.ipynb_checkpoints/xls.fit-checkpoint: Fitting an eXtreme Least Squares Model
In sametsoekel/eXtreme-Least-Squares: A Modeling Approach that Optimizes Future Errors in Least Squares
xls.fit R Documentation
Fitting an eXtreme Least Squares Model
Description
Almost the same interface as stats::lm.
Just includes two parameters more, error_weights and error_ahead_level
Usage
xls.fit(formula, data, error_weights = NULL, error_ahead_level = 4)
Arguments
formula
An object of class "formula": a symbolic description of the model to be fitted.
data
A "data.frame" (with no missing values) object containing the variables in the model.
error_weights
A numeric vector including error weights by order. If NULL, it is created automatically by error_ahead_level amount, decreasing at equal intervals.
error_ahead_level
An integer which represents how many steps further the parameters will be optimized for each data point.
Value
A lm object whose coefficients are optimized by the mentioned method.
Examples
df <- datasets::airquality
ordered_df <- df[with(df,order(Month,Day)),]
model <- xls.fit(Ozone ~ Solar.R + Wind + Temp,na.omit(ordered_df),error_weights = c(0.4,0.3,0.2,0.1),error_ahead_level = 4)
sametsoekel/eXtreme-Least-Squares documentation built on April 3, 2022, 12:08 a.m.
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| xls.fit | R Documentation |
Fitting an eXtreme Least Squares Model
Description
Almost the same interface as stats::lm.
Just includes two parameters more, error_weights and error_ahead_level
Usage
xls.fit(formula, data, error_weights = NULL, error_ahead_level = 4)
Arguments
formula |
An object of class "formula": a symbolic description of the model to be fitted. |
data |
A "data.frame" (with no missing values) object containing the variables in the model. |
error_weights |
A numeric vector including error weights by order. If NULL, it is created automatically by error_ahead_level amount, decreasing at equal intervals. |
error_ahead_level |
An integer which represents how many steps further the parameters will be optimized for each data point. |
Value
A lm object whose coefficients are optimized by the mentioned method.
Examples
df <- datasets::airquality ordered_df <- df[with(df,order(Month,Day)),] model <- xls.fit(Ozone ~ Solar.R + Wind + Temp,na.omit(ordered_df),error_weights = c(0.4,0.3,0.2,0.1),error_ahead_level = 4)
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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