iccCompare: Comparison of Dependent Intraclass Correlation Coefficients

Description

iccCompare: Tools for Comparing Dependent Intraclass Correlation Coefficients.

Provides functions to test the equality of dependent ICCs using various methods.

Details

See the package DESCRIPTION file for more information.

Author(s)

Maintainer: Josep Lluis Carrasco jlcarrasco@ub.edu

Authors:

• Gonzalo Peon Pena

Tests the equality of dependent ICCs using the likelihood ratio test (LRT)

Description

Tests the equality of dependent ICCs using the likelihood ratio test (LRT)

Usage

ICC_LR_test(data, ry = "y", rind = "ind", rtype = "type", optimizer = "nlminb")

Arguments

Details

The null hypothesis of equality of dependent ICCs is tested using the likelihood ratio test (LRT) proposed in Donner and Zou (2002).

Value

The output is an object of class htest with the following components:

• null.value. Vector. The value of the ICC in the null hypothesis of equality of ICCs.

• estimate. Vector. The estimates of the ICC

• statistic. Numerical. The value of the LR statistic.

• p.value. Numerical. The value of the LR statistic.

References

Donner, A. and Zou, G. (2002). Testing the equality of dependent intraclass correlation coefficients. Journal of the Royal Statistical Society: Series D (The Statistician), 51(3):367–379

Examples

sin_res<-ICC_LR_test(sin_data,ry="Sinuosity",rind="id",rtype="Section")

Fits the linear mixed model to estimate the dependent ICCs

Description

Fits the linear mixed model to estimate the dependent ICCs

Usage

fit_model_dep_icc(dataset, ry, rind, rtype, warnings = TRUE)

Arguments

Value

An object of class lme.

Computes the confidence interval for an ICC

Description

Computes the confidence interval for an ICC

Usage

ic_icc(icc, se, alpha = 0.05, m, N)

Arguments

Details

Confidence intervals are constructed using asymptotic methods assuming a Normal distribution. The implemented methods include: the asymptotic Normal approach, Fisher's Z transformation, and the Konishi-Gupta transformation.

Tests the equality of dependent ICCs

Description

Tests the equality of dependent ICCs

Usage

icc_dep_test(
  data,
  ry,
  rind,
  rtype,
  alpha = 0.05,
  Wald = FALSE,
  WL = 1:2,
  Boot = FALSE,
  nboot = 500,
  Perm = FALSE,
  nperm = 100,
  parallel = TRUE,
  workers = 15,
  future_seed = NULL,
  progress = TRUE
)

Arguments

Details

The variance components required for ICC estimation are obtained using a linear mixed-effects model that accounts for correlations across settings. The null hypothesis of equality between dependent ICCs is evaluated through the following methods:

- Wald test based on Fisher’s Z and Konishi–Gupta transformations, using either asymptotic or bootstrap standard errors;

- Chi-square test with asymptotic or bootstrap standard errors;

- Permutation test.

Value

The output is a list with the following components:

• Model. Object of class lme with the fitted model estimates.

• Estimates. List containing three objects: 1) a matrix with the ICCs etimates; 2) the variance-covariance matrix of the ICC estimates; 3) Confidence intervals of the ICC estimates.

• p_values. List with the computed p-values testing the equality of the ICCs.

• boot_res. List with three objects: 1) bootstrap estimates of the ICCs; 2) mean of the bootstrap ICC estimates; 3) variance-covariance matrix of the ICC estimates obtained by bootstrapping.

• perm_res. List with three elements: 1) P-value obtained by permutations to test the equality of the ICCs; 2) Vector containing the sum of the squared differences between the ICCs obtained in each permutation; 3) the sum of the squared differences between the ICCs obtained in the original sample.

Examples

sin_res_b<-icc_dep_test(sin_data,ry="Sinuosity",rind="id",rtype="Section",alpha=0.05,Wald=TRUE,
WL=1:2,)


sin_res<-icc_dep_test(sin_data,ry="Sinuosity",rind="id",rtype="Section",alpha=0.05,Wald=TRUE,
                     WL=1:2,Boot=TRUE,nboot=500,Perm=TRUE,nperm=100,
                     parallel=TRUE,workers=15,future_seed = NULL,progress=TRUE)



dia_res<-icc_dep_test(cccrm::bpres,ry="DIA",rind="ID",rtype="METODE",alpha=0.05,Wald=TRUE,
WL=1:2,Boot=TRUE,nboot=500,Perm=TRUE,nperm=100,
parallel=TRUE,workers=15,future_seed = NULL,progress=TRUE)

Sinuosity data

Description

A data frame containingthe sinuosity index from 90 trajectories

Usage

sin_data

Format

A data frame containingt he sinuosity index from 388 trips of 36 gulls

Sinuosity

Sinuosity index

id

Subject identifier

Section

Time section where the trip started: Day or Night