prep_me_data function

Prepare data for spatial measurement error models

Prepare data for spatial measurement error models

Prepares the list of data required for geostan's (spatial) measurement error models. Given a data frame of standard errors and any optional arguments, the function returns a list with all required data for the models, filling in missing elements with default values.

prep_me_data( se, car_parts, prior, logit = rep(FALSE, times = ncol(se)), bounds = c(-Inf, Inf) )

Arguments

  • se: Data frame of standard errors; column names must match (exactly) the variable names used in the model formula.

  • car_parts: A list of data required for spatial CAR models, as created by prep_car_data; optional. If omitted, the measurement error model will be a non-spatial Student's t model.

  • prior: A named list of prior distributions (see priors). If none are provided, default priors will be assigned. The list of priors may include the following parameters:

    • df: If using a non-spatial ME model, the degrees of freedom (df) for the Student's t model is assigned a gamma prior with default parameters of gamma2(alpha = 3, beta = 0.2). Provide values for each covariate in se, listing the values in the same order as the columns of se.
    • location: The prior for the location parameter (mu) is a normal (Gaussian) distribution (the default being normal(location = 0, scale = 100)). To adjust the prior distributions, provide values for each covariate in se, listing the values in the same order as the columns of se.
    • scale: The prior for the scale parameters is Student's t, and the default parameters are student_t(df = 10, location = 0, scale = 40). To adjust, provide values for each covariate in se, listing the values in the same order as the columns of se.
    • car_rho: The CAR model, if used, has a spatial autocorrelation parameter, rho, which is assigned a uniform prior distribution. You must specify values that are within the permissible range of values for rho; these are automatically printed to the console by the prep_car_data function.
  • logit: Optional vector of logical values (TRUE, FALSE) indicating if the latent variable should be logit-transformed. Only use for rates. This keeps rates between zero and one and may improve modeling of skewed variables (e.g., the poverty rate).

  • bounds: Rarely needed; an optional numeric vector of length two providing the upper and lower bounds, respectively, of the variables (e.g., a magnitudes must be greater than 0). If not provided, they will be set to c(-Inf, Inf) (i.e., unbounded).

Returns

A list of data as required for (spatial) ME models. Missing arguments will be filled in with default values, including prior distributions.

Examples

data(georgia) ## for a non-spatial prior model for two covariates se <- data.frame(ICE = georgia$ICE.se, college = georgia$college.se) ME <- prep_me_data(se) ## see default priors print(ME$prior) ## set prior for the scale parameters ME <- prep_me_data(se, prior = list(scale = student_t(df = c(10, 10), location = c(0, 0), scale = c(20, 20)))) ## for a spatial prior model (often recommended) A <- shape2mat(georgia, "B") cars <- prep_car_data(A) ME <- prep_me_data(se, car_parts = cars)

See Also

se_log

  • Maintainer: Connor Donegan
  • License: GPL (>= 3)
  • Last published: 2024-12-04