[Splm-commits] r32 - pkg

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

Author: gpiras
Date: 2009-10-13 16:06:03 +0200 (Tue, 13 Oct 2009)
New Revision: 32

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

Deleted: pkg/clmltest.model.R
===================================================================
--- pkg/clmltest.model.R	2009-10-13 14:05:41 UTC (rev 31)
+++ pkg/clmltest.model.R	2009-10-13 14:06:03 UTC (rev 32)
@@ -1,86 +0,0 @@
-`clmltest.model` <-
-function(x, listw, index){
-    ## depends on:
-    ## listw2dgCMatrix.R
-    ## REmod.R
-    ## spreml.R
-if(!inherits(x,"splm")) stop("argument should be an object of class splm")
-frm<-x$call
-if(x$type != "random effects ML") stop("argument should be of type random effects ML")
-
-  if(is.null(index))  stop("index should be specified to retrieve information on time and cross-sectional dimentions")
-
-  if(!inherits(listw,"listw")) stop("object w should be of class listw")
-
-
-
-  ind <- index[,1]
-  tind <- index[,2]
-
-if(names(x$coefficients)[1]=="(Intercept)")  X<-data.frame(cbind(rep(1,ncol(x$model)), x$model[,-1]))
-else X<-x$model[,-1]
-  y<-x$model[,1]
-  	eML<-x$residuals
-
-  ## reduce index accordingly
-
-  ## reorder data by cross-sections, then time
-  oo<-order(tind,ind)
-  X<-X[oo,]
-  y<-y[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
-
-	eme<-tapply(eML,inde1,mean)
-	emme<-eML - rep(eme,T)
-	sigmav<-crossprod(eML,emme)/(N*(T-1)) ####estimate of the variance component sigma_v
-	sigma1<-crossprod(eML,rep(eme,T))/N ####estimate of the variance component sigma_1
-	c1<-sigmav/sigma1^2
-	c2<-1/sigmav
-	c1e<-as.numeric(c1)*eML
-	Wst<-listw2dgCMatrix(listw) ###transform the listw object in a sparse matrix
-	Ws<-t(Wst)  ### this is the real W since listw2dgCMatrix generate W'
-	WpsW<-Wst+Ws
-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<-(WpsW)%*%q
-	wq<-as.matrix(wq)
-	}
-	Wc1e<-unlist(tapply(eML,inde,yybis)) #### (W'+W)*u
-	sumWc1e<-tapply(Wc1e,inde1,sum)
-	prod1<-as.numeric(c1)*rep(sumWc1e,T)/T
-	prod2<-as.numeric(c2)* (Wc1e - rep(sumWc1e,T)/T)
-	prod<-prod1+prod2
-	D<-1/2*crossprod(eML,prod) ###calculates D in the notation of the paper.
-	W2<-Ws%*%Ws ####generate W^2
-	WW<-crossprod(Ws) ####generate W'*W
-tr<-function(R) sum(diag(R))
-	b<-tr(W2+WW) ###generates b (same notation as the paper)
-	LMl1<-D^2/(((T-1)+as.numeric(sigmav)^2/as.numeric(sigma1)^2)*b) ###conditional LM test for lambda equal to zero
-	LMlstar<-sqrt(LMl1) ###one-sided version
-	statistics<-LMlstar
-  pval <- pnorm(LMlstar, lower.tail=FALSE)
-
-  names(statistics)="LM*-lambda"
-	method<- "Baltagi, Song and Koh LM*-lambda conditional LM test (assuming sigma^2_mu >= 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(frm), alternative="Spatial autocorrelation")
-  class(RVAL) <- "htest"
-  return(RVAL)
-
-}
-