| stepdown {crctStepdown} | R Documentation |
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.
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
)
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 |
A data frame with the point estimates, p-values, and confidence intervals
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)