An object containing the output of fitting a multinomial response model with
structured penalties for one concrete value of the tuning parameter (or one
concrete combination of all used tuning parameters).
Objects from the Class
Objects of this class are created by function MRSP.fit, which will
most likely be called internally by the end-user function MRSP.
Slots
coef:
Object of class "MRSP.coef" containing the estimated
regression coefficients. Structurally, it is simply a list
of one or two matrices.
coef.stand:
Same as slot coef, but contains the coefficients
belonging to standardized covariates.
coef.pretres:
Same as slot coef, but contains the coefficients
belonging to standardized covariates before any
(potential) thresholding took place.
dat:
Data object in the form required by MRSP.fit.
To save memory, this slot is usually set to NULL.
x.original:
Original matrix containing the "x"-variables.
To save memory, this slot is often times set to NULL.
x.stand:
The "x"-matrix after standardization.
V.original:
The original data object containing the category-specific
predictors. To save memory, this slot is often times NULL.
V.stand:
The "V"-object after standardization.
y:
Response matrix in the form required by MRSP.fit.
To save memory, this slot is often times set to NULL.
weights:
Vector of observation weights.
penindex:
Object specifying how each covariate is penalized. See
the documentation of MRSP.fit for details.
grpindex:
Object specifying which predictors form parameter groups. See
the documentation of MRSP.fit for details.
penweights:
Object specifying the weighting of the penalty on different
parameters or parameter groups. See MRSP.fit
for details.
guessed.active:
Object indexing the covariates that are found to
have an effect on the response. If necessary, this
refers to groups of coefficients.
guessed.active.coef:
An object with the same structure as slot coef,
indexing the atomic coefficients that are nonzero.
guessed.active.groupdiff:
Index of "columnwise" parameter groups
that contain at least two different parameter
values.
guessed.active.diff:
An index of nonzero pairwise differences between parameters
belonging to the same covariate.
df:
Estimated effective degrees of freedom.
tuning:
List of tuning parameter values as supplied to internal
function fista.
lambda:
The lambda value, which controls the degree of penalization of most
traditional penalties.
lambdaR:
The lambda value used for ridge penalties.
lambdaF:
The lambda value used for fusion penalties.
fusion:
Either FALSE or character string specifying the type of fusion
that was used in fusion penalties. Note that those fusion penalties
are not yet supported for end-users of MRSP.
gamma:
A numeric that weighs lasso penalties vs CATS lasso penalties.
See MRSP for details.
psi:
A numeric that weighs penalties on coefficients of global vs
category-specific predictors. See MRSP for details.
eta:
A nobs x K matrix of linear predictor values.
mu:
A nobs x K matrix of estimated probabilities for the response categories.
offset:
Vector of offset values that were added to the linear predictors.
residuals:
Object with residuals. Currently always NULL. Use method
residuals instead to compute residuals.
mlfit:
Not to be used by or of interest for end-users.
AIC:
The AIC of the fitted model.
BIC:
The BIC of the fitted model.
Brier:
The Brier score of the fitted model.
threshold:
The numeric threshold used.
refit:
Logical indicating whether the object results from a refitting procedure.
indg:
Not to be used by or of interest for end-users.
indcs:
Not to be used by or of interest for end-users.
model:
The model-object used. To save memory, this is often stored as an expression.
constr:
The identifiability constraint that was used. See MRSP for details.
control:
Object of class "MRSP.control" that contains control information.
fn.val:
The final value of the objective function that was minimized, i.e.
the negative penalized loglikelihood: -loglik + lambda*penweights*penalty.
loglik:
Loglikelihood value of the fitted model.
penalty:
Value of the penalty term for the fitted model, weighted with
the corresponding tuning parameters and penweights.
iter.count:
Number of iterations until convergence of the proximal gradient algorithm.
best.iter:
Iteration number with the best value of the objective function.
ridgestabil:
Logical indicating whether a small, untuned ridge penalty was
applied to all coefficients in order to stabilize otherwise
diverging estimates.
name:
A character string specifying the name and thus type of the fitted model.
fisher:
Fisher matrix. Currently not computed and thus always NULL.
arglist:
Not to be used by or of interest for end-users.
call:
The call to MRSP.fit that created this object.
