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#' @eval get_description('linear_model')
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#' @import struct
#' @export linear_model
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#' @examples
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#' D = iris_DatasetExperiment()
#' M = linear_model(formula = y~Species)
#'
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linear_model = function(formula,na_action='na.omit',contrasts=list(),...) {
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out=struct::new_struct('linear_model',
formula=formula,
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na_action=na_action,
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contrasts=contrasts,
...)
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return(out)
}
.linear_model<-setClass(
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"linear_model",
contains='model',
slots=c(
# INPUTS
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formula='entity',
na_action='enum',
contrasts='entity',
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# OUTPUTS
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lm='entity',
coefficients='entity',
residuals='entity',
fitted_values='entity',
predicted_values='entity',
r_squared='entity',
adj_r_squared='entity'
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),
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prototype = list(name='Linear model',
description=paste0(
'Linear models can be used to carry out ',
'regression, single stratum analysis of variance and analysis ',
'of covariance.'),
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type="regression",
predicted='predicted_values',
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.params=c('formula','na_action','contrasts'),
.outputs=c('lm','coefficients','residuals','fitted_values','predicted_values','r_squared','adj_r_squared'),
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libraries='stats',
formula=ents$formula,
na_action=enum(name='NA action',
description=c(
'na.omit' = 'Incomplete cases are removed.',
'na.fail' = 'An error is thrown if NA are present.',
'na.exclude'='Incomplete cases are removed, and the output result is padded to the correct size using NA.',
'na.pass' = 'Does not apply a linear model if NA are present.'
),
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value='na.omit',
type='character',
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allowed=c('na.omit','na.fail','na.exclude','na.pass')
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),
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contrasts=entity(name='Contrasts',
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description='The contrasts associated with a factor.',
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type='list'
),
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lm=entity(name='Linear model object',
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description='The lm object for this model_',
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type='lm'
),
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coefficients=entity(name='Model coefficients',
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description='The coefficients for the fitted model_',
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type='numeric'
),
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residuals=entity(name='Residuals',
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description='The residuals for the fitted model_',
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type='numeric'
),
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fitted_values=entity(name='Fitted values',
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description='The fitted values for the data used to train the model_',
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type='numeric'
),
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predicted_values=entity(name='Predicted values',
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description='The predicted values for new data using the fitted model_',
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type='numeric'
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),
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r_squared=entity(name='R Squared',
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description='The value of R Squared for the fitted model_',
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type='numeric'
),
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adj_r_squared=entity(name='Adjusted R Squared',
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description='The value ofAdjusted R Squared for the fitted model_',
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type='numeric'
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)
)
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)
#' @export
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#' @template model_train
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setMethod(f="model_train",
signature=c("linear_model",'DatasetExperiment'),
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definition=function(M,D)
{
X=cbind(D$data,D$sample_meta)
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if (length(M$contrasts)==0) {
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M$lm=lm(formula = M$formula, na.action = M$na_action,data=X) # default contrasts
} else {
M$lm=lm(formula = M$formula, na.action = M$na_action, contrasts = M$contrasts,data=X)
}
M$coefficients=coefficients(M$lm)
M$residuals=residuals(M$lm)
M$fitted_values=fitted(M$lm)
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M$r_squared=summary(M$lm)$r.squared
M$adj_r_squared=summary(M$lm)$adj.r.squared
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return(M)
}
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)
#' @export
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#' @template model_predict
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setMethod(f="model_predict",
signature=c("linear_model",'DatasetExperiment'),
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definition=function(M,D)
{
X=cbind(D$data,D$sample_meta)
M$predicted_values(predict(M$lm,newdata = X))
return(M)
}
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)
|
