Estimates hidden rates underlying the evolution of a binary character
corHMM(phy, data, rate.cat, rate.mat=NULL, model = "ARD", node.states = "marginal",
fixed.nodes=FALSE, p=NULL, root.p="yang", ip=NULL, nstarts=0, n.cores=1,
get.tip.states = FALSE, lewis.asc.bias = FALSE, collapse = TRUE, lower.bound = 1e-9,
upper.bound = 100, opts=NULL)a phylogenetic tree, in ape “phylo” format.
a data.frame containing species information. The first column must be species names matching the phylogeny. Additional columns contain discrete character data.
specifies the number of rate categories (see Details).
a user-supplied index of parameters to be optimized.
One of "ARD", "SYM", or "ER". ARD: all rates differ. SYM: rates between any two states do not differ. ER: all rates are equal.
method used to calculate ancestral states at internal nodes (see Details).
specifies that states for nodes in the phylogeny are assumed fixed. These are supplied as node labels in the “phylo” object.
a vector of transition rates. Allows the user to calculate the likelihood given a specified set of parameter values to specified as fixed and calculate the likelihood.
a vector used to fix the probabilities at the root, but “yang” and “maddfitz” can also be supplied to use the method of Yang (2006) and FitzJohn et al (2009) respectively (see details).
initial values used for the likelihood search. Can be a single value or a vector of unique values for each parameter. The default is ip=1.
the number of random restarts to be performed. The default is nstarts=0.
the number of processor cores to spread out the random restarts.
a boolean indicating whether tip reconstructions should be output. The default is FALSE.
a boolean indicating whether to correct for observing a dataset that is not univariate. The default is FALSE
a boolean indicating whether to collapse multiple character combinations into only the observed states. For example, if true a two character dataset contained (0,0), (1,0), and (1,1), this would be collapsed into 1,2,3. However, if set to false it would 1,2,4. In combination with a custom rate matrix this allows for the estimation of transitions between the unobserved character combination. The default is TRUE
lower bound for the likelihood search. The default is lower.bound=1e-9.
upper bound for the likelihood search. The default is upper.bound=100.
options to pass to nloptr. default is NULL.
corHMM returns an object of class corHMM. This is a list with elements:
the maximum negative log-likelihood.
Akaike information criterion.
Akaike information criterion corrected for sample size.
The number of rate categories specified.
a matrix containing the maximum likelihood estimates of the transition rates. Note that the rate classes are ordered from slowest (R1) to fastest (Rn) with respect to state 0.
The indices of the parameters being estimated are returned. This also is a way to allow the estimation of transition rates for parameters not oberved in the dataset. Say you have 2 traits X and Y, where the combinations 00, 01, and 11 are observed (10 is not). A 4 by 4 index matrix could be used to force 10 into the model.
User-supplied dataset.
User-supplied dataset with an extra column of trait values corresponding to how corHMM calls the user data.
User-supplied tree.
The likeliest states at each internal node. The state and rates reconstructed at internal nodes are in the order of the column headings of the rates matrix.
The likeliest state at each tip. The state and rates reconstructed at the tips are in the order of the column headings of the rates matrix.
a vector containing the amount of information (in bits) that the tip states and model gives to each node.
The number of iterations used by the optimization routine.
The root prior used in model estimation.
This function takes a tree and a trait file and estimates transition rates and ancestral states for any number of discrete characters using a Markov model with or without "hidden" states. Users are advised to read the _Generalized corHMM_ vignette for details on how to make full use of corHMM's new functionality. In general, these models describe evolution as discrete transitions between observed states. If rate.class > 1, then the model is a hidden Markov model (HMM; also known as a hidden rates model (HRM)). The HRM is a generalization of the covarion model that allows different rate classes to be treated as "hidden" states. Essentially a hidden Markov model allows for multiple processes to describe the evolution of your observed character. This could be another (hidden) state or a large group of them. Regardless of the reason, an HMM is saying that not all observed characters are expected to act the same way.
