提问人：M. Riera 提问时间：9/29/2023 更新时间：9/29/2023 访问量：46
在数据帧内使用变量名称进行子集疏浚 （MuMIn）
Subset dredge with variable names, inside a data frame (MuMIn)
问：
我已经安装了模型，并在它们上运行。我创建了一个存储模型中使用的变量名称和对象本身（在 ）.通过使用 ，我希望使用存储在特定列中的变量名称对对象进行子集化，并将子集保留在新列中。dredge()data framedredgelistrowwise()dredge
我的问题是我无法使这种行行为起作用。
如果我不尝试调用存储在其他列中的字符串，我可以很好地子集对象，请参阅可重现示例。dredge
我找到了 2019 年、2018 年和 2014 年的相关问题（这里，这里）。 一方面，这些问题在函数内部使用，而不是在存储在数据框中的已生成对象上使用。我尝试使用那里概述的解决方案，但没有成功，也可能是由于缺乏解析、表达式等方面的经验。subsetdredgesubsetdredge
library(MuMIn)
#> Warning: package 'MuMIn' was built under R version 4.2.3
library(tidyverse)
#> Warning: package 'ggplot2' was built under R version 4.2.2
#> Warning: package 'tibble' was built under R version 4.2.3
#> Warning: package 'tidyr' was built under R version 4.2.3
#> Warning: package 'purrr' was built under R version 4.2.3
#> Warning: package 'dplyr' was built under R version 4.2.3
#> Warning: package 'stringr' was built under R version 4.2.3
data(Cement)
fm <- lm(y ~ X1*X2 + X1*X3 + X4, Cement)
options(na.action = "na.fail")
d <- MuMIn::dredge(fm)
#> Fixed term is "(Intercept)"
d
#> Global model call: lm(formula = y ~ X1 * X2 + X1 * X3 + X4, data = Cement)
#> ---
#> Model selection table
#> (Int) X1 X2 X3 X4 X1:X2 X1:X3 df logLik AICc
#> 4 52.58 1.4680 0.6623 4 -28.156 69.3
#> 12 71.65 1.4520 0.4161 -0.2365 5 -26.933 72.4
#> 8 48.19 1.6960 0.6569 0.25000 5 -26.952 72.5
#> 10 103.10 1.4400 -0.6140 4 -29.817 72.6
#> 14 111.70 1.0520 -0.41000 -0.6428 5 -27.310 73.2
#> 20 53.71 1.2750 0.6358 0.004197 5 -28.072 74.7
#> 15 203.60 -0.9234 -1.44800 -1.5570 5 -29.734 78.0
#> 28 72.43 1.2870 0.3959 -0.2343 0.003577 6 -26.860 79.7
#> 24 49.05 1.5580 0.6387 0.24590 0.002914 6 -26.904 79.8
#> 16 62.41 1.5510 0.5102 0.10190 -0.1441 6 -26.918 79.8
#> 40 48.17 1.6690 0.6522 0.25180 0.007277 6 -26.933 79.9
#> 46 112.00 1.0680 -0.41490 -0.6462 -0.005210 6 -27.301 80.6
#> 13 131.30 -1.20000 -0.7246 4 -35.372 83.7
#> 32 66.63 1.3570 0.4556 0.06354 -0.1767 0.003398 7 -26.854 90.1
#> 56 49.02 1.5320 0.6342 0.24770 0.002888 0.007116 7 -26.885 90.2
#> 48 60.19 1.5560 0.5298 0.12600 -0.1218 0.004666 7 -26.911 90.2
#> 7 72.07 0.7313 -1.00800 4 -40.965 94.9
#> 9 117.60 -0.7382 3 -45.872 100.4
#> 3 57.42 0.7891 3 -46.035 100.7
#> 11 94.16 0.3109 -0.4569 4 -45.761 104.5
#> 2 81.48 1.8690 3 -48.206 105.1
#> 64 64.80 1.3650 0.4722 0.08329 -0.1585 0.003335 0.003693 8 -26.850 105.7
#> 6 72.35 2.3120 0.49450 4 -48.005 109.0
#> 38 64.05 0.9528 0.49550 0.311600 5 -45.678 109.9
#> 5 110.20 -1.25600 3 -50.980 110.6
