get_sim_data: get_sim_data

In mrecos/DistRegLMERR: Kernel Logistic Regression with Focal Mean Embeddings

get_sim_data

Description

'get_sim_data()' is a function to simulate archaeological site data for use in this package. Beacuse archaeological site data is often a protected class of data or because this package requires data in a particular format; this function helps to test run models without neededing real data.

Usage

get_sim_data(
  site_samples,
  N_site_bags,
  sites_var1_mean = 50,
  sites_var1_sd = 10,
  sites_var2_mean = 3,
  sites_var2_sd = 2,
  backg_var1_mean = 100,
  backg_var1_sd = 20,
  backg_var2_mean = 6,
  backg_var2_sd = 3,
  background_site_balance = 1
)

Arguments

site_samples	- [scaler] Number of sites to include in simulated data
N_site_bags	- [scaler] Number of background groups to use in simulated data
sites_var1_mean	- [scaler] mean of variable 1 for sites
sites_var1_sd	- [scaler] sd of variable 1 for sites
sites_var2_mean	- [scaler] mean of variable 2 for sites
sites_var2_sd	- [scaler] sd of variable 2 for sites
backg_var1_mean	- [scaler] mean of variable 1 for background
backg_var1_sd	- [scaler] sd of variable 1 for background
backg_var2_mean	- [scaler] mean of variable 2 for background
backg_var2_sd	- [scaler] sd of variable 2 for background
background_site_balance	- [scaler] Ratio of site to bacground groups

Details

This function requires only the setting of parameters and does not rely on input data. The function generated as data.frame of with four columns including 'presence' as a 1/0 variable for site presence/absence, 'SITENO' that is a group level label for each site (presence == 1) and for each collection of background data points (presence == 0), and 'var1' and 'var2' that are the simulated environmental variables used as regression features. The number of sites and background samples to generate is controlled by the 'site_samples' and 'N_site_bags' arguments respectively. The 'background_site_balance' controls the ratio of site to background groups in the output data.frame. The remaining arguments are used to control how similar the two simulated environmental features are within and between site presence/absence groups. The features are simulated from a normal distribution and can be controlled by adjusting the 'mean' and 'sd' (standard deviation) arguments for each. Similar distribution parameters between site and background approximate a more homogeneous environment where sites are found on less distinct landforms. Alternatively, greater difference in the distribution parameters between sites and background approximate an environment where sites are found on distinct landforms. Adjusting this can give insight into how the model works for a range of proxy environments.

Value

- data.frame of simulated training and test set data

Examples

## Not run: 
sim_data <- get_sim_data(site_samples = 800, N_site_bags = 75,
sites_var1_mean = 80, sites_var1_sd   = 10,
sites_var2_mean = 5,  sites_var2_sd   = 2,
backg_var1_mean = 100,backg_var1_sd   = 20,
backg_var2_mean = 6,  backg_var2_sd   = 3)
formatted_data <- format_site_data(sim_data, N_sites=10, train_test_split=0.8,
                                   sample_fraction = 0.9, background_site_balance=1)
train_data <- formatted_data[["train_data"]]
train_presence <- formatted_data[["train_presence"]]
test_presence <- formatted_data[["test_presence"]]

##### Logistic Mean Embedding KLR Model
#### Build Kernel Matrix
K <- build_K(train_data, sigma = sigma, dist_metric = dist_metric)
#### Train
train_log_pred <- KLR(K, train_presence, lambda, 100, 0.001, verbose = 2)
#### Predict
test_log_pred <- KLR_predict(test_data, train_data, dist_metric = dist_metric,
                            train_log_pred[["alphas"]], sigma)

## End(Not run)

mrecos/DistRegLMERR documentation built on April 9, 2022, 5:10 p.m.