The Cracked Bassoon

Bayesian SEM I:

Confirmatory factor analysis
Filed under bayesian, python.

Factor analysis tries to explain the relationships between observed variables in terms of a smaller number of unobserved variables. Observed variables are variously called manifest variables, indicators, or items, whereas unobserved variables are usually called latent variables or factors. When researchers talk about factor analysis, they usually mean exploratory factor analysis (EFA; Spearman, 1904), a collection of statistical techniques whose goal is to generate a factor solution from the data. A second, less common form of factor analysis is confirmatory factor analysis (CFA; Jöreskog, 1969). In contrast to EFA, in CFA the factor solution is specified beforehand. The factor solution may come from theory, other data, or perhaps a previous EFA of the same data. CFA is a special case of structural equation modeling (SEM; Bollen, 1989).

Most CFA is performed within the frequentist statistical framework. However, as discussed by Muthén and Asparouhov (2012), Bayesian CFA may hold several advantages over frequentist CFA. In this post, I describe how to implement Bayesian CFA using PyMC3 (Salvatier et al., 2016).

Building a CFA model


CFA requires two inputs. The first is a matrix of data denoted by \(\boldsymbol{Y}\) with shape \(n \times p\), where \(n\) is the number of cases and \(p\) is the number of items. Typically, \(\boldsymbol{Y}\) is residualized for nuisance covariates and standardized or transformed before CFA is performed.

The second input is a factor design matrix denoted by \(\boldsymbol{M}\) with shape \(p \times m\), where \(m\) is the number of factors. If the \(i\)th item loads on (is influenced by) the \(j\)th factor, the value of the cell with coordinate \((i, j)\) is 1; if not, this value is 0.

In frequentist CFA, \(\boldsymbol{M}\) is always a sparse matrix, meaning that most of the values are 0, and there are practical limitations on how many 1s \(\boldsymbol{M}\) can contain. If there are too many, the model is not identified, meaning that there is no unique maximum-likelihood solution because there are too many free parameters in the resulting model. Identifiability is not necessarily an issue for Bayesian models, but \(\boldsymbol{M}\) should still be sparse.

Model design

In basic CFA, data are assumed to be multivariate normal,

\[\begin{equation} \boldsymbol{Y}\sim\mathrm{Normal}\left(\boldsymbol{\mu},\boldsymbol{\Sigma}\right) \end{equation}\]

where \(\boldsymbol{\mu}\) is a \(p\)-length vector of means and \(\boldsymbol{\Sigma}\) is an \(n \times p\) covariance matrix. For former is given by

\[\begin{align} \boldsymbol{\mu}=\boldsymbol{\nu}+\boldsymbol{\Lambda}\boldsymbol{\alpha} \end{align}\]

where \(\boldsymbol{\nu}\) is \(p\)-length vector of item intercepts, \(\boldsymbol{\Lambda}\) is a \(p \times m\) matrix of factor loadings, and \(\boldsymbol{\alpha}\) is a \(m\)-length vector of factor intercepts.

The covariance matrix \(\boldsymbol{\Sigma}\) is given by

\[\begin{align} \boldsymbol{\Sigma}=\boldsymbol{\Lambda}\boldsymbol{\Psi}\boldsymbol{\Lambda}^\mathrm{T}+\boldsymbol{\Theta} \end{align}\]

where \(\boldsymbol{\Psi}\) is an \(m \times m\) factor covariance matrix and \(\boldsymbol{\Theta}\) is a \(p \times p\) item covariance matrix.

Priors on intercepts

The two intercept vectors, \(\boldsymbol{\nu}\) and \(\boldsymbol{\alpha}\), should have univariate normal priors. I haven’t spent a lot of time experimenting with different priors because my initial choices worked well on a moderately sized standardized data set (see later). If the data are not standardized or the data set is small, you may need to be more careful.

Factor loadings

Our factor design matrix \(\boldsymbol{M}\) dictates which items load on which factors, and the matrix \(\boldsymbol{\Lambda}\) contains the actual factor loadings. In frequentist CFA, both \(\boldsymbol{M}\) and \(\boldsymbol{\Lambda}\) must be sparse. Muthén and Asparouhov (2012) argue that the frequentist approach of assigning values of 0 to some elements of \(\boldsymbol{M}\) and \(\boldsymbol{\Lambda}\) is analogous to assigning them a univariate normal prior with mean standard deviation of 0 in a Bayesian model. (This is impossible of course, but you get the idea). Those authors go on to argue that from a Bayesian perspective, such a prior could be considered to be too informative: is it realistic to assume that a given item does not load on a given factor at all? An alternative approach is to assign them a prior with a small standard deviation. This way, loadings between all items and all factors will be estimated, but the so-called cross-loadings (non-hypothesized loadings) will be smaller than the hypothesized loadings.

