使用来自扫帚输出的多个模型格式化乳胶回归表？
Formatted latex regression tables with multiple models from broom output?
我有几个模型，例如下面的示例，我有估计、标准误差、p 值、r2 等 data.frames 格式，但我没有原始模型对象（分析是 运行 在另一台机器上进行的）。
require(broom)
model <- lm(mpg ~ hp + cyl, mtcars)
tidy_model <- tidy(model)
glance_model <- glance(model)
# tidy_model
# # A tibble: 3 x 5
# term estimate std.error statistic p.value
# <chr> <dbl> <dbl> <dbl> <dbl>
# 1 (Intercept) 36.9 2.19 16.8 1.62e-16
# 2 hp -0.0191 0.0150 -1.27 2.13e- 1
# 3 cyl -2.26 0.576 -3.93 4.80e- 4
# glance(model)
# # A tibble: 1 x 11
# r.squared adj.r.squared sigma ...
# * <dbl> <dbl> <dbl> ...
# 1 0.760 0.743 3.06 ...
存在多个包（例如 stargazer 或 texreg）可将一个或多个模型对象（lm、glm 等）转换为格式正确的对象并排回归 tables，请参见下面的 texreg:
示例
require(texreg)
screenreg(list(model1, model1)
# =================================
# Model 1 Model 2
# ---------------------------------
# (Intercept) 34.66 *** 34.66 ***
# (2.55) (2.55)
# cyl -1.59 * -1.59 *
# (0.71) (0.71)
# disp -0.02 -0.02
# (0.01) (0.01)
# ---------------------------------
# R^2 0.76 0.76
# Adj. R^2 0.74 0.74
# Num. obs. 32 32
# RMSE 3.06 3.06
# =================================
# *** p < 0.001, ** p < 0.01, * p < 0.05
是否有类似的包使用 broom 产生的整洁估计结果作为输入而不是模型对象来产生 table ，例如上面的例子？
Is there a similar package that uses tidy estimation results produced with broom as inputs
据我所知，stargazer 允许您使用自定义输入生成回归 table。这允许我们创建 "fake" shell tables，我们可以用整洁的 table 中的值填充它们。使用你的例子
# create fake models
dat <- lapply(tidy_model$term, function(...) rnorm(10))
dat <- as.data.frame(setNames(dat, c("mpg", tidy_model$term[-1])))
f <- as.formula(paste("mpg ~", paste(tidy_model$term[-1], collapse = " + ")))
fit <- lm(f, dat)
# set up model statistics
fit_stats <- data.frame(labels = names(glance_model),
mod1 = round(unlist(glance_model), 3),
mod2 = round(unlist(glance_model), 3),
row.names = NULL,
stringsAsFactors = FALSE)
然后我们可以将这些值输入 stargazer:
图书馆（观星者）
stargazer(fit, fit, type = "text",
coef = list(tidy_model$estimate, tidy_model$estimate),
se = list(tidy_model$std.error, tidy_model$std.error),
add.lines = lapply(1:nrow(fit_stats), function(i) unlist(fit_stats[i, ])),
omit.table.layout = "s"
)
# ==========================================
# Dependent variable:
# ----------------------------
# mpg
# (1) (2)
# ------------------------------------------
# hp -0.019 -0.019
# (0.015) (0.015)
# cyl -2.265*** -2.265***
# (0.576) (0.576)
# Constant 36.908*** 36.908***
# (2.191) (2.191)
# ------------------------------------------
# r.squared 0.741 0.741
# adj.r.squared 0.723 0.723
# sigma 3.173 3.173
# statistic 41.422 41.422
# p.value 0 0
# df 3 3
# logLik -80.781 -80.781
# AIC 169.562 169.562
# BIC 175.425 175.425
# deviance 291.975 291.975
# df.residual 29 29
# ==========================================
# Note: *p<0.1; **p<0.05; ***p<0.01
我又看了 texreg，受到 回答的启发，除了上一个答案：
extract_broom <- function(tidy_model, glance_model) {
# get estimates/standard errors from tidy
coef <- tidy_model$estimate
coef.names <- as.character(tidy_model$term)
se <- tidy_model$std.error
pvalues <- tidy_model$p.value
# get goodness-of-fit statistics from glance
glance_transposed <- as_tibble(cbind(name = names(glance_model), t(glance_model)))
gof.names <- as.character(glance_transposed$name)
gof <- as.double(glance_transposed$value)
gof.decimal <- gof %% 1 > 0
tr_object <- texreg::createTexreg(coef.names = coef.names,
coef = coef,
se = se,
pvalues = pvalues,
gof.names = gof.names,
gof = gof,
gof.decimal = gof.decimal)
return(tr_object)
}
这导致以下输出：
texreg_model <- extract_broom(tidy_model, glance_model)
screenreg(list(texreg_model, texreg_model))
# =====================================
# Model 1 Model 2
# -------------------------------------
# (Intercept) 36.91 *** 36.91 ***
# (2.19) (2.19)
# hp -0.02 -0.02
# (0.02) (0.02)
# cyl -2.26 *** -2.26 ***
# (0.58) (0.58)
# -------------------------------------
# r.squared 0.74 0.74
# adj.r.squared 0.72 0.72
# sigma 3.17 3.17
# statistic 41.42 41.42
# p.value 0.00 0.00
# df 3 3
# logLik -80.78 -80.78
# AIC 169.56 169.56
# BIC 175.42 175.42
# deviance 291.97 291.97
# df.residual 29 29
# =====================================
# *** p < 0.001, ** p < 0.01, * p < 0.05
我有几个模型，例如下面的示例，我有估计、标准误差、p 值、r2 等 data.frames 格式，但我没有原始模型对象（分析是 运行 在另一台机器上进行的）。
require(broom)
model <- lm(mpg ~ hp + cyl, mtcars)
tidy_model <- tidy(model)
glance_model <- glance(model)
