Bland-Altman analysis — bland

Functions that use the Bland-Altman method to assess the agreement between two numerical vectors.

Usage

s_bland_altman(x, y, conf_level = 0.95)

g_bland_altman(x, y, conf_level = 0.95)

Arguments

x: (numeric)
vector of numbers we want to analyze.
y: (numeric)
vector of numbers we want to analyze, to be compared with x.
conf_level: (proportion)
confidence level of the interval.

Value

s_bland_altman() returns a named list of the following elements: df, difference_mean, ci_mean, difference_sd, difference_se, upper_agreement_limit, lower_agreement_limit, agreement_limit_se, upper_agreement_limit_ci, lower_agreement_limit_ci, t_value, and n.

g_bland_altman() returns a ggplot Bland-Altman plot.

Functions

s_bland_altman(): Statistics function that compares two numeric vectors using the Bland-Altman method and calculates a variety of statistics.
g_bland_altman(): Graphing function that produces a Bland-Altman plot.

Examples

x <- seq(1, 60, 5)
y <- seq(5, 50, 4)
conf_level <- 0.9

# Derive statistics that are needed for Bland-Altman plot
s_bland_altman(x, y, conf_level = conf_level)
#> $df
#>    average difference
#> 1      3.0         -4
#> 2      7.5         -3
#> 3     12.0         -2
#> 4     16.5         -1
#> 5     21.0          0
#> 6     25.5          1
#> 7     30.0          2
#> 8     34.5          3
#> 9     39.0          4
#> 10    43.5          5
#> 11    48.0          6
#> 12    52.5          7
#> 
#> $difference_mean
#> [1] 1.5
#> 
#> $ci_mean
#> [1] -0.3692162  3.3692162
#> 
#> $difference_sd
#> [1] 3.605551
#> 
#> $difference_se
#> [1] 1.040833
#> 
#> $upper_agreement_limit
#> [1] 7.430604
#> 
#> $lower_agreement_limit
#> [1] -4.430604
#> 
#> $agreement_limit_se
#> [1] 1.802776
#> 
#> $upper_agreement_limit_ci
#> [1]  4.193027 10.668181
#> 
#> $lower_agreement_limit_ci
#> [1] -7.668181 -1.193027
#> 
#> $t_value
#> [1] 1.795885
#> 
#> $n
#> [1] 12
#> 

# Create a Bland-Altman plot
g_bland_altman(x = x, y = y, conf_level = conf_level)