effort function

Optimum k Measures Based Upon a Fixed Total Researcher Effort

Given a fixed researcher effort (e.g., total number of assays able to be run), this function plots the optimum k measurements per individual to use in order to obtain the smallest confidence interval at an expected intraclass correlation coefficient (ICC) estimate. The results are depicted graphically, showing the tradeoff in confidence interval width with changing k.

effort(
  est.type = c("hypothetical", "pilot"),
  e = NULL,
  ICC = NULL,
  x = NULL,
  y = NULL,
  data = NULL,
  alpha = 0.05
)

Arguments

• est.type: A character string of either "hypothetical" indicating usage of the given values of effort (e) and intraclass correlation coefficient (ICC) or if "pilot" is specified then to calculate these from the dataset provided. Just the first letter may be used.
• e: A numeric value indicating the total effort (n individuals times k measurements per individual). May be a vector of effort levels.
• ICC: A numeric value of the expected intraclass correlation coefficient.
• x: Column name of data indicating the individual or group ID from a pilot study.
• y: Column name of data indicating the measurements from a pilot study.
• data: A data.frame from a pilot experiment.
• alpha: A numeric indicating the alpha level to use when estimating the confidence interval.

Details

More than one e may be given. In this case, the graphical result portrays multiple lines - each representing a different e.

When est.type="pilot", the function automatically generates an effort 10 percent larger and smaller than the calculated effort from the pilot data.

Examples

#Example 1
  effort(est.type = "h", e = c(30, 60, 120), ICC = 0.2)
#Example 2
  data(ChickWeight)
  effort(est.type = "p", x = Chick, y = weight, data = ChickWeight)

See Also

Nest

Author(s)

matthewwolak@gmail.com