Создание многопланового «foreach» l oop для параллельного

Question

У меня есть функция, определенная на данный момент следующим образом:

gam_select <- function(dependent, scope_list, data, family){
  nrun <- length(scope_list[[1]]) * length(scope_list[[2]]) *
    length(scope_list[[3]]) * length(scope_list[[4]]) * length(scope_list[[5]]) *
    length(scope_list[[6]])*length(scope_list[[7]])
  ptm <- proc.time()
  all.gams <- data.frame(aic_val = rep(9999, nrun+1))
  counter = 0
  for(i in 1:length(scope_list[[1]])){
    for(j in 1:length(scope_list[[2]])){
      for(k in 1:length(scope_list[[3]])){
        for(l in 1:length(scope_list[[4]])){
          for(m in 1:length(scope_list[[5]])){
            for(n in 1:length(scope_list[[6]])){
              for(o in 1:length(scope_list[[7]])){
                counter = counter+1
                form.test <- paste0(dependent," ~ ",
                                    scope_list[[1]][i], " + ",
                                    scope_list[[2]][j], " + ",
                                    scope_list[[3]][k], " + ",
                                    scope_list[[4]][l] ," + ",
                                    scope_list[[5]][m], " + ",
                                    scope_list[[6]][n], " + ",
                                    scope_list[[7]][o])
                gam.test <- mgcv::gam(as.formula(form.test), data = data, family = family)
                all.gams$formula[counter] <- form.test
                all.gams$aic_val[counter] <- MuMIn::AICc(gam.test)
                all.gams$bic_val[counter] <- BIC(gam.test)
                print(paste(counter,"/",nrun, "  ", form.test))
              }
            }
          }
        }
      }
    }
  }
  all.gams$formula[all.gams$aic_val == min(all.gams$aic_val)]
  best_aic <- as.numeric(rownames(all.gams[all.gams$aic_val == min(all.gams$aic_val),]))
  print(paste(all.gams[best_aic,]))
  return(all.gams)
}

Эта функция работает нормально, но, как вы можете, вероятно, заметить, минимальные изменения (т.е. увеличение длины списка) области видимости могут значительно замедлиться функция.

Я хотел бы сделать это параллельно, если смогу, порядок не имеет значения, пока на выходе есть data.frame, который представляет все формулы гаммы с их AI C ценность. Сторона: Кто-то может спросить, почему бы не использовать для этого пошаговую программу AI C, краткий ответ: пошаговые функции действительно не ищут полный список областей.

Я попробовал несколько методов, но, похоже, он не проходил каждую итерацию, он останавливался на первой:

  all.gams <- foreach (i = 1:length(scope_list[[1]]),
                        j = 1:length(scope_list[[2]]),
                        k = 1:length(scope_list[[3]]), 
                        l = 1:length(scope_list[[4]]), 
                        m = 1:length(scope_list[[5]]), 
                        n = 1:length(scope_list[[6]]), 
                        o = 1:length(scope_list[[7]]), combine = 'cbind', .inorder = F) %dopar% {
                          counter = counter+1
                          form.test <- paste0(dependent," ~ ",
                                              scope_list[[1]][i], " + ",
                                              scope_list[[2]][j], " + ",
                                              scope_list[[3]][k], " + ",
                                              scope_list[[4]][l] ," + ",
                                              scope_list[[5]][m], " + ",
                                              scope_list[[6]][n], " + ",
                                              scope_list[[7]][o])
                          gam.test <- mgcv::gam(as.formula(form.test), data = data, family = family)
                          all.gams$formula[counter] <- form.test
                          all.gams$aic_val[counter] <- MuMIn::AICc(gam.test)
                          print(paste(counter,"/",nrun, "  ", form.test))
                          return(all.gams)
                        }