However, now a question arises: how small should the standard deviation of the cross-loading prior be? Muthén and Asparouhov provide some recommendations, such as fitting many models with different choices. In my opinion, this is not ideal because typically CFA is performed on large data sets and Bayesian inference takes a long time, so it may not be practical to fit numerous models.

An alternative approach is to make the standard deviation of the cross-loading prior a random variable itself. We can implement this approach using the equation

\[\begin{equation} \boldsymbol{\Lambda} = \boldsymbol{\Phi}\circ\left[\beta\left(1 - \boldsymbol{M}\right) + \boldsymbol{M}\right] \end{equation}\]

where \(\boldsymbol{\Phi}\) is a matrix of unscaled factor loadings, \(\beta\) is the cross-loading standard deviation, and \(\circ\) denotes the Hadamard product. We assign univariate standard normal priors to all elements in \(\boldsymbol{\Phi}\) and a beta prior to \(\beta\).

Priors on factor correlations

CFA runs into a second identifiability issue if neither the variance of each factor nor at least one loading per factor is fixed. I feel it makes the most sense to fix the factor variances. This makes \(\boldsymbol{\Psi}\) a correlation matrix rather than a covariance matrix.

The Inverse-Wishart distribution is defined on real-valued positive-definite matrices and is the conjugate prior for the covariance matrix of a multivariate normal distribution. However, since Inverse-Wishart has some problematic features (Alvarez et al., 2016) and we don’t care about conjugate priors much these days, a better choice is the LKJ prior (Lewandowski et al., 2009). The LKJ prior has a single shape parameter, \(\eta\). When \(\eta\) is large, the off-diagonal values of the correlation matrix tend to be small. Factors tend to be highly correlated, however, so we should set \(\eta\) to 1.

Unfortunately, while PyMC3 contains the LKJCorr distribution, it is currently broken, so we must construct an LKJ prior on \(\boldsymbol{\Psi}\) indirectly using LKJCholeskyCov instead. This isn’t a huge deal, but it’s something that could be easily overlooked. Hopefully this will get fixed in the future.

Priors on residual item variances and correlations

The item covariance matrix \(\boldsymbol{\Theta}\) is the thorniest part of CFA. It soaks up residual item variance and correlations between items not explained by the factors. We can model this matrix as

\[\begin{equation} \boldsymbol{\Theta}=\boldsymbol{D}\boldsymbol{\Omega}\boldsymbol{D} \end{equation}\]

where \(\boldsymbol{D}\) is a diagonal matrix of standard deviations and \(\boldsymbol{\Omega}\) is a correlation matrix. We can place half-Cauchy priors on all cells in \(\boldsymbol{D}\) and an LKJ prior on \(\boldsymbol{\Omega}\).

In the special case where \(\boldsymbol{\Omega}\) is an identity matrix, the model will attempt to completely explain all the correlations between the items using only the factors. Since using factors to explain the relationships between items is the whole point of CFA, one could argue that \(\boldsymbol{\Omega}\) should be an identity matrix all the time! Indeed, in frequentist CFA, it is fairly common (and somewhat questionable) practice to iteratively add non-zero residual correlations until the model’s goodness of fit passes some threshold of acceptability.

Nevertheless, there may be some circumstances where one should estimate residual correlations. In frequentist CFA, \(\boldsymbol{\Omega}\) needs to be sparse matrix to ensure identifiability. However, it is usually difficult to foresee which residuals should be correlated. Bayesian CFA provides a neat solution to this problem: we can choose a very large value of \(\eta\) for the LKJ prior on \(\boldsymbol{\Omega}\), which forces all residual correlations to be low.

Application to an example data set

The classic Holzinger and Swineford (1939) data set contains scores of children from two different schools on tests of cognitive ability. Muthén and Asparouhov (2012) performed EFA followed by Bayesian CFA on a subset of 19 of these tests. I’ve attempted to replicate their results using PyMC3. Below is the working code.

"""Example of Bayesian confirmatory factor analysis in PyMC3.

import numpy as np
import pandas as pd
import pymc3 as pm
import theano.tensor as tt
import matplotlib.pyplot as plt
from os.path import exists

from matplotlib import rcParams
from pymc3.math import matrix_dot
from tabulate import tabulate

def bcfa(
    r"""Constructs a Bayesian CFA model.

        items (np.array): Array of item data.
        factors (np.array): Factor design matrix.
        beta (:obj:`float` or `'estimate'`, optional): Standard deviation of normal
            prior on cross loadings. If `'estimate'`,  beta is estimated from the data.
        nu_sd (:obj:`float`, optional): Standard deviation of normal prior on item
        alpha_sd (:obj:`float`, optional): Standard deviation of normal prior on factor
        d_beta (:obj:`float`, optional): Scale parameter of half-Cauchy prior on factor
            standard deviation.
        corr_factors (:obj:`bool`, optional): Allow correlated factors.
        corr_items (:obj:`bool`, optional): Allow correlated items.
        g_eta (:obj:`float`, optional): Shape parameter of LKJ prior on residual item
            correlation matrix.
        l_eta (:obj:`float`, optional): Shape parameter of LKJ prior on factor
            correlation matrix.
        beta_beta (:obj:`float`, optional): Beta parameter of beta prior on beta.


