# Comparison with other packages

#### James T. Thorson and Wouter van der Bijl

    library(phylosem)

`phylosem` is an R package for fitting phylogenetic structural equation models (PSEMs). The package generalizes features in existing R packages:

- `sem` for structural equation models (SEMs);
- `phylosem` for comparison among alternative path models;
- `phylolm` for fitting large linear models that arise as when specifying a SEM with one endogenous variable and multiple exogenous and independent variables;
- `Rphylopars` for interpolating missing values when specifying a SEM with an unstructured (full rank) covariance among variables;

In model configurations that can be fitted by both `phylosem` and these other packages, we have confirmed that results are nearly identical or otherwise identified reasons that results differ.

`phylosem` involves a simple user-interface that specifies the SEM using notation from package `sem` and the phylogenetic tree using package `ape`. It allows uers to specify common models for the covariance including:

- Brownian motion (BM);
- Ornstein-Uhlenbeck (OU);
- Pagel’s lambda;
- Pagel’s kappa;

Output can be coerced to standard formats so that `phylosem` can use plotting and summary functions form other packages. Available output formats include:

- `sem`, for plotting the estimated SEM and summarizing direct and indirect effects;
- `phylopath`, for plotting and model comparison;
- `phylo4d` in R-package `phylobase` for plotting estimated traits;

Below, we specifically highlight the syntax, runtime, and output resulting from `phylosem` and other packages.

## Comparison with phylolm

We first compare syntax and run-times using simulated data against `phylolm`. This confirms that runtimes from `phylosem` are within an order of magnitude and that results are nearly identical for BM, OU, delta, and kappa models.

    # Settings
    Ntree = 100
    sd_x = 0.3
    sd_y = 0.3
    b0_x = 1
    b0_y = 0
    b_xy = 1

    # Simulate tree
    set.seed(1)
    tree = ape::rtree(n=Ntree)

    # Simulate data
    x = b0_x + sd_x * phylolm::rTrait(n = 1, phy=tree)
    ybar = b0_y + b_xy*x
    y_normal = ybar + sd_y * phylolm::rTrait(n = 1, phy=tree)

    # Construct, re-order, and reduce data
    Data = data.frame(x=x,y=y_normal)[]

    # Compare using BM model
    start_time = Sys.time()
    plm_bm = phylolm::phylolm(y ~ 1 + x, data=Data, phy=tree, model="BM" )
    Sys.time() - start_time
    #> Time difference of 0.005378962 secs
    knitr::kable(summary(plm_bm)$coefficients, digits=3)

| Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | ---
(Intercept) | -0.371 | 0.214 | -1.734 | 0.086
x | 1.117 | 0.101 | 11.053 | 0.000

    start_time = Sys.time()
    psem_bm = phylosem( sem = "x -> y, p",
              data = Data,
              tree = tree,
              control = phylosem_control(quiet = TRUE) )
    Sys.time() - start_time
    #> Time difference of 0.1166389 secs
    knitr::kable(summary(psem_bm)$coefficients, digits=3)

Path | VarName | Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | --- | ---
NA | Intercept_x | 1.087 | 0.183 | 5.945 | 0.000
NA | Intercept_y | -0.371 | 0.213 | 1.743 | 0.081
x -> y | p | 1.117 | 0.101 | 11.109 | 0.000
x <-> x | V[x] | 0.315 | 0.022 | 14.072 | 0.000
y <-> y | V[y] | 0.315 | 0.022 | 14.072 | 0.000

    # Compare using OU
    start_time = Sys.time()
    plm_ou = phylolm::phylolm(y ~ 1 + x, data=Data, phy=tree, model="OUrandomRoot" )
    Sys.time() - start_time
    #> Time difference of 0.01002812 secs

    start_time = Sys.time()
    psem_ou = phylosem( sem = "x -> y, p",
              data = Data,
              tree = tree,
              estimate_ou = TRUE,
              control = phylosem_control(quiet = TRUE) )
    Sys.time() - start_time
    #> Time difference of 0.1162949 secs

    knitr::kable(summary(psem_ou)$coefficients, digits=3)

