Extract Least Square Means from a GEE Model

Usage

lsmeans(object, conf_level = 0.95, weights = "proportional", ...)

# S3 method for tern_gee_logistic
lsmeans(object, conf_level = 0.95, weights = "proportional", ...)

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.
...: 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 0.5086584  0.02405795     0.4615684     0.5555953 420        NA
#> 2   TRT 0.4941228  0.02542517     0.4445127     0.5438489 380 0.9435112
#>   or_lower_cl or_upper_cl  log_or_est log_or_lower_cl log_or_upper_cl
#> 1          NA          NA          NA              NA              NA
#> 2   0.7170383    1.241514 -0.05814704       -0.332626       0.2163319
#>   conf_level
#> 1       0.95
#> 2       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 0.5086584  0.02405795     0.4615684     0.5555953 420        NA
#> 2   TRT 0.4941228  0.02542517     0.4445127     0.5438489 380 0.9435112
#>   or_lower_cl or_upper_cl  log_or_est log_or_lower_cl log_or_upper_cl
#> 1          NA          NA          NA              NA              NA
#> 2    0.749389    1.187919 -0.05814704       -0.288497        0.172203
#>   conf_level
#> 1        0.9
#> 2        0.9