从扫帚输出中格式化带有多个模型的乳胶回归表? [英] Formatted latex regression tables with multiple models from broom output?

问题描述

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(运行了分析在另一台机器上).

I have several models such as the example below for which I have estimates, standard errors, p-values, r2 etc. as data.frames in tidy format, but I don't have the original model objects (analysis was run on a different machine).

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      ...

存在几种可以转换一个或多个模型对象( 的包(例如，stargazer 或 texreg )的软件包.code> 等)并排放入格式良好的回归表中，请参见下面的 texreg 示例:

There exist several packages (e.g. stargazer or texreg) which transform one or more model objects (lm, glm, etc.) into well-formatted regression tables side-by-side, see below for an example of 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

是否有类似的程序包，将使用 broom 产生的整洁估计结果用作输入，而不是使用模型对象来生成如上例所示的表?

Is there a similar package that uses tidy estimation results produced with broom as inputs rather than model objects to produce a table such as the above example?

推荐答案

我又受

I had another look at texreg, inspired by this answer, and there is a more native way to do this by defining an additional extraction method for texreg in addition to the previous answer:

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)
}

这将导致以下输出:

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

这篇关于从扫帚输出中格式化带有多个模型的乳胶回归表?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！