Predict mean and variance of the outcome for a SensIAT within-group model

Predict mean and variance of the outcome for a SensIAT within-group model

Usage

# S3 method for class 'SensIAT_fulldata_model'
predict(object, time, ...)

# S3 method for class 'SensIAT_within_group_model'
predict(object, time, include.var = TRUE, ..., base = object$base)

Arguments

object

SensIAT_within_group_model object

time

Time points of interest

...

Currently ignored.

include.var

Logical. If TRUE, the variance of the outcome is also returned

base

A SplineBasis object used to evaluate the basis functions.

Value

If include.var is TRUE, a tibble with columns time, mean, and var is returned. otherwise if include.var is FALSE, only the mean vector is returned.

Functions

• predict(SensIAT_fulldata_model): For each combination of time and alpha estimate the mean response and variance for each group as well as estimate the mean treatment effect and variance.

Examples

# \donttest{
model <-
    fit_SensIAT_within_group_model(
        group.data = SensIAT_example_data,
        outcome_modeler = SensIAT_sim_outcome_modeler,
        alpha = c(-0.6, -0.3, 0, 0.3, 0.6),
        id = Subject_ID,
        outcome = Outcome,
        time = Time,
        End = 830,
        knots = c(60,260,460),
    )
predict(model, time = c(90, 180))
#> # A tibble: 10 × 4
#>    alpha  time  mean     var
#>    <dbl> <dbl> <dbl>   <dbl>
#>  1  -0.6    90  1.46 0.00966
#>  2  -0.6   180  1.55 0.00640
#>  3  -0.3    90  1.68 0.0103 
#>  4  -0.3   180  1.81 0.00688
#>  5   0      90  1.95 0.0113 
#>  6   0     180  2.10 0.00874
#>  7   0.3    90  2.29 0.0141 
#>  8   0.3   180  2.43 0.0125 
#>  9   0.6    90  2.70 0.0194 
#> 10   0.6   180  2.78 0.0178 
# }