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72 lines
2.9 KiB
R
72 lines
2.9 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/predict.R
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\name{predict.cube}
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\alias{predict.cube}
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\title{Model prediction}
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\usage{
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\method{predict}{cube}(object, model, ..., output_names = c("pred"), keep_bands = FALSE)
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}
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\arguments{
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\item{object}{a data cube proxy object (class cube)}
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\item{model}{model used for prediction (e.g. from \code{caret} or \code{tidymodels})}
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\item{...}{further arguments passed to the model-specific predict method}
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\item{output_names}{optional character vector for output variable(s)}
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\item{keep_bands}{logical; keep bands of input data cube, defaults to FALSE, i.e. original bands will be dropped}
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}
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\description{
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Apply a trained model on all pixels of a data cube.
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}
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\details{
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The model-specific predict method will be automatically chosen based on the class of the provided model. It aims at supporting
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models from the packages \code{tidymodels}, \code{caret}, and simple models as from \code{lm} or \code{glm}.
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For multiple output variables or output in form of lists or data.frames, \code{output_names} must be provided and match
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names of the columns / items of the result object returned from the underlying predict method. For example,
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predictions using \code{tidymodels} return a tibble (data.frame) with columns like \code{.pred_class} (classification case).
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This must be explicitly provided as \code{output_names}. Similarly, \code{predict.lm} and the like return lists
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if the standard error is requested by the user and \code{output_names} hence should be set to \code{c("fit","se.fit")}.
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For more complex cases or when predict expects something else than a \code{data.frame}, this function may not work at all.
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}
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\note{
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This function returns a proxy object, i.e., it will not immediately start any computations.
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}
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\examples{
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\donttest{
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# create image collection from example Landsat data only
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# if not already done in other examples
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if (!file.exists(file.path(tempdir(), "L8.db"))) {
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L8_files <- list.files(system.file("L8NY18", package = "gdalcubes"),
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".TIF", recursive = TRUE, full.names = TRUE)
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create_image_collection(L8_files, "L8_L1TP", file.path(tempdir(), "L8.db"), quiet = TRUE)
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}
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v = cube_view(extent=list(left=388941.2, right=766552.4,
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bottom=4345299, top=4744931, t0="2018-04", t1="2018-06"),
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srs="EPSG:32618", nx = 497, ny=526, dt="P3M")
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L8.col = image_collection(file.path(tempdir(), "L8.db"))
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x = sf::st_read(system.file("ny_samples.gpkg", package = "gdalcubes"))
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raster_cube(L8.col, v) |>
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select_bands(c("B02","B03","B04","B05")) |>
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extract_geom(x) -> train
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x$FID = rownames(x)
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train = merge(train, x, by = "FID")
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train$iswater = as.factor(train$class == "water")
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log_model <- glm(iswater ~ B02 + B03 + B04 + B05, data = train, family = "binomial")
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raster_cube(L8.col, v) |>
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select_bands(c("B02","B03","B04","B05")) |>
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predict(model=log_model, type="response") |>
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plot(key.pos=1)
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}
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}
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