resemble

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Last update: 21.10.2020

Think Globally, Fit Locally (Saul and Roweis, 2003)

About

The resemble package provides high-performing functionality for data-driven modeling (including local modeling), nearest neighbor search and orthogonal projections in spectral data.

Check the package vignette(s)!

Core functionality

The core functionality of the package can be summarized into the following functions:

mbl: implements memory-based learning (MBL) for modeling and predicting continuous response variables. For example, it can be used to reproduce the famous LOCAL algorithm proposed by Shenk et al. (1997). In general, this function allows you to easily customize your own MBL regression-prediction method.

dissimilarity: Computes dissimilarity matrices based on various methods (e.g. Euclidean, Mahalanobis, cosine, correlation, moving correlation, Spectral information divergence, principal components dissimilarity and partial least squares dissimilarity).

ortho_projection: A function for dimensionality reduction using either principal component analysis or partial least squares (a.k.a projection to latent structures).

search_neighbors: A function to efficiently retrieve from a reference set the k-nearest neighbors of another given data set.

New version

During the recent lockdown we invested some of our free time to come up with a new version of our package. This new resemble 2.0 comes with MAJOR improvements and new functions! For these improvements major changes were required. The most evident changes are in the function and argument names. These have been now adapted to properly follow the tydiverse style guide. A number of changes have been implemented for the sake of computational efficiency. These changes are documented in inst\changes.md.

New interesing functions and fucntionality are also available, for example, the mbl() function now allows sample spiking, where a set of reference observations can be forced to be included in the neighborhhoods of each sample to be predicted. The serach_neighbors() function efficiently retrieves from a refence set the k-nearest neighbors of another given data set. The dissimilarity() function computes dissimilarity matrices based on various metrics.

Installation

If you want to install the package and try its functionality, it is very simple, just type the following line in your R console:

install.packages('resemble')

If you do not have the following packages installed, it might be good to update/install them first

install.packages('Rcpp')
install.packages('RcppArmadillo')
install.packages('foreach')
install.packages('iterators')

Note: Apart from these packages we stronly recommend to download and install Rtools https://cran.r-project.org/bin/windows/Rtools/). This is important for obtaining the proper C++ toolchain that might be needed for resemble.

Then, install resemble

You can also install the development version of resemble directly from github using devtools:

devtools::install_github("l-ramirez-lopez/resemble")

Example

After installing resemble you should be also able to run the following lines:

library(resemble)
library(tidyr)
library(prospectr)
data(NIRsoil)

# Proprocess the data
NIRsoil <- NIRsoil[NIRsoil$CEC %>% complete.cases(),]
wavs <- as.numeric(colnames(NIRsoil$spc))

NIRsoil$spc_p <- NIRsoil$spc %>% 
  standardNormalVariate() %>% 
  resample(wavs, seq(min(wavs), max(wavs), by = 11)) %>% 
  savitzkyGolay(p = 1, w = 5, m = 1)

# split into calibration/training and test
train_x <- NIRsoil$spc_p[as.logical(NIRsoil$train), ]
train_y <- NIRsoil$CEC[as.logical(NIRsoil$train)]

test_x <- NIRsoil$spc_p[!as.logical(NIRsoil$train), ]
test_y <- NIRsoil$CEC[!as.logical(NIRsoil$train)]

# Use MBL as in Ramirez-Lopez et al. (2013)
sbl <- mbl(
  Xr = train_x, Yr = train_y, Xu = test_x,
  k = seq(50, 130, by = 20),
  method = local_fit_gpr(),
  control = mbl_control(validation_type = "NNv")
)
sbl
plot(sbl)
get_predictions(sbl)

Figure 1. Standard plot of the results of the mbl function.

resemble implements functions dedicated to non-linear modelling of complex visible and infrared spectral data based on memory-based learning (MBL, a.k.a instance-based learning or local modelling in the chemometrics literature). The package also includes functions for: computing and evaluate spectral dissimilarity matrices, projecting the spectra onto low dimensional orthogonal variables, spectral neighbor search, etc.

Memory-based learning (MBL)

To expand a bit more the explanation on the mbl function, let’s define first the basic input data:

To predict each value in Yu, the mbl function takes each sample in Xu and searches in Xr for its k-nearest neighbours (most spectrally similar samples). Then a (local) model is calibrated with these (reference) neighbours and it immediately predicts the correspondent value in Yu from Xu. In the function, the k-nearest neighbour search is performed by computing spectral dissimilarity matrices between observations. The mbl function offers the following regression options for calibrating the (local) models:

'gpr': Gaussian process with linear kernel.

'pls': Partial least squares.

'wapls': Weighted average partial least squares (Shenk et al., 1997).

Figure 2 illustrates the basic steps in MBL for a set of five observations.

Figure 2. Example of the main steps in memory-based learning for predicting a response variable in five different observations based on set of p-dimesnional variables.

Citing the package

Simply type and you will get the info you need:

citation(package = "resemble")

News

Bug report and development version

You can send an e-mail to the package maintainer (ramirez.lopez.leo@gmail.com) or create an issue on github.

References

Lobsey, C. R., Viscarra Rossel, R. A., Roudier, P., & Hedley, C. B. 2017. rs-local data-mines information from spectral libraries to improve local calibrations. European Journal of Soil Science, 68(6), 840-852.

Ramirez-Lopez, L., Behrens, T., Schmidt, K., Stevens, A., Dematte, J.A.M., Scholten, T. 2013. The spectrum-based learner: A new local approach for modeling soil vis-NIR spectra of complex data sets. Geoderma 195-196, 268-279.

Saul, L. K., & Roweis, S. T. 2003. Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of machine learning research, 4(Jun), 119-155.

Shenk, J., Westerhaus, M., and Berzaghi, P. 1997. Investigation of a LOCAL calibration procedure for near infrared instruments. Journal of Near Infrared Spectroscopy, 5, 223-232.