6

我有几个模型，例如下面的示例，我有估计、标准误差、p 值、r2 等作为整洁格式的 data.frames，但我没有原始模型对象（分析是在不同的机器）。

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      ...

有几个包（例如stargazeror texreg）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，请参见下面的示例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生成的整洁估计结果broom作为输入而不是模型对象来生成如上例所示的表格？

4

2 回答 2

8

是否有类似的软件包使用扫帚产生的整齐估计结果作为输入

据我所知，但stargazer允许您使用自定义输入来生成回归表。这使我们可以创建“假”的 shell 表，我们可以使用 tidy 表中的值填充这些表。使用您的示例

# 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
于 2018-08-26T17:31:11.580 回答
3

受此texreg答案的启发，我又看了看，并且除了先前的答案之外，还有一种更本地的方法可以通过定义额外的提取方法来做到这一点：texreg

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)
}

这将产生以下输出：

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
于 2018-08-27T10:33:50.457 回答