Package ‘lmap’

December 11, 2024

Type Package
Title Logistic Mapping
Version 0.1.3
Maintainer Mark de Rooij <rooijm@fsw.leidenuniv.nl>
Description Set of tools for mapping of categorical response variables based on principal compo-
nent analysis (pca) and multidimensional unfolding (mdu).
Depends R (>= 3.5.0), ggplot2, ggrepel, ggforce, fmdu
Imports nnet, stats, magrittr, dplyr, MASS, Rfast
License BSD_2_clause + file LICENSE
Encoding UTF-8
LazyData TRUE
Author Mark de Rooij [aut, cre, cph],
Frank Busing [aut, cph],
Juan Claramunt Gonzalez [aut]
NeedsCompilation yes
RoxygenNote 7.2.3
Repository CRAN
Date/Publication 2024-12-11 07:50:02 UTC

Contents
clmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
clpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
dataExample_clmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
dataExample_clpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
dataExample_lmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
dataExample_lpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
dataExample_mru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
esm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
fastmbu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
fastmru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1
2 clmdu

lmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
lpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
mru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
plot.clmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
plot.clpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
plot.lmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
plot.lpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
plot.mru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
predict.clmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
predict.clpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
predict.lmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
predict.lpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
predict.mru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
summary.clmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
summary.clpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
summary.esm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
summary.lmdu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
summary.lpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
summary.mru . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
twomodedistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Index 31

clmdu Cumulative Logistic (Restricted) MDU

Description
Cumulative Logistic (Restricted) MDU

Usage
clmdu(
Y,
X = NULL,
S = 2,
trace = FALSE,
start = "svd",
maxiter = 65536,
dcrit = 1e-06
)

Arguments
Y An N times R ordinal matrix coded with integers 1,2,.. .
X An N by P matrix with predictor variables
S Positive number indicating the dimensionality of the solution
clmdu 3

trace boolean to indicate whether the user wants to see the progress of the function
(default=TRUE)
start either starting values (list with (U,V) or (B,V)) or way to compute them (svd,
random, ca)
maxiter maximum number of iterations
dcrit convergence criterion

Value
Y Matrix Y from input
Xoriginal Matrix X from input
X Scaled X matrix
mx Mean values of X
sdx Standard deviations of X
ynames Variable names of responses
xnames Variable names of predictors
probabilities Estimated values of Y
m main effects
U matrix with coordinates for row-objects
B matrix with regression weight (U = XB)
V matrix with vectors for items/responses
iter number of main iterations from the MM algorithm
deviance value of the deviance at convergence

Examples
## Not run:
data(dataExample_clmdu)
Y<-dataExample_clmdu
X<-dataExample_clmdu
output1 = clmdu(Y)
plot(output1)
plot(output1, circles = NULL)
summary(output1)

output2 = clmdu(Y = Y, X = X)
plot(output2, circles = c(1,2))
summary(output2)

## End(Not run)
4 clpca

clpca Cumulative Logistic (Restrcited) PCA

Description
Cumulative Logistic (Restrcited) PCA

Usage
clpca(
Y,
X = NULL,
S = 2,
lambda = FALSE,
trace = FALSE,
maxiter = 65536,
dcrit = 1e-06
)

Arguments
Y An N times R ordinal matrix .
X An N by P matrix with predictor variables
S Positive number indicating the dimensionality of the solution
lambda if TRUE does lambda scaling (see Understanding Biplots, p24)
trace tracing information during iterations
maxiter maximum number of iterations
dcrit convergence criterion

Value
Y Matrix Y from input
Xoriginal Matrix X from input
X Scaled X matrix
mx Mean values of X
sdx Standard deviations of X
ynames Variable names of responses
xnames Variable names of predictors
probabilities Estimated values of Y
m main effects
U matrix with coordinates for row-objects
B matrix with regression weight (U = XB)
dataExample_clmdu 5

V matrix with vectors for items/responses

iter number of main iterations from the MM algorithm
deviance value of the deviance at convergence

Examples
## Not run:
data(dataExample_clpca)
Y<-as.matrix(dataExample_clpca[,5:8])
X<-as.matrix(dataExample_clpca[,1:4])
out = clpca(Y)
out = clpca(Y, X)

