Bootstrap procedure for Cumulative Logistic (Restricted) MDU

Description

Bootstrap procedure for Cumulative Logistic (Restricted) MDU

Usage

bootstrap.clmdu(object, Bsamples = 1000, myseed = 1)

Arguments

Value

BBdf Bootstrap estimates of B

BVdf Bootstrap estimates of V

Examples

## Not run: 
data(dataExample_lmdu)
Y = as.matrix(dataExample_clmdu[ , 1:8])
X = as.matrix(dataExample_clmdu[ , 9:13])
output2 = clmdu(Y = Y, X = X, S = 2)
boot.output = bootstrap.lmdu(output2, Bsamples = 100)
plot(boot.output)

## End(Not run)

Bootstrap procedure for Cumulative Logistic (Restricted) PCA

Description

Bootstrap procedure for Cumulative Logistic (Restricted) PCA

Usage

bootstrap.clpca(object, Bsamples = 1000, myseed = 1)

Arguments

Value

BBdf Bootstrap estimates of B

BVdf Bootstrap estimates of V

Examples

## Not run: 
data(dataExample_clpca)
Y<-as.matrix(dataExample_clpca[,5:8])
X<-as.matrix(dataExample_clpca[,1:4])
# supervised
output = clpca(Y = Y, X = X, S = 2)
boot.output = bootstrap.clpca(output, Bsamples = 100)
plot(boot.output)

## End(Not run)

Bootstrap procedure for Logistic (Restricted) MDU

Description

Bootstrap procedure for Logistic (Restricted) MDU

Usage

bootstrap.lmdu(object, Bsamples = 1000, myseed = 1)

Arguments

Value

BBdf Bootstrap estimates of B

BVdf Bootstrap estimates of V

Examples

## Not run: 
data(dataExample_lmdu)
Y = as.matrix(dataExample_lmdu[ , 1:8])
X = as.matrix(dataExample_lmdu[ , 9:13])
output2 = lmdu(Y = Y, X = X, S = 2)
boot.output = bootstrap.lmdu(output2, Bsamples = 100)
plot(boot.output)

## End(Not run)

Bootstrap procedure for Logistic (Restricted) PCA

Description

Bootstrap procedure for Logistic (Restricted) PCA

Usage

bootstrap.lpca(object, Bsamples = 1000, myseed = 1)

Arguments

Value

BBdf Bootstrap estimates of B

BVdf Bootstrap estimates of V

Examples

## Not run: 
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[, 1:8])
X = as.matrix(dataExample_lpca[, 9:13])
# supervised
output = lpca(Y = Y, X = X, S = 2)
boot.output = bootstrap.lpca(output, Bsamples = 100)
plot(boot.output)

## End(Not run)

Bootstrap procedure for Multonimal Canonical Decomposition Model

Description

Bootstrap procedure for Multonimal Canonical Decomposition Model

Usage

bootstrap.mcd(object, Bsamples = 1000)

Arguments

Value

BBdf Bootstrap estimates of B

BVdf Bootstrap estimates of V

Examples

## Not run: 
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[, 1:8])
X = as.matrix(dataExample_lpca[, 9:13])
# supervised
output = mcd1(X, Y, S = 2, ord.z = 2)
#' boot.output = bootstrap.mcd(output, Bsamples = 100)
plot(boot.output)

## End(Not run)

Bootstrap procedure for Multinomial Reduced Rank Model

Description

Bootstrap procedure for Multinomial Reduced Rank Model

Usage

bootstrap.mrrr(object, Bsamples = 1000, myseed = 1)

Arguments

Value

BBdf Bootstrap estimates of B

BVdf Bootstrap estimates of V

Examples

## Not run: 
data(dataExample_mru)
y = as.matrix(dataExample_mru[ , 1])
X = as.matrix(dataExample_mru[ , 2:6])
output = mrrr(y = y, X = X, S = 2)
boot.output = bootstrap.mrrr(output, Bsamples = 100)
plot(boot.output)

## End(Not run)

Bootstrap procedure for Multinomial Restricted Unfolding

Description

Bootstrap procedure for Multinomial Restricted Unfolding

Usage

bootstrap.mru(object, Bsamples = 1000, myseed = 1)

Arguments

Value

BBdf Bootstrap estimates of B

BVdf Bootstrap estimates of V

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)
boot.output = bootstrap.mru(output, Bsamples = 100)
plot(boot.output)

## End(Not run)

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

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)

Cumulative Logistic (Restricted) PCA

Description

Cumulative Logistic (Restricted) PCA

Usage

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

Arguments

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_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)

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.

