From 5da062de8f4d08aecc19d1eab007ecec71d7427f Mon Sep 17 00:00:00 2001
From: Stefano Nembrini <stefanonembrini@gmail.com>
Date: Sat, 19 Nov 2016 14:34:43 +0000
Subject: [PATCH] version 0.2.0

---
 DESCRIPTION       |  10 +++----
 MD5               |  19 +++++++------
 NAMESPACE         |   1 +
 R/trust.R         |  13 +++++++++
 R/urq.R           |  71 ++++++++++++++++++++++++++++++++++++++--------
 R/urqCI.R         |  52 +++++++++++++++++++--------------
 R/urqb.R          |  32 ++++++++++++---------
 data/trust.rda    | Bin 0 -> 4612 bytes
 man/Trust-Data.Rd |  22 ++++++++++++++
 man/urq.Rd        |  25 ++++++++++++++--
 man/urqCI.Rd      |  20 +++++++++----
 man/urqb.Rd       |   5 +---
 12 files changed, 198 insertions(+), 72 deletions(-)
 create mode 100644 R/trust.R
 create mode 100644 data/trust.rda
 create mode 100644 man/Trust-Data.Rd

diff --git a/DESCRIPTION b/DESCRIPTION
index 3e009fb..271fe05 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,11 +1,11 @@
 Package: uqr
 Type: Package
 Title: Unconditional Quantile Regression
-Version: 0.1.0
-Date: 2016-10-18
+Version: 0.2.0
+Date: 2016-11-19
 Author: Stefano Nembrini <stefanonembrini@gmail.com>
 Maintainer: Stefano Nembrini <stefanonembrini@gmail.com>
-Description: Estimation and Inference for Unconditional Quantile Regression (see Firpo et al. (2009). <DOI:10.3982/ECTA6822>).
+Description: Estimation and Inference for Unconditional Quantile Regression for cross-sectional and panel data (see Firpo et al. (2009). <DOI:10.3982/ECTA6822>).
 License: GPL (>= 2)
 LazyData: TRUE
 Imports: stats, base
@@ -13,6 +13,6 @@ Depends: R(>= 3.3.1), Hmisc(>= 3.17-4), gtools(>= 3.5.0)
 Suggests: knitr
 RoxygenNote: 5.0.1
 NeedsCompilation: no
-Packaged: 2016-10-30 10:38:18 UTC; Nembrini
+Packaged: 2016-11-19 13:59:28 UTC; Nembrini
 Repository: CRAN
-Date/Publication: 2016-10-30 16:58:08
+Date/Publication: 2016-11-19 15:34:43
diff --git a/MD5 b/MD5
index e967ba7..056bae8 100644
--- a/MD5
+++ b/MD5
@@ -1,11 +1,14 @@
-245fadf2e85be0245b74c7bc84d463bd *DESCRIPTION
-fa29a58a65859ad5953d644deb6b7819 *NAMESPACE
+b908ab14d23964d82d31b0c7df494269 *DESCRIPTION
+6d4df09a93753bb3b2433383048c0108 *NAMESPACE
 05811d56576c5db60c2a1ed898e7788f *R/engel.R
-c36e02ac88a7264bdf481731c8d933eb *R/urq.R
-53d1cc58de7604b3603e3bc6ebecde80 *R/urqCI.R
-0da5e41bb148ee8d51324c37c57a5765 *R/urqb.R
+f8bf6b110c60582ae8a97654666347b8 *R/trust.R
+6844e8bb00b4dd98a4f610ff856a5696 *R/urq.R
+3992635ed9aea52e4ae65595765f8d4e *R/urqCI.R
+936084ec5292cf56238f820833716fcb *R/urqb.R
 2cb41ec2ee5de9777a3577f2e441e17c *data/engel.rda
+f1680069020d86c8eb8ef2b06c7172b9 *data/trust.rda
 c8b6bbd3098f11a999aa85ec1f93a8b3 *man/Engel-Data.Rd
-5abde58b4881e1465c891ff745b57d28 *man/urq.Rd
-42f92ba60d60749b8f8ce2f6698b7e55 *man/urqCI.Rd
-8814ca35adf7e8dd8a6f4fcf5acb2853 *man/urqb.Rd
+e0474c586ab0c997f1611dfa3f26da7a *man/Trust-Data.Rd
+fa216a5f316b8e110f354bd3a44ad2c6 *man/urq.Rd
+2ed594aee10daaa11b89881671d855a0 *man/urqCI.Rd
+150d5e3a8302fbbebacdbc1d1e543f20 *man/urqb.Rd
diff --git a/NAMESPACE b/NAMESPACE
index 30af504..318946a 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -2,6 +2,7 @@
 
