Title:	Multivariate Ordinal Regression Models
Version:	1.2.6
Date:	2025-06-02
Description:	A flexible framework for fitting multivariate ordinal regression models with composite likelihood methods. Methodological details are given in Hirk, Hornik, Vana (2020) <doi:10.18637/jss.v093.i04>.
License:	GPL-3
Depends:	minqa, BB, ucminf, dfoptim, R (≥ 3.5.0)
Imports:	MASS, pbivnorm, stats, optimx, mnormt, numDeriv, Matrix, mvtnorm
Suggests:	knitr, rmarkdown, xtable, colorspace, VGAM
VignetteBuilder:	knitr
URL:	https://github.com/lauravana/mvord
BugReports:	https://github.com/lauravana/mvord/issues
Encoding:	UTF-8
NeedsCompilation:	yes
RoxygenNote:	7.3.2
Packaged:	2025-06-02 13:14:35 UTC; lauravanagur
Author:	Rainer Hirk [aut], Kurt Hornik [aut], Laura Vana [aut, cre], Alan Gentz [ctb]
Maintainer:	Laura Vana <laura.vana@tuwien.ac.at>
Repository:	CRAN
Date/Publication:	2025-06-02 14:00:02 UTC

Multivariate Ordinal Regression Models in R.

Description

The R package mvord implements composite likelihood estimation in the class of multivariate ordinal regression models with probit and logit link. A flexible modeling framework for multiple ordinal measurements on the same subject is set up, which takes into consideration the dependence among the multiple observations by employing different error structures. Heterogeneity in the error structure across the subjects can be accounted for by the package, which allows for covariate dependent error structures. In addition, regression coefficients and threshold parameters are varying across the multiple response dimensions in the default implementation. However, constraints can be defined by the user if a reduction of the parameter space is desired.

Details

see mvord

References

Hirk R, Hornik K, Vana L (2020). “mvord: An R Package for Fitting Multivariate Ordinal Regression Models.” Journal of Statistical Software, 93(4), 1–41, doi:10.18637/jss.v093.i04.

Coefficients of Multivariate Ordinal Regression Models.

Description

coef is a generic function which extracts regression coefficients from objects of class 'mvord'.

Usage

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

Arguments

Constraints on the Regression Coefficients of Multivariate Ordinal Regression Models.

Description

An extractor function for the constraint matrices corresponding to the regression coefficients from objects of class 'mvord'.

Usage

constraints(object)

## S3 method for class 'mvord'
constraints(object)

Arguments

Simulated credit ratings

Description

A data set containing simulated credit ratings and simulated perfomance measures from four raters.

Usage

data("data_cr", package = "mvord")

Format

A data frame with 690 rows and 11 columns

Details

• rater1 credit ratings assigned by rater 1

• rater2 credit ratings assigned by rater 2

• rater3 credit ratings assigned by rater 3

• rater4 credit ratings assigned by rater 4

• firm_id firm index

• rater_id rater index covered from the free operating cash-flow of a company

• LR liquidity ratio, relating the cash held by a company to the current liabilities

• LEV leverage ratio relating debt to earnings before interest and taxes

• PR profitability ratio of retained earnoings to assets

• RSIZE log of relative size of the company in the market

• BETA a measure of systematic risk

Simulated panel of credit ratings

Description

A data set containing simulated credit ratings assigned by one rater and simulated perfomance measures for firms in different years.

• rating credit ratings

• firm_id firm index

• year year index

• LR liquidity ratio, relating the cash held by a company to the current liabilities

• LEV leverage ratio relating debt to earnings before interest and taxes

• PR profitability ratio of retained earnings to assets

• RSIZE log of relative size of the company in the market

• BETA a measure of systematic risk

• BSEC business sector of a firm (factor with 8 levels)

Usage

data("data_cr_panel", package = "mvord")

Format

A data frame with 11320 rows and 9 variables

Simulated credit ratings

Description

A simulated data set where three different raters (rater1, rater2 and rater3) assign ordinal ratings on different firms. rater3 uses a different rating scale compared to rater1 and rater2, i.e., the number of threshold categories is different. For each firm we simulate five different covariates X1, ..., X5 from a standard normal distribution. Additionally, each firm is randomly assigned to a business sector (sector X, Y or Z), captured by the covariate X6. Furthermore, we simulate multivariate normally distributed errors. For a given set of parameters we obtain the three rating variables for each firm by slotting the latent scores according to the corresponding threshold parameters. The IDs for each subject i of the n = 1000 firms are stored in the column firm_id. The IDs of the raters are stored in the column rater_id. The ordinal ratings are provided in the column rating and all the covariates in the remaining columns. Overall, the data set has 3000 rows, for each of the n = 1000 firms it has three rating observations.

