# Estimating Phylogenetic Multilevel Models with brms
Author: Paul Bürkner
Date: 2024-03-19

#### Paul Bürkner

#### 2024-03-19

## Introduction

In the present vignette, we want to discuss how to specify phylogenetic multilevel models using brms. These models are relevant in evolutionary biology when data of many species are analyzed at the same time. The usual approach would be to model species as a grouping factor in a multilevel model and estimate varying intercepts (and possibly also varying slopes) over species. However, species are not independent as they come from the same phylogenetic tree and we thus have to adjust our model to incorporate this dependency. The examples discussed here are from chapter 11 of the book Modern Phylogenetic Comparative Methods and the application in Evolutionary Biology (de Villemeruil & Nakagawa, 2014). The necessary data can be downloaded from the corresponding website (https://www.mpcm-evolution.com/). Some of these models may take a few minutes to fit.

## A Simple Phylogenetic Model

Assume we have measurements of a phenotype, `phen` (say the body size), and a `cofactor` variable (say the temperature of the environment). We prepare the data using the following code.

    phylo <- ape::read.nexus("https://paul-buerkner.github.io/data/phylo.nex")
    data_simple <- read.table(
      "https://paul-buerkner.github.io/data/data_simple.txt",
      header = TRUE
    )
    head(data_simple)

           phen  cofactor phylo
    1 107.06595 10.309588  sp_1
    2  79.61086  9.690507  sp_2
    3 116.38186 15.007825  sp_3
    4 143.28705 19.087673  sp_4
    5 139.60993 15.658404  sp_5
    6  68.50657  6.005236  sp_6

The `phylo` object contains information on the relationship between species. Using this information, we can construct a covariance matrix of species (Hadfield & Nakagawa, 2010).

    A <- ape::vcv.phylo(phylo)

Now we are ready to fit our first phylogenetic multilevel model:

    model_simple <- brm(
      phen ~ cofactor + (1|gr(phylo, cov = A)),
      data = data_simple,
      family = gaussian(),
      data2 = list(A = A),
      prior = c(
        prior(normal(0, 10), "b"),
        prior(normal(0, 50), "Intercept"),
        prior(student_t(3, 0, 20), "sd"),
        prior(student_t(3, 0, 20), "sigma")
      )
    )

With the exception of `(1|gr(phylo, cov = A))` instead of `(1|phylo)` this is a basic multilevel model with a varying intercept over species (`phylo` is an indicator of species in this data set). However, by using `cov = A` in the `gr` function, we make sure that species are correlated as specified by the covariance matrix `A`. We pass `A` itself via the `data2` argument which can be used for any kinds of data that does not fit into the regular structure of the `data` argument. Setting priors is not required for achieving good convergence for this model, but it improves sampling speed a bit. After fitting, the results can be investigated in detail.

    summary(model_simple)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: phen ~ cofactor + (1 | gr(phylo, cov = A))
       Data: data_simple (Number of observations: 200)
      Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
             total post-warmup draws = 4000

    Multilevel Hyperparameters:
    ~phylo (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)    14.50      2.19    10.42    18.87 1.01      840     1407

    Regression Coefficients:
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept    37.95      7.14    23.55    51.91 1.00     2673     2583
    cofactor      5.18      0.14     4.91     5.45 1.00     7361     3468

    Further Distributional Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     9.22      0.73     7.84    10.71 1.00     1069     2237

    Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
    and Tail_ESS are effective sample size measures, and Rhat is the potential
    scale reduction factor on split chains (at convergence, Rhat = 1).

    plot(model_simple, N = 2, ask = FALSE)

    plot(conditional_effects(model_simple), points = TRUE)

The so called phylogenetic signal (often symbolize by \(\lambda\)) can be computed with the `hypothesis` method and is roughly \(\lambda = 0.7\) for this example.

    hyp <- "sd_phylo__Intercept^2 / (sd_phylo__Intercept^2 + sigma^2) = 0"
    (hyp <- hypothesis(model_simple, hyp, class = NULL))

    Hypothesis Tests for class :
                    Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
    1 (sd_phylo__Interc... = 0      0.7      0.09     0.51     0.84         NA        NA    *
    ---
    'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
    '*': For one-sided hypotheses, the posterior probability exceeds 95%;
    for two-sided hypotheses, the value tested against lies outside the 95%-CI.
    Posterior probabilities of point hypotheses assume equal prior probabilities.

    plot(hyp)

Note that the phylogenetic signal is just a synonym of the intra-class correlation (ICC) used in the context phylogenetic analysis.