 export(urq)
 export(urqCI)
+export(urqb)
 import(Hmisc)
 import(grDevices)
 import(graphics)
diff --git a/R/trust.R b/R/trust.R
new file mode 100644
index 0000000..44f978a
--- /dev/null
+++ b/R/trust.R
@@ -0,0 +1,13 @@
+#' @title Trust Data
+#' @name Trust Data
+#' @description Data on 12 European Countries 
+#' @format A data frame containing 180 observations for 12 countries. Data taken from the Eurobarometer, The Hertie School of Governance. 
+#' \describe{
+#' \item{Trust_in_the_ECB}{Trust in the European Central Bank}
+#' \item{Trust_in_the_EU}{Trust in the European Union}
+#' \item{countryname}{countryname identifier}
+#' \item{year}{year identifier}
+#' }
+#' @docType data
+#' @usage data(trust)
+"trust"
\ No newline at end of file
diff --git a/R/urq.R b/R/urq.R
index 9a62989..59c04f1 100644
--- a/R/urq.R
+++ b/R/urq.R
@@ -1,24 +1,39 @@
 #' @title Unconditional Quantile Regression
 #'
 #'@description Returns an object of class \code{urq}. that represents an Unconditional Quantile Regression Fit
-#'@usage urq(formula,data,tau=NULL,kernel=NULL)
+#'@usage urq(formula,data,tau=NULL,kernel=NULL,cre=NULL,id=NULL)
 #'@param formula a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right.
 #'@param data a \code{dataframe} in which to interpret the variables named in the formula 
 #'@param tau the quantile(s) to be estimated, this must be a number (or a vector of numbers) strictly between 0 and 1.
 #'@param kernel a character string giving the smoothing kernel to be used. This must match one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine", with default "gaussian".
-#'@details This function returns a Recentered Influence Function regression of given quantiles as proposed by Firpo, S., Fortin, N. M., & Lemieux, T. (2009).
+#'@param cre The CRE formula (right hand side only) is a specification of the variables in the CRE component. These are possibly endogenous variables (in the sense that they are affected by the fixed effects) and must be time-varying. If left empty, a cross-sectional analysis is performed.
+#'@param id defines the structure of the panel.
+#'@details This function returns a Recentered Influence Function regression of given quantiles as proposed by Firpo, S., Fortin, N. M., & Lemieux, T. (2009). Panel data analysis is performed extending the correlated random effects (CRE)  model by Mundlak (1978) and Chamberlain (1984) to an unconditional quantile regression framework. See Abrevaya and Dahl (2008) and by Bache et al (2011) for more details.
 #'@export 
 #'@import
 #'stats
 #'@references Firpo, S., Fortin, N. M., & Lemieux, T. (2009). Unconditional quantile regressions. Econometrica, 77(3), 953-973.
+#'@references Mundlak, Y. 1978. On the pooling of time series and cross section data. Econometrica 46: 69-85.
+#'@references Chamberlain G (1984) Panel Data. In: Griliches Z, Intriligator MD (eds) Handbook of Econometrics, vol 2, Elsevier Science B. V., pp 1247-1318
+#'@references Abrevaya, Jason and Christian M. Dahl. 2008. The effects of birth inputs on birthweight. Jounal of Business and Economic Statistics. 26-4. Pages 379-397.
+#'@references Bache, Stefan Holst; Christian M. Dahl; Johannes Tang Kristensen. 2011. Headlights on tobacco road to low birthweight - Evidence from a battery of quantile regression estimators and a heterogeneous panel.
 #'@keywords NULL
 #'@seealso \code{\link{density},\link[uqr]{urqCI}}
 #'@return NULL
 #'@examples 
+#' ### example for cross-sectional data ###
+#' 
 #'data(engel)
 #'formula = foodexp ~ income
 #' rifreg=urq(formula,data = engel)
-urq <- function(formula,data,tau=NULL,kernel=NULL) {
+#' 
+#' ### example for panel data ###
+#' 
+#' data(trust)
+#'formula=Trust_in_the_ECB~Trust_in_the_EU+Trust_in_National_Government
+#'cre=~Trust_in_the_EU+Trust_in_National_Government
+#'rif=urq(formula,data=trust,cre=cre,id="countryname")
+urq <- function(formula,data,tau=NULL,kernel=NULL,cre=NULL,id=NULL) {
   #call <- match.call()
   #mf <- match.call(expand.dots = FALSE)
   
