# Ancestral states reconstructions and missing value imputation with phylogenetic/time-series models This function imputes the missing cases (NA values) according to a given phylogenetic model (object of class "mvmorph"); it can also do ancestral state reconstruction. ```r estim(tree, data, object, error=NULL, asr=FALSE) ``` ## Arguments - `tree`: Phylogenetic tree (an object of class "phylo" or "simmap") or a time-series. - `data`: Matrix or data frame with species in rows and continuous traits with missing cases (NA values) in columns (preferentially with names and in the same order than in the tree). - `object`: A fitted object from an mvMORPH model (class "mvmorph"). - `error`: Matrix or data frame with species in rows and continuous traits sampling variance (squared standard errors) in columns. - `asr`: If asr=TRUE, the ancestral states are estimated instead of the missing cases. ## Details Missing observations for species in a phylogenetic tree are estimated according to a given evolutionary model (and parameters). Multivariate models are useful to recover the variance and covariance structure of the dataset to be imputed. When **asr=TRUE**, the estimates, their variances and standard errors are those of the ancestral states at each node of the tree (this option is not available for the time-series). Note that if there are missing cases, they are first imputed before estimating the ancestral states. Estimation of missing cases and ancestral states is performed using GLS (Generalized Least Squares) solution (See Cunningham et al. 1998). ## Returns - **estimates**: The imputed dataset - **var**: Variance of the estimates - **se**: Standard error of the estimates - **NA_index**: Position of the missing cases in the dataset ## References Clavel J., Merceron G., Escarguel G. 2014. Missing Data Estimation in Morphometrics: How Much is Too Much? Syst. Biol. 63:203-218. Cunningham C.W., Omland K.E., Oakley T.H. 1998. Reconstructing ancestral character states: a critical reappraisal. Trends Ecol. Evol. 13:361-366. ## Author(s) Julien Clavel ## See Also `mvMORPH` `mvOU` `mvEB` `mvBM` `mvSHIFT` ## Examples ```r ## Simulated dataset set.seed(14) # Generating a random tree tree<-pbtree(n=50) # Setting the regime states of tip species sta<-as.vector(c(rep("Forest",20),rep("Savannah",30))); names(sta)<-tree$tip.label # Making the simmap tree with mapped states tree<-make.simmap(tree,sta , model="ER", nsim=1) col<-c("blue","orange"); names(col)<-c("Forest","Savannah") # Plot of the phylogeny for illustration plotSimmap(tree,col,fsize=0.6,node.numbers=FALSE,lwd=3, pts=FALSE) # Simulate two correlated traits evolving along the phylogeny traits<-mvSIM(tree,nsim=1, model="BMM", param=list(sigma=list(matrix(c(2,1,1,1.5),2,2), matrix(c(4,1,1,4),2,2)), names_traits=c("head.size","mouth.size"))) # Introduce some missing cases (NA values) data<-traits data[8,2]<-NA data[25,1]<-NA # Fit of model 1 fit<-mvBM(tree,data,model="BMM") # Estimate the missing cases imp<-estim(tree, data, fit) # Check the imputed data imp$estim[1:10,] ## We want the ancestral states values at each nodes: nodelabels() # To see where the nodes are situated imp2<-estim(tree, data, fit, asr=TRUE) # Check the 10 firsts ancestral states imp2$estim[1:10,] ```

estim function