Type:	Package
Title:	Markov Random Field Models for Image Analysis
Version:	1.0
Maintainer:	Victor Freguglia <victorfreguglia@gmail.com>
Description:	Model fitting, sampling and visualization for the (Hidden) Markov Random Field model with pairwise interactions and general interaction structure from Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>, which has many popular models used in 2-dimensional lattices as particular cases, like the Ising Model and Potts Model. A complete manuscript describing the package is available in Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
Imports:	Rcpp (≥ 1.0.1), dplyr(≥ 0.8.1), tidyr, methods, ggplot2, Rdpack
Depends:	R (≥ 3.5.0)
LinkingTo:	Rcpp, RcppArmadillo
RdMacros:	Rdpack
RoxygenNote:	7.1.2
Suggests:	testthat (≥ 2.1.0), covr, knitr, rmarkdown, glue
VignetteBuilder:	knitr
URL:	https://github.com/Freguglia/mrf2d
BugReports:	https://github.com/Freguglia/mrf2d/issues
NeedsCompilation:	yes
Packaged:	2022-01-25 16:21:16 UTC; victor
Author:	Victor Freguglia [aut, cre]
Repository:	CRAN
Date/Publication:	2022-01-25 23:52:42 UTC

mrf2d: Markov Random Field Models for Image Analysis

Description

mrf2d contains tools for Markov Random Field models on two-dimensional lattices.

To learn more about mrf2d, read the paper at doi: 10.18637/jss.v101.i08.

Author(s)

Maintainer: Victor Freguglia victorfreguglia@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/Freguglia/mrf2d

• Report bugs at https://github.com/Freguglia/mrf2d/issues

Example objects from mrf2d

Description

Z_potts and theta_potts are example objects for mrf2d.

Z_potts is a matrix object containing an observed lattice of a 3 color (C = 2) Potts model.

theta_potts is the parameter array used to sample it, it consists of a configuration with one parameter (-1.0) and two relative positions (to be used with a nearest-neighbor structure).

Author(s)

Victor Freguglia

Examples

theta_potts
dplot(Z_potts)

Creation of basis functions

Description

fourier_2d() and polynomial_2d() creates a list of basis functions to be used as the fixed effect in fit_ghm.

Usage

fourier_2d(max_freqs, lattice_dim)

polynomial_2d(poly_deg, lattice_dim)

Arguments

Details

fourier_2d() is for 2-dimensional Fourier transform.

Value

A list of functions.

Author(s)

Victor Freguglia

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Examples

fourier_2d(c(10,10), dim(Z_potts))
polynomial_2d(c(3,3), dim(Z_potts))

BOLD5000 neuroimaging data

Description

An image extracted from the "BOLD5000" open dataset. It was read from the file in path ⁠BOLD5000/DS001499/SUB-CSI2/SES-16/ANAT/SUB-CSI2_SES-16_T1W.NII.GZ⁠, available at the OpenNeuro platform (https://openneuro.org/datasets/ds001499/versions/1.3.0).

Usage

bold5000

Format

An object of class matrix (inherits from array) with 176 rows and 256 columns.

Details

The file was read using the oro.nifti package and the image was extracted from the matrix in slice 160.

References

Chang, N., Pyles, J. A., Marcus, A., Gupta, A., Tarr, M. J., & Aminoff, E. M. (2019). BOLD5000, a public fMRI dataset while viewing 5000 visual images. Scientific data, 6(1), 1-18.

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Conditional probabilities in a pixel position

Description

Computes the vector of conditional probabilities for a pixel position given a field, an interaction structure and a parameter array.

Usage

cp_mrf2d(Z, mrfi, theta, pos)

Arguments

Value

A numeric vector with the conditional probabilities.

Author(s)

Victor Freguglia

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Examples

cp_mrf2d(Z_potts, mrfi(1), theta_potts, c(57,31))
cp_mrf2d(Z_potts, mrfi(1), theta_potts*0.1, c(57,31))

Example Data

Description

mrf2d contains a set of simulated fields to illustrate its usage.

field1

A binary field sampled from a sparse interaction structure: mrfi(1) + c(4,4)

hfield1

A continuous valued field, obtained by Gaussian mixture driven by field1.