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.

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.

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.

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.

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.

Diabetes data

Description

Data description

Usage

data(diabetes)

Format

A list of 3 matrices

• X: A 145 x 3 matrix containing observed values on three predictor variables. RW = Relative weight; IR = Insulin Response; SSPG = Steady state plasma glucose.

• G: A 145 x 3 indicator matrix containing the responses on three response classes Overt, Chemical, and Non

• y: A vector of length 145 containing the responses (Overt, Chemical, Non)

References

G. M. Reaven and R. G. Miller (1979) An Attempt to Define the Nature of Chemical Diabetes Using a Multidimensional Analysis. Diabetologia 16, 17-24 D. F. Andrews A. M. Herzberg (1985). Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag Inc.

Dutch Parliamentary Election Study

Description

Data description

Usage

data(dpes)

Format

A list of 3 matrices

• X: A 275 x 5 matrix containing observed values on five predictor variables. E = Euthanasia; ID = Income differences; AS = Asylum Seekers; C = Crime; LR = Left-right scaling

• G: A 275 x 8 indicator matrix containing the responses on eight response classes PvdA, CDA, VVD, D66, GL, CU, LPF, SP.

• y: A vector of length 275 containing the responses (PvdA, CDA, VVD, D66, GL, CU, LPF, SP)

References

Irwin, G., van Holsteyn, J., and den Ridder, J. (2003). Nationaal Kiezersonderzoek, NKO 2002 2003. DANS.

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

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

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,
  C = NULL,
  MAXINNER = 32,
  FCRIT = 0.001,
  MAXITER = 65536,
  DCRIT = 1e-06,
  error.check = FALSE
)

Arguments

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

Kieskompas data

Description

Data description

Usage

kieskompas

Format

A list of 7 matrices

• G: an indicator matrix of dimension 25001 by 21 indicating the vote intention of the participants

• Xs: responses to the 30 propositions of the participants

• Xs2: responses to the 30 propositions of the participants, with NA's coded as neutral (3)

• Xb: background variables Age, Gender (male = 0, female = 1), and Education (theoretical = 0, practical = 0)

• Z: position of the 18 political parties on the 30 propositions

• stellingen: the exact Dutch wording of the propositions

• grouping: the 30 propositions can be grouped in themes (mk, bb, ef, sc, ai, bo, et, oz). mk: Environment & Climate; bb: Foreign Policy; ef: Economy & Finance; sc: Social Affairs & Culture ai: Asylum & Immigration; cc: Construction & Environment; et: Ethics; oz: Education & Healthcare

References

van Lindert, J., Meijer, S., Etienne, T., van der Steen, S., Kutiyski, Y., Moreda Laguna, O., Brousianou, A., Blanken, W., & Krouwel, A. (2023). Het Kieskompas voor de Nederlandse Tweede Kamerverkiezingen van 2023 [dataset]. Kieskompas, Amsterdam, the Netherlands.

Liver

Description

Data description

Usage

data(liver)

Format

A list of 3 matrices

• X: A 218 x 3 matrix containing observed values on three liver function test (predictor variables). ASpartate aminotransferase (AS), ALanine aminotransferase (AL), and Glutamate Dehydrogenase (GD)

• G: A 218 x 4 indicator matrix containing the responses on Acute Viral Hepatitis (AVH), Persistent Chronic Hepatitis (PCH), Aggressive Chronic Hepatitis (ACH), Post-Necrotic Cirrhosis (PNC).

• y: A vector of length 218 containing the responses (AVH, PCH, )

References

Plomteux, G. (1980). Multivariate analysis of an enzymic profile for the differential diagnosis of viral hepatitis. Clinical Chemistry, 26(13), 1897–1899.

