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?
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