## A Phylogenetic Model with Repeated Measurements

Often, we have multiple observations per species and this allows to fit more complicated phylogenetic models.

    data_repeat <- read.table(
      "https://paul-buerkner.github.io/data/data_repeat.txt",
      header = TRUE
    )
    data_repeat$spec_mean_cf <-
      with(data_repeat, sapply(split(cofactor, phylo), mean)[phylo])
    head(data_repeat)

           phen  cofactor species phylo spec_mean_cf
    1 107.41919 11.223724    sp_1  sp_1    10.309588
    2 109.16403  9.805934    sp_1  sp_1    10.309588
    3  91.88672 10.308423    sp_1  sp_1    10.309588
    4 121.54341  8.355349    sp_1  sp_1    10.309588
    5 105.31638 11.854510    sp_1  sp_1    10.309588
    6  64.99859  4.314015    sp_2  sp_2     3.673914

The variable `spec_mean_cf` just contains the mean of the cofactor for each species. The code for the repeated measurement phylogenetic model looks as follows:

    model_repeat1 <- brm(
      phen ~ spec_mean_cf + (1|gr(phylo, cov = A)) + (1|species),
      data = data_repeat,
      family = gaussian(),
      data2 = list(A = A),
      prior = c(
        prior(normal(0,10), "b"),
        prior(normal(0,50), "Intercept"),
        prior(student_t(3,0,20), "sd"),
        prior(student_t(3,0,20), "sigma")
      ),
      sample_prior = TRUE, chains = 2, cores = 2,
      iter = 4000, warmup = 1000
    )

The variables `phylo` and `species` are identical as they are both identifiers of the species. However, we model the phylogenetic covariance only for `phylo` and thus the `species` variable accounts for any specific effect that would be independent of the phylogenetic relationship between species (e.g., environmental or niche effects). Again we can obtain model summaries as well as estimates of the phylogenetic signal.

    summary(model_repeat1)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: phen ~ spec_mean_cf + (1 | gr(phylo, cov = A)) + (1 | species)
       Data: data_repeat (Number of observations: 1000)
      Draws: 2 chains, each with iter = 4000; warmup = 1000; thin = 1;
             total post-warmup draws = 6000

    Multilevel Hyperparameters:
    ~phylo (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)    16.46      1.89    12.86    20.40 1.00     1627     2856

    ~species (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)     4.98      0.85     3.28     6.64 1.00     1019     1542

    Regression Coefficients:
                 Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept       36.39      7.65    21.06    51.22 1.00     4110     3987
    spec_mean_cf     5.10      0.10     4.89     5.30 1.00     7406     4088

    Further Distributional Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     8.10      0.20     7.73     8.50 1.00     5124     4331

    Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
    and Tail_ESS are effective sample size measures, and Rhat is the potential
    scale reduction factor on split chains (at convergence, Rhat = 1).

    hyp <- paste(
      "sd_phylo__Intercept^2 /",
      "(sd_phylo__Intercept^2 + sd_species__Intercept^2 + sigma^2) = 0"
    )
    (hyp <- hypothesis(model_repeat1, hyp, class = NULL))

    Hypothesis Tests for class :
                    Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
    1 (sd_phylo__Interc... = 0     0.74      0.06     0.62     0.84          0         0    *
    ---
    'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
    '*': For one-sided hypotheses, the posterior probability exceeds 95%;
    for two-sided hypotheses, the value tested against lies outside the 95%-CI.
    Posterior probabilities of point hypotheses assume equal prior probabilities.

    plot(hyp)