@@ -26,28 +41,60 @@ urq <- function(formula,data,tau=NULL,kernel=NULL) {
   if(is.null(tau)) tau<-1:9/10
   indicator<-function(condition) ifelse(condition,1,0)
   c1=NULL
+  idx.dep=which(colnames(data)==all.vars(formula)[1])
+  quantile(x=data[,idx.dep],probs=tau)->q
+  density(data[,idx.dep],kernel=kernel)->f
+  approx(f$x, f$y, q)$y->fq
   for (i in 1:length(tau)){
     #which(is.na(data[,idx.dep]))->miss
     #data2=data[-miss,]
-    idx.dep=which(colnames(data)==all.vars(formula)[1])
+
+
     
     
-    quantile(x=data[,idx.dep],probs=tau)->q
-    density(data[,idx.dep],kernel=kernel)->f
-    approx(f$x, f$y, q)$y->fq
+
     RIF=q[i]+((tau[i]-indicator(data[,idx.dep]<q[i]))/fq[i])
     data2=data
     data2[,idx.dep]=RIF
+    
+    if (!is.null(cre)){
+      if(is.null(id)){print("id variable is missing")}
+      vars=all.vars(cre)
+      new=rep(NA,length(vars))
+      old=all.vars(formula)[2:length(all.vars(formula))]
+      dep=all.vars(formula)[1]
+      for(i in 1:length(vars)){
+
+        data2[,dim(data2)[2]+1] <- ave(data[,which(colnames(data2)==vars[i])], group=data2[,which(colnames(data2)==id)])
+        new[i]=colnames(data2)[dim(data2)[2]]<-paste(vars[i],sep="","_M")
+      }
+      formula_cre=paste(dep, "~", paste(c(old,new), collapse=" + "))
+      lm=lm(formula_cre,data2,weights=NULL)
+      c=coef(lm)
+      c1=rbind(c1,c)
+      #print(i)
+      data2[,idx.dep]=data[,idx.dep]
+    }
+    
+  else{
     lm=lm(formula,data2,weights=NULL)
-    c=coef(lm)
-    c1=rbind(c1,c)
-    #print(i)
-    data2=NULL
+  c=coef(lm)
+  c1=rbind(c1,c)
+#print(i)
+  data2=NULL}
+    
+    
   }
   RIF=t(c1)
   colnames(RIF)<-paste("tau=",tau)
+  if (!is.null(cre)){formula=formula_cre}
+  if (!is.null(cre)){
+  data=data2
+    #data=data.frame(data,data2[,dim(data)[2]+1])
+  #colnames(data[,idx.dep])=all.vars(formula)[1]
+  }
   
