plotInteraction
Plot interaction effects of two predictors in linear regression model
Description
plotInteraction(
creates a plot of the main effects of the
two selected predictors mdl
,var1
,var2
)var1
and var2
and
their conditional effects
in the linear regression model mdl
. Horizontal lines through
the effect values indicate their 95% confidence intervals.
plotInteraction(
specifies the plot type mdl
,var1
,var2
,ptype
)ptype
. For example, if
ptype
is 'predictions'
, then
plotInteraction
plots the adjusted response function as a
function of the second predictor, with the first predictor fixed at specific values.
For details, see Conditional Effect.
returns line objects using any of the input argument combinations in the previous
syntaxes. Use h
= plotInteraction(___)h
to modify the properties of a specific line
after you create the plot. For a list of properties, see Line Properties.
Examples
Interaction Plot of Main Effects and Conditional Effects
Fit a model with an interaction term and create an interaction plot that shows the main effects and conditional effects.
Using the data in the carsmall
data set, create response values that include an interaction term. First, load the data set and normalize the predictor data.
load carsmall
Acceleration = normalize(Acceleration);
Horsepower = normalize(Horsepower);
Displacement = normalize(Displacement);
Define a response variable that includes the interaction term Acceleration*Horsepower
.
y = Acceleration + 4*Horsepower + Acceleration.*Horsepower + Displacement;
Add some noise to the response values.
rng('default') % For reproducibility y = y + normrnd(10,0.25*std(y,'omitnan'),size(y));
Create a table that includes the predictor data and response values.
tbl = table(Acceleration,Horsepower,Displacement,y);
Fit a linear regression model.
mdl = fitlm(tbl,'y ~ Acceleration + Horsepower + Acceleration*Horsepower + Displacement + Horsepower*Displacement')
mdl = Linear regression model: y ~ 1 + Acceleration*Horsepower + Horsepower*Displacement Estimated Coefficients: Estimate SE tStat pValue __________ _______ _________ __________ (Intercept) 9.8652 0.16177 60.982 8.587e-77 Acceleration 0.63726 0.1626 3.9191 0.00016967 Horsepower 3.6168 0.34 10.638 9.273e-18 Displacement 0.95032 0.31828 2.9858 0.0036144 Acceleration:Horsepower 0.60108 0.1851 3.2473 0.0016209 Horsepower:Displacement -0.0096069 0.20947 -0.045863 0.96352 Number of observations: 99, Error degrees of freedom: 93 Root Mean Squared Error: 1.07 R-squared: 0.93, Adjusted R-Squared: 0.927 F-statistic vs. constant model: 249, p-value = 3.3e-52
pValue
of the interaction term Acceleration*Horsepower
is very small, meaning that the interaction term is statistically significant.
Create an interaction plot that shows the main effects and conditional effects of Horsepower
and Acceleration
.
plotInteraction(mdl,'Horsepower','Acceleration')
For each predictor, the main effect point and its conditional effect points are not vertically aligned. Therefore, you cannot find any vertical lines that pass through the confidence intervals of the main and conditional effect points for each predictor. This plot indicates the existence of interaction effects on the response variable.
For comparison, create an interaction plot for Displacement
and Horsepower
. This p-value of this interaction term (Displacement*Horsepower
) is large, meaning that the interaction term is not statistically significant.
plotInteraction(mdl,'Displacement','Horsepower')
For each predictor, the main effect point and its conditional effect points are aligned vertically. This plot indicates no interaction.
Interaction Plot of Adjusted Response Curve
Fit a model with an interaction term and create an interaction plot of adjusted response curves.