Usage

field1

hfield1

Format

An object of class matrix (inherits from array) with 150 rows and 150 columns.

An object of class matrix (inherits from array) with 150 rows and 150 columns.

Author(s)

Victor Freguglia

Plotting functions for lattice data

Description

dplot() and cplot() are functions for plotting lattice data. They are an alternative to base R's image() function using ggplot2 instead. dplot is used for discrete data and cplot for continuous data, they only differ in the fact that pixel values are treated as a factor in dplot, therefore, a discrete scale is used.

Usage

dplot(Z, legend = FALSE)

cplot(Y, legend = TRUE)

Arguments

Details

Since returns a ggplot object, other layers can be added to it using the usual ggplot2 syntax in order to modify any aspect of the plot.

The data frame used to create the object has columns named x, y and value, which are mapped to x, y and fill, respectively, used with geom_tile().

Value

a ggplot object.

Author(s)

Victor Freguglia

Examples

# Plotting discrete data
dplot(Z_potts)

#Making it continuous
cplot(Z_potts + rnorm(length(Z_potts)))

#Adding extra ggplot layers
library(ggplot2)
dplot(Z_potts) + ggtitle("This is a title")
dplot(Z_potts, legend = TRUE) + scale_fill_brewer(palette = "Set1")

EM estimation for Gaussian Hidden Markov field

Description

fit_ghm fits a Gaussian Mixture model with hidden components driven by a Markov random field with known parameters. The inclusion of a linear combination of basis functions as a fixed effect is also possible.

The algorithm is a modification of of (Zhang et al. 2001), which is described in (Freguglia et al. 2020).

Usage

fit_ghm(
  Y,
  mrfi,
  theta,
  fixed_fn = list(),
  equal_vars = FALSE,
  init_mus = NULL,
  init_sigmas = NULL,
  maxiter = 100,
  max_dist = 10^-3,
  icm_cycles = 6,
  verbose = interactive(),
  qr = NULL
)

Arguments

Details

If either init_mus or init_sigmas is NULL an EM algorithm considering an independent uniform distriburion for the hidden component is fitted first and its estimated means and sample deviations are used as initial values. This is necessary because the algorithm may not converge if the initial parameter configuration is too far from the maximum likelihood estimators.

max_dist defines a stopping condition. The algorithm will stop if the maximum absolute difference between (\mu and \sigma) parameters in consecutive iterations is less than max_dist.

Value

A hmrfout containing:

• par: A data.frame with \mu and \sigma estimates for each component.

• fixed: A matrix with the estimated fixed effect in each pixel.

• Z_pred: A matrix with the predicted component (highest probability) in each pixel.

• predicted: A matrix with the fixed effect + the \mu value for the predicted component in each pixel.

• iterations: Number of EM iterations done.

Author(s)

Victor Freguglia

References

Freguglia V, Garcia NL, Bicas JL (2020). “Hidden Markov random field models applied to color homogeneity evaluation in dyed textile images.” Environmetrics, 31(4), e2613.

Zhang Y, Brady M, Smith S (2001). “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm.” IEEE transactions on medical imaging, 20(1), 45–57.

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Examples

# Sample a Gaussian mixture with components given by Z_potts
# mean values are 0, 1 and 2 and a linear effect on the x-axis.

set.seed(2)
Y <- Z_potts + rnorm(length(Z_potts), sd = 0.4) +
      (row(Z_potts) - mean(row(Z_potts)))*0.01
# Check what the data looks like
cplot(Y)

fixed <- polynomial_2d(c(1,0), dim(Y))
fit <- fit_ghm(Y, mrfi = mrfi(1), theta = theta_potts, fixed_fn = fixed)
fit$par
cplot(fit$fixed)

Maximum Pseudo-likelihood fitting of MRFs on 2d lattices.

Description

Parameter estimation for Markov random fields via Pseudo-Likelihood function optimization. See pl_mrf2d for information on the Pseudo-Likelihood function.

Usage

fit_pl(
  Z,
  mrfi,
  family = "onepar",
  init = 0,
  optim_args = list(method = "BFGS"),
  return_optim = FALSE
)

Arguments

Value

An object of class mrfout with elements:

• theta: The estimated array of potential values.

