vis_reg: Visualize Regression Coefficients Within the Context of...

In RegrCoeffsExplorer: Efficient Visualization of Regression Coefficients for Lm(), Glm(), and Glmnet() Objects

Visualize Regression Coefficients Within the Context of Empirical Data

Description

Typically, regression coefficients for continuous variables are interpreted on a per-unit basis and compared against coefficients for categorical variables. However, this method of interpretation is flawed as it overlooks the distribution of empirical data. This visualization tool provides a more nuanced understanding of the regression model's dynamics, illustrating not only the immediate effect of a unit change but also the broader implications of larger shifts such as interquartile changes.

Usage

vis_reg(object, ...)

Arguments

object	A fitted model object, expected to be one of the following classes: lm : Linear Models. ⁠glm lm⁠ : Generalized Linear Models. ⁠elnet glmnet⁠ : Regularized Linear Models. ⁠lognet glmnet⁠ : Regularized Logistic Models. fixedLassoInf : Inference for the lassso for the linear models. fixedLogitLassoInf : Inference for the lassso for the logistic models.
...	Additional parameters.Please refer to details.

Details

The following additional arguments can be passed:

• CI: A logical value indicating whether to include Confidence Intervals.

  • The default is FALSE.

  • For fixedLassoInf or fixedLogitLassoInf classes it is set to TRUE.

  • confint() is used to generate CIs for the lm and ⁠glm lm⁠ classes.

  • ⁠If CIs are desired for the regularized models, please, fit your model using ⁠fixedLassoInf()⁠function from the⁠selectiveInference⁠package following the steps outlined in the documentation for this package and pass the object of class⁠fixedLassoInforfixedLogitLassoInf'.

• x_data_orig: Original non-centered and non-scaled model matrix without intercept.

  • Please, pass the model matrix when CIs desired for fixedLassoInf and/or fixedLogitLassoInf object classes with penalty factors.

  • For objects fitted without penalty factors this argument is not required as original data can be reconstructed from the object passed.

• intercept: A logical value indicating whether to include the intercept.

  • The default is FALSE.

  • For the regularized models it is set to FALSE.

• title : Custom vectors of strings specifying titles for both plots.

• alpha : A numeric value between 0 and 1 specifying the significance level.

  • The default is 0.05.

• palette : Custom vector of colors to highlight the direction of estimated regression coefficients or Odds Ratio.

  • Grey scale is implemented by default.

  • Values at low and high ends of the grey scale palette can be specified.

• start : grey value at low end of palette.

  • The default value is 0.5.

• end : grey value at high end of palette.

  • The default value is 0.9.

• eff_size_diff : A vector specifying which values to utilize for realized effect size calculation.It is applied to all independent variables. By default it is c(4,2) which is Q3 - Q1. The following coding scheme is used:

  • 1 is the minimum.

  • 2 is the first quartile.

  • 3 is the second quartile.

  • 4 is the third quartile.

  • 5 is the maximum.

• round_func : A string specifying how to round the realized effect size.

  • Can be either "floor", "ceiling", or "none".

  • The default value is "none".

• glmnet_fct_var : names of categorical variables for regularized models.

  • Glmnet treats all variables as numeric.

  • If any of the variables utilized are, in fact, categorical, please, specify their name(s).

  • Please, note that that by default model.matrix()will create k-1 dummy variables in lieu of k levels of a categorical variable. For example,if you have a factor variable called "sex" with two levels 0 and 1, and 0 being the base level, mode.matrix() will create a dummy variable called "sex1". Please, utilize the names created by mode.matrix() here and not the original factor name.

• verbose : A logical value indicating whether to display warning messages.

  • The default is FALSE.

Please note the following:

• Only Gaussian and binomial families are currently supported.

• Certain steps should be followed in order to produce Confidence Intervals for the regularized models. Please, refer to the vignette for the vis_reg() function and the documentation of the selectiveInference package.

• Penalty factor of 0 is not currently supported and no Confidence Intervals will be produced in this case.

Value

A list with the following components:

• ⁠$PerUnitVis⁠: A ggplot object that visualizes regression coefficients on a per-unit basis

• ⁠$RealizedEffectVis⁠: A ggplot object that visualizes regression coefficients on a basis of realized effect calculation.

• ⁠$SidebySide⁠: A grob object containing both visualizations side-by-side.

See Also

• lm for linear models.

• glm for generalized linear models.

• glmnet and cv.glmnet for lasso and elastic-net regularized generalized linear models.

• model.matrix for design matrices.

• ggplot for ggplot objects.

• arrangeGrob for grobs, gtables, and ggplots.

• fixedLassoInf for post-selection inference.

Examples

# Set seed for reproducibility
set.seed(38)
# Set the number of observations
n = 1000
# Generate predictor variables
X1 = rnorm(n)
X2 = rnorm(n)
X3 = rnorm(n)
# Define coefficients for each predictor
beta_0 = -1
beta_1 = 0.5
beta_2 = -0.25
beta_3 = 0.75
# Generate the latent variable
latent_variable = beta_0 + beta_1 * X1+ beta_2 * X2 + beta_3 * X3
# convert it to probabilities
p = pnorm(latent_variable)
# Generate binomial outcomes based on these probabilities
y = rbinom(n, size = 1, prob = p)
# Fit a GLM with a probit link
glm_model <- glm(y ~ X1 + X2 + X3, family = binomial(link = "probit"),
                 data = data.frame(y, X1, X2, X3))
# Specify additional parameters and Plot Odds Ratio for the Realized Effect
vis_reg(glm_model, CI=TRUE,intercept=TRUE,
        palette=c("greenyellow","red4"))$RealizedEffectVis

RegrCoeffsExplorer documentation built on April 3, 2025, 10:33 p.m.