r - 从扫帚输出中格式化带有多个模型的 latex 回归表？

Question

我有几个模型，例如下面的示例，我以整齐的格式将估计值，标准误差，p值，r2等作为data.frames，但是我没有原始模型对象(分析是在不同的模型上运行的)机器)。

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      ...

存在多个软件包(例如stargazer或texreg)，它们可以将一个或多个模型对象(lm，glm等)并排转换为格式良好的回归表，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

是否有类似的程序包使用broom生成的整洁估计结果作为输入而不是模型对象来生成表格，例如上述示例？

Answer 1

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来生成回归表。这使我们能够创建“假” shell表，并用整齐的表中的值填充。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