Using the data in the carsmall
data set, create response values that include an interaction term. First, load the data set and normalize the predictor data.
load carsmall
Acceleration = normalize(Acceleration);
Horsepower = normalize(Horsepower);
Displacement = normalize(Displacement);
Define a response variable that includes the interaction term Acceleration*Horsepower
.
y = Acceleration + 4*Horsepower + Acceleration.*Horsepower + Displacement;
Add some noise to the response values.
rng('default') % For reproducibility y = y + normrnd(10,0.25*std(y,'omitnan'),size(y));
Create a table that includes the predictor data and response values.
tbl = table(Acceleration,Horsepower,Displacement,y);
Fit a linear regression model.
mdl = fitlm(tbl,'y ~ Acceleration + Horsepower + Acceleration*Horsepower + Displacement + Horsepower*Displacement')
mdl = Linear regression model: y ~ 1 + Acceleration*Horsepower + Horsepower*Displacement Estimated Coefficients: Estimate SE tStat pValue __________ _______ _________ __________ (Intercept) 9.8652 0.16177 60.982 8.587e-77 Acceleration 0.63726 0.1626 3.9191 0.00016967 Horsepower 3.6168 0.34 10.638 9.273e-18 Displacement 0.95032 0.31828 2.9858 0.0036144 Acceleration:Horsepower 0.60108 0.1851 3.2473 0.0016209 Horsepower:Displacement -0.0096069 0.20947 -0.045863 0.96352 Number of observations: 99, Error degrees of freedom: 93 Root Mean Squared Error: 1.07 R-squared: 0.93, Adjusted R-Squared: 0.927 F-statistic vs. constant model: 249, p-value = 3.3e-52
pValue
of the interaction term Acceleration*Horsepower
is very small, meaning that the interaction term is statistically significant.
Create an interaction plot that shows the adjusted response function as a function of Acceleration
, with Horsepower
fixed at specific values.
plotInteraction(mdl,'Horsepower','Acceleration','predictions')
The curves are not parallel. This plot indicates interactions between the predictors.
For comparison, create an interaction plot for the Displacement
and Horsepower
. The p-value of this interaction term (Displacement*Horsepower
) is large, meaning that the interaction term is not statistically significant.
plotInteraction(mdl,'Displacement','Horsepower','predictions')
The curves are parallel, indicating no interaction.
Input Arguments
mdl
— Linear regression model object
LinearModel
object | CompactLinearModel
object
Linear regression model object, specified as a LinearModel
object created by using fitlm
or stepwiselm
, or a CompactLinearModel
object created by using compact
.
var1
— First variable for plot
character vector | string array | positive integer
First variable for the plot, specified as a character vector or string
array of the variable name in mdl.VariableNames
(VariableNames
property of mdl
),
or a positive integer representing the index of a variable in
mdl.VariableNames
.
Data Types: char
| string
| single
| double
var2
— Second variable for plot
character vector | string array | positive integer
Second variable for the plot, specified as a character vector or string
array of the variable name in mdl.VariableNames
(VariableNames
property of mdl
),
or a positive integer representing the index of a variable in
mdl.VariableNames
.
Data Types: char
| string
| single
| double
ptype
— Plot type
'effects'
(default) | 'predictions'
Plot type, specified as one of these values:
'effects'
—plotInteraction
creates a plot of the main effects of the two selected predictorsvar1
andvar2
and their conditional effects. Horizontal lines through the effect values indicate their 95% confidence intervals.'predictions'
—plotInteraction
plots the adjusted response function as a function ofvar2
, withvar1
fixed at specific values.
For details, see Main Effect and Conditional Effect.
Output Arguments
h
— Line objects
vector
Line objects, returned as a vector. Use dot notation to query and set properties of the line objects. For details, see Line Properties.
If the plot type is 'effects'
(default),
h(1)
corresponds to the circles that represent the
main effect estimates, and h(2)
and
h(3)
correspond to the 95% confidence intervals for
the two main effects. The remaining entries in h
correspond to the conditional effects and their confidence intervals. The
line objects associated with the main effects have the tag
'main'
. The line objects associated with the
conditional effects of var1
and
var2
have the tags
'conditional1'
and 'conditional2'
,
respectively.
If the plot type is 'predictions'
, each entry in
h
corresponds to each curve on the plot.
More About
Main Effect
An effect, or main effect, of a predictor represents an effect of one predictor on the response from changing the predictor value while averaging out the effects of the other predictors.
