Specify Axis Limits

You can control where data appears in the axes by setting the x-axis, y-axis, and z-axis limits. You also can change where the x-axis and y-axis lines appear (2-D plots only) or reverse the direction of increasing values along each axis.

Change Axis Limits

Create a line plot. Specify the axis limits using the xlim and ylim functions. For 3-D plots, use the zlim function. Pass the functions a two-element vector of the form [min max].

x = linspace(-10,10,200); 
y = sin (4 * x) ./ exp (x);
plot(x,y)
xlim ([0 10])
ylim ([- 0.4 0.8])

Use Semiautomatic Axis Limits

Set the maximum x-axis limit to 0 and the minimum y-axis limit to -1. Let MATLAB choose the other limits. For an automatically calculated minimum or maximum limit, use -inf or inf, respectively.

[X,Y,Z] = peaks;
surf (X, Y, Z)
xlabel('x-axis')
ylabel('y-axis')
xlim ([- inf 0]) 
ylim ([- 1 inf])

Revert Back to Default Limits

Create a mesh plot and change the axis limits. Then revert back to the default limits.

[X,Y,Z] = peaks;
mesh(X,Y,Z)
xlim ([- 2 2])
ylim ([- 2 2])
evil ([- 5 5])

xlim car 
ylim car 
evil car

Reverse Axis Direction

Control the direction of increasing values along the x-axis and y-axis by setting the XDir and YDir properties of the Axes object. Set these properties to either 'reverse' or 'normal' (the default). Use the gca command to access the Axes object.

stem(1:10)
ax = gca;
ax.XDir = 'reverse';
ax.YDir = 'reverse';

Display Axis Lines through Origin

By default, the x-axis and y-axis appear along the outer bounds of the axes. Change the location of the axis lines so that they cross at the origin point (0,0) by setting the XAxisLocation and YAxisLocation properties of the Axes object. Set XAxisLocation to either 'top''bottom', or 'origin'. Set YAxisLocation to either 'left''right', or 'origin'. These properties only apply to axes in a 2-D view.

x = linspace(-5,5);
y = sin (x);
plot(x,y)

ax = gca;
ax.XAxisLocation = 'origin';
ax.YAxisLocation = 'origin';

Remove the axes box outline.

box off

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Machine Learning in MATLAB

Train Classification Models in Classification Learner App

Train Regression Models in Regression Learner App

Distribution Plots

Explore the Random Number Generation UI

Design of Experiments

Machine Learning Models

Logistic regression

Logistic regression create generalized linear regression model - MATLAB fitglm 2

Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification 2

Support Vector Machines for Binary Classification 3

Support Vector Machines for Binary Classification 4

Support Vector Machines for Binary Classification 5

Assess Neural Network Classifier Performance

Naive Bayes Classification

ClassificationTree class

Discriminant Analysis Classification

Ensemble classifier

ClassificationTree class 2

Train Generalized Additive Model for Binary Classification

Train Generalized Additive Model for Binary Classification 2

Classification Using Nearest Neighbors

Classification Using Nearest Neighbors 2

Classification Using Nearest Neighbors 3

Classification Using Nearest Neighbors 4

Classification Using Nearest Neighbors 5

Linear Regression

Linear Regression 2

Linear Regression 3

Linear Regression 4

Nonlinear Regression

Nonlinear Regression 2

Visualizing Multivariate Data

Generalized Linear Models

Generalized Linear Models 2

RegressionTree class

RegressionTree class 2

Neural networks

Gaussian Process Regression Models

Gaussian Process Regression Models 2

Understanding Support Vector Machine Regression

Understanding Support Vector Machine Regression 2

RegressionEnsemble



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