-  fit=list (coefficients=RIF,tau=tau,formula=formula)
+  fit=list (coefficients=RIF,tau=tau,formula=formula,id=id,data=data)
   fit$call <- sys.call()
   class(fit) <- "urq"
   #return(call)
diff --git a/R/urqCI.R b/R/urqCI.R
index a296ebf..1463183 100644
--- a/R/urqCI.R
+++ b/R/urqCI.R
@@ -1,9 +1,8 @@
-#' @title Unconditional Quantile Regression
+#' @title Inference for Unconditional Quantile Regression 
 #'
 #'@description Returns a summary list for an Unconditional Quantile Regression Fit.
-#'@usage urqCI(urq,data,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cluster=NULL)
+#'@usage urqCI(urq,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cluster=NULL)
 #'@param urq an object of class \code{urq}.
-#'@param data a \code{dataframe} in which to interpret the variables named in the \code{formula} .
 #'@param R the number of bootstrap replications to be used.
 #'@param seed random number generator.
 #'@param colour colour of plot: default is lightblue.
@@ -12,7 +11,7 @@
 #'@param cluster column name of variable to be used in order to obtain cluster robust standard errors and confidence intervals.
 #'@details This function provides standard errors and confidence intervals for the Recentered Influence Function regression fit \code{urq}. If the cluster option is used, standard errors are cluster robust according to the variable supplied by the user, otherwise observations are assumed to be iid. 
 #'Inference is obtained though a bayesian bootstrap drawing observation (or cluster) weights from a Dirichlet distribution. 
-#'If the option graph is TRUE, then a quantile plot is provided showing estimates and confidence intervals (t approximation [polygon] and percentile bootstrap [dashed lines]).
+#'If the option graph is TRUE, then a quantile plot is provided showing estimates and confidence intervals (t approximation).
 #' @references Rubin, D. B. (1981). The bayesian bootstrap. The annals of statistics, 9(1), 130-134.
 #'@export 
 #'@import
@@ -20,12 +19,23 @@
 #'@keywords NULL
 #'@seealso \code{\link{urq}}
 #'@return NULL
-#'@examples data(engel)
+#'@examples 
+#'### example for cross-sectional data ###
+#'
+#'data(engel)
 #'formula=foodexp ~ income
 #'rifreg=urq(formula=formula,data=engel)
-#'summary=urqCI(urq = rifreg,data = engel,R = 100,graph = TRUE,seed = 1234)
+#'summary=urqCI(urq = rifreg,R = 100,graph = TRUE,seed = 1234)
+#'
+#'### example for panel data ###
+#'
+#'data(trust)
+#'formula=Trust_in_the_ECB~Trust_in_the_EU+Trust_in_National_Government
+#'cre=~Trust_in_the_EU+Trust_in_National_Government
+#'rif=urq(formula,data=trust,cre=cre,id="countryname")
+#'summary=urqCI(urq = rif,R = 100,graph = TRUE,seed = 1234,cluster="countryname")
 
-urqCI=function(urq,data,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cluster=NULL){
+urqCI=function(urq,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cluster=NULL){
   if (is.null(seed)) {
     seed <- runif(1, 0, .Machine$integer.max)
   }
@@ -34,26 +44,26 @@ urqCI=function(urq,data,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cl
   set.seed(seed)
   boot=matrix(ncol=dim(urq$coefficients)[1]*length(urq$tau),nrow=R)
   
+  #data=urq$data
   cat("Bootstrapping\n")
   #print(R)
   for(i in 1:R){
-    if (is.null(cluster)){data$wts=as.numeric(rdirichlet(1, rep(1, dim(data)[1])))}
+    if (is.null(cluster)){urq$data$wts=as.numeric(rdirichlet(1, rep(1, dim(urq$data)[1])))}
     else{
-      idx.cluster=which(colnames(data)==cluster)
-      clusters=names(table(data[,idx.cluster]))
-      data=data[order(data[,idx.cluster]),] 
+      idx.cluster=which(colnames(urq$data)==cluster)
+      clusters=names(table(urq$data[,idx.cluster]))
+      urq$data=urq$data[order(urq$data[,idx.cluster]),] 
       freq=as.numeric(rdirichlet(1, rep(1, length(clusters))))
-      freq=rep(freq,times=table(data[,idx.cluster]))
-      data$wts=freq/sum(freq)
-      
+      freq=rep(freq,times=table(urq$data[,idx.cluster]))
+      urq$data$wts=freq/sum(freq)
     }
-    
-    boot[i,]=as.numeric(urqb(urq$formula,data=data,tau=urq$tau,cluster=cluster)$coefficients)
+    #data=data,tau=tau,formula=formula,kernel=NULL,cluster=cluster
+    boot[i,]=as.numeric(urqb(data=urq$data,tau=urq$tau,formula=urq$formula,kernel=NULL,cluster=cluster)$coefficients)
     #print(i)
     #weights=NULL
     if (!i %% 50)cat(".")
   }
-
+  
   if (R<=50) cat("Number of replications is small, please consider increasing it.")
   names.urq=rownames(urq$coefficients)
   if(is.null(confidence)) confidence<-0.95
@@ -75,8 +85,8 @@ urqCI=function(urq,data,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cl
   tP <- 2 * pt(-abs(urq.vector/se), dim(boot)[1] - 1)
   lower <- urq.vector + qt(alpha/2, dim(boot)[1] - 1) * se
   upper <- urq.vector + qt(1 - alpha/2, dim(boot)[1] - 1) * se
-  recap=cbind(urq.vector,t(apply(boot,2,quantile,c(alpha/2,1-alpha/2),na.rm=TRUE)),lower,upper,tP,se,rep(urq$tau,each=dim(urq$coefficients)[1]))
-  colnames(recap)=c("Coef","Perc. Lower","Perc. Upper","t Lower","t Upper","P>|t|","Std.Err.","tau")
+  recap=cbind(urq.vector,lower,upper,tP,se,rep(urq$tau,each=dim(urq$coefficients)[1]))
+  colnames(recap)=c("Coef","t Lower","t Upper","P>|t|","Std.Err.","tau")
   
