Path analysis usding d-separation

Usage

# S3 method for dSep
plot(
  x,
  y,
  plotCoef = TRUE,
  plotdSep = FALSE,
  legend = TRUE,
  lty = c(signif = 1, nonSignif = 2),
  color = c(causal = "black", dsep = "red"),
  alpha = 0.05,
  ...
)

Arguments

x: a dSep object.
plotCoef: if TRUE plot the path coefficients.
plotdSep: if TRUE plot the d-separation paths. By default in red.
legend: if TRUE add a legend.
lty: a vector of 2 elements with the line type of significant and non significant paths.
color: a vector of 2 elements with the color of the causal paths and the color of the d-separation paths.
alpha: significance level.
...: parameters passed to FUN. Parameters must be named following the FUN arguments (e.g. data=data.frame()).
FUN: a function or a the name of the function to test the conditional independences. Currently tested with lm, glm, gls, pgls, MCMCglmm and brm.
formulaArg: argument name from FUN that accepts the formula parameter.
orderResponse: parameter passed to condIndep.
n: sample size of the dataset. If omited the function tries to guess from the models using nobs and custom functions.
cl: the number of CPU cores or a cluster object to run the models in parallel. Cluster object can be defined with makeCluster in package parallel or makeCluster from snow package.
pathCoef: if TRUE calculates the path coefficient with pathCoef. It is advised to standardize the input data (see scale). By doing this, the standardized coefficients represents the relative strength of each causal relationship in the model.

Value

a dSep object.

References

Gonzalez-Voyer, Alejandro, and Achaz Von Hardenberg. 2014. "An Introduction to Phylogenetic Path Analysis." In Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology, edited by Laszlo Zsolt Garamszegi, 29. Berlin, Heidelberg: Springer Berlin Heidelberg. http://www.mpcm-evolution.org/practice/online-practical-material-chapter-8/chapter-8-2-step-step-guide-phylogenetic-path-analysis-using-d-sep-method-rhinograds-example

Author

Joan Maspons <j.maspons@creaf.uab.cat>

Examples

## Dummy data
g1<- gRbase::dag(~a:c:d + b:d)
g2<- gRbase::dag(~a:c:d + b:d:a)
g<- list(m1=g1, m2=g2)
d<- data.frame(a=rnorm(100), b=rnorm(100), c=rnorm(100), d=rnorm(100))

## Use of different functions
m.lm<- dSep(g1, FUN="lm", n=nrow(d), data=d)
#> Error in UseMethod("dSep"): no applicable method for 'dSep' applied to an object of class "igraph"
m.glm<- dSep(g, FUN="glm", n=nrow(d), data=d)
#> Error in UseMethod("dSep"): no applicable method for 'dSep' applied to an object of class "list"

if (require("nlme"))
  m.gls<- dSep(g1, FUN=gls, formulaArg="model", n=nrow(d), data=d)
#> Loading required package: nlme
#> Registered S3 method overwritten by 'nlme':
#>   method   from
#>   nobs.lme dSep
#> Error in UseMethod("dSep"): no applicable method for 'dSep' applied to an object of class "igraph"

if (require(caper)){
  data(shorebird, package="caper")
  shorebird.data[,2:5]<- scale(log(shorebird.data[,2:5]))
  shorebird<- comparative.data(shorebird.tree, shorebird.data, 'Species')
  g3<- gRbase::dag(~Egg.Mass:M.Mass:F.Mass)
  g4<- gRbase::dag(~Egg.Mass:M.Mass:Cl.size + Cl.size:F.Mass:M.Mass)
  m1.pgls<- dSep(list(mPhy1=g3, mPhy2=g4), FUN=caper::pgls, n=nrow(d), cl=2, data=shorebird, lambda='ML')

  rhino.dat <- read.csv("http://mpcm-evolution.org/OPM/Chapter8_OPM/download/rhino.csv")
  rhino.tree <- read.tree("http://mpcm-evolution.org/OPM/Chapter8_OPM/download/rhino.tree")
  com.dat<- caper::comparative.data(rhino.tree, rhino.dat, SP, vcv=TRUE, vcv.dim=3, warn.dropped=TRUE)
  m<- list()
  m$h1<- gRbase::dag(~LS:BM + NL:BM + DD:NL + RS:DD)
  m$h2<- gRbase::dag(~LS:BM + NL:BM + DD:NL + RS:DD:LS)
  m$h3<- gRbase::dag(~LS:BM + NL:BM + DD:NL + RS:NL)
  m$h4<- gRbase::dag(~LS:BM + NL:BM + DD:NL + RS:BM:NL)
  m$h5<- gRbase::dag(~LS:BM + NL:BM + DD:NL + RS:BM:NL:DD)
  m$h6<- gRbase::dag(~LS:BM + NL:BM:RS + DD:NL + RS:BM)
  m$h7<- gRbase::dag(~LS:BM + NL:BM:RS + DD:NL + RS:BM:LS)
  m$h8<- gRbase::dag(~LS:BM + NL:BM:RS + DD:NL)
  m$h9<- gRbase::dag(~LS:BM + NL:BM:RS + DD:NL + RS:LS)

  m2.pgls<- dSep(m, FUN=pgls, cl=2, orderResponse=c("BM", "RS", "LS", "NL", "DD"), data=com.dat, lambda="ML")
}
#> Loading required package: caper
#> Loading required package: ape
#> Loading required package: MASS
#> Loading required package: mvtnorm
#> Registered S3 method overwritten by 'caper':
#>   method    from
#>   nobs.pgls dSep
#> Error in UseMethod("dSep"): no applicable method for 'dSep' applied to an object of class "list"

if (require("brms"))
  m.brmfit<- dSep(g1, FUN=brm, n=nrow(d), cl=2, pathCoef=FALSE, data=d)
#> Loading required package: brms
#> Loading required package: Rcpp
#> Loading 'brms' package (version 2.20.4). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#> 
#> Attaching package: ‘brms’
#> The following object is masked from ‘package:stats’:
#> 
#>     ar
#> Error in UseMethod("dSep"): no applicable method for 'dSep' applied to an object of class "igraph"

if (require("MCMCglmm"))
  m.MCMCglmm<- dSep(g1, FUN=MCMCglmm, formulaArg="fixed", n=nrow(d), cl=2, data=d, verbose=FALSE)
#> Loading required package: MCMCglmm
#> Loading required package: Matrix
#> Loading required package: coda
#> 
#> Attaching package: ‘MCMCglmm’
#> The following object is masked from ‘package:brms’:
#> 
#>     me
#> Error in UseMethod("dSep"): no applicable method for 'dSep' applied to an object of class "igraph"

##
plot(m.glm)
#> Error in eval(expr, envir, enclos): object 'm.glm' not found