R：为什么使用do.call会给我带来更大的结果？_R_Do.call

R：为什么使用do.call会给我带来更大的结果？

R：为什么使用do.call会给我带来更大的结果？,r,do.call,R,Do.call,我发现用do.call调用waic或loo时似乎有一些奇怪的行为。就内存使用而言，结果是非常非常大的。（我的总体目标是能够将任意数量的模型列表传递给一个函数，使用waic/loo比较每个列表中的模型，并将结果作为列表返回。请注意，如果我使用lappy，它会将waic单独应用于每个模型，这不会导致任何比较，这不是什么。）例如： library(brms) d <- data.frame(x = rnorm(100)) d$y <- rnorm(100) d$z <- rnorm

我发现用

do.call调用waic
或loo
时似乎有一些奇怪的行为。就内存使用而言，结果是非常非常大的。（我的总体目标是能够将任意数量的模型列表传递给一个函数，使用waic
/loo
比较每个列表中的模型，并将结果作为列表返回。请注意，如果我使用lappy
，它会将waic
单独应用于每个模型，这不会导致任何比较，这不是什么。）例如：
library(brms)

d <- data.frame(x = rnorm(100))
d$y <- rnorm(100)
d$z <- rnorm(100)

m1 <- brm(formula = y ~ x, data = d)
m2 <- brm(formula = y ~ z, data = d)

w1 <- waic(m1, m2)
w2 <- do.call(waic, list(m1, m2))

class(w1)
class(w2)

object.size(w1)
object.size(w2)

如果我使用loo
，也会发生同样的情况，因此没有指定waic
函数
如果我看一下str
的结果，似乎l2
正在存储完整的模型，而不仅仅是loo
结果，这可能是导致膨胀的原因<代码>str（w1）

产生：

List of 3
 $ loos      :List of 2
  ..$ m1:List of 8
  .. ..$ estimates   : num [1:3, 1:2] -152.802 2.757 305.604 6.602 0.457 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:3] "elpd_waic" "p_waic" "waic"
  .. .. .. ..$ : chr [1:2] "Estimate" "SE"
  .. ..$ pointwise   : num [1:100, 1:3] -1.11 -1.08 -1.04 -2.05 -1.97 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : NULL
  .. .. .. ..$ : chr [1:3] "elpd_waic" "p_waic" "waic"
  .. ..$ elpd_waic   : num -153
  .. ..$ p_waic      : num 2.76
  .. ..$ waic        : num 306
  .. ..$ se_elpd_waic: num 6.6
  .. ..$ se_p_waic   : num 0.457
  .. ..$ se_waic     : num 13.2
  .. ..- attr(*, "dims")= int [1:2] 4000 100
  .. ..- attr(*, "class")= chr [1:2] "waic" "loo"
  .. ..- attr(*, "yhash")= chr "543895fddfbc9e53e6fdf43fd5d57f2d95a3893f"
  .. ..- attr(*, "model_name")= chr "m1"
  ..$ m2:List of 8
  .. ..$ estimates   : num [1:3, 1:2] -153.55 2.958 307.1 6.72 0.592 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:3] "elpd_waic" "p_waic" "waic"
  .. .. .. ..$ : chr [1:2] "Estimate" "SE"
  .. ..$ pointwise   : num [1:100, 1:3] -1.05 -1.12 -1.04 -1.95 -1.89 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : NULL
  .. .. .. ..$ : chr [1:3] "elpd_waic" "p_waic" "waic"
  .. ..$ elpd_waic   : num -154
  .. ..$ p_waic      : num 2.96
  .. ..$ waic        : num 307
  .. ..$ se_elpd_waic: num 6.72
  .. ..$ se_p_waic   : num 0.592
  .. ..$ se_waic     : num 13.4
  .. ..- attr(*, "dims")= int [1:2] 4000 100
  .. ..- attr(*, "class")= chr [1:2] "waic" "loo"
  .. ..- attr(*, "yhash")= chr "543895fddfbc9e53e6fdf43fd5d57f2d95a3893f"
  .. ..- attr(*, "model_name")= chr "m2"
 $ diffs     : 'compare.loo' num [1:2, 1:8] 0 -0.748 0 1.384 -152.802 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "m1" "m2"
  .. ..$ : chr [1:8] "elpd_diff" "se_diff" "elpd_waic" "se_elpd_waic" ...
 $ ic_diffs__: num [1, 1:2] -1.5 2.77
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr "m1 - m2"
  .. ..$ : chr [1:2] "WAIC" "SE"
 - attr(*, "class")= chr "loolist"

