mse: Compute the Mean Squared Error
In ryan-heslin/RegLesson: Linear Regression from Scratch
mse R Documentation
Compute the Mean Squared Error
Description
Compute the Mean Squared Error
Usage
mse(Y, Y_hat, n = NROW(Y), p)
Arguments
Y
Response vector.
Y_hat
Vector of predictions.
n
Number of rows in design matrix.
p
Number of columns in design matrix.
Details
This function computes the mean squared error, given coefficient and prediction vectors and the parameters for computing degrees of freedom.
Value
Estimated mean squared error.
ryan-heslin/RegLesson documentation built on Aug. 5, 2022, 9:03 p.m.
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| mse | R Documentation |
Compute the Mean Squared Error
Description
Compute the Mean Squared Error
Usage
mse(Y, Y_hat, n = NROW(Y), p)
Arguments
Y |
Response vector. |
Y_hat |
Vector of predictions. |
n |
Number of rows in design matrix. |
p |
Number of columns in design matrix. |
Details
This function computes the mean squared error, given coefficient and prediction vectors and the parameters for computing degrees of freedom.
Value
Estimated mean squared error.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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