R/PlotSymptomsLogisticResgression.R

In crtahlin/medplot: Web application developed to facilitate the exploration and analysis of longitudinal data.

tableLogist <- function(data,
                           measurementVar,
                           selectedMeasurement,
                           covariate,
                           selectedSymptoms,
                           thresholdValue) {

  
  # subset the data, using only the selected evaluation
  data <- data[data[,measurementVar]==selectedMeasurement, ]
  
  # binarize the data
  data[, selectedSymptoms] <- ifelse(data[, selectedSymptoms]>thresholdValue, 1, 0)
  
  table <- data.frame("Variable"=selectedSymptoms) # table of printable results - Fixed effect
  table2 <- data.frame("Variable"=selectedSymptoms) # table of raw results
  table3 <- data.frame("Variable"=selectedSymptoms) # table of printable results - Intercept
  
# check if covariate is binary and generate text which levels we are comparing
   if (determineTypeofVariable(data[,covariate])[["nLevels"]]=="binary") { # binary var
  levels <- levels(as.factor(data[,covariate])) # levels of the covariate
  oddsFor <- paste(levels[2],"vs",levels[1]) # text describing which variables were compared
   }

  if (determineTypeofVariable(data[,covariate])[["nLevels"]]=="multilevel" &  # numerical var w multi levels
        ( determineTypeofVariable(data[,covariate])[["type"]]=="integer") 
      | determineTypeofVariable(data[,covariate])[["type"]]=="numeric") { 
    #levels <- levels(as.factor(data[,covariate])) # levels of the covariate
    oddsFor <- paste("unit difference in", covariate) # text describing which variables were compared
  }
  
  for (symptom in selectedSymptoms) {
    model <- glm(data[,symptom] ~ data[,covariate], family=binomial())
    table[table["Variable"]==symptom, "Odds ratio"] <- 
      format(exp(model$coef[2]), digits=2)
    table[table["Variable"]==symptom, "95% conf. interval"] <- 
      paste(format(exp(confint(model)[2,1]), digits=2),
            " to ",
            format(exp(confint(model)[2,2]), digits=2))
    table2[table2["Variable"]==symptom, "OR"] <- exp(model$coef[2])
    table2[table2["Variable"]==symptom, "CILower"] <- exp(confint(model)[2,1])
    table2[table2["Variable"]==symptom, "CIUpper"] <- exp(confint(model)[2,2])
    table[table["Variable"]==symptom, "P value"] <- 
      format(summary(model)$coefficients[2,"Pr(>|z|)"],digits=2)
    
    # printable intercept table
    table3[table3["Variable"]==symptom, "Odds (intercept)"] <- 
      format(exp(model$coef[1]), digits=2)
    table3[table3["Variable"]==symptom, "95% conf. interval"] <- 
      paste(format(exp(confint(model)[1,1]), digits=2),
            " to ",
            format(exp(confint(model)[1,2]), digits=2))
    table3[table3["Variable"]==symptom, "P value"] <- 
      format(summary(model)$coefficients[1,"Pr(>|z|)"],digits=2)
  }
  return(list(printableResultsTable=table,
              rawResultsTable=table2,
              referenceValue=oddsFor,
              printableInterceptTable=table3))

}