I am trying to build some tables, each for every elemnt (13 in total) of a list containing statistics from an lsmeans statistics, computed via this commands lines
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"))
Each element contains the following statistics (i.e. the first one):
$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
Desired outcome
In the case I need to build elegant tables for them, or specifically one single table for each elements, including both lsmeans statistics and constrast ones, how should I proceed?
Here the data I’m working on
> 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"))
>
>Solution :
Does this give you the desired output?
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
To produce flextables for different objects in one go, you can the following:
library(tidyverse)
library(lubridate)
library(purrr)
md[[1]] %>%
map(
~broom::tidy(.x) %>%
flextable::flextable()
)