I'm trying to save the summary of a model as a data frame in R. The model is a stepwise regression model using the MASS package. I'm primarily interested in saving the coefficients, their t value and the R-squared of the model. I tried
ModelSummary Error in as.data.frame.default(x[[i]], optional = TRUE, stringsAsFactors = stringsAsFactors) : cannot coerce class ‘"lm"’ to a data.frame The broom package has a lot of great ways to turn your regression summaries into data frames. Since you do not have a directly reproducible dataset included, I will just use a simple regression on the iris dataset as an example. First, you can load the packages broom for the tidy dataframes and tidyverse for wrangling/plotting the data.
#### Load Libraries #### library(tidyverse) library(broom) Then fit a regression like so:
#### Fit Regression Model #### fit The tidy function turns your main coefficients into a dataframe instantly.
#### Tidy Dataframe #### fit.tidy # A tibble: 2 × 5 term estimate std.error statistic p.value 1 (Intercept) 1.08 0.0730 14.8 4.04e-31 2 Petal.Width 2.23 0.0514 43.4 4.68e-86 The augment function fits your model data and several other useful metrics into a dataframe, such as fitted values, residuals, and other information.
#### Augmented Dataframe #### fit.aug # A tibble: 150 × 8 Petal.Length Petal.Width .fitted .resid .hat .sigma .cooksd .std.resid 1 1.4 0.2 1.53 -0.130 0.0182 0.480 0.000693 -0.273 2 1.4 0.2 1.53 -0.130 0.0182 0.480 0.000693 -0.273 3 1.3 0.2 1.53 -0.230 0.0182 0.479 0.00218 -0.484 4 1.5 0.2 1.53 -0.0295 0.0182 0.480 0.0000360 -0.0624 5 1.4 0.2 1.53 -0.130 0.0182 0.480 0.000693 -0.273 6 1.7 0.4 1.98 -0.276 0.0140 0.479 0.00240 -0.580 7 1.4 0.3 1.75 -0.353 0.0160 0.479 0.00449 -0.743 8 1.5 0.2 1.53 -0.0295 0.0182 0.480 0.0000360 -0.0624 9 1.4 0.2 1.53 -0.130 0.0182 0.480 0.000693 -0.273 10 1.5 0.1 1.31 0.193 0.0206 0.480 0.00176 0.409 To glance your final model fit metrics, glance checks things like adjusted R square, etc. and places them into a data frame.
#### Glance Dataframe #### fit.glance # A tibble: 1 × 12 r.squared adj.r.squ…¹ sigma stati…² p.value df logLik AIC BIC devia…³ 1 0.927 0.927 0.478 1882. 4.68e-86 1 -101. 208. 217. 33.8 These data frame variables can also be very quickly selected using pull from the tidyverse (specifically dplyr ).
fit.glance %>% pull(adj.r.squared) Giving you a quick adjusted R square value:
[1] 0.9266173 I will show you how this can be used for one case with the augment code I just used. Here is a plot of the fitted values and residuals on a scatterplot using the model data frame:
fit.aug %>% ggplot(aes(x=.fitted, y=.resid))+ geom_point() 
Since you are interested in combining some of these values in a data frame, you can also do this by adding bits of each into each other.
fit.tidy %>% add_column(r.square = fit.glance$r.squared, adj.r.square = fit.glance$adj.r.squared) # A tibble: 2 × 7 term estimate std.error statistic p.value r.square adj.r.square 1 (Intercept) 1.08 0.0730 14.8 4.04e-31 0.927 0.927 2 Petal.Width 2.23 0.0514 43.4 4.68e-86 0.927 0.927 