ssym.l: Fitting Semi-parametric Log-symmetric Regression Models
In ssym: Fitting Semi-Parametric log-Symmetric Regression Models
ssym.l R Documentation
Fitting Semi-parametric Log-symmetric Regression Models
Description
ssym.l is used to fit a semi-parametric regression model suitable for
analysis of data sets in which the response variable is continuous, strictly
positive, and asymmetric. Under this setup, both median and skewness of the response
variable distribution are explicitly modeled through semi-parametric functions,
whose nonparametric components may be approximated by natural cubic splines or P-splines.
Usage
ssym.l(formula, family, xi, data, epsilon, maxiter, subset, link.mu, link.phi,
local.influence, spec, std.out)
Arguments
formula
a symbolic description of the systematic component of the model to be fitted. See details for further information.
family
a description of the (log) error distribution to be used in the model. Supported families include Normal, Student, Contnormal,
Powerexp, Hyperbolic, Slash, Sinh-normal and Sinh-t, which correspond to normal, Student-t, contaminated normal,
power exponential, symmetric hyperbolic, slash, sinh-normal and sinh-t distributions, respectively.
xi
a numeric value or numeric vector that represents the extra parameter of the specified error distribution.
data
an optional data frame, list or environment containing the variables in the model.
epsilon
an optional positive value, which represents the convergence criterion. Default value is 1e-07.
maxiter
an optional positive integer giving the maximal number of iterations for the estimating process. Default value is 1e03.
subset
an optional expression that specifies a subset of individuals to be used in the fitting process.
link.mu
an optional character that specifies the link function of the median submodel.
link.phi
an optional character that specifies the link function of the skewness submodel.
local.influence
logical. If TRUE, local influence measures under two perturbation schemes are calculated. Default is FALSE.
spec
an optional character. The smoothing parameter is estimated by minimizing a overall goodness-of-fit criterion such as AIC or BIC. spec is an optional
string to specify the goodness-of-fit measure to be used. Default value is AIC.
std.out
logical. If FALSE, just a reduced set of attributes is returned by the model-fitting function. Default is TRUE
.
Details
The argument formula comprises of three parts (separated by the symbols "~" and "|"), namely: observed response variable in log-scale, predictor of the
median submodel (having logarithmic link) and predictor of the skewness (or the relative dispersion) submodel (having logarithmic link). An arbitrary number of
nonparametric effects may be specified in the predictors. These effects are specified to be approximated by natural cubic splines or P-splines using the functions
ncs() or psp(), respectively.
The iterative estimation process is based on the Fisher scoring and backfitting algorithms. Because some distributions such as log-Student-t,
log-contaminated-normal, log-power-exponential, log-slash and log-hyperbolic may be obtained as a power mixture of the log-normal distribution, the
expectation-maximization (EM) algorithm is applied in those cases to obtain a more efficient iterative process of parameter estimation. Furthermore,
because the Birnbaum-Saunders-t distribution can be obtained as a scale mixture of the Birnbaum-Saunders distribution, the expectation-maximization algorithm
is also applied in this case to obtain a more efficient iterative process of parameter estimation. The smoothing parameter is chosen by minimizing
the AIC or BIC criteria.
The function ssym.l() calculates overall goodness-of-fit statistics, deviance-type residuals for both submodels, as well as local influence measures
under the case-weight and response perturbation schemes.
Value
theta.mu
a vector of parameter estimates associated with the median submodel.
theta.phi
a vector of parameter estimates associated with the skewness (or the relative dispersion) submodel.
vcov.mu
approximate variance-covariance matrix associated with the median submodel.
vcov.phi
approximate variance-covariance matrix associated with the skewness (or the relative dispersion) submodel.
weights
final weights of the iterative process.
lambdas.mu
estimate of the smoothing parameter(s) associated with the nonparametric part of the median submodel.
lambdas.phi
estimate of the smoothing parameter(s) associated with the nonparametric part of the skewness (or the relative dispersion) submodel.
gle.mu
degrees of freedom associated with the nonparametric part of the median submodel.
gle.phi
degrees of freedom associated with the nonparametric part of the skewness (or the relative dispersion) submodel.
deviance.mu
a vector with the individual contributions to the deviance associated with the median submodel.
deviance.phi
a vector with the individual contributions to the deviance associated with the skewness (or the relative dispersion) submodel.
mu.fitted
a vector with the fitted values of the (in log-scale) median submodel.
phi.fitted
a vector with the fitted values of the skewness (or the relative dispersion) submodel.
lpdf
a vector of individual contributions to the log-likelihood function.