## End(Not run)

dataExample_clmdu Dummy data for clmdu example

Description
Dummy data for clmdu example

Usage
dataExample_clmdu

Format
A data frame with 200 observations on the following variables:

X1 Continuous variable 1.
X2 Continuous variable 2.
X3 Continuous variable 3.
X4 Continuous variable 4.
Y1 Discrete variable 1.
Y2 Discrete variable 2.
Y3 Discrete variable 3.
Y4 Discrete variable 4.
Y5 Discrete variable 5.
6 dataExample_lmdu

dataExample_clpca Dummy data for clpca example

Description
Dummy data for clpca example

Usage
dataExample_clpca

Format
A data frame with 200 observations on the following variables:
X1 Continuous variable 1.
X2 Continuous variable 2.
X3 Continuous variable 3.
X4 Continuous variable 4.
Y1 Discrete variable 1.
Y2 Discrete variable 2.
Y3 Discrete variable 3.
Y4 Discrete variable 4.

dataExample_lmdu Dummy data for lmdu example

Description
Dummy data for lmdu example

Usage
dataExample_lmdu

Format
A data frame with 234 observations on the following variables:
Y1 Dichotomous variable 1.
Y2 Dichotomous variable 2.
Y3 Dichotomous variable 3.
Y4 Dichotomous variable 4.
dataExample_lpca 7

Y5 Dichotomous variable 5.
Y6 Dichotomous variable 6.
Y7 Dichotomous variable 7.
Y8 Dichotomous variable 8.
X1 Continuous variable 1.
X2 Continuous variable 2.
X3 Continuous variable 3.
X4 Continuous variable 4.
X5 Continuous variable 5.

dataExample_lpca Dummy data for lpca example

Description
Dummy data for lpca example

Usage
dataExample_lpca

Format
A data frame with 234 observations on the following variables:

Y1 Dichotomous variable 1.
Y2 Dichotomous variable 2.
Y3 Dichotomous variable 3.
Y4 Dichotomous variable 4.
Y5 Dichotomous variable 5.
Y6 Dichotomous variable 6.
Y7 Dichotomous variable 7.
Y8 Dichotomous variable 8.
X1 Continuous variable 1.
X2 Continuous variable 2.
X3 Continuous variable 3.
X4 Continuous variable 4.
X5 Continuous variable 5.
8 esm

dataExample_mru Dummy data for mru example

Description
Dummy data for mru example

Usage
dataExample_mru

Format
A data frame with 234 observations on the following variables:
y Categorical variable.
X1 Continuous variable 1.
X2 Continuous variable 2.
X3 Continuous variable 3.
X4 Continuous variable 4.
X5 Continuous variable 5.

esm Extended Stereotype Model

Description
The function esm performs extended stereotype model analysis for multivariate logistic analysis i.e.
a double constrained reduced rank multinomial logistic model

Usage
esm(
X,
Y,
S = 2,
Z = NULL,
W = NULL,
ord.z = 1,
ord.m = R,
scale.x = FALSE,
trace = FALSE,
maxiter = 65536,
dcrit = 1e-06
)
esm 9

Arguments
X An N by P matrix with predictor variables
Y An N times R binary matrix .
S Positive number indicating the dimensionality of teh solution
Z design matrix for response
W design matrix for intercepts
ord.z if Z = NULL, the function creates Z having order ord.z
ord.m if W = NULL, the function creates W having order ord.m
scale.x whether X should be scaled to zero mean and standard deviation one
trace whether progress information should be printed on the screen
maxiter maximum number of iterations
dcrit convergence criterion

Value
This function returns an object of the class esm with components:
call function call
Xoriginal Matrix X from input
X Scaled X matrix
mx Mean values of X
sdx Standard deviations of X
Y Matrix Y from input
pnames Variable names of profiles
xnames Variable names of predictors
znames Variable names of responses
Z Design matrix Z
W Design matrix W
G Profile indicator matrix G
m main effects
bm regression weights for main effects
Bx regression weights for X
Bz regression weights for Z
A regression weights (Bx Bz’)
U matrix with coordinates for row-objects
V matrix with coordinates for column-objects
Ghat Estimated values of G
deviance value of the deviance at convergence
df number of paramters
AIC Akaike’s informatoin criterion
iter number of main iterations from the MM algorithm
svd Singular value decomposition in last iteration
10 fastmbu

Examples
## Not run:
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[ , 1:5])
X = as.matrix(dataExample_lpca[ , 9:13])
#unsupervised
output = esm(X, Y, S = 2, ord.z = 2)

## End(Not run)

fastmbu Fast version of mbu. It runs mbu without input checks.

