plotSlice

Plot of slices through fitted generalized linear regression surface

Syntax

plotSlice(mdl)

Description

plotSlice(mdl) creates a figure containing one or more plots, each representing a slice through the regression surface predicted by mdl. Each plot shows the fitted response values as a function of a single predictor variable, with the other predictor variables held constant.

plotSlice also displays the 95% confidence bounds for the response values. Use the Bounds menu to choose the type of confidence bounds, and use the Predictors menu to select which predictors to use for each slice plot. For details, see Tips.

Examples

Slice Plot for Generalized Linear Regression Model

Plot slices through a fitted generalized linear regression model surface.

Generate sample data using Poisson random numbers with two underlying predictors X(:,1) and X(:,2).

rng('default') % For reproducibility
rndvars = randn(100,2);
X = [2 + rndvars(:,1),rndvars(:,2)];
mu = exp(1 + X*[1;2]);
y = poissrnd(mu);

Create a generalized linear regression model of Poisson data.

mdl = fitglm(X,y,'y ~ x1 + x2','Distribution','poisson');

Create a slice plot.

plotSlice(mdl)

The green line in each plot represents the predicted response values as a function of a single predictor variable, with the other predictor variables held constant. The red dotted lines are the 95% confidence bounds. The y-axis label includes the predicted response value and the corresponding confidence bound for the point selected by the vertical and horizontal lines. The x-axis label shows the predictor variable name and the predictor value for the selected point.

Move the vertical line in the x1 plot to the right and observe the change in the y-axis label and the changes in the x2 plot.

Input Arguments

mdl — Generalized linear regression model

GeneralizedLinearModel object | CompactGeneralizedLinearModel object

Generalized linear regression model, specified as a GeneralizedLinearModel object created using fitglm or stepwiseglm, or a CompactGeneralizedLinearModel object created using compact.

Tips

• Use the Bounds menu in the figure window to choose the type of confidence bounds. You can choose Simultaneous or Non-Simultaneous. You can also choose No Bounds to have no confidence bounds.

  • Simultaneous (default) — plotSlice computes confidence bounds for the curve of the response values using Scheffe's method. The range between the upper and lower confidence bounds contains the curve consisting of true response values with 95% confidence.

  • Non-Simultaneous — plotSlice computes confidence bounds for the response value at each observation. The confidence interval for a response value at a specific predictor value contains the true response value with 95% confidence.

  Simultaneous bounds are wider than separate bounds, because requiring the entire curve of response values to be within the bounds is stricter than requiring the response value at a single predictor value to be within the bounds.

• Use the Predictors menu in the figure window to select which predictors to use for each slice plot. If the regression model mdl includes more than eight predictors, plotSlice creates plots for the first five predictors by default.

Alternative Functionality

• Use predict to return the predicted response values and confidence bounds. You can also specify the confidence level for confidence bounds by using the 'Alpha' name-value pair argument of the predict function. Note that predict finds nonsimultaneous bounds by default, whereas plotSlice finds simultaneous bounds by default.

• A GeneralizedLinearModel object provides multiple plotting functions.

  • When verifying a model, use plotDiagnostics to find questionable data and to understand the effect of each observation. Also, use plotResiduals to analyze the residuals of the model.

  • After fitting a model, use plotPartialDependence to understand the effect of a particular predictor. Also, use plotSlice to plot slices through the prediction surface.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• This function supports model objects fitted with GPU array input arguments.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).