• mrfi: The interaction structure considered.

• family: The parameter restriction family considered.

• method: The estimation method ("Pseudolikelihood").

• value: The optimal pseudo-likelihood value.

• opt.xxx(if return_optim is TRUE): Information returned by the optim() function used for the optimization.

Author(s)

Victor Freguglia

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Examples

fit_pl(Z_potts, mrfi(1), family = "onepar")
fit_pl(Z_potts, mrfi(1), family = "oneeach")
fit_pl(Z_potts, mrfi(2), family = "onepar")

Stochastic Approximation fitting of MRFs on 2d lattices

Description

Estimates the parameters of a MRF by successively sampling from a parameter configuration and updating it by comparing the sufficient statistics of the sampled field and the observed field.

This method aims to find the parameter value where the gradient of the likelihood function is equal to zero.

Usage

fit_sa(
  Z,
  mrfi,
  family = "onepar",
  gamma_seq,
  init = 0,
  cycles = 5,
  refresh_each = length(gamma_seq) + 1,
  refresh_cycles = 60,
  verbose = interactive()
)

Arguments

Details

The stochastic approximation method consists of, given an observed field Z, and a starting parameters configuration \theta_0, successively sample a field Z_t from the current parameter configuration and estimate the direction of the gradient of the likelihood function by comparing the sufficient statistics in the current sample and the observed field.

The solution is updated by moving in the estimated direction with a predefined step size \gamma_t, a new field Z_{t+1} is sampled using the new parameter configuration and Z_t as an initial value, and the process is repeated.

\theta_{t+1} = \theta_t - \gamma_t(T(Z_t) - T(Z)),

where T(Z) is the sufficient statistics for the reference field, T(Z_t) is the sufficient statistics for a field sampled from \theta_t.

gamma_seq is normalized internally by diving values by length(Z), so the choice of the sequence is invariant to the lattice dimensions. Typically, a sequence like seq(from = 1, to = 0, length.out = 1000) should be used for defining a sequence with 1000 steps. Some tuning of this sequence is required.

Value

A mrfout object with the following elements:

• theta: The estimated array of potentials.

• mrfi: The interaction structure considered.

• family: The parameter restriction family considered.

• method: The estimation method ("Stochastic Approximation").

• metrics: A data.frame containing the the euclidean distance between the sufficient statics computed for Z and the current sample.

Note

Stochastic Approximation is called "Controllable Simulated Annealing" in some references.

Examples where Stochastic Approximation is used with MRFs are (Gimel'farb 1996), (Atchadé et al. 2013).

Author(s)

Victor Freguglia

References

Wikipedia (2019). “Stochastic approximation.” https://en.wikipedia.org/wiki/Stochastic_approximation.

Atchadé YF, Lartillot N, Robert C, others (2013). “Bayesian computation for statistical models with intractable normalizing constants.” Brazilian Journal of Probability and Statistics, 27(4), 416–436.

Gimel'farb GL (1996). “Texture modeling by multiple pairwise pixel interactions.” IEEE Transactions on pattern analysis and machine intelligence, 18(11), 1110–1114.

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Examples

set.seed(2)
fit1 <- fit_sa(Z_potts, mrfi(1), family = "oneeach", gamma_seq = seq(1, 0, length.out = 50))
# Estimated parameters
fit1$theta
# A visualization of estimated gradient norm over iterations.
plot(fit1$metrics, type = "l")

fit_sa(Z_potts, mrfi(1), family = "oneeach", gamma_seq = seq(1, 0, length.out = 50))

MRF fitting functions output

Description

MRF fitting functions output

Usage

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

## S3 method for class 'hmrfout'
summary(object, ...)

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

Arguments

Parameter restriction families

Description

Different parameter restrictions can be included in estimation processes to make sure mrf2d can successfully include a wide range of model types in its inference functions.

For model identifiability, at least one linear restriction is necessary. mrf2d always assume \theta_{0,0,r} = 0 for all relative positions r.