Author(s)
Luis Hernando Vanegas <hvanegasp@gmail.com> and Gilberto A. Paula
References
Vanegas, L.H. and Paula, G.A. (2015) A semiparametric approach for joint modeling of median and skewness. TEST 24, 110-135.
Vanegas, L.H. and Paula, G.A. (2016) Log-symmetric distributions: statistical properties and parameter estimation. Brazilian Journal of Probability and Statistics 30, 196-220.
Vanegas, L.H. and Paula, G.A. (2016) An extension of log-symmetric regression models: R codes and applications. Journal of Statistical Computation and Simulation 86, 1709-1735.
See Also
ssym.nl, ssym.l2
Examples
###################################################################################
######### Fraction of Cell Volume Data - a log-power-exponential model ###########
###################################################################################
#data("Ovocytes", package="ssym")
#fit <- ssym.l(log(fraction) ~ type + psp(time) | type + psp(time),
# data=Ovocytes, family='Powerexp', xi=-0.55, local.influence=TRUE)
#summary(fit)
#
################## Graph of the nonparametric effects ##################
#par(mfrow=c(1,2))
#np.graph(fit, which=1, exp=TRUE)
#np.graph(fit, which=2, exp=TRUE)
#
################## Graph of deviance-type residuals ##################
#plot(fit)
#
################### Simulated envelopes ##################
#envelope(fit)
#
################### Graph of local influence measures ##################
#out <- influence(fit)
###################################################################################
############### Textures of snacks Data - a log-Student-t model #################
###################################################################################
#data("Snacks", package="ssym")
#fit <- ssym.l(log(texture) ~ type + ncs(week) | type, data=Snacks,
# family='Student', xi=15, local.influence=TRUE)
#summary(fit)
#
############################ Extra parameter ###########################
#extra.parameter(fit,5,50)
#
################### Graph of the nonparametric effect ##################
#np.graph(fit, which=1, exp=TRUE)
#
################### Graph of deviance-type residuals ##################
#plot(fit)
#
################### Simulated envelopes ##################
#envelope(fit)
#
################### Plot of influence measures ##################
#out <- influence(fit)
###################################################################################
################### Daphnia Data - a log-normal model ########################
###################################################################################
#data("daphnia", package="nlreg")
#fit <- ssym.l(log(time) ~ ncs(conc) | ncs(conc), data=daphnia, family="Normal")
#summary(fit)
#
################### Graph of the nonparametric effects ##################
#par(mfrow=c(1,2))
#np.graph(fit, which=1, exp=TRUE)
#np.graph(fit, which=2, exp=TRUE)
#
################### Simulated envelopes ##################
#envelope(fit)
###################################################################################
####################### gam.data - a Power-exponential model ####################
###################################################################################
#data("gam.data", package="gam")
#
#fit <- ssym.l(y~psp(x),data=gam.data,family="Powerexp",xi=-0.5)
#summary(fit)
#
################## Graph of the nonparametric effect ##################
#np.graph(fit, which=1)
#
###################################################################################
######### Personal Injury Insurance Data - a Birnbaum-Saunders-t model ##########
###################################################################################
#data("Claims", package="ssym")
#fit <- ssym.l(log(total) ~ op_time | op_time, data=Claims,
# family='Sinh-t', xi=c(0.1,4), local.influence=TRUE)
#summary(fit)
#
################## Plot of deviance-type residuals ##################
#plot(fit)
#
################### Simulated envelopes ##################
#envelope(fit)
################## Plot of influence measures ##################
#out <- influence(fit)
###################################################################################
######### Body Fat Percentage Data - a Birnbaum-Saunders-t model ##########
###################################################################################
#data("ais", package="sn")
#fit <- ssym.l(log(Bfat)~1, data=ais, family='Sinh-t', xi=c(4.5,4))
#summary(fit)
#
########################### Extra parameter ###########################
