dissimilarity: Dissimilarity computation between matrices

In resemble: Memory-Based Learning in Spectral Chemometrics

Dissimilarity computation between matrices

Description

\loadmathjax

This is a wrapper to integrate the different dissimilarity functions of the offered by package.It computes the dissimilarities between observations in numerical matrices by using an specifed dissmilarity measure.

Usage

dissimilarity(Xr, Xu = NULL,
              diss_method = c("pca", "pca.nipals", "pls", "mpls",
                              "cor", "euclid", "cosine", "sid"),
              Yr = NULL, gh = FALSE, pc_selection = list("var", 0.01),
              return_projection = FALSE, ws = NULL,
              center = TRUE, scale = FALSE, documentation = character(),
              ...)

Arguments

Xr	a matrix of containing n observations/rows and p variables/columns.
Xu	an optional matrix containing data of a second set of observations with p variables/columns.
diss_method	a character string indicating the method to be used to compute the dissimilarities between observations. Options are: "pca": Mahalanobis distance computed on the matrix of scores of a Principal Component (PC) projection of Xr (and Xu if provided). PC projection is done using the singular value decomposition (SVD) algorithm. See ortho_diss function. "pca.nipals": Mahalanobis distance computed on the matrix of scores of a Principal Component (PC) projection of Xr (and Xu if provided). PC projection is done using the non-linear iterative partial least squares (nipals) algorithm. See ortho_diss function. "pls": Mahalanobis distance computed on the matrix of scores of a partial least squares projection of Xr (and Xu if provided). In this case, Yr is always required. See ortho_diss function. "mpls": Mahalanobis distance computed on the matrix of scores of a modified partial least squares projection (Shenk and Westerhaus, 1991; Westerhaus, 2014) of Xr (and Xu if provided). In this case, Yr is always required. See ortho_diss function. "cor": based on the correlation coefficient between observations. See cor_diss function. "euclid": Euclidean distance between observations. See f_diss function. "cosine": Cosine distance between observations. See f_diss function. "sid": spectral information divergence between observations. See sid function.
Yr	a numeric matrix of n observations used as side information of Xr for the ortho_diss methods (i.e. pca, pca.nipals or pls). It is required when: diss_method = "pls" diss_method = "pca" with "opc" used as the method in the pc_selection argument. See ortho_diss. gh = TRUE
gh	a logical indicating if the Mahalanobis distance (in the pls score space) between each observation and the pls centre/mean must be computed.
pc_selection	a list of length 2 to be passed onto the ortho_diss methods. It is required if the method selected in diss_method is any of "pca", "pca.nipals" or "pls" or if gh = TRUE. This argument is used for optimizing the number of components (principal components or pls factors) to be retained. This list must contain two elements in the following order: method (a character indicating the method for selecting the number of components) and value (a numerical value that complements the selected method). The methods available are: "opc": optimized principal component selection based on Ramirez-Lopez et al. (2013a, 2013b). The optimal number of components (of set of observations) is the one for which its distance matrix minimizes the differences between the Yr value of each observation and the Yr value of its closest observation. In this case value must be a value ((larger than 0 and below the minimum dimension of Xr or Xr and Xu combined) indicating the maximum number of principal components to be tested. See the ortho_projection function for more details. "cumvar": selection of the principal components based on a given cumulative amount of explained variance. In this case, value must be a value (larger than 0 and below or equal to 1) indicating the minimum amount of cumulative variance that the combination of retained components should explain. "var": selection of the principal components based on a given amount of explained variance. In this case, value must be a value (larger than 0 and below or equal to 1) indicating the minimum amount of variance that a single component should explain in order to be retained. "manual": for manually specifying a fix number of principal components. In this case, value must be a value (larger than 0 and below the minimum dimension of Xr or Xr and Xu combined). indicating the minimum amount of variance that a component should explain in order to be retained. The default is list(method = "var", value = 0.01). Optionally, the pc_selection argument admits "opc" or "cumvar" or "var" or "manual" as a single character string. In such a case the default "value" when either "opc" or "manual" are used is 40. When "cumvar" is used the default "value" is set to 0.99 and when "var" is used, the default "value" is set to 0.01.
return_projection	a logical indicating if the projection(s) must be returned. Projections are used if the ortho_diss methods are called (i.e. diss_method = "pca", diss_method = "pca.nipals" or diss_method = "pls") or when gh = TRUE. In case gh = TRUE and a ortho_diss method is used (in the diss_method argument), both projections are returned.
ws	an odd integer value which specifies the window size, when diss_method = "cor" (cor_diss method) for moving correlation dissimilarity. If ws = NULL (default), then the window size will be equal to the number of variables (columns), i.e. instead moving correlation, the normal correlation will be used. See cor_diss function.
center	a logical indicating if Xr (and Xu if provided) must be centered. If Xu is provided the data is centered around the mean of the pooled Xr and Xu matrices (\mjeqnXr \cup XuXr U Xu). For dissimilarity computations based on diss_method = pls, the data is always centered.
scale	a logical indicating if Xr (and Xu if provided) must be scaled. If Xu is provided the data is scaled based on the standard deviation of the the pooled Xr and Xu matrices (\mjeqnXr \cup XuXr U Xu). If center = TRUE, scaling is applied after centering.
documentation	an optional character string that can be used to describe anything related to the mbl call (e.g. description of the input data). Default: character(). NOTE: his is an experimental argument.
...	other arguments passed to the dissimilarity functions (ortho_diss, cor_diss, f_diss or sid).

Details

This function is a wrapper for ortho_diss, cor_diss, f_diss, sid. Check the documentation of these functions for further details.

Value

A list with the following components:

• dissimilarity: the resulting dissimilarity matrix.

• projection: an ortho_projection object. Only output if return_projection = TRUE and if diss_method = "pca", diss_method = "pca.nipals", diss_method = "pls" or diss_method = "mpls". This object contains the projection used to compute the dissimilarity matrix. In case of local dissimilarity matrices, the projection corresponds to the global projection used to select the neighborhoods (see ortho_diss function for further details).

• gh: a list containing the GH distances as well as the pls projection used to compute the GH.

Author(s)

Leonardo Ramirez-Lopez

References

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.

Westerhaus, M. 2014. Eastern Analytical Symposium Award for outstanding Wachievements in near infrared spectroscopy: my contributions to Wnear infrared spectroscopy. NIR news, 25(8), 16-20.

See Also

ortho_diss cor_diss f_diss sid.

Examples

library(prospectr)
data(NIRsoil)

# Filter the data using the first derivative with Savitzky and Golay
# smoothing filter and a window size of 11 spectral variables and a
# polynomial order of 4
sg <- savitzkyGolay(NIRsoil$spc, m = 1, p = 4, w = 15)

# Replace the original spectra with the filtered ones
NIRsoil$spc <- sg

Xu <- NIRsoil$spc[!as.logical(NIRsoil$train), ]
Yu <- NIRsoil$CEC[!as.logical(NIRsoil$train)]

Yr <- NIRsoil$CEC[as.logical(NIRsoil$train)]
Xr <- NIRsoil$spc[as.logical(NIRsoil$train), ]

Xu <- Xu[!is.na(Yu), ]
Xr <- Xr[!is.na(Yr), ]

Yu <- Yu[!is.na(Yu)]
Yr <- Yr[!is.na(Yr)]

dsm_pca <- dissimilarity(
  Xr = Xr, Xu = Xu,
  diss_method = c("pca"),
  Yr = Yr, gh = TRUE,
  pc_selection = list("opc", 30),
  return_projection = TRUE
)

resemble documentation built on April 21, 2023, 1:13 a.m.