report.lm.md
In report: Automated Reporting of Results and Statistical Models

report.lm - glm

Code
  report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))
Output
  We fitted a probit model (estimated using ML) to predict vs with disp (formula:
  vs ~ disp). The model's explanatory power is substantial (Nagelkerke's R2 =
  0.66). The model's intercept, corresponding to disp = 0, is at 2.51 (95% CI
  [1.13, 4.28], p = 0.001). Within this model:

    - The effect of disp is statistically significant and negative (beta = -0.01,
  95% CI [-0.02, -6.45e-03], p < .001; Std. beta = -1.62, 95% CI [-2.79, -0.80])

  Standardized parameters were obtained by fitting the model on a standardized
  version of the dataset. 95% Confidence Intervals (CIs) and p-values were
  computed using a Wald z-distribution approximation.

Code
  report(glm(vs ~ mpg, data = mtcars, family = "poisson"))
Output
  We fitted a poisson model (estimated using ML) to predict vs with mpg (formula:
  vs ~ mpg). The model's explanatory power is substantial (Nagelkerke's R2 =
  0.39). The model's intercept, corresponding to mpg = 0, is at -3.27 (95% CI
  [-5.46, -1.36], p = 0.002). Within this model:

    - The effect of mpg is statistically significant and positive (beta = 0.11, 95%
  CI [0.03, 0.19], p = 0.007; Std. beta = 0.66, 95% CI [0.18, 1.15])

  Standardized parameters were obtained by fitting the model on a standardized
  version of the dataset. 95% Confidence Intervals (CIs) and p-values were
  computed using a Wald z-distribution approximation.

report.lm - lm intercept-only

Code
  out
Output
  We fitted a constant (intercept-only) linear model (estimated using OLS) to
  predict d_wide$group0 - d_wide$group1 (formula: d_wide$group0 - d_wide$group1 ~
  1). The model's intercept is at -1.58 (95% CI [-2.46, -0.70], t(9) = -4.06, p =
  0.003).

  Standardized parameters were obtained by fitting the model on a standardized
  version of the dataset. 95% Confidence Intervals (CIs) and p-values were
  computed using a Wald t-distribution approximation.