   if (isTRUE(graph) && isTRUE(length(urq$tau)>1)){
     for(i in c(2:dim(urq$coefficients)[1])){
@@ -89,10 +99,8 @@ urqCI=function(urq,data,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cl
       plot(rep(urq$tau, 2), c(cfi[, 2], cfi[, 3]),xaxt = "n" ,type = "n",main=cond,xlab="",ylab="",ylim=c(min=min(cfi[,1:5],na.rm=TRUE),max=max(cfi[,1:5],na.rm=TRUE)))
       axis(1, at=urq$tau, labels=urq$tau,cex.axis=.8,las=2)
       
-      polygon(c(urq$tau, rev(urq$tau)),c(cfi[,4],rev(cfi[,5])),col=colour,border=NA)
+      polygon(c(urq$tau, rev(urq$tau)),c(cfi[,2],rev(cfi[,3])),col=colour,border=NA)
       points(urq$tau,cfi[,1],type="b",lty="longdash",cex = 0.5,pch = 20,col="blue")
-      points(urq$tau,cfi[,2],type="l",lty="longdash",cex = 0.5,pch = 20,col="black")
-      points(urq$tau,cfi[,3],type="l",lty="longdash",cex = 0.5,pch = 20,col="black")
       abline(h=0)
     }
   }
diff --git a/R/urqb.R b/R/urqb.R
index be862bc..fb9b3ba 100644
--- a/R/urqb.R
+++ b/R/urqb.R
@@ -1,43 +1,49 @@
 #' @title Unconditional Quantile Regression
 #'
 #'@description Function Not intended for user. Returns an object of class "urq" that represents an Unconditional Quantile Regression Fit. 
-#'@usage urqb(data,tau=NULL,formula,kernel=NULL,cluster=cluster,wts=wts)
+#'@usage urqb(data,tau,formula,kernel=NULL,cluster=cluster)
 #'@param formula a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right.
 #'@param data a data.frame in which to interpret the variables named in the formula 
 #'@param tau the quantile(s) to be estimated, this must be a number (or a vector of numbers) strictly between 0 and 1.
 #'@param kernel a character string giving the smoothing kernel to be used. This must match one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine", with default "gaussian".
 #'@param cluster column name of variable to be used in order to obtain cluster robust standard errors.
-#'@param wts weights
 #'@import gtools Hmisc
-#'@keywords internal
+#'@keywords NULL
+#'@export
 #'@seealso \code{\link{density},\link{urq}}
 #'@return NULL
 #'@examples NULL
-urqb <- function(data,tau=NULL,formula,kernel=NULL,cluster=cluster,wts=wts) {
+urqb <- function(data=data,tau=tau,formula=formula,kernel=NULL,cluster=cluster) {
   #library(Hmisc)
   if(is.null(kernel)) kernel<-"gaussian"
-  if(is.null(tau)) tau<-1:9/10
+  #if(is.null(tau)) tau<-1:9/10
   indicator<-function(condition) ifelse(condition,1,0)
   c1=NULL
   for (i in 1:length(tau)){
     #which(is.na(data[,idx.dep]))->miss
     #data2=data[-miss,]
+    formula=as.formula(formula)
+    as.data.frame(data)
     idx.dep=which(colnames(data)==all.vars(formula)[1])
     data2=data
     
-    wtd.quantile(data2[,idx.dep],tau,weights=data2$wts,normwt=TRUE)->q
-    density(data2[,idx.dep],kernel=kernel,weights=data2$wts)->f
+    
+    wtd.quantile(data2[,1],tau,weights=data$wts,normwt=TRUE)->q
+    density(data[,1],kernel=kernel,weights=data$wts)->f
     approx(f$x, f$y, q)$y->fq
     RIF=q[i]+((tau[i]-indicator(data2[,idx.dep]<q[i]))/fq[i])
     
     data2[,idx.dep]=RIF
-    lm=lm(formula,data2,weights=wts)
-    c=coef(lm)
-    c1=rbind(c1,c)
-    #print(i)
-    data2=NULL
+
+
+      lm=lm(formula,data2,weights=NULL)
+      c=coef(lm)
+      c1=rbind(c1,c)
+      #print(i)
+      data2=NULL
+    }
     