Path | VarName | Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | --- | ---
NA | Intercept_x | 1.028 | 0.208 | 4.946 | 0.000
NA | Intercept_y | -0.274 | 0.235 | 1.165 | 0.244
x -> y | p | 1.099 | 0.101 | 10.887 | 0.000
x <-> x | V[x] | 0.332 | 0.026 | 12.712 | 0.000
y <-> y | V[y] | 0.332 | 0.026 | 12.860 | 0.000

    knitr::kable(summary(plm_ou)$coefficients, digits=3)

| Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | ---
(Intercept) | -0.781 | 0.389 | -2.006 | 0.048
x | 1.095 | 0.101 | 10.850 | 0.000

    knitr::kable(c( "phylolm_alpha"=plm_ou$optpar,
                    "phylosem_alpha"=exp(psem_ou$parhat$lnalpha) ), digits=3)

| x
--- | ---
phylolm_alpha | 0.120
phylosem_alpha | 0.105

    # Compare using Pagel's lambda
    start_time = Sys.time()
    plm_lambda = phylolm::phylolm(y ~ 1 + x, data=Data, phy=tree, model="lambda" )
    Sys.time() - start_time
    #> Time difference of 0.01735187 secs

    start_time = Sys.time()
    psem_lambda = phylosem( sem = "x -> y, p",
              data = Data,
              tree = tree,
              estimate_lambda = TRUE,
              control = phylosem_control(quiet = TRUE) )
    Sys.time() - start_time
    #> Time difference of 0.09754896 secs

    knitr::kable(summary(psem_lambda)$coefficients, digits=3)

Path | VarName | Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | --- | ---
NA | Intercept_x | 1.092 | 0.162 | 6.740 | 0.000
NA | Intercept_y | -0.346 | 0.200 | 1.726 | 0.084
x -> y | p | 1.092 | 0.103 | 10.559 | 0.000
x <-> x | V[x] | 0.284 | 0.025 | 11.367 | 0.000
y <-> y | V[y] | 0.290 | 0.024 | 11.897 | 0.000

    knitr::kable(summary(plm_lambda)$coefficients, digits=3)

| Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | ---
(Intercept) | -0.356 | 0.207 | -1.718 | 0.089
x | 1.102 | 0.103 | 10.744 | 0.000

    knitr::kable(c( "phylolm_lambda"=plm_lambda$optpar,
                    "phylosem_lambda"=plogis(psem_lambda$parhat$logitlambda) ), digits=3)

| x
--- | ---
phylolm_lambda | 0.980
phylosem_lambda | 0.957

    # Compare using Pagel's kappa
    start_time = Sys.time()
    plm_kappa = phylolm::phylolm(y ~ 1 + x, data=Data, phy=tree, model="kappa", lower.bound = 0, upper.bound = 3 )
    Sys.time() - start_time
    #> Time difference of 0.005357981 secs

    start_time = Sys.time()
    psem_kappa = phylosem( sem = "x -> y, p",
              data = Data,
              tree = tree,
              estimate_kappa = TRUE,
              control = phylosem_control(quiet = TRUE) )
    Sys.time() - start_time
    #> Time difference of 0.08427882 secs

    knitr::kable(summary(psem_kappa)$coefficients, digits=3)

Path | VarName | Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | --- | ---
NA | Intercept_x | 1.078 | 0.186 | 5.783 | 0.000
NA | Intercept_y | -0.368 | 0.216 | 1.705 | 0.088
x -> y | p | 1.113 | 0.101 | 11.025 | 0.000
x <-> x | V[x] | 0.299 | 0.029 | 10.183 | 0.000
y <-> y | V[y] | 0.300 | 0.029 | 10.343 | 0.000

    knitr::kable(summary(plm_kappa)$coefficients, digits=3)

| Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | ---
(Intercept) | -0.370 | 0.216 | -1.716 | 0.089
x | 1.115 | 0.101 | 11.015 | 0.000

    knitr::kable(c( "phylolm_kappa"=plm_kappa$optpar,
                    "phylosem_kappa"=exp(psem_kappa$parhat$lnkappa) ), digits=3)

| x
--- | ---
phylolm_kappa | 0.930
phylosem_kappa | 0.857

## Generalized linear models

We also compare results among software for fitting phylogenetic generalized linear models (PGLM).

### Poisson-distributed response

First, we specifically explore a Poisson-distributed PGLM, comparing `phylosem` against `phylolm::phyloglm` (which uses Generalized Estimating Equations) and `phyr::pglmm_compare` (which uses maximum likelihood).

