Nothing
phylolm <- function(formula, data=list(), phy,
model=c("BM","OUrandomRoot","OUfixedRoot","lambda","kappa","delta","EB","trend"),
lower.bound=NULL, upper.bound=NULL, starting.value=NULL, measurement_error = FALSE,
boot=0,full.matrix = TRUE, save = FALSE, REML = FALSE, ...)
{
## initialize
if (!inherits(phy, "phylo")) stop("object \"phy\" is not of class \"phylo\".")
model = match.arg(model)
if ((model=="trend")&(is.ultrametric(phy)))
stop("the trend is unidentifiable for ultrametric trees.")
if ((model=="lambda") && measurement_error)
stop("the lambda transformation and measurement error cannot be used together: they are not distinguishable")
if (is.null(phy$edge.length)) stop("the tree has no branch lengths.")
if (is.null(phy$tip.label)) stop("the tree has no tip labels.")
tol = 1e-10
mf = model.frame(formula=formula,data=data)
if (is.null(rownames(mf))) {
if (nrow(mf)!=length(phy$tip.label))
stop("number of rows in the data does not match the number of tips in the tree.")
warning("the data has no names, order assumed to be the same as tip labels in the tree.\n")
}
else { # the data frame has row names
taxa_without_data = setdiff(phy$tip.label, rownames(mf))
if (length(taxa_without_data)>0){
warning("will drop from the tree ", length(taxa_without_data), " taxa with missing data")
phy = drop.tip(phy, taxa_without_data)
}
if (length(phy$tip.label)<2)
stop("names of the data do not match with tip labels.")
taxa_notin_tree = setdiff(rownames(mf), phy$tip.label)
if (length(taxa_notin_tree)>0){
warning(length(taxa_notin_tree), " taxa not in the tree: their data will be ignored")
mf = mf[-which(rownames(mf) %in% taxa_notin_tree),,drop=F]
}
# now we should have that: nrow(mf)==length(phy$tip.label)
ordr = match(phy$tip.label, rownames(mf))
if (any(is.na(ordr))) # should never happen given earlier checks
stop("data names do not match with the tip labels.\n")
mf = mf[ordr,,drop=F]
}
X = model.matrix(attr(mf, "terms"), data=mf)
y = model.response(mf)
d = ncol(X)
## Check that response is a vector
if (is.matrix(y) && ncol(y) > 1) {
stop(paste0("The response vector y in the formula is multivariate (it has several columns).\n",
" Please fit each column one by one: 'phylolm' can only handle a simple (univariate) response vector y."))
}
phy = reorder(phy,"pruningwise")
n <- length(phy$tip.label)
N <- dim(phy$edge)[1]
ROOT <- n + 1L
anc <- phy$edge[, 1]
des <- phy$edge[, 2]
externalEdge <- des<=n
OU = c("OUrandomRoot","OUfixedRoot")
flag = 0 # flag and D are used for OU model if tree is not ultrametric:
D = NULL # for the generalized 3-point structure
## preparing for OU model
if (model %in% OU) {
D = numeric(n)
if (!is.ultrametric(phy)) {
flag = 1
dis = pruningwise.distFromRoot(phy)
Tmax = max(dis[1:n])
D = Tmax - dis[1:n]
D = D - mean(D)
phy$edge.length[externalEdge] <- phy$edge.length[externalEdge] + D[des[externalEdge]]
## phy is now ultrametric, with height Tmax:
Tmax = Tmax + min(D)
}
}
## preparing for trend model
if (model == "trend") {
trend = pruningwise.distFromRoot(phy)[1:n]
X = cbind(X,trend)
d = d+1
}
## calculate Tmax = average distance from root to tips,
## to choose appropriate starting values later # fixit
dis = pruningwise.distFromRoot(phy)[1:n]
Tmax = mean(dis)
## Default bounds
bounds.default = matrix(c(1e-7/Tmax,50/Tmax,1e-7,1,1e-6,1,1e-5,3,-3/Tmax,0,1e-16,1e16), ncol=2, byrow=TRUE)
rownames(bounds.default) = c("alpha","lambda","kappa","delta","rate","sigma2_error")
colnames(bounds.default) = c("min","max")
## Default starting values
starting.values.default = c(0.5/Tmax,0.5,0.5,0.5,-1/Tmax,1)
names(starting.values.default) = c("alpha","lambda","kappa","delta","rate","sigma2_error")
## Utility functions to get values
get_value_param <- function(values, values.default, param) {
if (length(values) == 1 && is.null(names(values))) {
# For backward compatibility : if an unnamed single value is given, assume that it is for "optpar", i.e. the parameter to optimize that is not sigma2_error.