#> 1 95.42 2 -53.168 111.5
#> delta weight
#> 4 0.00 0.539
#> 12 3.13 0.113
#> 8 3.16 0.111
#> 10 3.32 0.102
#> 14 3.88 0.078
#> 20 5.40 0.036
#> 15 8.73 0.007
#> 28 10.41 0.003
#> 24 10.49 0.003
#> 16 10.52 0.003
#> 40 10.55 0.003
#> 46 11.29 0.002
#> 13 14.43 0.000
#> 32 20.80 0.000
#> 56 20.86 0.000
#> 48 20.91 0.000
#> 7 25.62 0.000
#> 9 31.10 0.000
#> 3 31.42 0.000
#> 11 35.21 0.000
#> 2 35.77 0.000
#> 64 36.39 0.000
#> 6 39.70 0.000
#> 38 40.61 0.000
#> 5 41.31 0.000
#> 1 42.22 0.000
#> Models ranked by AICc(x)
data.frame(test = c("X1", "X2")) %>%
rowwise() %>%
mutate(dredge = list(d)) %>%
mutate(subset = list(subset(dredge, delta <= 6)))
#> # A tibble: 2 × 3
#> # Rowwise:
#> test dredge subset
#> <chr> <list> <list>
#> 1 X1 <mdl.slct [26 × 12]> <mdl.slct [6 × 12]>
#> 2 X2 <mdl.slct [26 × 12]> <mdl.slct [6 × 12]>
data.frame(test = c("X1", "X2")) %>%
rowwise() %>%
mutate(dredge = list(d)) %>%
mutate(subset = list(subset(dredge, has("X1"))))
#> # A tibble: 2 × 3
#> # Rowwise:
#> test dredge subset
#> <chr> <list> <list>
#> 1 X1 <mdl.slct [26 × 12]> <mdl.slct [18 × 12]>
#> 2 X2 <mdl.slct [26 × 12]> <mdl.slct [18 × 12]>
data.frame(test = c("X1", "X2")) %>%
rowwise() %>%
mutate(dredge = list(d)) %>%
mutate(subset = list(subset(dredge, has(test))))
#> # A tibble: 2 × 3
#> # Rowwise:
#> test dredge subset
#> <chr> <list> <list>
#> 1 X1 <mdl.slct [26 × 12]> <mdl.slct [26 × 12]>
#> 2 X2 <mdl.slct [26 × 12]> <mdl.slct [26 × 12]>
data.frame(test = c("X1", "X2")) %>%
rowwise() %>%
mutate(dredge = list(d)) %>%
mutate(subset = list(subset(dredge, parse(text = sprintf("has(%s)", test)))))
#> Error in `mutate()`:
#> ℹ In argument: `subset = list(subset(dredge, parse(text =
#> sprintf("has(%s)", test))))`.
#> ℹ In row 1.
#> Caused by error in `xj[i]`:
#> ! invalid subscript type 'expression'
#> Backtrace:
#> ▆
#> 1. ├─... %>% ...
#> 2. ├─dplyr::mutate(...)
#> 3. ├─dplyr:::mutate.data.frame(...)
#> 4. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
#> 5. │ ├─base::withCallingHandlers(...)
#> 6. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
#> 7. │ └─mask$eval_all_mutate(quo)
#> 8. │ └─dplyr (local) eval()
#> 9. ├─base::subset(dredge, parse(text = sprintf("has(%s)", test)))
#> 10. ├─MuMIn:::subset.model.selection(...)
#> 11. │ └─MuMIn:::`[.model.selection`(...)
#> 12. │ ├─MuMIn:::subset_model_selection(item(x, j, i, ...), origattrib <- attributes(x))
#> 13. │ └─MuMIn:::item(x, j, i, ...)
#> 14. │ └─base::`[.data.frame`(x, i, name, ...)
#> 15. └─base::.handleSimpleError(...)
