ccc_est: Concordance correlation Coefficient estimation from a linear...

In cccrm: Concordance Correlation Coefficient for Repeated (and Non-Repeated) Measures

Concordance correlation Coefficient estimation from a linear mixed model

Description

It computes the Concordance Correlation Coefficient and its asymptotic confidence interval.

Usage

ccc_est(model, D = NULL, cl = 0.95, transf = "F2", sd_est = TRUE, ...)

Arguments

model	The lme model.
D	Weights vector.
cl	Confidence level (0.95 as a default). Bounded between 0 and 1.
transf	Character string. Whether to apply a transformation of the coefficient for inference. Valid options are: "F" for Fisher's Z-transformation; "F2" For Fisher's Z-transformation setting m=2 (default); "KG" Konishi-Gupta transformation; "None", no transformation is applied. See *Details* for further information.
sd_est	Logical. Whether to estimate the asymptotic standard deviation (defaults to TRUE) or to only report the ccc value.
...	To pass further arguments.

Value

A ccc class object.

See Also

ccc_vc

Examples

set.seed(1984)
df <- ccc_sim_data(n=50,b = c(0,1), mu = -0.25, sa = 1.5, se = 1, nrep=2)
mod <- lme_model(df,"y","id",rmet="met")
ccc_est(mod)

cccrm documentation built on Oct. 19, 2024, 9:06 a.m.