simstudy (version 0.2.0)

genOrdCat: Generate ordinal categorical data

Description

Ordinal categorical data is added to an existing data set. Correlations can be added via correlation matrix or rho and corstr.

Usage

genOrdCat(
  dtName,
  adjVar = NULL,
  baseprobs,
  catVar = "cat",
  asFactor = TRUE,
  idname = "id",
  prefix = "grp",
  rho = 0,
  corstr = "ind",
  corMatrix = NULL
)

Arguments

dtName

Name of complete data set

adjVar

Adjustment variable name in dtName - determines logistic shift. This is specified assuming a cumulative logit link.

baseprobs

Baseline probability expressed as a vector or matrix of probabilities. The values (per row) must sum to <= 1. If rowSums(baseprobs) < 1, an additional category is added with probability 1 - rowSums(baseprobs). The number of rows represents the number of new categorical variables. The number of columns represents the number of possible responses - if an particular category has fewer possible responses, assign zero probability to non-relevant columns.

catVar

Name of the new categorical field. Defaults to "cat". Can be a character vector with a name for each new variable defined via baseprobs. Will be overridden by prefix if more than one variable is defined and length(catVar) == 1.

asFactor

If asFactor == TRUE (default), new field is returned as a factor. If asFactor == FALSE, new field is returned as an integer.

idname

Name of the id column in dtName.

prefix

A string. The names of the new variables will be a concatenation of the prefix and a sequence of integers indicating the variable number.

rho

Correlation coefficient, -1 < rho < 1. Use if corMatrix is not provided.

corstr

Correlation structure of the variance-covariance matrix defined by sigma and rho. Options include "ind" for an independence structure, "cs" for a compound symmetry structure, and "ar1" for an autoregressive structure.

corMatrix

Correlation matrix can be entered directly. It must be symmetrical and positive definite. It is not a required field; if a matrix is not provided, then a structure and correlation coefficient rho must be specified. (The matrix created via rho and corstr must also be positive definite.)

Value

Original data.table with added categorical field.

Examples

# NOT RUN {
# Ordinal Categorical Data ----

def1 <- defData(
    varname = "male",
    formula = 0.45, dist = "binary", id = "idG"
)
def1 <- defData(def1,
    varname = "z",
    formula = "1.2*male", dist = "nonrandom"
)
def1

## Generate data

set.seed(20)

dx <- genData(1000, def1)

probs <- c(0.40, 0.25, 0.15)
dx <- genOrdCat(dx,
  adjVar = "z", idname = "idG", baseprobs = probs,
  catVar = "grp"
)
dx

# Correlated Ordinal Categorical Data ----

baseprobs <- matrix(c(
    0.2, 0.1, 0.1, 0.6,
    0.7, 0.2, 0.1, 0,
    0.5, 0.2, 0.3, 0,
    0.4, 0.2, 0.4, 0,
    0.6, 0.2, 0.2, 0
),
nrow = 5, byrow = TRUE
)

set.seed(333)
dT <- genData(1000)

dX <- genOrdCat(dT,
    adjVar = NULL, baseprobs = baseprobs,
    prefix = "q", rho = .125, corstr = "cs", asFactor = FALSE
)
dX

dM <- data.table::melt(dX, id.vars = "id")
dProp <- dM[, prop.table(table(value)), by = variable]
dProp[, response := c(1:4, 1:3, 1:3, 1:3, 1:3)]

data.table::dcast(dProp, variable ~ response,
    value.var = "V1", fill = 0
)
# }