modeLLtest (version 1.0.4)

cvll: Cross-Validated Log Likelihood (CVLL)

Description

Extracts the leave-one-out cross-validated log-likelihoods from a method of estimating a formula.

Usage

cvll(
  formula,
  data,
  method = c("OLS", "MR", "RLM", "RLM-MM"),
  subset,
  na.action,
  ...
)

Arguments

formula

A formula object, with the dependent variable on the left of a ~ operator, and the independent variables on the right.

data

A data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model.

method

A method to estimate the model. Currently takes Ordinary Least Squares ("OLS"), Median Regression ("MR"), Robust Linear Regression ("RLM") using M-estimation, and Robust Linear Regression using MM-estimation ("RLM-MM"). The algorithm method used to compute the fit for the median regression is the modified version of the Barrodale and Roberts algorithm for l1-regression, which is the rq default by R package quantreg. See quantreg rq function documentation for more details. Fitting for the robust regressions is done by iterated re-weighted least squares (IWLS) and is taken from the MASS package rlm function. The MM-estimation is the M-estimation with Tukey's biweight initialized by a specific S-estimate. The M-estimation, which can be achieved in this package with the option "RLM", is the default for the MASS rlm function. See MASS package rlm documentation for details.

subset

Expression indicating which subset of the rows of data should be used in the fit. All observations are included by default.

na.action

A missing-data filter function, applied to the model.frame, after any subset argument has been used.

...

Optional arguments, currently unsupported.

Value

An object of class cvll computed by the cross-validated log likelihood (CVLL). See cvdm_object for more details.

Details

This function extracts a vector of leave-one-out cross-validated log likelihoods (CVLLs) from a method of estimating a formula. Singular matrices during the leave-one-out cross-validation process are skipped.

References

• Harden, J. J., & Desmarais, B. A. (2011). Linear Models with Outliers: Choosing between Conditional-Mean and Conditional-Median Methods. State Politics & Policy Quarterly, 11(4), 371-389. 10.1177/1532440011408929

• Desmarais, B. A., & Harden, J. J. (2014). An Unbiased Model Comparison Test Using Cross-Validation. Quality & Quantity, 48(4), 2155-2173. 10.1007/s11135-013-9884-7

Examples

# NOT RUN {
  set.seed(123456)
  b0 <- .2 # True value for the intercept
  b1 <- .5 # True value for the slope
  n <- 500 # Sample size
  X <- runif(n, -1, 1)

  Y <- b0 + b1 * X + rnorm(n, 0, 1) # N(0, 1 error)

  obj_cvll <- cvll(Y ~ X, data.frame(cbind(Y, X)), method = "OLS")
# }