и

all.gams <- foreach (i = 1:length(scope_list[[1]]), combine = 'cbind', .inorder = F) %:%
  foreach (j = 1:length(scope_list[[2]]), combine = 'cbind', .inorder = F) %:%
  foreach (k = 1:length(scope_list[[3]]), combine = 'cbind', .inorder = F) %:%
  foreach (l = 1:length(scope_list[[4]]), combine = 'cbind', .inorder = F) %:%
  foreach (m = 1:length(scope_list[[5]]), combine = 'cbind', .inorder = F) %:%
  foreach (n = 1:length(scope_list[[6]]), combine = 'cbind', .inorder = F) %:%
  foreach (o = 1:length(scope_list[[7]]), combine = 'cbind', .inorder = F) %dopar% {
    counter = counter+1
    form.test <- paste0(dependent," ~ ",
                        scope_list[[1]][i], " + ",
                        scope_list[[2]][j], " + ",
                        scope_list[[3]][k], " + ",
                        scope_list[[4]][l] ," + ",
                        scope_list[[5]][m], " + ",
                        scope_list[[6]][n], " + ",
                        scope_list[[7]][o])
    gam.test <- mgcv::gam(as.formula(form.test), data = data, family = family)
    all.gams$formula[counter] <- form.test
    all.gams$aic_val[counter] <- MuMIn::AICc(gam.test)
    print(paste(counter,"/",nrun, "  ", form.test))
    return(all.gams)
  }

Вот список scope_list:

scope_list <- list(
  TagID =          c("s(TagID, bs = 're')"),
  mwoy =           c( "1", "mwoy", "s(mwoy, bs = 'cs',k = 5)"),
  EstimatedAge =   c("1", "EstimatedAge", "s(EstimatedAge, bs = 'cs',k = 5)"),
  Avg_Flow=        c("1", "Avg_Flow", "s(Avg_Flow, bs = 'cs',k = 5)"),
  Avg_Temperature= c("1 ", " Avg_Temperature ", " s(Avg_Temperature, bs = 'cs',k = 3)"),
  Avg_Turbidity=   c("1 ", " Avg_Turbidity ", " s(Avg_Turbidity, bs = 'cs',k = 5)"),
  Avg_TDS=         c("1 ", " Avg_TDS", " s(Avg_TDS, bs = 'cs', k = 5)"),
  n_Salmonids=     c("1 ", " n_Salmonids ", " s(n_Salmonids, bs = 'cs',k = 5)")
)

наконец, вот как я бы назвал эту функцию: entry <- gam_select("entry", scope_list, weekly_stb_gam, binomial())

и некоторые данные для воспроизведения:

structure(list(Species = structure(c(3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Catfish", 
"Largemouth Bass", "Striped Bass"), class = "factor"), EstimatedAge = c(3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2), TagID = c(5004.24, 
5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 
5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 
5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 
5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 
5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 5004.24, 
5004.24, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 5011.03, 
5011.03), Week = c(74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 
85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 
101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 164, 165, 166, 
167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 
180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 
193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 
206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 
219, 220, 221, 222, 223, 224, 225, 226), transit = c(0, 0, 0, 
1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 
0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), exit = c(0, 0, 0, 1, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), entry = c(0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Avg_Flow = c(2001.28571428571, 
648.285714285714, 290.571428571429, 291.571428571429, 290.285714285714, 
1003.28571428571, 1288.42857142857, 2243.28571428571, 3107.42857142857, 
3488.85714285714, 5468.14285714286, 5070.28571428571, 3079.71428571429, 
3346.85714285714, 3369.14285714286, 4855.71428571429, 3861.42857142857, 
3773.71428571429, 2534.28571428571, 4373.85714285714, 4994.57142857143, 
3556.28571428571, 3874.14285714286, 1369.14285714286, 644.857142857143, 
540.428571428571, 538.857142857143, 536.714285714286, 506.857142857143, 
533.142857142857, 663.571428571429, 316.857142857143, 326.714285714286, 
535.285714285714, 946.714285714286, 606.285714285714, 402.857142857143, 
4135.85714285714, 3296.42857142857, 6417.85714285714, 7159.28571428571, 
3200.28571428571, 6569.71428571429, 6975, 6374.57142857143, 7077, 
7172.42857142857, 7171.28571428571, 7167.57142857143, 4707.71428571429, 
6062.28571428571, 4204.42857142857, 2992.57142857143, 2489.85714285714, 
2170.28571428571, 2852.14285714286, 4984.85714285714, 5823, 2992.14285714286, 
4081, 6673, 6337, 6247.57142857143, 3637.57142857143, 3489.85714285714, 
3207.85714285714, 5407.14285714286, 9413.42857142857, 10106.7142857143, 
10234, 10240, 9745.85714285714, 3753.71428571429, 3502, 1728.28571428571, 
1336.57142857143, 0, 0, 0, 0, 1471.57142857143, 2488.42857142857, 
5209, 6673.71428571429, 4270, 2421.14285714286, 2560.71428571429, 
6648.42857142857, 6670.85714285714, 6671, 6669.57142857143, 6281.85714285714, 
5545.57142857143, 7102.57142857143, 7173.85714285714, 7125.28571428571, 
7165.85714285714, 7069.71428571429, 7171.42857142857, 7138.14285714286
), Avg_Wind = c(9.33333333333333, 4.57142857142857, 8.14285714285714, 
5.71428571428571, 5.42857142857143, 6, 4.14285714285714, 5.42857142857143, 
4, 5.85714285714286, 4.85714285714286, 5.71428571428571, 8.14285714285714, 
9.14285714285714, 2.42857142857143, 2.71428571428571, 3.14285714285714, 
3.85714285714286, 4, 7.28571428571429, 4, 8, 7.42857142857143, 
4.28571428571429, 6.28571428571429, 8.42857142857143, 11.5714285714286, 
9, 11, 11.1428571428571, 12.8571428571429, 13.2857142857143, 
13.7142857142857, 19.2857142857143, 16, 13.1428571428571, 8.85714285714286, 
12.7142857142857, 10.8571428571429, 13, 12.8571428571429, 13.1428571428571, 
7.28571428571429, 9.28571428571429, 10.1428571428571, 7, 8.71428571428571, 
12.4285714285714, 10.5714285714286, 9.71428571428571, 8.85714285714286, 
6.14285714285714, 12.1428571428571, 6.57142857142857, 7, 3.42857142857143, 
4, 2.14285714285714, 5.28571428571429, 4.85714285714286, 8.28571428571429, 
8.14285714285714, 4.57142857142857, 6.14285714285714, 6.57142857142857, 
8.71428571428571, 10.5714285714286, 6.28571428571429, 8.57142857142857, 
4.71428571428571, 7.16666666666667, 5.57142857142857, 12.2857142857143, 
6.14285714285714, 6.57142857142857, 4.71428571428571, 7.14285714285714, 
11.1428571428571, 8.85714285714286, 9.42857142857143, 6.57142857142857, 
