m <- lm(Sepal.Length ~ Species + Sepal.Width, data = iris)
nd <- ggeffects::new_data(m, "Species", condition = c(Sepal.Width = 50))
m <- lm(Sepal.Length ~ Species + Sepal.Width, data = iris)
Terms <- delete.response(terms(m))
mf <- model.frame(Terms, data = nd)
X <- model.matrix(Terms, mf)
# predictions
beta <- coef(m)
result <- as.numeric(crossprod(beta, t(X)))
# SE
sqrt(diag(X %*% vcov(m) %*% t(X)))
# Pool se?
# like: sqrt(mean(SEs^2)), see parameters::pool_parameters()
ggeffects:::.safe_se_from_vcov(
m,
nd,
value_adjustment = "mean",
vcov.fun = NULL,
terms = "Species",
condition = c(Sepal.Width = 50),
type = "fixed",
vcov.args = NULL,
vcov.type = NULL,
interval = NULL,
model_class = "lm"
)
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