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Type: Package
Title: A Zero-Inflated Beta Random Effect Model
Version: 1.0.2
Date: 2023-10-16
Description: A two-part zero-inflated Beta regression model with random effects (ZIBR) for testing the association between microbial abundance and clinical covariates for longitudinal microbiome data. Eric Z. Chen and Hongzhe Li (2016) <doi:10.1093/bioinformatics/btw308>.
License: MIT + file LICENSE
LazyData: TRUE
RoxygenNote: 7.2.3
Encoding: UTF-8
Suggests: betareg, dplyr, lme4 (≥ 1.1-34), nlme, knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Depends: R (≥ 2.10), statmod
VignetteBuilder: knitr
URL: https://github.com/PennChopMicrobiomeProgram/ZIBR
BugReports: https://github.com/PennChopMicrobiomeProgram/ZIBR/issues
NeedsCompilation: no
Packaged: 2023-10-16 19:47:32 UTC; ctbus
Author: Eric Zhang Chen [aut, cph], Charlie Bushman [cre]
Maintainer: Charlie Bushman <ctbushman@gmail.com>
Repository: CRAN
Date/Publication: 2023-10-17 09:50:08 UTC

Fit beta random effect

Description

Fit beta random effect

Usage

fit_beta_random_effect(
  Z = Z,
  Y = Y,
  subject.ind = subject.ind,
  time.ind = time.ind,
  quad.n = 30,
  verbose = FALSE
)

Arguments

Z

FILL

Y

FILL

subject.ind

the subject index

time.ind

the time index

quad.n

number of points in gaussian quadrature

verbose

a boolean to enable more output

Value

a named list


Fit logisitic random effect

Description

Fit logisitic random effect

Usage

fit_logistic_random_effect(
  X = X,
  Y = Y,
  subject.ind = subject.ind,
  time.ind = time.ind,
  quad.n = 30,
  verbose = FALSE
)

Arguments

X

FILL

Y

FILL

subject.ind

the subject index

time.ind

the time index

quad.n

number of points in gaussian quadrature

verbose

a boolean to enable more output

Value

a named list


Fit zero inflated beta random effect

Description

Fit zero inflated beta random effect

Usage

fit_zero_inflated_beta_random_effect(
  X = X,
  Z = Z,
  Y = Y,
  subject_ind = subject_ind,
  time_ind = time_ind,
  component_wise_test = TRUE,
  joint_test = TRUE,
  quad_n = 30,
  verbose = FALSE
)

Arguments

X

FILL

Z

FILL

Y

FILL

subject_ind

the subject index

time_ind

the time index

component_wise_test

boolean to run component-wise test

joint_test

boolean to run joint test

quad_n

number of points in gaussian quadrature

verbose

a boolean to enable more output

Value

a named list


Longitudinal human microbiome data

Description

A dataset containing the bacterial abundance and clinical information from a longitudinal human microbiome study

Usage

ibd

Format

A data frame with 236 rows and 5 variables:

Sample

Sample IDs

Subject

Subject IDs

Time

Time points

Treatment

Treatment, 0 for antiTNF, 1 for EEN

Abundance

Abundance for Eubacterium

...

References

Lewis and Chen et al. (2016) Cell Host & Microbe 18 (4), 489-500


Simulate beta data

Description

Simulate beta data

Usage

simulate_beta_random_effect_data(
  subject_n = 50,
  time_n = 5,
  v = 2,
  beta = as.matrix(c(-0.5, -0.5, 0.5)),
  Z = NA,
  s2 = 1,
  sim_seed = 100
)

Arguments

subject_n

the number of subjects

time_n

the number of time points

v

FILL

beta

FILL

Z

FILL

s2

FILL

sim_seed

the random seed with which to simulate the data

Value

a named list


Simulate logistic data

Description

Simulate logistic data

Usage

simulate_logistic_data(
  subject_n = 50,
  time_n = 5,
  alpha = as.matrix(c(0, 0.5, -1)),
  s1 = 0.5,
  sim_seed = 100
)

Arguments

subject_n

the number of subjects

time_n

the number of time points

alpha

FILL

s1

FILL

sim_seed

the random seed with which to simulate the data

Value

a named list


Simulate data according to zero-inflated beta random effects model

Description

Simulate data according to zero-inflated beta random effects model

Usage

simulate_zero_inflated_beta_random_effect_data(
  subject_n = 50,
  time_n = 5,
  v = 2,
  alpha = as.matrix(c(0, 0.5, -1)),
  beta = as.matrix(c(-0.5, -0.5, 0.5)),
  X = NA,
  Z = NA,
  s1 = 0.2,
  s2 = 0.2,
  sim_seed = 100
)

Arguments

subject_n

number of subjects

time_n

number of time points for each subject

v

the dispersion parameter in beta component

alpha

the coefficients in logistic component

beta

the coefficients in beta component

X

the covariates in logistic component

Z

the covariates in beta component

s1

the stardard deviation of random effect in logistic component

s2

the stardard deviation of random effect in beta component

sim_seed

the random seed

Value

a named list

Examples

simulate_zero_inflated_beta_random_effect_data(
  subject_n = 100, time_n = 5,
  X = as.matrix(c(rep(0, 50 * 5), rep(1, 50 * 5))),
  alpha = as.matrix(c(-0.5, 1)),
  beta = as.matrix(c(-0.5, 0.5)),
  s1 = 1, s2 = 0.8,
  v = 5,
  sim_seed = 100
)

Fit zero-inflated beta regression with random effects

Description

Fit zero-inflated beta regression with random effects

Usage

zibr(
  logistic_cov,
  beta_cov,
  Y,
  subject_ind,
  time_ind,
  component_wise_test = TRUE,
  quad_n = 30,
  verbose = FALSE
)

Arguments

logistic_cov

the covariates in logistic component

beta_cov

the covariates in beta component

Y

the response variable in the regression model

subject_ind

the variable for subject IDs

time_ind

the variable for time points

component_wise_test

whether to perform component wise test. If true, ZIBR will calculate p-values for logistic and beta component respectively.

quad_n

Gaussian quadrature points

verbose

print the fitting process

Value

a named list

Examples

## simulate some data
sim <- simulate_zero_inflated_beta_random_effect_data(
  subject_n = 100, time_n = 5,
  X = as.matrix(c(rep(0, 50 * 5), rep(1, 50 * 5))),
  Z = as.matrix(c(rep(0, 50 * 5), rep(1, 50 * 5))),
  alpha = as.matrix(c(-0.5, 1)),
  beta = as.matrix(c(-0.5, 0.5)),
  s1 = 1, s2 = 0.8,
  v = 5,
  sim_seed = 100
)

## run zibr on the simulated data
zibr_fit <- zibr(
  logistic_cov = sim$X, beta_cov = sim$Z, Y = sim$Y,
  subject_ind = sim$subject_ind, time_ind = sim$time_ind
)

zibr_fit

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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