13.5714285714286, 11.4285714285714, 12.8571428571429, 14.2857142857143, 
8.42857142857143, 14.7142857142857, 11.7142857142857, 12.5714285714286, 
9.85714285714286, 15, 11.5714285714286, 10, 11.8571428571429, 
8.14285714285714, 8.42857142857143, 13.4285714285714, 13.8571428571429, 
10, 6.57142857142857), Avg_Conductivity = c(569, 610.428571428571, 
637.428571428571, 651.857142857143, 688.428571428571, 661, 636.142857142857, 
689.428571428571, 672.285714285714, 617.428571428571, 591.714285714286, 
629.571428571429, 741.571428571429, 662.428571428571, 535.285714285714, 
551.571428571429, 473.142857142857, 464.285714285714, 489.428571428571, 
590, 557.285714285714, 572.142857142857, 454.428571428571, 420.714285714286, 
450.428571428571, 498.857142857143, 588.142857142857, 482.142857142857, 
511.285714285714, 513.857142857143, 536.285714285714, 598.714285714286, 
680.714285714286, 767, 821.285714285714, 813.428571428571, 835.285714285714, 
290.142857142857, 284, 295.857142857143, 276, 280.714285714286, 
271.142857142857, 257.428571428571, 297.142857142857, 311, 327.285714285714, 
375, 414.142857142857, 498, 554.285714285714, 544.428571428571, 
571.857142857143, 588, 562.571428571429, 560.571428571429, 507, 
462.857142857143, 482.428571428571, 588.571428571429, 585.428571428571, 
567.428571428571, 610.714285714286, 699, 524.285714285714, 444.857142857143, 
342.857142857143, 299.428571428571, 281.571428571429, 242.714285714286, 
226.285714285714, 207, 179.142857142857, 184.428571428571, 166.857142857143, 
158.285714285714, 139.142857142857, 142.857142857143, 148.142857142857, 
141.285714285714, 124.285714285714, 123.428571428571, 125.285714285714, 
121.428571428571, 98, 116.857142857143, 128, 140.857142857143, 
138.285714285714, 138.857142857143, 109.142857142857, 107.285714285714, 
138.142857142857, 155.428571428571, 149.285714285714, 158.428571428571, 
180.285714285714, 184.285714285714, 182.714285714286, 190.142857142857
), Avg_pH = c(7.42857142857143, 7.44285714285714, 7.48571428571429, 
7.44285714285714, 7.51428571428571, 7.5, 7.45714285714286, 7.44285714285714, 
7.5, 7.5, 7.41428571428571, 7.6, 7.58571428571429, 7.7, 7.7, 
7.61428571428571, 7.55714285714286, 7.54285714285714, 7.51428571428571, 
7.57142857142857, 7.41428571428571, 7.51428571428571, 7.61428571428571, 
7.71428571428571, 7.61428571428571, 7.51428571428571, 7.54285714285714, 
7.57142857142857, 7.81428571428571, 8.4, 7.68571428571429, 7.58571428571429, 
7.7, 7.84285714285714, 7.78571428571429, 7.68571428571429, 7.67142857142857, 
7.58571428571429, 7.55714285714286, 7.47142857142857, 7.62857142857143, 
7.47142857142857, 7.32857142857143, 7.34285714285714, 7.52857142857143, 
7.64285714285714, 7.72857142857143, 7.87142857142857, 7.91428571428571, 
7.91428571428571, 7.9, 7.8, 7.82857142857143, 7.81428571428571, 
7.8, 7.72857142857143, 7.52857142857143, 7.5, 7.51428571428571, 
7.62857142857143, 7.67142857142857, 7.85714285714286, 7.81428571428571, 
7.88571428571429, 7.82857142857143, 7.92857142857143, 7.91428571428571, 
7.52857142857143, 7.61428571428571, 7.44285714285714, 7.68571428571429, 
7.48571428571429, 7.5, 7.52857142857143, 7.6, 7.55714285714286, 
7.54285714285714, 7.57142857142857, 7.61428571428571, 7.6, 7.54285714285714, 
7.48571428571429, 7.3, 7.34285714285714, 7.58571428571429, 7.55714285714286, 
7.58571428571429, 7.6, 7.5, 7.35714285714286, 7.48571428571429, 
7.4, 7.54285714285714, 7.57142857142857, 7.55714285714286, 7.55714285714286, 
7.8, 7.8, 7.8, 7.85714285714286), Avg_Flourescence = c(109.665714285714, 
98.5928571428571, 96.0385714285714, 93.37, 95.0514285714286, 
90.9, 96.7657142857143, 100.264285714286, 101.232857142857, 80.0242857142857, 
73.8085714285714, 70.5685714285714, 79.9571428571429, 84.5257142857143, 
90.8442857142857, 82.9971428571428, 83.6971428571429, 83.4871428571429, 
84.1842857142857, 84.5557142857143, 88.3257142857143, 88.9857142857143, 
94.6928571428571, 96.51, 96.1185714285714, 95.7128571428571, 
86.0571428571428, 84.8957142857143, 281.365714285714, 455.407142857143, 
130.278571428571, 90.2957142857143, 90.8657142857143, 99.1642857142857, 
83.8957142857143, 81.9971428571429, 78.8128571428571, 93.7585714285714, 
97.1857142857143, 110.777142857143, 110.71, 101.081428571429, 