#> 16. └─dplyr (local) h(simpleError(msg, call))
#> 17. └─rlang::abort(message, class = error_class, parent = parent, call = error_call)
data.frame(test = c("X1", "X2")) %>%
rowwise() %>%
mutate(dredge = list(d)) %>%
mutate(subset = list(subset(dredge, eval(parse(text = sprintf("has(%s)", test))))))
#> Error in `mutate()`:
#> ℹ In argument: `subset = list(subset(dredge, eval(parse(text =
#> sprintf("has(%s)", test)))))`.
#> ℹ In row 1.
#> Caused by error in `has()`:
#> ! no se pudo encontrar la función "has"
#> Backtrace:
#> ▆
#> 1. ├─... %>% ...
#> 2. ├─dplyr::mutate(...)
#> 3. ├─dplyr:::mutate.data.frame(...)
#> 4. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
#> 5. │ ├─base::withCallingHandlers(...)
#> 6. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
#> 7. │ └─mask$eval_all_mutate(quo)
#> 8. │ └─dplyr (local) eval()
#> 9. ├─base::subset(dredge, eval(parse(text = sprintf("has(%s)", test))))
#> 10. ├─MuMIn:::subset.model.selection(...)
#> 11. │ ├─MuMIn:::`[.model.selection`(...)
#> 12. │ │ ├─MuMIn:::subset_model_selection(item(x, j, i, ...), origattrib <- attributes(x))
#> 13. │ │ └─MuMIn:::item(x, j, i, ...)
#> 14. │ │ └─base::`[.data.frame`(x, i, name, ...)
#> 15. │ └─MuMIn:::subset_eval(substitute(subset), x, parent.frame())
#> 16. │ └─base::eval(...)
#> 17. │ └─base::eval(...)
#> 18. │ └─base::eval(parse(text = sprintf("has(%s)", test)))
#> 19. │ └─base::eval(parse(text = sprintf("has(%s)", test)))
#> 20. └─base::.handleSimpleError(...)
#> 21. └─dplyr (local) h(simpleError(msg, call))
#> 22. └─rlang::abort(message, class = error_class, parent = parent, call = error_call)
data.frame(test = c("X1", "X2")) %>%
rowwise() %>%
mutate(dredge = list(d)) %>%
mutate(subset = list(subset(dredge, has(substitute(v, list(v = as.name("test")))))))
#> Error in `mutate()`:
#> ℹ In argument: `subset = list(subset(dredge, has(substitute(v, list(v =
#> as.name("test"))))))`.
#> ℹ In row 1.
#> Caused by error:
#> ! objeto 'substitute(v, list(v = as.name("test")))' no encontrado
#> Backtrace:
#> ▆
#> 1. ├─... %>% ...
#> 2. ├─dplyr::mutate(...)
#> 3. ├─dplyr:::mutate.data.frame(., subset = list(subset(dredge, has(substitute(v, list(v = as.name("test")))))))
#> 4. │ └─dplyr:::mutate_cols(.data, dplyr_quosures(...), by)
#> 5. │ ├─base::withCallingHandlers(...)
#> 6. │ └─dplyr:::mutate_col(dots[[i]], data, mask, new_columns)
#> 7. │ └─mask$eval_all_mutate(quo)
#> 8. │ └─dplyr (local) eval()
#> 9. ├─base::subset(dredge, has(substitute(v, list(v = as.name("test")))))
#> 10. ├─MuMIn:::subset.model.selection(dredge, has(substitute(v, list(v = as.name("test")))))
#> 11. │ ├─MuMIn:::`[.model.selection`(...)
#> 12. │ │ ├─MuMIn:::subset_model_selection(item(x, j, i, ...), origattrib <- attributes(x))
#> 13. │ │ └─MuMIn:::item(x, j, i, ...)
#> 14. │ │ └─base::`[.data.frame`(x, i, name, ...)
#> 15. │ └─MuMIn:::subset_eval(substitute(subset), x, parent.frame())
#> 16. │ └─base::eval(...)
#> 17. │ └─base::eval(...)
