The Beta-Binomial Distribution

Description

Density and random generation for the beta-binomial distribution.

Usage

dBB(m,p,phi)
rBB(k,m,p,phi)

Arguments

Details

The beta-binomial distribution consists of a finite sum of Bernoulli dependent variables whose probability parameter is random and follows a beta distribution. Assume that we have y_j a set of variables, j=1,...,m, with m integer, that conditioned on a random variable u, are independent and follow a Bernoulli distribution with probability parameter u. On the other hand, the random variable u follows a beta distribution with parameter p/phi and (1-p)/phi. Namely,

y_j \sim Ber(u), u \sim Beta(p/phi,(1-p)/phi),

where 0<p<1 and phi>0. The first and second order marginal moments of this distribution are defined as

E[y_j]=p, Var[y_j]=p(1-p),

and correlation between observations is defined as

Corr[y_j,y_k]=phi/(1+phi),

where j,k=1,...,m are different. Consequently, phi can be considered as a dispersion parameter.

If we sum up all the variables we will define a new variable which follows a new distribution that is called beta-binomial distribution, and it is defined as follows. The variable y follows a beta-binomial distribution with parameters m, p and phi if

y|u \sim Bin(m,u), u\sim Beta(p/phi,(1-p)/phi).

Value

dBB gives the density of a beta-binomial distribution with the defined m, p and phi parameters.

rBB generates k random observations based on a beta-binomial distribution with the defined m, p and phi parameters.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2006): Analysis of short-form-36 (SF-36): The beta-binomial distribution approach, Statistics in Medicine, 26, 1318-1342

See Also

The rbeta and rbinom functions of package stats.

Examples

set.seed(12)
# We define
m <- 10     
p <- 0.4    
phi <- 1.8  

# We perform k beta-binomial simulations for those parameters.
k <- 100
bb <- rBB(k,m,p,phi)
bb
dd <- dBB(m,p,phi)

# We are going to plot the histogram of the created variable,
# and using dBB() function we are going to fit the distribution:
hist(bb,col="grey",breaks=seq(-0.5,m+0.5,1),probability=TRUE,
  main="Histogram",xlab="Beta-binomial random variable")
lines(seq(0,m),dd,col="red",lwd=4)

Estimation of the parameters of a beta-binomial distribution

Description

This function performs the estimation of the parameters of a beta-binomial distribution for the given data and maximum score number in each observation.

There are two different approaches available for performing the estimation of the parameters: (i) Method of moments, and, (ii) maximum likelihood approach.

Usage

BBest(y,m,method="MM")

Arguments

Details

BBest function performs estimations in the parameters of a beta-binomial distribution for the given data. The estimations can be performed using two different approaches, the methods of moments and the maximum likelihood estimation approach.

The density function of a given observation y that follows a beta-binomial distribution with paramters m, p and phi is defined as

f(y)=[\Gamma(1/phi)*\Gamma(p/phi+m)*\Gamma((1-p)/phi+m)]/[\Gamma(1/phi+m)*\Gamma(p/phi)*\Gamma((1-p)/phi)].

The first and second order moments are defined as

E[y]=mp

Var[y]=mp(1-p)[1+(n-1)phi/(1+phi)].

Hence, if y=(y_1,...,y_n) is the given data, we can conclude the method of moments from the previous as

p=E/m,

phi=[V-mp(1-p)]/[mp(1-p)m-V],

where E is the sample mean and V is the sample variance.

On the other hand, the maximum likelihood estimation of both parameters consits of solving the derivative of the log-likelihood defined by the density function with respect to each parameter and equaling them to zero. An iterative algorithm is needed for both parameter estimation as the score equations the parameters depend each other.

The variance of the estimation of the probability parameter of the beta-binomial distribution for the given data set is computed by the inverse of the Fisher information, i.e., the inverse of the negative second derivate of the log-likelihodd remplacing p by its estimation.

Value

BBest returns an object of class "BBest".

The function summary (i.e., summary.BBest) can be used to obtain or print a summary of the results.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2006): Analysis of short-form-36 (SF-36): The beta-binomial distribution approach, Statistics in Medicine, 26, 1318-1342

Examples

# We simulate 1000 observations of a beta-binomial distribution
# for the fixed paramters.
m <- 10
k <- 1000
p <- 0.7
phi <- 1.6

set.seed(5)
y <- rBB(k,m,p,phi)

# Performing the estimation of the parameters

# Method of moments:
MM <- BBest(y,m)
MM

# Maximum likelihood approach
MLE <- BBest(y,m,method="MLE")
MLE

Beta-binomial mixed-effects model

Description

BBmm function performs beta-binomial mixed-effects models, i.e., it allows the inclusion of gaussian random effects in the linear predictor of a logistic beta-binomial regression model.