98.2857142857143, 91.4285714285714, 85.5671428571429, 83.8628571428571, 
79.1071428571429, 74.7871428571429, 71.4485714285714, 79.2642857142857, 
68.1442857142857, 74.9485714285714, 83.6857142857143, 59.42, 
60.49, 65.31, 61.5114285714286, 58.63, 62.9671428571429, 62.9614285714286, 
59.6214285714286, 60.8528571428571, 60.5142857142857, 56.52, 
58.1714285714286, 61.7057142857143, 70.1157142857143, 95.2528571428571, 
102.661428571429, 85.2157142857143, 84.6642857142857, 103.77, 
90.5628571428571, 77.2414285714286, 75.5028571428571, 76.53, 
69.2514285714286, 67.6314285714286, 74.49, 69.9857142857143, 
73.2771428571429, 72.0171428571429, 70.4785714285714, 75.9371428571429, 
70.5014285714286, 63.25, 74.7842857142857, NaN, 105.005714285714, 
100.607142857143, 112.18, 111.237142857143, 113.255714285714, 
111.084285714286, 124.067142857143, 103.008571428571, 113.164285714286, 
110.241428571429, 110.295714285714, 96.9785714285714), Avg_Temperature = c(22.8285714285714, 
22.7714285714286, 22.1, 20.6142857142857, 19.1, 17.5142857142857, 
17.1571428571429, 16.1571428571429, 14.5, 13.8428571428571, 14.5571428571429, 
12.9428571428571, 12.8857142857143, 10.6428571428571, 8.78571428571429, 
10.1571428571429, 10.6142857142857, 10.8571428571429, 11.8285714285714, 
13.6, 14.9142857142857, 14.7571428571429, 14.6428571428571, 16.4285714285714, 
18.5142857142857, 19.1428571428571, 20.0285714285714, 18.0571428571429, 
18.8428571428571, 20.1571428571429, 20.5428571428571, 21.5571428571429, 
20.1571428571429, 19.4285714285714, 20.6571428571429, 22.5, 24.3714285714286, 
23.0142857142857, 24.8, 25.0857142857143, 24.2285714285714, 24.4571428571429, 
25.3714285714286, 25.3857142857143, 24.3857142857143, 24.7, 23.7571428571429, 
22.4857142857143, 22.6428571428571, 22.3142857142857, 22.2571428571429, 
21.8285714285714, 20.1428571428571, 19.4142857142857, 18.8, 18.6285714285714, 
18.1714285714286, 17.8714285714286, 17.7142857142857, 15.3428571428571, 
13.4571428571429, 11.4142857142857, 11.6428571428571, 10.8285714285714, 
9.57142857142857, 9.25714285714286, 10.6285714285714, 10.6714285714286, 
10.4714285714286, 10.7428571428571, 12.5714285714286, 13.2285714285714, 
12.6571428571429, 12.3857142857143, 12.8714285714286, 15.3714285714286, 
15.9142857142857, 14.9285714285714, 15.8714285714286, 15.0428571428571, 
15.4714285714286, 16.4714285714286, 17.9428571428571, 18.6428571428571, 
16.6142857142857, 18.3, 18.6571428571429, 20.1428571428571, 20.2714285714286, 
24.1142857142857, 23.7857142857143, 23.5857142857143, 24.6571428571429, 
25.9571428571429, 25.6714285714286, 25.9571428571429, 24.1857142857143, 
23.7, 23.2571428571429, 23.6142857142857), Avg_Turbidity = c(6.42428571428571, 
4.91428571428571, 4.51142857142857, 3.93, 3.47571428571429, 3.69142857142857, 
4.21857142857143, 4.32857142857143, 4.69428571428571, 5.19285714285714, 
5.99857142857143, 14.2128571428571, 11.9871428571429, 10.8742857142857, 
17.0657142857143, 11.8457142857143, 10.71, 8.25, 6.77285714285714, 
6.58285714285714, 6.54, 6.87571428571429, 8.56, 7.46571428571429, 
6.71285714285714, 7.14714285714286, 6.28142857142857, 6.20571428571429, 
8.248, NaN, 16.71, 12.8171428571429, 8.67, 8.87571428571428, 
7.17285714285714, 7.26285714285714, 6.64571428571429, 10.5885714285714, 
8.97142857142857, 11.4442857142857, 11.0457142857143, 24.9557142857143, 
11.02, 8.63285714285714, 8.62571428571429, 9.84714285714286, 
9.91714285714286, 8.31571428571429, 7.93142857142857, 6.55428571428571, 
7.63571428571429, 6.16, 5.9, 4.66142857142857, 5.00857142857143, 
4.68714285714286, 5.26714285714286, 5.86142857142857, 5.02142857142857, 
5.44714285714286, 4.95285714285714, 5.73714285714286, 6.54285714285714, 
6.65285714285714, 7.70571428571429, 11.65, 22.3928571428571, 
53.1457142857143, 67.5757142857143, 37.7942857142857, 27.8814285714286, 
52.6671428571429, 66.5185714285714, 47.0714285714286, 32.6957142857143, 
23.9042857142857, 17.6128571428571, 18.7814285714286, 15.2457142857143, 
12.7328571428571, 14.0857142857143, 14.19, 15.0828571428571, 
16.9657142857143, 17.5985714285714, 16.6657142857143, 20.7014285714286, 
25.5228571428571, 24.8, 19.8942857142857, 26.1514285714286, 28.9985714285714, 
26.48, 21.6671428571429, 22.9442857142857, 15.65, 14.9228571428571, 
12.6314285714286, 27.3671428571429, 10.9428571428571), Avg_TDS = c(284.5, 
