refineR (version 2.0.0)

findRI: Function to estimate reference intervals for a single population

Description

The function estimates the optimal parameters lambda, mu and sigma for a raw data set containing pathological and non-pathological values. The optimization is carried out via a multi-level grid search to minimize the cost function (negative log-likelihood with regularization) and to find a model that fits the distribution of the physiological values and thus separates pathological from non-pathological values.

Usage

findRI(
  Data = NULL,
  model = c("BoxCox", "modBoxCoxFast", "modBoxCox"),
  NBootstrap = 0,
  seed = 123,
  ...
)

Value

(object) of class "RWDRI" with parameters optimized

Arguments

Data

(numeric) values specifying data points comprising pathological and non-pathological values

model

(character) specifying the applied model (can be either "BoxCox" (default), "modBoxCoxFast" or "modBoxCox"), option "modBoxCoxFast" and "modBoxCox" first runs the original optimization using the Box-Cox transformation, afterwards the modified Box-Cox transformation is utilized and an optimal shift is identified ('fast': only 1 iteration is carried out to find a shift)

NBootstrap

(integer) specifying the number of bootstrap repetitions

seed

(integer) specifying the seed used for bootstrapping

...

additional arguments to be passed to the method

Author

Tatjana Ammer

Examples

# first example
# \donttest{
resRI <- findRI(Data = testcase1)
print(resRI)
plot(resRI, showPathol = FALSE)

# second example
resRI <- findRI(Data = testcase2)
print(resRI, RIperc = c(0.025, 0.5, 0.975))
plot(resRI, showPathol = FALSE)

# third example, with bootstrapping 
resRI <- findRI(Data = testcase3, NBootstrap = 30, seed = 123)
print(resRI)
getRI(resRI, RIperc = c(0.025, 0.5, 0.975), CIprop = 0.95, pointEst ="fullDataEst")
getRI(resRI, RIperc = c(0.025, 0.5, 0.975), CIprop = 0.95, pointEst ="medianBS")
plot(resRI)

# forth example, without values and pathological distribution in plot function 
resRI <- findRI(Data = testcase4)
print(resRI)
plot(resRI, showValue = FALSE, showPathol =FALSE) 

# fifth example, with bootstrapping
resRI <- findRI(Data = testcase5, NBootstrap = 30)
plot(resRI,  RIperc = c(0.025, 0.5, 0.975), showPathol = FALSE, showCI = TRUE)
# }