R/nerve/ExploreGLM.R

In premodelling/mixed: Modelling Multilevel & Longitudinal Problems

# Title     : ExploreGLM.R
# Objective : Explorevia GLM
# Created by: greyhypotheses
# Created on: 31/03/2022


#' Modelling via glm(.)
#'
#' @param nerve: The data frame of the nerve data set
#'
ExploreGLM <- function (nerve) {

  # The residual deviance measures how well a response variable can be predicted by a model
  # with p predictor variables →
  #
  #       Null Hypothesis: The model fits the data well,
  #                      p.value = 1 - pchisqr(residual deviance, residual degrees of freedom, lower.tail = TRUE)
  #                              = pchisqr(residual deviance, residual degrees of freedom, lower.tail = FALSE)
  #       Alternative Hypothesis: Otherwise


  # ... formula set-up: [success, failure] ~ variables
  model <- glm( cbind(reactionCount, 15 - reactionCount) ~ painScoreStd,
                family = binomial(link = 'logit'), data = nerve )


  # ... a significant relationship between pain score and reaction count
  summary(model)
  anova(model)


  # ... but a better model is required; the p.value suggests evidence against the null hypothesis
  estimates <- NULL
  estimates$residual.deviance <- model$deviance
  estimates$residual.freedom <- model$df.residual
  estimates$p.value <- 1 - pchisq(q = estimates$residual.deviance,
                                  df = estimates$residual.freedom, lower.tail = TRUE)
  estimates$p.value <- pchisq(q = estimates$residual.deviance,
                              df = estimates$residual.freedom, lower.tail = FALSE)


  return(list(model = model, estimates = estimates))

}