knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  echo = FALSE,
  comment = "#>",
  fig.path = "../figures/"
)

library(klrfome)

Introduction

Harris, Matthew D., (2017). klrfome - Kernel Logistic Regression with Focal Mean Embeddings. Accessed 10 Sep 2017. Online at https://doi.org/10.5281/zenodo.888409

Background

library("dplyr")
library("corrplot")
library("ggplot2")

#Parameters
set.seed(1337)
sigma = 0.5
lambda = 0.1
dist_metric = "euclidean"

### Simulate Training Data
sim_data <- get_sim_data(site_samples = 800, N_site_bags = 75)
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_data <- formatted_data[["test_data"]]
test_presence <- formatted_data[["test_presence"]]

##### Logistic Mean Embedding KRR Model
#### Build Kernel Matrix
K <- build_K(train_data, sigma = sigma, dist_metric = dist_metric, progress = FALSE)
#### Train
train_log_pred <- KLR(K, train_presence, lambda, 100, 0.01)
#### Predict
test_log_pred <- KLR_predict(test_data, train_data, dist_metric = dist_metric,
                             train_log_pred[["alphas"]], sigma, progress = FALSE)

### Plot K Matrix
K_corrplot(K,train_data,clusters=4)

### Plot Prediction
predicted_log <- data.frame(pred = test_log_pred, obs = test_presence)
ggplot(predicted_log, aes(x = as.factor(obs), y = pred, color = as.factor(obs))) +
  geom_jitter(width = 0.1) +
  theme_bw() +
  ylim(c(0,1)) +
  labs(y = "Predicted Probability", x = "Site Presence",
       title = "Kernel Logistic Regression",
       subtitle = "test set predictions; simulated data") +
  theme(
    legend.position = "none"
  )

Methods

Results

# Note the path that we need to use to access our data files when rendering this document
my_data <- readr::read_csv("../data/raw_data/my_csv_file.csv")

Discussion

Conclusion

Acknowledgements

References

Colophon

This report was generated on r Sys.time() using the following computational environment and dependencies:

# which R packages and versions?
devtools::session_info()

The current Git commit details are:

# what commit is this file at? You may need to change the path value
# if your Rmd is not in analysis/paper/
git2r::repository("../..")


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