tmt (version 0.3.6-4)

tmt_sim: Function for the Simulation of Multistage-Designs

Description

This function simulates data according to the specified and submitted multistage design. The persons are drawn from a standard normal distribution if the amount of persons are specified. As an additional argument, a seed can also be set. If requested, it is also possible to submit a vector ore list of person parameters to specify different person distributions.

Usage

tmt_sim(
  mstdesign = NULL,
  items = NULL,
  persons = NULL,
  preconditions = NULL,
  ...
)

Value

List with following entries

data

Matrix with item responses

data_mst

Data frame with item responses and additional a vector of used modules per person

persons

Generated and used person parameters

mstdesign

Submitted multistage design

Arguments

mstdesign

definition of desired multistage design

items

vector of difficulty parameters for each items

persons

amount of persons per starting module

preconditions

definition of preconditions can optionally be specified. In the case of probabilistic routing preconditions such as a pre-test, which are taken into account in the MST design. For the specification the correlation with the true person parameter have to be specified. The submitted correlation is adjusted in the function according to Demirtas and Yavuz (2015; <doi:10.1080/10543406.2014.920868>) It is also possible to submit your own vector with integers for the preconditions.

...

further optional arguments like setting a seed

Author

Jan Steinfeld

References

• Demirtas, H., & Yavuz, Y. (2015). Concurrent Generation of Ordinal and Normal Data. Journal of Biopharmaceutical Statistics, 25(4), 635-650. https://doi.org/10.1080/10543406.2014.920868

Examples

#############################################################################
# translate multistage model 1
#############################################################################
 mstdesign <- "
	 M1 =~ c(i1, i2, i3, i4, i5)
	 M2 =~ c(i6, i7, i8, i9, i10)
	 M3 =~ c(i11, i12, i13, i14, i15)

	 # define branches
	 p1 := M2(0,2) + M1
	 p2 := M2(3,5) + M3
 "
items <- seq(-3,3,length.out = 15)
names(items) <- paste0("i", seq(items))

data_1 <- tmt_sim(mstdesign = mstdesign, 
    items = items, 
    persons = 500, 
    seed = 1111)

#############################################################################
# translate multistage model 2
#############################################################################
mstdesign <- "
    M1 =~ c(i1, i2, i3, i4, i5)
    M2 =~ c(i6, i7, i8, i9, i10)
    M3 =~ c(i11, i12, i13, i14, i15)
    M4 =~ c(i16, i17, i18, i19, i20)
    M5 =~ c(i21, i22, i23, i24, i25)
    M6 =~ c(i26, i27, i28, i29, i30)

    # define branches
    p1 := M4(0,2) + M2(0,2) + M1
    p2 := M4(0,2) + M2(3,5) + M3
    p3 := M4(3,5) + M5(0,2) + M3
    p4 := M4(3,5) + M5(3,5) + M6
  "

items <- seq(-3,3,length.out = 30)
names(items) <- paste0("i", seq(items))

data_2 <- tmt_sim(mstdesign = mstdesign, 
    items = items, 
    persons = 500,
    seed = 1111)