if (param == "sigma2_error") {
val <- NULL
} else {
val <- values
}
} else {
val <- values[[param]]
}
# If no one named "param" in the list of values (val = NULL), return the default.
if (is.null(val)) return(values.default[[param]])
return(val)
}
get_value_model <- function(values, values.default, model, measurement_error) {
if (model != "BM") {
if (model %in% OU) param <- "alpha"
if (model == "lambda") param <- "lambda"
if (model == "kappa") param <- "kappa"
if (model == "delta") param <- "delta"
if (model == "EB") param <- "rate"
vals <- get_value_param(values, values.default, param)
} else {
vals <- NULL
}
if (measurement_error) vals <- c(vals, get_value_param(values, values.default, "sigma2_error"))
return(vals)
}
## User defined bounds and starting values
lower.bound <- get_value_model(lower.bound, bounds.default[ , 1], model, measurement_error)
upper.bound <- get_value_model(upper.bound, bounds.default[ , 2], model, measurement_error)
starting.value <- get_value_model(starting.value, starting.values.default, model, measurement_error)
## preparing for general use of "parameter" for branch length transformation
prm = list(myname = starting.value[1])
names(prm) = model # good for lambda, kappa, delta
if (model %in% OU) names(prm) = "alpha"
if (model == "EB") names(prm) = "rate"
if (measurement_error) {
if (model %in% c("BM","trend")) names(prm) = "sigma2_error"
else prm[["sigma2_error"]] = starting.value[2]
}
## log-likelihood, computation using the three-point structure
ole= 4 + 2*d + d*d # output length
loglik <- function(parameters,y,X) {
tree = transf.branch.lengths(phy,model,parameters=parameters,
check.pruningwise=F,check.ultrametric=F,D=D,check.names=F)$tree
if (flag) { # need diagonal terms in D
y = exp(-parameters$alpha*D) * y # variables local to this function
X = exp(-parameters$alpha*D) * X
}
tmp=.C("threepoint", as.integer(N),as.integer(n),as.integer(phy$Nnode),
as.integer(1),as.integer(d),as.integer(ROOT),as.double(tree$root.edge),as.double(tree$edge.length),
as.integer(des), as.integer(anc), as.double(as.vector(y)), as.double(as.vector(X)),
result=double(ole))$result # tmp has, in this order:
## logdetV, 1'V^{-1}1, y'V^{-1}1, y'V^{-1}y, X'V^{-1}1, X'V^{-1}X, X'V^{-1}y
comp = list(vec11=tmp[2], y1=tmp[3], yy=tmp[4], X1=tmp[5:(4+d)],
XX=matrix(tmp[(5+d):(ole-d)], d,d),Xy=tmp[(ole-d+1):ole],logd=tmp[1])
invXX = solve(comp$XX)
betahat = invXX%*%comp$Xy
sigma2hat = as.numeric((comp$yy - 2*t(betahat)%*%comp$Xy + t(betahat)%*%comp$XX%*%betahat)/n)
if (sigma2hat<0) {
resdl = X%*%betahat - y
tmpyy=.C("threepoint", as.integer(N),as.integer(n),as.integer(phy$Nnode),
as.integer(1),as.integer(d),as.integer(ROOT),as.double(tree$root.edge),as.double(tree$edge.length),
as.integer(des), as.integer(anc), as.double(as.vector(resdl)), as.double(as.vector(X)),
result=double(ole))$result[4]
sigma2hat = tmpyy/n
}
vcov = sigma2hat*invXX*n/(n-d)
if (!REML) {
n2llh = as.numeric( n*log(2*pi) + n + n*log(sigma2hat) + comp$logd) # -2 log-likelihood
} else {
sigma2hat <- sigma2hat * n / (n-d)
# log|X'V^{-1}X|
ldXX <- determinant(comp$XX, logarithm = TRUE)$modulus
n2llh <- as.numeric( (n - d) * (log(2*pi) + 1 + log(sigma2hat)) + comp$logd + ldXX) # -2 restricted log-likelihood
}
if (flag)
n2llh = n2llh + parameters$alpha * 2*sum(D)
## because diag matrix used for generalized 3-point structure is exp(alpha diag(D))
return(list(n2llh=n2llh, betahat = as.vector(betahat), sigma2hat=sigma2hat,vcov=vcov))
}
## Fitting
lower = lower.bound
upper = upper.bound
start = starting.value
if ((model %in% c("BM","trend"))&&(!measurement_error)) {
BMest = loglik(prm, y, X) # root edge taken to be 0
results <- list(coefficients=BMest$betahat, sigma2=BMest$sigma2hat, optpar=NULL, sigma2_error = 0,
logLik=-BMest$n2llh/2, p=1+d, aic=2*(1+d)+BMest$n2llh, vcov = BMest$vcov)
} else {
##------- Optimization of phylogenetic correlation parameter is needed ---------#
# first: checks of bounds
if (sum(lower>start)+sum(upper<start)>0 )
stop("The starting value is not within the bounds of the parameter.")
