select_parameters.mc {geocmeans} | R Documentation |
Function to select the parameters for a clustering algorithm. This version of the function allows to use a plan defined with the package future to reduce calculation time.
select_parameters.mc( algo, data, k, m, alpha = NA, beta = NA, nblistw = NULL, lag_method = "mean", spconsist = TRUE, classidx = TRUE, standardize = TRUE, maxiter = 500, tol = 0.01, seed = NULL, chunk_size = 100, verbose = FALSE ) selectParameters.mc( algo, data, k, m, alpha = NA, beta = NA, nblistw = NULL, lag_method = "mean", spconsist = TRUE, classidx = TRUE, standardize = TRUE, maxiter = 500, tol = 0.01, seed = NULL, chunk_size = 100, verbose = FALSE )
algo |
A string indicating which method to use (FCM, GFCM, SFCM, SGFCM) |
data |
A dataframe with numeric columns |
k |
A sequence of values for k to test (>=2) |
m |
A sequence of values for m to test |
alpha |
A sequence of values for alpha to test (NULL if not required) |
beta |
A sequence of values for beta to test (NULL if not required) |
nblistw |
A list of list.w objects describing the neighbours typically produced by the spdep package (NULL if not required) |
lag_method |
A string indicating if a classical lag must be used ("mean") or if a weighted median must be used ("median"). Both can be tested by specifying a vector : c("mean","median") |
spconsist |
A boolean indicating if the spatial consistency must be calculated |
classidx |
A boolean indicating if the quality of classification indices must be calculated |
standardize |
A boolean to specify if the variable must be centered and reduce (default = True) |
maxiter |
An integer for the maximum number of iteration |
tol |
The tolerance criterion used in the evaluateMatrices function for convergence assessment |
seed |
An integer used for random number generation. It ensures that the start centers will be the same if the same integer is selected. |
chunk_size |
The size of a chunk used for multiprocessing. Default is 100. |
verbose |
A boolean indicating if a progressbar should be displayed |
A dataframe with indicators assessing the quality of classifications
data(LyonIris) AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img", "TxChom1564","Pct_brevet","NivVieMed") dataset <- LyonIris@data[AnalysisFields] queen <- spdep::poly2nb(LyonIris,queen=TRUE) Wqueen <- spdep::nb2listw(queen,style="W") future::plan(future::multiprocess(workers=2)) #set spconsist to TRUE to calculate the spatial consistency indicator #FALSE here to reduce the time during package check values <- select_parameters.mc("SFCM", dataset, k = 5, m = seq(1,2.5,0.1), alpha = seq(0,2,0.1), nblistw = Wqueen, spconsist=FALSE) data(LyonIris) AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img", "TxChom1564","Pct_brevet","NivVieMed") dataset <- LyonIris@data[AnalysisFields] queen <- spdep::poly2nb(LyonIris,queen=TRUE) Wqueen <- spdep::nb2listw(queen,style="W") future::plan(future::multiprocess(workers=2)) #set spconsist to TRUE to calculate the spatial consistency indicator #FALSE here to reduce the time during package check values <- select_parameters.mc("SFCM", dataset, k = 5, m = seq(1,2.5,0.1), alpha = seq(0,2,0.1), nblistw = Wqueen, spconsist=FALSE)