FSM (version 1.0.0)

tasmd_rand: Target Absolute Standardized Mean Differences (TASMD)

Description

Computes the mean and standard deviation of Target Absolute Standardized Mean Differences (TASMD) of multiple covariates (or transformations thereof) in a treatment group relative to a target population or a target individual for a set of assignments under one or two designs.

Usage

tasmd_rand(
  data_frame,
  index_col = FALSE,
  alloc1,
  alloc2,
  treat_lab = 1,
  legend = c("CRD", "FSM"),
  mean_tar = NULL,
  sd_tar = NULL,
  denom = "target",
  roundoff = 3
)

Arguments

data_frame

Data frame containing a column of unit indices (optional) and covariates (or transformations thereof).

index_col

if TRUE, data_frame contains a column of unit indices.

alloc1

A matrix or vector of treatment assignments. If alloc1 is a matrix, then each row should correspond to an assignment vector.

alloc2

A (optional) matrix or vector of treatment assignment. If alloc2 is a matrix, then each row should correspond to an assignment vector.

treat_lab

Label of the treatment group in which the TASMD is computed.

legend

Legend of the two designs under consideration.

mean_tar

A (optional) vector of target profile of the covariates under consideration, e.g., mean of the covariates in the target population. Applicable only when imbalance = 'TASMD'. If mean_tar = NULL, the full-sample average of the covariates is considered as the target profile.

sd_tar

A optional vector of the standard deviation of the covariates in the target population. Applicable only when imbalance = 'TASMD'.

denom

Specifies the denominator for the computation of TASMD. If denom = 'target', the standard deviations of the covariates in the target population are used. If denom = 'group', the standard deviations of the covariates in the treatment group given by treat_lab are used. Applicable only when imbalance = 'TASMD'.

roundoff

A number indicating the number of decimal places to be used for rounding off the TASMDs.

Value

A list containing the following items (if alloc1 and alloc2 are matrices)

tasmd_table: A matrix containing the means (standard deviations in parenthesis) of the TASMDs for the designs under consideration. If alloc1 or alloc2 is a vector, the TASMD of the corresponding assignment is returned.

tasmd_mean: A matrix containing the means of the TASMDs for the designs under consideration.

tasmd_sd: A matrix containing the standard deviations of the TASMDs for the designs under consideration.

If alloc1 and alloc2 are vectors, tasmd_rand produces a data frame of the corresponding TASMDs.

References

Chattopadhyay, A., Morris, C. N., and Zubizarreta, J. R. (2020), ``Randomized and Balanced Allocation of Units into Treatment Groups Using the Finite Selection Model for R".

Examples

# NOT RUN {
# Consider the Lalonde dataset.
# Get the full sample size.
N = nrow(Lalonde)
# Get the treatment group sizes.
n1 = floor(N/2)
n2 = N-n1
# Generate an SOM.
som_obs = som(n_treat = 2, treat_sizes = c(n1,n2),include_discard = FALSE,
method = 'SCOMARS', marginal_treat = rep((n2/N),N), control = FALSE)
# Generate a treatment assignment given som_obs.
f = fsm(data_frame = Lalonde, SOM = som_obs, s_function = 'Dopt', eps = 0.0001, 
ties = 'random', intercept = TRUE, standardize = TRUE, units_print = FALSE)
# Get assignment vector under the FSM.
Z_fsm_obs = f$data_frame_allocated$Treat
# Draw a random CRD.
Z_crd_obs = crd(data_frame = Lalonde, n_treat = 2, treat_sizes = c(n1, n2), 
control = FALSE)$Treat
# Calculate the TASMD.
TASMD = tasmd_rand(data_frame = Lalonde, index_col = TRUE, alloc1 = Z_crd_obs, 
alloc2 = Z_fsm_obs, treat_lab = 1, mean_tar = NULL, sd_tar = NULL, 
denom = 'target', legend = c('CRD','FSM'), roundoff = 3)
# }