    # Settings
    Ntree = 100
    sd_x = 0.3
    sd_y = 0.3
    b0_x = 1
    b0_y = 0
    b_xy = 1

    # Simulate tree
    set.seed(1)
    tree = ape::rtree(n=Ntree)

    # Simulate data
    x = b0_x + sd_x * phylolm::rTrait(n = 1, phy=tree)
    ybar = b0_y + b_xy*x
    y_normal = ybar + sd_y * phylolm::rTrait(n = 1, phy=tree)
    y_pois = rpois( n=Ntree, lambda=exp(y_normal) )

    # Construct, re-order, and reduce data
    Data = data.frame(x=x,y=y_pois)

    # Compare using phylolm::phyloglm
    pglm = phylolm::phyloglm(y ~ 1 + x, data=Data, phy=tree, method="poisson_GEE" )
    knitr::kable(summary(pglm)$coefficients, digits=3)

| Estimate | StdErr | z.value | p.value
--- | --- | --- | --- | ---
(Intercept) | -1.098 | 0.633 | -1.736 | 0.083
x | 1.314 | 0.247 | 5.320 | 0.000

    #
    pglmm = phyr::pglmm_compare(
      y ~ 1 + x,
      family = "poisson",
      data = Data,
      phy = tree )
    knitr::kable(summary(pglmm), digits=3)
    #> Generalized linear mixed model for poisson data fit by restricted maximum likelihood
    #>
    #> Call:y ~ 1 + x
    #>
    #> logLik    AIC    BIC
    #> -173.4  354.7  360.6
    #>
    #> Phylogenetic random effects variance (s2):
    #>    Variance Std.Dev
    #> s2  0.05511  0.2348
    #>
    #> Fixed effects:
    #>                Value Std.Error  Zscore    Pvalue
    #> (Intercept) -0.57009   0.30469 -1.8710   0.06134 .
    #> x            1.18137   0.19807  5.9645 2.454e-09 ***
    #> ---
    #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    #
    pgsem = phylosem( sem = "x -> y, p",
              data = Data,
              family = c("fixed","poisson"),
              tree = tree,
              control = phylosem_control(quiet = TRUE) )
    knitr::kable(summary(pgsem)$coefficients, digits=3)

Path | VarName | Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | --- | ---
NA | Intercept_x | 1.087 | 0.183 | 5.945 | 0.000
NA | Intercept_y | -0.581 | 0.305 | 1.904 | 0.057
x -> y | p | 1.190 | 0.199 | 5.971 | 0.000
x <-> x | V[x] | 0.315 | 0.022 | 14.072 | 0.000
y <-> y | V[y] | 0.232 | 0.054 | 4.310 | 0.000

We also compare results against `brms` (which fits a Bayesian hierarchical model), although we load results from compiled run of `brms` to avoid users having to install STAN to run vignettes for `phylosem`:

    # Comare using Bayesian implementation in brms
    library(brms)
    Amat <- ape::vcv.phylo(tree)
    Data$tips <- rownames(Data)
    mcmc <- brm(
      y ~ 1 + x + (1 | gr(tips, cov = A)),
      data = Data, data2 = list(A = Amat),
      family = 'poisson',
      cores = 4
    )
    knitr::kable(fixef(mcmc), digits = 3)

    # Plot them together
    library(ggplot2)
    pdat <- rbind.data.frame(
      coef(summary(pglm))[, 1:2],
      data.frame(Estimate = pglmm$B, StdErr = pglmm$B.se),
      setNames(as.data.frame(fixef(mcmc))[1:2], c('Estimate', 'StdErr')),
      setNames(summary(pgsem)$coefficients[2:3, 3:4], c('Estimate', 'StdErr'))
    )
    pdat$Param <- rep(c('Intercept', 'Slope'), 4)
    pdat$Method <- rep( c('phylolm::phyloglm', 'phyr::pglmm_compare',
                         'brms::brm', 'phylosem::phylosem'), each = 2)
    figure = ggplot(pdat, aes(
      x = Estimate, xmin = Estimate - StdErr,
      xmax = Estimate + StdErr, y = Param, color = Method
    )) +
      geom_pointrange(position = position_dodge(width = 0.6)) +
      theme_classic() +
      theme(panel.grid.major.x = element_line(), panel.grid.minor.x = element_line())

    #> Warning: package 'ggplot2' was built under R version 4.3.3

In this instance (and in others we have explored), results from `phylolm::phyloglm` are generally different while those from `phylosem`, `phyr::pglmm_compare`, and `brms` are close but not quite identical.

### Binomial regression

We also compare results for a Bernoulli-distributed response using PGLM. We again compare `phylosem` against `phyr::pglmm_compare`, and do not explore threshold models which we expect to give different results due differences in assumptions about how latent variables affect measurements.

