让函数接受参数的列表列 [英] Have a function accept a list column of parameters
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问题描述
与Pass a list of lists of unquoted character parameters to an apply/map/pmap call相关。
我有一个如下所示的函数:
causal_med_so <- function(predictor, mediator, outcome, data, ...){
predictor <- rlang::ensym(predictor)
mediator <- rlang::ensym(mediator)
outcome <- rlang::ensym(outcome)
if(!missing(...)) {
data <- get(data, envir = .GlobalEnv) %>%
dplyr::select(!!predictor, !!mediator, !!outcome, ...) %>%
dplyr::filter(across(.cols = everything(), .fns = ~ !is.na(.)))
predictor <- enquo(predictor)
mediator <- enquo(mediator)
outcome <- enquo(outcome)
med.form <- formula(paste0(
quo_name(mediator), "~",
paste0(
quo_name(predictor), "+",
paste0(c(...), collapse = "+"),
collapse = "+"
)
))
med.fit <- eval(bquote(lm(.(med.form), data = data)))
out.form <- formula(paste0(quo_name(outcome), "~",
paste0(
quo_name(predictor), "+",
quo_name(mediator), "+",
paste0(c(...), collapse = "+"),
collapse = "+"
)))
out.fit <- eval(bquote(lm(.(out.form), data = data)))
med.out <- mediation::mediate(med.fit, out.fit,
treat = quo_name(predictor),
mediator = quo_name(mediator),
boot=T, boot.ci.type = "bca")
return(med.out)
} else {
data <- get(data, envir = .GlobalEnv) %>%
dplyr::select(!!predictor, !!mediator, !!outcome) %>%
dplyr::filter(across(.cols = everything(), .fns = ~ !is.na(.)))
med.form <- formula(paste0(quo_name(mediator), "~", quo_name(predictor)))
med.fit <- eval(bquote(lm(.(med.form), data = data)))
out.form <- formula(paste0(quo_name(outcome), "~",
quo_name(predictor), "+", quo_name(mediator)))
out.fit <- eval(bquote(lm(.(out.form), data = data)))
med.out <- mediation::mediate(med.fit, out.fit,
treat = quo_name(predictor),
mediator = quo_name(mediator),
boot=T, boot.ci.type = "bca")
return(med.out)
}
}
我创建了一个参数列表以提供给该函数:
param_dat <- list(
predictor = c("mpg", "cyl"),
mediator = c("drat", "disp", "wt", "cyl"),
outcome = c("qsec", "gear", "carb", "hp"),
data = c("mtcars")
) %>% cross_df
我要针对模型中的某些变量进行调整,因此我bind_cols
其他协变量:
param_dat <- bind_cols(
list(
predictor = c("mpg", "cyl"),
mediator = c("drat", "disp", "wt", "cyl"),
outcome = c("qsec", "gear", "carb"),
data = c("mtcars")
) %>% cross_df(),
tibble(covariates = rep(list(c("vs", "hp")), 24))
)
并运行模型:
out <- param_dat %>%
slice_head(n = 2)%>%
pmap(., causal_med_so)
我收到一个错误:
计算时出错(predvar,data,env):找不到对象‘vs’
推荐答案
我们可以使用
library(purrr)
out <- param_dat %>%
slice_head(n = 2) %>%
pmap(., ~ invoke(causal_med_so, ..1, ..2, ..3, ..4, ..5))
Running nonparametric bootstrap
Running nonparametric bootstrap
-结构
> str(out)
List of 2
$ :List of 56
..$ d0 : num -0.0631
..$ d1 : num -0.0631
..$ d0.ci : Named num [1:2] -0.14458 -0.00145
.. ..- attr(*, "names")= chr [1:2] "1.777555%" "96.54248%"
..$ d1.ci : Named num [1:2] -0.14458 -0.00145
.. ..- attr(*, "names")= chr [1:2] "1.777555%" "96.54248%"
..$ d0.p : num 0.062
..$ d1.p : num 0.062
..$ d0.sims : num [1:1000, 1] -0.0135 -0.0658 -0.1265 -0.1003 -0.0049 ...
..$ d1.sims : num [1:1000, 1] -0.0135 -0.0658 -0.1265 -0.1003 -0.0049 ...
..$ z0 : num -0.0799
..$ z1 : num -0.0799
..$ z0.ci : Named num [1:2] -0.2338 0.0353
.. ..- attr(*, "names")= chr [1:2] "2.142035%" "97.09108%"
..$ z1.ci : Named num [1:2] -0.2338 0.0353
.. ..- attr(*, "names")= chr [1:2] "2.142035%" "97.09108%"
..$ z0.p : num 0.224
..$ z1.p : num 0.224
..$ z0.sims : num [1:1000, 1] -0.1527 -0.0219 0.0202 -0.0953 -0.1865 ...
..$ z1.sims : num [1:1000, 1] -0.1527 -0.0219 0.0202 -0.0953 -0.1865 ...
..$ n0 : num 0.441
..$ n1 : num 0.441
..$ n0.ci : Named num [1:2] 0.0806 2.7754
.. ..- attr(*, "names")= chr [1:2] "7.723976%" "99.76811%"
..$ n1.ci : Named num [1:2] 0.0806 2.7754
...
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