# def of function to be optimized
# minus2llh_sinvar: return the -loglik given a single variable: measure-err
# logvalue=log of measure-err variance
# if not BM: prm[[1]] needs up-to-date before calling this fcn:
# prm[[1]] = alpha or lamda or kappa or rate
minus2llh_sinvar=function(logvalue) {
if(model %in% c("BM","trend")){
prm[[1]] = exp(logvalue)
} else
prm[[2]] = exp(logvalue)
loglik(prm, y, X)$n2llh
}
# minus2llh: returns -loglik given 'alpha' and possibly measurement error
minus2llh <- function(logvalue, y) {
if (model == "EB") prm[[1]]=logvalue[1] # which is 'rate', not log(rate)
else prm[[1]]=exp(logvalue[1]) # first element is the parameter of the model
if ((measurement_error)&&(!(model %in% c("BM","trend")))){
prm[[2]]=exp(logvalue[2])
}
loglik(prm, y, X)$n2llh
}
# get objects to start the search, and to bound the search
if (lower[1]==upper[1] && !(measurement_error)) { # no optimization in fact
prm[[1]] = lower[1]
BMest = loglik(prm, y, X)
} else {
if(model !="EB"){ # logstart = (log(alpha/lambda...), log(m.e. variance)) or just log(alpha,...)
logstart = log(start)
loglower = log(lower)
logupper = log(upper)
} else{
# logstart = (rate, log(m.e. variance)) or just rate
logstart = start # do *not* log transform 'rate' because it is <= 0
loglower = lower
logupper = upper
if(measurement_error){
logstart[2] = log(start[2])
loglower[2] = log(lower[2])
logupper[2] = log(upper[2])
}
}
if (!(model %in% c("BM","trend")) && lower[1] != upper[1]){
opt <- optim(logstart, fn = minus2llh, method = "L-BFGS-B",lower=loglower, upper = logupper, y = y, ...)
# next: get (co)variance parameters alpha/lambda... and m.e. variance into "prm"
# to be used later in loglik to get the estimated beta and sigma2.
if (model == "EB") MLEvalue = as.numeric(opt$par[1]) else MLEvalue = as.numeric(exp(opt$par[1]))
prm[[1]] = MLEvalue
if (measurement_error){
MLEsigma2_error = as.numeric(exp(opt$par[2]))
prm[[2]] = MLEsigma2_error
}
} else { # then there must be measurement error
# if BM or trend, logstart = log(m.e. variance), already set above.
if (!(model %in% c("BM","trend"))) { # then we must have (lower[1]==upper[1])
prm[[1]]=lower[1]
logstart=logstart[2]
loglower=loglower[2]
logupper=logupper[2]
}
opt <- optim(logstart, fn = minus2llh_sinvar,method = "L-BFGS-B",lower=loglower, upper = logupper, ...)
