Version: | 1.0.8 |
Date: | 2023-05-01 |
Title: | Ordinal Regression for High-Dimensional Data |
Depends: | R (≥ 4.2.0), survival |
Description: | Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
Imports: | methods |
BuildResaveData: | best |
SystemRequirements: | C++11 |
NeedsCompilation: | yes |
BuildVignettes: | TRUE |
LazyData: | true |
Packaged: | 2023-05-04 12:01:04 UTC; archer.43 |
Author: | Kellie J. Archer |
Maintainer: | Kellie J. Archer <archer.43@osu.edu> |
Repository: | CRAN |
Date/Publication: | 2023-05-05 07:30:06 UTC |
Ordinal Response Regression for High-Dimensional Data
Description
This package provides a function, ordinalgmifs, for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
Details
Package: | ordinalgmifs |
Version: | 1.0.8 |
Date: | 2023-05-01 |
Title: | Ordinal Regression for High-Dimensional Data |
Authors@R: | c(person(c("Kellie", "J."), "Archer", email = "archer.43@osu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1555-5781")), person("Jiayi", "Hou", role = "aut"), person("Qing", "Zhou", role = "aut"), person("Kyle","Ferber", role = "aut"), person(c("John", "G."), "Layne", role = c("com","ctr")), person("Amanda", "Gentry", role = "rev") ) |
Depends: | R (>= 4.2.0), survival |
Description: | Provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method. |
License: | GPL (>= 2) |
Imports: | methods |
BuildResaveData: | best |
SystemRequirements: | C++11 |
NeedsCompilation: | yes |
BuildVignettes: | TRUE |
LazyData: | true |
Author: | Kellie J. Archer [aut, cre] (<https://orcid.org/0000-0003-1555-5781>), Jiayi Hou [aut], Qing Zhou [aut], Kyle Ferber [aut], John G. Layne [com, ctr], Amanda Gentry [rev] |
Maintainer: | Kellie J. Archer <archer.43@osu.edu> |
Index of help topics:
coef.ordinalgmifs Extract Model Coefficients eyedisease Eye Disease Risk Factors hccframe Liver Cancer Methylation Data ordinalgmifs Ordinal Generalized Monotone Incremental Forward Stagewise Regression ordinalgmifs-package Ordinal Response Regression for High-Dimensional Data plot.ordinalgmifs Plot Solution Path for Ordinal GMIFS Fitted Model. predict.ordinalgmifs Predicted Probabilities and Class for Ordinal GMIFS Fit. print.ordinalgmifs Print the Contents of an Ordinal GMIFS Fitted Object. summary.ordinalgmifs Summarize an Ordinal GMIFS Object.
This package contains generic methods (coef, plot, predict, print, summary) that can be invoked for an object fitted using ordinalgmifs.
Author(s)
NA Kellie J. Archer, Jiayi Hou, Qing Zhou, Kyle Ferber, John G. Layne, Amanda Gentry
Maintainer: NA Kellie J. Archer <archer.43@osu.edu>
References
Hastie T., Taylor J., Tibshirani R., and Walther G. (2007) Forward stagewise regression and the monotone lasso. Electronic Journal of Statistics, 1, 1-29.
See Also
See Also ordinalgmifs
. For models where no predictor is penalized see vglm
Extract Model Coefficients
Description
coef.ordinalgmifs
is a generic function which extracts the model coefficients from a fitted model object fit using ordinalgmifs
Usage
## S3 method for class 'ordinalgmifs'
coef(object, model.select = "AIC", ...)
Arguments
object |
an |
model.select |
when |
... |
other arguments. |
Value
Coefficients extracted from the model object.
Author(s)
Kellie J. Archer
References
Hastie T., Taylor J., Tibshirani R., and Walther G. (2007) Forward stagewise regression and the monotone lasso. Electronic Journal of Statistics, 1, 1-29.
See Also
See Also ordinalgmifs
, summary.ordinalgmifs
, plot.ordinalgmifs
, predict.ordinalgmifs
Eye Disease Risk Factors
Description
Eye Disease Risk Factors data from Section 9.1 of Agresti's Analysis of Ordinal Categorical Data. The primary data are from the Wisconsin Epidemiological Study of Diabetic Retinopathy. The primary outcome is severity of retinopathy which was measured in the left and right eye of every subject.
