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Last update: 21.10.2020

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


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\

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.


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


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


Note: Apart from these packages we stronly recommend to download and install 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:



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


# 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")

<p align="center">
<img src="./man/figures/mbl.png" width="80%">
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:

* __Reference (training) set__: Dataset with *n* reference samples (e.g. spectral 
library) to be used in the calibration of spectral models. Xr represents the 
matrix of samples (containing the spectral predictor variables) and Yr represents 
a response variable corresponding to Xr.

* __Prediction set__ : Data set with _m_ samples where the response variable (Yu) 
is unknown. However it can be predicted by applying a spectral model 
(calibrated by using Xr and Yr) on the spectra of these samples (Xu). 

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.

<p align="center">
<img src="./vignettes/MBL.gif" width="50%">
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") ```


Other R'elated stuff

Bug report and development version

You can send an e-mail to the package maintainer ( or create an issue on github.


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.

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resemble documentation built on Nov. 9, 2020, 5:08 p.m.