Faux

rep_len(x, len)

Repeat a value or vector to create a vector of specified length

Arguments

• x: The value or vector to repeat
• len: The length of the resulting vector

Examples

julia> rep_len(1, 3)
[1, 1, 1]
julia> rep_len(1:2, 3)
[1, 2, 1]
julia> rep_len([1,2,3], 2)
[1,2]

rnorm_multi(n = 100, vars = 1, mu = 0, sd = 1, r = 0, 
            varnames = "X", 
            empirical = false,
            as_matrix = false)

Make normally distributed vectors with specified relationships.

Arguments

• @n: the number of samples required
• @vars: the number of variables to return
• @mu: a vector giving the means of the variables (numeric vector of length 1 or vars)
• @sd: the standard deviations of the variables (numeric vector of length 1 or vars)
• @r: the correlations among the variables (can be a single number, varsvars matrix, varsvars vector, or a vars*(vars-1)/2 vector)
• @varnames: optional names for the variables (string vector of length vars) defaults if r is a matrix with column names
• @empirical: logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance
• @as.matrix: logical. If true, returns a matrix

Examples

julia> tovector(1) == [1]
julia> tovector(1) != 1
julia> tovector(1:3) == [1,2,3]

select_by_type(df)

Select columns from a DataFrame of a specific type

Arguments

• df: The DataFrame
• type: The type of column to select

Examples

julia> df = DataFrame(s = ["A", "B"], 
                      i = [1,2], 
                      n = [1.1, 2.2], 
                      b = [true, false]);
julia> select_by_type(df)
julia> select_by_type(df, Int64)
julia> select_by_type(df, String)
julia> select_by_type(df, Bool)

sim_design(within = [], 
           between = [], 
            n = 100, mu = 0, sd = 1, r = 0, 
            empirical::Bool = false, 
            long::Bool = false, 
            dv::String = "y", 
            id::String = "id",
            vardesc::Dict = Dict(),
            sep::String = "_",
            rep::Int64 = 1)

Simulate data from design

Generates a data table with a specified within and between design.

Arguments

• within: an array of Pairs for the within-subject factors
• between: an array of Pairs for the between-subject factors
• n: the number of samples required per between-subject cell
• mu: the means of the variables
• sd: the standard deviations of the variables
• r: the correlations among the variables (can be a single number, full correlation matrix as a matrix or vector, or a vector of the upper right triangle of the correlation matrix
• empirical: if true, mu, sd and r specify the empirical not population mean, sd and covariance
• long: Whether the returned tbl is in wide or long format (defaults to value of faux_options("long"))
• dv: the name of the dv column for long plots (defaults to y)
• id: the name of the id column (defaults to id)
• vardesc: a Dict of variable descriptions having the names of the within- and between-subject factors
• rep: the number of data frames to simulate (default 1); if greater than 1, the returned data frame contains a rep column
• sep: separator for factor levels

Examples

julia> using Faux, DataStructures
julia> b = ["condition" => ["ctl", "exp"]]
julia> w = ["version" => ["A", "B"]]
julia> df = sim_design(within = w, between = b, n = 10)

tovector(x)

Convert a UnitRange or single value to a vector

Arguments

• x: The value to convert

Examples

julia> tovector(1)
[1]
julia> tovector(1:3)
[1,2,3]