Usage
data(eyedisease)
Format
A data frame with 720 observations on the following 19 variables.
rme
right eye macular oedema (absent = 0, present = 1)
lme
left eye macular oedema (absent = 0, present = 1)
rre
right eye refraction index
lre
left eye refraction index
riop
right eye intraocular eye pressure
liop
left eye intraocular eye pressure
age
age
diab
duration of diabetes (in years)
gh
glycosylated haemoglobin level
sbp
systolic blood pressure
dbp
diastolic blood pressure
bmi
body mass index
pr
pulse rate?
sex
gender (male=1, female=2)
prot
proteinuria (absent = 0, present = 1)
dose
a numeric vector
rerl
right eye severity of retinopathy, an ordered factor with levels
None
<Mild
<Moderate
<Proliferative
lerl
left eye severity of retinopathy, an ordered factor with levels
None
<Mild
<Moderate
<Proliferative
id
subject identifier
References
R. Klein and B.E.K. Klein and S.E. Moss and M.D. Davis and D.L. DeMets. (1984) The Wisconsin Epidemiologic Study of Diabetic Retinopathy II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Archives of Opthalmology 101, 520-526.
J. Williamson and K. Kim. (1996) A global odds ratio regression model for bivariate ordered categorical data from opthalmologic studies. Statistics in Medicine 15: 1507-1518.
A. Agresti. (2010) Analysis of Ordered Categorical Data, Second Edition. Wiley. Hoboken, NJ.
See Also
See Also as ordinalgmifs
Examples
data(eyedisease)
Liver Cancer Methylation Data
Description
These data are a subset of subjects and CpG sites reported in the original paper where liver samples were assayed using the Illumina GoldenGate Methylation BeadArray Cancer Panel I. Technical replicate samples were removed to ensure all samples were independent. The matched cirrhotic samples from subjects with hepatocellular carcinoma (HCC, labeled Tumor) were also excluded. Therefore methylation levels in liver tissue are provided for independent subjects whose liver was Normal (N=20), cirrhotic but not having HCC (N=16, Cirrhosis non-HCC), and HCC (N=20, Tumor).
Usage
data(hccframe)
Format
A data frame with 56 observations on the following 46 variables.
group
an ordered factor with levels
Normal
<Cirrhosis non-HCC
<Tumor
CDKN2B_seq_50_S294_F
a numeric vector representing a CpG site proportion methylation for CDKN2B
DDIT3_P1313_R
a numeric vector representing a CpG site proportion methylation for DDIT3
ERN1_P809_R
a numeric vector representing a CpG site proportion methylation for ERN1
GML_E144_F
a numeric vector representing a CpG site proportion methylation for GML
HDAC9_P137_R
a numeric vector representing a CpG site proportion methylation for HDAC9
HLA.DPA1_P205_R
a numeric vector representing a CpG site proportion methylation for HLA.DPA1
HOXB2_P488_R
a numeric vector representing a CpG site proportion methylation for HOXB2
IL16_P226_F
a numeric vector representing a CpG site proportion methylation for IL16
IL16_P93_R
a numeric vector representing a CpG site proportion methylation for IL16
IL8_P83_F
a numeric vector representing a CpG site proportion methylation for IL8
MPO_E302_R
a numeric vector representing a CpG site proportion methylation for MPO
MPO_P883_R
a numeric vector representing a CpG site proportion methylation for MPO
PADI4_P1158_R
a numeric vector representing a CpG site proportion methylation for PADI4
SOX17_P287_R
a numeric vector representing a CpG site proportion methylation for SOX17
TJP2_P518_F
a numeric vector representing a CpG site proportion methylation for TJP2
WRN_E57_F
a numeric vector representing a CpG site proportion methylation for WRN
CRIP1_P874_R
a numeric vector representing a CpG site proportion methylation for CRIP1
SLC22A3_P634_F
a numeric vector representing a CpG site proportion methylation for SLC22A3
CCNA1_P216_F
a numeric vector representing a CpG site proportion methylation for CCNA1
SEPT9_P374_F
a numeric vector representing a CpG site proportion methylation for SEPT9
ITGA2_E120_F
a numeric vector representing a CpG site proportion methylation for ITGA2
ITGA6_P718_R
a numeric vector representing a CpG site proportion methylation for ITGA6
HGF_P1293_R
a numeric vector representing a CpG site proportion methylation for HGF
DLG3_E340_F
a numeric vector representing a CpG site proportion methylation for DLG3
APP_E8_F
a numeric vector representing a CpG site proportion methylation for APP
SFTPB_P689_R
a numeric vector representing a CpG site proportion methylation for SFTPB
PENK_P447_R
a numeric vector representing a CpG site proportion methylation for PENK
COMT_E401_F
a numeric vector representing a CpG site proportion methylation for COMT
NOTCH1_E452_R
a numeric vector representing a CpG site proportion methylation for NOTCH1
EPHA8_P456_R
a numeric vector representing a CpG site proportion methylation for EPHA8
WT1_P853_F
a numeric vector representing a CpG site proportion methylation for WT1
KLK10_P268_R
a numeric vector representing a CpG site proportion methylation for KLK10
PCDH1_P264_F
a numeric vector representing a CpG site proportion methylation for PCDH1
TDGF1_P428_R
a numeric vector representing a CpG site proportion methylation for TDGF1
EFNB3_P442_R
a numeric vector representing a CpG site proportion methylation for EFNB3
MMP19_P306_F
a numeric vector representing a CpG site proportion methylation for MMP19
FGFR2_P460_R
a numeric vector representing a CpG site proportion methylation for FGFR2
RAF1_P330_F
a numeric vector representing a CpG site proportion methylation for RAF1
BMPR2_E435_F
a numeric vector representing a CpG site proportion methylation for BMPR2
GRB10_P496_R
a numeric vector representing a CpG site proportion methylation for GRB10
CTSH_P238_F
a numeric vector representing a CpG site proportion methylation for CTSH
SLC6A8_seq_28_S227_F
a numeric vector representing a CpG site proportion methylation for SLC6A8
PLXDC1_P236_F
a numeric vector representing a CpG site proportion methylation for PLXDC1
TFE3_P421_F
a numeric vector representing a CpG site proportion methylation for TFE3
TSG101_P139_R
a numeric vector representing a CpG site proportion methylation for TSG101
Source
The full dataset is available as GSE18081 from Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE18081
References
Archer KJ, Mas VR, Maluf DG, Fisher RA. High-throughput assessment of CpG site methylation for distinguishing between HCV-cirrhosis and HCV-associated hepatocellular carcinoma. Molecular Genetics and Genomics, 283(4): 341-349, 2010.