#extra.parameter(fit,c(3,4),c(5,7))
#
################## Plot of the fitted model ##################
#id <- sort(ais$Bfat, index=TRUE)$ix
#par(mfrow=c(1,2))
#hist(ais$Bfat[id],xlim=range(ais$Bfat),ylim=c(0,0.1),prob=TRUE,breaks=15,
# col="light gray",border="dark gray",xlab="",ylab="",main="")
#par(new=TRUE)
#plot(ais$Bfat[id],exp(fit$lpdf[id])/ais$Bfat[id],xlim=range(ais$Bfat),
# ylim=c(0,0.1),type="l",xlab="",ylab="Density",main="Histogram")
#
#plot(ais$Bfat[id],fit$cdfz[id],xlim=range(ais$Bfat),ylim=c(0,1),type="l",
# xlab="",ylab="",main="")
#par(new=TRUE)
#plot(ecdf(ais$Bfat[id]),xlim=range(ais$Bfat),ylim=c(0,1),verticals=TRUE,
# do.points=FALSE,col="dark gray",ylab="Probability",xlab="",main="ECDF")
###################################################################################
################### ALCOA Aluminium Data - a log-slash model ####################
###################################################################################
#data("alcoa", package="robustloggamma")
#alcoa2 <- data.frame(alcoa$dist[alcoa$label=="C"])
#colnames(alcoa2) <- "dist"
#
#fit <- ssym.l(log(dist) ~ 1, data=alcoa2, family="Slash", xi=1.212)
#
################## Plot of the fitted model ##################
#id <- sort(alcoa2$dist, index=TRUE)$ix
#par(mfrow=c(1,2))
#hist(alcoa2$dist[id],xlim=c(0,45),ylim=c(0,0.1),prob=TRUE,breaks=60,
# col="light gray",border="dark gray",xlab="",ylab="",main="")
#par(new=TRUE)
#plot(alcoa2$dist[id],exp(fit$lpdf[id])/alcoa2$dist[id],xlim=c(0,45),
#ylim=c(0,0.1), type="l",xlab="",ylab="",main="")
#
#plot(alcoa2$dist[id],fit$cdfz[id],xlim=range(alcoa2$dist),ylim=c(0,1),type="l",
# xlab="",ylab="",main="")
#par(new=TRUE)
#plot(ecdf(alcoa2$dist[id]),xlim=range(alcoa2$dist),ylim=c(0,1),verticals=TRUE,
# do.points=FALSE,col="dark gray",ylab="",xlab="",main="")
##################################################################################
################### Boston Housing Data - a log-Slash model ####################
###################################################################################
#data("Boston", package="MASS")
#fit <- ssym.l(log(medv) ~ crim + rm + tax + psp(lstat) + psp(dis) | psp(lstat),
# data=Boston, family="Slash", xi=1.56, local.influence=TRUE)
#summary(fit)
#
########################### Extra parameter ###########################
#extra.parameter(fit,1.0,2.3)
#
################## Plot of deviance-type residuals ##################
#plot(fit)
#
################## Plot of nonparametric effects ##################
#par(mfrow=c(1,3))
#np.graph(fit,which=1,exp=TRUE,"lstat")
#np.graph(fit,which=1,exp=TRUE,"dis")
#np.graph(fit,which=2,exp=TRUE,"lstat")
#
################## Plot of influence measures ##################
#out <- influence(fit)
#
################### Simulated envelopes ##################
#envelope(fit)
###################################################################################
####################### mcycle Data - a Power-exponential model #################
###################################################################################
#data("mcycle", package="MASS")
#fit <- ssym.l(accel ~ ncs(times)|ncs(times), data=mcycle, family="Powerexp",xi=-0.6)
#summary(fit)
#
################## Plot of nonparametric effects ##################
#par(mfrow=c(1,2))
#np.graph(fit,which=1,obs=TRUE)
#np.graph(fit,which=2,exp=TRUE,obs=TRUE)
#
################### Simulated envelopes ##################
#envelope(fit)
###################################################################################
################### Steel Data - a log-hyperbolic model ####################
###################################################################################
#data("Steel", package="ssym")
#fit <- ssym.l(log(life)~psp(stress), data=Steel, family="Hyperbolic", xi=1.25)
#summary(fit)
#
########################### Extra parameter ###########################
#extra.parameter(fit,0.5,2)
#
################## Plot of nonparametric effects ##################
#np.graph(fit,which=1,exp=TRUE)
ssym documentation built on April 22, 2023, 1:13 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| ssym.l | R Documentation |
Fitting Semi-parametric Log-symmetric Regression Models
Description
ssym.l is used to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, and asymmetric. Under this setup, both median and skewness of the response variable distribution are explicitly modeled through semi-parametric functions, whose nonparametric components may be approximated by natural cubic splines or P-splines.
Usage
ssym.l(formula, family, xi, data, epsilon, maxiter, subset, link.mu, link.phi,
local.influence, spec, std.out)
Arguments
formula |
a symbolic description of the systematic component of the model to be fitted. See details for further information. |
family |
a description of the (log) error distribution to be used in the model. Supported families include |
xi |
a numeric value or numeric vector that represents the extra parameter of the specified error distribution. |
data |
an optional data frame, list or environment containing the variables in the model. |
epsilon |
an optional positive value, which represents the convergence criterion. Default value is 1e-07. |
maxiter |
an optional positive integer giving the maximal number of iterations for the estimating process. Default value is 1e03. |
subset |
an optional expression that specifies a subset of individuals to be used in the fitting process. |
link.mu |
an optional character that specifies the link function of the median submodel. |
link.phi |
an optional character that specifies the link function of the skewness submodel. |
local.influence |
logical. If TRUE, local influence measures under two perturbation schemes are calculated. Default is |
spec |
an optional character. The smoothing parameter is estimated by minimizing a overall goodness-of-fit criterion such as AIC or BIC. |
std.out |
logical. If FALSE, just a reduced set of attributes is returned by the model-fitting function. Default is |
.
Details
The argument formula comprises of three parts (separated by the symbols "~" and "|"), namely: observed response variable in log-scale, predictor of the
median submodel (having logarithmic link) and predictor of the skewness (or the relative dispersion) submodel (having logarithmic link). An arbitrary number of
nonparametric effects may be specified in the predictors. These effects are specified to be approximated by natural cubic splines or P-splines using the functions
ncs() or psp(), respectively.
The iterative estimation process is based on the Fisher scoring and backfitting algorithms. Because some distributions such as log-Student-t, log-contaminated-normal, log-power-exponential, log-slash and log-hyperbolic may be obtained as a power mixture of the log-normal distribution, the expectation-maximization (EM) algorithm is applied in those cases to obtain a more efficient iterative process of parameter estimation. Furthermore, because the Birnbaum-Saunders-t distribution can be obtained as a scale mixture of the Birnbaum-Saunders distribution, the expectation-maximization algorithm is also applied in this case to obtain a more efficient iterative process of parameter estimation. The smoothing parameter is chosen by minimizing the AIC or BIC criteria.
The function ssym.l() calculates overall goodness-of-fit statistics, deviance-type residuals for both submodels, as well as local influence measures
under the case-weight and response perturbation schemes.
Value
theta.mu |
a vector of parameter estimates associated with the median submodel. |
theta.phi |
a vector of parameter estimates associated with the skewness (or the relative dispersion) submodel. |
vcov.mu |
approximate variance-covariance matrix associated with the median submodel. |
vcov.phi |
approximate variance-covariance matrix associated with the skewness (or the relative dispersion) submodel. |
weights |
final weights of the iterative process. |
lambdas.mu |
estimate of the smoothing parameter(s) associated with the nonparametric part of the median submodel. |
lambdas.phi |
estimate of the smoothing parameter(s) associated with the nonparametric part of the skewness (or the relative dispersion) submodel. |
gle.mu |
degrees of freedom associated with the nonparametric part of the median submodel. |
gle.phi |
degrees of freedom associated with the nonparametric part of the skewness (or the relative dispersion) submodel. |
deviance.mu |
a vector with the individual contributions to the deviance associated with the median submodel. |
deviance.phi |
a vector with the individual contributions to the deviance associated with the skewness (or the relative dispersion) submodel. |
mu.fitted |
a vector with the fitted values of the (in log-scale) median submodel. |
phi.fitted |
a vector with the fitted values of the skewness (or the relative dispersion) submodel. |
lpdf |
a vector of individual contributions to the log-likelihood function. |
Author(s)
Luis Hernando Vanegas <hvanegasp@gmail.com> and Gilberto A. Paula
References
Vanegas, L.H. and Paula, G.A. (2015) A semiparametric approach for joint modeling of median and skewness. TEST 24, 110-135.