MLEsigma2_error = as.numeric(exp(opt$par[1]))
if (model %in% c("BM","trend")){
prm[[1]] = MLEsigma2_error
} else {
prm[[2]] = MLEsigma2_error
}
}
## Bound
matchbound <- NULL
# param
if ((isTRUE(all.equal(prm[[1]], lower[1], tol=tol)))
|| (isTRUE(all.equal(prm[[1]], upper[1],tol=tol)))) {
matchbound <- c(matchbound, 1)
if ((model %in% c("lambda","kappa"))&&(prm[[1]] == 1)) matchbound <- NULL
if ((model == "EB")&&(prm[[1]] == 0)) matchbound <- NULL
}
# error
if (length(lower) > 1
&& (isTRUE(all.equal(prm[[2]], lower[2], tol=tol))
|| isTRUE(all.equal(prm[[2]], upper[2],tol=tol)))) {
matchbound <- c(matchbound, 2)
}
if (length(matchbound) > 0) {
for (i in matchbound) {
warning(paste("the estimation of", names(prm)[i],
'matches the upper/lower bound for this parameter.
You may change the bounds using options "upper.bound" and "lower.bound".\n'))
}
}
# estimate beta and sigma2:
BMest = loglik(prm, y, X)
}
# rescaling measurement error by sigma2
sigma2_errorhat = 0
if (measurement_error)
sigma2_errorhat = MLEsigma2_error * BMest$sigma2hat
# rescale sigma2 if OU, because it was "gamma" originally: sigma2 = 2 alpha gamma:
if (model %in% OU)
BMest$sigma2hat = 2*prm[[1]] * BMest$sigma2hat
results <- list(coefficients=BMest$betahat, sigma2=BMest$sigma2hat, optpar=prm[[1]], sigma2_error = sigma2_errorhat,
logLik=-BMest$n2llh/2, p=2+d, aic=2*(2+d)+BMest$n2llh, vcov = BMest$vcov)
if (model %in% c("BM","trend")) { # adjust for BM and trend models
results$optpar = NULL
results$p = results$p - 1
results$aic = results$aic - 2
}
if (measurement_error) {
results$p = results$p + 1 # adjust the number of parameters
results$aic = results$aic + 2 # adjust AIC value
}
} # end of cases that are not BM only
names(results$coefficients) = colnames(X)
colnames(results$vcov) = colnames(X)
rownames(results$vcov) = colnames(X)
results$fitted.values = drop(X %*% results$coefficients)
# drop: simplifies to vector, instead of matrix with 1 column
results$residuals = y - results$fitted.values
results$mean.tip.height = Tmax
results$y = y
results$X = X
results$n = n
results$d = d
results$formula = formula
results$call = match.call()
results$model = model
results$boot = boot
results$REML = REML
results$model.frame = mf
## starting the bootstrap
if (boot>0) {
# Turn off warnings
options(warn=-1)
# simulate all bootstrap data sets
simmodel=model
OU = c("OUrandomRoot","OUfixedRoot")
if (model %in% OU){
simmodel="OU"
}
# prm has "alpha" and "sigma2_error". Here we want "alpha" and "sigma2"
prmsimul = list(sigma2 = results$sigma2)
if (!(simmodel %in% c("BM","trend"))){
Noname=prm[[1]]
names(Noname)=NULL
prmsimul[[2]] = Noname
names(prmsimul)[2] = names(prm)[1]
}
# booty = bootstrap y response. parameter list depends on the model
booty <- results$fitted.values +
rTrait(n = boot, phy = phy,model = simmodel, parameters=prmsimul)
# by default: ancestral.state=0 and optimal.value=0 --used for OU model only
if (measurement_error){
booty <- booty + rnorm(boot*n, mean=0, sd=sqrt(results$sigma2_error))
}
# analyze these bootstrapped data
ncoeff = length(results$coefficients)
colnumberalpha=ncoeff+2
# nparam_var: number of parameters that affect the variances and covariances
nparam_var = 1 # for sigma2
if (measurement_error){
nparam_var = nparam_var+1
colnumberalpha=colnumberalpha+1
}
if (!(model %in% c("BM","trend"))) { nparam_var=nparam_var+1}
# initialize bootvector: vector with bootstrap estimated parameters
bootvector <- vector(length = ncoeff + nparam_var)
names(bootvector) <- paste0("v",1:(ncoeff + nparam_var)) # temporary names, to initialize
names(bootvector)[1:ncoeff] <- names(results$coefficients)
names(bootvector)[ncoeff+1] <- "sigma2"
if (measurement_error){
names(bootvector)[ncoeff+2] <- "sigma2_error"
}
if (!(model %in% c("BM","trend"))) {
names(bootvector)[colnumberalpha] <- names(prm)[1]
