Я пытаюсь создать лесной участок в R, используя функцию meta :: forest (), и у меня возникают проблемы с форматированием при попытке изменить левые столбцы. Общая статистика по эффектам исчезает, и столбец «автора» выравнивается по центру.
Вот пример того, как выглядит лесной участок, когда я не изменяю имена левых столбцов:
Исходный лесной участок 
Как видно из нижней части каждой «подгруппы», и для общего эффекта отображается статистика. Однако, когда Я пытаюсь изменить левые столбцы на «Автор» и «Измерение», добавив строку в мой код «leftcols ()», эти статистические данные удаляются, и столбцы выравниваются по центру:
Лес График с ошибками 
В частности, я хотел бы помочь с:
- Изменение названия левого столбца на «Автор» и «Измерение» без удаления общей статистики
- Сохранение выравнивания столбцов влево
- Увеличение разрыва между каждым из столбцов для улучшения читабельности
Код для воспроизводимого кода пример:
ForestPlot <- data.frame(
stringsAsFactors = FALSE,
Author = c("Author1","Author2","Author3",
"Author4","Author5","Author6","Author7","Author8",
"Author9","Author10","Author11","Author12","Author13",
"Author14","Author15","Author16","Author17",
"Author18","Author19","Author20","Author21","Author22",
"Author23","Author24","Author25","Author26","Author27",
"Author28","Author29","Author30","Author31","Author32",
"Author33"),
TE = c(-0.816425,-1.6769,-0.1843,
0.2024333,0.6572,-1.2798,-0.3335,-3.3999,-0.7975,
-0.2593,-0.1745,-0.1681,-0.816425,-1.7009,-0.2606,
-0.2007,-1.1554,-0.2615,-0.8014,-0.2953,-1.0333,-0.7091,
0.2301,-0.8444,-0.1545,-0.7629,-1.0476,-0.354,
0.0736,-0.4139,0.1353,-0.918,-0.384),
seTE = c(0.250827211,0.3012,0.3034,
0.3539,0.2456,0.212,0.1861,0.4002,0.2711,0.2964,
0.3251,0.325,0.250827211,0.371,0.196,0.2527,0.2363,
0.2246,0.2806,0.2844,0.2753,0.3112,0.3173,0.4287,
0.4089,0.324,0.3381,0.2242,0.2756,0.2041,0.177,0.3849,
0.2828),
var = c(0.06291429,0.09072144,
0.09205156,0.06713355,0.06031936,0.044944,0.03463321,
0.16016004,0.07349521,0.08785296,0.10569001,0.105625,
0.06291429,0.137641,0.038416,0.06385729,0.05583769,
0.05044516,0.07873636,0.08088336,0.07579009,0.09684544,
0.10067929,0.18378369,0.16719921,0.104976,0.11431161,
0.05026564,0.10556001,0.04165681,0.031329,0.14814801,
0.07997584),
Subgroup = c("Group1","Group1","Group2",
"Group2","Group2","Group1","Group2","Group1","Group2",
"Group2","Group2","Group1","Group2","Group1",
"Group1","Group2","Group1","Group2","Group1","Group2",
"Group1","Group2","Group2","Group2","Group2","Group2",
"Group2","Group2","Group1","Group2","Group1",
"Group1","Group1"),
Measure = c("ER","ER","ER","ER","ER",
"ER","ER","ER","ER","ER","ER","ER","ER","ER","ER",
"ER","ER","ER","ER","ER","ER","ER","ER","ER",
"ER","ER","ER","ER","ER","ER","ER","ER","ER")
)
library(meta)
library(metafor)
#Initial Random Effect Analysis - required for production of Forest Plot
Forest.Analysis <- metagen(TE,
seTE,
data = ForestPlot,
studlab = paste(Author),
comb.fixed = FALSE,
comb.random = TRUE,
method.tau = "SJ",
hakn = TRUE,
prediction = TRUE,
sm = "SMD")
Forest.Analysis
#Updating this random effects model to sort by subgroup
forest.subgroup<-update.meta(Forest.Analysis,
byvar=Subgroup,
comb.random = TRUE,
comb.fixed = FALSE)
forest.subgroup
#Original forest plot which includes overall statistic at the bottom (for reference)
forest(forest.subgroup,
sortvar = TE,
xlim = c(-4.5,2),
studylab = "Author",
rightlabs = c("g","95% CI","weight"),
hetstat = TRUE,
addrow.overall = TRUE,
pooled.totals = FALSE,
smlab = "Emotion Recognition Accuracy",
text.random = "Overall effect",
overall = TRUE,
comb.random = TRUE,
print.tau2 = FALSE,
print.I2 = TRUE,
print.I2.ci = TRUE,
col.diamond = "blue",
col.diamond.lines = "black",
col.predict = "black",
prediction = TRUE)
#Forest plot when I try to change left columns
forest(forest.subgroup,
sortvar = TE,
xlim = c(-4.5,2),
studylab = "Author",
rightlabs = c("g","95% CI","weight"),
leftcols = c("Author", "Measure"),
hetstat = TRUE,
addrow.overall = TRUE,
pooled.totals = FALSE,
smlab = "Emotion Recognition Accuracy",
text.random = "Overall effect",
overall = TRUE,
comb.random = TRUE,
print.tau2 = FALSE,
print.I2 = TRUE,
print.I2.ci = TRUE,
col.diamond = "blue",
col.diamond.lines = "black",
col.predict = "black",
prediction = TRUE)
#sessionInfo output
#R version 3.5.3 (2019-03-11)
#Platform: x86_64-apple-darwin15.6.0 (64-bit)
#Running under: macOS Mojave 10.14.6
#locale:
# [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
#attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#other attached packages:
#[1] metafor_2.1-0 Matrix_1.2-15 meta_4.9-9 readr_1.3.1
#loaded via a namespace (and not attached):
#[1] Rcpp_1.0.1 lattice_0.20-38 zeallot_0.1.0 crayon_1.3.4
#[5] grid_3.5.3 R6_2.4.0 nlme_3.1-137 backports_1.1.3
#[9] pillar_1.4.3 rlang_0.4.2 rstudioapi_0.10 vctrs_0.2.1
#[13] tools_3.5.3 hms_0.5.3 yaml_2.2.0 compiler_3.5.3
#[17] pkgconfig_2.0.2 tibble_2.1.3
* 1 033 * Заранее спасибо за помощь.