Description
Fast version of mbu. It runs mbu without input checks.

Usage
fastmbu(
Y = NULL,
W = NULL,
XU = NULL,
BU = NULL,
XV = NULL,
BV = NULL,
mains = TRUE,
MAXINNER = 32,
FCRIT = 0.001,
MAXITER = 65536,
DCRIT = 1e-06
)

Arguments
Y matrix with dichotomous responses
W matrix with weights for each entrance of Y or vector with weights for each row
of Y
XU in unsupervised analysis starting values for row coordinates; in supervised anal-
ysis matrix with predictor variables for rows
BU for supervised analysis matrix with regression weights for the row coordinates
XV in unsupervised analysis starting values for column coordinates; in supervised
analysis matrix with predictor variables for columns
BV for supervised analysis matrix with regression weights for the column coordi-
nates
fastmru 11

mains whether offsets for the items should be estimated

MAXINNER maximum number of iterations in the inner loop
FCRIT convergence criterion for STRESS in the inner loop
MAXITER maximum number of iterations in the outer loop
DCRIT convergence criterion for the deviance

Value

U estimated coordinate matrix for row objects

BU for supervised analysis the estimated matrix with regression weights for the rows
V estimated coordinate matrix for column objects
BV for supervised analysis the estimated matrix with regression weights for the columns
Mu estimated offsets
Lastinner number of iterations in the last call to STRESS
Lastfdif last difference in STRESS values in the inner loop
lastouter number of iterations in the outer loop
lastddif last difference in deviances in outer loop
deviance obtained deviance

fastmru Fast version of mru. It runs mru without input checks.

Description

Fast version of mru. It runs mru without input checks.

Usage

fastmru(
G = NULL,
X = NULL,
B = NULL,
Z = NULL,
MAXINNER = 32,
FCRIT = 0.001,
MAXITER = 65536,
DCRIT = 1e-06,
error.check = FALSE
)
12 lmdu

Arguments
G indicator matrix of the response variable
X matrix with predictor variables
B starting values of the regression weights
Z starting values for class locations
MAXINNER maximum number of iterations in the inner loop
FCRIT convergence criterion for STRESS in the inner loop
MAXITER maximum number of iterations in the outer loop
DCRIT convergence criterion for the deviance
error.check extensive check validity input parameters (default = FALSE).

Value
B estimated regression weights
V estimated class locations
Lastinner number of iterations in the last call to STRESS
Lastfdif last difference in STRESS values in the inner loop
lastouter number of iterations in the outer loop
lastddif last difference in deviances in outer loop
deviance obtained deviance

lmdu Logistic (Restricted) MDU

Description
This function runs: logistic multidimensional unfolding (if X = NULL) logistic restricted multidi-
mensional unfolding (if X != NULL)

Usage
lmdu(
Y,
f = NULL,
X = NULL,
S = 2,
start = "svd",
maxiter = 65536,
dcrit = 1e-06
)
lmdu 13

Arguments

Y An N times R binary matrix .

f Vector with frequencies of response patterns in Y (only applicable if (X = NULL))
X An N by P matrix with predictor variables
S Positive number indicating the dimensionality of the solution
start Either user provided starting values (start should be a list with U and V) or a
way to compute starting values (choices: random, svd, ca)
maxiter maximum number of iterations
dcrit convergence criterion

Value

deviance

call Call to the function

Yoriginal Matrix Y from input
Y Matrix Y from input
f frequencies of rows of Y
Xoriginal Matrix X from input
X Scaled X matrix
mx Mean values of X
sdx Standard deviations of X
ynames Variable names of responses
xnames Variable names of predictors
probabilities Estimated values of Y
m main effects
U matrix with coordinates for row-objects
B matrix with regression weight (U = XB)
V matrix with vectors for items/responses
iter number of main iterations from the MM algorithm
deviance value of the deviance at convergence
npar number of estimated parameters
AIC Akaike’s Information Criterion
BIC Bayesian Information Criterion
14 lpca