}
# define a function that will calculate the boot statistics. It is only dependent on `y`
boot_model <- function(y) {
# all other cases: first get 'prm' up-to-date.
if (!(model %in% c("BM","trend")) && lower[1]==upper[1])
bootvector[colnumberalpha] = lower[1] # will be used later to update 'prm'
# otherwise: something needs to be optimized: m.e., alpha, or both.
# below: storing 'standardized' estimated of m.e. variance in MLEsigma2_error. Rescaled later.
if (!(model %in% c("BM","trend")) && lower[1] != upper[1]){
try(opt <- optim(logstart, fn = minus2llh, method = "L-BFGS-B",lower=loglower, upper = logupper, y = y, ...),silent=TRUE)
if (!inherits(opt, 'try-error')){
if (model == "EB") {
bootvector[colnumberalpha] = as.numeric(opt$par[1]); # will be used later to update 'prm'
} else {
bootvector[colnumberalpha] = as.numeric(exp(opt$par[1]));
}
if(measurement_error) MLEsigma2_error = as.numeric(exp(opt$par[2]))
} else {
if(measurement_error){
try(opt <- optim(logstart, fn = minus2llh_sinvar,method = "L-BFGS-B",lower=loglower, upper = logupper, ...),silent = TRUE)
if (!inherits(opt, 'try-error')){
MLEsigma2_error = as.numeric(exp(opt$par[1]))
}
}
}
}
if (!(model %in% c("BM","trend")))
prm[[1]] = bootvector[colnumberalpha] # update of 'prm'
if (measurement_error){
if (!(model %in% c("BM","trend"))) {
prm[[2]] = MLEsigma2_error
} else {
prm[[1]] = MLEsigma2_error
}
}
BMest = loglik(prm, y, X)
if (model %in% OU)
BMest$sigma2hat = 2*prm[[1]] * BMest$sigma2hat # was "gamma" originally: sigma2 = 2 alpha gamma
if (measurement_error)
bootvector[ncoeff+2] <- MLEsigma2_error * BMest$sigma2hat
bootvector[1:ncoeff] <- BMest$betahat
bootvector[ncoeff+1] <- BMest$sigma2hat
return(bootvector)
}
bootmatrix <- future.apply::future_lapply(as.data.frame(booty), boot_model)
bootmatrix <- do.call(rbind, bootmatrix)
# summarize bootstrap estimates
ind.na <- which(is.na(bootmatrix[,1]))
# indices of replicates that failed: phylolm had an error
if (length(ind.na)>0) {
bootmatrix <- bootmatrix[-ind.na,]
numOnes <- range(apply(booty[,ind.na],2,sum))
}
bootmean <- apply(bootmatrix, 2, mean)
bootsd <- apply(bootmatrix, 2, sd)
bootconfint95 <- apply(bootmatrix, 2, quantile, probs = c(.025, .975))
bootmeansdLog = matrix(NA,2,nparam_var) # will give NaN for rate, but won't be printed.