Methods
AIC
signature(object = "MRSP"): Compute the AIC of an MRSP object.
BIC
signature(object = "MRSP"): Compute the BIC of an MRSP object.
coef
signature(object = "MRSP"): See coef-methods.
fitted
signature(object = "MRSP"): ompute fitted values, which for multinomial response correspond to class probabilities P(Y = r).
If option convert2hazard=TRUE and a sequential model is used, “discrete” hazard rates P(Y = r | Y >= r) are returned instead.
logLik
signature(object = "MRSP"): Returns the loglikelihood and the (estimated, effective) degrees of freedom.
nobs
signature(object = "MRSP"): Returns the number of invidual observations.
predict
signature(object = "MRSP"): predict(object, newdata, type=c("response", "link"), ...) predicts the response values (type="response")
or the linear predictors (type="link") for the observations given in newdata. Additional arguments offset and weights
can specify offsets and weights to be used. An argument convert2hazard can be supplied for sequential models, see fitted above.
residuals
signature(object = "MRSP"): Depending on argument type = c("deviance", "pearson"),
which is matched via match.arg, deviance or pearson residuals are returned.
show
signature(object = "MRSP"): Print some basic infos about the MRSP object.
summary
signature(object = "MRSP"): Show some slots of an MRSP object which are typically of interest.
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> library(MRSP)
Loading required package: parallel
Loading required package: compiler
Loading required package: matrixcalc
Loading required package: Formula
-----------------------------
This is MRSP version 0.4.3
Author: Wolfgang Poessnecker
Date: December 09, 2014
-----------------------------
Attaching package: 'MRSP'
The following object is masked from 'package:stats':
coefficients
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/MRSP/MRSP-class.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MRSP-class
> ### Title: Class '"MRSP"'
> ### Aliases: MRSP-class AIC,MRSP-method BIC,MRSP-method
> ### bootstrap,MRSP-method cv,MRSP-method extract,MRSP-method
> ### fitted,MRSP-method getCall,MRSP-method logLik,MRSP-method
> ### nobs,MRSP-method predict,MRSP-method pval,MRSP-method
> ### refit,MRSP,list-method refit,MRSP,missing-method
> ### residuals,MRSP-method se,MRSP-method show,MRSP-method
> ### summary,MRSP-method
> ### Keywords: classes
>
> ### ** Examples
>
> showClass("MRSP")
Class "MRSP" [package "MRSP"]
Slots:
Name: coef coef.stand
Class: list list
Name: coef.pretres dat
Class: list ANY
Name: x.original x.stand
Class: ANY ANY
Name: V.original V.stand
Class: ANY ANY
Name: y weights
Class: ANY vector
Name: penindex grpindex
Class: list list
Name: penweights guessed.active
Class: list list
Name: guessed.active.coef guessed.active.groupdiff
Class: list list
Name: guessed.active.diff df
Class: list numeric
Name: tuning lambda
Class: ANY numeric
Name: lambdaR lambdaF
Class: numeric numeric
Name: fusion gamma
Class: ANY numeric
Name: psi eta
Class: numeric ANY
Name: mu offset
Class: ANY vector
Name: residuals mlfit
Class: ANY ANY
Name: AIC BIC
Class: numeric numeric
Name: Brier threshold
Class: numeric ANY
Name: refit indg
Class: ANY ANY
Name: indcs model
Class: ANY MRSP.model
Name: constr control
Class: ANY MRSP.control
Name: fn.val loglik
Class: numeric numeric
Name: penalty iter.count
Class: numeric numeric
Name: best.iter ridgestabil
Class: numeric ANY
Name: name fisher
Class: character ANY
Name: arglist call
Class: ANY ANY
> ## for examples, see ?MRSP
>
>
>
>
>
> dev.off()
null device
1
>
providing 
.