIter = 100,
    responses = c(-1L, 0L, 1L), method = c("MH", "direct"),
    CFTPretry = 10, constrain)

Arguments

n

Number of states to draw.

graph

Square matrix indicating the weights of the network. Must be symmetrical with 0 as diagonal.

thresholds

Vector indicating the thresholds (external field). A single value is recycled over all nodes.

delta

The quadratic (Blume-Capel) parameter, entering the Hamiltonian as delta_i * s_i^2. Either a single value (recycled over all nodes) or one value per node. Must be supplied. A positive delta favours the middle category; a negative delta favours the extreme categories.

beta

Scalar indicating the inverse temperature.

nIter

Number of iterations in the Metropolis sampler.

responses

Ordered response options, defaulting to the spin-1 values c(-1L, 0L, 1L). Other (numeric) response options may be supplied, but the delta term only has an identifiable effect with more than two options.

method

The sampling method, either "MH" (Metropolis-Hastings, default) or "direct". Exact sampling ("CFTP") is implemented for two response options only and is therefore not available here; delta has no identifiable effect on binary responses in any case.

CFTPretry

Passed to IsingSampler; unused for the methods offered here.

constrain

A (number of samples) by (number of nodes) matrix with samples that need be constrained; NA indicates that the sample is unconstrained.

Details

The Blume-Capel Hamiltonian implemented here is

$H(s) = -\sum_i \tau_i s_i - \sum_{i<j} \omega_{ij} s_i s_j + \sum_i \delta_i s_i^2,$

with thresholds $\tau$ , network weights $\omega$ (graph) and quadratic terms $\delta$ (delta); states are drawn with probability proportional to $\exp(-\beta H(s))$ . Setting delta = 0 recovers the ordinary (multi-level) Ising model, so BlumeCapelSampler(..., delta = 0) is equivalent to IsingSampler.

Value

A matrix containing samples of states.

Author(s)

Sacha Epskamp ([email protected])

Examples

## Not run: 
# A small ferromagnetic network:
P <- 5
W <- matrix(0.5, P, P); diag(W) <- 0

# Low crystal field: ordered (mostly +1 / -1):
X0 <- BlumeCapelSampler(1000, W, thresholds = 0, delta = 0, beta = 1)

# High crystal field: disordered "0" phase (mostly the middle category):
X3 <- BlumeCapelSampler(1000, W, thresholds = 0, delta = 3, beta = 1)

mean(X0 == 0)  # low
mean(X3 == 0)  # high

## End(Not run)
## Not run: 
# A small ferromagnetic network:
P <- 5
W <- matrix(0.5, P, P); diag(W) <- 0

# Low crystal field: ordered (mostly +1 / -1):
X0 <- BlumeCapelSampler(1000, W, thresholds = 0, delta = 0, beta = 1)

# High crystal field: disordered "0" phase (mostly the middle category):
X3 <- BlumeCapelSampler(1000, W, thresholds = 0, delta = 3, beta = 1)

mean(X0 == 0)  # low
mean(X3 == 0)  # high

## End(Not run)

non-regularized estimation methods for the Ising Model

Description

This function can be used for several non-regularized estimation methods of the Ising Model. See details.

Usage

EstimateIsing(data, responses, beta = 1, method = c("uni", "pl",
                 "bi", "ll"), adj = matrix(1, ncol(data), ncol(data)),
                 ...)
EstimateIsingUni(data, responses, beta = 1, adj = matrix(1, ncol(data),
                 ncol(data)), min_sum = -Inf, thresholding = FALSE, alpha = 0.01, 
                 AND = TRUE, ...)
EstimateIsingBi(data, responses, beta = 1, ...)
EstimateIsingPL(data, responses, beta = 1, ...)
EstimateIsingLL(data, responses, beta = 1, adj = matrix(1, ncol(data),
                 ncol(data)), ...)
EstimateIsing(data, responses, beta = 1, method = c("uni", "pl",
                 "bi", "ll"), adj = matrix(1, ncol(data), ncol(data)),
                 ...)
EstimateIsingUni(data, responses, beta = 1, adj = matrix(1, ncol(data),
                 ncol(data)), min_sum = -Inf, thresholding = FALSE, alpha = 0.01, 
                 AND = TRUE, ...)
EstimateIsingBi(data, responses, beta = 1, ...)
EstimateIsingPL(data, responses, beta = 1, ...)
EstimateIsingLL(data, responses, beta = 1, adj = matrix(1, ncol(data),
                 ncol(data)), ...)