So far, we have completely ignored the variability of the cofactor within species. To incorporate this into the model, we define

    data_repeat$within_spec_cf <- data_repeat$cofactor - data_repeat$spec_mean_cf

and then fit it again using `within_spec_cf` as an additional predictor.

    model_repeat2 <- update(
      model_repeat1, formula = ~ . + within_spec_cf,
      newdata = data_repeat, chains = 2, cores = 2,
      iter = 4000, warmup = 1000
    )

The results are almost unchanged, with apparently no relationship between the phenotype and the within species variance of `cofactor`.

    summary(model_repeat2)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: phen ~ spec_mean_cf + (1 | gr(phylo, cov = A)) + (1 | species) + within_spec_cf
       Data: data_repeat (Number of observations: 1000)
      Draws: 2 chains, each with iter = 4000; warmup = 1000; thin = 1;
             total post-warmup draws = 6000

    Multilevel Hyperparameters:
    ~phylo (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)    16.37      1.90    12.86    20.35 1.00     1464     2173

    ~species (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)     5.00      0.84     3.28     6.61 1.00     1029     1268

    Regression Coefficients:
                   Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept         36.20      7.77    20.77    51.34 1.00     4647     4059
    spec_mean_cf       5.10      0.10     4.90     5.30 1.00     8295     4796
    within_spec_cf    -0.06      0.19    -0.42     0.31 1.00     8485     3488

    Further Distributional Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     8.11      0.21     7.73     8.53 1.00     5106     4048

    Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
    and Tail_ESS are effective sample size measures, and Rhat is the potential
    scale reduction factor on split chains (at convergence, Rhat = 1).

Also, the phylogenetic signal remains more or less the same.

    hyp <- paste(
      "sd_phylo__Intercept^2 /",
      "(sd_phylo__Intercept^2 + sd_species__Intercept^2 + sigma^2) = 0"
    )
    (hyp <- hypothesis(model_repeat2, hyp, class = NULL))

    Hypothesis Tests for class :
                    Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
    1 (sd_phylo__Interc... = 0     0.74      0.06     0.62     0.84          0         0    *
    ---
    'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
    '*': For one-sided hypotheses, the posterior probability exceeds 95%;
    for two-sided hypotheses, the value tested against lies outside the 95%-CI.
    Posterior probabilities of point hypotheses assume equal prior probabilities.

## A Phylogenetic Meta-Analysis

Let’s say we have Fisher’s z-transformed correlation coefficients \(Zr\) per species along with corresponding sample sizes (e.g., correlations between male coloration and reproductive success):

    data_fisher <- read.table(
      "https://paul-buerkner.github.io/data/data_effect.txt",
      header = TRUE
    )
    data_fisher$obs <- 1:nrow(data_fisher)
    head(data_fisher)

              Zr  N phylo obs
    1 0.28917549 13  sp_1   1
    2 0.02415579 40  sp_2   2
    3 0.19513651 39  sp_3   3
    4 0.09831239 40  sp_4   4
    5 0.13780152 66  sp_5   5
    6 0.13710587 41  sp_6   6

We assume the sampling variance to be known and as \(V(Zr) = \frac{1}{N - 3}\) for Fisher’s values, where \(N\) is the sample size per species. Incorporating the known sampling variance into the model is straight forward. One has to keep in mind though, that brms requires the sampling standard deviation (square root of the variance) as input instead of the variance itself. The group-level effect of `obs` represents the residual variance, which we have to model explicitly in a meta-analytic model.

    model_fisher <- brm(
      Zr | se(sqrt(1 / (N - 3))) ~ 1 + (1|gr(phylo, cov = A)) + (1|obs),
      data = data_fisher, family = gaussian(),
      data2 = list(A = A),
      prior = c(
        prior(normal(0, 10), "Intercept"),
        prior(student_t(3, 0, 10), "sd")
      ),
      control = list(adapt_delta = 0.95),
      chains = 2, cores = 2, iter = 4000, warmup = 1000
    )