-  }
+  
   RIF=t(c1)
   colnames(RIF)<-paste("tau=",tau)
   fit=list (coefficients=RIF,tau=tau,formula=formula)
diff --git a/data/trust.rda b/data/trust.rda
new file mode 100644
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diff --git a/man/Trust-Data.Rd b/man/Trust-Data.Rd
new file mode 100644
index 0000000..16413b8
--- /dev/null
+++ b/man/Trust-Data.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/trust.R
+\docType{data}
+\name{Trust Data}
+\alias{Trust Data}
+\alias{trust}
+\title{Trust Data}
+\format{A data frame containing 180 observations for 12 countries. Data taken from the Eurobarometer, The Hertie School of Governance. 
+\describe{
+\item{Trust_in_the_ECB}{Trust in the European Central Bank}
+\item{Trust_in_the_EU}{Trust in the European Union}
+\item{countryname}{countryname identifier}
+\item{year}{year identifier}
+}}
+\usage{
+data(trust)
+}
+\description{
+Data on 12 European Countries
+}
+\keyword{datasets}
+
diff --git a/man/urq.Rd b/man/urq.Rd
index 84726d7..f52a369 100644
--- a/man/urq.Rd
+++ b/man/urq.Rd
@@ -4,7 +4,7 @@
 \alias{urq}
 \title{Unconditional Quantile Regression}
 \usage{
-urq(formula,data,tau=NULL,kernel=NULL)
+urq(formula,data,tau=NULL,kernel=NULL,cre=NULL,id=NULL)
 }
 \arguments{
 \item{formula}{a formula object, with the response on the left of a ~ operator, and the terms, separated by + operators, on the right.}
@@ -14,20 +14,41 @@ urq(formula,data,tau=NULL,kernel=NULL)
 \item{tau}{the quantile(s) to be estimated, this must be a number (or a vector of numbers) strictly between 0 and 1.}
 
 \item{kernel}{a character string giving the smoothing kernel to be used. This must match one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine", with default "gaussian".}
+
+\item{cre}{The CRE formula (right hand side only) is a specification of the variables in the CRE component. These are possibly endogenous variables (in the sense that they are affected by the fixed effects) and must be time-varying. If left empty, a cross-sectional analysis is performed.}
+
+\item{id}{defines the structure of the panel.}
 }
 \description{
 Returns an object of class \code{urq}. that represents an Unconditional Quantile Regression Fit
 }
 \details{
-This function returns a Recentered Influence Function regression of given quantiles as proposed by Firpo, S., Fortin, N. M., & Lemieux, T. (2009).
+This function returns a Recentered Influence Function regression of given quantiles as proposed by Firpo, S., Fortin, N. M., & Lemieux, T. (2009). Panel data analysis is performed extending the correlated random effects (CRE)  model by Mundlak (1978) and Chamberlain (1984) to an unconditional quantile regression framework. See Abrevaya and Dahl (2008) and by Bache et al (2011) for more details.
 }
 \examples{
+### example for cross-sectional data ###
+
 data(engel)
 formula = foodexp ~ income
 rifreg=urq(formula,data = engel)
+
+### example for panel data ###
+
+data(trust)
+formula=Trust_in_the_ECB~Trust_in_the_EU+Trust_in_National_Government
+cre=~Trust_in_the_EU+Trust_in_National_Government
+rif=urq(formula,data=trust,cre=cre,id="countryname")
 }
 \references{
 Firpo, S., Fortin, N. M., & Lemieux, T. (2009). Unconditional quantile regressions. Econometrica, 77(3), 953-973.
+
+Mundlak, Y. 1978. On the pooling of time series and cross section data. Econometrica 46: 69-85.
+
+Chamberlain G (1984) Panel Data. In: Griliches Z, Intriligator MD (eds) Handbook of Econometrics, vol 2, Elsevier Science B. V., pp 1247-1318
+
+Abrevaya, Jason and Christian M. Dahl. 2008. The effects of birth inputs on birthweight. Jounal of Business and Economic Statistics. 26-4. Pages 379-397.
+
+Bache, Stefan Holst; Christian M. Dahl; Johannes Tang Kristensen. 2011. Headlights on tobacco road to low birthweight - Evidence from a battery of quantile regression estimators and a heterogeneous panel.
 }
 \seealso{
 \code{\link{density},\link[uqr]{urqCI}}
diff --git a/man/urqCI.Rd b/man/urqCI.Rd
index 1617b84..bfc16eb 100644
--- a/man/urqCI.Rd
+++ b/man/urqCI.Rd
@@ -2,15 +2,13 @@
 % Please edit documentation in R/urqCI.R
 \name{urqCI}
 \alias{urqCI}
-\title{Unconditional Quantile Regression}
+\title{Inference for Unconditional Quantile Regression}
 \usage{
-urqCI(urq,data,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cluster=NULL)
+urqCI(urq,R=20,seed=NULL,colour=NULL,confidence=NULL,graph=TRUE,cluster=NULL)
 }
 \arguments{
 \item{urq}{an object of class \code{urq}.}
 