Arguments

data

Data frame with binary responses to estimate the Ising model over

responses

Vector of length two indicating the response coding (usually c(0L, 1L) pr c(-1L, 1L))

beta

Inverse temperature parameter

method

The method to be used. pl uses pseudolikelihood estimation, uni sequential univariate regressions, bi bivariate regressions and ll estimates the Ising model as a loglinear model.

adj

Adjacency matrix of the Ising model.

min_sum

The minimum sum score that is artifically possible in the dataset. Defaults to -Inf. Set this only if you know a lower sum score is not possible in the data, for example due to selection bias.

thresholding

Logical, should the model be thresholded for significance?

alpha

Alpha level used in thresholding

AND

Logical, should an AND-rule (both regressions need to be significant) or OR-rule (one of the regressions needs to be significant) be used?

...

Arguments sent to estimator functions

Details

The following algorithms can be used (see Epskamp, Maris, Waldorp, Borsboom; in press).

pl: Estimates the Ising model by maximizing the pseudolikelihood (Besag, 1975).
uni: Estimates the Ising model by computing univariate logistic regressions of each node on all other nodes. This leads to a single estimate for each threshold and two estimates for each network parameter. The two estimates are averaged to produce the final network. Uses glm.
bi: Estimates the Ising model using multinomial logistic regression of each pair of nodes on all other nodes. This leads to a single estimate of each network parameter and $p$ estimates of each threshold parameter. Uses multinom.
ll: Estimates the Ising model by phrasing it as a loglinear model with at most pairwise interactions. Uses loglin.

Value

A list containing the estimation results:

graph

The estimated network

thresholds

The estimated thresholds

results

The results object used in the analysis

Author(s)

Sacha Epskamp ([email protected])

References

Epskamp, S., Maris, G., Waldorp, L. J., and Borsboom, D. (in press). Network Psychometrics. To appear in: Irwing, P., Hughes, D., and Booth, T. (Eds.), Handbook of Psychometrics. New York: Wiley.

Besag, J. (1975), Statistical analysis of non-lattice data. The statistician, 24, 179-195.

Examples

# Input:
N <- 5 # Number of nodes
nSample <- 500 # Number of samples

# Ising parameters:
Graph <- matrix(sample(0:1,N^2,TRUE,prob = c(0.7, 0.3)),N,N) * rnorm(N^2)
Graph <- Graph + t(Graph)
diag(Graph) <- 0
Thresholds <- rep(0,N)
Beta <- 1

# Response options (0,1 or -1,1):
Resp <- c(0L,1L)
Data <- IsingSampler(nSample,Graph, Thresholds)

# Pseudolikelihood:
resPL <- EstimateIsing(Data, method = "pl")
cor(Graph[upper.tri(Graph)], resPL$graph[upper.tri(resPL$graph)])

# Univariate logistic regressions:
resUni <- EstimateIsing(Data, method = "uni")
cor(Graph[upper.tri(Graph)], resUni$graph[upper.tri(resUni$graph)])

# bivariate logistic regressions:
resBi <- EstimateIsing(Data, method = "bi")
cor(Graph[upper.tri(Graph)], resBi$graph[upper.tri(resBi$graph)])

# Loglinear model:
resLL <- EstimateIsing(Data, method = "ll")
cor(Graph[upper.tri(Graph)], resLL$graph[upper.tri(resLL$graph)])
# Input:
N <- 5 # Number of nodes
nSample <- 500 # Number of samples

# Ising parameters:
Graph <- matrix(sample(0:1,N^2,TRUE,prob = c(0.7, 0.3)),N,N) * rnorm(N^2)
Graph <- Graph + t(Graph)
diag(Graph) <- 0
Thresholds <- rep(0,N)
Beta <- 1

