rACMEMEEV (version 1.0.1)

plot_covariates: Diagnostics for Models

Description

Using the previously fit models, add the diagnostics for assessing the adjusted covariates based on the naive, univariate, and multivariate models.

Usage

plot_covariates(model_output, columns)

Value

plotted objects

Arguments

model_output

list List of the model output

columns

vector The target columns to assess

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
)
plot_covariates(model_output, columns)