latentcor is an R package for estimation of
latent correlations with mixed data types (continuous, binary,
truncated, and ternary) under the latent Gaussian copula model. For
references on the estimation framework, see
Fan, J., Liu, H., Ning, Y., and Zou, H. (2017), “High Dimensional Semiparametric Latent Graphical Model for Mixed Data.” JRSS B. Continuous/binary types.
Quan X., Booth J.G. and Wells M.T.”Rank-based approach for estimating correlations in mixed ordinal data.” arXiv Ternary type.
Yoon G., Carroll R.J. and Gaynanova I. (2020). “Sparse semiparametric canonical correlation analysis for data of mixed types”. Biometrika. Truncated type for zero-inflated data.
Yoon G., Müller C.L. and Gaynanova I. (2021). “Fast computation of latent correlations” JCGS. Approximation method of computation, see vignette for details.
No R software package is currently available that allows accurate and
fast correlation estimation from mixed variable data in a unifying
manner. The R package latentcor,
introduced here, thus represents the first stand-alone R package for
computation of latent correlation that takes into account all variable
types (continuous/binary/ordinal/zero-inflated), comes with an optimized
memory footprint, and is computationally efficient, essentially making
latent correlation estimation almost as fast as rank-based correlation
estimation.
Multi-linear interpolation: Earlier versions of
latentcor used multi-linear interpolation based on
functionality of R package chebpol
written by Simen Gaure. This functionality is needed for faster
computations of latent correlations with approximation method. However,
chebpol was removed from CRAN on 2022-02-07. The current
version of latentcor reuses the multi-linear interpolation
part of the chebpol (provided under Artistic-2 license)
integrated directly within latentcor. To cite multi-linear
interpolation only, please use original chebpol.
Accuracy: The approximation method for ternary/ternary, truncated(zero-inflated)/ternary, and ternary/binary cases are less accurate close to boundary (zero proportions) due to size limitations of CRAN packages on the pre-stored grid. If higher accuracy is desired and original method is computationally prohibitive, latencor is also available as Python package with Github development python version
To use latentcor, you need to install R. To enhance your
user experience, you may use some IDE for it (e.g. RStudio).
The development version of latentcor
is available on GitHub. You can download it with the help of the
devtools package in R as follow:
install.packages("devtools")
devtools::install_github("https://github.com/mingzehuang/latentcor", build_vignettes = TRUE)The stable release version latentcor
is available on CRAN. You can download it in R as
follow:
install.packages("latentcor")A simple example estimating latent correlation is shown below.
library(latentcor)
# Generate two variables of sample size 100
# The first variable is ternary (pi0 = 0.3, pi1 = 0.5, pi2 = 1-0.3-0.5 = 0.2)
# The second variable is continuous.
# No copula transformation is applied.
X = gen_data(n = 1000, types = c("ter", "con"), XP = list(c(0.3, .5), NA))$X
# Estimate latent correlation matrix with the original method
latentcor(X = X, types = c("ter", "con"), method = "original")$R
# Estimate latent correlation matrix with the approximation method
latentcor(X = X, types = c("ter", "con"))$R
# Speed improvement by approximation method compared with original method
library(microbenchmark)
microbenchmark(latentcor(X, types = c("ter", "con"), method = "original"),
latentcor(X, types = c("ter", "con")))
# Unit: milliseconds
# min lq mean median uq max neval
# 5.3444 5.8301 7.033555 6.06740 6.74975 20.8878 100
# 1.5049 1.6245 2.009371 1.73805 1.99820 5.0027 100
# This is run on Windows 10 with Intel(R) Core(TM) i5-4570 CPU @ 3.20GHz 3.20 GHz
# Heatmap for latent correlation matrix.
latentcor(X = X, types = c("ter", "con"), showplot = TRUE)$plotRAnother example with the mtcars dataset.
library(latentcor)
# Use build-in dataset mtcars
X = mtcars
# Check variable types for manual determination
apply(mtcars, 2, table)
# Or use built-in get_types function to get types suggestions
get_types(mtcars)
# Estimate latent correlation matrix with original method
latentcor(mtcars, types = c("con", "ter", "con", "con", "con", "con", "con", "bin",
"bin", "ter", "con"), method = "original")$R
# Estimate latent correlation matrix with approximation method
latentcor(mtcars, types = c("con", "ter", "con", "con", "con", "con", "con", "bin",
"bin", "ter", "con"))$R
# Speed improvement by approximation method compared with original method
library(microbenchmark)
microbenchmark(latentcor(mtcars, types = types, method = "original"),
latentcor(mtcars, types = types, method = "approx"))
# Unit: milliseconds
# min lq mean median uq max neval
# 201.9872 215.6438 225.30385 221.5364 226.58330 411.4940 100
# 71.8457 75.1681 82.42531 80.1688 84.77845 238.3793 100
# This is run on Windows 10 with Intel(R) Core(TM) i5-4570 CPU @ 3.20GHz 3.20 GHz
# Heatmap for latent correlation matrix with approximation method.
latentcor(mtcars, types = c("con", "ter", "con", "con", "con", "con", "con", "bin",
"bin", "ter", "con"), showplot = TRUE)$plotRInteractive heatmap see: interactive heatmap of latent correlations (approx) for mtcars
We thank Dr. Grace Yoon for providing implementation details of the
mixedCCA R
package.