estimate_contrasts dealing with three factors about modelbased HOT 3 CLOSED

df <- iris
df$factor1 <- ifelse(df$Sepal.Width > 3, "A", "B")
df$factor2 <- ifelse(df$Petal.Length > 3.5, "C", "D")
df$factor3 <- ifelse(df$Sepal.Length > 5, "E", "F")

model <- lm(Petal.Width ~ factor1 * factor2 * factor3, data=df)
emmeans::emmeans(model, "factor1")
#> NOTE: Results may be misleading due to involvement in interactions
#>  factor1 emmean    SE  df lower.CL upper.CL
#>  A       nonEst    NA  NA       NA       NA
#>  B         1.19 0.101 143    0.993     1.39
#> 
#> Results are averaged over the levels of: factor2, factor3 
#> Confidence level used: 0.95

df <- iris
df$factor1 <- ifelse(df$Sepal.Width > 3, "A", "B")
df$factor2 <- ifelse(df$Petal.Length > 3.5, "C", "D")
df$factor3 <- ifelse(df$Sepal.Length > 5, "E", "F")

model <- lm(Petal.Width ~ factor1 * factor2 + factor3 + factor2:factor3, data=df)
modelbased::estimate_contrasts(model, levels = "factor1")
#> NOTE: Results may be misleading due to involvement in interactions
#> Level1 | Level2 | Difference |   SE |        95% CI |    t |  df |     p | Difference (std.)
#> --------------------------------------------------------------------------------------------
#> A      |      B |       0.05 | 0.07 | [-0.08, 0.18] | 0.69 | 144 | 0.489 |              0.06