Approach of different packages to generate predicted values

predictions.R

library(insight)
library(datawizard)
library(modelbased)
library(ggeffects)
library(emmeans)
library(marginaleffects)

data(efc, package = "ggeffects")
efc <- data_to_factor(efc, select = "c161sex")
model <- lm(neg_c_7 ~ barthtot + c160age * c161sex, data = efc)

# create reference grid
grid <- get_datagrid(model, at = list(c161sex = c("Male", "Female"), c160age = 50))

# ggeffects
ggpredict(model, c("c160age [50]", "c161sex"))
#> # Predicted values of Negative impact with 7 items
#> 
#> # c161sex = Male
#> 
#> c160age | Predicted |         95% CI
#> ------------------------------------
#>      50 |     11.54 | [11.05, 12.03]
#> 
#> # c161sex = Female
#> 
#> c160age | Predicted |         95% CI
#> ------------------------------------
#>      50 |     11.87 | [11.59, 12.15]
#> 
#> Adjusted for:
#> * barthtot = 64.66
ggpredict(model, "c161sex", condition = c(c160age = 50))
#> # Predicted values of Negative impact with 7 items
#> 
#> c161sex | Predicted |         95% CI
#> ------------------------------------
#> Male    |     11.54 | [11.05, 12.03]
#> Female  |     11.87 | [11.59, 12.15]
#> 
#> Adjusted for:
#> * barthtot = 64.66

# marginaleffects
predictions(model, newdata = grid)
#>   rowid     type predicted std.error conf.low conf.high c160age c161sex
#> 1     1 response  11.53973 0.2487972 11.05141  12.02804      50    Male
#> 2     2 response  11.86866 0.1411197 11.59168  12.14563      50  Female
#>   barthtot
#> 1  64.6617
#> 2  64.6617

# modelbased
estimate_expectation(model, grid)
#> Model-based Expectation
#> 
#> c160age | c161sex | barthtot | Predicted |   SE |         95% CI
#> ----------------------------------------------------------------
#> 50.00   |    Male |    64.66 |     11.54 | 0.25 | [11.05, 12.03]
#> 50.00   |  Female |    64.66 |     11.87 | 0.14 | [11.59, 12.15]
#> 
#> Variable predicted: neg_c_7
#> Predictors modulated: c("Male", "Female"), 50
#> Predictors controlled: barthtot

# emmeans
emmeans(model, "c161sex", at = list(c160age = 50))
#> NOTE: Results may be misleading due to involvement in interactions
#>  c161sex emmean    SE  df lower.CL upper.CL
#>  Male      11.5 0.249 867     11.1     12.0
#>  Female    11.9 0.141 867     11.6     12.1
#> 
#> Confidence level used: 0.95

# insight
get_predicted(model, data = grid) |> as.data.frame()
#>   Predicted        SE   CI_low  CI_high
#> 1  11.53973 0.2487972 11.05141 12.02804
#> 2  11.86866 0.1411197 11.59168 12.14563