,
Aleksandr Tsybakin [aut],
Jane Heffernan [aut],
Hanna Jankowski [aut],
Kevin McGregor [aut]| Title: | Efficient Visualization of Regression Coefficients for lm(), glm(), and glmnet() Objects |
|---|---|
| Description: | The visualization tool offers a nuanced understanding of regression dynamics, going beyond traditional per-unit interpretation of continuous variables versus categorical ones. It highlights the impact of unit changes as well as larger shifts like interquartile changes, acknowledging the distribution of empirical data. Furthermore, it generates visualizations depicting alterations in Odds Ratios for predictors across minimum, first quartile, median, third quartile, and maximum values, aiding in comprehending predictor-outcome interplay within empirical data distributions, particularly in logistic regression frameworks. |
| Authors: | Vadim Tyuryaev [aut, cre] ,
Aleksandr Tsybakin [aut],
Jane Heffernan [aut],
Hanna Jankowski [aut],
Kevin McGregor [aut] |
| Maintainer: | Vadim Tyuryaev <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.1.0 |
| Built: | 2024-11-15 07:31:12 UTC |
| Source: | https://github.com/vadimtyuryaev/regrcoeffsexplorer |
This function back transforms centered/scaled data.
detransform(x_data, ...)detransform(x_data, ...)
x_data |
Model matrix that has been centered and/or scaled. |
... |
Additional arguments specifying centering/scaling attributes. |
The following additional arguments can be passed:
attr_center : Centering attributes.
If none specified, attr(x_data,'scaled:center') is utilized.
attr_scale : Scaling attributes.
If none specified, attr(x_data,'scaled:scale') is utilized.
Returns de-centered and de-scaled model matrix.
# Set seed for reproducibility set.seed(1964) # Generate a 10x10 matrix with random numbers original_data <- matrix(rnorm(100), nrow = 10) # Scale and center the data scaled_centered_data <- scale(original_data, center = TRUE, scale = TRUE) # Transform the scaled/centered data back to its original form original_data_recovered <- detransform(scaled_centered_data) # Compare the original data and the recovered data all.equal(original_data,original_data_recovered)# Set seed for reproducibility set.seed(1964) # Generate a 10x10 matrix with random numbers original_data <- matrix(rnorm(100), nrow = 10) # Scale and center the data scaled_centered_data <- scale(original_data, center = TRUE, scale = TRUE) # Transform the scaled/centered data back to its original form original_data_recovered <- detransform(scaled_centered_data) # Compare the original data and the recovered data all.equal(original_data,original_data_recovered)
The function accepts input in the form of a generalized linear model (GLM) or a glmnet object, specifically those employing binomial families, and proceeds to generate a suite of visualizations illustrating alterations in Odds Ratios for given predictor variable corresponding to changes between minimum, first quartile (Q1), median (Q2), third quartile (Q3), and maximum values observed in empirical data. These plots offer a graphical depiction of the influence exerted by individual predictors on the odds of the outcome, facilitating a clear interpretation of their respective significance. Such a tool aids in comprehending the interplay between predictors and outcomes within the logistic regression framework, particularly within the context of empirical data distributions.
plot_OR( func, data, var_name, color_filling = grey.colors(4, start = 0.1, end = 0.9), verbose = FALSE )plot_OR( func, data, var_name, color_filling = grey.colors(4, start = 0.1, end = 0.9), verbose = FALSE )
func |
A fitted model object with binomial family, expected to be one of the following classes:
|
data |
Input data frame that was used to fit the input function
( |
var_name |
Name of a variable to plot graphs for ( |
color_filling |
Vector with color numbers to plot in bar plot
( |
verbose |
|
A list with the following components:
$BarPlot : A ggplot object that visualizes dependency of a change
in Variable values on function's Odds Ratio values.
$BoxPlot : A ggplot object that visualizes distribution of data
points for a given variable.
$SidebySide : A ggarrange object containing both visualizations
side-by-side.