The structure of the random part of the model can be expecified by two different ways: (i) determining the random.formula argument, or (ii) especifying the model matrix of the random effects, Z, and determining the number of random effects in each random component, nRandComp.

For the estimation of the parameters the joint log-likelihood can be optimized by means of two different approaches: (i) BB-Delta, specific Delta algorithm developed for beta-binomial mixed-effect regression models, and (ii) usual Newton-Raphson method.

Usage

BBmm(fixed.formula,X,y,random.formula,Z=NULL,nRandComp=NULL,m,data,
      method="BB-Delta",maxiter=50,show=FALSE,nDim=1,tolerance=10^(-6))

Arguments

Details

BBmm function performs beta-binomial mixed-effects regression models. It extends the beta-binomial regression model including gaussian random effects in the linear predictor. The model is defined as, conditional on some gaussian random effects u, the response variable y follows a beta-binomial distribution with parameters m, p and \phi,

y|u \sim BB(m,p,\phi), \text{and } u \sim N(0,D)

where the regression model is defined as,

\log(p/(1-p))=X*\beta+Z*u

and D is determined by some dispersion parameters icluded in the parameter vector \theta.

The estimation of the regression parameters \beta and the prediction of the random effects u is done via the extended likelihood, where the marginal likelihood is approximated to the h-likelihood by a first order Laplace approximation,

h=f(y|\beta,u,\theta)+f(u|\theta).

The previous formula do not have a closed form and numerical methods are needed for the estimation precedure. Two approches are available in the function in order to perform the fixed and random effects estimation: (i) A special case of the Delta algorithm developed for beta-binomial mixed-effects model estimations, and (ii) the general Newton-Raphson algorithm available in R-package rootSolve.

On the other hand, the estimation of dispersion parameters by the h-likelihood can be bias due to the previous estimation of the regression parameters. Consequenlty, a penalization of the h-likelihood must be performed to get unbiased estimations of the dispersion parameters. Lee and Nelder (1996) proposed the adjusted profile h-likelihood, where the following penalization is applied,

h(\theta)=h+(1/2)*\log[det(2\pi H^{-1})],

being H the Hessian matrix of the model, replacing the terms \beta and u involved in the previous formula with their estimates.

The method iterates between both estimation processes until convergence is reached based on the pre-established tolerance of the linear predictor.

Value

BBmm returns an object of class "BBmm".

The function summary (i.e., summary.BBmm) can be used to obtain or print a summary of the results..

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Najera-Zuloaga, J.; Lee, D.-J.; Esteban, C. & Arostegui, I. (2023): Multidimensional beta-binomial regression model: a joint analysis of patient-reported outcomes. Statistical Modelling, 0(0).

Najera-Zuloaga J., Lee D.-J. & Arostegui, I. (2019): A beta-binomial mixed effects model approach for analysing longitudinal discrete and bounded outcomes. Biometrical Journal, 61(3), 600-615.

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2018): Comparison of beta-binomial regression model approaches to analyze health related quality of life data. Statistical Methods in Medical Research, 27(10), 2989-3009.

Lee Y. & Nelder J. A. (1996): Hierarchical generalized linear models, Journal of the Royal Statistical Society. Series B, 58, 619-678

See Also

The multiroot and uniroot functions of the R-package rootSolve for the general Newton-Raphson algorithm.

Examples

set.seed(7)

# Defining the parameters
k <- 100
m <- 10
phi <- 0.5
beta <- c(1.5,-1.1)
sigma <- 0.5

# Simulating the covariate and random effects
x <- runif(k,0,10)
X <- model.matrix(~x)
z <- as.factor(rBI(k,4,0.5,2))
Z <- model.matrix(~z-1)
u <- rnorm(5,0,sigma)


# The linear predictor and simulated response variable
eta <- beta[1]+beta[2]*x+crossprod(t(Z),u)
p <- 1/(1+exp(-eta))
y <- rBB(k,m,p,phi)
dat <- data.frame(cbind(y,x,z))
dat$z <- as.factor(dat$z)