-\item{data}{a \code{dataframe} in which to interpret the variables named in the \code{formula} .}
-
 \item{R}{the number of bootstrap replications to be used.}
 
 \item{seed}{random number generator.}
@@ -29,13 +27,23 @@ Returns a summary list for an Unconditional Quantile Regression Fit.
 \details{
 This function provides standard errors and confidence intervals for the Recentered Influence Function regression fit \code{urq}. If the cluster option is used, standard errors are cluster robust according to the variable supplied by the user, otherwise observations are assumed to be iid. 
 Inference is obtained though a bayesian bootstrap drawing observation (or cluster) weights from a Dirichlet distribution. 
-If the option graph is TRUE, then a quantile plot is provided showing estimates and confidence intervals (t approximation [polygon] and percentile bootstrap [dashed lines]).
+If the option graph is TRUE, then a quantile plot is provided showing estimates and confidence intervals (t approximation).
 }
 \examples{
+### example for cross-sectional data ###
+
 data(engel)
 formula=foodexp ~ income
 rifreg=urq(formula=formula,data=engel)
-summary=urqCI(urq = rifreg,data = engel,R = 100,graph = TRUE,seed = 1234)
+summary=urqCI(urq = rifreg,R = 100,graph = TRUE,seed = 1234)
+
+### example for panel data ###
+
+data(trust)
+formula=Trust_in_the_ECB~Trust_in_the_EU+Trust_in_National_Government
+cre=~Trust_in_the_EU+Trust_in_National_Government
+rif=urq(formula,data=trust,cre=cre,id="countryname")
+summary=urqCI(urq = rif,R = 100,graph = TRUE,seed = 1234,cluster="countryname")
 }
 \references{
 Rubin, D. B. (1981). The bayesian bootstrap. The annals of statistics, 9(1), 130-134.
diff --git a/man/urqb.Rd b/man/urqb.Rd
index 46d0afb..96aa000 100644
--- a/man/urqb.Rd
+++ b/man/urqb.Rd
@@ -4,7 +4,7 @@
 \alias{urqb}
 \title{Unconditional Quantile Regression}
 \usage{
-urqb(data,tau=NULL,formula,kernel=NULL,cluster=cluster,wts=wts)
+urqb(data,tau,formula,kernel=NULL,cluster=cluster)
 }
 \arguments{
 \item{data}{a data.frame in which to interpret the variables named in the formula}
@@ -16,8 +16,6 @@ urqb(data,tau=NULL,formula,kernel=NULL,cluster=cluster,wts=wts)
 \item{kernel}{a character string giving the smoothing kernel to be used. This must match one of "gaussian", "rectangular", "triangular", "epanechnikov", "biweight", "cosine" or "optcosine", with default "gaussian".}
 
 \item{cluster}{column name of variable to be used in order to obtain cluster robust standard errors.}
-
-\item{wts}{weights}
 }
 \description{
 Function Not intended for user. Returns an object of class "urq" that represents an Unconditional Quantile Regression Fit.
@@ -28,5 +26,4 @@ NULL
 \seealso{
 \code{\link{density},\link{urq}}
 }
-\keyword{internal}