generate_test_data: Generate test data
In mppalves/GSTools: Grass Tools

Description Usage Arguments Details Author(s) Examples

View source: R/generate_test_data.R

Function that utilizes the original dataset, the centers of the model to be analysed and weather information to create a test dataset that will be scaled and read to use in posteriror evaluations.

1 2	generate_test_data(input_data, center_mean, center_stddev, grid, weather_dataset, wt_f=NULL, scaled = T, soil_type = NULL, points=NULL)

`input_data`	Dataset in sprad format containing LSUs as columns and cell numbers as rows
`center_mean`	Center attributes from scaled training data of the model used in the parameter model. Will be used if scale is set to TRUE. Example: train_data <- scale(train_data) col_means_train_harvest <- attr(train_data, "scaled:center")
`center_stddev`	Scale attributes from scaled training data of the model. Will be used if scale is set to TRUE.
`grid`	dataset with grid cells information. Has to be the same size as the `input_data`
`weather_dataset`	Data set containing weather information.
`wt_f`	koppen geiger climate classification dataset created by the file: koppen_geiger_pre_processing.R
`scaled`	Boolean that to define wheater the data will be scalled or not.
`soil_type`	Dataset with information on soil type on cell level.
`points`	A list of values that will substitute the orinal columns with the number of LSUs in case a more fine LSU resolution is desired for finding the optminal LSU. Ex: points = as.list(seq(0,25,0.5))

If wt_t.Rdata is null all cells will be scalled and outputed, if wt_f is filled with the koppen geiger climate classification generated by the file: koppen_geiger_pre_processing.R. the EF an d ET climate zones will be removed from the analysis.

Marcos Alves mppalves@gmail.com

##Scalled test data
generate_test_data(map_harvest_real, col_means_train_harvest, col_stddevs_train_harvest, grid, weather_dataset, soil_type = soil)

##Unscaled test data
generate_test_data(map_harvest_real, col_means_train_harvest, col_stddevs_train_harvest, grid, weather_dataset, scaled = F, soil_type = soil)

##Unscaled test data with LSU points substitutes
generate_test_data(map_harvest_real, col_means_train_harvest, col_stddevs_train_harvest, grid, weather_dataset, scaled = F, soil_type = soil, points = as.list(seq(0,25,0.5)))