mcotram {cotram} | R Documentation |
Multivariate Count Conditional Transformation Models
Description
A proof-of-concept implementation of multivariate conditional transformation models for count data.
Usage
mcotram(..., formula = ~ 1, data, conditional = FALSE, theta = NULL,
fixed = NULL, scale = FALSE, optim = mmltoptim(),
M = 1000, dofit = TRUE, domargins = TRUE)
Arguments
... |
marginal count transformation models, one for each response |
formula |
a model formula describing a model for the dependency
structure via the lambda parameters. The default is set to |
data |
a data.frame. |
conditional |
logical; parameters are defined conditionally (only
possible when all models are probit models). This is the default as
described by Klein et al. (2022). If |
theta |
an optional vector of starting values. |
fixed |
an optional named numeric vector of predefined parameter values. |
scale |
a logical indicating if (internal) scaling shall be applied to the model coefficients. |
optim |
a list of optimisers as returned by |
M |
number of Halton sequences used to approximate the log-likelihood in |
dofit |
logical; parameters are fitted by default, otherwise a list with log-likelihood and score function is returned. |
domargins |
logical; all model parameters are fitted by default, including the parameters of marginal models. |
Details
The function implements multivariate count conditional transformation models. The response is assumed to be a vector of counts.
Value
An object of class mmlt
with coef
and predict
methods.
References
Luisa Barbani, Roland Brandl, Torsten Hothorn (2022), Multi-species Count Transformation Models, doi:10.48550/arXiv.2201.13095.
Nadja Klein, Torsten Hothorn, Luisa Barbanti, Thomas Kneib (2020), Multivariate Conditional Transformation Models. Scandinavian Journal of Statistics, doi:10.1111/sjos.12501.
Examples
library("cotram")
data("spiders", package = "cotram")
### for illustration only
OR <- 1 ### order of transformation function
### OR = 1 means log-linear, use OR ~ 6
M <- 100 ### number of Halton sequences, seem sufficient here
## fit conditional marginal count transformation models
## one for each species
## (don't test as it takes too long for CRAN)
m_PF <- cotram(Pardosa_ferruginea ~ Elevation + Canopy_openess,
data = spiders, method = "probit", order = OR)
m_HL <- cotram(Harpactea_lepida ~ Elevation + Canopy_openess,
data = spiders, method = "probit", order = OR)
m_CC <- cotram(Callobius_claustrarius ~ Elevation + Canopy_openess,
data = spiders, method = "probit", order = OR)
m_CT <- cotram(Coelotes_terrestris ~ Elevation + Canopy_openess,
data = spiders, method = "probit", order = OR)
m_PL <- cotram(Pardosa_lugubris ~ Elevation + Canopy_openess,
data = spiders, method = "probit", order = OR)
m_PR <- cotram(Pardosa_riparia ~ Elevation + Canopy_openess,
data = spiders, method = "probit", order = OR)
### fit dependence parameters
mm <- mcotram(m_PF, m_HL, m_CC, m_CT, m_PL, m_PR, data = spiders,
M = M, scale = TRUE)
logLik(mm)
### Kendall's tau: Dependence of species after accounting
### for elevation and canopy openess in marginal models
coef(mm, type = "Kendall")
### regress dependencies on elevation and canopy openess
mmc <- mcotram(m_PF, m_HL, m_CC, m_CT, m_PL, m_PR, data = spiders,
formula = ~ Elevation + Canopy_openess, M = M, scale = TRUE)
logLik(mmc)
### weak evidence for such effects
pchisq(2 * (logLik(mmc) - logLik(mm)), df = 30, lower.tail = FALSE)
### plot Kendall's tau for different elevations / openess levels
nd <- expand.grid(Elevation = 80:120 * 10, Canopy_openess = 1:10 * 10)
KD <- Lower_tri(coef(mmc, newdata = nd, type = "Kendall"))
f <- factor(rownames(KD))
nd <- cbind(f = rep(f, nrow(nd)), nd[rep(1:nrow(nd), each = nlevels(f)),])
nd$KD <- c(KD)
if (require("lattice")) {
contourplot(KD ~ Elevation + Canopy_openess | f, data = nd,
cuts = 18, xlab = "Elevation", ylab = "Canopy openess")
}
### for example:
### => constant negative dependence of Pardosa_lugubris and Coelotes_terrestris
### => weak dependence of Harpactea_lepida and Pardosa_ferruginea
### for low elevations, negative dependence increasing with elevation