fsm_batch: Batched FSM for sequential experiments

In FSM: Finite Selection Model

Description

Extension of the FSM to cases where units arrive sequentially in batches.

Usage

fsm_batch(
  data_frame,
  data_frame_past,
  t_ind,
  SOM,
  s_function = "Dopt",
  Q_initial = NULL,
  eps = 0.001,
  ties = "random",
  intercept = TRUE,
  index_col_past = TRUE,
  standardize = TRUE,
  units_print = TRUE,
  index_col = TRUE,
  Pol_mat = NULL,
  w_pol = NULL
)

Arguments

data_frame	Data frame containing a column of unit indices (optional) and covariates (or transformations thereof).
data_frame_past	A data frame of units already allocated to treatment groups. Data frame contains a column of unit indices (optional), columns of covariates (or transformations thereof), and a column for treatment indicator.
t_ind	column name containing the treatment indicator in data_frame_past.
SOM	Selection Order Matrix.
s_function	Specifies a selection function, a string among 'constant', 'Dopt', 'Aopt', 'max pc', 'min pc', 'Dopt pc', 'max average', 'min average', 'Dopt average'. 'constant' selection function puts a constant value on every unselected unit. 'Dopt' use the D-optimality criteria based on the full set of covariates to select units. 'Aopt' uses the A-optimality criteria. 'max pc' (respectively, 'min pc') selects that unit that has the maximum (respectively, minimum) value of the first principal component. 'Dopt pc' uses the D-optimality criteria on the first principal component, 'max average' (respectively, 'min average') selects that unit that has the maximum (respectively, minimum) value of the simple average of the covariates. 'Dopt average' uses the D-optimality criteria on the simple average of the covariates.
Q_initial	A (optional) non-singular matrix (called 'initial matrix') that is added the (X^T X) matrix of the choosing treatment group at any stage, when the (X^T X) matrix of that treatment group at that stage is non-invertible. If FALSE, the (X^T X) matrix for the full set of observations is used as the non-singular matrix. Applicable if s_function = 'Dopt' or 'Aopt'.
eps	Proportionality constant for Q_initial, the default value is 0.001.
ties	Specifies how to deal with ties in the values of the selection function. If ties = 'random', a unit is selected randomly from the set of candidate units. If ties = 'smallest', the unit that appears earlier in the data frame, i.e. the unit with the smallest index gets selected.
intercept	if TRUE, the design matrix of each treatment group includes a column of intercepts.
index_col_past	TRUE if column of unit indices is present in data_frame_past.
standardize	if TRUE, the columns of the X matrix other than the column for the intercept (if any), are standardized.
units_print	if TRUE, the function automatically prints the candidate units at each step of selection.
index_col	if TRUE, data_frame contains a column of unit indices.
Pol_mat	Policy matrix. Applicable only when s_function = 'Aopt'.
w_pol	A vector of policy weights. Applicable only when s_function = 'Aopt'.

Value

A list containing the following items.

data_frame_allocated: The original data frame augmented with the column of the treatment indicator.

som_appended: The SOM with augmented columns for the indices and covariate values for units selected.

som_split: som_appended, split by the levels of the treatment.

data_frame_allocated_augmented: data frame combining data_frame_allocated and data_frame_past.

Author(s)

Ambarish Chattopadhyay, Carl N. Morris and Jose R. Zubizarreta

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

# Consider N=18, number of treatments = 2, n1 = n2 = 9, batch sizes = 6,6,6.
# Get data frame for the first batch.
df_sample_1 = data.frame(index = 1:6, age = c(20,30,40,40,50,60))
# Obtain SOM for all the 12 units.
som_gen = som(data_frame = NULL, n_treat = 2, treat_sizes = c(9,9), 
include_discard = FALSE, method = 'SCOMARS', marginal_treat = rep((9/18),18), control = FALSE)
# Assign the first batch.
f1 = fsm(data_frame = df_sample_1, SOM = som_gen[1:6,], s_function = 'Dopt', 
eps = 0.0001, ties = 'random', intercept = TRUE, standardize = TRUE, units_print = TRUE)
f1_app = f1$data_frame_allocated
# Get data frame for the second batch.
df_sample_2 = data.frame(index = 7:12, age = c(20,30,40,40,50,60))
# Assign the second batch.
f2 = fsm_batch(data_frame = df_sample_2, SOM = som_gen[7:12,], s_function = 'Dopt', 
eps = 0.0001, ties = 'random', intercept = TRUE, standardize = TRUE, units_print = TRUE,
data_frame_past = f1_app, t_ind = 'Treat', index_col_past = TRUE)
f2_app = f2$data_frame_allocated_augmented
# Get data frame for the third batch.
df_sample_3 = data.frame(index = 13:18, age = c(20,30,40,40,50,60))
# Assign the third batch.
f3 = fsm_batch(data_frame = df_sample_3, SOM = som_gen[13:18,], s_function = 'Dopt', 
eps = 0.0001, ties = 'random', intercept = TRUE, standardize = TRUE, units_print = TRUE,
data_frame_past = f2_app, t_ind = 'Treat', index_col_past = TRUE)
f3_app = f3$data_frame_allocated_augmented

FSM documentation built on March 11, 2021, 1:06 a.m.