psychonetrics (version 0.9)

Ising: Ising model

Description

This is the family of Ising models fit to dichotomous datasets. Note that the input matters (see also https://arxiv.org/abs/1811.02916) in this model! Models based on a dataset that is encoded with -1 and 1 are not entirely equivalent to models based on datasets encoded with 0 and 1 (non-equivalences occur in multi-group settings with equality constrains).

Usage

Ising(data, omega = "full", tau, beta, vars, groups, covs,
                    means, nobs, covtype = c("choose", "ML", "UB"),
                    responses, missing = "listwise", equal = "none",
                    baseline_saturated = TRUE, estimator = "default",
                    optimizer, storedata = FALSE, WLS.W, sampleStats,
                    identify = TRUE, verbose = FALSE, maxNodes = 20)

Arguments

data

A data frame encoding the data used in the analysis. Can be missing if covs and nobs are supplied.

omega

The network structure. Either "full" to estimate every element freely, "empty" to set all elements to zero, or a matrix of the dimensions nNode x nNode with 0 encoding a fixed to zero element, 1 encoding a free to estimate element, and higher integers encoding equality constrains. For multiple groups, this argument can be a list or array with each element/slice encoding such a matrix.

tau

Optional vector encoding the threshold/intercept structure. Set elements to 0 to indicate fixed to zero constrains, 1 to indicate free intercepts, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such a vector.

beta

Optional scalar encoding the inverse temperature. 1 indicate free beta parameters, and higher integers to indicate equality constrains. For multiple groups, this argument can be a list or array with each element/column encoding such scalers.

vars

An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in data.

groups

An optional character vector encoding the variables used in the analyis. Must equal names of the dataset in data.

covs

A sample variance--covariance matrix, or a list/array of such matrices for multiple groups. Make sure covtype argument is set correctly to the type of covariances used.

means

A vector of sample means, or a list/matrix containing such vectors for multiple groups.

nobs

The number of observations used in covs and means, or a vector of such numbers of observations for multiple groups.

covtype

If 'covs' is used, this is the type of covariance (maximum likelihood or unbiased) the input covariance matrix represents. Set to "ML" for maximum likelihood estimates (denominator n) and "UB" to unbiased estimates (denominator n-1). The default will try to find the type used, by investigating which is most likely to result from integer valued datasets.

responses

A vector of dichotemous responses used (e.g., c(-1,1) or c(0,1). Only needed when 'covs' is used.)

missing

How should missingness be handled in computing the sample covariances and number of observations when data is used. Can be "listwise" for listwise deletion, or "pairwise" for pairwise deletion. NOT RECOMMENDED TO BE USED YET IN ISING MODEL.

equal

A character vector indicating which matrices should be constrained equal across groups.

baseline_saturated

A logical indicating if the baseline and saturated model should be included. Mostly used internally and NOT Recommended to be used manually.

estimator

The estimator to be used. Currently implemented are "ML" for maximum likelihood estimation, "FIML" for full-information maximum likelihood estimation, "ULS" for unweighted least squares estimation, "WLS" for weighted least squares estimation, and "DWLS" for diagonally weighted least squares estimation. Only ML estimation is currently supported for the Ising model.

optimizer

The optimizer to be used. Can be one of "nlminb" (the default R nlminb function), "ucminf" (from the optimr package), and C++ based optimizers "cpp_L-BFGS-B", "cpp_BFGS", "cpp_CG", "cpp_SANN", and "cpp_Nelder-Mead". The C++ optimizers are faster but slightly less stable. Defaults to "nlminb".

storedata

Logical, should the raw data be stored? Needed for bootstrapping (see bootstrap).

WLS.W

Optional WLS weights matrix. CURRENTLY NOT USED.

sampleStats

An optional sample statistics object. Mostly used internally.

identify

Logical, should the model be identified?

verbose

Logical, should messages be printed?

maxNodes

The maximum number of nodes allowed in the analysis. This function will stop with an error if more nodes are used (it is not recommended to set this higher).

Value

An object of the class psychonetrics

Details

The Ising Model takes the following form:

\(\Pr(\boldsymbol{Y} = \boldsymbol{y}) = \frac{\exp\left( -\beta H\left(\boldsymbol{y}; \boldsymbol{\tau}, \boldsymbol{\Omega}\right)\right)}{Z(\boldsymbol{\tau}, \boldsymbol{\Omega})}\)

With Hamiltonian:

\(H\left(\boldsymbol{y}; \boldsymbol{\tau}, \boldsymbol{\Omega}\right) = -\sum_{i=1}^{m} \tau_i y_{i} - \sum_{i=2}^{m} \sum_{j=1}^{i-1} \omega_{ij} y_i y_j.\)

And Z representing the partition function or normalizing constant.

References

Epskamp, S., Maris, G., Waldorp, L. J., & Borsboom, D. (2018). Network Psychometrics. In: Irwing, P., Hughes, D., & Booth, T. (Eds.), The Wiley Handbook of Psychometric Testing, 2 Volume Set: A Multidisciplinary Reference on Survey, Scale and Test Development. New York: Wiley.

Examples

# NOT RUN {
library("dplyr")
data("Jonas")

# Variables to use:
vars <- names(Jonas)[1:10]

# Arranged groups to put unfamiliar group first (beta constrained to 1):
Jonas <- Jonas[order(Jonas$group),]

# Form saturated model:
model1 <- Ising(Jonas, vars = vars, groups = "group")

# Run model:
model1 <- model1 %>% runmodel

# Prune-stepup to find a sparse model:
model1b <- model1 %>% prune(alpha = 0.05) %>%  stepup(alpha = 0.05)

# Equal networks:
suppressWarnings(
  model2 <- model1 %>% groupequal("omega") %>% runmodel
)

# Prune-stepup to find a sparse model:
model2b <- model2 %>% prune(alpha = 0.05) %>% stepup(mi = "mi_equal", alpha = 0.05)

# Equal thresholds:
model3 <- model2 %>% groupequal("tau") %>% runmodel

# Prune-stepup to find a sparse model:
model3b <- model3 %>% prune(alpha = 0.05) %>% stepup(mi = "mi_equal", alpha = 0.05)

# Equal beta:
model4 <- model3 %>% groupequal("beta") %>% runmodel

# Prune-stepup to find a sparse model:
model4b <- model4 %>% prune(alpha = 0.05) %>% stepup(mi = "mi_equal", alpha = 0.05)

# Compare all models:
compare(
  `1. all parameters free (dense)` = model1,
  `2. all parameters free (sparse)` = model1b,
  `3. equal networks (dense)` = model2,
  `4. equal networks (sparse)` = model2b,
  `5. equal networks and thresholds (dense)` = model3,
  `6. equal networks and thresholds (sparse)` = model3b,
  `7. all parameters equal (dense)` = model4,
  `8. all parameters equal (sparse)` = model4b
) %>% arrange(BIC)
# }