R: Create plots visualizing the ML estimation process and...

plot_ML {emery}

R Documentation

Create plots visualizing the ML estimation process and results.

Description

plot_ML() is a general function for visualizing results generated by estimate_ML().

Usage

plot_ML(ML_est, params = NULL)

plot_ML_binary(
  ML_est,
  params = list(prev = NULL, se = NULL, sp = NULL, D = NULL)
)

plot_ML_ordinal(
  ML_est,
  params = list(pi_1_1 = NULL, phi_1ij_1 = NULL, phi_0ij_1 = NULL, D = NULL)
)

plot_ML_continuous(
  ML_est,
  params = list(prev_1 = NULL, mu_i1_1 = NULL, sigma_i1_1 = NULL, mu_i0_1 = NULL,
    sigma_i0_1 = NULL, D = NULL)
)

Arguments

`ML_est`	A MultiMethodMLEstimate class object
`params`	A list of population parameters. This is primarily used to evaluate results from a simulation where the target parameters are known, but can be used to visualize results with respect to some True value.

Value

A list of ggplot2 plots.

Binary:

`prev`	A plot showing how the prevalence estimate changes with each iteration of the EM algorithm
`se`	A plot showing how the sensitivity estimates of each method change with each iteration of the EM algorithm
`sp`	A plot showing how the specificity estimates of each method change with each iteration of the EM algorithm
`qk`	A plot showing how the q values for each observation k change over each iteration of the EM algorithm
`qk_hist`	A histogram of q values. Observations, k, can be colored by True state if it is passed by `params$D`.
`se_sp`	A plot showing the path the sensitivity and specificity estimates for each method follows during the EM algorithm. True sensitivity and specificity values can be passed by `params$se` and `params$sp`, respectively. This is useful for comparing algorithm results when applied to simulation data where True parameter values are known.

Ordinal:

`ROC`	The Receiver Operator Characteristic (ROC) curves estimated for each method
`q_k1`	A plot showing how the q values for each observation, k, change when d=1 over each iteration of the EM algorithm. Observations can be colored by True state if it is passed (`params$D`).
`q_k0`	A plot showing how the q values for each observation, k, change when d=0 over each iteration of the EM algorithm. Observations can be colored by True state if it is passed by `params$D`.
`q_k1_hist`	A histogram of q_1 values. Observations, k, can be colored by True state if it is passed by `params$D`.
`phi_d`	A stacked bar graph representing the estimated CMFs of each method when `d=0` and `d=1`.

Continuous:

`ROC`	The Receiver Operator Characteristic (ROC) curves estimated for each method
`z_k1`	A plot showing how the z_k1 values for each observation change over each iteration of the EM algorithm. Observations can be colored by True state if it is passed (`params$D`).
`z_k0`	A plot showing how the z_k0 values for each observation change over each iteration of the EM algorithm. Observations can be colored by True state if it is passed (`params$D`).
`z_k1_hist`	A histogram of z_k1 values. Observations can be colored by True state if it is passed (`params$D`).

Examples

# Set seed for this example
set.seed(11001101)

# Generate data for 4 binary methods
my_sim <- generate_multimethod_data(
  "binary",
  n_obs = 75,
  n_method = 4,
  se = c(0.87, 0.92, 0.79, 0.95),
  sp = c(0.85, 0.93, 0.94, 0.80),
  method_names = c("alpha", "beta", "gamma", "delta"))

# View the data
my_sim$generated_data

# View the parameters used to generate the data
my_sim$params

# Estimate ML accuracy values by EM algorithm
my_result <- estimate_ML(
  "binary",
  data = my_sim$generated_data,
  save_progress = FALSE # this reduces the data stored in the resulting object
)

# View results of ML estimate
my_result@results

[Package emery version 0.5.1 Index]