305.214285714286, 318.714285714286, 325.928571428571, 344.214285714286, 
330.5, 318.071428571429, 344.714285714286, 336.142857142857, 
308.714285714286, 295.857142857143, 314.785714285714, 370.785714285714, 
331.214285714286, 267.642857142857, 275.785714285714, 236.571428571429, 
232.142857142857, 244.714285714286, 295, 278.642857142857, 286.071428571429, 
227.214285714286, 210.357142857143, 225.214285714286, 249.428571428571, 
294.071428571429, 241.071428571429, 255.642857142857, 256.928571428571, 
268.142857142857, 299.357142857143, 340.357142857143, 383.5, 
410.642857142857, 406.714285714286, 417.642857142857, 145.071428571429, 
142, 147.928571428571, 138, 140.357142857143, 135.571428571429, 
128.714285714286, 148.571428571429, 155.5, 163.642857142857, 
187.5, 207.071428571429, 249, 277.142857142857, 272.214285714286, 
285.928571428571, 294, 281.285714285714, 280.285714285714, 253.5, 
231.428571428571, 241.214285714286, 294.285714285714, 292.714285714286, 
283.714285714286, 305.357142857143, 349.5, 262.142857142857, 
222.428571428571, 171.428571428571, 149.714285714286, 140.785714285714, 
121.357142857143, 113.142857142857, 103.5, 89.5714285714286, 
92.2142857142857, 83.4285714285714, 79.1428571428571, 69.5714285714286, 
71.4285714285714, 74.0714285714286, 70.6428571428571, 62.1428571428571, 
61.7142857142857, 62.6428571428571, 60.7142857142857, 49, 58.4285714285714, 
64, 70.4285714285714, 69.1428571428571, 69.4285714285714, 54.5714285714286, 
53.6428571428571, 69.0714285714286, 77.7142857142857, 74.6428571428571, 
79.2142857142857, 90.1428571428571, 92.1428571428571, 91.3571428571429, 
95.0714285714286), Chinook = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 22, 22, 9, 3, 2, 3, 0, 1, 0, 1, 3, 0, 1, 0, 0, 4, 3, 0, 5, 
3, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 1, 1, 0, 1, 5, 8, 4, 107, 71, 32, 217, 581, 530, 
627, 198, 548, 75, 41, 5, 8, 31, 166, 175, 480, 534, 1116, 1462, 
1433, 1421, 783, 831, 1165, 654, 182, 41, 12, 1, 3, 0, 0, 0, 
0, 1, 0), DeltaSmelt = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 8, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 3, 1, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 5, 0, 1, 1, 
6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0), Threadfin = c(1862, 1837, 94, 200, 417, 135, 167, 
354, 1085, 343, 421, 1790, 638, 459, 369, 129, 29, 25, 5, 35, 
10, 1, 15, 18, 11, 1, 0, 0, 1, 17, 5, 10, 7, 0, 5, 131, 2265, 
19085, 23944, 30359, 58128, 16975, 88344, 139973, 48927, 43486, 
43579, 26303, 10583, 2062, 2780, 1498, 342, 245, 79, 6173, 1287, 
435, 28, 281, 511, 657, 262, 943, 305, 442, 970, 963, 555, 380, 
261, 121, 32, 19, 35, 113, 62, 48, 52, 116, 174, 233, 152, 71, 
44, 27, 42, 235, 454, 3713, 14463, 42205, 27640, 24824, 11934, 
30972, 41203, 77515, 36054, 14975), Steelhead = c(0, 0, 0, 0, 
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 1, 4, 33, 42, 45, 15, 
7, 0, 3, 4, 4, 3, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 
0, 1, 1, 1, 3, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 3, 0, 
3, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), woy = c(39L, 40L, 41L, 
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
17L, 18L, 19L, 20L, 21L, 22L, 23L, 25L, 26L, 27L, 28L, 29L, 30L, 
31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 
44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 
18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 
31L, 32L, 33L, 34L), mwoy = c(52, 53, 1, 2, 3, 4, 5, 6, 7, 8, 
9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 
26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40, 41, 42, 
43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 1, 2, 3, 4, 5, 6, 
7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 
23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 
39, 40, 41, 42, 43, 44, 45, 46, 47), n_Salmonids = c(0, 0, 0, 
0, 0, 0, 0, 1, 0, 0, 0, 22, 22, 9, 3, 2, 3, 6, 2, 4, 34, 45, 
45, 16, 7, 0, 7, 7, 4, 8, 4, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 6, 8, 4, 
107, 71, 34, 217, 582, 531, 628, 201, 556, 75, 41, 5, 8, 31, 
166, 175, 480, 534, 1117, 1462, 1436, 1423, 786, 831, 1168, 654, 
183, 41, 12, 1, 3, 0, 0, 0, 0, 1, 0)), class = c("tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -100L))

