70 lines
2.9 KiB
R
70 lines
2.9 KiB
R
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### Method Compair plots.
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for (file in list.files('results', pattern = 'method_compair_[0-9T-]+\\.csv')) {
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# Read simulation result data set.
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ds <- read.csv(file.path('results', file), stringsAsFactors = FALSE)
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# Get dataset groups
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datasets <- unlist(unique(ds["dataset"]))
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# All seperate dataset plots into one file.
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pdf(file.path('results', sub('.csv', '.pdf', file, fixed = TRUE)))
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for (ds.name in datasets) {
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boxplot(ds[ds["dataset"] == ds.name, colnames(ds) != "dataset"],
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main = ds.name, ylab = 'Error', las = 2)
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}
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dev.off()
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# Plot each dataset seperately.
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for (ds.name in datasets) {
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pdf(file.path('results', sub('.csv',
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paste0('_', ds.name, '.pdf'),
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file, fixed = TRUE)))
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boxplot(ds[ds["dataset"] == ds.name, colnames(ds) != "dataset"],
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main = ds.name, ylab = 'Error', las = 2)
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dev.off()
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}
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}
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### Method Compair plots for parameterized M2.
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for (file in list.files('results', 'method_compair_M2_[0-9T-]+\\.csv')) {
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# Read simulation results.
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ds <- read.csv(file.path('results', file), stringsAsFactors = FALSE)
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# Extract M2 dataset parameters.
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pmix <- unique(ds$pmix)
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lambda <- unique(ds$lambda)
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# setup pdf file and plot layout.
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pdf(file.path('results', sub('.csv', '.pdf', file, fixed = TRUE)),
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width = 15, height = 10)
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layout(matrix(seq(length(pmix) * length(lambda)),
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ncol = length(lambda), byrow = TRUE))
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for (p in pmix) {
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for (l in lambda) {
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boxplot(ds[ds$pmix == p & ds$lambda == l,
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!(colnames(ds) %in% c("pmix", "lambda"))],
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main = substitute(expression(paste(
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lambda, ' = ', l, ', ', p[mix], ' = ', q
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)), list(l = l, q = p)))
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}
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}
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dev.off()
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}
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### Analyse predict dimension simulation.
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for (file in list.files('results', 'predict_dim_compair_[0-9T-]+\\.csv')) {
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# Read simulation result data set.
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ds <- read.csv(file.path('results', file), stringsAsFactors = FALSE)
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# Over all datasets (including M2 with default parameters)
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true.dim <- ds$true.dim
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dataset <- ds$dataset
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df <- ds[!(colnames(ds) %in% c("true.dim", "dataset"))]
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accuracy <- data.frame(
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all = colMeans(df == true.dim),
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M1 = colMeans(df[dataset == "M1", ] == true.dim[dataset == "M1"]),
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M2 = colMeans(df[dataset == "M2", ] == true.dim[dataset == "M2"]),
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M3 = colMeans(df[dataset == "M3", ] == true.dim[dataset == "M3"]),
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M4 = colMeans(df[dataset == "M4", ] == true.dim[dataset == "M4"]),
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M5 = colMeans(df[dataset == "M5", ] == true.dim[dataset == "M5"]),
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M6 = colMeans(df[dataset == "M6", ] == true.dim[dataset == "M6"]),
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M7 = colMeans(df[dataset == "M7", ] == true.dim[dataset == "M7"])
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)
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cat("Accuracy of simulation:", file, '\n')
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print(accuracy)
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}
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