Additionally, each family of restrictions may introduce other restrictions:

'onepar'

This family assumes the model is defined by a single parameter by adding the restriction

\theta_{a,b,r} = \phi * 1(a != b).

Here 1() denotes de indicator function. In words, the parameter must be the same value for any pair with different values and 0 for any equal-valued pair.

'oneeach'

Similar to 'onepar', parameters are 0 for equal-valued pairs and a constant for pairs with different values, but the constant may differ between different relative positions r:

\theta{a,b,r} = \phi_r * 1(a != b).

'absdif'

All parameters \theta_{a,b,r} with the same absolute difference between a and b must be equal within each relative position r. (Note that 'absdif' is equal to 'oneeach' for binary images).

\theta_{a,b,r} = \sum_d \phi_{d,r} * 1(|a-b| == d)

'dif'

The same as 'absdif', but parameters may differ between positive and negative differences.

\theta_{a,b,r} = \sum_d \phi_{d,r} * 1(a-b == d)

'free'

No additional restriction, all parameters other than \theta_{0,0,r} vary freely.

Author(s)

Victor Freguglia

See Also

vignette("mrf2d-family", package = "mrf2d")

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

mrfi: MRF interaction structure

Description

The mrfi S4 class is a representation of the interaction structure for a spatially-stationary Markov Random Field.

The function mrfi() provides an interface for creation mrfi objects. A plot method is also available for visualization, as well as conversion methods like as.list and operators like +.

mrfi() creates an object of class mrfi based on a distance rule and optionally a list of relative positions. The argument max_norm and norm_type can be used to automatically include all positions within a "range" defined by the norm type chosen and distance using that norm.

A list of relative positions may also be included to specify sparse interaction structures, for example. Alternatively, rpositions() can be used to create a based exclusively on a list of relative positions.

Simple operations are provided to include (set union) new interacting positions to a mrfi object with the '+' operator and remove positions (set difference) with -. Individual positions can be included/excluded using length-2 vectors in the right hand side. Union and set difference of complete structures can also be computed by adding or subtracting two mrfi objects.

These operations deal with opposite directions filtering to avoid redundancy in the interaction structure.

Usage

mrfi(max_norm = 1, norm_type = "1", positions = NULL)

rpositions(positions)

## S3 method for class 'mrfi'
as.list(x, ...)

## S4 method for signature 'mrfi'
length(x)

## S4 method for signature 'mrfi,numeric,missing'
x[[i]]

## S4 method for signature 'mrfi,numeric,missing'
x[i]

## S4 method for signature 'mrfi,numeric'
e1 + e2

## S4 method for signature 'mrfi,numeric'
e1 - e2

## S4 method for signature 'mrfi,mrfi'
e1 + e2

## S4 method for signature 'mrfi,mrfi'
e1 - e2

mrfi_to_string(mrfi)

Arguments

Details

The interaction structure is defined by the list of relative positions in it. For a specific pixel, conditional to the values of pixels in these relative positions from it, its value is independent of any other pixel in the image.

The relative positions are indentified by two integers rx and ry representing the "shift" in the x-axis and y-axis respectively. As an example: The relative position ⁠(1,0)⁠ representes the pixel in the immediate right position, while ⁠(-1,0)⁠ the left one.

Note that the inclusion of a relative position to the dependence also implies its opposite direction is not conditionally independent (commutativeness of dependence), but only one is included in the mrfi object.

To illustrate that, a nearest neighbor dependence structure can be specified by:

mrfi(1)

Note that it only includes the positions ⁠(1,0)⁠ and ⁠(0,1)⁠, but when visualizing it, for example, mrf2d understands the opposite directions are also conditionally dependent, as in

plot(mrfi(1)).

Value

A mrfi object.

as.list(): converts the mrfi object to a list of interacting positions (list of length-2 vectors).

[[: converts to list and subsets it.

[: subsets the mrfi object and returns another mrfi object.

+: computes the union of the interaction structure in a mrfi object with a numeric representing an additional position to include or another mrfi object.

Slots

Rmat

A 2-column matrix where each row represents a relative position of interaction.

Note

If a position in positions is already included due to the max_norm and norm_type specification, the second ocurrence is ignored. The same is valid for repeated or opposite positions in positions.