Usage

data("data_mvord", package = "mvord")

Format

A data frame with 3000 rows and 9 variables

Details

• firm_id firm index

• rater_id rater index

• rating ordinal credit ratings

• X1 covariate X1

• X2 covariate X2

• X3 covariate X3

• X4 covariate X4

• X5 covariate X5

• X6 covariate X6 (factor)

Simulated credit ratings

Description

A simulated data set where three different raters (rater1, rater2 and rater3) assign ordinal ratings on different firms. rater3 uses a different rating scale compared to rater1 and rater2. The IDs for each subject i of the n = 1000 firms are stored in the column firm_id.

Usage

data("data_mvord2", package = "mvord")

Format

A data frame with 1000 rows and 10 variables

Details

• firm_id firm index

• rater1 ordinal rating outcome of rater 1

• rater2 ordinal rating outcome of rater 2

• rater3 ordinal rating outcome of rater 3

• X1 covariate X1

• X2 covariate X2

• X3 covariate X3

• X4 covariate X4

• X5 covariate X5

• X6 covariate X6 (factor)

Simulated panel of credit ratings

Description

A simulated data set where one rater assigns ratings over the years 2001 to 2010 for a set of firms. The IDs for each subject i of the n = 1000 firms are stored in the column firm_id. The year of the rating observation is stored in the column year. The ordinal ratings are provided in the column rating and all the covariates in the remaining columns.

Usage

data("data_mvord_panel", package = "mvord")

Format

A data frame with 10000 rows and 9 variables

Details

• firm_id firm index

• year year index (2001 - 2010)

• rating ordinal credit ratings

• X1 covariate X1

• X2 covariate X2

• X3 covariate X3

• X4 covariate X4

• X5 covariate X5

• X6 covariate X6 (factor)

Data set toy example

Description

A data set containing two simulated ordinal responses with three categories, two quantitative covariates X1 and X2 and two categorical covariates f1 and f2.

Usage

data("data_mvord_toy", package = "mvord")

Format

A data frame with 100 rows and 6 variables

Details

• Y1 ordinal outcome Y1 (three categories)

• Y2 ordinal outcome Y2 (three categories)

• X1 covariate X1

• X2 covariate X2

• f1 categorical covariate f1

• f2 categorical covariate f2

Error Structures in mvord

Description

Different error.structures are available in mvord:

• general correlation structure (default) cor_general(~ 1),

• general covariance structure cov_general(~ 1),

• factor dependent correlation structure cor_general(~ f),

• factor dependent covariance structure cov_general(~ f),

• equicorrelation structure cor_equi(~ 1),

• covariate dependent equicorrelation structure cor_equi(~ S),

• AR(1) correlation structure cor_ar1(~ 1), or

• covariate dependent AR(1) correlation structure cor_ar1(~ S).

For more details see vignette.

Usage

cov_general(formula = ~1, value = numeric(0), fixed = FALSE)

cor_general(formula = ~1, value = numeric(0), fixed = FALSE)

cor_ar1(formula = ~1, value = numeric(0), fixed = FALSE)

cor_equi(formula = ~1, value = numeric(0), fixed = FALSE)

Arguments

Extracts Error Structure of Multivariate Ordinal Regression Models.

Description

A generic function which extracts for each subject the estimated error structure parameters from objects of class 'mvord'.

Usage

error_structure(eobj, type, ...)