Examples
## Not run:
data(dataExample_lmdu)
Y = as.matrix(dataExample_lmdu[ , 1:8])
X = as.matrix(dataExample_lmdu[ , 9:13])
# unsupervised
output = lmdu(Y = Y, S = 2)
# supervised
output2 = lmdu(Y = Y, X = X, S = 2)

## End(Not run)

lpca Logistic (Restricted) PCA

Description
This function runs: logistic principal component analysis (if X = NULL) logistic reduced rank
regression (if X != NULL)

Usage
lpca(
Y,
X = NULL,
S = 2,
dim.indic = NULL,
eq = FALSE,
lambda = FALSE,
maxiter = 65536,
dcrit = 1e-06
)

Arguments
Y An N times R binary matrix .
X An N by P matrix with predictor variables
S Positive number indicating the dimensionality of the solution
dim.indic An R by S matrix indicating which response variable pertains to which dimen-
sion
eq Only applicable when dim.indic not NULL; equality restriction on regression
weighhts per dimension
lambda if TRUE does lambda scaling (see Understanding Biplots, p24)
maxiter maximum number of iterations
dcrit convergence criterion
lpca 15

Value

This function returns an object of the class lpca with components:

call Call to the function

Y Matrix Y from input
Xoriginal Matrix X from input
X Scaled X matrix
mx Mean values of X
sdx Standard deviations of X
ynames Variable names of responses
xnames Variable names of predictors
probabilities Estimated values of Y
m main effects
U matrix with coordinates for row-objects
B matrix with regression weight (U = XB)
V matrix with vectors for items/responses
iter number of main iterations from the MM algorithm
deviance value of the deviance at convergence
npar number of estimated parameters
AIC Akaike’s Information Criterion
BIC Bayesian Information Criterion

Examples

## Not run:
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[, 1:8])
X = as.matrix(dataExample_lpca[, 9:13])
# unsupervised
output = lpca(Y = Y, S = 2)

## End(Not run)
16 mru

mru Multinomial Restricted MDU

Description

The function mru performs multinomial restricted unfolding for a nominal response variable and a
set of predictor variables.

Usage

mru(y, X, S = 2, start = "da", maxiter = 65536, dcrit = 1e-05)

Arguments

y An N vector of the responses (categorical).

X An N by P matrix with predictor variables
S Positive number indicating the dimensionality of teh solution
start Type of starting values (da: discriminant analysis, random or list with B and V)
maxiter maximum number of iterations
dcrit convergence criterion

Value

Y Matrix Y from input

Xoriginal Matrix X from input
X Scaled X matrix
G class indicator matrix
ynames class names of response variable
xnames variable names of the predictors
mx means of the predictor variables
sdx standard deviations of the predictor variables
U coordinate matrix of row objects
B matrix with regression coefficients
Class coordinate matrix
iters number of iterations
deviance value of the deviance at convergence
plot.clmdu 17

Examples
## Not run:
data(dataExample_mru)
y = as.matrix(dataExample_mru[1:20 , 1])
X = as.matrix(dataExample_mru[1:20 , 2:6])
output = mru(y = y, X = X, S = 2)

## End(Not run)

plot.clmdu Plots a Cumulative Logistic MDU model

Description
Plots a Cumulative Logistic MDU model

Usage
## S3 method for class 'clmdu'
plot(
x,
dims = c(1, 2),
circles = seq(1, R),
ycol = "darkgreen",
xcol = "lightskyblue",
ocol = "grey",
...
)

Arguments
x an object of type clmdu
dims which dimensions to visualize
circles which circles to visualize
ycol colour for representation of response variables
xcol colour for representation of predictor variables
ocol colour for representation of row objects
... additional arguments to be passed.

Value
Plot of the results obtained from clmdu
18 plot.clpca

Examples
## Not run:
data(dataExample_clmdu)
Y = as.matrix(dataExample_clmdu[ , 1:8])
X = as.matrix(dataExample_clmdu[ , 9:13])
# unsupervised
output = clmdu(Y = Y, S = 2)
plot(output)

## End(Not run)

plot.clpca Plots a Cumulative Logistic PCA model

Description
Plots a Cumulative Logistic PCA model

Usage
## S3 method for class 'clpca'
plot(
x,
dims = c(1, 2),
ycol = "darkgreen",
xcol = "lightskyblue",
ocol = "grey",
...
)

Arguments
x an object of type clpca
dims which dimensions to visualize
ycol colour for representation of response variables
xcol colour for representation of predictor variables
ocol colour for representation of row objects
... additional arguments to be passed.

Value
Plot of the results obtained from clpca
plot.lmdu 19

Examples
## Not run:
data(dataExample_clpca)
Y<-as.matrix(dataExample_clpca[,5:8])
X<-as.matrix(dataExample_clpca[,1:4])
out = clpca(Y, X)
plot(out)

## End(Not run)

plot.lmdu Plots a Logistic MDU model

Description

Plots a Logistic MDU model

Usage

## S3 method for class 'lmdu'

plot(
x,
dims = c(1, 2),
ycol = "darkgreen",
xcol = "lightskyblue",
ocol = "grey",
...
)

Arguments

x an object of type lmdu

dims which dimensions to visualize
ycol colour for representation of response variables
xcol colour for representation of predictor variables
ocol colour for representation of row objects
... additional arguments to be passed.

Value

Plot of the results obtained from lmdu

20 plot.lpca

Examples
## Not run:
data(dataExample_lmdu)
Y = as.matrix(dataExample_lmdu[ , 1:8])
X = as.matrix(dataExample_lmdu[ , 9:13])
# unsupervised
output = lmdu(Y = Y, S = 2)
plot(output)

## End(Not run)

plot.lpca Plots a Logistic PCA Model

Description
Plots a Logistic PCA Model

Usage
## S3 method for class 'lpca'
plot(
x,
dims = c(1, 2),
type = "H",
ycol = "darkgreen",
xcol = "lightskyblue",
ocol = "grey",
...
)

Arguments
x an object of type lpca
dims which dimensions to visualize
type either H (hybrid), I (inner product/pca), or D (distance/melodic)
ycol colour for representation of response variables
xcol colour for representation of predictor variables
ocol colour for representation of row objects
... additional arguments to be passed.

Value
Plot of the results obtained from lpca
plot.mru 21

Examples
## Not run:
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[, 1:8])
X = as.matrix(dataExample_lpca[, 9:13])
# unsupervised
output = lpca(Y = Y, S = 2)
plot(output)

## End(Not run)

plot.mru Plots a Multinomial Restricted MDU model

Description
Plots a Multinomial Restricted MDU model

Usage
## S3 method for class 'mru'
plot(
x,
dims = c(1, 2),
class.regions = FALSE,
ycol = "darkgreen",
xcol = "lightskyblue",
ocol = "grey",
...
)

Arguments
x an object of type mru
dims which dimensions to visualize
class.regions whether a voronoi diagram with classification regions should be included
ycol colour for representation of response variables
xcol colour for representation of predictor variables
ocol colour for representation of row objects
... additional arguments to be passed.

Value
Plot of the results obtained from mru
22 predict.clmdu

Examples
## Not run:
data(dataExample_mru)
y = as.matrix(dataExample_mru[ , 1])
X = as.matrix(dataExample_mru[ , 2:6])
output = mru(y = y, X = X, S = 2)
plot(output)

## End(Not run)

predict.clmdu The function predict.clmdu makes predictions for a test/validation

set based on a fitted cl restricted multidimensional unfolding model
(clmdu with X)

Description

The function predict.clmdu makes predictions for a test/validation set based on a fitted cl restricted
multidimensional unfolding model (clmdu with X)

Usage

## S3 method for class 'clmdu'

predict(object, newX, newY = NULL, ...)

Arguments

object An clmdu object

newX An N by P matrix with predictor variables for a test/validation set
newY An N by R matrix with response variables for a test/validation set
... additional arguments to be passed.