    # Settings
    Ntree = 100
    sd_x = 0.3
    sd_y = 0.3
    b0_x = 1
    b0_y = 0
    b_xy = 1

    # Simulate tree
    set.seed(1)
    tree = ape::rtree(n=Ntree)

    # Simulate data
    x = b0_x + sd_x * phylolm::rTrait(n = 1, phy=tree)
    ybar = b0_y + b_xy*x
    y_normal = ybar + sd_y * phylolm::rTrait(n = 1, phy=tree)
    y_binom = rbinom( n=Ntree, size=1, prob=plogis(y_normal) )

    # Construct, re-order, and reduce data
    Data = data.frame(x=x,y=y_binom)

    #
    pglmm = phyr::pglmm_compare(
      y ~ 1 + x,
      family = "binomial",
      data = Data,
      phy = tree )
    knitr::kable(summary(pglmm), digits=3)
    #> Generalized linear mixed model for binomial data fit by restricted maximum likelihood
    #>
    #> Call:y ~ 1 + x
    #>
    #> logLik    AIC    BIC
    #> -63.74 135.47 141.32
    #>
    #> Phylogenetic random effects variance (s2):
    #>    Variance Std.Dev
    #> s2   0.1076   0.328
    #>
    #> Fixed effects:
    #>               Value Std.Error Zscore Pvalue
    #> (Intercept) 0.23179   0.60507 0.3831 0.7017
    #> x           0.44548   0.45708 0.9746 0.3297

    #
    pgsem = phylosem( sem = "x -> y, p",
              data = Data,
              family = c("fixed","binomial"),
              tree = tree,
              control = phylosem_control(quiet = TRUE) )
    knitr::kable(summary(pgsem)$coefficients, digits=3)

Path | VarName | Estimate | StdErr | t.value | p.value
--- | --- | --- | --- | --- | ---
NA | Intercept_x | 1.087 | 0.183 | 5.945 | 0.000
NA | Intercept_y | 0.204 | 0.589 | 0.346 | 0.730
x -> y | p | 0.458 | 0.468 | 0.977 | 0.328
x <-> x | V[x] | -0.315 | 0.022 | 14.072 | 0.000
y <-> y | V[y] | 0.290 | 0.284 | 1.020 | 0.308

In this instance, `phylosem` and `phyr::pglmm_compare` give similar estimates and standard errors for the slope term.

### Summary of PGLM results

Based on these two comparisons, we conclude that phylosem provides an interface for maximum-likelihood estimate of phylogenetic generalized linear models (PGLM), and extends this class to include mixed data (i.e., a combination of different measurement types), missing data, and non-recursive structural linkages. However, we also encourage further cross-testing of different software for fitting phylogenetic generalized linear models.

## Compare with phylopath

We next compare with a single run of `phylopath`. This again confirms that runtimes are within an order of magnitude and results are identical for standardized or unstandardized coefficients.

    library(phylopath)
    library(phylosem)

    # make copy of data that's rescaled
    rhino_scaled = rhino
      rhino_scaled[,c("BM","NL","LS","DD","RS")] = scale(rhino_scaled[,c("BM","NL","LS","DD","RS")])

    # Fit and plot using phylopath
    dag <- DAG(RS ~ DD, LS ~ BM, NL ~ BM, DD ~ NL)
    start_time = Sys.time()
    result <- est_DAG( DAG = dag,
                        data = rhino,
                        tree = rhino_tree,
                        model = "BM",
                        measurement_error = FALSE )
    Sys.time() - start_time
    #> Time difference of 0.009079933 secs
    plot(result)

    # Fit and plot using phylosem
    model = "
      DD -> RS, p1
      BM -> LS, p2
      BM -> NL, p3
      NL -> DD, p4
    "
    start_time = Sys.time()
    psem = phylosem( sem = model,
              data = rhino_scaled[,c("BM","NL","DD","RS","LS")],
              tree = rhino_tree,
              control = phylosem_control(quiet = TRUE) )
    Sys.time() - start_time
    #> Time difference of 0.212764 secs
    plot( as_fitted_DAG(psem) )

## Comparison with sem

We next compare syntax and runtime against R-package `sem`. This confirms that runtimes are within an order of magnitude when specifying a star-phylogeny in `phylosem` to match the assumed structure in `sem`

    library(sem)
    #> Warning: package 'sem' was built under R version 4.3.3
    library(TreeTools)
    #> Warning: package 'TreeTools' was built under R version 4.3.3
    #> Warning: package 'ape' was built under R version 4.3.3

    # Simulation parameters
    n_obs = 50
    # Intercepts
    a1 = 1
    a2 = 2
    a3 = 3
    a4 = 4
    # Slopes
    b12 = 0.3
    b23 = 0
    b34 = 0.3
    # Standard deviations
    s1 = 0.1
    s2 = 0.2
    s3 = 0.3
    s4 = 0.4