## S3 method for class 'mvord'
error_structure(eobj, type = NULL, ...)

Arguments

Details

sigmas

extracts the correlation/covariance matrices corresponding to each subject. Applicable in line with cor_general, cov_general, cor_equi, cor_ar1.

alpha

extracts the parameters of the covariate dependent error structure. Applicable in line with cor_equi, cor_ar1.

corr

extracts the subject-specific correlation parameters. Applicable in line with cor_equi, cor_ar1.

z

extracts the subject-specific Fisher-z score. Applicable in line with cor_equi, cor_ar1.

Essay data

Description

The multirater agreement data set is taken from Chapter 5 in "Ordinal Data Modeling", from Johnson, Valen E and Albert, J. The data consists of grades assigned to 198 essays by 5 experts, each of whom rated all essays on a 10-point scale. A score of 10 indicates an excellent essay. In addition, the average word length is also available as an essay characteristic.

Usage

data("essay_data", package = "mvord")

Format

A data frame with 198 rows and 6 variables

Details

• Judge1 ordinal outcome: grades assigned by expert 1

• Judge2 ordinal outcome: grades assigned by expert 2

• Judge3 ordinal outcome: grades assigned by expert 3

• Judge4 ordinal outcome: grades assigned by expert 4

• Judge5 ordinal outcome: grades assigned by expert 5

• wl covariate: word length

Fitted Probabilities of Multivariate Ordinal Regression Models.

Description

A generic function which extracts fitted probabilities for the observed categories from objects of class 'mvord'.

Usage

## S3 method for class 'mvord'
fitted(object, ...)

Arguments

Fitted probabilities for multivariate ordinal regression models for given response categories.

Description

Extracts fitted probabilities for given combination of response categories from a fitted model of class 'mvord'.

Usage

joint_probabilities(
  object,
  response.cat,
  newdata = NULL,
  type = "prob",
  subjectID = NULL,
  newoffset = NULL,
  ...
)

Arguments

Details

The function provides a convenient way to extract probabilities for a given combination of response categories, given a set of covariates. The results obtained are the same as the ones by predict() with type = "prob" or type = "cum.prob". The difference is that in joint_probabilities(), for the same set of covariates, only the response.cat argument must be changed to obtain predictions for new classes. In predict(), one would need to reconstruct a new data frame newdata everytime a new response combination should be investigated.

From newdata only the columns corresponding to the covariates will be considered. Any columns corresponding to the responses will be ignored.

The row names of the output correspond to the subjectIDs.

See Also

predict.mvord, marginal_predict

Pairwise Log-Likelihood of Multivariate Ordinal Regression Models.

Description

logLik is a generic function which extracts the pairwise log-likelihood from objects of class
'mvord'.

Usage

## S3 method for class 'mvord'
logLik(object, ...)

Arguments

Marginal Predictions for Multivariate Ordinal Regression Models.

Description

Obtains marginal predictions/fitted measures for objects of class 'mvord'.

Usage

marginal_predict(
  object,
  newdata = NULL,
  type = NULL,
  subjectID = NULL,
  newoffset = NULL,
  ...
)

Arguments

Details

The following types can be chosen in marginal_predict:

If provided, the row names of the output correspond to the subjectIDs, otherwise they correspond to the row id of the observations.

See Also

predict.mvord, joint_probabilities

model.matrix of Multivariate Ordinal Regression Models.

Description

model.matrix is a generic function which extracts the model matrix from objects of class 'mvord'.

Usage

## S3 method for class 'mvord'
model.matrix(object, ...)

Arguments

Multivariate link functions in mvord

Description

Different link functions are available in mvord:

Usage

mvprobit()

mvlogit(df = 8L)

Arguments

Details

We allow for two different link functions, the multivariate probit link and the multivariate logit link. For the multivariate probit link a multivariate normal distribution for the errors is applied. The normal bivariate probabilities which enter the pairwise log-likelihood are computed with the package pbivnorm.

For the multivariate logit link a t copula based multivariate distribution with logistic margins is used. The mvlogit() function has an optional integer valued argument df which specifies the degrees of freedom to be used for the t copula. The default value of the degrees of freedom parameter is 8. We restrict the degrees of freedom to be integer valued because the most efficient routines for computing bivariate t probabilities do not support non-integer degrees of freedom. For further details see vignette.

