Extract Least Square Means from a GEE Model

Usage

lsmeans(
  object,
  conf_level = 0.95,
  weights = "proportional",
  specs = object$vars$arm,
  ...
)

# S3 method for class 'tern_gee_logistic'
lsmeans(
  object,
  conf_level = 0.95,
  weights = "proportional",
  specs = object$vars$arm,
  ...
)

Arguments

object: (tern_gee)
result of fit_gee().
conf_level: (proportion)
confidence level
weights: (string)
type of weights to be used for the least square means, see emmeans::emmeans() for details.
specs: (string or formula) specifications passed to emmeans::emmeans()
...: additional arguments for methods

Value

A data.frame with least-square means and contrasts. Additional classes allow to dispatch downstream methods correctly, too.

Examples

df <- fev_data
df$AVAL <- rbinom(n = nrow(df), size = 1, prob = 0.5)
fit <- fit_gee(vars = vars_gee(arm = "ARMCD"), data = df)

lsmeans(fit)
#>   ARMCD  prop_est prop_est_se prop_lower_cl prop_upper_cl   n   or_est
#> 1   PBO 0.5202839  0.02106086     0.4788982     0.5613931 420       NA
#> 2   TRT 0.5414730  0.02440801     0.4933255     0.5888583 380 1.088819
#>   or_lower_cl or_upper_cl log_or_est log_or_lower_cl log_or_upper_cl conf_level
#> 1          NA          NA         NA              NA              NA       0.95
#> 2   0.8443247    1.404113 0.08509393      -0.1692181        0.339406       0.95

lsmeans(fit, conf_level = 0.90, weights = "equal")
#>   ARMCD  prop_est prop_est_se prop_lower_cl prop_upper_cl   n   or_est
#> 1   PBO 0.5202839  0.02106086     0.4788982     0.5613931 420       NA
#> 2   TRT 0.5414730  0.02440801     0.4933255     0.5888583 380 1.088819
#>   or_lower_cl or_upper_cl log_or_est log_or_lower_cl log_or_upper_cl conf_level
#> 1          NA          NA         NA              NA              NA        0.9
#> 2   0.8796287    1.347759 0.08509393      -0.1282554       0.2984433        0.9