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Standard deviation of binary data

Source: R/sd-binary.R
sd-binary.Rd

Compute the sample SD of binary data (i.e., only 0 and 1 values) in either of four ways, each based on different inputs:

  • sd_binary_groups() takes the cell sizes of both groups, those coded as 0 and those coded as 1.

  • sd_binary_0_n() takes the cell size of the group coded as 0 and the total sample size.

  • sd_binary_1_n() takes the cell size of the group coded as 1 and the total sample size.

  • sd_binary_mean_n() takes the mean and the total sample size.

These functions are used as helpers inside debit(), and consequently debit_map().

Usage

sd_binary_groups(group_0, group_1)

sd_binary_0_n(group_0, n)

sd_binary_1_n(group_1, n)

sd_binary_mean_n(mean, n)

Arguments

group_0

Integer. Cell size of the group coded as 0.

group_1

Integer. Cell size of the group coded as 1.

n

Integer. Total sample size.

mean

Numeric. Mean of the binary data.

Value

Numeric. Sample standard deviation.

References

Heathers, James A. J., and Brown, Nicholas J. L. 2019. DEBIT: A Simple Consistency Test For Binary Data. https://osf.io/5vb3u/.

See also

is_subset_of_vals(x, 0, 1) checks whether x (a list or atomic vector) contains nothing but binary numbers.

Examples

# If 127 values are coded as 0 and 153 as 1...
sd_binary_groups(group_0 = 127, group_1 = 153)
#> [1] 0.4987311

# ...so that n = 280:
sd_binary_0_n(group_0 = 127, n = 280)
#> [1] 0.4987311
sd_binary_1_n(group_1 = 153, n = 280)
#> [1] 0.4987311

# If only the mean and total sample size are
# given, or these are more convenient to use,
# they still lead to the same result as above
# if the mean is given with a sufficiently
# large number of decimal places:
sd_binary_mean_n(mean = 0.5464286, n = 280)
#> [1] 0.4987311