# Response options (0,1 or -1,1):
Resp <- c(0L,1L)
Data <- IsingSampler(nSample,Graph, Thresholds)

# Pseudolikelihood:
resPL <- EstimateIsing(Data, method = "pl")
cor(Graph[upper.tri(Graph)], resPL$graph[upper.tri(resPL$graph)])

# Univariate logistic regressions:
resUni <- EstimateIsing(Data, method = "uni")
cor(Graph[upper.tri(Graph)], resUni$graph[upper.tri(resUni$graph)])

# bivariate logistic regressions:
resBi <- EstimateIsing(Data, method = "bi")
cor(Graph[upper.tri(Graph)], resBi$graph[upper.tri(resBi$graph)])

# Loglinear model:
resLL <- EstimateIsing(Data, method = "ll")
cor(Graph[upper.tri(Graph)], resLL$graph[upper.tri(resLL$graph)])

Entropy of the Ising Model

Description

Returns (marginal/conditional) Shannon information of the Ising model.

Usage

IsingEntrophy(graph, thresholds, beta = 1, conditional = numeric(0), 
          marginalize = numeric(0), base = 2, responses = c(0L, 1L))
IsingEntrophy(graph, thresholds, beta = 1, conditional = numeric(0), 
          marginalize = numeric(0), base = 2, responses = c(0L, 1L))

Arguments

graph

Weights matrix

thresholds

Thresholds vector

beta

Inverse temperature

conditional

Indices of nodes to condition on

marginalize

Indices of nodes to marginalize over

base

Base of the logarithm

responses

Vector of outcome responses.

Author(s)

Sacha Epskamp <[email protected]>

Likelihood of all states from tractible Ising model.

Description

This function returns the likelihood of all possible states. Is only tractible up to rougly 10 nodes.

Usage

IsingLikelihood(graph, thresholds, beta, responses = c(0L, 1L),
                 potential = FALSE, delta = 0)
IsingLikelihood(graph, thresholds, beta, responses = c(0L, 1L),
                 potential = FALSE, delta = 0)

Arguments

graph

Square matrix indicating the weights of the network. Must be symmetrical with 0 as diagonal.

thresholds

Vector indicating the thresholds, also known as the external field.

beta

Scalar indicating the inverse temperature.

responses

Response options. Typically set to c(-1L, 1L) or c(0L, 1L) (default). Two or more numeric response options are supported, including non-integer values such as seq(-1, 1, by = 0.5).

potential

Logical, return the potential instead of the probability of each state?

delta

Optional per-node quadratic (Blume-Capel) term added to the Hamiltonian as delta_i * s_i^2; a single value (recycled over nodes) or one value per node. The default 0 corresponds to the ordinary Ising model. See BlumeCapelSampler.

Author(s)

Sacha Epskamp

Pseudo-likelihood

Description

Computes the pseudolikelihood of a dataset given an Ising Model.

Usage

IsingPL(x, graph, thresholds, beta, responses = c(0L, 1L))
IsingPL(x, graph, thresholds, beta, responses = c(0L, 1L))

Arguments

x

A binary dataset

graph

Square matrix indicating the weights of the network. Must be symmetrical with 0 as diagonal.

thresholds

Vector indicating the thresholds, also known as the external field.

beta

Scalar indicating the inverse temperature.

responses

Response options. Typically set to c(-1L, 1L) or c(0L, 1L) (default). Must be integers!

Value

The pseudolikelihood

Author(s)

Sacha Epskamp ([email protected])

Sample states from the Ising model

Description

This function samples states from the Ising model using one of three methods. See details.

