Iv 使用 tbl_summary 函数创建了一个带有 gysummary 的表。
按三分位数分组：“低三分位数”、“中三分位数”、“高三分位数”
使用 add_p，Kruskal Wallis 检验比较这 3 组的不同特征。
我希望从 Kendall tau 相关性中添加一列 p 值（使用 1、2、3 作为数字而不是“低三分位数”、“中三分位数”、“高三分位数”。
这可能吗？
我尝试计算相关性并将 p 值保存为向量，但无法将其添加为列。
编辑： 这是代表：
library(reprex)
#> Warning: package 'reprex' was built under R version 4.2.3
library(quantreg)
#> Warning: package 'quantreg' was built under R version 4.2.3
#> Loading required package: SparseM
#>
#> Attaching package: 'SparseM'
#> The following object is masked from 'package:base':
#>
#> backsolve
library(readxl)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.2.3
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(SciViews)
#> Warning: package 'SciViews' was built under R version 4.2.1
library(gtsummary)
#> Warning: package 'gtsummary' was built under R version 4.2.3
library(labelled)
#> Warning: package 'labelled' was built under R version 4.2.3
set.seed(123)
# Create sample data
n <- 20
data <- data.frame(
VAT_Quantile_3 = factor(sample(c("Low tertile", "Medium tertile", "High tertile"), n, replace = TRUE)),
Gender = factor(sample(c("Male", "Female"), n, replace = TRUE)),
Age = c(50, 49, 48, 51, 52, 50, 49, 48, 51, 52, 50, 49, 48, 51, 52,50, 49, 48, 51, 52),
WC_0 = c(100, 99, 98, 101, 102, 100, 99, 98, 101, 102, 100, 99, 98, 101, 102, 100, 99, 98, 101, 102),
HOMAIR_0 = c(1, 2, 3, 4, 5,1, 2, 3, 4, 5,1, 2, 3, 4, 5,1, 2, 3, 4, 5)
)
head(data)
#> VAT_Quantile_3 Gender Age WC_0 HOMAIR_0
#> 1 High tertile Male 50 100 1
#> 2 High tertile Male 49 99 2
#> 3 High tertile Male 48 98 3
#> 4 Medium tertile Female 51 101 4
#> 5 High tertile Female 52 102 5
#> 6 Medium tertile Male 50 100 1
summary(data)
#> VAT_Quantile_3 Gender Age WC_0 HOMAIR_0
#> High tertile :8 Female: 9 Min. :48 Min. : 98 Min. :1
#> Low tertile :5 Male :11 1st Qu.:49 1st Qu.: 99 1st Qu.:2
#> Medium tertile:7 Median :50 Median :100 Median :3
#> Mean :50 Mean :100 Mean :3
#> 3rd Qu.:51 3rd Qu.:101 3rd Qu.:4
#> Max. :52 Max. :102 Max. :5
tab_1<- data %>%
select(VAT_Quantile_3, Gender , Age , WC_0, HOMAIR_0) %>%
set_variable_labels(WC_0 = "WC", HOMAIR_0 = "HOMA-IR") %>%
tbl_summary(by = VAT_Quantile_3,
type = list(all_continuous() ~ 'continuous2'),
missing = "no",
digits = list(all_continuous() ~ c(1, 1),
all_categorical() ~ c(0,1))) %>%
add_overall() %>%
add_p(,pvalue_fun = ~style_pvalue(., digits = 3)) %>%
add_q() %>%
add_n() %>%
modify_caption("**Table 1. Baseline characteristics across VAT area sex-specific tertials**") %>%
bold_p(t = 0.05, q = TRUE) %>%
bold_labels() %>%
italicize_levels() %>%
modify_footnote(all_stat_cols() ~ "Values are presented as median (p25, p75) for continuous variables, and as number (%) for categorical variables.")
#> add_q: Adjusting p-values with
#> `stats::p.adjust(x$table_body$p.value, method = "fdr")`
#code for Kendell's tau:
cor.test(data$Age, as.numeric(data$VAT_Quantile_3), method = "kendall")
#> Warning in cor.test.default(data$Age, as.numeric(data$VAT_Quantile_3), method =
#> "kendall"): Cannot compute exact p-value with ties
#>
#> Kendall's rank correlation tau
#>
#> data: data$Age and as.numeric(data$VAT_Quantile_3)
#> z = -0.21336, p-value = 0.831
#> alternative hypothesis: true tau is not equal to 0
#> sample estimates:
#> tau
#> -0.04144342
cor.test(data$WC_0, as.numeric(data$VAT_Quantile_3), method = "kendall")
#> Warning in cor.test.default(data$WC_0, as.numeric(data$VAT_Quantile_3), :
#> Cannot compute exact p-value with ties
#>
#> Kendall's rank correlation tau
#>
#> data: data$WC_0 and as.numeric(data$VAT_Quantile_3)
#> z = -0.21336, p-value = 0.831
#> alternative hypothesis: true tau is not equal to 0
#> sample estimates:
#> tau
#> -0.04144342
cor.test(data$HOMAIR_0, as.numeric(data$VAT_Quantile_3), method = "kendall")
#> Warning in cor.test.default(data$HOMAIR_0, as.numeric(data$VAT_Quantile_3), :
#> Cannot compute exact p-value with ties
#>
#> Kendall's rank correlation tau
#>
#> data: data$HOMAIR_0 and as.numeric(data$VAT_Quantile_3)
#> z = -0.85343, p-value = 0.3934
#> alternative hypothesis: true tau is not equal to 0
#> sample estimates:
#> tau
#> -0.1657737
library(flextable)
#> Warning: package 'flextable' was built under R version 4.2.3
#>
#> Attaching package: 'flextable'
#> The following objects are masked from 'package:gtsummary':
#>
#> as_flextable, continuous_summary
tab_1 %>%
as_flex_table() %>%
save_as_image(path = "C:/דוקטורט בכונן/מאמר עם הילה/Area proportion paper/AREA.png")
#> [1] "C:/דוקטורט בכונן/מאמר עם הילה/Area proportion paper/AREA.png"
创建于 2023-04-11 与 reprex v2.0.2