我每天进行回归分析。就我而言，这通常意味着估计连续和分类预测变量对各种结果的影响。生存分析可能是我执行的最常见的分析。此类分析通常以非常方便的方式在期刊中进行介绍。这是一个例子：
我想知道是否有人遇到过任何可以公开使用的功能或包：
直接使用回归对象（coxph，lm，lmer，glm或您拥有的任何对象）
在森林图上绘制每个预测变量的效果，或者甚至允许绘制预测变量的选择。
类别预测变量也显示参考类别
显示因子变量在每个类别中的事件数（请参见上图）。显示p值。
最好使用ggplot
提供某种定制
我知道sjPlot包允许绘制lme4，glm和lm结果。但是没有软件包允许上述用于coxph结果，并且coxph是最常用的回归方法之一。我试图自己创建这样的功能，但没有成功。我已经阅读了这篇很棒的文章：Reproduce table and plot from journal，但无法弄清楚如何“通用”代码。
任何建议都非常欢迎。
最佳答案
编辑我现在将其放到package on github中。我已经使用coxph，lm和glm的输出进行了测试。
例：
devtools::install_github("NikNakk/forestmodel")
library("forestmodel")
example(forest_model)
在SO上发布的原始代码（已被github软件包取代）：
尽管可以将相同的技术扩展到其他回归模型，但是我专门针对
coxph模型进行了这项工作，特别是因为它使用了broom包来提取系数。提供的forest_cox函数将coxph的输出作为其参数。 （使用model.frame提取数据以计算每个组中的个体数量并查找因子的参考水平。）它还采用了许多格式化参数。返回值是ggplot，可以打印，保存等。输出以问题中显示的NEJM图为模型。
library("survival")
library("broom")
library("ggplot2")
library("dplyr")
forest_cox <- function(cox, widths = c(0.10, 0.07, 0.05, 0.04, 0.54, 0.03, 0.17),
colour = "black", shape = 15, banded = TRUE) {
data <- model.frame(cox)
forest_terms <- data.frame(variable = names(attr(cox$terms, "dataClasses"))[-1],
term_label = attr(cox$terms, "term.labels"),
class = attr(cox$terms, "dataClasses")[-1], stringsAsFactors = FALSE,
row.names = NULL) %>%
group_by(term_no = row_number()) %>% do({
if (.$class == "factor") {
tab <- table(eval(parse(text = .$term_label), data, parent.frame()))
data.frame(.,
level = names(tab),
level_no = 1:length(tab),
n = as.integer(tab),
stringsAsFactors = FALSE, row.names = NULL)
} else {
data.frame(., n = sum(!is.na(eval(parse(text = .$term_label), data, parent.frame()))),
stringsAsFactors = FALSE)
}
}) %>%
ungroup %>%
mutate(term = paste0(term_label, replace(level, is.na(level), "")),
y = n():1) %>%
left_join(tidy(cox), by = "term")
rel_x <- cumsum(c(0, widths / sum(widths)))
panes_x <- numeric(length(rel_x))
forest_panes <- 5:6
before_after_forest <- c(forest_panes[1] - 1, length(panes_x) - forest_panes[2])
panes_x[forest_panes] <- with(forest_terms, c(min(conf.low, na.rm = TRUE), max(conf.high, na.rm = TRUE)))
panes_x[-forest_panes] <-
panes_x[rep(forest_panes, before_after_forest)] +
diff(panes_x[forest_panes]) / diff(rel_x[forest_panes]) *
(rel_x[-(forest_panes)] - rel_x[rep(forest_panes, before_after_forest)])
forest_terms <- forest_terms %>%
mutate(variable_x = panes_x[1],
level_x = panes_x[2],
n_x = panes_x[3],
conf_int = ifelse(is.na(level_no) | level_no > 1,
sprintf("%0.2f (%0.2f-%0.2f)", exp(estimate), exp(conf.low), exp(conf.high)),
"Reference"),
p = ifelse(is.na(level_no) | level_no > 1,
sprintf("%0.3f", p.value),
""),
estimate = ifelse(is.na(level_no) | level_no > 1, estimate, 0),
conf_int_x = panes_x[forest_panes[2] + 1],
p_x = panes_x[forest_panes[2] + 2]
)
forest_lines <- data.frame(x = c(rep(c(0, mean(panes_x[forest_panes + 1]), mean(panes_x[forest_panes - 1])), each = 2),