Logistic (Restricted) MDU

Description

This function runs: logistic multidimensional unfolding (if X = NULL) logistic restricted multidimensional unfolding (if X != NULL)

Usage

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

Arguments

Value

deviance

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)

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,
  start = NULL,
  dim.indic = NULL,
  eq = FALSE,
  lambda = FALSE,
  maxiter = 65536,
  dcrit = 1e-06
)

Arguments

Value

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

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)

Helper function for the plot functions

Description

Helper function for the plot functions to add variable labels to the predictor and response variable axes

Usage

make.df.for.varlabels(BV, margins, names, P, R)

Arguments

Value

df with information for the placement of the variable labels

Helper function for the plot functions

Description

Helper function for the plot functions to add variable markers and labels to the predictor variable axes

Usage

make.dfs.for.X(Xo, P, B, xnames, mx, sdx)

Arguments

Value

output with information for the placement of variable markers and labels

Multinomial Canonical Decomposition Model for Multivariate Binary Data

Description

The function mcd1 fits the multinomial canonical decomposition model to multivariate binary responses i.e. a double constrained reduced rank multinomial logistic model

Usage

mcd1(
  X,
  Y,
  S = 2,
  Z = NULL,
  W = NULL,
  ord.z = 1,
  ord.m = R,
  trace = FALSE,
  maxiter = 65536,
  dcrit = 1e-06
)

Arguments

Value

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

Examples

## Not run: 
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[ , 1:5])
X = as.matrix(dataExample_lpca[ , 9:13])
#unsupervised
output = mcd1(X, Y, S = 2, ord.z = 2)

## End(Not run)

Multinomial Canonical Decomposition Model for a multinomial outcome

Description

The function mcd2 fits the multinomial canonical decomposition model to a multinomial outcome i.e. a double constrained reduced rank multinomial logistic model

Usage

mcd2(X, G, Z, S = 2, trace = TRUE, maxiter = 65536, dcrit = 1e-06)

Arguments

Value

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

Examples

## Not run: 
data(dataExample_lpca)
Y = as.matrix(dataExample_lpca[ , 1:5])
X = as.matrix(dataExample_lpca[ , 9:13])
#unsupervised
output = mcd1(X, Y, S = 2, ord.z = 2)

## End(Not run)

Multinomial Logistic Regression

Description

The function mlr performs multinomial logistic regression for a nominal response variable and a set of predictor variables. It uses an MM algorithm

Usage

mlr(y, X, base = "largest", maxiter = 65536, dcrit = 1e-06)

Arguments

Value

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

A matrix with regression coefficients

iter number of iterations

deviance value of the deviance at convergence

Examples

## Not run: 
data(dataExample_mru)
y = as.matrix(dataExample_mru[ , 1])
X = as.matrix(dataExample_mru[ , 2:6])
output = mlr(y = y, X = X, base = 1)

## End(Not run)

Multinomial Reduced Rank Regression

Description

The function mrrr performs multinomial reduced rank regression for a nominal response variable and a set of predictor variables.

Usage

mrrr(y, X, S = 2, trace = FALSE, maxiter = 65536, dcrit = 1e-06, start = NULL)

Arguments

Value

Xoriginal Matrix X from input

X Scaled X matrix

G class indicator matrix

ynames class names of response classes

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

V Class coordinate matrix

iters number of iterations

deviance value of the deviance at convergence

Examples

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

## End(Not run)

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, Z = NULL, S = 2, start = "da", maxiter = 65536, dcrit = 1e-06)

Arguments

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

V Class coordinate matrix

iters number of iterations

deviance value of the deviance at convergence

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)

## End(Not run)

Netherlands Study for Depression and Anxiety

Description

Synthetic data generated on the basis of characteristics of the data described in Spinhoven e.a. (2009).

Usage

data(nesda)

Format

A list of 2 matrices

• X: A 845 x 8 matrix containing observed values on eight predictor variables. Gender (female = 1), Age, Education, Neuroticism, Extraversion, Openess, Agreeableness, Conscientiousness

• Y: A 845 x 5 matrix Indicating whether a participants suffers from a mental illness D (dysthymia), M (Major depressive disorder), G (Generalized Anxiety disorder), S (Social Phobia), P (Panic disorder)

References

Penninx BWJH, Beekman ATF, Smit JH, Zitman FG, Nolen WA, Spinhoven P, et al. The Netherlands Study of Depression and Anxiety (NESDA): rationale, objectives, and methods. International Journal of Methods in Psychiatric Research 2008;17:121–40.