The first column of the input data must be species names (as in the previous version), but there can be any number of data columns. If your dataset does have 2 or more columns of trait information, each column is taken to describe a separate character. The separation of character and state is an important one because corHMM will automatically remove dual transitions from your model. For example, say you had 3 characters each with 2 states (0 or 1), but only three of these combinations were ever observed 0_0_1, 0_1_0, or 1_0_0. With dual transitions disallowed, it is impossible to move between these combinations because it would mean simultaneously losing and gaining a state (0_0_1 -> 0_0_0 -> 0_1_0 in one step.) One way around this is to provide a custom rate matrix to corHMM where transitions are allowed between these states. However, this is also a case where it would seem appropriate to code the data as a single character with 3 states.
Ambiguities (polymorphic taxa or taxa missing data) are assigned likelihoods following Felsenstein (2004, p. 255). Taxa with missing data are coded “?” with all states observed at a tip. Polymorphic taxa are coded with states separated by an “&”. For example, if a trait has four states and taxonA is observed to be in state 1 and 3, the character would be coded as “1&3”. corHMM then uses this information to assign a likelihood of 1.0 to both states. Missing data are treated as ambiguous for all states, thus all states for taxa missing data are assigned a likelihood of 1.0. For example, for a four-state character (i.e. DNA), a taxon missing data will have likelihoods of all four states equal to 1.0 [e.g. L(A)=1.0, L(C)=1.0, L(G)=1.0, L(T)=1.0].
The likelihood function is maximized using the bounded subplex optimization routine implemented in the R package nloptr, which provides a common interface to NLopt, an open-source library for nonlinear optimization. In the former case, however, it is recommended that nstarts is set to a large value (e.g. 100) to ensure that the maximum likelihood solution is found. Users can set n.cores to parse the random restarts onto multiple processors.
The user can fix the root state probabilities by supplying a vector to root.p. For example, if the hypothesis is that the root is 0_S in a model with two hidden rates, then the root vector would be root.p=c(1,0,0,0) for state combinations 0_S, 1_S, 0_F, and 1_F, respectively. If the user supplies the flag root.p=“NULL”, then there is equal weighting among all possible states in the model. If the user supplies the flag root.p=“yang”, then the estimated transition rates are used to set the weights at the root (see pg. 124 Yang 2006), whereas specifying root.p=“maddfitz” employs the same procedure described by Maddison et al. (2007) and FitzJohn et al. (2009). Note that the default root.p="yang".
Ancestral states can be estimated using marginal, joint, scaled, or none approaches. Marginal gives the likelihood of state at each node, integrating over the states at other nodes. Joint gives the optimal state at each node for the entire tree at once (it can only return the most likely state, i.e. it is not a probability like the marginal reconstruction). Scaled is included for compatibility with ape's ace() function. None suppresses calculation of ancestral states, which can dramatically speed up calculations if you're comparing models but make plotting difficult.
Beaulieu J.M., B.C. O'Meara, and M.J. Donoghue. 2013. Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in campanulid angiosperms. Systematic Biology 62:725-737.
Boyko, J. D., and J. M. Beaulieu. 2021. Generalized hidden Markov models for phylogenetic comparative datasets. Methods in Ecology and Evolution 12:468-478.
Felsenstein, J. 1981. A likelihood approach to character weighting and what it tells us about parsimony and compatibility. Biological Journal of the Linnean Society 16: 183-196.
Felsenstein J. 2004. Inferring phylogenies. Sunderland MA: Sinauer Associates.
FitzJohn, R.G., W.P. Maddison, and S.P. Otto. 2009. Estimating trait-dependent speciation and extinction rates from incompletely resolved phylogenies. Systematic Biology 58:595-611.
Maddison, W.P., P.E. Midford, and S.P. Otto. 2007. Estimating a binary characters effect on speciation and extinction. Systematic Biology 56:701-710.
Pagel, M. 1994. Detecting correlated evolution on phylogenies: a gneeral method for the comparative analysis of discrete characters. Proc. R. Soc. Lond. B 255:37-45.
Yang, Z. 2006. Computational Molecular Evolution. Oxford Press:London.
# NOT RUN {
data(primates)
phy <- multi2di(primates[[1]])
data <- primates[[2]]
MK_3state <- corHMM(phy = phy, data = data, rate.cat = 1)
MK_3state
# }
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