# tidy_model
# # A tibble: 3 x 5
# term estimate std.error statistic p.value
# <chr> <dbl> <dbl> <dbl> <dbl>
# 1 (Intercept) 36.9 2.19 16.8 1.62e-16
# 2 hp -0.0191 0.0150 -1.27 2.13e- 1
# 3 cyl -2.26 0.576 -3.93 4.80e- 4
# glance(model)
# # A tibble: 1 x 11
# r.squared adj.r.squared sigma ...
# * <dbl> <dbl> <dbl> ...
# 1 0.760 0.743 3.06 ...
存在多个包（例如 stargazer 或 texreg）可将一个或多个模型对象（lm、glm 等）转换为格式正确的对象并排回归 tables，请参见下面的 texreg:
require(texreg)
screenreg(list(model1, model1)
# =================================
# Model 1 Model 2
# ---------------------------------
# (Intercept) 34.66 *** 34.66 ***
# (2.55) (2.55)
# cyl -1.59 * -1.59 *
# (0.71) (0.71)
# disp -0.02 -0.02
# (0.01) (0.01)
# ---------------------------------
# R^2 0.76 0.76
# Adj. R^2 0.74 0.74
# Num. obs. 32 32
# RMSE 3.06 3.06
# =================================
# *** p < 0.001, ** p < 0.01, * p < 0.05
是否有类似的包使用 broom 产生的整洁估计结果作为输入而不是模型对象来产生 table ，例如上面的例子？
Is there a similar package that uses tidy estimation results produced with broom as inputs
据我所知，stargazer 允许您使用自定义输入生成回归 table。这允许我们创建 "fake" shell tables，我们可以用整洁的 table 中的值填充它们。使用你的例子
# create fake models
dat <- lapply(tidy_model$term, function(...) rnorm(10))
dat <- as.data.frame(setNames(dat, c("mpg", tidy_model$term[-1])))
f <- as.formula(paste("mpg ~", paste(tidy_model$term[-1], collapse = " + ")))
fit <- lm(f, dat)
# set up model statistics
fit_stats <- data.frame(labels = names(glance_model),
mod1 = round(unlist(glance_model), 3),
mod2 = round(unlist(glance_model), 3),
row.names = NULL,
stringsAsFactors = FALSE)
然后我们可以将这些值输入 stargazer:
图书馆（观星者）
stargazer(fit, fit, type = "text",
coef = list(tidy_model$estimate, tidy_model$estimate),
se = list(tidy_model$std.error, tidy_model$std.error),
add.lines = lapply(1:nrow(fit_stats), function(i) unlist(fit_stats[i, ])),
omit.table.layout = "s"
)
# ==========================================
# Dependent variable:
# ----------------------------
# mpg
# (1) (2)
# ------------------------------------------
# hp -0.019 -0.019
# (0.015) (0.015)
# cyl -2.265*** -2.265***
# (0.576) (0.576)
# Constant 36.908*** 36.908***
# (2.191) (2.191)
# ------------------------------------------
# r.squared 0.741 0.741
# adj.r.squared 0.723 0.723
# sigma 3.173 3.173
# statistic 41.422 41.422
# p.value 0 0
# df 3 3
# logLik -80.781 -80.781
# AIC 169.562 169.562
# BIC 175.425 175.425
# deviance 291.975 291.975
# df.residual 29 29
# ==========================================
# Note: *p<0.1; **p<0.05; ***p<0.01
我又看了 texreg，受到
extract_broom <- function(tidy_model, glance_model) {
# get estimates/standard errors from tidy
coef <- tidy_model$estimate
coef.names <- as.character(tidy_model$term)
se <- tidy_model$std.error
pvalues <- tidy_model$p.value
# get goodness-of-fit statistics from glance
glance_transposed <- as_tibble(cbind(name = names(glance_model), t(glance_model)))
gof.names <- as.character(glance_transposed$name)
gof <- as.double(glance_transposed$value)
gof.decimal <- gof %% 1 > 0
tr_object <- texreg::createTexreg(coef.names = coef.names,
coef = coef,
se = se,
pvalues = pvalues,
gof.names = gof.names,
gof = gof,
gof.decimal = gof.decimal)
return(tr_object)
}
这导致以下输出：
texreg_model <- extract_broom(tidy_model, glance_model)
screenreg(list(texreg_model, texreg_model))
# =====================================
# Model 1 Model 2
# -------------------------------------
# (Intercept) 36.91 *** 36.91 ***
# (2.19) (2.19)
# hp -0.02 -0.02
# (0.02) (0.02)
# cyl -2.26 *** -2.26 ***
# (0.58) (0.58)
# -------------------------------------
# r.squared 0.74 0.74
# adj.r.squared 0.72 0.72
# sigma 3.17 3.17
# statistic 41.42 41.42
# p.value 0.00 0.00
# df 3 3
# logLik -80.78 -80.78
# AIC 169.56 169.56
# BIC 175.42 175.42
# deviance 291.97 291.97
# df.residual 29 29
# =====================================
# *** p < 0.001, ** p < 0.01, * p < 0.05