Spinhoven, P., De Rooij, M., Heiser, W.J., Penninx, B. and Smit, J. (2009). The Role of Personality in Comorbidity among Anxiety and Depressive Disorders in Primary Care and Specialty Care: A Cross-Sectional Analysis. General Hospital Psychiatry, 31, 470-477.

This function compares the predictive performance of several models fitted on the same data

Description

The number of bootstraps should be the same for each model Ideally, the seed used in bootstrapping should also be the same

Usage

oos.comparison(objectlist, xlabel = "Model")

Arguments

Value

plot A boxplot with prediction errors for each model

pe A data frame with average prediction error for each bootstrap

fit A matrix with prediction error statistics for each model

Examples

## Not run: 
data(dataExample_mru)
y = as.matrix(dataExample_mru[ , 1])
X = as.matrix(dataExample_mru[ , 2:6])
output2 = mrrr(y = y, X = X, S = 2)
b2 = bootstrap.mrrr(output2)
output3 = mrrr(y = y, X = X, S = 3)
b3 = bootstrap.mrrr(output3)
myobjects = list(b2, b3)
comparison = oos.comparison(myobjects)
comparison$plot
comparison$fit

## End(Not run)

Plot an object obtained using one of the bootstrap functions

Description

Plot an object obtained using one of the bootstrap functions

Arguments

Value

Plot of the results obtained from bootstrap

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 = "darkblue",
  ocol = "grey",
  markersize = 2.5,
  labelsize = 3,
  ...
)

Arguments

Value

Plot of the results obtained from clmdu

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)

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 = "darkblue",
  ocol = "grey",
  markersize = 2.5,
  labelsize = 3,
  ...
)

Arguments

Value

Plot of the results obtained from clpca

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)

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 = "darkblue",
  ocol = "grey",
  markersize = 2.5,
  labelsize = 3,
  ...
)

Arguments

Value

Plot of the results obtained from lmdu

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)

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",
  pmarkers = seq(0.1, 0.9, by = 0.1),
  ycol = "darkgreen",
  xcol = "darkblue",
  ocol = "grey",
  ...
)

Arguments

Value

Plot of the results obtained from lpca

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)

Plots a Multinomial Reduced Rank Model

Description

Plots a Multinomial Reduced Rank Model

Usage

## S3 method for class 'mrrr'
plot(
  x,
  dims = c(1, 2),
  ycol = "darkgreen",
  xcol = "darkblue",
  ocol = "grey",
  markersize = 2.5,
  labelsize = 3,
  ...
)

Arguments

Value

Plot of the results obtained from mrrr

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)

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 = "darkblue",
  ocol = "grey",
  markersize = 2.5,
  labelsize = 3,
  ...
)

Arguments

Value

Plot of the results obtained from mru

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)

Plotting function for object of class trioscale

Description

Plotting function for object of class trioscale

Usage

## S3 method for class 'trioscale'
plot(
  x,
  ycol = "darkgreen",
  xcol = "darkblue",
  ocol = "grey",
  markersize = 2.5,
  labelsize = 3,
  classlabels = NULL,
  s1 = 2.5,
  s2 = 1.05,
  s3 = 1.15,
  ...
)

Arguments

Value

This function returns an plot

Examples

## Not run: 
out = trioscale(data)
plot.trioscale(out)

## End(Not run)

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

Value

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

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)

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

Value

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

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)

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

Value

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

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)

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

Value

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

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)

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

Description

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

Usage

## S3 method for class 'mlr'
predict(object, newX, ...)

Arguments

Value

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

Examples

## Not run: 
data(dataExample_mru)
y = as.matrix(dataExample_mru[ , 1])
X = as.matrix(dataExample_mru[ , 2:6])
output = mlr(y = y, X = X, base = 1)
preds = predict(output, newX = X[1:4, ])

## End(Not run)

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

Description

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

Usage

## S3 method for class 'mrrr'
predict(object, newX, ...)

Arguments

Value

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

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])
output = mrrr(Y = Y, X = X, S = 2)
preds = predict(output, newX = newX)

## End(Not run)

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

Value

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

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)

Two procedures for procrustes analysis

Description

procrustes1 does orthogonal procrustes analysis procrustes2 does similarity procrustes analysis

Usage

procrustes1(V1, V2)

Arguments

Value

either a rotation matrix (procrustes1) or a list with a rotation matrix (R), a stretching factor (s) and a translation (t) (procrustes2)

Helper function for pre-processing the predictors

Description

Continuous predictor variables are standardized - mean zero, standard deviation one. Categorical predictor variables are represented as dummy (0, 1) variables.