        None: Places model in context.

    # get numbers of cases, items, and factors
    n, p = items.shape
    p_, m = factors.shape
    assert p == p_, "Mismatch between data and factor-loading matrices"

    # place priors on item and factor intercepts
    nu = pm.Normal(name=r"$\nu$", mu=0, sd=nu_sd, shape=p, testval=items.mean(axis=0))
    alpha = pm.Normal(name=r"$\alpha$", mu=0, sd=alpha_sd, shape=m, testval=np.zeros(m))

    # place priors on unscaled factor loadings
    Phi = pm.Normal(name=r"$\Phi$", mu=0, sd=1, shape=factors.shape, testval=factors)

    # create masking matrix for factor loadings
    if isinstance(beta, str):
        assert beta == "estimate", f"Don't know what to do with '{beta}'"
        beta = pm.Beta(name=r"$\beta$", alpha=1, beta=beta_beta, testval=0.1)
    M = (1 - np.asarray(factors)) * beta + np.asarray(factors)

    # create scaled factor loadings
    Lambda = pm.Deterministic(r"$\Lambda$", Phi * M)

    # determine item means
    mu = nu + matrix_dot(Lambda, alpha)

    # place priors on item standard deviations
    D = pm.HalfCauchy(name=r"$D$", beta=d_beta, shape=p, testval=items.std(axis=0))

    # place priors on item correlations
    f = pm.Lognormal.dist(sd=0.25)
    if not corr_items:
        Omega = np.eye(p)
        G = pm.LKJCholeskyCov(name=r"$G$", eta=g_eta, n=p, sd_dist=f)
        ch1 = pm.expand_packed_triangular(p, G, lower=True)
        K =, ch1.T)
        sd1 = tt.sqrt(tt.diag(K))
        Omega = pm.Deterministic(r"$\Omega$", K / sd1[:, None] / sd1[None, :])

    # determine residual item variances and covariances
    Theta = pm.Deterministic(r"$\Theta$", D[None, :] * Omega * D[:, None])

    # place priors on factor correlations
    if not corr_factors:
        Psi = np.eye(m)
        L = pm.LKJCholeskyCov(name=r"$L$", eta=l_eta, n=m, sd_dist=f)
        ch = pm.expand_packed_triangular(m, L, lower=True)
        _psi =, ch.T)
        sd = tt.sqrt(tt.diag(_psi))
        Psi = pm.Deterministic(r"$\Psi$", _psi / sd[:, None] / sd[None, :])

    # determine variances and covariances of items
    Sigma = matrix_dot(Lambda, Psi, Lambda.T) + Theta

    # place priors on observations
    pm.MvNormal(name="$Y$", mu=mu, cov=Sigma, observed=items, shape=items.shape)

def main():

    # load the data
    df = pd.read_csv("../../assets/data/HS.csv", index_col=0)

    # define items to keep
    item_names = [

    # define the factor structure
    factors = np.array(
            [1, 0, 0, 0],
            [1, 0, 0, 0],
            [1, 0, 0, 0],
            [1, 0, 0, 0],
            [0, 1, 0, 0],
            [0, 1, 0, 0],
            [0, 1, 0, 0],
            [0, 1, 0, 0],
            [0, 1, 0, 0],
            [0, 0, 1, 0],
            [0, 0, 1, 0],
            [0, 0, 1, 0],
            [0, 0, 1, 0],
            [0, 0, 0, 1],
            [0, 0, 0, 1],
            [0, 0, 0, 1],
            [0, 0, 0, 1],
            [0, 0, 0, 1],
            [0, 0, 0, 1],

    # iterate over the two schools
    for school, sdf in df.groupby("school"):

        # define the path to save results
        f = f"../data/BCFA examples/{school}"

        # select the 19 commonly used variables
        items = sdf[item_names]

        # for numerical convenience, standardize the data
        items = (items - items.mean()) / items.std()

        with pm.Model():

            # construct the model
            bcfa(items, factors)

            if not exists(f):

                # sample and save
                trace = pm.sample(19000, tune=1000, chains=2)
                pm.save_trace(trace, f)
                pm.traceplot(trace, compact=True)
                rcParams["font.size"] = 14


                trace = pm.load_trace(f)

        # create a nice summary table
        loadings = pd.DataFrame(
            index=[v.title() for v in item_names],
            columns=["Spatial", "Verbal", "Speed", "Memory"],
        print(tabulate(loadings, tablefmt="pipe", headers="keys"))
        correlations = pd.DataFrame(
            index=["Spatial", "Verbal", "Speed", "Memory"],
            columns=["Spatial", "Verbal", "Speed", "Memory"],
        print(tabulate(correlations, tablefmt="pipe", headers="keys"))
        correlations = pd.DataFrame(

if __name__ == "__main__":

Anyone wishing to rerun this model or adapt my code for their own purposes may find the following stray observations useful:


Here are the factor loadings for the first school. They are pretty close to the values reported by Muthén and Asparouhov (2012).

