pcops: Profile COPS Function (aka COPS Variant 2)

Description Usage Arguments Value Examples

View source: R/cops.R

Description

Metaparameter selection for MDS models baseed on the Profile COPS approach (COPS Variant 2). It uses copstress for hyperparameter selection. It is a special case of a STOPS model.

Usage

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pcops(
  dis,
  loss = c("stress", "smacofSym", "smacofSphere", "strain", "sammon", "rstress",
    "powermds", "sstress", "elastic", "powersammon", "powerelastic", "powerstress",
    "sammon2", "powerstrain", "apstress", "rpowerstress"),
  weightmat = NULL,
  ndim = 2,
  init = NULL,
  theta = c(1, 1, 1),
  stressweight = 1,
  cordweight,
  q = 2,
  minpts = ndim + 1,
  epsilon = 100,
  rang,
  optimmethod = c("ALJ", "pso", "SANN", "DIRECT", "DIRECTL", "stogo", "MADS", "hjk"),
  lower = c(1, 1, 0.5),
  upper = c(5, 5, 2),
  verbose = 0,
  scale = c("proc", "sd", "none", "std"),
  normed = TRUE,
  s = 4,
  stresstype = "default",
  acc = 1e-07,
  itmaxo = 200,
  itmaxi = 10000,
  ...
)

Arguments

dis

numeric matrix or dist object of a matrix of proximities

loss

which loss function to be used for fitting, defaults to strain. Currently allows for the following models:

  • Power transformations of observed proximities only: Strain loss or classical scaling (strain, workhorse is cmdscale), Kruskall's stress for symmetric matrices (smacofSym or stress and smacofSphere for scaling onto a sphere; workhorse is smacof), Sammon mapping (sammon or sammon2; for the earlier the workhorse is sammon from MASS for the latter it is smacof), elastic scaling (elastic, the workhorse is smacof), Takane et al's S-Stress sstress (workhorse is powerstressMin)

  • Power transformations of fitted distances only: De Leeuw's r-stress rstress (workhorse is powerstressMin)

  • Power transformations of fitted distances and observed proximities: Powermds (powermds), Sammon mapping/elastic scaling with powers (powersammon, powerelastic ), powerstress (POST-MDS, powerstress), restricted powerstress with equal transformations for delta and d (rpowerstress); workhorse is powerstressMin)

  • Approximation to power stress: Approximated power stress (apstress; workhorse is smacof)

weightmat

(optional) a matrix of nonnegative weights; defaults to 1 for all off diagonals

ndim

number of dimensions of the target space

init

(optional) initial configuration. If not supplied, the Torgerson scaling result of the dissimilarity matrix dis^theta[2]/enorm(dis^theta[2],weightmat) is used.

theta

the theta vector of powers; the first is kappa (for the fitted distances if it exists), the second lambda (for the observed proximities if it exist), the third is nu (for the weights if it exists). If a scalar is given as argument, it will take the role designated by the loss argument (typially recycled). Defaults to 1 1 1

stressweight

weight to be used for the fit measure; defaults to 1

cordweight

weight to be used for the cordillera; if missing gets estimated from the initial configuration so that copstress = 0 for theta=c(1,1)

q

the norm of the cordillera; defaults to 1

minpts

the minimum points to make up a cluster in OPTICS; defaults to ndim+1

epsilon

the epsilon parameter of OPTICS, the neighbourhood that is checked; defaults to 10

rang

range of the minimum reachabilities to be considered. If missing it is found from the initial configuration by taking 1.5 times the maximal minimum reachability of the model with theta=c(1,1). If NULL it will be normed to each configuration's minimum and maximum distance, so an absolute value of goodness-of-clusteredness. Note that the latter is not necessarily desirable when comparing configurations for their relative clusteredness. See also cordillera.

optimmethod

What general purpose optimizer to use? Defaults to our adaptive LJ version (ALJ). Also allows particle swarm optimization with s particles ("pso") and simulated annealing ("SANN"), "DIRECT" and "DIRECTL", Hooke-Jeeves ("hjk"), StoGo ("stogo"), and "MADS". We recommend not using SANN and pso with the rstress, sstress and the power stress models. We amde good experiences with ALJ, stogo, DIRECT and DIRECTL and also MADS.

lower

The lower contraints of the search region

upper

The upper contraints of the search region

verbose

numeric value hat prints information on the fitting process; >2 is extremely verbose. Note that for models with some parameters fixed, the iteration progress of the optimizer shows different values also for the fixed parameters because due to the modular setup we always optimize over a three parameter vector. These values are inconsequential however as internally they will be fixed.

scale

should the configuration be scaled and/or centered for calculating the cordillera? "std" standardizes each column of the configurations to mean=0 and sd=1 (typically not a good idea), "sd" scales the configuration by the maximum standard devation of any column (default), "proc" adjusts the fitted configuration to the init configuration (or the Togerson scaling solution if init=NULL). This parameter only has an effect for calculating the cordillera, the fitted and returned configuration is NOT scaled.

normed

should the cordillera be normed; defaults to TRUE

s

number of particles if pso is used

stresstype

what stress to be used for comparisons between solutions. Currently not implemented and pcops uses explicitly normalized stress for copstress (not stress-1). Stress-1 is reported by the print function though.

acc

termination threshold difference of two successive outer minimization steps.

itmaxo

iterations of the outer step (optimization over the hyperparmeters; if solver allows it). Defaults to 200.

itmaxi

iterations of the inner step (optimization of the MDS). Defaults to 10000 (whichis huge).

...

additional arguments to be passed to the optimization procedure

Value

A list with the components

Examples

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dis<-as.matrix(smacof::kinshipdelta)
set.seed(210485)
#configuration is scaled with highest column sd for calculating cordilera 
res1<-pcops(dis,loss="strain",lower=0.1,upper=5,minpts=2) 
res1
summary(res1)
plot(res1)

cops documentation built on March 24, 2021, 1:06 a.m.