intubate (version 1.0.0)

ape: Interfaces for ape package for data science pipelines.

Description

Interfaces to ape functions that can be used in a pipeline implemented by magrittr.

Usage

ntbt_binaryPGLMM(data, ...)
ntbt_compar.gee(data, ...)
ntbt_correlogram.formula(data, ...)
ntbt_lmorigin(data, ...)
ntbt_yule.cov(data, ...)

Arguments

data

data frame, tibble, list, ...

...

Other arguments passed to the corresponding interfaced function.

Value

Object returned by interfaced function.

Details

Interfaces call their corresponding interfaced function.

Examples

## Not run: 
# library(intubate)
# library(magrittr)
# library(ape)
# 
# ## ntbt_binaryPGLMM: Phylogenetic Generalized Linear Mixed Model for Binary Data
# n <- 100
# phy <- compute.brlen(rtree(n=n), method = "Grafen", power = 1)
# X1 <- rTraitCont(phy, model = "BM", sigma = 1)
# X1 <- (X1 - mean(X1))/var(X1)
# sim.dat <- data.frame(Y=array(0, dim=n), X1=X1, row.names=phy$tip.label)
# sim.dat$Y <- binaryPGLMM.sim(Y ~ X1, phy = phy, data = sim.dat, s2 = .5,
#                              B = matrix(c(0, .25), nrow = 2, ncol = 1), nrep = 1)$Y
# 
# ## Original function to interface
# binaryPGLMM(Y ~ X1, phy = phy, data = sim.dat)
# 
# ## The interface puts data as first parameter
# ntbt_binaryPGLMM(sim.dat, Y ~ X1, phy = phy)
# 
# ## so it can be used easily in a pipeline.
# sim.dat %>%
#   ntbt_binaryPGLMM(Y ~ X1, phy = phy)
# 
# 
# ## ntbt_compar.gee: Comparative Analysis with GEEs
# tr <- "((((Homo:0.21,Pongo:0.21):0.28,Macaca:0.49):0.13,Ateles:0.62):0.38,Galago:1.00);"
# tree.primates <- read.tree(text = tr)
# dta <- data.frame(X = c(4.09434, 3.61092, 2.37024, 2.02815, -1.46968),
#                   Y = c(4.74493, 3.33220, 3.36730, 2.89037, 2.30259))
# rownames(dta) <- tree.primates$tip.label
# 
# ## Original function to interface
# compar.gee(X ~ Y, phy = tree.primates, data = dta)
# 
# ## The interface puts data as first parameter
# ntbt_compar.gee(dta, X ~ Y, phy = tree.primates)
# 
# ## so it can be used easily in a pipeline.
# dta %>%
#   ntbt_compar.gee(X ~ Y, phy = tree.primates)
# 
# 
# ## ntbt_correlogram.formula: Phylogenetic Correlogram
# data(carnivora)
# 
# ## Original function to interface
# correlogram.formula(SW ~ Order/SuperFamily/Family/Genus,
#                     data = carnivora)
# 
# ## The interface puts data as first parameter
# ntbt_correlogram.formula(carnivora, SW ~ Order/SuperFamily/Family/Genus)
# 
# ## so it can be used easily in a pipeline.
# carnivora %>%
#   ntbt_correlogram.formula(SW ~ Order/SuperFamily/Family/Genus)
# 
# 
# ## ntbt_lmorigin: Multiple regression through the origin
# data(lmorigin.ex1)
# 
# ## Original function to interface
# lmorigin(SO2 ~ ., data = lmorigin.ex1, origin = FALSE, nperm = 99)
# 
# ## The interface puts data as first parameter
# ntbt_lmorigin(lmorigin.ex1, SO2 ~ ., origin = FALSE, nperm = 99)
# 
# ## so it can be used easily in a pipeline.
# lmorigin.ex1 %>%
#   ntbt_lmorigin(SO2 ~ ., origin = FALSE, nperm = 99)
# 
# ## ntbt_yule.cov: Fits the Yule Model With Covariates
# data(bird.orders)
# dta <- data.frame (x = rnorm(45))
# 
# ## Original function to interface
# yule.cov(bird.orders, ~ x, data = dta)
# 
# ## The interface puts data as first parameter
# ntbt_yule.cov(dta, bird.orders, ~ x)
# 
# ## so it can be used easily in a pipeline.
# dta %>%
#   ntbt_yule.cov(bird.orders, ~ x)
# ## End(Not run)