Usage

IsingSampler(n, graph, thresholds, beta = 1, nIter = 100, responses = c(0L, 1L),
    method = c("MH", "CFTP", "direct"), CFTPretry = 10, constrain, delta = 0)
IsingSampler(n, graph, thresholds, beta = 1, nIter = 100, responses = c(0L, 1L),
    method = c("MH", "CFTP", "direct"), CFTPretry = 10, constrain, delta = 0)

Arguments

n

Number of states to draw

graph

Square matrix indicating the weights of the network. Must be symmetrical with 0 as diagonal.

thresholds

Vector indicating the thresholds, also known as the external field.

beta

Scalar indicating the inverse temperature.

nIter

Number of iterations in the Metropolis and exact sampling methods.

responses

Response options. Typically set to c(-1L, 1L) or c(0L, 1L) (default). Two or more response options are supported, and they may be any numeric values, including non-integers (e.g. c(-1L, 0L, 1L), 1:5 or seq(-1, 1, by = 0.5)). With more than two options the conditional distribution of each node is the categorical (softmax) generalization of the binary Ising conditional. The pairwise interaction always enters the Hamiltonian as J_ij * s_i * s_j, i.e. through the (numeric) product of the response values, and each node has a single threshold. When all response options are integer-valued the result is an integer matrix (as before); otherwise it is a numeric (double) matrix.

method

The sampling method to use. Must be "MH", "CFTP" or "direct". See details.

CFTPretry

The amount of times a sample from CFTP may be retried. If after 100 couplings from the past the chain still results in NA values the chain is reset with different random numbers. Be aware that data that requies a lot of CFTP resets might not resemble exact samples anymore.

constrain

A (number of samples) by (number of nodes) matrix with samples that need be constrained; NA indicates that the sample is unconstrained. Defaults to a matrix of NAs.

delta

Optional per-node quadratic (Blume-Capel single-ion / crystal-field) term added to the Hamiltonian as delta_i * s_i^2. Either a single value (recycled over nodes) or a vector with one value per node. The default 0 reproduces the ordinary Ising model. A positive delta favours response options near zero (the middle category); a negative delta favours the extreme categories. This term only has an identifiable effect with more than two response options; with two response options it is absorbed into the thresholds (and is constant, hence has no effect, for symmetric responses such as c(-1, 1)). See BlumeCapelSampler.

Details

This function uses one of three sampling methods. "MH" can be used to sample using a Metropolis-Hastings algorithm. The chain is initiated with random values from the response options, then for each iteration each node is redrawn from its full-conditional distribution given all other nodes and parameters. For two response options this is the usual binary update; for more than two response options the node is drawn from the categorical (softmax) full-conditional over all response options. Typically, 100 of such iterations should suffice for the chain to converge, though strongly coupled models with more than two response options may require a larger nIter.

The second method, "CFTP" enhances the Metropolis-Hastings algorithm with Coupling from the Past (CFTP; Murray, 2007) to draw exact samples from the distribution. This is slower than the default Metropolis-Hastings but guarantees exact samples. However, it does depend on the graph structure and the number of nodes if these exact samples can be obtained in feasable time. "CFTP" is currently only implemented for two response options; for more than two response options use "MH" or "direct".

The third option, "direct", simply computes for every possibly state the probability and draws samples directly from the distribution of states by using these probabilities. This also guarantees exact samples, but quickly becomes intractible (roughly above 10 nodes).

Value

A matrix containing samples of states.

Author(s)

Sacha Epskamp ([email protected])

References

Murray, I. (2007). Advances in Markov chain Monte Carlo methods.

Examples

## See IsingSampler-package help page
## See IsingSampler-package help page

Likelihood of single state from tractible Ising model.

Description

This function returns the likelihood of a single possible state. Is only tractible up to rougly 10 nodes.

Usage

IsingStateProb(s, graph, thresholds, beta, responses = c(0L, 1L), delta = 0)
IsingStateProb(s, graph, thresholds, beta, responses = c(0L, 1L), delta = 0)

Arguments

s

Vector contaning the state to evaluate.

graph

Square matrix indicating the weights of the network. Must be symmetrical with 0 as diagonal.

thresholds

Vector indicating the thresholds, also known as the external field.

beta

Scalar indicating the inverse temperature.

responses

Response options. Typically set to c(-1L, 1L) or c(0L, 1L) (default). Two or more numeric response options are supported, including non-integer values such as seq(-1, 1, by = 0.5).

delta

Author(s)

Sacha Epskamp ([email protected])

Likelihood of sumscores from tractible Ising model.

Description

This function returns the likelihood of all possible sumscores. Is only tractible up to rougly 10 nodes.