See Also
See Also as ordinalgmifs
Examples
data(hccframe)
Ordinal Generalized Monotone Incremental Forward Stagewise Regression
Description
This function can fit a cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response model when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
Usage
ordinalgmifs(formula, data, x = NULL, subset, epsilon = 0.001, tol = 1e-05,
scale = TRUE, probability.model = "Cumulative", link = "logit",
verbose=FALSE, assumption=NULL, ...)
Arguments
formula |
an object of class " |
data |
an optional data frame, list or environment (or object coercible by |
x |
an optional matrix of predictors that are to be penalized in the model fitting process. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
epsilon |
small incremental amount used to update a coefficient at a given step. |
tol |
the iterative process stops when the difference between successive log-likelihoods is less than this specified level of tolerance. |
scale |
logical, if TRUE the penalized predictors are centered and scaled. |
probability.model |
the type of ordinal response model to be fit. Can be |
link |
the link function used. Allowable links for |
verbose |
logical, if TRUE the step number is printed to the console (default is FALSE). |
assumption |
integer, only use with |
... |
additional arguments |
Details
A model specified as response~terms, x=penalized.terms
where response
is the ordinal response vector and terms
is the series of variables in the model that are not to be penalized and x
is a matrix of variables that are to be penalized. For example, terms
may include the variables age and gender while x
includes hundreds to thousands of features from a high-throughput genomic experiment. In the event that no baseline demographic/clinical characteristics/subject level variables are available or needed in terms
(all variables are to be penalized) then the model is specified as response~1, x=penalized.terms
.
Value
AIC |
a vector of AIC values for each step (if |
BIC |
a vector of BIC values for each step (if |
alpha |
the ordinal threshold estimates for the fitted model. |
theta |
the coefficient estimates for the unpenalized variables (if |
beta |
the coefficient estimates for the penalized variables (if |
phi |
the scaling coefficient estimates (if a |
logLik |
a vector of log-likelihood values for each step(if |
link |
the link function used in the model fit. |
model.select |
the step at which the minimum AIC was observed (if |
probability.model |
the model fit. |
scale |
logical indicating whether penalized variables were centered and scaled. |
w |
the unpenalized variables in the model (if any). |
x |
the penalized variables in the model (if any). |
y |
the ordinal response. |
Author(s)
Kellie J. Archer, Jiayi Hou, Qing Zhou, Kyle Ferber, John G. Layne, Amanda Gentry
References
Hastie T., Taylor J., Tibshirani R., and Walther G. (2007) Forward stagewise regression and the monotone lasso. Electronic Journal of Statistics, 1, 1-29.
See Also
See Also coef.ordinalgmifs
, summary.ordinalgmifs
, plot.ordinalgmifs
, predict.ordinalgmifs
Examples
data(hccframe)
# To minimize processing time, MPO_E302_R is coerced into the model and only a subset of
# two CpG sites (DDIT3_P1313_R and HDAC9_P137_R) are included as penalized covariates
# in this demonstration, and epsilon is set to 0.01
hcc.fit <- ordinalgmifs(group ~ MPO_E302_R, x = c("DDIT3_P1313_R", "HDAC9_P137_R"),
data = hccframe, epsilon = 0.01)
coef(hcc.fit)
summary(hcc.fit)
phat <- predict(hcc.fit)
head(phat$predicted)
table(phat$class, hccframe$group)
Functions Called by ordinalgmifs Functions, Not by the User
Description
These functions are called my other ordinalgmifs
functions and are not intended to be directly called by the user.
Details
The du.adjcat
, du.bcr
, du.cum
, du.fcr
, and du.stereo
functions calculate the derivatives at the current step for the adjacent category, backward CR, cumulative link, forward CR, and stereotype logit models, respectively, are used to identify which penalized parameter is updated. The fn.acat
, fn.bcr
, fn.cum
, fn.fcr
, and fn.stereo
are the log-likelihood functions for the adjacent category, backward CR, cumulative link, forward CR, and stereotype logit models, respectively, are used to estimate the thresholds and non-penalized subset parameters (if included) at each step of the algorithm.
The G
function returns the probability for the indicated link function. The gradient
function returns the gradient of the log-likelihood for the cumulative link models and is used for the cumulative link constrained optimization.
Value
these functions are called for intermediate results used internally by user-invoked functions
Author(s)
Kellie J. Archer, archer.43@osu.edu
See Also
See Also as ordinalgmifs
Plot Solution Path for Ordinal GMIFS Fitted Model.
Description
This function plots either the coefficient path, the AIC, or the log-likelihood for a fitted ordinalgmifs
object.
Usage
## S3 method for class 'ordinalgmifs'
plot(x, type = "trace", xlab=NULL, ylab=NULL, main=NULL, ...)
Arguments
x |
an |
type |
default is |
xlab |
a default x-axis label will be used which can be changed by specifying a user-defined x-axis label. |
ylab |
a default y-axis label will be used which can be changed by specifying a user-defined y-axis label. |
main |
a default main title will be used which can be changed by specifying a user-defined main title. |
... |
other arguments. |
Value
No return value, called for side effects
Author(s)
Kellie J. Archer
See Also
See Also ordinalgmifs
, coef.ordinalgmifs
, summary.ordinalgmifs
, predict.ordinalgmifs
Predicted Probabilities and Class for Ordinal GMIFS Fit.
Description
This function returns a list the includes the predicted probabilities as well as the predicted class for an ordinalgmifs
fitted object.
Usage
## S3 method for class 'ordinalgmifs'
predict(object, neww = NULL, newdata, newx = NULL, model.select = "AIC", ...)
Arguments
object |
an |
neww |
an optional formula that includes the unpenalized variables to use for predicting the response. If omitted, the training data are used. |
newdata |
an optional data.frame that minimally includes the unpenalized variables to use for predicting the response. If omitted, the training data are used. |
newx |
an optional matrix of penalized variables to use for predicting the response. If omitted, the training data are used. |
model.select |
when |
... |
other arguments. |
Value
predicted |
a matrix of predicted probabilities from the fitted model. |
class |
a vector containing the predicted class taken as that class having the largest predicted probability. |
... |
other arguments. |
Author(s)
Kellie J. Archer, Jiayi Hou, Qing Zhou, Kyle Ferber, John G. Layne, Amanda Gentry
See Also
See Also ordinalgmifs
, coef.ordinalgmifs
, summary.ordinalgmifs
, plot.ordinalgmifs
Print the Contents of an Ordinal GMIFS Fitted Object.
Description
This function prints the names of the list objects from an ordinalgmifs
fitted model.
Usage
## S3 method for class 'ordinalgmifs'
print(x, ...)
Arguments
x |
an |
... |
other arguments. |
Value
returns the object names in the fitted ordinalgmifs object
Note
The contents of an ordinalgmifs
fitted object differ depending upon whether x
is specified in the ordinalgmifs
model (i.e., penalized variables are included in
the model fit hence a solution path is returned) or only terms
on the right hand side of the equation are included (unpenalized variables). In the
latter case, we recommend using the VGAM package.
Author(s)
Kellie J. Archer
See Also
See Also ordinalgmifs
, coef.ordinalgmifs
, summary.ordinalgmifs
, plot.ordinalgmifs
, predict.ordinalgmifs
Summarize an Ordinal GMIFS Object.
Description
summary
method for class ordinalgmifs
.
Usage
## S3 method for class 'ordinalgmifs'
summary(object, model.select = "AIC", ...)
Arguments
object |
an |
model.select |
when |
... |
other arguments. |
Details
Prints the following items extracted from the fitted ordinalgmifs
object:
the probability model and link used and model parameter estimates. For models that include
x
, the parameter estimates, AIC, BIC, and log-likelihood are printed for indicated model.select
step or if model.select
is not supplied the step at which the minimum AIC was observed.
Value
extracts the relevant information from the step in the solution
path that attained the minimum AIC (default) or at the user-defined
model.select
step
Author(s)
Kellie J. Archer
See Also
See Also ordinalgmifs
, coef.ordinalgmifs
, plot.ordinalgmifs
, predict.ordinalgmifs