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Examples

plot(mrfi(max_norm = 2, norm_type = "1"))
plot(mrfi(max_norm = 2, norm_type = "m"))
plot(mrfi(max_norm = 2, norm_type = "1", positions = list(c(4,4))))

as.list(mrfi(1))
mrfi(1)[[1]]
mrfi(2)[[1:3]]

mrfi(1)
rpositions(list(c(1,0), c(0,1)))
mrfi(2)
mrfi(2, norm_type = "m")
mrfi(1, positions = list(c(4,4), c(-4,4)))


#Repeated positions are handled automatically
mrfi(1, positions = list(c(1,0), c(2,0)))

mrfi(1) + c(2,0)
mrfi(1) - c(1,0)
mrfi(1) + mrfi(0, positions = list(c(2,0)))
mrfi(2) - mrfi(1)

MRF fitting functions output

Description

MRF fitting functions output

Usage

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

## S3 method for class 'mrfout'
summary(object, ...)

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

Arguments

Pseudo-likelihood function for MRFs on 2d lattices

Description

Computes the pseudo-likelihood function of a Markov Random Field on a 2-dimensional lattice.

Usage

pl_mrf2d(Z, mrfi, theta, log_scale = TRUE)

Arguments

Details

The pseudo-likelihood function is defined as the product of conditional probabilities of each pixel given its neighbors:

\prod_i P(Z_i | Z_{{N}_i}, \theta).

Value

A numeric with the pseudo-likelihood value.

Author(s)

Victor Freguglia

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

Examples

pl_mrf2d(Z_potts, mrfi(1), theta_potts)

Plotting of mrfi objects.

Description

Plots a visual representation of the interaction structure described in a mrfi object. The black tile represents a reference pixel and gray tiles are shown in relative positions with dependent pixels.

A ggplot object is used, therefore, the user can load the ggplot2 package and add more ggplot layers to freely customize the plot.

Usage

## S3 method for class 'mrfi'
plot(x, include_axis = FALSE, include_opposite = TRUE, ...)

Arguments

Details

The data.frame used for the ggplot call has columns names rx and ry repŕesenting the relative positions.

Value

A ggplot object using geom_tile() to represent interacting relative positions.

Author(s)

Victor Freguglia

Examples

plot(mrfi(1))

library(ggplot2)
plot(mrfi(1)) + geom_tile(fill = "red")
plot(mrfi(1)) + geom_tile(fill = "blue") + theme_void()

plot(mrfi(1)) + geom_text(aes(label = paste0("(",rx,",",ry,")")))

Sampling of Markov Random Fields on 2d lattices

Description

Performs pixelwise updates based on conditional distributions to sample from a Markov random field.

Usage

rmrf2d(
  init_Z,
  mrfi,
  theta,
  cycles = 60,
  sub_region = NULL,
  fixed_region = NULL
)

Arguments

Details

This function implements a Gibbs Sampling scheme to sample from a Markov random field by iteratively sampling pixel values from the conditional distribution

P(Z_i | Z_{{N}_i}, \theta).

A cycle means exactly one update to each pixel. The order pixels are sampled is randomized within each cycle.

If init_Z is passed as a length 2 vector with lattice dimensions, the initial field is sampled from independent discrete uniform distributions in ⁠{0,...,C}⁠. The value of C is obtained from the number of rows/columns of theta.

A MRF can be sampled in a non-rectangular region of the lattice with the use of the sub_region argument or by setting pixels to NA in the initial configuration init_Z. Pixels with NA values in init_Z are completely disconsidered from the conditional probabilities and have the same effect as setting sub_region = is.na(init_Z). If init_Z has NA values, sub_region is ignored and a warning is produced.

A specific region can be kept constant during the Gibbs Sampler by using the fixed_region argument. Keeping a subset of pixels constant is useful when you want to sample in a specific region of the image conditional to the rest, for example, in texture synthesis problems.

Value

A matrix with the sampled field.

Note

As in any Gibbs Sampling scheme, a large number of cycles may be required to achieve the target distribution, specially for strong interaction systems.