Value

This function returns an object of the class predclpca with components:

Yhat Predicted values for the test set

devr Estimated prediction deviance for separate responses
devtot Estimated prediction deviance for all responses
predict.clpca 23

Examples
## Not run:
data(dataExample_clpca)
Y = as.matrix(dataExample_clmdu[ , 1:8])
X = as.matrix(dataExample_clmdu[ , 9:13])
newY = as.matrix(dataExample_clmdu[1:20 , 1:8])
newX = as.matrix(dataExample_clmdu[1:20 , 9:13])
# supervised
output = clmdu(Y = Y, X = X, S = 2)
preds = predict(output, newX = newX, newY = newY)

## End(Not run)

predict.clpca The function predict.clpca makes predictions for a test/validation set

based on a fitted clrrr model (clpca with X)

Description

The function predict.clpca makes predictions for a test/validation set based on a fitted clrrr model
(clpca with X)

Usage

## S3 method for class 'clpca'

predict(object, newX, newY = NULL, ...)

Arguments

object An clpca object

newX An N by P matrix with predictor variables for a test/validation set
newY An N by R matrix with response variables for a test/validation set
... additional arguments to be passed.

Value

This function returns an object of the class predclpca with components:

Yhat Predicted values for the test set

devr Estimated prediction deviance for separate responses
devtot Estimated prediction deviance for all responses
24 predict.lmdu

Examples
## Not run:
data(dataExample_clpca)
Y = as.matrix(dataExample_clpca[ , 1:8])
X = as.matrix(dataExample_clpca[ , 9:13])
newY = as.matrix(dataExample_clpca[1:20 , 1:8])
newX = as.matrix(dataExample_clpca[1:20 , 9:13])
# supervised
output = clpca(Y = Y, X = X, S = 2)
preds = predict(output, newX = newX, newY = newY)

## End(Not run)

predict.lmdu The function predict.lmdu makes predictions for a test/validation set

based on a fitted lrmdu model (lmdu with X)

Description
The function predict.lmdu makes predictions for a test/validation set based on a fitted lrmdu model
(lmdu with X)

Usage
## S3 method for class 'lmdu'
predict(object, newX, newY = NULL, ...)

Arguments
object An lmdu object
newX An N by P matrix with predictor variables for a test/validation set
newY An N by R matrix with response variables for a test/validation set
... additional arguments to be passed.

Value
This function returns an object of the class lpca with components:

Yhat Predicted values for the test set

devr Estimated prediction deviance for separate responses
devtot Estimated prediction deviance for all responses
Brier.r Estimated Brier score for separate responses
Brier Estimated Brier score for all responses
predict.lpca 25

Examples
## Not run:
data(dataExample_lpca)
Y = as.matrix(dataExample_lmdu[-c(1:20) , 1:8])
X = as.matrix(dataExample_lmdu[-c(1:20) , 9:13])
newY = as.matrix(dataExample_lmdu[1:20 , 1:8])
newX = as.matrix(dataExample_lmdu[1:20 , 9:13])
# supervised
output = lmdu(Y = Y, X = X, S = 2)
preds = predict(output, newX = newX, newY = newY)

## End(Not run)

predict.lpca The function predict.lpca makes predictions for a test/validation set

based on a fitted lrrr model (lpca with X)

Description
The function predict.lpca makes predictions for a test/validation set based on a fitted lrrr model
(lpca with X)

Usage
## S3 method for class 'lpca'
predict(object, newX, newY = NULL, ...)

Arguments
object An lpca object
newX An N by P matrix with predictor variables for a test/validation set
newY An N by R matrix with response variables for a test/validation set
... additional arguments to be passed.

Value
This function returns an object of the class lpca with components:

Yhat Predicted values for the test set

devr Estimated prediction deviance for separate responses
devtot Estimated prediction deviance for all responses
Brier.r Estimated Brier score for separate responses
Brier Estimated Brier score for all responses
26 predict.mru

Examples

## Not run:
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[-c(1:20) , 1:8])
X = as.matrix(dataExample_lpca[-c(1:20) , 9:13])
newY = as.matrix(dataExample_lpca[1:20 , 1:8])
newX = as.matrix(dataExample_lpca[1:20 , 9:13])
# supervised
output = lpca(Y = Y, X = X, S = 2)
preds = predict(output, newX = newX, newY = newY)

## End(Not run)

predict.mru The function predict.mru makes predictions for a test/validation set

based on a fitted mru model

Description

The function predict.mru makes predictions for a test/validation set based on a fitted mru model

Usage

## S3 method for class 'mru'

predict(object, newX, newG = NULL, ...)