Value

The functions mvlogit() and mvprobit() returns an object of class 'mvlink'. An object of class 'mvlink' is a list containing the following components:

name

name of the multivariate link function

df

degrees of freedom of the t copula; returned only for mvlogit()

F_uni

a function corresponding to the univariate margins of the multivariate distribution F of the subject errors; the function returns Pr(X \leq x) = F_1(x)

F_biv

a function corresponding to the bivariate distribution of the multivariate distribution F of the subject errors Pr(X \leq x, Y\leq y|r) = F_2(x, y, r);

F_biv_rect

the function computes the rectangle probabilities from based on F_biv; the function has the matrices U (upper bounds) and L (lower bounds) as well as vector r containing the correlation coefficients corresponding to the bivariate distribution as arguments; the matrices U and L both have two columns, first corresponding to the bounds of x, second to the bounds of y; the number of rows corresponds to the number of observations; the rectangle probabilities are defined as Pr(L[,1]\leq X\leq U[,1], L[,2]\leq Y \leq U[,2]|r) = F_2(U[,1], U[,2],r) - F_2(U[,1], L[,2],r)- F_2(L[,1], U[,2],r) + F_2(L[,1], L[,2],r)

F_multi

the function computes the multivariate probabilities for distribution function F; the function has the matrices U (upper bounds) and L (lower bounds) as well as the list list_R containing for each observation the correlation matrix; F is needed for the computation of the fitted/predicted joint probabilities. If NULL only marginal probabilities can be computed.

deriv.fun

(needed for computation of analytic standard errors) a list containing the following gradient functions:

dF1dx

derivative dF_1(x)/dx function,

dF2dx

derivative dF_2(x,y,r)/dx function,

dF2dr

derivative dF_2(x,y,r)/dr function.

If deriv.fun = NULL numeric standard errors will be computed.

Multivariate Ordinal Regression Models.

Description

Multivariate ordinal regression models in the R package mvord can be fitted using the function mvord(). Two different data structures can be passed on to mvord() through the use of two different multiple measurement objects MMO and MMO2 in the left-hand side of the model formula. MMO uses a long data format, which has the advantage that it allows for varying covariates across multiple measurements. This flexibility requires the specification a subject index as well as a multiple measurement index. In contrast to MMO, the function MMO2 has a simplified data structure, but is only applicable in settings where the covariates do not vary between the multiple measurements. In this case, the multiple ordinal observations as well as the covariates are stored in different columns of a data.frame. We refer to this data structure as wide data format.

Usage

mvord(
  formula,
  data,
  error.structure = cor_general(~1),
  link = mvprobit(),
  response.levels = NULL,
  coef.constraints = NULL,
  coef.values = NULL,
  threshold.constraints = NULL,
  threshold.values = NULL,
  weights.name = NULL,
  offset = NULL,
  PL.lag = NULL,
  contrasts = NULL,
  control = mvord.control()
)

Arguments

Details

Implementation MMO:

data:

In MMO we use a long format for the input of data, where each row contains a subject index (i), a multiple measurement index (j), an ordinal observation (Y) and all the covariates (X1 to Xp). This long format data structure is internally transformed to a matrix of responses which contains NA in the case of missing entries and a list of covariate matrices. This is performed by the multiple measurement object MMO(Y, i, j) specifying the column names of the subject index and the multiple measurement index in data. The column containing the ordinal observations can contain integer or character values or can be of class (ordered) 'factor'. When using the long data structure, this column is basically a concatenated vector of each of the multiple ordinal responses. Internally, this vector is then split according to the measurement index. Then the ordinal variable corresponding to each measurement index is transformed into an ordered factor. For an integer or a character vector the natural ordering is used (ascending, or alphabetical). If for character vectors the alphabetical order does not correspond to the ordering of the categories, the optional argument response.levels allows to specify the levels for each response explicitly. This is performed by a list of length q, where each element contains the names of the levels of the ordered categories in ascending (or if desired descending) order. If all the multiple measurements use the same number of classes and same labelling of the classes, the column Y can be stored as an ordered 'factor' (as it is often the case in longitudinal studies). The order of the multiple measurements is needed when specifying constraints on the thresh- old or regression parameters. This order is based on the type of the multiple measurement index column in data. For 'integer', 'character' or 'factor' the natural ordering is used (ascending, or alphabetical). If a different order of the multiple responses is desired, the multiple measurement index column should be an ordered factor with a corresponding ordering of the levels.

