Incomplete info in documentation regarding the combinations for <group>=NA and re_formula=NULL

[mattansb]

Currently, the cods for prepare_predictions() read:

• newdata
  [...] NA values within factors are interpreted as if all dummy variables of this factor are zero.
• re_formula
  [...] If NULL (default), include all group-level effects; if NA, include no group-level effects.

The newdata argument seems to suggest that setting newdata = data.frame(..., group = NA) should have the same effect as re_formula = NA since in both cases the group-specific coefficients are set to 0.

But this is not the case.

Instead, it seem that

prepare_predictions(
  newdata = data.frame(..., group = NA), 
  re_formula = NULL, # default
  allow_new_levels = FALSE # default
)

is closer to

prepare_predictions(
  newdata = data.frame(..., group = "<NEW>"), 
  re_formula = NULL, # default
  allow_new_levels = TRUE
)

(even though newlevels throw an error when allow_new_levels = FALSE).

It is not clear which of sample_new_levels = c("uncertainty", "gaussian") is used in this case.

[paul-buerkner]

newdata = data.frame(..., group = NA) just defines a new grouping level, which does not affect any dummy variables, since random effects don't have dummy variables. Such variables only apply for fixed effects. How can we make this clearer?

[mattansb]

I was expecting newdata = data.frame(..., group = NA) to be the same as re_formula = NA be cause I interpreted "NA values within factors are interpreted as if all dummy variables of this factor are zero." to mean that in a mixed model

$$ y = bX + uZ + e $$

Then all $Z$ are set to 0, similar to how if group was a fixed effect all $X$ would be set to 0.

But if newdata = data.frame(..., group = NA) is just another "new" level, than it should also give an error if not setting allow_new_levels:

library(brms)

fit <- brm(count ~ 1 + (1|patient),
           data = epilepsy, family = poisson())


posterior_epred(fit,
  newdata = data.frame(patient = "<NEW>")
)
#> Error: Levels '<NEW>' of grouping factor 'patient' cannot be found in the 
#> fitted model. Consider setting argument 'allow_new_levels' to TRUE.

# Does not throw an error...
posterior_epred(fit,
  newdata = data.frame(patient = NA)
)
#>           [,1]
#> [1,]  1.772992
#> [2,]  4.682992
#> [3,] 11.606553
#> [4,]  2.182194
#> [5,]  1.660112
#> [6,]  2.234523
#> .....

If this is the intended behavior, it should also require setting allow_new_levels = TRUE, and maybe the docs should read:

NA values within fixed factors are interpreted as if all dummy variables of this factor are zero. NA values within random factors are treated as a new level.

[paul-buerkner]

good points. let me check in more detail. Mattan S. Ben-Shachar ***@***.***> schrieb am Mi., 15. Mai 2024, 07:06:

…

I was expecting newdata = data.frame(..., group = NA) to be the same as re_formula = NA be cause I interpreted "*NA values within factors are interpreted as if all dummy variables of this factor are zero.*" to mean that in a mixed model $$ y = bX + uZ + e $$ Then all $Z$ are set to 0, similar to how if group was a fixed effect all $X$ would be set to 0. But if newdata = data.frame(..., group = NA) is just another "new" level, than it should also give an error if not setting allow_new_levels: library(brms) fit <- brm(count ~ 1 + (1|patient), data = epilepsy, family = poisson()) posterior_epred(fit, newdata = data.frame(patient = "<NEW>") )#> Error: Levels '<NEW>' of grouping factor 'patient' cannot be found in the #> fitted model. Consider setting argument 'allow_new_levels' to TRUE. # Does not throw an error... posterior_epred(fit, newdata = data.frame(patient = NA) )#> [,1]#> [1,] 1.772992#> [2,] 4.682992#> [3,] 11.606553#> [4,] 2.182194#> [5,] 1.660112#> [6,] 2.234523#> ..... If this is the intended behavior, it should also require setting allow_new_levels = TRUE, and maybe the docs should read: NA values within *fixed* factors are interpreted as if all dummy variables of this factor are zero. *NA values within random factors are treated as a new level.* — Reply to this email directly, view it on GitHub <#1652 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADCW2ABDT7CHOMJQRT2JVLLZCLUMJAVCNFSM6AAAAABHSR3ZOWVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCMJRGU4TIMRVG4> . You are receiving this because you commented.Message ID: ***@***.***>