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Thanks again for your effort in developping the package.
How would you recommend to run mixed models thourgh taxatree_models?
Following your tutorial, I am not able specify type = lmer
lm_models <- phylo %>%
tax_fix() %>%
tax_prepend_ranks() %>%
# it makes sense to perform the compositional transformation BEFORE filtering
tax_transform("compositional", rank = "Genus", keep_counts = TRUE) %>%
tax_filter(min_prevalence = 0.1, undetected = 0, use_counts = TRUE) %>%
tax_transform(
trans = "log2", chain = TRUE, zero_replace = "halfmin"
) %>%
taxatree_models(
type = lmer,
ranks = NULL, # uses every rank available except the first
# variables = c("UC", "female", "antibiotics", "steroids", "age_scaled")
formula = ~ UC + (1| female)
)
Proportional min_prevalence given: 0.1 --> min 7/67 samples.
2022-06-02 10:33:27 - modelling at rank: Phylum
Error in `[[<-`(`*tmp*`, "call", value = f) :
[[<- defined for objects of type "S4" only for subclasses of environment
Using the R random effect syntax and type = lm does not generate any issue.
lm_models <- phylo %>%
tax_fix() %>%
tax_prepend_ranks() %>%
# it makes sense to perform the compositional transformation BEFORE filtering
tax_transform("compositional", rank = "Genus", keep_counts = TRUE) %>%
tax_filter(min_prevalence = 0.1, undetected = 0, use_counts = TRUE) %>%
tax_transform(
trans = "log2", chain = TRUE, zero_replace = "halfmin"
) %>%
taxatree_models(
type = lm,
ranks = NULL, # uses every rank available except the first
# variables = c("UC", "female", "antibiotics", "steroids", "age_scaled")
formula = ~ UC + (1| female)
)
However, the random effect is not evaluated - which is not surprising.
lm_models %>%
taxatree_models2stats() %>%
.["taxatree_stats"]
$taxatree_stats
# A tibble: 268 × 7
term taxon rank estimate std.error statistic p.value
<fct> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
1 UC P: Firmicutes Phyl… -47.4 10.5 -4.50 2.84e-5
2 1 | femaleTRUE P: Firmicutes Phyl… NA NA NA NA
3 UC P: Bacteroidetes Phyl… -16.7 2.92 -5.72 2.97e-7
4 1 | femaleTRUE P: Bacteroidetes Phyl… NA NA NA NA
Thanks.
The text was updated successfully, but these errors were encountered:
I fixed this particular bug in a new branch https://github.com/david-barnett/microViz/tree/tax-model-lmer-fix
Feel free to install from there and play around, you'll probably need to load broom.mixed or even explicitly provide a tidier function for the lmer models in the taxatree_models2statsfun argument.
I'm reluctant to push this fix to the main branch at the moment as I have not tested out mixed models with tax_model and taxatree_models and don't want to encourage its general use before I do so.
I likely will do this in the next month or so, but I would be grateful to hear more from you if you get this working or encounter further problems 🙂
Hi @david-barnett,
Thanks again for your effort in developping the package.
How would you recommend to run mixed models thourgh
taxatree_models
?Following your tutorial, I am not able specify type = lmer
Using the
R
random effect syntax andtype = lm
does not generate any issue.However, the random effect is not evaluated - which is not surprising.
Thanks.
The text was updated successfully, but these errors were encountered: