Help for package DMRnet

Type:

Package

Title:

Delete or Merge Regressors Algorithms for Linear and Logistic Model Selection and High-Dimensional Data

Version:

0.4.0

Description:

Model selection algorithms for regression and classification, where the predictors can be continuous or categorical and the number of regressors may exceed the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Szymon Nowakowski, Piotr Pokarowski, Wojciech Rejchel and Agnieszka Sołtys, 2023. Improving Group Lasso for High-Dimensional Categorical Data. In: Computational Science – ICCS 2023. Lecture Notes in Computer Science, vol 14074, p. 455-470. Springer, Cham. <doi:10.1007/978-3-031-36021-3_47>. Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf. Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. <doi:10.1111/sjos.12546>.

License:

GPL-2

Encoding:

UTF-8

LazyData:

true

Imports:

hclust1d, glmnet, grpreg, stats, graphics, utils

Suggests:

knitr

URL:

https://github.com/SzymonNowakowski/DMRnet

BugReports:

https://github.com/SzymonNowakowski/DMRnet/issues

RoxygenNote:

7.2.3

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2023-08-02 12:36:36 UTC; Szym

Author:

Agnieszka Prochenka-Sołtys [aut] (previous maintainer for versions <= 0.2.0), Piotr Pokarowski [aut], Szymon Nowakowski

[aut, cre]

Maintainer:

Szymon Nowakowski <s.nowakowski@mimuw.edu.pl>

Repository:

CRAN

Date/Publication:

2023-08-07 14:40:09 UTC

DMRnet-package

Description

DMRnet Functions

Similar in use to glmnet. It consists of the following functions:

DMR - Model selection algorithm for p<n; produces a path of models.

DMRnet - Model selection algorithm both for p<n and for p>=n; produces a path of models.

print.DMR, coef.DMR, plot.DMR, predict.DMR - Functions for inspection of the models on the path.

gic.DMR, cv.DMR, cv.DMRnet - Functions for final model selection, resulting with one model from the path.

coef.gic.DMR, coef.cv.DMR, plot.gic.DMR, plot.cv.DMR, predict.gic.DMR, predict.cv.DMR - Functions for inspection of the final model.

miete, promoter - Two data sets used for vignettes, examples, etc.

For more information see a friendly "Getting started" vignette:

Author(s)

Agnieszka Prochenka-Sołtys, Piotr Pokarowski, Szymon Nowakowski

Maintainer: Szymon Nowakowski <s.nowakowski@mimuw.edu.pl>

References

Aleksandra Maj-Kańska, Piotr Pokarowski and Agnieszka Prochenka, 2015. Delete or merge regressors for linear model selection. Electronic Journal of Statistics 9(2): 1749-1778. doi:10.1214/15-EJS1050

Piotr Pokarowski and Jan Mielniczuk, 2015. Combined l1 and greedy l0 penalized least squares for linear model selection. Journal of Machine Learning Research 16(29): 961-992. https://www.jmlr.org/papers/volume16/pokarowski15a/pokarowski15a.pdf

Piotr Pokarowski, Wojciech Rejchel, Agnieszka Sołtys, Michał Frej and Jan Mielniczuk, 2022. Improving Lasso for model selection and prediction. Scandinavian Journal of Statistics, 49(2): 831–863. doi:10.1111/sjos.12546

Examples

## Not run: 
vignette("getting-started", package="DMRnet")

## End(Not run)

Delete or Merge Regressors

Description

Fits a path of linear (family="gaussian") or logistic (family="binomial") regression models, where the number of parameters changes from 1 to p (p is the number of columns in the model matrix). Models are subsets of continuous predictors and partitions of levels of factors in X.

Usage

DMR(
  X,
  y,
  family = "gaussian",
  clust.method = "complete",
  lam = 10^(-7),
  lambda = NULL
)

Arguments

X

Input data frame; each row is an observation vector; each column can be numerical or integer for a continuous predictor or a factor for a categorical predictor; DMR works only if p<n (n is the number of observations, p the number of columns in the model matrix), for p>=n see DMRnet.

y

Response variable; Numerical for family="gaussian" or a factor with two levels for family="binomial". For family="binomial" the last level in alphabetical order is the target class.

family

Response type; one of: "gaussian", "binomial".

clust.method

Clustering method used for partitioning levels of factors; see function hclust in package stats for details. clust.method="complete" is the default.

lam

The amount of penalization in ridge regression (used for logistic regression in order to allow for parameter estimation in linearly separable setups) or the amount of matrix regularization in case of linear regression. Used only for numerical reasons. The default is 1e-7.

lambda

The net of lambda values. It is optional and serves only for consistency with DMRnet. It is not used in DMR.

Details

DMR algorithm is based on a traditional stepwise method. A nested family of models is built based on the values of squared Wald statistics:

1. For each continuous variable the squared Wald statistic is calculated for a hypothesis that the variable is equal to zero (it should be deleted).

2. For each factor a dissimilarity matrix is constructed using squared Wald statistics for hypotheses that two parameters are equal (the two levels of factor should be merged). Next, hierarchical clustering is preformed using the dissimilarity matrix. All cutting heights are recorded.

3. Squared Wald statistics and cutting heights and values of from steps 2 and 3 are concatenated and sorted, resulting in vector h.

4. Nested family of models of size 1 to p is built by accepting hypotheses according to increasing values in vector h.

Value

An object with S3 class "DMR", which is a list with the ingredients:

beta

Matrix p times p of estimated parameters; each column corresponds to a model on the nested path having from p to 1 parameter (denoted as df).

df

Vector of degrees of freedom; from p to 1.

rss/loglik

Measure of fit for the nested models: rss (residual sum of squares) is returned for family="gaussian" and loglik (loglikelihood) is returned for family="binomial".

n

Number of observations.

levels.listed

Minimal set of levels of respective factors present in data.

lambda

The net of lambda values used in the screening step, empty vector in case of DMR.

arguments

List of the chosen arguments from the function call.

interc

If the intercept was fitted: for DMR always equal to TRUE.

Examples

## DMR for linear regression
data(miete)
ytr <- miete[1:1500,1]
Xtr <- miete[1:1500,-1]
Xte <- miete[1501:2053,-1]
m1 <- DMR(Xtr, ytr)
print(m1)
plot(m1)
g <- gic.DMR(m1, c = 2.5)
plot(g)
coef(m1, df = g$df.min)
ypr <- predict(m1, newx = Xte, df = g$df.min)

## DMR for logistic regression
# notice that only part of dataset promoter was used: DMR works only if p<n, for p>=n use DMRnet
data(promoter)
ytr <- factor(promoter[1:80,1])
Xtr <- promoter[1:80,2:11]
Xte <- promoter[81:106,2:11]
m2 <- DMR(Xtr, ytr, family = "binomial")
print(m2)
plot(m2)
g <- gic.DMR(m2, c = 2)
plot(g)
coef(m2, df = g$df.min)
ypr <- predict(m2, newx = Xte, df = g$df.min)

Delete or Merge Regressors net

Description

Fits a path of linear (family="gaussian") or logistic (family="binomial") regression models, where models are subsets of continuous predictors and partitions of levels of factors in X. Works even if p>=n (the number of observations is greater than the number of columns in the model matrix).

Usage

DMRnet(
  X,
  y,
  family = "gaussian",
  o = 5,
  nlambda = 100,
  lam = 10^(-7),
  interc = TRUE,
  maxp = ifelse(family == "gaussian", ceiling(length(y)/2), ceiling(length(y)/4)),
  lambda = NULL,
  algorithm = "DMRnet",
  clust.method = ifelse(algorithm == "glamer", "single", "complete")
)

Arguments

X

Input data frame; each row is an observation vector; each column can be numerical or integer for a continuous predictor or a factor for a categorical predictor.

y

Response variable; Numerical for family="gaussian" or a factor with two levels for family="binomial". For family="binomial" the last level in alphabetical order is the target class.

family

Response type; one of: "gaussian", "binomial".

o

Parameter of the group lasso screening step, described in Details, the default value is 5.

nlambda

Parameter of the group lasso screening step, described in Details, the default value is 100.

lam

interc

Should intercept(s) be fitted (the default, interc=TRUE) or set to zero (interc=FALSE). If in X there are any categorical variables, interc=TRUE must be set.

maxp

Maximal number of parameters of the model, smaller values result in quicker computation

lambda

Explicitly provided net of lambda values for the group lasso screening step, described in Details. If provided, it overrides the value of nlambda parameter.

algorithm

The algorithm to be used; for partition selection (merging levels) use one of: "DMRnet" (the default), "glamer" or "PDMR". Alternatively, use "var_sel" for variable (group) selection with no partition selection.

clust.method

Clustering method used for partitioning levels of factors; see function hclust in package stats for details. clust.method="complete" is the default for all algorithms except algorithm="glamer", for which clust.method="single" is the default.

Details

DMRnet algorithm is a generalization of DMR to high-dimensional data. It uses a screening step in order to decrease the problem to p<n and then uses DMR subsequently. The screening is done with the group lasso algorithm implemented in the grpreg package.

First, the group lasso for the problem is solved for nlambda values of lambda parameter, or for the net of lambda values (if lambda is explicitly provided). Next, for each value of lambda, the scaled nonzero second norms of the groups' coefficients are sorted in decreasing order. Finally, the first i over o fraction of the groups with the largest nonzero values are chosen for further analysis, i = 1,2,...,o-1. E.g., if o=5, first 1/5, first 2/5,..., 4/5 groups with the largest scaled nonzero second norm of coefficients are chosen.

The final path of models is chosen by minimizing the likelihood of the models for the number of parameters df equal to 1,...,l<=maxp for some integer l. Note that, in contrast to DMR, the models on the path need not to be nested.

Value

An object with S3 class "DMR", which is a list with the ingredients:

beta

Matrix p times l of estimated parameters; each column corresponds to a model on the nested path having from l to 1 parameter (denoted as df).

df

Vector of degrees of freedom; from l to 1.

rss/loglik

Measure of fit for the nested models: rss (residual sum of squares) is returned for family="gaussian" and loglik (loglikelihood) is returned for family="binomial".

n

Number of observations.

levels.listed

Minimal set of levels of respective factors present in data.

lambda

The net of lambda values used in the screening step.

arguments

List of the chosen arguments from the function call.

interc

If the intercept was fitted: value of parameter interc is returned.

Examples

## DMRnet for linear regression
data(miete)
ytr <- miete[1:200,1]
Xtr <- miete[1:200,-1]
Xte <- miete[201:250,-1]
m1 <- DMRnet(Xtr, ytr)
print(m1)
plot(m1)
g <- gic.DMR(m1, c = 2.5)
plot(g)
coef(m1, df = g$df.min)
ypr <- predict(m1, newx = Xte, df = g$df.min)

## DMRnet for logistic regression
data(promoter)
ytr <- factor(promoter[1:70,1])
Xtr <- promoter[1:70,-1]
Xte <- promoter[71:106,-1]
m2 <- DMRnet(Xtr, ytr, family = "binomial")
print(m2)
plot(m2)
g <- gic.DMR(m2, c = 2)
plot(g)
coef(m2, df = g$df.min)
ypr <- predict(m2, newx = Xte, df = g$df.min)

## PDMR for linear regression
data(miete)
ytr <- miete[1:200,1]
Xtr <- miete[1:200,-1]
Xte <- miete[201:250,-1]
m1 <- DMRnet(Xtr, ytr, algorithm="PDMR")
print(m1)
plot(m1)
g <- gic.DMR(m1, c = 2.5)
plot(g)
coef(m1, df = g$df.min)
ypr <- predict(m1, newx = Xte, df = g$df.min)

coef.DMR

Description

Extracts coefficients from a DMR object.

Usage

## S3 method for class 'DMR'
coef(object, df = NULL, ...)

Arguments

object

Fitted DMR object.

df

Number of parameters in the model for which coefficients are required. Default is the entire path of models.

...

Further arguments passed to or from other methods.

Details

Similar to other coef methods, this function extracts coefficients from a fitted DMR object.

Value

Vector or matrix of coefficients.

Examples

data(miete)
y <- miete[,1]
X <- miete[,-1]
m <- DMR(X, y)
coef(m, df = 12)

coef.cv.DMR

Description

Extracts coefficients from a cv.DMR object (for the model with minimal cross-validated error /the default/ or the smallest model falling under the upper curve of a prediction error plus one standard deviation).

Usage

## S3 method for class 'cv.DMR'
coef(object, md = "df.min", ...)

Arguments

object

Fitted cv.DMR object.

md

Value of the model dimension parameter at which predictions are required. The default is md="df.min" value indicating the model minimizing the cross validation error. Alternatively, md="df.1se" can be used, indicating the smallest model falling under the upper curve of a prediction error plus one standard deviation.

...

Further arguments passed to or from other methods.

Details

Similar to other coef methods, this function extracts coefficients from a fitted cv.DMR object.

Value

Vector of coefficients.

Examples

## cv.DMR for linear regression
set.seed(13)
data(miete)
y <- miete$rent
X <- miete$area
cv = cv.DMR(X,y)
coef(cv)

coef.gic.DMR

Description

Extracts coefficients from a gic.DMR object (for the model with minimal gic).

Usage

## S3 method for class 'gic.DMR'
coef(object, ...)

Arguments

object

Fitted gic.DMR object.

...

Further arguments passed to or from other methods.

Details

Similar to other coef methods, this function extracts coefficients from a fitted gic.DMR object for the model with minimal gic.

Value

Vector of coefficients.

Examples

data(miete)
y <- miete[,1]
X <- miete[,-1]
m <- DMR(X, y)
g <- gic.DMR(m, c = 2.5)
coef(g)

cross-validation for DMR

Description

Executes k-fold cross-validation for DMR and returns a value for df.

Usage

cv.DMR(
  X,
  y,
  family = "gaussian",
  clust.method = "complete",
  lam = 10^(-7),
  nfolds = 10,
  indexation.mode = "GIC"
)

Arguments

X

Input data frame, of dimension n x p; DMR works only if p<n, for p>=n see DMRnet; each row is an observation vector. Columns can be numerical or integer for continuous predictors or factors for categorical predictors.

y

Response variable. Numerical for family="gaussian" or a factor with two levels for family="binomial". For family="binomial" the last level in alphabetical order is the target class.

family

Response type; one of: "gaussian", "binomial".

clust.method

Clustering method used for partitioning levels of factors; see function hclust in package stats for details. clust.method="complete" is the default.

lam

nfolds

Number of folds in cross-validation. The default value is 10.

indexation.mode

How the cross validation algorithm should index the models for internal quality comparisons; one of: "GIC" (the default) for GIC-indexed cross validation, "dimension", for model dimension-indexed cross validation.

Details

cv.DMR algorithm does cross-validation for DMR with nfolds folds. The df for the minimal estimated prediction error is returned.

Value

An object with S3 class "cv.DMR" is returned, which is a list with the ingredients of the cross-validation fit.

df.min: df (number of parameters) of the model with minimal cross-validated error.
df.1se: df (number of parameters) of the smallest model falling under the upper curve of a prediction error plus one standard deviation.
dmr.fit: Fitted DMR object for the full data.
cvm: The mean cross-validated error for the entire sequence of models.
foldid: The fold assignments used.

Examples

## cv.DMR for linear regression
set.seed(13)
data(miete)
ytr <- miete$rent[1:1500]
Xtr <- miete$area[1:1500]
Xte <- miete$area[1501:2053]
cv <- cv.DMR(Xtr, ytr)
print(cv)
plot(cv)
coef(cv)
ypr <- predict(cv, newx = Xte)

cross-validation for DMRnet

Description

Executes k-fold cross-validation for DMR and returns a value for df.

Usage

cv.DMRnet(
  X,
  y,
  family = "gaussian",
  o = 5,
  nlambda = 100,
  lam = 10^(-7),
  interc = TRUE,
  maxp = ifelse(family == "gaussian", ceiling(length(y)/2), ceiling(length(y)/4)),
  nfolds = 10,
  indexation.mode = "GIC",
  algorithm = "DMRnet",
  clust.method = ifelse(algorithm == "glamer", "single", "complete")
)

Arguments

X

Input data frame, of dimension n x p; each row is an observation vector. Columns can be numerical or integer for continuous predictors or factors for categorical predictors.

y

Response variable. Numerical for family="gaussian" or a factor with two levels for family="binomial". For family="binomial" the last level in alphabetical order is the target class.

family

Response type; one of: "gaussian", "binomial".

o

Parameter of the group lasso screening step, described in DMRnet.

nlambda

Parameter of the group lasso screening step, described in DMRnet. The default value is 100.

lam

interc

Should intercept(s) be fitted (the default, interc=TRUE) or set to zero (interc=FALSE). If in X there are any categorical variables, interc=TRUE must be set.

maxp

Maximal number of parameters of the model, smaller values result in quicker computation.

nfolds

Number of folds in cross-validation. The default value is 10.

indexation.mode

algorithm

clust.method

Details

cv.DMRnet algorithm does nfold-fold cross-validation for DMRnet. The df for the minimal estimated prediction error is returned.

Value

An object with S3 class "cv.DMR" is returned, which is a list with the ingredients of the cross-validation fit.

df.min: df (number of parameters) of the model with minimal cross-validated error.
df.1se: df (number of parameters) of the smallest model falling under the upper curve of a prediction error plus one standard deviation.
dmr.fit: Fitted DMR object for the full data.
cvm: The mean cross-validated error for the entire sequence of models.
foldid: The fold assignments used.

Examples

## cv.DMRnet for linear regression
set.seed(13)
data(miete)
ytr <- miete$rent[1:1500]
Xtr <- miete$area[1:1500]
Xte <- miete$area[1501:2053]
cv <- cv.DMRnet(Xtr, ytr)
print(cv)
plot(cv)
coef(cv)
ypr <- predict(cv, newx = Xte)

gic.DMR

Description

Computes values of Generalized Information Criterion for the entire sequence of models from a DMR object.

Usage

gic.DMR(
  x,
  c = ifelse(x$arguments$family == "gaussian", constants()$RIC_gaussian_constant,
    constants()$RIC_binomial_constant)
)

Arguments

x

Fitted DMR object.

c

Parameter controlling amount of penalization for complexity of the model in the generalized information criterion (GIC). For linear regression GIC for model M is defined as

GIC_M = RSS_M + df_M*c* log{p}*s^2,

where RSS_M is the residual sum of squares and df_M is the number of parameters in the model M; s^2 is an estimator of sigma^2 based on the model in the DMR object with the largest number of parameters. For logistic regression GIC for model M is defined as

GIC_M = -2*loglik_M + |M|*c* log{p},

where loglik_M is the logarithm of the likelihood function and df_M is the number of parameters in the model M. Recommended values are c=2.5 for linear regression and c=2 for logistic regression.

Value

An object of class "gic.DMR" is returned, which is a list with the ingredients of the gic fit.

df.min: df (number of parameters) for the model with minimal GIC.
dmr.fit: Fitted DMR object.
gic: Vector of GIC values for the entire sequence of models.

Examples

data(miete)
y <- miete[,1]
X <- miete[,-1]
m <- DMR(X, y)
(g <- gic.DMR(m, c = 2.5))

miete dataset

Description

The miete data contains the rent index for Munich in 2003.

Usage

data(miete)

Format

A data frame with 2053 observations on the following 12 variables.

rent: Rent in euros.
bathextra: Special furniture in bathroom, yes = 1, no = 0.
tiles: Bathroom with tiles, yes = 0, no = 1.
area: Municipality.
kitchen: Upmarket kitchen, yes = 1, no = 0.
rooms: Number of rooms.
best: Best address, yes = 1, no = 0.
good: Good address, yes = 1, no =0.
warm: Warm water, yes = 0, no = 1.
central: Central heating, yes = 0, no = 1.
year: Year of construction.
size: Living space in square meter.

References

Fahrmeir, L., Künstler, R., Pigeot, I., Tutz, G. (2004) Statistik: der Weg zur Datenanalyse. 5. Auflage, Berlin: Springer-Verlag.

Examples

data(miete)
summary(miete)

plot.DMR

Description

Plots coefficients from a DMR object.

Usage

## S3 method for class 'DMR'
plot(x, ...)

Arguments

x

Fitted DMR object.

...

Further arguments passed to or from other methods.

Details

Produces a coefficient profile plot of the coefficient paths for a fitted DMR object.

Examples

data(miete)
y <- miete[,1]
X <- miete[,-1]
m <- DMR(X, y)
plot(m)

plot.cv.DMR

Description

Plots cross-validated error values from a cv.DMR object.

Usage

## S3 method for class 'cv.DMR'
plot(x, ...)

Arguments

x

Fitted cv.DMR object.

...

Further arguments passed to or from other methods.

Details

Produces a plot of cross-validated error values for the entire sequence of models from the fitted cv.DMR object. The horizontal level indicating separation of one standard deviation from the minimum error is indicated with a blue dashed line. The df.min (the smallest model minimizing the cross-validated error) and df.1se (the smallest model falling under the blue dashed line) are marked with red and blue points, respectively.

Examples

## cv.DMR for linear regression
set.seed(13)
data(miete)
y <- miete$rent
X <- miete$area
cv = cv.DMR(X,y)
plot(cv)

plot.gic.DMR

Description

Plots gic values from a gic.DMR object.

Usage

## S3 method for class 'gic.DMR'
plot(x, ...)

Arguments

x

Fitted gic.DMR object.

...

Further arguments passed to or from other methods.

Details

Produces a plot of Generalized Information Criterion for the entire sequence of models from the fitted gic.DMR object.

Examples

data(miete)
y <- miete[,1]
X <- miete[,-1]
m <- DMR(X, y)
g <- gic.DMR(m, c = 2.5)
plot(g)

predict.DMR

Description

Makes predictions from a DMR object.

Usage

## S3 method for class 'DMR'
predict(
  object,
  newx,
  df = NULL,
  type = "link",
  unknown.factor.levels = "error",
  ...
)

Arguments

object

Fitted DMR object.

newx

Data frame of new values for X at which predictions are to be made. The intercept column should NOT be passed in a call to predict.

df

Number of parameters in the model for which predictions are required. Default is the entire sequence of models for df=1 to df=p.

type

One of: "link", "response", "class". For family="gaussian" for all values of type it gives the fitted values. For family="binomial" and type="link" it returns the linear predictors, for type="response" it returns the fitted probabilities and for type="class" it produces the class labels corresponding to the maximum probability.

unknown.factor.levels

The way of handling factor levels in test data not seen while training a model. One of "error" (the default - throwing an error) or "NA" (returning NA in place of legitimate value for problematic rows).

...

Further arguments passed to or from other methods.

Details

Similar to other predict methods, this function predicts fitted values from a fitted DMR object.

Value

Vector or matrix of predictions.

Examples

data(miete)
ytr <- miete[1:1500,1]
Xtr <- miete[1:1500,-1]
Xte <- miete[1501:2053,-1]
m <- DMR(Xtr, ytr)
ypr <- predict(m, newx = Xte, df = 11)

predict.cv.DMR

Description

Makes predictions from a cv.DMR object (for the model with minimal cross-validated error /the default/ or the smallest model falling under the upper curve of a prediction error plus one standard deviation).

Usage

## S3 method for class 'cv.DMR'
predict(
  object,
  newx,
  type = "link",
  md = "df.min",
  unknown.factor.levels = "error",
  ...
)

Arguments

object

Fitted cv.DMR object.

newx

Data frame of new values for X at which predictions are to be made. The intercept column should NOT be passed in a call to predict.

type

md

unknown.factor.levels

...

Further arguments passed to or from other methods.

Details

Similar to other predict methods, this function predicts fitted values from a fitted cv.DMR object.

Value

Vector of predictions.

Examples

## cv.DMR for linear regression
set.seed(13)
data(miete)
ytr <- miete$rent[1:1500]
Xtr <- miete$area[1:1500]
Xte <- miete$area[1501:2053]
cv <- cv.DMR(Xtr, ytr)
print(cv)
plot(cv)
coef(cv)
ypr <- predict(cv, newx = Xte)

predict.gic.DMR

Description

Makes predictions from a gic.DMR object (for the model with minimal GIC).

Usage

## S3 method for class 'gic.DMR'
predict(object, newx, type = "link", unknown.factor.levels = "error", ...)

Arguments

object

Fitted gic.DMR object.

newx

Data frame of new values for X at which predictions are to be made. The intercept column should NOT be passed in a call to predict.

type

unknown.factor.levels

...

Further arguments passed to or from other methods.

Details

Similar to other predict methods, this function predicts fitted values from a fitted gic.DMR object for the model with minimal GIC.

Value

Vector of predictions.

Examples

data(miete)
ytr <- miete[1:1500,1]
Xtr <- miete[1:1500,-1]
Xte <- miete[1501:2053,-1]
m <- DMR(Xtr, ytr)
g <- gic.DMR(m, c = 2.5)
ypr <- predict(g, newx = Xte)

print.DMR

Description

Prints a DMR object.

Usage

## S3 method for class 'DMR'
print(x, ...)

Arguments

x

Fitted DMR object.

...

Further arguments passed to or from other methods.

Details

Print a summary of the DMR path at each step along the path.

Value

The summary is silently returned.

Examples

data(miete)
y <- miete[,1]
X <- miete[,-1]
m <- DMR(X, y)
print(m)

promoter dataset

Description

It consists of E. coli promoter gene sequences starting at position -50 (p-50) and ending at position +7 (p7). Each of these 57 Fields is filled by one of a, g, t, c. The task is to recognize promoters, which are genetic regions which initiate the first step in the expression of adjacent genes (transcription). There are 53 promoters and 53 non-promoter sequences.

Usage

data(promoter)

Format

A data frame with 106 observations on the following 58 variables.

