stepdown {crctStepdown}R Documentation

Conduct the randomisation-based stepdown procedure

Description

For a set of models fit with lme4, the function will conduct the randomisation tests and generate p-values for the null hypotheses of no treatment effect that controls the family-wise error rate, and generates a 100(1-alpha)% confidence set for the treatment effect model parameters.

Usage

stepdown(
  fitlist,
  tr_var = "treat",
  cl_var = "cl",
  data,
  alpha = 0.05,
  plots = TRUE,
  n_permute = 1000,
  nsteps = 1000,
  type = "rw",
  rand_func = NULL,
  confint = TRUE,
  verbose = TRUE
)

Arguments

fitlist

A list of models fitted with lme4. All models should be fit using the same data frame.

tr_var

String indicating the name of the column in data that is a binary indicator for whether the observation was under the treatment (1=treatment, 0=control)

cl_var

String specifying the name of the column identifying the clusters/cluster-time

data

A data frame containing the data used to fit the models in fitlist

alpha

Numeric. 100(1-alpha)% confidence intervals are calculated. Default it 0.05

plots

Logical indicating whether to plot permutational distributions and confidence interval search during running of function. Default is TRUE

n_permute

Number of permutations of the randomisation test to run

nsteps

Number of steps of the confidence interval search process

type

Method of correction: options are "rw" = Romano-Wolf randomisation test based stepdown, "h" = Holm standard stepdown, "h" = Holm stepdown using randomisation test, "b" = standard Bonferroni, "br" = Bonerroni using randomisation test, or "none" = randomisation test with no correction.

rand_func

String of the name of a function that re-randomises the clusters. The function should produce a data frame that identifies the clusters in the treatment group under the new randomisation scheme. The data frame can either have a single column with name cl_var or two columns of cl_var and t identifying the cluster ID and time period a cluster joins the treatment group. If NULL then clusters are randomised in a 1:1 ratio to treatment and control

confint

Logical indicating whether to run the confidence interval search process

verbose

Logical indicating whether to provide detailed output

Value

A data frame with the point estimates, p-values, and confidence intervals

Examples

out <- twoarm_sim()
data <- out[[1]]
  fit1 <- lme4::glmer(y1 ~ treat + (1|cl) ,
data=data,
family="poisson")

fit2 <- lme4::glmer(y2 ~ treat + (1|cl),
                    data=data,
                    family="poisson")
 stepdown(fitlist=list(fit1,fit2),
          data=data,
          n_permute = 100,
          nsteps=100,
          plots=FALSE,
          verbose=TRUE)

[Package crctStepdown version 0.2.1 Index]