Description Usage Arguments Details Value Author(s) Examples
Interfaces to ape
functions that can be used
in a pipeline implemented by magrittr
.
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data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Interfaces call their corresponding interfaced function.
Object returned by interfaced function.
Roberto Bertolusso
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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)
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