If the categories differ across multiple measurements (either the number of categories or the category labels) one needs to specify the response.levels explicitly. This is performed by a list of length J (number of multiple measurements), where each element contains the names of the levels of the ordered categories in ascending or descending order.

response.levels = list(c("G","F","E", "D", "C", "B", "A"),
                       c("G","F","E", "D", "C", "B", "A"),
                       c("O","N","M","L", "K", "J", "I", "H"))

formula

The ordinal responses (e.g., rating) are passed by a formula object. Intercepts can be included or excluded in the model depending on the model paramterization:

Model without intercept:

If the intercept should be removed the formula for a given response (rating) and covariates (X1 to Xp) has the following form:

formula = MMO(rating, firm_id, rater_id) ~ 0 + X1 + ... + Xp.

Model with intercept:

If one wants to include an intercept in the model, there are two equivalent possibilities to set the model formula. Either one includes the intercept explicitly by:

formula = MMO(rating, firm_id, rater_id) ~ 1 + X1 + ... + Xp,

or by

formula = MMO(rating, firm_id, rater_id) ~ X1 + ... + Xp.

Implementation MMO2:

data:

The data structure applied by MMO2 is slightly simplified, where the multiple ordinal observations as well as the covariates are stored as columns in a data.frame. Each subject i corresponds to one row of the data frame, where all outcomes (with missing observations set to NA) and all the covariates are stored in different columns. Ideally each outcome column is of type ordered factor. For column types like 'integer', 'character' or 'factor' a warning is given and the natural ordering is used (ascending, or alphabetical).

formula

The ordinal responses (e.g., rating) are passed by a formula object. Intercepts can be included or excluded in the model depending on the model parameterization:

formula = MMO2(rater1, rater2, rater3) ~ X1 + ... + Xp.

error.structure

We allow for different error structures depending on the model parameterization:

• Correlation:

  • cor_general The most common parameterization is the general correlation matrix.

    error.structure = cor_general(~ 1)

    This parameterization can be extended by allowing a factor dependent correlation structure, where the correlation of each subject i depends on a given subject-specific factor f. This factor f is not allowed to vary across multiple measurements j for the same subject i and due to numerical constraints only up to maximum 30 levels are allowed.

    error.structure = cor_general(~ f)

  • cor_equi A covariate dependent equicorrelation structure, where the correlations are equal across all J dimensions and depend on subject-specific covariates S1, ..., Sm. It has to be noted that these covariates S1, ..., Sm are not allowed to vary across multiple measurements j for the same subject i.

    error.structure = cor_equi(~ S1 + ... + Sm)

  • cor_ar1 In order to account for some heterogeneity the AR(1) error structure is allowed to depend on covariates X1, ..., Xp that are constant over time for each subject i.

    error.structure = cor_ar1(~ S1 + ... + Sm)

• Covariance:

  • cov_general

    In case of a full variance-covariance parameterization the standard parameterization with a full variance-covariance is obtained by:

    error.structure = cov_general(~ 1)

    This parameterization can be extended to the factor dependent covariance structure, where the covariance of each subject depends on a given factor f:

    error.structure = cov_general(~ f)

coef.constraints

The package supports constraints on the regression coefficients. Firstly, the user can specify whether the regression coefficients should be equal across some or all response dimensions. Secondly, the values of some of the regression coefficients can be fixed.

As there is no unanimous way to specify such constraints, we offer two options. The first option is similar to the specification of constraints on the thresholds. The constraints can be specified in this case as a vector or matrix of integers, where coefficients getting same integer value are set equal. Values of the regression coefficients can be fixed through a matrix. Alternatively constraints on the regression coefficients can be specified by using the design employed by the VGAM package. The constraints in this setting are set through a named list, where each element of the list contains a matrix full-column rank. If the values of some regression coefficients should be fixed, offsets can be used. This design has the advantage that it supports constraints on outcome-specific as well as category-specific regression coefficients. While the first option has the advantage of requiring a more concise input, it does not support category-specific coefficients. The second option offers a more flexible design in this respect. For further information on the second option we refer to the vignette and to the documentation of vglm.