  Spatial Verbal Speed Memory
Visual 0.611 0.015 0.04 0.035
Cubes 0.467 -0.001 -0.003 -0.001
Paper 0.477 0.023 0.028 0.027
Flags 0.66 0.029 -0.017 0.003
General 0.033 0.794 0.033 -0.019
Paragrap 0.007 0.824 -0.02 0.014
Sentence -0.033 0.881 0.007 -0.028
Wordc 0.036 0.637 0.063 0.035
Wordm -0.006 0.877 -0.053 0.012
Addition -0.088 0.019 0.722 -0
Code 0.001 0.032 0.631 0.07
Counting 0.009 -0.059 0.766 -0.023
Straight 0.103 0.035 0.675 -0.015
Wordr -0.018 0.029 -0.007 0.517
Numberr 0.004 -0 -0.021 0.528
Figurer 0.068 -0.009 -0.022 0.57
Object -0.069 0.012 0.017 0.666
Numberf 0.054 -0.043 0.05 0.617
Figurew 0.01 0.03 0.006 0.452

And here are the factor correlations.

  Spatial Verbal Speed Memory
Spatial 1 0.548 0.499 0.574
Verbal 0.548 1 0.424 0.474
Speed 0.499 0.424 1 0.549
Memory 0.574 0.474 0.549 1

While my code also produces the residual item correlations, I’ve omitted it because it’s a pretty big table.


Here are the factor loadings for the other school. Again, everything compares well with Muthén and Asparouhov (2012).

  Spatial Verbal Speed Memory
Visual 0.676 0.119 0.033 0.023
Cubes 0.452 -0.016 -0.034 -0.017
Paper 0.501 0.015 -0.032 -0.093
Flags 0.627 -0.08 0.031 0.082
General -0.051 0.871 0.025 -0.077
Paragrap 0.035 0.787 0.005 0.042
Sentence -0.071 0.932 -0.019 -0.033
Wordc 0.054 0.688 0.021 0.058
Wordm 0.082 0.821 -0.012 0.03
Addition -0.114 -0.002 0.632 0.007
Code 0.011 0.088 0.708 0.039
Counting 0.02 -0.036 0.566 -0.031
Straight 0.109 -0.051 0.53 0.006
Wordr -0.042 0.002 -0.075 0.7
Numberr 0.001 -0.095 -0.084 0.611
Figurer 0.122 0.041 0.063 0.526
Object -0.072 0.011 0.1 0.541
Numberf -0.013 0.039 -0.002 0.47
Figurew 0.035 0.019 0.081 0.405

And here are the factor correlations.

  Spatial Verbal Speed Memory
Spatial 1 0.352 0.276 0.316
Verbal 0.352 1 0.432 0.155
Speed 0.276 0.432 1 0.399
Memory 0.316 0.155 0.399 1

Future directions

The obvious next thing to do is to implement Bayesian SEM more generally. This may be a topic of a future post.

Oftentimes researchers may wish to analyze data that are not normally distributed, such as binary disease states. Unfortunately, however, CFA gets really complicated when one tries to relax the assumption of multivariate normal data, and to date I have had no luck implementing such models. If I do ever crack this, I’ll write about it here.


Alvarez, I., Niemi, J., & Simpson, M. (2016). Bayesian inference for a covariance matrix. arXiv:1408.4050.

Bollen, K. A. (1989). Structural equations with latent variables. Wiley.

Holzinger, K. J., & Swineford, F. (1939). A study in factor analysis: The stability of a bi-factor solution. Supplementary Educational Monographs, 48, 91.

Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202. 10.1007/BF02289343

Lewandowski, D., Kurowicka, D., & Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100(9), 1989–2001. 10.1016/j.jmva.2009.04.008

Muthén, B., & Asparouhov, T. (2012). Bayesian structural equation modeling: A more flexible representation of substantive theory. Psychological Methods, 17(3), 313–335. 10.1037/a0026802

Salvatier, J., Wiecki, T. V., & Fonnesbeck, C. (2016). Probabilistic programming in Python using PyMC3. PeerJ Comput Science, 2, e55. 10.7717/peerj-cs.55

Spearman, C. (1904). “General intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), 201. 10.2307/1412107

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