# Apply the model
model <- BBmm(fixed.formula = y~x,random.formula = ~z,m=m,data=dat)
model

#---------#

# Multidimensional regression with 2 dimensions

set.seed(5)
nId <- 25

# Simulation

m <- 10
beta1 <- c(1,-0.5)
beta2 <- c(-1,0.5)
beta <- cbind(beta1,beta2)

x1 <- rnorm(nId, 1,2)
X1 <- model.matrix(~x1)
x2 <- rnorm(nId, -1,1)
X2 <- model.matrix(~x2)

sigma <- 0.6
u <- rnorm(nId,0,sigma)

eta1 <- beta1[1]+x1*beta1[2]+u
p1 <- 1/(1+exp(-eta1))
eta2 <- beta2[1]+x2*beta2[2]+u
p2 <- 1/(1+exp(-eta2))

phi1 <- 0.3
phi2 <- 1
phi <- c(phi1,phi2)

y1 <- rBB(nId,m,p1,phi1)
y2 <- rBB(nId,m,p2,phi2)
y <- c(y1,y2)

# Define matrices

X <- Matrix::bdiag(X1,X2)
X <- as.matrix(X)
Z. <- diag(nId)
Z <- kronecker(rbind(1,1),Z.)

# Fit the model

Model.multi <- BBmm(X=X,y=y,Z=Z,nRandComp = nId,m=m,nDim=2)
Model.multi

Fit a beta-binomial logistic regression model

Description

BBreg function fits a beta-binomial logistic regression model, i.e., it links the probability parameter of a beta-binomial distribution with the given covariates by means of a logistic link function. The estimation of the parameters in the model is done via maximum likelihood estimation.

Usage

  BBreg(formula,m,data,maxiter=100)

Arguments

Details

There are two different ways of defining a regression model based on the beta-binomial distribution: (i) the marginal regression approach, (ii) hierarchical generalized linear model approach. Najera-Zuloaga et al. (2017) proved that the first approach is more adequate when the interest consists of the interpretation of the regression coefficients. Consequenlty, this function is based on the first approach, i.e., the marginal regression approach.

Once the marginal density function of the beta-binomial distribution is explicity calculated, we connect the probability parameter with the given covariates by means of a logistic link function:

logit(p)=log(p/(1-p))=X*beta

where X a model matrix composed by the given covariates and beta are the regression coefficients of the model.

Remplacing the previous linear predictor in the marginal density function of the beta-binomial distribution, we can derive maximum likelihood estimations of both regression and dispersion parameters. Forcina and Franconi (1988) presented an estimation algorithm based on the Newton-Raphson approach. This function performs the estimation of the parameters following the presented methodology.

Value

BBreg returns an object of class "BBreg".

The function summary (i.e., summary.BBreg) can be used to obtain or print a summary of the results.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Forcina A. & Franconi L. (1988): Regression analysis with Beta-Binomial distribution, Revista di Statistica Applicata, 21, 7-12

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2017): Comparison of beta-binomial regression model approaches to analyze health related quality of life data, Statistical Methods in Medical Research, DOI: 10.1177/0962280217690413

Examples

# We simulate a covariate, fix the paramters of the beta-binomial 
# distribution and simulate a response variable.

# Then we apply the model, and try to get the same values.
set.seed(18)
k <- 1000
m <- 10
x <- rnorm(k,5,3)

beta <- c(-10,2)
p <- 1/(1+exp(-(beta[1]+beta[2]*x)))
phi <- 1.2

y <- rBB(k,m,p,phi)

model <- BBreg(y~x,m)
model

The Binomial distribution with optional Dispersion Parameter

Description

Density and random generation for the binomial distribution with optional dispersion parameter.

Usage

dBI(m,p,phi)
rBI(k,m,p,phi)

Arguments

Details

The binomial distribution belongs to the exponential family of distributions. Consequenlty, although the usual binomial distribution only consists of two paramters, an additional dispersion parameter can be included. The inclusion of a dispersion parameter softens the relationship between the expectation and variance that the binomial distribution keeps, i.e. the model allows overdispersion to be included,

E[y]=mp, Var[y]=phi*mp(1-p).

The density function of the binomial distribution with dispersion parameter is based on the exponential family approach and it is defined as

f(y)=exp\{[y*log(p/(1-p))+m*log(1-p)]/phi+c(y,phi)\},

where c() is a function that it is approximated with the deviance of the model by quadratic approximations of the log-likelihood function.

