55 lines
1.8 KiB
R
55 lines
1.8 KiB
R
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# Comparison of dimension prediction methods including maves cross-validation
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# with meanMAVE as basis for reference.
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#
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# Results are written to a CSV file with a dataset column and its simulation nr.
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#
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# Note:
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# All methods are called via interface functions defined in
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# 'simulation_interfaces.R'. In addition this file provides a few convenience
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# functions.
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source('simulation_interfaces.R')
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# Number of simulations
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NR.SIM <- 100L
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NR.DS <- 7L
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max.dim <- 5L
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# List of dimension prediction methods.
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methods <- c("cv", "elbow", "wilcoxon")
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# Build result data matrix (repeat each dataset simulation times).
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result <- matrix(NA, nrow = NR.DS * NR.SIM,
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ncol = length(methods) + 2) # +2 for dataset and meanMAVE.
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result[, 1L] <- rep(paste0("M", seq(NR.DS)), NR.SIM)
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colnames(result) <- c("dataset", "true.dim", "mave.cv", methods)
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# Create a progress logger for length many reports.
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logger <- progress_logger(NR.DS * NR.SIM)
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for (sim in seq_len(nrow(result))) {
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# Create new dataset.
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with(dataset(result[sim, 1L]), { X <<- X; Y <<- Y; B <<- B })
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# Write true dr dimension.
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result[sim, "true.dim"] <- ncol(B)
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# Report simulation progress to user.
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logger('')
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# Call CVE and let each dimension prediction method estimate.
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dr <- cve.call(X = X, Y = Y, max.dim = max.dim)
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for (method in methods) {
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result[sim, method] <- predict_dim(dr, method = method)$k
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}
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# The same using meanMAVE and MAVE's 'mave.dim'.
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dr <- meanMAVE(X = X, Y = Y, max.dim = max.dim)
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result[sim, "mave.cv"] <- which.min(mave.dim(dr)$cv)
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# explicit call to the garbage collector.
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gc()
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}
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path <- paste0(getwd(),
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format(Sys.time(), '/results/predict_dim_compair_%Y-%m-%dT%H%M%S'))
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# Write entire simulation results into a single file.
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write.csv(result, file = paste0(path, ".csv"), row.names = FALSE)
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