str（w2）

生成的东西开头是这样的（我对它进行了一些格式化，所以它不是全部在一行）：

如果你这样称呼它，试着称呼它（“waic”，list（引号（m1），引号（m2））。这是一种更直接的翻译，不会将数据对象放在调用本身中。谢谢！我能够找到如何将其作为起点来做我想做的事情，但我仍然很好奇为什么会有不同。我会将解决方案作为编辑添加到原始帖子中，以便代码可读，但我仍然很好奇我想@MrFlick解释了这两种方法之间的区别。你可以看到类似这样的代码：

x
List of 3
 $ loos      :List of 2
  ..$ m1:List of 8
  .. ..$ estimates   : num [1:3, 1:2] -152.802 2.757 305.604 6.602 0.457 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:3] "elpd_waic" "p_waic" "waic"
  .. .. .. ..$ : chr [1:2] "Estimate" "SE"
  .. ..$ pointwise   : num [1:100, 1:3] -1.11 -1.08 -1.04 -2.05 -1.97 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : NULL
  .. .. .. ..$ : chr [1:3] "elpd_waic" "p_waic" "waic"
  .. ..$ elpd_waic   : num -153
  .. ..$ p_waic      : num 2.76
  .. ..$ waic        : num 306
  .. ..$ se_elpd_waic: num 6.6
  .. ..$ se_p_waic   : num 0.457
  .. ..$ se_waic     : num 13.2
  .. ..- attr(*, "dims")= int [1:2] 4000 100
  .. ..- attr(*, "class")= chr [1:2] "waic" "loo"
  .. ..- attr(*, "yhash")= chr "543895fddfbc9e53e6fdf43fd5d57f2d95a3893f"
  .. ..- attr(*, "model_name")= chr "m1"
  ..$ m2:List of 8
  .. ..$ estimates   : num [1:3, 1:2] -153.55 2.958 307.1 6.72 0.592 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : chr [1:3] "elpd_waic" "p_waic" "waic"
  .. .. .. ..$ : chr [1:2] "Estimate" "SE"
  .. ..$ pointwise   : num [1:100, 1:3] -1.05 -1.12 -1.04 -1.95 -1.89 ...
  .. .. ..- attr(*, "dimnames")=List of 2
  .. .. .. ..$ : NULL
  .. .. .. ..$ : chr [1:3] "elpd_waic" "p_waic" "waic"
  .. ..$ elpd_waic   : num -154
  .. ..$ p_waic      : num 2.96
  .. ..$ waic        : num 307
  .. ..$ se_elpd_waic: num 6.72
  .. ..$ se_p_waic   : num 0.592
  .. ..$ se_waic     : num 13.4
  .. ..- attr(*, "dims")= int [1:2] 4000 100
  .. ..- attr(*, "class")= chr [1:2] "waic" "loo"
  .. ..- attr(*, "yhash")= chr "543895fddfbc9e53e6fdf43fd5d57f2d95a3893f"
  .. ..- attr(*, "model_name")= chr "m2"
 $ diffs     : 'compare.loo' num [1:2, 1:8] 0 -0.748 0 1.384 -152.802 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "m1" "m2"
  .. ..$ : chr [1:8] "elpd_diff" "se_diff" "elpd_waic" "se_elpd_waic" ...
 $ ic_diffs__: num [1, 1:2] -1.5 2.77
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr "m1 - m2"
  .. ..$ : chr [1:2] "WAIC" "SE"
 - attr(*, "class")= chr "loolist"