Arguments

object An lmdu object

newX An N by P matrix with predictor variables for a test/validation set
newG An N by R matrix with response variables for a test/validation set
... additional arguments to be passed.

Value

This function returns an object of the class p.mru with components:

Yhat Predicted values for the test set

dev Estimated prediction deviance
summary.clmdu 27

Examples

## Not run:
data(dataExample_lpca)
Y = as.matrix(dataExample_mru[-c(1:20) , 1:8])
X = as.matrix(dataExample_mru[-c(1:20) , 9:13])
newY = as.matrix(dataExample_mru[1:20 , 1:8])
newX = as.matrix(dataExample_mru[1:20 , 9:13])
# supervised
output = mru(Y = Y, X = X, S = 2)
preds = predict(output, newX = newX, newY = newY)

## End(Not run)

summary.clmdu Summarizing Cumulative Logistic MDU models The function sum-

mary.lmdu gives a summary from an object from clmdu()

Description

Summarizing Cumulative Logistic MDU models

The function summary.lmdu gives a summary from an object from clmdu()

Usage

## S3 method for class 'clmdu'

summary(object, ...)

Arguments

object An object resulting from clmdu

... additional arguments to be passed.

Value

Summary of the results obtained from clmdu

28 summary.esm

summary.clpca Summarizing Cumulative Logistic PCA models

Description
The function summary.clpca gives a summary from an object from clpca()

Usage
## S3 method for class 'clpca'
summary(object, ...)

Arguments
object An object resulting from clpca
... additional arguments to be passed.

Value
Summary of the results obtained from clpca

summary.esm Summarizing an Extended Steretype Model

Description
The function summary.esm gives a summary from an object from esm()

Usage
## S3 method for class 'esm'
summary(object, ...)

Arguments
object An object resulting from esm
... additional arguments to be passed.

Value
Summary of the results obtained from esm
summary.lmdu 29

summary.lmdu Summarizing Logistic MDU models

Description
The function summary.lmdu gives a summary from an object from lmdu()

Usage
## S3 method for class 'lmdu'
summary(object, ...)

Arguments
object An object resulting from lmdu
... additional arguments to be passed.

Value
Summary of the results obtained from lmdu

summary.lpca Summarizing Logistic PCA models

Description
The function summary.lpca gives a summary from an object from lpca()

Usage
## S3 method for class 'lpca'
summary(object, ...)

Arguments
object An object resulting from lpca
... additional arguments to be passed.

Value
Summary of the results obtained from lpca
30 twomodedistance

summary.mru Summarizing Multinomial Logistic Unfolding model The function

summary.mru gives a summary from an object from mru()

Description
Summarizing Multinomial Logistic Unfolding model
The function summary.mru gives a summary from an object from mru()

Usage
## S3 method for class 'mru'
summary(object, ...)

Arguments
object An object resulting from mru
... additional arguments to be passed.

Value
Summary of the results obtained from mru

twomodedistance The function twomodedistance computes the two mode (unfolding) dis-
tance

Description
The function twomodedistance computes the two mode (unfolding) distance

Usage
twomodedistance(U, V)

Arguments
U An N times S matrix with coordinates in S dimensional Euclidean space.
V An R times S matrix with coordinates in S dimensional Euclidean space.

Value
D a N by R matrix with Euclidean distances
Index

∗ datasets summary.lpca, 29
dataExample_clmdu, 5 summary.mru, 30
dataExample_clpca, 6
dataExample_lmdu, 6 twomodedistance, 30
dataExample_lpca, 7
dataExample_mru, 8

clmdu, 2
clpca, 4

dataExample_clmdu, 5
dataExample_clpca, 6
dataExample_lmdu, 6
dataExample_lpca, 7
dataExample_mru, 8

esm, 8

fastmbu, 10
fastmru, 11

lmdu, 12
lpca, 14

mru, 16

plot.clmdu, 17
plot.clpca, 18
plot.lmdu, 19
plot.lpca, 20
plot.mru, 21
predict.clmdu, 22
predict.clpca, 23
predict.lmdu, 24
predict.lpca, 25
predict.mru, 26

summary.clmdu, 27
summary.clpca, 28
summary.esm, 28
summary.lmdu, 29

31