panes_x[1], panes_x[length(panes_x)]),
y = c(rep(c(0.5, max(forest_terms$y) + 1.5), 3),
rep(max(forest_terms$y) + 0.5, 2)),
linetype = rep(c("dashed", "solid"), c(2, 6)),
group = rep(1:4, each = 2))
forest_headings <- data.frame(term = factor("Variable", levels = levels(forest_terms$term)),
x = c(panes_x[1],
panes_x[3],
mean(panes_x[forest_panes]),
panes_x[forest_panes[2] + 1],
panes_x[forest_panes[2] + 2]),
y = nrow(forest_terms) + 1,
label = c("Variable", "N", "Hazard Ratio", "", "p"),
hjust = c(0, 0, 0.5, 0, 1)
)
forest_rectangles <- data.frame(xmin = panes_x[1],
xmax = panes_x[forest_panes[2] + 2],
y = seq(max(forest_terms$y), 1, -2)) %>%
mutate(ymin = y - 0.5, ymax = y + 0.5)
forest_theme <- function() {
theme_minimal() +
theme(axis.ticks.x = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.y = element_blank(),
strip.text = element_blank(),
panel.margin = unit(rep(2, 4), "mm")
)
}
forest_range <- exp(panes_x[forest_panes])
forest_breaks <- c(
if (forest_range[1] < 0.1) seq(max(0.02, ceiling(forest_range[1] / 0.02) * 0.02), 0.1, 0.02),
if (forest_range[1] < 0.8) seq(max(0.2, ceiling(forest_range[1] / 0.2) * 0.2), 0.8, 0.2),
1,
if (forest_range[2] > 2) seq(2, min(10, floor(forest_range[2] / 2) * 2), 2),
if (forest_range[2] > 20) seq(20, min(100, floor(forest_range[2] / 20) * 20), 20)
)
main_plot <- ggplot(forest_terms, aes(y = y))
if (banded) {
main_plot <- main_plot +
geom_rect(aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
forest_rectangles, fill = "#EFEFEF")
}
main_plot <- main_plot +
geom_point(aes(estimate, y), size = 5, shape = shape, colour = colour) +
geom_errorbarh(aes(estimate,
xmin = conf.low,
xmax = conf.high,
y = y),
height = 0.15, colour = colour) +
geom_line(aes(x = x, y = y, linetype = linetype, group = group),
forest_lines) +
scale_linetype_identity() +
scale_alpha_identity() +
scale_x_continuous(breaks = log(forest_breaks),
labels = sprintf("%g", forest_breaks),
expand = c(0, 0)) +
geom_text(aes(x = x, label = label, hjust = hjust),
forest_headings,
fontface = "bold") +
geom_text(aes(x = variable_x, label = variable),
subset(forest_terms, is.na(level_no) | level_no == 1),
fontface = "bold",
hjust = 0) +
geom_text(aes(x = level_x, label = level), hjust = 0, na.rm = TRUE) +
geom_text(aes(x = n_x, label = n), hjust = 0) +
geom_text(aes(x = conf_int_x, label = conf_int), hjust = 0) +
geom_text(aes(x = p_x, label = p), hjust = 1) +
forest_theme()
main_plot
}
样本数据和图
pretty_lung <- lung %>%
transmute(time,
status,
Age = age,
Sex = factor(sex, labels = c("Male", "Female")),
ECOG = factor(lung$ph.ecog),
`Meal Cal` = meal.cal)
lung_cox <- coxph(Surv(time, status) ~ ., pretty_lung)
print(forest_cox(lung_cox))
关于r - 生存/回归分析结果的最佳/有效图，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/31289111/