Author(s)

Victor Freguglia

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08.

rmrf2d_mc for generating multiple points of a Markov Chain to be used in Monte-Carlo methods.

Examples

# Sample using specified lattice dimension
Z <- rmrf2d(c(150,150), mrfi(1), theta_potts)

#Sample using itial configuration

Z2 <- rmrf2d(Z, mrfi(1), theta_potts)

# View results
dplot(Z)
dplot(Z2)

# Using sub-regions
subreg <- matrix(TRUE, 150, 150)
subreg <- abs(row(subreg) - 75) + abs(col(subreg) - 75) <= 80
# view the sub-region
dplot(subreg)

Z3 <- rmrf2d(c(150,150), mrfi(1), theta_potts, sub_region = subreg)
dplot(Z3)

# Using fixed regions
fixreg <- matrix(as.logical(diag(150)), 150, 150)
# Set initial configuration: diagonal values are 0.
init_Z4 <- Z
init_Z4[fixreg] <- 0

Z4 <- rmrf2d(init_Z4, mrfi(1), theta_potts, fixed_region = fixreg)
dplot(Z4)

# Combine fixed regions and sub-regions
Z5 <- rmrf2d(init_Z4, mrfi(1), theta_potts,
fixed_region = fixreg, sub_region = subreg)
dplot(Z5)

Markov Chain sampling of MRFs for Monte-Carlo methods

Description

Generates a Markov Chain of random fields and returns the sufficient statistics for each of the observations.

This function automatizes the process of generating a random sample of MRFs to be used in Monte-Carlo methods by wrapping rmrf2d and executing it multiple time while storing sufficient statistics instead of the entire lattice.

Usage

rmrf2d_mc(
  init_Z,
  mrfi,
  theta,
  family,
  nmc = 100,
  burnin = 100,
  cycles = 4,
  verbose = interactive()
)

Arguments

Value

A matrix where each row contains the vector of sufficient statistics for an observation.

Note

Fixed regions and incomplete lattices are not supported.

Author(s)

Victor Freguglia

Examples

rmrf2d_mc(c(80, 80), mrfi(1), theta_potts, family = "oneeach", nmc = 8)

Summarized representation of theta arrays

Description

smr_array creates a vector containing only the free parameters from an array given a restriction family. exapand_array is the reverse operation, expanding a complete array from the vector of sufficient statistics.

Usage

smr_array(theta, family)

expand_array(theta_vec, family, mrfi, C)

Arguments

Details

The order the parameters appear in the summarized vector matches the order in smr_stat().

vec_description() provides a data.frame object describing which are the relative positions and interactions associated with each element of the summarized vector in the same order.

Value

smr_array returns a numeric vector with the free parameters of theta.

expand_array returns a three-dimensional array of potentials.

Author(s)

Victor Freguglia

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08

Examples

smr_array(theta_potts, "onepar")
smr_array(theta_potts, "oneeach")

expand_array(0.99, family = "onepar", mrfi = mrfi(1), C = 2)
expand_array(c(0.1, 0.2), family = "oneeach", mrfi = mrfi(1), C = 3)

Summary Statistics

Description

Computes the summary count statistics of a field given an interaction structure and a restriction family.

• cohist() computes the co-ocurrence histogram.

• smr_stat() computes the co-ocurrence histogram, then converts it into a vector of sufficient statistics given a family of restrictions.

Usage

smr_stat(Z, mrfi, family)

cohist(Z, mrfi)

vec_description(mrfi, family, C)

Arguments

Details

The order the summarized counts appear in the summary vector matches the order in smr_array().

Value

A numeric vector with the summarized counts.

An array representing the co-ocurrence histogram of Z in the relative positions contained in mrfi. Each row and column corresponds a pair of values in ⁠(0, ..., C)⁠ and each slice corresponds to

A data.frame describing the relative position and interaction associated with each potential in the vector form in each row, in the same order.

Author(s)

Victor Freguglia

See Also

A paper with detailed description of the package can be found at doi: 10.18637/jss.v101.i08

Examples

smr_stat(Z_potts, mrfi(1), "onepar")
smr_stat(Z_potts, mrfi(1), "oneeach")

cohist(Z_potts, mrfi(1))