A summary of the fitted model is obtained via

    summary(model_fisher)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: Zr | se(sqrt(1/(N - 3))) ~ 1 + (1 | gr(phylo, cov = A)) + (1 | obs)
       Data: data_fisher (Number of observations: 200)
      Draws: 2 chains, each with iter = 4000; warmup = 1000; thin = 1;
             total post-warmup draws = 6000

    Multilevel Hyperparameters:
    ~obs (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)     0.05      0.03     0.00     0.11 1.00      751     1787

    ~phylo (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)     0.06      0.04     0.00     0.15 1.00      828     2043

    Regression Coefficients:
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept     0.16      0.04     0.08     0.24 1.00     3393     2551

    Further Distributional Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     0.00      0.00     0.00     0.00   NA       NA       NA

    Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
    and Tail_ESS are effective sample size measures, and Rhat is the potential
    scale reduction factor on split chains (at convergence, Rhat = 1).

    plot(model_fisher)

The meta-analytic mean (i.e., the model intercept) is \(0.16\) with a credible interval of \([0.08, 0.25]\). Thus the mean correlation across species is positive according to the model.

## A phylogenetic count-data model

Suppose that we analyze a phenotype that consists of counts instead of being a continuous variable. In such a case, the normality assumption will likely not be justified and it is recommended to use a distribution explicitly suited for count data, for instance the Poisson distribution. The following data set (again retrieved from mpcm-evolution.org) provides an example.

    data_pois <- read.table(
      "https://paul-buerkner.github.io/data/data_pois.txt",
      header = TRUE
    )
    data_pois$obs <- 1:nrow(data_pois)
    head(data_pois)

      phen_pois   cofactor phylo obs
    1         1  7.8702830  sp_1   1
    2         0  3.4690529  sp_2   2
    3         1  2.5478774  sp_3   3
    4        14 18.2286628  sp_4   4
    5         1  2.5302806  sp_5   5
    6         1  0.5145559  sp_6   6

As the Poisson distribution does not have a natural overdispersion parameter, we model the residual variance via the group-level effects of `obs` (e.g., see Lawless, 1987).

    model_pois <- brm(
      phen_pois ~ cofactor + (1|gr(phylo, cov = A)) + (1|obs),
      data = data_pois, family = poisson("log"),
      data2 = list(A = A),
      chains = 2, cores = 2, iter = 4000,
      control = list(adapt_delta = 0.95)
    )

Again, we obtain a summary of the fitted model via

    summary(model_pois)

     Family: poisson
      Links: mu = log
    Formula: phen_pois ~ cofactor + (1 | gr(phylo, cov = A)) + (1 | obs)
       Data: data_pois (Number of observations: 200)
      Draws: 2 chains, each with iter = 4000; warmup = 2000; thin = 1;
             total post-warmup draws = 4000

    Multilevel Hyperparameters:
    ~obs (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)     0.18      0.09     0.01     0.34 1.01      575      597

    ~phylo (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)     0.18      0.10     0.02     0.42 1.00      671      769

    Regression Coefficients:
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept    -2.08      0.20    -2.49    -1.70 1.00     3411     3009
    cofactor      0.25      0.01     0.23     0.27 1.00     4458     2808

    Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
    and Tail_ESS are effective sample size measures, and Rhat is the potential
    scale reduction factor on split chains (at convergence, Rhat = 1).

    plot(conditional_effects(model_pois), points = TRUE)

Now, assume we ignore the fact that the phenotype is count data and fit a linear normal model instead.

    model_normal <- brm(
      phen_pois ~ cofactor + (1|gr(phylo, cov = A)),
      data = data_pois, family = gaussian(),
      data2 = list(A = A),
      chains = 2, cores = 2, iter = 4000,
      control = list(adapt_delta = 0.95)
    )

    summary(model_normal)

     Family: gaussian
      Links: mu = identity; sigma = identity
    Formula: phen_pois ~ cofactor + (1 | gr(phylo, cov = A))
       Data: data_pois (Number of observations: 200)
      Draws: 2 chains, each with iter = 4000; warmup = 2000; thin = 1;
             total post-warmup draws = 4000