List of 3
 $ loos      :List of 2
  ..$ structure(list(formula = structure(list(formula = y ~ x, pforms = list(
),     pfix = list(
), resp = "y", family = structure(list(family = "gaussian",         link = "identity", linkfun = function (mu)         link(mu, link = slink
), linkinv = function (eta)         ilink(eta, link = slink
), dpars = c("mu", "sigma"
), type = "real",         ybounds = c(-Inf, Inf
), closed = c(NA, NA
), ad = c("weights",         "subset", "se", "cens", "trunc", "mi"
), specials = c("residuals",         "rescor")
), class = c("brmsfamily", "family")
), mecor = TRUE
), class = c("brmsformula", "bform")
), data = structure(list(y = c(0.222865037574825, 0.570128066864863, -0.135685571969938, 1.51400901767955, -1.42294537600286, 3.05953211020977, 1.77319875465707, -0.560055434573721, -2.17233843283651, 0.244891176968169, 0.852334318135536, -0.600487439599353, 0.37978067519301, -2.39510231005513, 0.595899684607949, 1.03714876676312, -1.41064279609684, -0.233463367708363, 1.83336396424474, 0.265639715825803, 0.896216012007527, -0.511005506576839, -0.990258505999856, -0.558980878174761, 1.05346814525893, -0.0899664221908207, -1.33110641888477, -0.188959731751976, -0.679800813713052, -0.321769958513427, 0.668316298445943, -0.427812609325669, -0.961094772216214, -0.194752817585835, 0.296422206866777, 0.274271008303485, -0.723313689099733, 1.39275639468154, -1.91894463948922, 0.322229704542086, -0.840174756057233, 1.46068495897767, -2.20030669514299, 0.766461853640603, -0.932998510936173, -0.389747230010412, 1.21222512351531, -0.367388491045232, 1.4680382011575, -0.248155597915535, 0.881321799678558, 0.928975863372169, -0.40097077944702, -0.932753769947884, -0.0787743622003075, 0.934773284552975, 0.802022931150372, 0.539236485182244, 1.90920483698261, 0.0959007938340177, 1.90355811256047, 1.23880369756146, 0.399161008234409, -1.61444070712025, 1.09228096133471, -0.334304984354992, 1.94919630176386, 0.388723697364589, -0.676560429449814, -0.486799514926963, -1.69814420604508, 0.610643539656719, -1.7358003804893, -1.18947570640705, -0.2048251685502, -0.244308782100457, 0.680149129765254, -0.194941685094996, 1.2495923072767, -0.508497293855665, -2.01720057673334, 0.717582169549046, -0.974164402299986, 0.191975856490787, -0.47647793799195, 1.30995888921046, 0.612325939317696, 1.68149269824028, 0.186230025956939, 1.4523083782372, 0.247246158932475, -0.749612962281774, 1.68680561431638, -1.4512951423047, -1.93333213179369, 1.20046240239089, -0.13573165368954, -0.545721678032106, -0.0145610725616504, -0.268693593357222
), x = c(1.55551806854814, -2.30450038318989, 0.296995230680729, 0.668154099392684, -0.1547006917081, -0.309984806283829, 0.495221682683779, -0.562225239149049, 0.347241974318799, -0.124006626810845, -1.44831536829081, 0.00816231155818208, 1.19269682091828, 1.09657509945784, -0.635792796784607, 0.387962591093174, 0.0706000447502193, -1.00525115255797, -0.289935304272473, -0.473562989522596, -0.783401194401419, -0.847650167792549, 0.275627321425429, -0.135952419089467, -0.77243964938835, 1.1657114904043, -1.01856242301745, -0.463154535428835, -0.586387548561369, 0.286936164713612, -0.940443323746505, 0.340709452808652, -0.894584173330604, 0.591347212942816, 0.949953660301979, 0.321956819738089, 0.580290486749746, -0.68755984894602, 1.46867714260446, 0.535535223822069, 0.80492806522492, 1.32291579645674, -1.08656956060417, 1.31416520144562, 