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)

Arguments

Value

plotted objects

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)

Model the Data

Description

Using the methodology generated by Muoko et. al (see README for full citation), run the modelling with the JAGS sampler. This function accepts a number of different arguments, but defaulted arguments are assumed to be best for most systems. Obviously, if there is prior knowledge, this can be adjusted at the user's own discretion.

Usage

acme_model(
  data,
  columns,
  n_chains = 1,
  n_adapt_steps = 500,
  n_burn = 1000,
  n_thin = 1,
  n_steps = 10000,
  seed = 42,
  stan = FALSE
)

Arguments

Value

List with all the appropriate things needed for modelling with JAGS

Examples

data <- data.frame(list("fruit" = c(1, 2), "veg" = c(3, 4)))
acme_model(data, names(data))

Create Attenuation Contamination Matrix

Description

From the output JAGS model, create the needed attenuation contamination matrix for further analysis. Please be aware that this is multivariate so a minimum of 3 columns is needed.

Usage

attenuation_matrix(model_output, columns, validity_coefficients, stan = FALSE)

Arguments

Value

List with the attenuation-contamination matrix and the standard deviations

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)
attenuation_matrix(
  output,
  columns,
  validity_coefficients,
)

Define a Model that is JAGS Usable

Description

Create the model string, save it to a temporary folder, and return back the location of the temporary model file for usage by JAGS later.

Usage

create_model_string()

Value

Full path to the model specification

Examples

create_model_string()

Data Preparation and Formatting for Modelling

Description

Create the needed list that gets based to the JAGS backend for modelling. This should be a standard data.frame object that can then be standardized and reshaped into a format appropriate for modeling. Errors will be thrown if the data is not a class, or inherit from, a data.frame or if the specified columns do not exist in the data.frame.

Usage

create_modelling_data(data, columns)

Arguments

Value

List with all the appropriate things needed for modelling with JAGS

Examples

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)
 )
)
create_modelling_data(data, c("BMI", "fruit", "veg", "tobacco"))

Define the Pre-Model Using Stan

Description

Create the model string, save it to a temporary folder, and return back the location of the temporary model file for usage by Stan later. This is experimental and has been validated, but caution should be used when utilizing the Stan backend as the pre-model.

Usage

create_stan_model_string()

Value

Full path to the model specification

Examples

create_stan_model_string()

Perform the Fisher Z Transformation

Description

Take a validity coefficient and perform the Fisher Z Transformation to approximate a normal distribution

Usage

fisher_z_transform(coefficient)

Arguments

Value

Transformed validity coefficient

Examples

coef <- 0.3
fisher_z_transform(coef)

Generate updated validity coefficient using Fisher Z Transformation

Description

Using the Fisher Z Transformation, generate new validity coefficients based on proposed upper and lower boundaries. This new validity coefficient is based on transformed upper and lower boundaries as well as a fitted normal distribution to the se new proposed upper and lower boundaries.

Usage

generate_coefficient(n, lower_bound, upper_bound, interval = 0.95, seed = 42)

Arguments

Value

Validity coefficient

Examples

n <- 1000
coef_upper <- 0.9
coef_lower <- 0.3
ci <- 0.95
generate_coefficient(n, coef_lower, coef_upper, ci)

Model the Data with Multivariate Adjustment

Description

Using the methodology generated by Muoko et. al (see README for full citation), run the modelling with the JAGS sampler. This model uses the calculated attenuation-contamination matrix to adjust the various covariates needed in the model. The function accepts the computed pre-model, the attenuation-contamination coefficient,, a model string, and standard deviations. There is an optional argument for also fitting the univariate model to run further comparisons between the naive, univariate, and multivariate models in later steps.

Usage

multivariate_model(
  formula,
  data,
  columns,
  a_c_matrix,
  n_burn = 1000,
  n_thin = 1,
  n_steps = 10000,
  seed = 42,
  b0 = 0,
  capital_b_0 = 1e-06,
  sampler = "Metropolis",
  c0 = 0.001,
  d0 = 0.001,
  univariate = FALSE,
  sds = NULL,
  variances = NULL
)

Arguments

Value

List with fitted models ready for further analysis

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
)

Pipeline used for running a model start to finish.

Description

This is an unexported function that can run the entire pre-model, attenuation-contamination matrix, and model fitting from start to finish.

Usage

pipeline(data, formula, parameters, columns, stan = FALSE, seed = 42)

Arguments

Value

list The output parameters

Examples

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)
 )
)
parameters <- list(
  fruit = c(0.3, 0.55, 0.8),
  veg = c(0.25, 0.5, 0.75),
  tobacco = c(0.4, 0.55, 0.7)
)
grid <- expand.grid(parameters)
param_grid <- list()
for (i in seq_len(nrow(grid))) {
  name <- paste0("iteration_", i)
  param_grid[[name]] <- list(
    parameters = grid[i, ]
  )
}
output <- pipeline(
   data,
   "BMI ~ fruit + veg + tobacco",
   as.numeric(param_grid[[i]][["parameters"]]),
   c("fruit", "veg", "tobacco")
)

Custom Function to Use Patchwork

Description

Uses patchwork to make gridding easier for plotting the traceplots and acf plots for the Stan pre-model.

Usage

plot_a_list(plot_list, rows, columns)

Arguments

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)

Arguments

Value

plotted objects

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)

Run a sensitivity analysis on the error adjustment

Description

Create a sensitivity analysis based on a grid of validity coefficient parameters. The input of the parameter space should be a list with each key being the name of one of the covariates to vary and a vector of the parameter space to test the sensitivity. Be very careful because all combinations of the parameters will be tested so you can very easily run this for too long. Also please note that the parameters should be be bound between 0 and 1, the theoretical limits of the validity coefficients. An example of this parameter grid is:

Usage

sensitivity_analysis(
  parameters,
  data,
  formula,
  columns,
  stan = FALSE,
  seed = 42
)

Arguments

Details

params <- list( fruit = c(0.1, 0.2, 0.3), veg = c(0.1, 0.2, 0.3), tobacco = c(0.1, 0.2, 0.3) )

But, again please note that this will then fit 3 * 3 * 3 different models so the run time here can explode. Also parallel computation will be utilized here, but it is much more difficult to debug should errors arise. All this to say is: buyer beware.

Value

list with the means and the SDs of the parameters

Examples

columns <- c("fruit", "veg", "tobacco")
data <- data.frame(
 list(
   "BMI" = rnorm(5, mean = 0, sd = 1),
   "fruit" = rnorm(5, mean = 0, sd = 1),
   "veg" = rnorm(5, mean = 0, sd = 1),
   "tobacco" = rnorm(5, mean = 0, sd = 1)
 )
)
parameters <- list(
  fruit = c(0.3),
  veg = c(0.25),
  tobacco = c(0.4)
)
output_jags <- sensitivity_analysis(
  parameters,
  data,
  "BMI ~ fruit + veg + tobacco",
  columns
)

Standardize the data of 1-D vector

Description

Perform standardization of data on a 2-D dataframe type object. Standardization in this case refers to (x - mean(x)) / sd(x) where X is a 1-dimensional vector.

Usage

standardize_with_return(data)

Arguments

Value

List with the original standard deviation, mean, and the standardized data

Examples

data <- rnorm(100, mean = 0, sd = 1)
standardize_with_return(data)

Create Traceplots for the Parameters

Description

Create sampler traceplots to run diagnostics on whether or not the sampler converged when creating the posterior. Columns should always be in the order that they appear in the model.

Usage

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

Arguments

Value

plotted objects

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$variances,
  univariate = TRUE
)
traceplots(model_output$naive, columns)