simstudy (version 0.7.0)

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,
  npVar = NULL,
  npAdj = NULL
)

Value

Original data.table with added categorical field.

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.)

npVar

Vector of variable names that indicate which variables are to violate the proportionality assumption.

npAdj

Matrix with a row for each npVar and a column for each category. Each value represents the deviation from the proportional odds assumption on the logistic scale.

Examples

# 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
)

# proportional odds assumption violated

d1 <- defData(varname = "rx", formula = "1;1", dist = "trtAssign")
d1 <- defData(d1, varname = "z", formula = "0 - 1.2*rx", dist = "nonrandom")

dd <- genData(1000, d1)

baseprobs <- c(.4, .3, .2, .1)
npAdj <- c(0, 1, 0, 0)

dn <- genOrdCat(
  dtName = dd, adjVar = "z",
  baseprobs = baseprobs,
  npVar = "rx", npAdj = npAdj
)