Value

dBI gives the density of the binomial distribution for those m, p and phi parameters.

rBI generates k random observations based on a binomial distribution for those m, p and phi parameters.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Pawitan Y. (2001): In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford University Press

See Also

The rbinom functions of package stats. This function performs simulations based on a binomial distribution without dispersion parameter.

Examples

k <- 1000
m <- 10
p <- 0.765
phi <- 4.35

#simulating
y <- rBI(k,m,p,phi)
y

#density function
d <- dBI(m,p,phi)
d

#plot the simulated variable and fit the density
hist(y,col="lightgrey")
lines(seq(0,m),k*d,col="blue",lwd=2)

Estimation of the parameters of a binomial distribution with optional dispersion parameter.

Description

BIest function estimates the probability parameter of a binomial distribution for the given data and number of trials. It is possible to include a dispersion parameter in the binomial distribution and get the estimation by the method of moments or maximum quasi-likelihood approach. This function also returns the standard deviation of the estimated probability parameter and the upper and lower bounds of the 95% confidence interval.

Usage

BIest(y,m,disp=FALSE,method="MM")

Arguments

Details

This function performs the estimation of the parameters involved in a binomial distribution for a given data.

The estimation of the probability parameter is done by either maximum likelihood approach or method of moments due to the fact that both approaches give the same estimation,

p=sum(y)/(m*n),

where m is the number of trials and n is the number of observations.

If the dispersion parameter is included in the model, BIest function performs its estimation by the method of moments or maximum quasi-likelihood methodology. The method of moments is based on the variance equation of a binomial distribution with dispersion parameter

Var[y]=phi*mp(1-p).

The maximum quasi-likelihood approach is based on the quadratic approximation of the log-likelihood function of a binomial distribution with dispersion parameter, where the unknown term involving phi is estimated with the deviance of the model.

The standard deviation of the estimated probability parameter is calculated by the Fisher information, i.e., the negative of the second derivative of the log-likelihood (log-quasi-likelihood) function.

Value

BIest returns an object of class "BIest".

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Pawitan Y. (2001): In All Likelihood: Statistical Modelling and Inference Using Likelihood, Oxford University Press

See Also

The rBI and dBI functions of package HRQoL. The functions perform simulations and estimate the density of a binomial distribution with optional dispersion parameter for a given set of parameters.

Examples

set.seed(9)
# We simulate the binomial data with some fixed parameters and
# then try to reach the same estimations.
m <- 10   
k <- 100
p <- 0.654
y <- rBI(k,m,p) #Simulations

# without dispersion parameter
BIest(y,m)

# with dispersion parameter
# estimation by method of moments.
BIest(y,m,disp=TRUE,method="MM")
# estimation by maximum quasi-likelihood.
BIest(y,m,disp=TRUE,method="MLE")

Eating Disorders patient-reported outcome data.

Description

A prospective study of patients diagnosed with an eating disorder who were followed up for two years in the Eating Disorders Outpatient Clinic of the Psychiatric Service at Galdakao Hospital in Bizkaia, Basque Country.

Usage

data(EDpro)

Format

The EDpro data frame has 525 rows and 18 columns. The variables included in the data frame are the following:

-id: Patient identifier.

-visit: Visit number.

-bmi: Body Mass Index.

age: Age.

-duration: Duration of the illness in years.

-diagnosis: Diagnosis of the eating disorder (1: Anorexia Nervosa, 2: Bulimic Anorexia, 3: Bulimia Nervosa/Binge Eating).

-severity: Severity of the disease, using the Eating Attitudes Test (1: Mild, 2: Moderate, 3: Severe).

-anxiety: Level of anxiety using the Hospital Anxiety and Depression Scale (0: No, 1: Yes).

-depression: Level of depression using the Hospital Anxiety and Depression Scale (0: No, 1: Yes).

-pf: Physical Function dimension measured by the SF-36 questionnaire.

-rp: Role Physical dimension measured by the SF-36 questionnaire.

-bp: Body Pain dimension measured by the SF-36 questionnaire.

-gh: General Health dimension measured by the SF-36 questionnaire.

-vt: Vitality dimension measured by the SF-36 questionnaire.

-sf: Social Functioning dimension measured by the SF-36 questionnaire.

-re: Role Emotional dimension measured by the SF-36 questionnaire.

-mh: Mental Health dimension measured by the SF-36 questionnaire.

-time: Years elapsed since the beginning of the study.

