r-从包含lsmeans()统计信息的列表元素创建表

Question

我正在尝试构建一些表，每个表针对列表的每一个元素(总共13个(，包含lsmeans统计信息的统计信息，通过以下命令行计算

md <- out_long %>%
group_by(signals) %>%
do(fit = lmerTest::lmer(value ~ COND + (1 |ID), data = .)) %>% 
pull(fit) %>% 
lapply(., function(m) lsmeans(m, pairwise ~ COND, adjust="tukey"))

每个元素都包含以下统计信息(即第一个(：

$lsmeans
COND    lsmean    SE   df lower.CL upper.CL
NEG-CTR  2.471 0.772 38.9    0.909     4.03
NEG-NOC  3.024 0.772 38.9    1.463     4.59
NEU-NOC  0.711 0.772 38.9   -0.850     2.27
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 
$contrasts
contrast              estimate    SE df t.ratio p.value
(NEG-CTR) - (NEG-NOC)   -0.554 0.644 48  -0.860  0.6678
(NEG-CTR) - (NEU-NOC)    1.760 0.644 48   2.735  0.0233
(NEG-NOC) - (NEU-NOC)    2.314 0.644 48   3.595  0.0022
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 3 estimates 

期望结果

如果我需要为它们构建优雅的表，或者为每个元素专门构建一个表，包括lsmeans统计和constrast统计，我应该如何进行？

这是我在上工作的数据

> dput(head(out_long,50))
structure(list(ID = c("01", "01", "01", "04", "04", "04", "06", 
"06", "06", "07", "07", "07", "08", "08", "08", "09", "09", "09", 
"10", "10", "10", "11", "11", "11", "12", "12", "12", "13", "13", 
"13", "15", "15", "15", "16", "16", "16", "17", "17", "17", "18", 
"18", "18", "19", "19", "19", "21", "21", "21", "22", "22"), 
GR = c("RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", "RP", 
"RP"), SES = c("V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", "V", 
"V", "V", "V", "V", "V"), COND = c("NEG-CTR", "NEG-NOC", 
"NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
"NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
"NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
"NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
"NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
"NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
"NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC", 
"NEU-NOC", "NEG-CTR", "NEG-NOC", "NEU-NOC", "NEG-CTR", "NEG-NOC"
), signals = c("P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", "P3FCz", 
"P3FCz", "P3FCz", "P3FCz"), value = c(-11.6312151716924, 
-11.1438413285935, -3.99591470944713, -0.314155675382471, 
0.238885648959708, 5.03749946898385, -0.213621915029167, 
-2.96032491743069, -1.97168681693488, -2.83109425298642, 
1.09291198163802, -6.692991645215, 4.23849942428043, 2.9898889629932, 
3.5510699900835, 9.57481668808606, 5.4167795618285, 1.7067607715475, 
-6.13036076093477, -2.82955734597919, -2.50672211111696, 
0.528517585832501, 8.16418133488309, 1.88777321897925, -7.73588468896919, 
-9.83058052401056, -6.97442700196932, 1.27327945355082, 2.11962397764132, 
0.524299677616254, -1.83310726842883, 0.658810483381172, 
-0.261373488428192, 4.37524298634374, 0.625555654900511, 
3.19617639836154, 0.0405517582137798, -3.29357103412113, 
-0.381435057304614, -5.73445509910268, -6.1129152355645, 
-2.45744234877604, 2.95352732001065, 0.527721249096473, 1.91803490989119, 
-3.46703346467546, -2.40438419043702, -5.35374408162217, 
-7.27028665849262, -7.1532211375959)), row.names = c(NA, 
-50L), class = c("tbl_df", "tbl", "data.frame"))
> 

Answer 1

这能为您提供所需的输出吗？

broom::tidy(md[[1]]$lsmeans)
# A tibble: 3 x 6
COND    estimate std.error    df statistic p.value
<chr>      <dbl>     <dbl> <dbl>     <dbl>   <dbl>
1 NEG-CTR    -1.42      1.16  23.4    -1.22    0.234
2 NEG-NOC    -1.41      1.16  23.4    -1.21    0.239
3 NEU-NOC    -1.10      1.18  24.3    -0.935   0.359
broom::tidy(md[[1]]$contrasts)
# A tibble: 3 x 8
term  contrast              null.value estimate std.error    df statistic adj.p.value
<chr> <chr>                      <dbl>    <dbl>     <dbl> <dbl>     <dbl>       <dbl>
1 COND  (NEG-CTR) - (NEG-NOC)          0  -0.0166     0.869  31.0   -0.0191       1.00 
2 COND  (NEG-CTR) - (NEU-NOC)          0  -0.320      0.888  31.1   -0.360        0.931
3 COND  (NEG-NOC) - (NEU-NOC)          0  -0.303      0.888  31.1   -0.342        0.938

要一次性为不同的对象生成柔性表格，可以执行以下操作：

library(tidyverse)
library(lubridate)
library(purrr)
md[[1]] %>%
map(
~broom::tidy(.x) %>%
flextable::flextable()
)