[Splm-commits] r33 - pkg

[noreply at r-forge.r-project.org]

Author: gpiras
Date: 2009-10-13 16:06:21 +0200 (Tue, 13 Oct 2009)
New Revision: 33

Removed:
   pkg/clmmtest.R
Log:
eliminate all functions erroneously  uploaded

Deleted: pkg/clmmtest.R
===================================================================
--- pkg/clmmtest.R	2009-10-13 14:06:03 UTC (rev 32)
+++ pkg/clmmtest.R	2009-10-13 14:06:21 UTC (rev 33)
@@ -1,104 +0,0 @@
-`clmmtest` <-
-function(formula, data, index=NULL, listw){
-    ## depends on listw2dgCMatrix.R, spfeml.R
-
-	ml <- spfeml(formula, data=data, index=index, listw, model="error", effects="pooled")
-
-	  if(!is.null(index)) {
-    require(plm)
-    data <- plm.data(data, index)
-    }
-  index <- data[,1]
-  tindex <- data[,2]
-  X<-model.matrix(formula,data=data)
-  y<-model.response(model.frame(formula,data=data))
-  ## reduce index accordingly
-   names(index)<-row.names(data)
-  ind<-index[which(names(index)%in%row.names(X))]
-  tind<-tindex[which(names(index)%in%row.names(X))]
-
-  ## reorder data by cross-sections, then time
-  oo<-order(tind,ind)
-  X<-X[oo,]
-  y<-y[oo]
-  ind<-ind[oo]
-  tind<-tind[oo]
-
-  ## det. number of groups and df
-  N<-length(unique(ind))
-  k<-dim(X)[[2]]
-  ## det. max. group numerosity
-  T<-max(tapply(X[,1],ind,length))
-  ## det. total number of obs. (robust vs. unbalanced panels)
-  NT<-length(ind)
-
-###maximum likelihood estimation under the null hypothesis that lambda is equal to zero. extract the residuals.
-	indic<-seq(1,T)
-	inde<-as.numeric(rep(indic,each=N)) ####indicator to get the cross-sectional observations
-	ind1<-seq(1,N)
-	inde1<-as.numeric(rep(ind1,T)) ####indicator to get the time periods observations
-
-	lambda<-ml$spat.coef
-	#print(lambda)
-	eML<-residuals(ml)
-#	print(length(eML))
- 	Wst<-listw2dgCMatrix(listw) ###transform the listw object in a sparse matrix
-	Ws<-t(Wst)  ### this is the real W since listw2dgCMatrix generate W'
-	B<- -lambda*Ws
-	diag(B)<- 1
-	BpB<-crossprod(B)
-	BpBi<- solve(BpB)
-vc<-function(R) {
-	BBu<-BpBi%*%R
-	BBu<-as.matrix(BBu)
-	}
-	eme<-unlist(tapply(eML,inde,vc))
-	sigmav2<-crossprod(eML,eme)/(N*(T-1)) ####estimate of the variance component sigma_v
-	sigmav4<-sigmav2^2
-
-tr<-function(R) sum(diag(R))
-	trBpB<-tr(BpB)
-	BpB2<-BpB%*%BpB
-yybis<-function(q){ #### for very big dimension of the data this can be changed looping over the rows and columns of W or either the listw object
-	wq<-rep(q,T)
-	tmp<-wq%*%eML
-					}
-	BBu<-apply(BpB2,1,yybis)
-	BBu<-rep(BBu,T)
-	upBBu<-crossprod(eML,BBu)
-	Dmu<--((T/(2*sigmav2))*trBpB) + ((1/(2*sigmav4))*upBBu)
-	WpB<-Wst%*%B
-	BpW<-t(B)%*%Ws
-	WpBplBpW <-WpB + BpW
-	G<-WpBplBpW %*% BpBi
-	e<-tr(BpB2)
-	d<-tr(WpBplBpW)
-	h<-trBpB
-	g<-tr(G)
-	c<-tr(G%*%G)
-	#print(c(e,d,h,g,c))
-	NUM<- ((2*sigmav4)/T)*((N*sigmav4*c)-(sigmav4*g^2))   ###equation 2.30 in the paper
-	DENft<- NT*sigmav4*e*c
-	DENst<- N*sigmav4*d^2
-	DENtt<- T*sigmav4*g^2 * e
-	DENfot<- 2* sigmav4 *g*h*d
-	DENfit<- sigmav4*h^2* c
-	DEN<- DENft - DENst - DENtt + DENfot - DENfit
-	LMmu <- Dmu^2*NUM / DEN
-	LMmustar<- sqrt(LMmu)
-	statistics<-LMmustar
-  pval <- pnorm(LMmustar, lower.tail=FALSE)
-
-  names(statistics)="LM*-mu"
-	method<- "Baltagi, Song and Koh LM*- mu conditional LM test (assuming lambda may or may not be = 0)"
-	#alt<-"serial corr. in error terms, sub RE and spatial dependence"
-  ##(insert usual htest features)
-  dname <- deparse(formula)
-  RVAL <- list(statistic = statistics,
-               method = method,
-               p.value = pval, data.name=deparse(formula), alternative="Random regional effects")
-  class(RVAL) <- "htest"
-  return(RVAL)
-
-}
-