Details

All consecutive patients seen at the Eating Disorders Outpatient Clinic of the Psychiatric Service at Galdakao Hospital between October 1996 and October 1997 were eligible for the study. Patients were included if they were ambulatory; had received a diagnosis of anorexia nervosa or bulimia nervosa based on criteria in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV); and were between the ages of 14 and 65 years.

Throughout the 2-year study period, each of the 193 patient received a psychopharmacologic and psychotherapeutic treatment program consisting of cognitive-behavioural treatment; nutritional orientation and counselling; psychoeducation; motivationaltherapy; social skills training; and therapy to modify distorted perception of body image. These interventions were adjusted to each patient's needs by a multidisciplinary team.

At most, 3 measurements were obtained from each of the 193 patients: at baseline, at one year, and at two years from the start of the study. Clinical measurements and Helath-Related Quality of Life (HQRoL) data were recorded at each visit using the Short Form-36 (SF-36) and the Hospital Anxiety and Depression (HAD) Scale helath questionnaires.

More information about the study can be found in Padierna et al. (2002) and Arostegui et al. (2007).

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2007): Analysis of short-form-36 (SF-36): The beta-binomial distribution approach, Statistics in Medicine, 26, 1318-1342.

Padierna A., Quintana J.M., Arostegui I., Gonzalez N. & Horcajo M.J.(2002): Changes in health related quality of life among patients treated for eating disorders, Quality of Life Research, 11, 545-552.

Spider plot of the dimensions of the Short Form-36 Health Survey

Description

This function creates a spider plot with the 8 health realted quality of life dimensions provided by the Short Form-36 Health Survey.

Usage

HRQoLplot(data,legend=FALSE,title="Short Form-36 Health Survey",
  dimlabel.cex=NULL,legend.cex=1,linewidth=3,title.cex=1,lty=1)

Arguments

Details

The Short Form-36 Health Survey is a commonly used technique to measure the Health Related Quality of Life (HRQoL) in chronich diseases. It was developed within the Medical Outcomes Study (Ware et al. (1993)). It measures generic HRQoL concepts and provides an objective way to measure HRQoL from the patients point of view by scoring standardized responses to standardized questions. The validity and reability of this instrument has been broadly tested (Stansfeld et al. (1997)). The SF-36 has 36 items, with different answer options. It was constructed to respresent eight health dimensions, which are physical functioning (PF), role physical (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH). Each item is assigned to a unique helath dimension. Each of the multi-item dimensions contains two to ten items. The first four dimensions are mainly physical, whereas the last four measure mental aspects of HRQoL. The resulting raw scores are tipically transformed to standardized scale scores from 0 to 100, where a higher score indicates a better health status.

Arostegui et al. (2013) proposed a recoding methodology for the Short Form-36 Health Survey (SF-36) dimensions in order to apply a beta-binomial distribution. The HRQoLplot function plots the SF-36 dimensions scores in a spider plot. Each axis of the plot refers to an expecific SF-36 dimension. Hence, the order of the dimensions in the data frame object of the function has been stablished as it has been explained in Arguments section. Each observation of the data frame, the value of each observation in all the dimensions, is drawn with a line of a different color in the plot. The plot shows the name of each dimension and the maximum number of scores each dimension can obtain in each axis of the plot.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

This function depends on the function radarchart of the package fmsb created by Minato Nakazawa.

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2013): On the recoding of continuous and bounded indexes to a binomial form: an application to quality-of-life scores, Journal of Applied Statistics, 40, 563-583

See Also

As it is said in the author section, the function depends on the function radarchart of the package fmsb

Examples

set.seed(5)
# We insert the columns in the order that has been determined:
n <- c(20,4,9,20,20,8,3,13)
k=3
p=runif(8,0,1)
phi <- runif(8,1,3)
dat <- data.frame(
  PF=rBB(k,n[1],p[1],phi[1]),
  RP=rBB(k,n[2],p[2],phi[2]),
  BP=rBB(k,n[3],p[3],phi[3]),
  GH=rBB(k,n[4],p[4],phi[4]),
  VT=rBB(k,n[5],p[5],phi[5]),
  SF=rBB(k,n[6],p[6],phi[6]),
  RE=rBB(k,n[7],p[7],phi[7]),
  MH=rBB(k,n[8],p[8],phi[8]))

rownames(dat) <- c("ID1", "ID2", "ID3")
HRQoLplot(dat,TRUE)

Short Form-36 Health Survey recode

Description

The SF36rec function recodes to a binomial form the 0-100 original standardized scores of the dimensions provided by the Short Form-36 Health Survey (SF-36) based on Arostegui et al. (2013).