1.48015770667234, 0.977243130228241, 0.0222292740372709, 1.39536233198176, 2.37899133963515, 2.55012059548256, -0.601237284663627, -1.10848718399703, -0.718810050747427, 1.0122041455424, -0.582118731616551, 0.0958348014993671, -0.983464989091578, 0.0985791907557584, 0.239011227739015, 0.172632248363633, -0.758182492239057, -1.48857289853501, 0.0685537104701556, 0.51044243736898, 0.482978773054692, 0.394488590480919, 0.608583888747901, -0.535321605973326, 0.617601681089166, -0.142879029528284, -0.727413272882995, -1.14984815574316, -0.34637735244817, 0.784041932914986, 1.02247745933603, 0.512966807729552, -1.43056324735507, 0.649198431445028, 0.783348344111711, -1.44908235653234, 0.216245543136908, -0.324521163713685, 1.25515815254712, -0.0190663771221899, -0.543757718828948, -0.193192758506608, 1.11354415805662, 0.484296073217596, -0.00441288006210047, -1.59834412372779, -0.463597727522575, 0.95437185173169, -0.00258922453282936, -0.595437835005253, 1.1527271628746, -0.00240938398175376, -0.733006155939103, -0.0418069102112059, -0.97696234693589, -0.913221059230367)
), class = "data.frame", row.names = c(NA, 100L
), terms = y ~ y + x, data_name = "d"
), prior = structure(list(    prior = c("", "", "student_t(3, 0, 2.5)", "student_t(3, 0, 2.5)"    
), class = c("b", "b", "Intercept", "sigma"
), coef = c("",     "x", "", ""
), group = c("", "", "", ""
), resp = c("", "",     "", ""
), dpar = c("", "", "", ""
), nlpar = c("", "", "",     ""
), bound = c("", "", "", "")
), special = list(mu = list()
), row.names = c(NA, -4L
), class = c("brmsprior", "data.frame"
), sample_prior = "no"
),     data2 = list(
), stanvars = structure(list(
), class = "stanvars"
),     model = structure("// generated with brms 2.13.5\nfunctions {\n}\ndata {\n  int<lower=1> N;  // number of observations\n  vector[N] Y;  // response variable\n  int<lower=1> K;  // number of population-level effects\n  matrix[N, K] X;  // population-level design matrix\n  int prior_only;  // should the likelihood be ignored?\n}\ntransformed data {\n  int Kc = K - 1;\n  matrix[N, Kc] Xc;  // centered version of X without an intercept\n  vector[Kc] means_X;  // column means of X before centering\n  for (i in 2:K) {\n    means_X[i - 1] = mean(X[, i]);\n    Xc[, i - 1] = X[, i] - means_X[i - 1];\n  }\n}\nparameters {\n  vector[Kc] b;  // population-level effects\n  real Intercept;  // temporary intercept for centered predictors\n  real<lower=0> sigma;  // residual SD\n}\ntransformed parameters {\n}\nmodel {\n  // priors including all constants\n  target += student_t_lpdf(Intercept | 3, 0, 2.5);\n  target += student_t_lpdf(sigma | 3, 0, 2.5)\n    - 1 * student_t_lccdf(0 | 3, 0, 2.5);\n  // likelihood including all constants\n  if (!prior_only) {\n    target += normal_id_glm_lpdf(Y | Xc, Intercept, b, sigma);\n  }\n}\ngenerated quantities {\n  // actual population-level intercept\n  real b_Intercept = Intercept - dot_product(means_X, b);\n}\n", class = c("character",     "brmsmodel")
), ranef = structure(list(id = numeric(0
), group = character(0
),         gn = numeric(0
), ...

foo <- function(...){
     dots <- eval(substitute(alist(...)))
     res <- list()
     for(i in 1:length(dots)){
          res[[i]] <- as.character(dots[[i]])[-1]
          res[[i]] <- lapply(res[[i]], FUN = function(x) eval(parse(text = paste0('quote(', x, ')'))))
          res[[i]] <- do.call('waic', res[[i]])
     }
    return(res)
}