predict.robu produces the predicted mean and confidence interval of a fitted robumeta model object given a prediction vector.
# S3 method for robu
predict(object, pred.vector, level = 0.95, ...)prediction the predicted value based on the prediction vector.
se The standard error for the predicted mean.
t The t-statistic calculated based on the predicted mean.
df The small sample corrected degrees of freedom of the distribution of the t-statistic.
lower The lower bound of the confidence interval for the predicted mean.
upper The upper bound of the confidence interval for the predicted mean.
A fitted robumeta model object.
A prediction vector containing the new covariate values.
Confidence level.
Additional arguments to predict.
intercept
If an intercept is included in the robumeta model,
the first element should always be 1, representing the intercept,
followed by the covariate values in appropriate order.
If the robumeta model does not have an intercept, the prediction
vector should begin with the first covariate value.
variable
For continuous variables, use the variable value as the
corresponding element value in pred.vector.
For a categorical variable the original variable value should
be transformed to match the coding system used in the robumeta
model (e.g. dummy coding, deviation coding, etc.).
NA
If the vector contains NAs, predict.robu will remove the
corresponding covariates from the original data, and refit a new
robumeta model. The prediction and confidence interval will be
estimated based on the new model.
robu_mod <- robu(LOR1 ~ study_design + duration + service_hrs,
data = dropoutPrevention,
studynum = studyID,
var.eff.size = varLOR,
modelweights = "HIER",
small = TRUE)
In this robumeta model, the first covariate is a categorical variable that contains three levels: "Matched" (33 percent, dummy code: 00), "Randomized"(24 percent, 01) and "non-match non-randomized"(43 percent, 10). The corresponding prediction vector begins with 1 (intercept), and followed by 0, 0, the dummy code for "Matched". The last two elements are 38 and 5, the values for duration and sevice_hrs.
predict(object = robu_mod, pred.vector = c(1,0,0,38,5),level = 0.95)
If we do not know the value of duration, the prediction vector should be c(1,0,0,NA,5). predict.robu() will refit a new model without the covariate duration, and the prediction will be based on it.
predict(object = robu_mod, pred.vector = c(1,0,0,NA,5),level = 0.95)