y: One of 1/0, indicating the class (1 = promoter).
X1: Sequence; filled by one of a, g, t, c.
X2: Sequence; filled by one of a, g, t, c.
X3: Sequence; filled by one of a, g, t, c.
X4: Sequence; filled by one of a, g, t, c.
X5: Sequence; filled by one of a, g, t, c.
X6: Sequence; filled by one of a, g, t, c.
X7: Sequence; filled by one of a, g, t, c.
X8: Sequence; filled by one of a, g, t, c.
X9: Sequence; filled by one of a, g, t, c.
X10: Sequence; filled by one of a, g, t, c.
X11: Sequence; filled by one of a, g, t, c.
X12: Sequence; filled by one of a, g, t, c.
X13: Sequence; filled by one of a, g, t, c.
X14: Sequence; filled by one of a, g, t, c.
X15: Sequence; filled by one of a, g, t, c.
X16: Sequence; filled by one of a, g, t, c.
X17: Sequence; filled by one of a, g, t, c.
X18: Sequence; filled by one of a, g, t, c.
X19: Sequence; filled by one of a, g, t, c.
X20: Sequence; filled by one of a, g, t, c.
X21: Sequence; filled by one of a, g, t, c.
X22: Sequence; filled by one of a, g, t, c.
X23: Sequence; filled by one of a, g, t, c.
X24: Sequence; filled by one of a, g, t, c.
X25: Sequence; filled by one of a, g, t, c.
X26: Sequence; filled by one of a, g, t, c.
X27: Sequence; filled by one of a, g, t, c.
X28: Sequence; filled by one of a, g, t, c.
X29: Sequence; filled by one of a, g, t, c.
X30: Sequence; filled by one of a, g, t, c.
X31: Sequence; filled by one of a, g, t, c.
X32: Sequence; filled by one of a, g, t, c.
X33: Sequence; filled by one of a, g, t, c.
X34: Sequence; filled by one of a, g, t, c.
X35: Sequence; filled by one of a, g, t, c.
X36: Sequence; filled by one of a, g, t, c.
X37: Sequence; filled by one of a, g, t, c.
X38: Sequence; filled by one of a, g, t, c.
X39: Sequence; filled by one of a, g, t, c.
X40: Sequence; filled by one of a, g, t, c.
X41: Sequence; filled by one of a, g, t, c.
X42: Sequence; filled by one of a, g, t, c.
X43: Sequence; filled by one of a, g, t, c.
X44: Sequence; filled by one of a, g, t, c.
X45: Sequence; filled by one of a, g, t, c.
X46: Sequence; filled by one of a, g, t, c.
X47: Sequence; filled by one of a, g, t, c.
X48: Sequence; filled by one of a, g, t, c.
X49: Sequence; filled by one of a, g, t, c.
X50: Sequence; filled by one of a, g, t, c.
X51: Sequence; filled by one of a, g, t, c.
X52: Sequence; filled by one of a, g, t, c.
X53: Sequence; filled by one of a, g, t, c.
X54: Sequence; filled by one of a, g, t, c.
X55: Sequence; filled by one of a, g, t, c.
X56: Sequence; filled by one of a, g, t, c.
X57: Sequence; filled by one of a, g, t, c.

Source

UCI machine learning repository: promoter

References

Towell, G., Shavlik, J., Noordewier, M. Refinement of approximate domain theories by knowledge-based neural networks. In Proceedings of the eighth National conference on Artificial intelligence, pages 861-866. Boston, MA, 1990.

Examples

data(promoter)
summary(promoter)

DMRnet-package

Description

DMRnet Functions

Author(s)

References

Examples

Delete or Merge Regressors

Description

Usage

Arguments

Details

Value

See Also

Examples

Delete or Merge Regressors net

Description

Usage

Arguments

Details

Value

See Also

Examples

coef.DMR

Description

Usage

Arguments

Details

Value

Examples

coef.cv.DMR

Description

Usage

Arguments

Details

Value

Examples

coef.gic.DMR

Description

Usage

Arguments

Details

Value

Examples

cross-validation for DMR

Description

Usage

Arguments

Details

Value

See Also

Examples

cross-validation for DMRnet

Description

Usage

Arguments

Details

Value

See Also

Examples

gic.DMR

Description

Usage

Arguments

Value

See Also

Examples

miete dataset

Description

Usage

Format

References

Examples

plot.DMR

Description

Usage

Arguments

Details

Examples

plot.cv.DMR

Description