I have this function:
mweFitModelsGLMER <- function(longData, strModelName = "ID Rand Model", strFormula = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)", ...) { output = list() # Initialise output Ptn <- unique(longData$Ptn) Model <- longData %>% lme4::glmer(formula = stats::as.formula(strFormula), ...) #Do a few other things like prepare summary table etc. output$Models <- # list( tibble( Protein = Ptn, Formula = strFormula, `Model Name` = strModelName, Model = list(Model), ) #) return(output) } Whose primary purpose is to fit a mixed model, do a few other things and then output a big tibble. This works well on its own with the data at the end of the post:
mweFitModelsGLMER(allData, family = "binomial", na.action = na.pass) Now I try to create a helper function, which can take in a named list of formula specifications, pass it on to the function above and return the output:
mweMultipleModelsGLMER <- function(longData, namLstFormula = list(Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)", Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"), ...) { purrr::imap( namLstFormula, ~ mweFitModelsGLMER( longData = longData, strModelName = paste(.y), strFormula = .x, ... ) ) } And run it like so:
mweMultipleModelsGLMER(allData, namLstFormula = list(Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)", Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"), family = "binomial") To which, R responds with
Error in
map2(): ℹ In index: 1. ℹ With name: Model1. Caused by error inlme4::glmer(): ! 'control' is not a list; use glmerControl()
Ok, then I be explicit in my call..
mweMultipleModelsGLMER( allData, namLstFormula = list(Model1 = "phMeta_Toxicity ~ 1 + (1 | phMeta_UID)", Model2 = "phMeta_Toxicity ~ value + (1 | phMeta_UID)"), family = "binomial", control = lme4::glmerControl(optimizer = "bobyqa") ) To which R comes back to me with:
Error in
map2(): ℹ In index: 1. ℹ With name: Model1. Caused by error inlme4::glmer(): ! 'control' is not a list; use glmerControl()
Backtrace: ▆ 1. ├─global mweMultipleModelsGLMER(...) 2. │ └─purrr::imap(...) 3. │ └─purrr::map2(.x, vec_index(.x), .f, ...) 4. │ └─purrr:::map2_("list", .x, .y, .f, ..., .progress = .progress) 5. │ ├─purrr:::with_indexed_errors(...) 6. │ │ └─base::withCallingHandlers(...) 7. │ ├─purrr:::call_with_cleanup(...) 8. │ └─.f(.x[[i]], .y[[i]], ...) 9. │ └─global mweFitModelsGLMER(...) 10. │ └─longData %>% ... 11. └─lme4::glmer(., formula = stats::as.formula(strFormula), ...) 12. └─base::stop("'control' is not a list; use glmerControl()") For reference, I did help("glmer")
glmer(formula, data = NULL, family = gaussian , control = glmerControl() , start = NULL , verbose = 0L , nAGQ = 1L , subset, weights, na.action, offset, contrasts = NULL , mustart, etastart , devFunOnly = FALSE)
What am I missing?
Data below:
library(tidyverse) allData <- structure(list(phMeta_UID = c("Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 BRS", "Pat 1 SHF", "Pat 1 SHF", "Pat 1 SHF", "Pat 1 SHF", "Pat 1 SHF", "Pat 1 SHF", "Pat 1 SHF", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 SHF", "Pat 2 SHF", "Pat 2 SHF", "Pat 2 SHF", "Pat 2 SHF", "Pat 2 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 3 SHF", "Pat 1 SHF", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 BRS", "Pat 2 SHF"), phMeta_Time = c(0, 0.5, 1, 2, 3, 4, 6, 8, 12, 0, 0.5, 2, 3, 4, 8, 12, 0, 0.5, 2, 0, 0.5, 2, 3, 4, 10, 3, 4, 8, 9, 12, 0, 0.5, 2, 1, 1, 3, 4, 6, 8, 12, 1), phMeta_Batch = c(1, 1, 2, 1, 1, 1, 2, 2, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2), phMeta_Site = c("BRS", "BRS", "BRS", "BRS", "BRS", "BRS", "BRS", "BRS", "BRS", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "BRS", "BRS", "BRS", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "SHF", "BRS", "BRS", "BRS", "BRS", "BRS", "BRS", "SHF"), phMeta_Toxicity = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), levels = c("N", "Y"), class = "factor"), phMeta_Patient = c(4, 4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 5, 5, 5, 5, 5, 5, 2), phMeta_SiteXPatient = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 1, 2, 2, 2, 2, 2, 2, 2), phMeta_Phlebotomy = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, "Venous", "Capillary", "Venous", NA, "Venous", "Capillary", "Venous", NA, NA, NA, "Venous", "Capillary", "Capillary", "Capillary", "Capillary", "Venous", "Venous", "Venous", "Venous", NA, "Venous", "Venous", "Capillary", "Venous", "Venous", NA, NA, NA, NA, NA, NA, "Capillary"), value = structure(c(0.821944197243299, -0.0198715543825022, 0.631293040769747, 0.0849008002934989, 0.0887812578740912, 1.05453893286552, -2.12824809442977, 0.276304235154362, 0.859985670512456, -1.04724028808727, -0.00411159277206202, 1.67249503748148, -0.54509175851945, -1.46818604842327, -0.498407201908304, 0.51962029081445, -1.08569307903582, -0.270959349353233, -0.211864905388811, 0.158115347225517, 0.0227322978830837, 0.852349233070034, -0.401612245382643, -2.98257656282869, -0.191542564781942, -1.45732401444245, 0.494605681417659, 0.464925863604591, 0.856056504259303, 1.63695935481179, -1.33574175565861, 0.42694933523218, -0.0213328145592944, -0.966697791972374, -0.634006734239892, 0.128202810199108, 0.960861331383678, -0.258051551124902, 1.25488311517846, 1.08015428255721, 1.18190128745978 ), dim = c(41L, 1L)), Ptn = c("phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand", "phOSI_ICOS ligand" )), row.names = c(NA, -41L), class = c("tbl_df", "tbl", "data.frame" )) EDIT 1:
The function works if I ignore the list names:
fFitMultipleModelsGLMER2 <- function(nestlistData, strPredictorROC = "phMeta_Toxicity", LstFormula = c("phMeta_Toxicity ~ 1 + (1 | phMeta_UID)", "phMeta_Toxicity ~ value + (1 | phMeta_UID)"), ...) { purrr::map( namLstFormula, ~fFitModelsGLMER( longData = nestlistData, strPredictor = strPredictorROC, strFormula = .x, ... ) ) } EDIT2: Editing to improve discoverability
