This function simulates meta-variables with varying distributions to append to some data.
Usage
simulateMeta(
data,
outcome = colnames(data)[1],
metaInfo = data.frame(var = c("randUnif", "randBin", "rNorm", "corrUnif", "corrBin",
"corrNorm"), rdist = c("runif", "rbinom", "rnorm", "runif", "rbinom", "rnorm"),
outcome_0 = c("0.5", "0.5", "1", "0.5", "0.6", "1"), outcome_1 = c("0.5", "0.5", "1",
"0.75", "0.65", "1.5"), outcome_2 = c("0.5", "0.5", "1", "0.95", "0.75", "1.5"))
)Arguments
- data
Data.frame with the outcome and unit. Typically this also includes PCA data as it is run after computing the principle components (see examples).
- outcome
(Optional) String for column title of the data's outcome. Default is the first column.
- metaInfo
(Optional) Data.frame indicating the meta-variables (and properties) to generate. Default has some examples of possible options.
The data.frame has a var column, rdist column, and columns for each outcome. The var column names the meta-variables, rdist indicates the distribution (options are runif, rbinom, and rnorm), and the outcome columns indicate mean of the variable for that outcome.
In order to allow designation of the expected values, the following rules are imposed on each distribution:
runif: a=0, so b is modified,
rbinom: n=1, so this defines the probability
runif: variance is set to 1
Examples
data <- simulatePP(
agentVarData = data.frame(
"outcome" = c(0, 1, 2),
"A" = c(0, 0, 0),
"B" = c(1 / 100, 1 / 500, 1 / 1000)
),
agentKappaData = data.frame(
"agent" = c("A", "B"),
"clusterAgent" = c(NA, "A"),
"kappa" = c(10, 3)
),
unitsPerOutcome = 5,
replicatesPerUnit = 1
)
#> Outcome: 0 (1/3)
#> Outcome: 1 (2/3)
#> Outcome: 2 (3/3)
pcaData <- getKsPCAData(
data = data, replicate = "replicate",
xRange = c(0, 1), yRange = c(0, 1)
)
#> PCA Pairs (4): 1, 2, 3, 4
pcaMeta <- simulateMeta(pcaData)
## Another simple example
data <- simulateMeta(
data.frame("outcome" = c(0, 0, 0, 1, 1, 1), "unit" = 1:6)
)