Usage

SF36rec(x,k)

Arguments

Details

The Short Form-36 Health Survey is a commonly used technique to measure the Health Related Quality of Life (HRQoL) in chronich diseases. It was developed within the Medical Outcomes Study (Ware et al. (1993)). It measures generic HRQoL concepts and provides an objective way to measure HRQoL from the patients point of view by scoring standardized responses to standardized questions. The validity and reability of this instrument has been broadly tested (Stansfeld et al. (1997)). The SF-36 has 36 items, with different answer options. It was constructed to respresent eight health dimensions, which are physical functioning (PF), role physical (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role emotional (RE) and mental health (MH). Each item is assigned to a unique helath dimension. Each of the multi-item dimensions contains two to ten items. The first four dimensions are mainly physical, whereas the last four measure mental aspects of HRQoL. The resulting raw scores are tipically transformed to standardized scale scores from 0 to 100, where a higher score indicates a better health status.

Arostegui et al. (2013) proposed a recoding methodology of the SF-36 standardized scores to a binomial form in order to apply the beta-binomial distribution. The method was mainly based on the possible number of values each dimension can obtain, which comes from the number of items related with the construction of each dimension.

The SF36rec function performes the cited recoding methodology to the specified SF-36 dimension. It has two inputs. The first one is the dimension that will be recoded, and the second one identifies which SF-36 dimension is.

Value

The score values of the recoded dimension.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2013): On the recoding of continuous and bounded indexes to a binomial form: an application to quality-of-life scores, Journal of Applied Statistics, 40, 563-583

Ware J. E., Snow K. K., Kosinski M. A. & Gandek B. (1993): SF-36 Health Survey, Manual and Interpretation Guides. The Health Institute, New England Medical Center.

Stansfeld S. A., Roberts R. & Foot S. P. (1997): Assessing the validity of the SF-36 general health survey. Quality of Life Research, 6, 217-224.

Examples

set.seed(2)
# We simulate a variable bounded between 0 and 100.
BodyPain <- runif(1000,0,100)

# We specify that the simulated dimension corresponds
# with body pain dimension.
k <- 3

# We perform the recoding.
BodyPain.rec <- SF36rec(BodyPain,k)

Print a BBest class model.

Description

print.BBest is the BBest specific method fot the generic function print which prints objects returned by modelling functions.

Usage

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

Arguments

Value

Prints a BBest object.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2006): Analysis of short-form-36 (SF-36): The beta-binomial distribution approach, Statistics in Medicine, 26, 1318-1342

See Also

BBest

Examples

set.seed(9)
# Simulate a binomial distribution
y <- rBB(100,10,0.5,2)

# Apply the model
model <- BBest(y,10)
print(model)   # or just model

Print a BBmm class model.

Description

print.BBmm is the BBmm specific method fot the generic function print which prints objects returned by modelling functions.

Usage

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

Arguments

Value

Prints a BBmm object.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Breslow N. E. & Calyton D. G. (1993): Approximate Inference in Generalized Linear Mixed Models, Journal of the American Statistical Association, 88, 9-25

Lee Y. & Nelder J. A. (1996): Hierarchical generalized linear models, Journal of the Royal Statistical Society. Series B, 58, 619-678

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2017): Comparison of beta-binomial regression model approaches to analyze health related quality of life data, Statistical Methods in Medical Research, DOI: 10.1177/0962280217690413

See Also

BBmm

Examples

set.seed(14)

# Defining the parameters
k <- 100
m <- 10
phi <- 0.5
beta <- c(1.5,-1.1)
sigma <- 0.5

# Simulating the covariate and random effects
x <- runif(k,0,10)
X <- model.matrix(~x)
z <- as.factor(rBI(k,4,0.5,2))
Z <- model.matrix(~z-1)
u <- rnorm(5,0,sigma)


# The linear predictor and simulated response variable
eta <- beta[1]+beta[2]*x+crossprod(t(Z),u)
p <- 1/(1+exp(-eta))
y <- rBB(k,m,p,phi)
dat <- data.frame(cbind(y,x,z))
dat$z <- as.factor(dat$z)

# Apply the model
model <- BBmm(fixed.formula = y~x,random.formula = ~z,m=m,data=dat)
print(model) # or just model

Print a BBreg class model.