Using the first option, constraints can be specified by a vector or a matrix
coef.constraints. First, a simple and less flexible way by specifying a vector
coef.constraints of dimension J. This vector is allocated in the following way: The first element of the vector coef.constraints gets a value of 1. If the coefficients of the multiple measurement j = 2 should be equal to the coefficients of the first dimension (j=1) again a value of 1 is set. If the coefficients should be different to the coefficients of the first dimension a value of 2 is set. In analogy, if the coefficients of dimensions two and three should be the same one sets both values to 2 and if they should be different, a value of 3 is set. Constraints on the regression coefficients of the remaining multiple measurements are set analogously.

coef.constraints <- c(1,1,2,3)

This vector coef.constraints sets the coefficients of the first two raters equal

\beta_{1\cdot} = \beta_{2\cdot}

A more flexible way to specify constraints on the regression coefficients is a matrix with J rows and p columns, where each column specifies constraints on one of the p coefficients in the same way as above. In addition, a value of NA excludes a corresponding coefficient (meaning it should be fixed to zero).

coef.constraints <- cbind(c(1,2,3,4), c(1,1,1,2), c(NA,NA,NA,1),
                          c(1,1,1,NA), c(1,2,3,4), c(1,2,3,4))

This matrix coef.constraints gives the following constraints:

• \beta_{12} = \beta_{22} = \beta_{32}

• \beta_{13} = 0

• \beta_{23} = 0

• \beta_{33} = 0

• \beta_{44} = 0

• \beta_{14} = \beta_{24} = \beta_{34}

coef.values

In addition, specific values on regression coefficients can be set in the matrix
coef.values. Parameters are removed if the value is set to zero (default for NA's in
coef.constraints) or to some fixed value. If constraints on parameters are set, these dimensions need to have the same value in coef.values. Again each column corresponds to one regression coefficient.

Together with the coef.constraints from above we impose:

coef.constraints <- cbind(c(1,2,2), c(1,1,2), c(NA,1,2),
                          c(NA,NA,NA), c(1,1,2))

coef.values <- cbind(c(NA,NA,NA), c(NA,NA,NA), c(0,NA,NA),
                     c(1,1,1), c(NA,NA,NA))

Interaction terms: When constraints on the regression coefficient should be specified in models with interaction terms, the coef.constraints matrix has to be expanded manually. In case of interaction terms (specified either by X1 + X2 + X1:X2 or equivalently by X1*X2), one additional column at the end of coef.constraints for the interaction term has to be specified for numerical variables. For interaction terms including factor variables suitably more columns have to be added to the coef.constraints matrix.

threshold.constraints

Similarly, constraints on the threshold parameters can be imposed by a vector of positive integers, where dimensions with equal threshold parameters get the same integer. When restricting the thresholds of two outcome dimensions to be the same, one has to be careful that the number of categories in the two outcome dimensions must be the same. In our example with J=4 different outcomes we impose:

threshold.constraints <- c(1,1,2)

gives the following restrictions:

• \bm\theta_{1} = \bm\theta_{2}

• \bm\theta_{3} arbitrary.

threshold.values

In addition, threshold parameter values can be specified by threshold.values in accordance with identifiability constraints. For this purpose we use a list with J elements, where each element specifies the constraints of the particular dimension by a vector of length of the number of threshold parameters (number of categories - 1). A number specifies a threshold parameter to a specific value and NA leaves the parameter flexible. For data_mvord we have

threshold.constraints <- NULL

threshold.values <- list(c(-4,NA,NA,NA,NA,4.5),
                         c(-4,NA,NA,NA,NA,4.5),
                         c(-5,NA,NA,NA,NA,NA,4.5))

Value

The function mvord returns an object of class "mvord".

The functions summary and print are used to display the results. The function coef extracts the regression coefficients, a function thresholds the threshold coefficients and the function
error_structure returns the estimated parameters of the corresponding error structure.