colnames(bootmeansdLog) = colnames(bootmatrix)[(ncoeff+1):(ncoeff+nparam_var)]
for (i in 1:nparam_var){
bootmeansdLog[1,i] <- mean(log(bootmatrix[, ncoeff + i]))
bootmeansdLog[2,i] <- sd(log(bootmatrix[, ncoeff + i]))
}
results$bootmean = bootmean
results$bootsd = bootsd
results$bootconfint95 = bootconfint95
results$bootmeansdLog = bootmeansdLog
results$bootnumFailed = length(ind.na)
if (full.matrix) results$bootstrap = bootmatrix
if (save) results$bootdata = booty
### Turn on warnings
options(warn=0)
}
## R squared
if (model %in% OU) {
RMS <- results$sigma2 / 2/results$optpar * n/(n-d)
RSSQ <- results$sigma2 / 2/results$optpar * n
} else {
RMS <- results$sigma2 * n/(n-d)
RSSQ <- results$sigma2 * n
}
xdummy <- matrix(rep(1, length(y)))
# local variables used in loglik function
d <- ncol(xdummy)
ole <- 4 + 2*d + d*d # output length
nullMod <- loglik(prm, y, xdummy)
NMS <- nullMod$sigma2hat * n/(n-1)
NSSQ <- nullMod$sigma2hat * n
results$r.squared <- (NSSQ - RSSQ) / NSSQ
results$adj.r.squared <- (NMS - RMS) / NMS
class(results) = "phylolm"
return(results)
}
################################################
################################################
print.phylolm <- function(x, digits = max(3, getOption("digits") - 3), ...){
cat("Call:\n")
print(x$call)
cat("\n")
aiclogLik = c(x$aic,x$logLik)
names(aiclogLik) = c("AIC","logLik")
print(aiclogLik, digits = digits)
cat("\nParameter estimate(s) using ML:\n")
if (!is.null(x$optpar)) {
if (x$model %in% c("OUrandomRoot","OUfixedRoot")) cat("alpha:",x$optpar)
if (x$model %in% c("lambda","kappa","delta")) cat(x$model,":",x$optpar)
if (x$model=="EB") cat("rate:",x$optpar)
cat("\n")
}
cat("sigma2:",x$sigma2,"\n")
if (x$sigma2_error > 0) cat("sigma2_error:",x$sigma2_error,"\n")
cat("\nCoefficients:\n")
print(x$coefficients)
}
################################################
summary.phylolm <- function(object, ...) {
se <- sqrt(diag(object$vcov))
tval <- coef(object) / se
if (object$boot == 0)
TAB <- cbind(Estimate = coef(object), StdErr = se, t.value = tval,
p.value = 2*pt(-abs(tval), df=object$n - object$d))
else
TAB <- cbind(Estimate = coef(object), StdErr = se, t.value = tval,
lowerbootCI = object$bootconfint95[1,1:object$d],
upperbootCI = object$bootconfint95[2,1:object$d],
p.value = 2*pt(-abs(tval), df=object$n - object$d)) # need p-value last for printCoefmat
res <- list(call=object$call, coefficients=TAB,
residuals = object$residuals, sigma2 = object$sigma2,
optpar=object$optpar, sigma2_error = object$sigma2_error, logLik=object$logLik,
df=object$p, aic=object$aic, model=object$model,
mean.tip.height=object$mean.tip.height,
bootNrep = ifelse(object$boot>0, object$boot - object$bootnumFailed, 0),
r.squared=object$r.squared, adj.r.squared=object$adj.r.squared)
if (res$bootNrep>0) {
res$bootmean = object$bootmean
res$bootsd = object$bootsd
res$bootconfint95 = object$bootconfint95
res$bootmeansdLog <- object$bootmeansdLog
}
class(res) = "summary.phylolm"
res
}
################################################
print.summary.phylolm <- function(x, digits = max(3, getOption("digits") - 3), ...){
cat("\nCall:\n")
print(x$call)
cat("\n")
aiclogLik = c(x$aic,x$logLik)
names(aiclogLik) = c("AIC","logLik")
print(aiclogLik, digits = digits)
r <- zapsmall(quantile(x$residuals), digits + 1)
names(r) <- c("Min", "1Q", "Median", "3Q", "Max")
cat("\nRaw residuals:\n")
print(r, digits = digits)
cat("\nMean tip height:",x$mean.tip.height)
cat("\nParameter estimate(s) using ML:\n")
if (!is.null(x$optpar)) {
if (x$model %in% c("OUrandomRoot","OUfixedRoot")) cat("alpha:",x$optpar)
if (x$model %in% c("lambda","kappa","delta")) cat(x$model,":",x$optpar)
if (x$model=="EB") cat("rate:",x$optpar)
cat("\n")
}
cat("sigma2:",x$sigma2,"\n")
if (x$sigma2_error > 0) cat("sigma2_error:",x$sigma2_error,"\n")
cat("\nCoefficients:\n")
printCoefmat(x$coefficients, P.values=TRUE, has.Pvalue=TRUE)
cat("\nR-squared:", formatC(x$r.squared, digits = digits))
cat("\tAdjusted R-squared:", formatC(x$adj.r.squared, digits = digits), "\n")
if (!is.null(x$optpar)) {
cat("\nNote: p-values and R-squared are conditional on ")
if (x$model %in% c("OUrandomRoot","OUfixedRoot")) cat("alpha=",x$optpar,".",sep="")
if (x$model %in% c("lambda","kappa","delta")) cat(x$model,"=",x$optpar,".",sep="")
if (x$model=="EB") cat("rate=",x$optpar,".",sep="")
cat("\n")
}
if (x$bootNrep > 0) {
cat("\n")
ncoef = nrow(x$coefficients)
nparam_var=length(x$bootmean)-ncoef # number of variance/covariance parameters
for (i in 1:nparam_var){
tmp = colnames(x$bootmeansdLog)[i]# sigma2, sigma2_error or lambda/alpha/...