Description

print.BBreg is the BBreg specific method fot the generic function print which prints objects returned by modelling functions.

Usage

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

Arguments

Value

Prints a BBreg object.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Forcina A. & Franconi L. (1988): Regression analysis with Beta-Binomial distribution, Revista di Statistica Applicata, 21, 7-12

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2017): Comparison of beta-binomial regression model approaches to analyze health related quality of life data, Statistical Methods in Medical Research, DOI: 10.1177/0962280217690413

See Also

BBreg

Examples

# We simulate a covariate, fix the paramters of the beta-binomial 
# distribution and simulate a response variable.

# Then we apply the model, and try to get the same values.
set.seed(18)
k <- 1000
m <- 10
x <- rnorm(k,5,3)

beta <- c(-10,2)
p <- 1/(1+exp(-(beta[1]+beta[2]*x)))
phi <- 1.2

y <- rBB(k,m,p,phi)

model <- BBreg(y~x,m)
print(model) # or just model

Print a summary.BBest class model.

Description

print.summary.BBest is the summary.BBest specific method fot the generic function print which prints objects returned by modelling functions.

Usage

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

Arguments

Value

Prints a summary.BBest object.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2006): Analysis of short-form-36 (SF-36): The beta-binomial distribution approach, Statistics in Medicine, 26, 1318-1342

See Also

BBest,summary.BBest

Examples

set.seed(9)
# Simulate a binomial distribution
y <- rBB(100,10,0.5,2)

# Apply the model
model <- BBest(y,10)
sum.model <- summary(model)
print(sum.model) # or just sum.model

Print a summary.BBmm class model.

Description

print.summary.BBmm is the summary.BBmm specific method fot the generic function print which prints objects returned by modelling functions.

Usage

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

Arguments

Value

Prints a summary.BBmm object.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Breslow N. E. & Calyton D. G. (1993): Approximate Inference in Generalized Linear Mixed Models, Journal of the American Statistical Association, 88, 9-25

Lee Y. & Nelder J. A. (1996): Hierarchical generalized linear models, Journal of the Royal Statistical Society. Series B, 58, 619-678

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2017): Comparison of beta-binomial regression model approaches to analyze health related quality of life data, Statistical Methods in Medical Research, DOI: 10.1177/0962280217690413

See Also

BBmm, summary.BBmm

Examples

set.seed(14)

# Defining the parameters
k <- 100
m <- 10
phi <- 0.5
beta <- c(1.5,-1.1)
sigma <- 0.5

# Simulating the covariate and random effects
x <- runif(k,0,10)
X <- model.matrix(~x)
z <- as.factor(rBI(k,4,0.5,2))
Z <- model.matrix(~z-1)
u <- rnorm(5,0,sigma)


# The linear predictor and simulated response variable
eta <- beta[1]+beta[2]*x+crossprod(t(Z),u)
p <- 1/(1+exp(-eta))
y <- rBB(k,m,p,phi)
dat <- data.frame(cbind(y,x,z))
dat$z <- as.factor(dat$z)

# Apply the model
model <- BBmm(fixed.formula = y~x,random.formula = ~z,m=m,data=dat)
sum.model <- summary(model)
print(sum.model) # or just sum.model

Print a summary.BBreg class model.

Description

print.summary.BBreg is the summary.BBreg specific method fot the generic function print which prints objects returned by modelling functions.

Usage

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

Arguments

Value

Prints a summary.BBreg object.

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Forcina A. & Franconi L. (1988): Regression analysis with Beta-Binomial distribution, Revista di Statistica Applicata, 21, 7-12

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2017): Comparison of beta-binomial regression model approaches to analyze health related quality of life data, Statistical Methods in Medical Research, DOI: 10.1177/0962280217690413

See Also

BBreg,summary.BBreg

Examples

# We simulate a covariate, fix the paramters of the beta-binomial 
# distribution and simulate a response variable.

# Then we apply the model, and try to get the same values.
set.seed(18)
k <- 1000
m <- 10
x <- rnorm(k,5,3)

beta <- c(-10,2)
p <- 1/(1+exp(-(beta[1]+beta[2]*x)))
phi <- 1.2

y <- rBB(k,m,p,phi)

model <- BBreg(y~x,m)
sum.model <- summary(model)
print(sum.model) # or just sum.model

Summarizes a BBest class model.