An object of class "mvord" is a list containing the following components:

beta

a named matrix of regression coefficients

theta

a named list of threshold parameters

error.struct

an object of class error_struct containing the parameters of the error structure

sebeta

a named matrix of the standard errors of the regression coefficients

setheta

a named list of the standard errors of the threshold parameters

seerror.struct

a vector of standard errors for the parameters of the error structure

rho

a list of all objects that are used in mvord()

References

Hirk R, Hornik K, Vana L (2020). “mvord: An R Package for Fitting Multivariate Ordinal Regression Models.” Journal of Statistical Software, 93(4), 1–41, doi:10.18637/jss.v093.i04.

See Also

print.mvord, summary.mvord, coef.mvord, thresholds.mvord, error_structure.mvord,
mvord.control, data_cr_panel,data_cr, data_mvord_panel,data_mvord, data_mvord2

Examples

library(mvord)

#toy example
data(data_mvord_toy)

#wide data format with MMO2
res <- mvord(formula = MMO2(Y1, Y2) ~ 0 + X1 + X2,
             data = data_mvord_toy)
print(res)
summary(res)
thresholds(res)
coefficients(res)
head(error_structure(res))

# convert data_mvord_toy into long format
df <- cbind.data.frame("i" = rep(1:100,2), "j" = rep(1:2,each = 100),
                       "Y" = c(data_mvord_toy$Y1,data_mvord_toy$Y2),
                       "X1" = rep(data_mvord_toy$X1,2),
                       "X2" = rep(data_mvord_toy$X2,2))

#for long format data, use MMO instead of MMO2
res <- mvord(formula = MMO(Y, i, j) ~ 0 + X1 + X2, #or formula = MMO(Y) ~ 0 + X1 + X2
               data = df)
print(res)
summary(res)
thresholds(res)
coefficients(res)
head(error_structure(res))

res2 <- mvord(formula = MMO(Y) ~ 0 + X1 + X2,
               data = df,
               control = mvord.control(solver = "BFGS"),
               threshold.constraints = c(1,1),
               coef.constraints = c(1,1))
print(res2)
summary(res2)
thresholds(res2)
coefficients(res2)
head(error_structure(res2))

## examples
#load data
data(data_mvord)
head(data_mvord)

#-------------
# cor_general
#-------------
# approx 2 min
res_cor <- mvord(formula = MMO(rating) ~ 0 + X1 + X2 + X3 + X4 + X5,
                 data = data_mvord,
                 coef.constraints = cbind(c(1,2,2),
                                          c(1,1,2),
                                          c(NA,1,2),
                                          c(NA,NA,NA),
                                          c(1,1,2)),
                 coef.values = cbind(c(NA,NA,NA),
                                     c(NA,NA,NA),
                                     c(0,NA,NA),
                                     c(1,1,1),
                                     c(NA,NA,NA)),
                 threshold.constraints = c(1,1,2),
                 control = mvord.control(solver = "newuoa"))
print(res_cor)
summary(res_cor)
thresholds(res_cor)
coefficients(res_cor)
head(error_structure(res_cor))

#-------------
# cov_general
#-------------
#approx 4 min
res_cov <- mvord(formula = MMO(rating) ~ 1 + X1 + X2 + X3 + X4 + X5,
                 data = data_mvord,
                 error.structure = cov_general(~1),
                 threshold.values = list(c(-4,NA,NA,NA,NA,4.5),
                                         c(-4,NA,NA,NA,NA,4),
                                         c(-5,NA,NA,NA,NA,NA,4.5))
) #does not converge with BFGS

print(res_cov)
summary(res_cov)
thresholds(res_cov)
coefficients(res_cov)
head(error_structure(res_cov))


#-------------
# cor_ar1
#-------------
#approx 4min
data(data_mvord_panel)
head(data_mvord_panel)