    # Simulate data
    E1 = rnorm(n_obs, sd=s1)
    E2 = rnorm(n_obs, sd=s2)
    E3 = rnorm(n_obs, sd=s3)
    E4 = rnorm(n_obs, sd=s4)
    Y1 = a1 + E1
    Y2 = a2 + b12*Y1 + E2
    Y3 = a3 + b23*Y2 + E3
    Y4 = a4 + b34*Y3 + E4
    Data = data.frame(Y1=Y1, Y2=Y2, Y3=Y3, Y4=Y4)

    # Specify path diagram (in this case, using correct structure)
    equations = "
      Y2 = b12 * Y1
      Y4 = b34 * Y3
    "
    model <- specifyEquations(text=equations, exog.variances=TRUE, endog.variances=TRUE)

    # Fit using package:sem
    start_time = Sys.time()
    Sem <- sem(model, data=Data)
    Sys.time() - start_time
    #> Time difference of 0.008908033 secs

    # Specify star phylogeny
    tree_null = TreeTools::StarTree(n_obs)
      tree_null$edge.length = rep(1,nrow(tree_null$edge))
      rownames(Data) = tree_null$tip.label

    # Fit using phylosem
    start_time = Sys.time()
    psem = phylosem( data = Data,
              sem = equations,
              tree = tree_null,
              control = phylosem_control(quiet = TRUE) )
    Sys.time() - start_time
    #> Time difference of 0.03409982 secs

We then compare estimated values for standardized coefficients

| x
--- | ---
b12 | 0.326
b34 | 0.325

Path | Parameter | Estimate
--- | --- | ---
Y1 -> Y2 | b12 | 0.345
Y3 -> Y4 | b34 | 0.343

and also compare values for unstandardized coefficients:

| x
--- | ---
b12 | 0.660
b34 | 0.390
V[Y1] | 0.010
V[Y2] | 0.038
V[Y3] | 0.098
V[Y4] | 0.126

Path | Parameter | Estimate
--- | --- | ---
Y1 -> Y2 | b12 | 0.660
Y3 -> Y4 | b34 | 0.390
Y1 <-> Y1 | V[Y1] | 0.010
Y2 <-> Y2 | V[Y2] | 0.038
Y3 <-> Y3 | V[Y3] | 0.098
Y4 <-> Y4 | V[Y4] | 0.126

## Comparison with Rphylopars

Finally, we compare syntax and runtime against R-package `Rphylopars`. This confirms that we can impute identical estimates using both packages, when specifying a full-rank covariance in `phylosem`

We note that `phylosem` also allows parsimonious representations of the trait covariance via the inputted SEM structure.

    library(Rphylopars)

    # Format data, within no values for species t1
    Data = rhino[,c("BM","NL","DD","RS","LS")]
      rownames(Data) = tree$tip.label
    Data['t1',] = NA

    # fit using phylopars
    start_time = Sys.time()
    pars <- phylopars( trait_data = cbind(species=rownames(Data),Data),
                      tree = tree,
                      pheno_error = FALSE,
                      phylo_correlated = TRUE,
                      pheno_correlated = FALSE)
    Sys.time() - start_time
    #> Time difference of 0.09994388 secs

    # Display estimates for missing values
    knitr::kable(cbind( "Estimate"=pars$anc_recon["t1",], "Var"=pars$anc_var["t1",] ), digits=3)

| Estimate | Var
--- | --- | ---
BM | 1.266 | 1.941
NL | 1.600 | 1.856
DD | 2.301 | 1.708
RS | 0.431 | 1.909
LS | 1.083 | 1.347

    # fit using phylosem
    start_time = Sys.time()
    psem = phylosem( data = Data,
                     tree = tree,
                     sem = "",
                     covs = "BM, NL, DD, RS, LS",
                     control = phylosem_control(quiet = TRUE) )
    Sys.time() - start_time
    #> Time difference of 0.3152001 secs

    # Display estimates for missing values
    knitr::kable(cbind(
      "Estimate"=as.list(psem$sdrep,"Estimate")$x_vj[ match("t1",tree$tip.label), ],
      "Var"=as.list(psem$sdrep,"Std. Error")$x_vj[ match("t1",tree$tip.label), ]^2
    ), digits=3)

Estimate | Var
--- | ---
1.266 | 1.941
1.600 | 1.856
2.301 | 1.708
0.431 | 1.910
1.083 | 1.347