rACMEMEEV (version 1.0.1)

acf_plots: Create Auto Correlation Plots for Models

Description

Create auto correlation plots for the models to test if there is any high autocorrelation in the sampling space, which would imply that sampler should be adjusted or the data needs to be adjusted.

Usage

acf_plots(input, columns, pre_model = FALSE, stan = FALSE)

Value

plotted objects

Arguments

input

list List of the model output

columns

vector The target columns to assess

pre_model

bool If you are passing the pre-model

stan

bool If you are using the stan model

Examples

columns <- c("fruit", "veg", "tobacco")
fruit_v_coef <- generate_coefficient(100, 0.3, 0.8, 0.95)
veg_v_coef <- generate_coefficient(100, 0.25, 0.75, 0.95)
tob_v_coef <- generate_coefficient(100, 0.4, 0.7, 0.95)
validity_coefficients <- c(fruit_v_coef, veg_v_coef, tob_v_coef)
data <- data.frame(
 list(
   "BMI" = rnorm(100, mean = 0, sd = 1),
   "fruit" = rnorm(100, mean = 0, sd = 1),
   "veg" = rnorm(100, mean = 0, sd = 1),
   "tobacco" = rnorm(100, mean = 0, sd = 1)
 )
)
output <- acme_model(data, columns)
lambda <- attenuation_matrix(
  output,
  columns,
  validity_coefficients,
)
model_output <- multivariate_model(
  "BMI ~ fruit + veg + tobacco",
  data = data,
  columns = columns,
  a_c_matrix = lambda$matrix,
  sds = lambda$sds,
  variances = lambda$variances,
  univariate = TRUE
)
acf_plots(model_output$naive, columns)