Usage

procx(X)

Arguments

Value

Xoriginal

dichotomous indicator which predictor variables are dichotomous

X standardized matrix

mx averages of original variables

sdx standard deviation of original variables

Function for reading the drug consumption data from the UCI repository

Description

Function for reading the drug consumption data from the UCI repository

Usage

read_drugdata()

Value

X first coordinate matrix

Y matrix with response variables (Alcohol,Am,Amyl,Be,Caff,Ca,Choc,Co,Crack,Ex,Heroin,Ke,Le,LSD,Me,Mu,Ni,Semer,VSA)

idx indicator which response variables have a probability between 0.1 and 0.9

References

Fehrman, E., Muhammad, A. K., Mirkes, E. M., Egan, V., & Gorban, A. N. (2017). The five factor model of personality and evaluation of drug consumption risk. In Data science: innovative developments in data analysis and clustering (pp. 231-242). Springer International Publishing.

Function to read in the ISSP data It requires the file ZA7650_v1-0-0.sav to be on your computer this file can be obtained from /www.gesis.org/en/issp/modules/issp-modules-by-topic/environment/2020 ZA7650 Data file Version 1.0.0, https://doi.org/10.4232/1.13921.

Description

Function to read in the ISSP data It requires the file ZA7650_v1-0-0.sav to be on your computer this file can be obtained from /www.gesis.org/en/issp/modules/issp-modules-by-topic/environment/2020 ZA7650 Data file Version 1.0.0, https://doi.org/10.4232/1.13921.

Usage

read_isspdata_peb(path)

Arguments

Value

X matrix containing the predictor variables

Y matrix with response variables

References

ISSP Research Group (2022). International social survey programme: Environment IV - issp 2020. GESIS, Cologne.

Summarizing Cumulative Logistic MDU models The function summary.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

Value

Summary of the results obtained from clmdu

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

Value

Summary of the results obtained from clpca

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

Value

Summary of the results obtained from lmdu

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

Value

Summary of the results obtained from lpca

Summarizing an Multinomial Canonical Decomposition Model

Description

The function summary.mcd1 gives a summary from an object from mcd()

Usage

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

Arguments

Value

Summary of the results obtained from mcd

Summarizing Multinomial Logistic Regression Model

Description

The function summary.mlr gives a summary from an object from mlr()

Usage

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

Arguments

Value

Summary of the results obtained from mlr

Summarizing Multinomial Reduced Rank Model

Description

The function summary.mrrr gives a summary from an object from mrrr()

Usage

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

Arguments

Value

Summary of the results obtained from mrrr

Summarizing Multinomial Restricted Unfolding Model The function summary.mru gives a summary from an object from mru()

Description

Summarizing Multinomial Restricted Unfolding Model

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

Usage

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

Arguments

Value

Summary of the results obtained from mru

Summarizing TrioScale

Description

The function summary.trioscale gives a summary from the MLR in trioscale

Usage

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

Arguments

Value

Summary of the results obtained from trioscale

Theme_lmda

Description

produces the logistic MDA theme for ggplots

Usage

theme_lmda()

Function for TRIOSCALE

Description

Function for TRIOSCALE

Usage

trioscale(y, X)

Arguments

Value

This function returns an object of class trioscale

Examples

## Not run: 
data(diabetes)
output = trioscale(y = diabetes$y, X = diabetes$X)
plot(output)

## End(Not run)

The function twomodedistance computes the two mode (unfolding) distance

Description

The function twomodedistance computes the two mode (unfolding) distance

Usage

twomodedistance(U, V)

Arguments

Value

D a N by R matrix with Euclidean distances