mse: Calculate the mean-squared error for predicted vs observed...
In gweissman/gmish: Miscellaneous Functions for Predictive Modeling
mse R Documentation
Calculate the mean-squared error for predicted vs observed continuous outcomes.
Description
Calculate the mean-squared error for predicted vs observed continuous outcomes.
Usage
mse(preds, obs)
Arguments
preds
A vector of predicted continuous outcomes.
obs
A vector containing the observed continuous outcomes.
Value
The Mean Squared Error (MSE) given by
MSE = \frac{1}{N} \sum (y - \hat y)^2
Examples
# Generate some predictions
predictions <- rnorm(1000)
# Generate some observed outcomes
observations <- rnorm(1000)
# Calculate the MSE
mse(predictions, observations)
gweissman/gmish documentation built on Feb. 21, 2025, 1:20 a.m.
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| mse | R Documentation |
Calculate the mean-squared error for predicted vs observed continuous outcomes.
Description
Calculate the mean-squared error for predicted vs observed continuous outcomes.
Usage
mse(preds, obs)
Arguments
preds |
A vector of predicted continuous outcomes. |
obs |
A vector containing the observed continuous outcomes. |
Value
The Mean Squared Error (MSE) given by
MSE = \frac{1}{N} \sum (y - \hat y)^2
Examples
# Generate some predictions
predictions <- rnorm(1000)
# Generate some observed outcomes
observations <- rnorm(1000)
# Calculate the MSE
mse(predictions, observations)
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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