confint.lmm: Statistical Inference for Linear Mixed Model

In LMMstar: Repeated Measurement Models for Discrete Times

Statistical Inference for Linear Mixed Model

Description

Compute confidence intervals (CIs) and p-values for the coefficients of a linear mixed model.

Usage

## S3 method for class 'lmm'
confint(
  object,
  parm = NULL,
  level = 0.95,
  effects = NULL,
  robust = FALSE,
  null = NULL,
  columns = NULL,
  df = NULL,
  type.information = NULL,
  transform.sigma = NULL,
  transform.k = NULL,
  transform.rho = NULL,
  transform.names = TRUE,
  backtransform = NULL,
  ...
)

Arguments

object	a lmm object.
parm	Not used. For compatibility with the generic method.
level	[numeric,0-1] the confidence level of the confidence intervals.
effects	[character] Should the CIs/p-values for all coefficients be output ("all"), or only for mean coefficients ("mean" or "fixed"), or only for variance coefficients ("variance"), or only for correlation coefficients ("correlation").
robust	[logical] Should robust standard errors (aka sandwich estimator) be output instead of the model-based standard errors. Not feasible for variance or correlation coefficients estimated by REML.
null	[numeric vector] the value of the null hypothesis relative to each coefficient.
columns	[character vector] Columns to be output. Can be any of "estimate", "se", "statistic", "df", "null", "lower", "upper", "p.value".
df	[logical] Should a Student's t-distribution be used to model the distribution of the coefficient. Otherwise a normal distribution is used.
type.information, transform.sigma, transform.k, transform.rho, transform.names	are passed to the vcov method. See details section in coef.lmm.
backtransform	[logical] should the variance/covariance/correlation coefficient be backtransformed?
...	Not used. For compatibility with the generic method.

Value

A data.frame containing some of the following coefficient (in rows):

• column estimate: the estimate.

• column se: the standard error.

• column statistic: the test statistic.

• column df: the degree of freedom.

• column lower: the lower bound of the confidence interval.

• column upper: the upper bound of the confidence interval.

• column null: the null hypothesis.

• column p.value: the p-value relative to the null hypothesis.

See Also

the function anova to perform inference about linear combinations of coefficients and adjust for multiple comparisons.

coef.lmm for a simpler output (e.g. only estimates).
model.tables.lmm for a more detailed output (e.g. with p-value).

Examples

#### simulate data in the long format ####
set.seed(10)
dL <- sampleRem(100, n.times = 3, format = "long")

#### fit Linear Mixed Model ####
eUN.lmm <- lmm(Y ~ X1 + X2 + X5, repetition = ~visit|id, structure = "UN", data = dL)

#### Confidence intervals ####
## based on a Student's t-distribution with transformation
confint(eUN.lmm, effects = "all")
## based on a Student's t-distribution without transformation
confint(eUN.lmm, effects = "all",
        transform.sigma = "none", transform.k = "none", transform.rho = "none")
## based on a Student's t-distribution transformation but not backtransformed
confint(eUN.lmm, effects = "all", backtransform = FALSE)
## based on a Normal distribution with transformation
confint(eUN.lmm, df = FALSE)

LMMstar documentation built on Nov. 9, 2023, 1:06 a.m.