probabilities of assignment: Simple Random Assignment

simple_ra_probabilities(N, prob = NULL, prob_unit = NULL,
prob_each = NULL, num_arms = NULL, conditions = NULL,
check_inputs = TRUE, simple = TRUE)

## Arguments

N The number of units. N must be a positive integer. (required) Use for a two-arm design. prob is the probability of assignment to treatment and must be a real number between 0 and 1 inclusive and must be length 1. (optional) Use for a two-arm design. prob is the probability of assignment to treatment and must be a real number between 0 and 1 inclusive and must be length N. (optional) Use for a multi-arm design in which the values of prob_each determine the probabilities of assignment to each treatment condition. prob_each must be a numeric vector giving the probability of assignment to each condition. All entries must be nonnegative real numbers between 0 and 1 inclusive and the total must sum to 1. It may be a conditions-length vector or a N-by-conditions matrix. (optional) The number of treatment arms. If unspecified, num_arms will be determined from the other arguments. (optional) A character vector giving the names of the treatment groups. If unspecified, the treatment groups will be named 0 (for control) and 1 (for treatment) in a two-arm trial and T1, T2, T3, in a multi-arm trial. An exception is a two-group design in which num_arms is set to 2, in which case the condition names are T1 and T2, as in a multi-arm trial with two arms. (optional) logical. Defaults to TRUE. logical. internal use only.

## Value

A matrix of probabilities of assignment

## Examples

# Two Group Designs
prob_mat <- simple_ra_probabilities(N=100)
#>      prob_0 prob_1
#> [1,]    0.5    0.5
#> [2,]    0.5    0.5
#> [3,]    0.5    0.5
#> [4,]    0.5    0.5
#> [5,]    0.5    0.5
#> [6,]    0.5    0.5
prob_mat <- simple_ra_probabilities(N=100, prob=0.5)
#>      prob_0 prob_1
#> [1,]    0.5    0.5
#> [2,]    0.5    0.5
#> [3,]    0.5    0.5
#> [4,]    0.5    0.5
#> [5,]    0.5    0.5
#> [6,]    0.5    0.5
prob_mat <- simple_ra_probabilities(N=100, prob_each = c(0.3, 0.7),
conditions = c("control", "treatment"))
#>      prob_control prob_treatment
#> [1,]          0.3            0.7
#> [2,]          0.3            0.7
#> [3,]          0.3            0.7
#> [4,]          0.3            0.7
#> [5,]          0.3            0.7
#> [6,]          0.3            0.7
# Multi-arm Designs
prob_mat <- simple_ra_probabilities(N=100, num_arms=3)
#>        prob_T1   prob_T2   prob_T3
#> [1,] 0.3333333 0.3333333 0.3333333
#> [2,] 0.3333333 0.3333333 0.3333333
#> [3,] 0.3333333 0.3333333 0.3333333
#> [4,] 0.3333333 0.3333333 0.3333333
#> [5,] 0.3333333 0.3333333 0.3333333
#> [6,] 0.3333333 0.3333333 0.3333333
prob_mat <- simple_ra_probabilities(N=100, prob_each=c(0.3, 0.3, 0.4))
#>      prob_T1 prob_T2 prob_T3
#> [1,]     0.3     0.3     0.4
#> [2,]     0.3     0.3     0.4
#> [3,]     0.3     0.3     0.4
#> [4,]     0.3     0.3     0.4
#> [5,]     0.3     0.3     0.4
#> [6,]     0.3     0.3     0.4
prob_mat <- simple_ra_probabilities(N=100, prob_each=c(0.3, 0.3, 0.4),
conditions=c("control", "placebo", "treatment"))
#>      prob_control prob_placebo prob_treatment
#> [1,]          0.3          0.3            0.4
#> [2,]          0.3          0.3            0.4
#> [3,]          0.3          0.3            0.4
#> [4,]          0.3          0.3            0.4
#> [5,]          0.3          0.3            0.4
#> [6,]          0.3          0.3            0.4
prob_mat <- simple_ra_probabilities(N=100, conditions=c("control", "placebo", "treatment"))