### Prepare Sample Binomial Data set.seed(42) obs_num = 100 x1 = rnorm(obs_num) x2 = rnorm(obs_num) x3 = rnorm(obs_num) prob = plogis(1 + 0.3 * x1 + 0.2 * x2 + 0.1 * x3) y = rbinom(obs_num, 1, prob) data = data.frame(x1, x2, x3, y) ### GLM Object Exmaple # Get GLM model glm_object = glm(y ~ x1 + x2 + x3, family=binomial(link="logit"), data=data) summary(glm_object) # Plot Odds Ratio graphs plot_OR(glm_object, data, var_name="x2")$"SidebySide" ### GLMNET Object Example require(glmnet) # Get Lasso model y_lasso = data$y x_lasso = model.matrix(as.formula(paste("~", paste(colnames(subset(data, select=-c(y))), collapse = "+"), sep = "")), data=data) x_lasso = x_lasso[,-1] ndim_lasso = dim(x_lasso)[1] # Select the 1se lambda from cross validation cv_model_lasso = cv.glmnet(x_lasso, y_lasso, family="binomial", alpha=1) lambda_lasso = cv_model_lasso$lambda.1se plot(cv_model_lasso) # Get a model with the specified lambda model_lasso = glmnet(x_lasso, y_lasso, family="binomial", alpha=0.5, lambda=lambda_lasso) summary(model_lasso) # Plot Odds Ratio graphs plot_OR(model_lasso, data, var_name="x2")$"SidebySide"### Prepare Sample Binomial Data set.seed(42) obs_num = 100 x1 = rnorm(obs_num) x2 = rnorm(obs_num) x3 = rnorm(obs_num) prob = plogis(1 + 0.3 * x1 + 0.2 * x2 + 0.1 * x3) y = rbinom(obs_num, 1, prob) data = data.frame(x1, x2, x3, y) ### GLM Object Exmaple # Get GLM model glm_object = glm(y ~ x1 + x2 + x3, family=binomial(link="logit"), data=data) summary(glm_object) # Plot Odds Ratio graphs plot_OR(glm_object, data, var_name="x2")$"SidebySide" ### GLMNET Object Example require(glmnet) # Get Lasso model y_lasso = data$y x_lasso = model.matrix(as.formula(paste("~", paste(colnames(subset(data, select=-c(y))), collapse = "+"), sep = "")), data=data) x_lasso = x_lasso[,-1] ndim_lasso = dim(x_lasso)[1] # Select the 1se lambda from cross validation cv_model_lasso = cv.glmnet(x_lasso, y_lasso, family="binomial", alpha=1) lambda_lasso = cv_model_lasso$lambda.1se plot(cv_model_lasso) # Get a model with the specified lambda model_lasso = glmnet(x_lasso, y_lasso, family="binomial", alpha=0.5, lambda=lambda_lasso) summary(model_lasso) # Plot Odds Ratio graphs plot_OR(model_lasso, data, var_name="x2")$"SidebySide"
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.
vis_reg(object, ...)vis_reg(object, ...)
object |
A fitted model object, expected to be one of the following classes:
|
... |
Additional parameters.Please refer to 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.
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.
$SidebySide: A grob object containing both visualizations side-by-side.
lm for linear models.
glm for generalized linear models.
glmnet and cv.glmnet for
lasso and elastic-net regularized generalized linear models.
model.matrix for design matrices.
ggplot for ggplot objects.
arrangeGrob for grobs, gtables, and ggplots.
fixedLassoInf for post-selection inference.
# Set seed for reproducibility set.seed(38) # Set the number of observations n = 1000 # Generate predictor variables X1 = rnorm(n) X2 = rnorm(n) X3 = rnorm(n) # Define coefficients for each predictor beta_0 = -1 beta_1 = 0.5 beta_2 = -0.25 beta_3 = 0.75 # Generate the latent variable latent_variable = beta_0 + beta_1 * X1+ beta_2 * X2 + beta_3 * X3 # convert it to probabilities p = pnorm(latent_variable) # Generate binomial outcomes based on these probabilities y = rbinom(n, size = 1, prob = p) # Fit a GLM with a probit link glm_model <- glm(y ~ X1 + X2 + X3, family = binomial(link = "probit"), data = data.frame(y, X1, X2, X3)) # Specify additional parameters and Plot Odds Ratio for the Realized Effect vis_reg(glm_model, CI=TRUE,intercept=TRUE, palette=c("greenyellow","red4"))$RealizedEffectVis# 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