Vanegas, L.H. and Paula, G.A. (2016) Log-symmetric distributions: statistical properties and parameter estimation. Brazilian Journal of Probability and Statistics 30, 196-220.
Vanegas, L.H. and Paula, G.A. (2016) An extension of log-symmetric regression models: R codes and applications. Journal of Statistical Computation and Simulation 86, 1709-1735.
See Also
ssym.nl, ssym.l2
Examples
###################################################################################
######### Fraction of Cell Volume Data - a log-power-exponential model ###########
###################################################################################
#data("Ovocytes", package="ssym")
#fit <- ssym.l(log(fraction) ~ type + psp(time) | type + psp(time),
# data=Ovocytes, family='Powerexp', xi=-0.55, local.influence=TRUE)
#summary(fit)
#
################## Graph of the nonparametric effects ##################
#par(mfrow=c(1,2))
#np.graph(fit, which=1, exp=TRUE)
#np.graph(fit, which=2, exp=TRUE)
#
################## Graph of deviance-type residuals ##################
#plot(fit)
#
################### Simulated envelopes ##################
#envelope(fit)
#
################### Graph of local influence measures ##################
#out <- influence(fit)
###################################################################################
############### Textures of snacks Data - a log-Student-t model #################
###################################################################################
#data("Snacks", package="ssym")
#fit <- ssym.l(log(texture) ~ type + ncs(week) | type, data=Snacks,
# family='Student', xi=15, local.influence=TRUE)
#summary(fit)
#
############################ Extra parameter ###########################
#extra.parameter(fit,5,50)
#
################### Graph of the nonparametric effect ##################
#np.graph(fit, which=1, exp=TRUE)
#
################### Graph of deviance-type residuals ##################
#plot(fit)
#
################### Simulated envelopes ##################
#envelope(fit)
#
################### Plot of influence measures ##################
#out <- influence(fit)
###################################################################################
################### Daphnia Data - a log-normal model ########################
###################################################################################
#data("daphnia", package="nlreg")
#fit <- ssym.l(log(time) ~ ncs(conc) | ncs(conc), data=daphnia, family="Normal")
#summary(fit)
#
################### Graph of the nonparametric effects ##################
#par(mfrow=c(1,2))
#np.graph(fit, which=1, exp=TRUE)
#np.graph(fit, which=2, exp=TRUE)
#
################### Simulated envelopes ##################
#envelope(fit)
###################################################################################
####################### gam.data - a Power-exponential model ####################
###################################################################################
#data("gam.data", package="gam")
#
#fit <- ssym.l(y~psp(x),data=gam.data,family="Powerexp",xi=-0.5)
#summary(fit)
#
################## Graph of the nonparametric effect ##################
#np.graph(fit, which=1)
#
###################################################################################
######### Personal Injury Insurance Data - a Birnbaum-Saunders-t model ##########
###################################################################################
#data("Claims", package="ssym")
#fit <- ssym.l(log(total) ~ op_time | op_time, data=Claims,
# family='Sinh-t', xi=c(0.1,4), local.influence=TRUE)
#summary(fit)
#
################## Plot of deviance-type residuals ##################
#plot(fit)
#
################### Simulated envelopes ##################
#envelope(fit)
################## Plot of influence measures ##################
#out <- influence(fit)
###################################################################################
######### Body Fat Percentage Data - a Birnbaum-Saunders-t model ##########
###################################################################################
#data("ais", package="sn")
#fit <- ssym.l(log(Bfat)~1, data=ais, family='Sinh-t', xi=c(4.5,4))