Usage

IsingSumLikelihood(graph, thresholds, beta, responses = c(0L, 1L), delta = 0)
IsingSumLikelihood(graph, thresholds, beta, responses = c(0L, 1L), delta = 0)

Arguments

graph

Square matrix indicating the weights of the network. Must be symmetrical with 0 as diagonal.

thresholds

Vector indicating the thresholds, also known as the external field.

beta

Scalar indicating the inverse temperature.

responses

Response options. Typically set to c(-1L, 1L) or c(0L, 1L) (default). Two or more numeric response options are supported, including non-integer values such as seq(-1, 1, by = 0.5).

delta

Author(s)

Sacha Epskamp ([email protected])

Transform parameters for linear transformations on response catagories

Description

This function is mainly used to translate parameters estimated with response options set to 0 and 1 to a model in which the response options are -1 and 1, but can be used for any linear transformation of response options.

Usage

LinTransform(graph, thresholds, from = c(0L, 1L), to = c(-1L, 1L), a, b)
LinTransform(graph, thresholds, from = c(0L, 1L), to = c(-1L, 1L), a, b)

Arguments

graph

A matrix containing an Ising graph

thresholds

A vector containing thresholds

from

The original response encoding

to

The response encoding to transform to

a

The slope of the transformation. Overwrites to.

b

The intercept of the transformation. Overwrites to.

Author(s)

Sacha Epskamp <[email protected]>

Examples

N <- 4 # Number of nodes

# Ising parameters:
Graph <- matrix(sample(0:1,N^2,TRUE,prob = c(0.7, 0.3)),N,N) * rnorm(N^2)
Graph <- pmax(Graph,t(Graph)) / N
diag(Graph) <- 0
Thresh <- -(rnorm(N)^2)
Beta <- 1

p1 <- IsingLikelihood(Graph, Thresh, Beta, c(0,1))

a <- 2
b <- -1

# p2 <- IsingLikelihood(Graph/(a^2), Thresh/a - (b*rowSums(Graph))/a^2, Beta, c(-1,1))

p2 <- IsingLikelihood(LinTransform(Graph,Thresh)$graph, 
                      LinTransform(Graph,Thresh)$thresholds , 
                      Beta, c(-1,1))
LinTransform

round(cbind(p1[,1],p2[,1]),5)

plot(p1[,1],p2[,1])
abline(0,1)
N <- 4 # Number of nodes

# Ising parameters:
Graph <- matrix(sample(0:1,N^2,TRUE,prob = c(0.7, 0.3)),N,N) * rnorm(N^2)
Graph <- pmax(Graph,t(Graph)) / N
diag(Graph) <- 0
Thresh <- -(rnorm(N)^2)
Beta <- 1

p1 <- IsingLikelihood(Graph, Thresh, Beta, c(0,1))

a <- 2
b <- -1

# p2 <- IsingLikelihood(Graph/(a^2), Thresh/a - (b*rowSums(Graph))/a^2, Beta, c(-1,1))

p2 <- IsingLikelihood(LinTransform(Graph,Thresh)$graph, 
                      LinTransform(Graph,Thresh)$thresholds , 
                      Beta, c(-1,1))
LinTransform

round(cbind(p1[,1],p2[,1]),5)

plot(p1[,1],p2[,1])
abline(0,1)

Package 'IsingSampler'

Help Index

Sampling methods and distribution functions for the Ising model

Description

Author(s)

Examples

Sample states from the Blume-Capel model

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

non-regularized estimation methods for the Ising Model

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Entropy of the Ising Model

Description

Usage

Arguments

Author(s)

Likelihood of all states from tractible Ising model.

Description

Usage

Arguments

Author(s)

Pseudo-likelihood

Description

Usage

Arguments

Value

Author(s)

Sample states from the Ising model

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Likelihood of single state from tractible Ising model.

Description

Usage

Arguments

Author(s)

Likelihood of sumscores from tractible Ising model.

Description

Usage

Arguments

Author(s)

Transform parameters for linear transformations on response catagories

Description

Usage

Arguments

Author(s)

Examples