tmp = ifelse(tmp %in% c("sigma2", "sigma2_error"), tmp, "optpar")
cat(colnames(x$bootmeansdLog)[i],": ",x[[tmp]],"\n", sep="")
cat(" bootstrap mean: ", x$bootmean[ncoef+i] ," (on raw scale)","\n",sep="")
if (!(x$model %in% c("EB","lambda")) || tmp != "optpar")
cat(" ",exp(x$bootmeansdLog[1,i])," (on log scale, then back transformed)","\n",sep="")
cat(" bootstrap 95% CI: (",x$bootconfint95[1,ncoef+i],",",x$bootconfint95[2,ncoef+i],")\n\n", sep="")
}
cat("Parametric bootstrap results based on",x$bootNrep,"fitted replicates\n")
}
}
################################################
residuals.phylolm <-function(object,type=c("response"), ...){
type <- match.arg(type)
object$residuals
}
################################################
vcov.phylolm <- function(object, ...){
object$vcov
}
################################################
logLik.phylolm <- function(object, ...){
res <- object$logLik
attr(res, "df") <- object$p
attr(res, "nobs") <- object$n
class(res) <- "logLik.phylolm"
res
}
print.logLik.phylolm <- function (x, ...) {
cat("'log Lik.' ", x," (df=", attr(x, 'df'), ")\n", sep = "")
}
AIC.logLik.phylolm <- function(object, k=2, ...) {
ll <- as.numeric(object)
return(k*attr(object, 'df') - 2*ll)
}
AIC.phylolm <- function(object, k=2, ...) {
return(AIC(logLik(object),k))
}
extractAIC.phylolm <- function(fit, scale, k=2, ...) {
c(fit$p, - 2*fit$logLik + k * fit$p)
}
nobs.phylolm <- function(object, ...){
return(object$n)
}
################################################
predict.phylolm <- function(object, newdata=NULL, se.fit = FALSE, ...){
se = rep(NA, object$n)
# Standard error of prediction is computed as follows:
# V(predictor) = Var(x*bhat) = x*V(bhat)*t(x)
# SE(predictor) = sqrt(V(predictor))
if (object$model=="trend")
stop("Predicting for trend model has not been implemented.")
if(is.null(newdata)) {
predictor <- fitted(object)
if (se.fit)
for (i in 1:object$n) se[i] = sqrt(as.numeric(t(object$X[i,]) %*% object$vcov %*% object$X[i,]))
}
else{
# For new data, we kind of ignore the tree structure
X = model.matrix(delete.response(terms(formula(object))),data = newdata)
predictor <- X %*% coef(object)
if (se.fit)
for (i in 1:nrow(X)) se[i] = sqrt(as.numeric(t(X[i,]) %*% object$vcov %*% X[i,]))
}
if (se.fit) list(fit = predictor, se.fit = se, df = object$n - object$d, residual.scale = sd(object$residuals))
else predictor
}
################################################
plot.phylolm <-function(x, ...){
plot(x$y, fitted(x), xlab = "Observed value", ylab = "Fitted value", ...)
}
################################################
confint.phylolm <- function(object, parm, level = 0.95, ...){
object$df.residuals <- object$n - object$d
return(stats::confint.lm(object, parm, level, ...))
}
################################################
model.frame.phylolm <- function(formula, ...) {
return(formula$model.frame)
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.