Description

summary.BBest si the BBest specific method for the generic function summary which summarizes objects returned by modelling functions.

Usage

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

Arguments

Details

summary.BBest summarizes all the relevant information about the estimation of the parameters in a BBest class model.

Value

summary.BBest returns an object of class "summary.BBest".

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Arostegui I., Nunez-Anton V. & Quintana J. M. (2006): Analysis of short-form-36 (SF-36): The beta-binomial distribution approach, Statistics in Medicine, 26, 1318-1342

See Also

BBest

Examples

set.seed(9)
# Simulate a binomial distribution
y <- rBB(100,10,0.5,2)

# Apply the model
model <- BBest(y,10)
sum.model <- summary(model)

Summarizes a BBmm class model.

Description

summary.BBmm si the BBmm specific method for the generic function summary which summarizes objects returned by modelling functions.

Usage

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

Arguments

Details

summary.BBmm summarizes all the relevant information about the estimation of the parameters in a BBmm class model.

The function performs statistical significance hypothesis about the estimated fixed parameters based on the normal distribution of the estimates. It also performs a goodness of fit test based on the difference between the calculated deviance of the model and the null deviance or deviance of the null model, which it is suppose to follow a Chi-square distribution with degrees of freedom equal to the difference in degrees of freedom of the models.

Value

summary.BBmm returns an object of class "summary.BBmm".

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Breslow N. E. & Calyton D. G. (1993): Approximate Inference in Generalized Linear Mixed Models, Journal of the American Statistical Association, 88, 9-25

Lee Y. & Nelder J. A. (1996): Hierarchical generalized linear models, Journal of the Royal Statistical Society. Series B, 58, 619-678

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2017): Comparison of beta-binomial regression model approaches to analyze health related quality of life data, Statistical Methods in Medical Research, DOI: 10.1177/0962280217690413

See Also

The multiroot and uniroot functions of the R-package rootSolve for the general Newton-Raphson algorithm.

BBmm.

Examples

set.seed(14)

# Defining the parameters
k <- 100
m <- 10
phi <- 0.5
beta <- c(1.5,-1.1)
sigma <- 0.5

# Simulating the covariate and random effects
x <- runif(k,0,10)
X <- model.matrix(~x)
z <- as.factor(rBI(k,4,0.5,2))
Z <- model.matrix(~z-1)
u <- rnorm(5,0,sigma)


# The linear predictor and simulated response variable
eta <- beta[1]+beta[2]*x+crossprod(t(Z),u)
p <- 1/(1+exp(-eta))
y <- rBB(k,m,p,phi)
dat <- data.frame(cbind(y,x,z))
dat$z <- as.factor(dat$z)

# Apply the model
model <- BBmm(fixed.formula = y~x,random.formula = ~z,m=m,data=dat)
sum.model <- summary(model)

Summarizes a BBreg class model.

Description

summary.BBreg si the BBreg specific method for the generic function summary which summarizes objects returned by modelling functions.

Usage

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

Arguments

Details

summary.BBreg summarizes all the relevant information about the estimation of the parameters in a BBreg class model.

The function performs statistical significance hypothesis about the estimated regression parameters based on the normal distribution of the estimates. It also performs a goodness of fit test based on the difference between the calculated deviance of the model and the null deviance or deviance of the null model, which it is suppose to follow a Chi-square distribution with degrees of freedom equal to the difference in degrees of freedom of the models.

Value

summary.BBreg returns an object of class "summary.BBreg".

Author(s)

J. Najera-Zuloaga

D.-J. Lee

I. Arostegui

References

Forcina A. & Franconi L. (1988): Regression analysis with Beta-Binomial distribution, Revista di Statistica Applicata, 21, 7-12

Najera-Zuloaga J., Lee D.-J. & Arostegui I. (2017): Comparison of beta-binomial regression model approaches to analyze health related quality of life data, Statistical Methods in Medical Research, DOI: 10.1177/0962280217690413

Examples

# We simulate a covariate, fix the paramters of the beta-binomial 
# distribution and simulate a response variable.

# Then we apply the model, and try to get the same values.
set.seed(18)
k <- 1000
m <- 10
x <- rnorm(k,5,3)

beta <- c(-10,2)
p <- 1/(1+exp(-(beta[1]+beta[2]*x)))
phi <- 1.2

y <- rBB(k,m,p,phi)

model <- BBreg(y~x,m)
sum.model <- summary(model)