For a predictor variable x_{s}, the effect is defined by
g(x_{si}) – g(x_{sj}) ,
where g is an Adjusted Response function. The
plotEffects
function chooses the observations
i and j as follows. For a categorical
variable that is not ordinal,
x_{si}
and
x_{sj}
are the predictor values that produce the maximum and minimum adjusted responses,
respectively, so that the effect value is always positive. For a numeric variable or
an ordinal categorical variable, the function chooses two predictor values that
produce the minimum and maximum adjusted responses where x_{si}
<
x_{sj}.
plotEffects
plots the effect value and the 95% confidence interval of the effect value for each predictor variable.
Adjusted Response
An adjusted response function describes the relationship between the fitted response and a single predictor, with the other predictors averaged out by averaging the fitted values over the data used in the fit.
A regression model for the predictor variables (x_{1}, x_{2}, …, x_{p}) and the response variable y has the form
y_{i} = f(x_{1i}, x_{2i}, …, x_{pi}) + r_{i},
where f is a fitted regression function and r is a residual. The subscript i represents the observation number.
The adjusted response function for the first predictor variable x_{1}, for example, is defined as
$$g\left({x}_{1}\right)=\frac{1}{n}{\displaystyle \sum _{i=1}^{n}f\left({x}_{1},{x}_{2i},{x}_{3i},\mathrm{...},{x}_{pi}\right)},$$
where n is the number of observations. The adjusted response data value is the sum of the adjusted fitted value and the residual for each observation.
$${\tilde{y}}_{i}=g\left({x}_{1i}\right)+{r}_{i}.$$
plotAdjustedResponse
plots the adjusted response
function and the adjusted response data values for a selected predictor variable.
Conditional Effect
When a model contains an interaction term, the main effect of one predictor depends on the value of another predictor that interacts with it. In this case, a conditional effect of one predictor given a specific value of another is helpful in understanding the actual effect of both predictors. You can examine whether the effect of one predictor depends on the value of another by using conditional effect values.
To define a conditional effect, define the adjusted response function as a function of two predictor variables. For example, the adjusted response function of x_{1} and x_{2} is
$$h\left({x}_{1},{x}_{2}\right)=\frac{1}{n}{\displaystyle \sum _{i=1}^{n}f\left({x}_{1},{x}_{2},{x}_{3i},\mathrm{...},{x}_{pi}\right)},$$
where f is a fitted regression function, and n is the number of observations.
The conditional effect of one predictor (x_{2}) given a specific value of another predictor (x_{1k}) is defined by
h(x_{1k},x_{2i}) - h(x_{1k},x_{2j}).
To compute conditional effect values,
plotInteraction
chooses the observations
i and j of
x_{2} in the same way as when the
function computes the Main Effect and chooses the
x_{1k} values. If
x_{1} is a categorical variable, then
plotInteraction
computes the conditional effect for all
levels of x_{1}. If
x_{1} is a numeric variable, then
plotInteraction
computes the conditional effect for three
values of x_{1}: the minimum value of
x_{1}, the maximum value of
x_{1}, and the average value of the
minimum and maximum.
If the plot type is 'effects'
(default),
plotInteraction
plots the main effects of the two selected
predictors, their conditional effects, and the 95% confidence bounds for the effect
values.
If the plot type is 'predictions'
,
plotInteraction
plots the adjusted response function as a
function of the second predictor, with the first predictor fixed at specific values.
For example, plotInteraction(mdl,'x1','x2','predictions')
plots
the curve of h(x_{1k},
x_{2}) for each
x_{1k} value.
Tips
The data cursor displays the values of the selected plot point in a data tip (small text box located next to the data point). The data tip includes the x-axis and y-axis values for the selected point, along with the observation name or number.
Alternative Functionality
A
LinearModel
object provides multiple plotting functions.When creating a model, use
plotAdded
to understand the effect of adding or removing a predictor variable.When verifying a model, use
plotDiagnostics
to find questionable data and to understand the effect of each observation. Also, useplotResiduals
to analyze the residuals of the model.After fitting a model, use
plotAdjustedResponse
,plotPartialDependence
, andplotEffects
to understand the effect of a particular predictor. UseplotInteraction
to understand the interaction effect between two predictors. Also, useplotSlice
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).
See Also
CompactLinearModel
| LinearModel
| plotAdjustedResponse
| plotEffects
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