#select subset of data
subset_dat <- data_mvord_panel$year %in% c("year3", "year4", "year5", "year6", "year7")
data_mvord_panel <- data_mvord_panel[subset_dat,]

mult.obs <- 5
res_AR1 <- mvord(formula = MMO(rating) ~ 0 + X1 + X2 + X3 + X4 + X5,
                 data = data_mvord_panel,
                 error.structure = cor_ar1(~1),
                 threshold.constraints = c(1,1,1,2,2),
                 coef.constraints = c(1,1,1,2,2),
                 control = mvord.control(solver = "BFGS"))
print(res_AR1)
summary(res_AR1)
thresholds(res_AR1)
coefficients(res_AR1)
head(error_structure(res_AR1))
head(error_structure(res_AR1, type = "corr"))

data(data_mvord2)
# approx 2 min
res_cor <- mvord(formula = MMO2(rater1, rater2, rater3) ~ 0 + X1 + X2 + X3 + X4 + X5,
                 data = data_mvord2,
                 coef.constraints = cbind(c(1,2,2),
                                          c(1,1,2),
                                          c(NA,1,2),
                                          c(NA,NA,NA),
                                          c(1,1,2)),
                 coef.values = cbind(c(NA,NA,NA),
                                     c(NA,NA,NA),
                                     c(0,NA,NA),
                                     c(1,1,1),
                                     c(NA,NA,NA)),
                 threshold.constraints = c(1,1,2),
                 control = mvord.control(solver = "newuoa"))
print(res_cor)
summary(res_cor)
thresholds(res_cor)
coefficients(res_cor)
head(error_structure(res_cor))

Control functions for mvord()

Description

Control arguments are set for mvord().

Usage

mvord.control(
  se = TRUE,
  start.values = NULL,
  combis = NULL,
  solver = "newuoa",
  solver.optimx.control = list(maxit = 2e+05, trace = 0, kkt = FALSE)
)

Arguments

See Also

mvord

Names of regression coefficient constraints in mvord

Description

An extractor function for the names of the regression coefficient constraints based on the model formula and data.

Usage

names_constraints(formula, data, contrasts = NULL)

Arguments

nobs of Multivariate Ordinal Regression Models.

Description

nobs is a generic function which extracts the number of observations from objects of class
'mvord'.

Usage

## S3 method for class 'mvord'
nobs(object, ...)

Arguments

Computes polychoric correlations

Description

This function computes polychoric correleations among two or more variables.

Usage

polycor(x, y = NULL)

Arguments

Predict method for Multivariate Ordinal Regression Models.

Description

Obtains predicted or fitted values for objects of class 'mvord'.

Usage

## S3 method for class 'mvord'
predict(
  object,
  newdata = NULL,
  type = NULL,
  subjectID = NULL,
  newoffset = NULL,
  ...
)

Arguments

Details

If provided, the (row) names of the output correspond to the subjectIDs, otherwise they correspond to the row id of the observations.

See Also

marginal_predict, joint_probabilities

Print Method for class error_struc.

Description

Prints error structure of class error_struct.

Usage

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

Arguments

Print Method for Multivariate Ordinal Regression Models.

Description

Prints thresholds, regression coefficients and parameters of the error structure of class 'mvord'.

Usage

## S3 method for class 'mvord'
print(x, call = TRUE, ...)

Arguments

Pseudo R^2 for objects of class 'mvord'

Description

This function computes Mc Fadden's Pseudo R^2 for objects of class 'mvord'.

Usage

pseudo_R_squared(object, adjusted = FALSE)

Arguments

See Also

mvord

Summary method for Multivariate Ordinal Regression Models.

Description

Summary of thresholds, regression coefficients and parameters of the error structure of class 'mvord'.

Usage

## S3 method for class 'mvord'
summary(object, short = TRUE, call = TRUE, ...)

Arguments

terms of Multivariate Ordinal Regression Models.

Description

terms is a generic function which can be used to extract terms from objects of class
'mvord'.

Usage

## S3 method for class 'mvord'
terms(x, ...)

Arguments

Thresholds of Multivariate Ordinal Regression Models.

Description

thresholds is a generic function which extracts threshold coefficients from objects of class
'mvord'.

Usage

thresholds(object, ...)

## S3 method for class 'mvord'
thresholds(object, ...)

Arguments

vcov of Multivariate Ordinal Regression Models.

Description

vcov is a generic function which extracts the Godambe information matrix from objects of class
'mvord'.

Usage

## S3 method for class 'mvord'
vcov(object, ...)

Arguments