Ordinal categorical data is added to an existing data set.
Correlations can be added via correlation matrix or rho and corstr.
genOrdCat(
dtName,
adjVar = NULL,
baseprobs,
catVar = "cat",
asFactor = TRUE,
idname = "id",
prefix = "grp",
rho = 0,
corstr = "ind",
corMatrix = NULL,
npVar = NULL,
npAdj = NULL
)Name of complete data set
Adjustment variable name in dtName - determines logistic shift. This is specified assuming a cumulative logit link.
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.
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.
If asFactor == TRUE (default), new field is returned
as a factor. If asFactor == FALSE, new field is returned as an integer.
Name of the id column in dtName.
A string. The names of the new variables will be a concatenation of the prefix and a sequence of integers indicating the variable number.
Correlation coefficient, -1 < rho < 1. Use if corMatrix is not provided.
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.
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.)
Vector of variable names that indicate which variables are to violate the proportionality assumption.
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.
Original data.table with added categorical field.
# 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
#> varname formula variance dist link
#> <char> <char> <num> <char> <char>
#> 1: male 0.45 0 binary identity
#> 2: z 1.2*male 0 nonrandom identity
## 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"
)
#> Warning: Probabilities do not sum to 1. Adding category to all rows!
dx
#> Key: <idG>
#> idG male z grp
#> <int> <int> <num> <fctr>
#> 1: 1 1 1.2 2
#> 2: 2 1 1.2 3
#> 3: 3 0 0.0 4
#> 4: 4 0 0.0 1
#> 5: 5 1 1.2 4
#> ---
#> 996: 996 1 1.2 2
#> 997: 997 0 0.0 1
#> 998: 998 0 0.0 1
#> 999: 999 0 0.0 2
#> 1000: 1000 0 0.0 1
# 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
#> Key: <id>
#> id q1 q2 q3 q4 q5
#> <int> <int> <int> <int> <int> <int>
#> 1: 1 4 3 1 3 3
#> 2: 2 4 1 3 3 1
#> 3: 3 4 1 3 3 1
#> 4: 4 3 1 3 1 1
#> 5: 5 4 1 1 3 1
#> ---
#> 996: 996 4 1 2 2 1
#> 997: 997 2 1 3 2 1
#> 998: 998 4 3 1 3 1
#> 999: 999 2 2 3 3 1
#> 1000: 1000 4 2 2 3 1
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)]
#> variable V1 response
#> <fctr> <table> <int>
#> 1: q1 0.192 1
#> 2: q1 0.096 2
#> 3: q1 0.109 3
#> 4: q1 0.603 4
#> 5: q2 0.672 1
#> 6: q2 0.209 2
#> 7: q2 0.119 3
#> 8: q3 0.492 1
#> 9: q3 0.190 2
#> 10: q3 0.318 3
#> 11: q4 0.379 1
#> 12: q4 0.222 2
#> 13: q4 0.399 3
#> 14: q5 0.592 1
#> 15: q5 0.203 2
#> 16: q5 0.205 3
data.table::dcast(dProp, variable ~ response,
value.var = "V1", fill = 0
)
#> Key: <variable>
#> variable 1 2 3 4
#> <fctr> <table> <table> <table> <table>
#> 1: q1 0.192 0.096 0.109 0.603
#> 2: q2 0.672 0.209 0.119 0.000
#> 3: q3 0.492 0.190 0.318 0.000
#> 4: q4 0.379 0.222 0.399 0.000
#> 5: q5 0.592 0.203 0.205 0.000
# 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
)