Package 'easySimData'

Convert calls into quantile functions

Description

Calls come from ... in simulateTrialData. For individual level data, empirical quantile will be generated using simdata::quantile_functions_from_data. For calls specifying common distribution families and their parameters in plain text, quantile functions will be generated by quantile functions in stats, e.g., qnorm, qunif, etc. For calls of user-customized quantile functions, it returns those directly.

Usage

configureTrial(..., var_prefix)

Arguments

...	calls describing the marginal distributions of endpoints. See ... in simulateTrialData.
var_prefix	prefix to name variables that users do not give to those.

Value

a list of quantile functions.

---

Example data from simdata package

Description

A data frame containing the variables as follows:

Usage

data(df)

Format

A data frame with 4709 rows and 7 variables:

Gender

Gender

Age

Age

Race

Race

Weight

Weight

BMI

BMI

BPsys

BPsys

BPdia

BPdia

Source

simdata R package.

Examples

data(df)
df

---

Plot data generated by the easySimData package.

Description

Plot data generated by the easySimData package.

Usage

## S3 method for class 'easySimData'
plot(x, ...)

Arguments

x	an object of class easySimData returned by simulateTrialData.
...	ignored.

Value

an object returned by gridExtra::grid.arrange, a grid of plots comparing the simulated marginal distribution with the ones specified by users.

---

Generate simulation endpoint data for an arm in a trial.

Description

Parameters of marginal distribution of endpoints and their target correlation are specified. This function returns endpoint data of one arm. To generate data of multiple arms, one need to call this function for each of the arms.

Usage

simulateTrialData(
  ...,
  cor_matrix,
  trial_size = 100,
  arm_label = NULL,
  var_prefix = "easySimData"
)

Arguments

...	calls describing the marginal distributions of endpoints. They can be data frames, e.g., df, or valid R expressions evaluated to be data frames, e.g., data.frame(x = runif(10), y = 1), df %>% filter(x < 1), myFunctionReturnDataFrame(), or simply any quantile functions (see the dist argument of simdata::simdesign_norta).
cor_matrix	a target correlation matrix. See the cor_target_final argument of simdata::simdesign_norta.
trial_size	size of an arm.
arm_label	a character to name the arm. When specified, a column arm will be added to the end of the simulated data frame. NULL by default.
var_prefix	a character as the prefix of endpoints specified by common distributions.

Value

A data frame of endpoints.

Examples

library(dplyr)
data(df)
head(df)

cmat <- matrix(.5, 10, 10)
diag(cmat) <- 1.

## the first argument should always be x if a user-defined quantile function
## is supplied. This is required by the `simdata` package.
user_qfun <- function(x, shape1 = .6, shape2 = 1.2){
  qbeta(x, shape1 = shape1, shape2 = shape2)
}

data <- simulateTrialData(
  df[, c('Gender', 'Age')] %>% dplyr::filter(Age < 50), # an expression with
                                                        # its value a data frame
  df %>% dplyr::select(-c(Gender, Age, Race)), # same as above
  norm(mean = 0.2,sd = 1.2), # distribution name. Note that it is not a valid
                             # R function but a symbol.
  binom(prob =.4, size=1  ), # spaces are ignore, order does not matter
  norm(mean =-0.4, sd=0.9),  # same distribution with different parameters
  user_qfun,                 # user-defined quantile

  cor_matrix = cmat,
  trial_size = 1000,
  arm_label = 'placebo'
)

## for diagnosis
plot(data)
cor(data %>% dplyr::select(-matches('arm')))

## Note that user_qfun is simply the quantile function of a beta distribution,
## so a preferred way to use easySimData is as follow
data <- simulateTrialData(
  df[, c('Gender', 'Age')] %>% dplyr::filter(Age < 50), # an expression with
                                                        # its value a data frame
  df %>% dplyr::select(-c(Gender, Age, Race)), # same as above
  norm(mean = 0.2,sd = 1.2), # distribution name. Note that it is not a valid
                             # R function but a symbol.
  binom(prob =.4, size=1  ), # spaces are ignored, order does not matter
  norm(mean =-0.4, sd=0.9),  # same distribution with different parameters
  beta(shape1 = .5, shape2 = 1.2), # distribution name

  cor_matrix = cmat,
  trial_size = 1000,
  arm_label = 'placebo'
)

---