anova.lmm | R Documentation |
Test linear combinations of parameters from a linear mixed model using Wald test or Likelihood Ratio Test (LRT).
## S3 method for class 'lmm'
anova(
object,
effects = NULL,
rhs = NULL,
robust = NULL,
df = NULL,
univariate = TRUE,
multivariate = TRUE,
transform.sigma = NULL,
transform.k = NULL,
transform.rho = NULL,
simplify = NULL,
...
)
object |
a |
effects |
[character or numeric matrix] Should the Wald test be computed for all variables ( |
rhs |
[numeric vector] the right hand side of the hypothesis. Only used when the argument |
robust |
[logical] Should robust standard errors (aka sandwich estimator) be output instead of the model-based standard errors.
Can also be |
df |
[logical] Should degrees-of-freedom be estimated using a Satterthwaite approximation? If yes F-distribution (multivariate) and Student's t-distribution (univariate) are used. Other chi-squared distribution and normal distribution are used. |
univariate |
[logical] Should an estimate, standard error, confidence interval, and p-value be output for each hypothesis? |
multivariate |
[logical] Should all hypotheses be simultaneously tested using a multivariate Wald test? |
transform.sigma , transform.k , transform.rho |
are passed to the |
simplify |
[logical] should only the estimates, variance-covariance with its gradient, and degrees-of-freedom relative to the parameters involved in the Wald test be stored (TRUE) or relative to all model parameters (0.5) along with their iid decomposition (FALSE). |
... |
Not used. For compatibility with the generic method. |
By default adjustment of confidence intervals and p-values for multiple comparisons is based on the distribution of the maximum-statistic.
This is refered to as a single-step Dunnett multiple testing procedures in table II of Dmitrienko et al. (2013).
It is performed using the multcomp package with the option test = adjusted("single-step")
with equal degrees-of-freedom
or by simulation using a Student's t copula with unequal degrees-of-freedom (more in the note of the details section of confint.Wald_lmm
).
A data.frame (LRT) or a list of containing the following elements (Wald):
multivariate
: data.frame containing the multivariate Wald test.
The column df.num
refers to the degrees-of-freedom for the numerator (i.e. number of hypotheses)
wherease the column df.denum
refers to the degrees-of-freedom for the denominator (i.e. Satterthwaite approximation).
univariate
: data.frame containing each univariate Wald test.
glht
: used internally to call functions from the multcomp package.
object
: list containing key information about the linear mixed model.
args
: list containing argument values from the function call.
Dmitrienko, A. and D'Agostino, R., Sr (2013), Traditional multiplicity adjustment methods in clinical trials. Statist. Med., 32: 5172-5218. https://doi.org/10.1002/sim.5990.
summary.Wald_lmm
or confint.Wald_lmm
for a summary of the results.
autoplot.Wald_lmm
for a graphical display of the results.
rbind.Wald_lmm
for combining result across models and adjust for multiple comparisons.
#### 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 ~ visit + X1 + X2 + X5,
repetition = ~visit|id, structure = "UN", data = dL)
#### Multivariate Wald test ####
## F-tests
anova(eUN.lmm)
anova(eUN.lmm, effects = "all")
anova(eUN.lmm, robust = TRUE, df = FALSE)
summary(anova(eUN.lmm), method = "bonferroni")
## user defined F-test
summary(anova(eUN.lmm, effects = c("X1=0","X2+X5=10")))
## chi2-tests
anova(eUN.lmm, df = FALSE)
## with standard contrast
if(require(multcomp)){
amod <- lmm(breaks ~ tension, data = warpbreaks)
e.amod <- anova(amod, effect = mcp(tension = "Tukey"))
summary(e.amod)
}
#### Likelihood ratio test ####
eUN0.lmm <- lmm(Y ~ X1 + X2, repetition = ~visit|id, structure = "UN", data = dL)
anova(eUN.lmm, eUN0.lmm)
eCS.lmm <- lmm(Y ~ X1 + X2 + X5, repetition = ~visit|id, structure = "CS", data = dL)
anova(eUN.lmm, eCS.lmm)
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