#> 18. └─base::.handleSimpleError(...)
#> 19. └─dplyr (local) h(simpleError(msg, call))
#> 20. └─rlang::abort(message, class = error_class, parent = parent, call = error_call)
sessionInfo()
#> R version 4.2.0 (2022-04-22 ucrt)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 22000)
#>
#> Matrix products: default
#>
#> locale:
#> [1] LC_COLLATE=Spanish_Spain.utf8 LC_CTYPE=Spanish_Spain.utf8
#> [3] LC_MONETARY=Spanish_Spain.utf8 LC_NUMERIC=C
#> [5] LC_TIME=Spanish_Spain.utf8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] forcats_0.5.1 stringr_1.5.0 dplyr_1.1.2 purrr_1.0.1
#> [5] readr_2.1.2 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.0
#> [9] tidyverse_1.3.1 MuMIn_1.47.5
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.0 xfun_0.40 haven_2.5.0 lattice_0.20-45
#> [5] colorspace_2.0-3 vctrs_0.6.3 generics_0.1.2 htmltools_0.5.6
#> [9] stats4_4.2.0 yaml_2.3.5 utf8_1.2.2 rlang_1.1.1
#> [13] pillar_1.9.0 glue_1.6.2 withr_2.5.0 DBI_1.1.2
#> [17] dbplyr_2.1.1 readxl_1.4.0 modelr_0.1.8 lifecycle_1.0.3
#> [21] cellranger_1.1.0 munsell_0.5.0 gtable_0.3.0 rvest_1.0.2
#> [25] evaluate_0.15 knitr_1.39 tzdb_0.3.0 fastmap_1.1.0
#> [29] fansi_1.0.3 highr_0.9 broom_0.8.0 backports_1.4.1
#> [33] scales_1.2.1 jsonlite_1.8.0 fs_1.5.2 hms_1.1.1
#> [37] digest_0.6.29 stringi_1.7.6 grid_4.2.0 cli_3.6.1
#> [41] tools_4.2.0 magrittr_2.0.3 crayon_1.5.1 pkgconfig_2.0.3
#> [45] ellipsis_0.3.2 Matrix_1.4-1 xml2_1.3.3 lubridate_1.8.0
#> [49] reprex_2.0.2 assertthat_0.2.1 rmarkdown_2.14 httr_1.4.3
#> [53] rstudioapi_0.13 R6_2.5.1 nlme_3.1-157 compiler_4.2.0
创建于 2023-09-29 使用 reprex v2.0.2
答：
1赞
DaveArmstrong
9/29/2023
#1
我不太确定为什么该子集不能以通常的方式工作。我能够在函数内部获得所需的结果。疏浚对象的文档说这等同于 ，所以我改用后者（在这种情况下仍然不起作用）。mutate()map()mutate()subsethas("X1")!is.na(X1)has()
library(MuMIn)
library(tidyverse)
data(Cement)
fm <- lm(y ~ X1*X2 + X1*X3 + X4, Cement)
options(na.action = "na.fail")
d <- MuMIn::dredge(fm)
#> Fixed term is "(Intercept)"
data.frame(test = c("X1", "X2")) %>%
rowwise() %>%
mutate(dredge = list(d)) %>%
mutate(subset = map(test, \(x)subset(d, !is.na(d[[x]]))))
#> # A tibble: 2 × 3
#> # Rowwise:
#> test dredge subset
#> <chr> <list> <list>
#> 1 X1 <mdl.slct [26 × 12]> <mdl.slct [18 × 12]>
#> 2 X2 <mdl.slct [26 × 12]> <mdl.slct [16 × 12]>
创建于 2023-09-29 使用 reprex v2.0.2
评论
0赞
M. Riera
9/29/2023
您的解决方案有效，谢谢！我意识到你也可以用filter（）做子集，因为疏浚也是类data.frame。我在 filter（） 中使用了 get（），以便在数据框列中使用变量名称 （filter（get（column_storing_variable_name））。
1赞
DaveArmstrong
9/29/2023
@M.Riera也是一个很好的解决方案。
评论