Formatted latex regression tables with multiple models from broom output? - r

Question

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 ...
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
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?

Answer 1

Is there a similar package that uses tidy estimation results produced with broom as inputs
Not to my knowledge, but stargazer allows you to use custom inputs to generate regression tables. This allows us to create "fake" shell tables that we can populate with values from the tidy table. Using your example
# 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$term[-1], collapse = " + ")))
fit <- lm(f, dat)
# set up model statistics
fit_stats <- data.frame(labels = names(glance_model),
mod1 = round(unlist(glance_model), 3),
mod2 = round(unlist(glance_model), 3),
row.names = NULL,
stringsAsFactors = FALSE)
We can then feed these values into stargazer:
library(stargazer)
stargazer(fit, fit, type = "text",
coef = list(tidy_model$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

Answer 2

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)
}
This results in the following output:
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