    Multilevel Hyperparameters:
    ~phylo (Number of levels: 200)
                  Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sd(Intercept)     0.67      0.47     0.04     1.77 1.00     1394     1837

    Regression Coefficients:
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    Intercept    -3.06      0.62    -4.29    -1.82 1.00     4039     2843
    cofactor      0.68      0.04     0.59     0.76 1.00     8063     2426

    Further Distributional Parameters:
          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
    sigma     3.44      0.18     3.10     3.81 1.00     5522     2514

    Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
    and Tail_ESS are effective sample size measures, and Rhat is the potential
    scale reduction factor on split chains (at convergence, Rhat = 1).

We see that `cofactor` has a positive relationship with the phenotype in both models. One should keep in mind, though, that the estimates of the Poisson model are on the log-scale, as we applied the canonical log-link function in this example. Therefore, estimates are not comparable to a linear normal model even if applied to the same data. What we can compare, however, is the model fit, for instance graphically via posterior predictive checks.

    pp_check(model_pois)

    pp_check(model_normal)

Apparently, the distribution of the phenotype predicted by the Poisson model resembles the original distribution of the phenotype pretty closely, while the normal models fails to do so. We can also apply leave-one-out cross-validation for direct numerical comparison of model fit.

    loo(model_pois, model_normal)

    Output of model 'model_pois':

    Computed from 4000 by 200 log-likelihood matrix.

             Estimate   SE
    elpd_loo   -348.2 17.0
    p_loo        30.1  3.5
    looic       696.4 33.9
    ------
    MCSE of elpd_loo is NA.
    MCSE and ESS estimates assume MCMC draws (r_eff in [0.4, 1.6]).

    Pareto k diagnostic values:
                             Count Pct.    Min. ESS
    (-Inf, 0.7]   (good)     197   98.5%   240
       (0.7, 1]   (bad)        3    1.5%   <NA>
       (1, Inf)   (very bad)   0    0.0%   <NA>
    See help('pareto-k-diagnostic') for details.

    Output of model 'model_normal':

    Computed from 4000 by 200 log-likelihood matrix.

             Estimate   SE
    elpd_loo   -536.1 15.9
    p_loo        10.0  2.3
    looic      1072.1 31.8
    ------
    MCSE of elpd_loo is 0.1.
    MCSE and ESS estimates assume MCMC draws (r_eff in [0.7, 1.6]).

    All Pareto k estimates are good (k < 0.7).
    See help('pareto-k-diagnostic') for details.

    Model comparisons:
                 elpd_diff se_diff
    model_pois      0.0       0.0
    model_normal -187.9      18.0

Since smaller values of loo indicate better fit, it is again evident that the Poisson model fits the data better than the normal model. Of course, the Poisson model is not the only reasonable option here. For instance, you could use a negative binomial model (via family `negative_binomial`), which already contains an overdispersion parameter so that modeling a varying intercept of `obs` becomes obsolete.

## Phylogenetic models with multiple group-level effects

In the above examples, we have only used a single group-level effect (i.e., a varying intercept) for the phylogenetic grouping factors. In brms, it is also possible to estimate multiple group-level effects (e.g., a varying intercept and a varying slope) for these grouping factors. However, it requires repeatedly computing Kronecker products of covariance matrices while fitting the model. This will be very slow especially when the grouping factors have many levels and matrices are thus large.

## References

de Villemeruil P. & Nakagawa, S. (2014) General quantitative genetic methods for comparative biology. In: Modern phylogenetic comparative methods and their application in evolutionary biology: concepts and practice (ed. Garamszegi L.) Springer, New York. pp. 287-303.

Hadfield, J. D. & Nakagawa, S. (2010) General quantitative genetic methods for comparative biology: phylogenies, taxonomies, and multi-trait models for continuous and categorical characters. Journal of Evolutionary Biology. 23. 494-508.

Lawless, J. F. (1987). Negative binomial and mixed Poisson regression. Canadian Journal of Statistics, 15(3), 209-225.