model_opt_r.Rd 1.69 KB
 Romulo Pereira Goncalves committed Jan 25, 2021 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 % Generated by roxygen2: do not edit by hand % Please edit documentation in R/model_opt.r \name{model_opt_r} \alias{model_opt_r} \title{Perform Habitat Sampling and Probability Mapping} \usage{ model_opt_r( k, raster, sample_type, buffer, model, area, seed, n, sample_size, n_channel, seed2, mtry, pbtn1, pbtn2, ras_vx, max_samples_per_class ) } \arguments{ \item{k}{Iteration value for the models.} \item{raster}{satellite time series stack (rasterBrickObject) or just any type of image (*rasterObject)} \item{sample_type}{distribution of spatial locations c("random","regular")} \item{buffer}{distance (in m) for new sample collection around initial samples (depends on pixel size)} \item{model}{which machine learning classifier to use c("rf", "svm") for random forest or support vector machine implementation} \item{area}{extent where the the classification is happening} \item{seed}{set seed for reproducible results} \item{n}{number of iterations for model accuracy} \item{sample_size}{number of spatial locations} \item{n_channel}{number of channels} \item{seed2}{spatial points sample} \item{mtry}{number of predictor used at random forest splitting nodes (mtry << n predictors)} \item{pbtn1}{matrix for points} \item{pbtn2}{matrix for points} \item{ras_vx}{velox raster} \item{max_samples_per_class}{maximum number of samples per class} } \value{ a list with 4 elements: \enumerate{ \item k To identify the used model \cr \item model The model - mmax \cr \item points_list List of points used part of the sample. \cr \item oobe The accuracy achieved by the model \cr } } \description{ This function finds the best model (mmax) for a set of sampled points. } \keyword{internal}