findRI {refineR}

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,
  ...
)

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

Value

(object) of class "RWDRI" with parameters optimized

Author(s)

Tatjana Ammer [e-mail]

Examples

# first example

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)