Answer 1

Рекомендуется подготовить весь шаблон вместо использования for в for. И если вы хотите сделать параллельное, все выходные данные, которые вы хотите, должны быть возвращены функцией foreach.

Вот мой пример;

library(foreach)

all_scope_pattern <- do.call("expand.grid", scope_list)
formula_right_part <- apply(all_scope_pattern, 1, function(x) paste0(x, collapse = " + ")) 

f <- function(counter, formula_r, data, dependent, family){
  form.test <- paste0(dependent, " ~ ", formula_r)
  gam.test <- mgcv::gam(as.formula(form.test), data = data, family = family)

  tmp_aic_val <- MuMIn::AICc(gam.test)
  tmp_bic_val <- BIC(gam.test)

  data.frame(counter = counter, formula = form.test, aic_val = tmp_aic_val, vic_val = tmp_bic_val)
}

# please do preparation of parallel here

res <- foreach(i = 1:length(formula_right_part), .combine = "rbind") %dopar% {
  f(counter = i, 
    formula_r = formula_right_part[i],
    data = weekly_stb_gam,
    dependent = "entry",
    family = binomial())
}

# if you want 
all_scope_pattern_with_counter <- cbind(all_scope_pattern, counter = 1:nrow(all_scope_pattern))
dplyr::inner_join(all_scope_pattern_with_counter, res, by = "counter")