#summary(fit)
#
########################### Extra parameter ###########################
#extra.parameter(fit,c(3,4),c(5,7))
#
################## Plot of the fitted model ##################
#id <- sort(ais$Bfat, index=TRUE)$ix
#par(mfrow=c(1,2))
#hist(ais$Bfat[id],xlim=range(ais$Bfat),ylim=c(0,0.1),prob=TRUE,breaks=15,
# col="light gray",border="dark gray",xlab="",ylab="",main="")
#par(new=TRUE)
#plot(ais$Bfat[id],exp(fit$lpdf[id])/ais$Bfat[id],xlim=range(ais$Bfat),
# ylim=c(0,0.1),type="l",xlab="",ylab="Density",main="Histogram")
#
#plot(ais$Bfat[id],fit$cdfz[id],xlim=range(ais$Bfat),ylim=c(0,1),type="l",
# xlab="",ylab="",main="")
#par(new=TRUE)
#plot(ecdf(ais$Bfat[id]),xlim=range(ais$Bfat),ylim=c(0,1),verticals=TRUE,
# do.points=FALSE,col="dark gray",ylab="Probability",xlab="",main="ECDF")
###################################################################################
################### ALCOA Aluminium Data - a log-slash model ####################
###################################################################################
#data("alcoa", package="robustloggamma")
#alcoa2 <- data.frame(alcoa$dist[alcoa$label=="C"])
#colnames(alcoa2) <- "dist"
#
#fit <- ssym.l(log(dist) ~ 1, data=alcoa2, family="Slash", xi=1.212)
#
################## Plot of the fitted model ##################
#id <- sort(alcoa2$dist, index=TRUE)$ix
#par(mfrow=c(1,2))
#hist(alcoa2$dist[id],xlim=c(0,45),ylim=c(0,0.1),prob=TRUE,breaks=60,
# col="light gray",border="dark gray",xlab="",ylab="",main="")
#par(new=TRUE)
#plot(alcoa2$dist[id],exp(fit$lpdf[id])/alcoa2$dist[id],xlim=c(0,45),
#ylim=c(0,0.1), type="l",xlab="",ylab="",main="")
#
#plot(alcoa2$dist[id],fit$cdfz[id],xlim=range(alcoa2$dist),ylim=c(0,1),type="l",
# xlab="",ylab="",main="")
#par(new=TRUE)
#plot(ecdf(alcoa2$dist[id]),xlim=range(alcoa2$dist),ylim=c(0,1),verticals=TRUE,
# do.points=FALSE,col="dark gray",ylab="",xlab="",main="")
##################################################################################
################### Boston Housing Data - a log-Slash model ####################
###################################################################################
#data("Boston", package="MASS")
#fit <- ssym.l(log(medv) ~ crim + rm + tax + psp(lstat) + psp(dis) | psp(lstat),
# data=Boston, family="Slash", xi=1.56, local.influence=TRUE)
#summary(fit)
#
########################### Extra parameter ###########################
#extra.parameter(fit,1.0,2.3)
#
################## Plot of deviance-type residuals ##################
#plot(fit)
#
################## Plot of nonparametric effects ##################
#par(mfrow=c(1,3))
#np.graph(fit,which=1,exp=TRUE,"lstat")
#np.graph(fit,which=1,exp=TRUE,"dis")
#np.graph(fit,which=2,exp=TRUE,"lstat")
#
################## Plot of influence measures ##################
#out <- influence(fit)
#
################### Simulated envelopes ##################
#envelope(fit)
###################################################################################
####################### mcycle Data - a Power-exponential model #################
###################################################################################
#data("mcycle", package="MASS")
#fit <- ssym.l(accel ~ ncs(times)|ncs(times), data=mcycle, family="Powerexp",xi=-0.6)
#summary(fit)
#
################## Plot of nonparametric effects ##################
#par(mfrow=c(1,2))
#np.graph(fit,which=1,obs=TRUE)
#np.graph(fit,which=2,exp=TRUE,obs=TRUE)
#
################### Simulated envelopes ##################
#envelope(fit)
###################################################################################
################### Steel Data - a log-hyperbolic model ####################
###################################################################################
#data("Steel", package="ssym")
#fit <- ssym.l(log(life)~psp(stress), data=Steel, family="Hyperbolic", xi=1.25)
#summary(fit)
#
########################### Extra parameter ###########################
#extra.parameter(fit,0.5,2)
#
################## Plot of nonparametric effects ##################
#np.graph(fit,which=1,exp=TRUE)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.