##' Performs Likelihood-ratio, Wald and score tests
##' @title Statistical tests
##' @aliases compare test_wald
##' @export
##' @param object \code{lvmfit}-object
##' @param \dots Additional arguments to low-level functions
##' @return Matrix of test-statistics and p-values
##' @author Klaus K. Holst
##' @seealso \code{\link{modelsearch}}, \code{\link{equivalence}}
##' @keywords htest
##' @examples
##' m <- lvm();
##' regression(m) <- c(y1,y2,y3) ~ eta; latent(m) <- ~eta
##' regression(m) <- eta ~ x
##' m2 <- regression(m, c(y3,eta) ~ x)
##' set.seed(1)
##' d <- sim(m,1000)
##' e <- estimate(m,d)
##' e2 <- estimate(m2,d)
##'
##' compare(e)
##'
##' compare(e,e2) ## LRT, H0: y3<-x=0
##' compare(e,scoretest=y3~x) ## Score-test, H0: y3~x=0
##' compare(e2,par=c("y3~x")) ## Wald-test, H0: y3~x=0
##'
##' B <- diag(2); colnames(B) <- c("y2~eta","y3~eta")
##' compare(e2,contrast=B,null=c(1,1))
##'
##' B <- rep(0,length(coef(e2))); B[1:3] <- 1
##' compare(e2,contrast=B)
##'
##' compare(e,scoretest=list(y3~x,y2~x))
compare <- function(object,...) UseMethod("compare")

##' @export
compare.default <- function(object,...,
                            par,contrast,null,
                            scoretest,Sigma,level=.95,df=NULL) {
    if (!missing(par) || (!missing(contrast) && is.character(contrast))) {
        if (!missing(contrast) && is.character(contrast)) par <- contrast
        contrast <- rep(0,length(coef(object)))
        myidx <- parpos(Model(object),p=par)
        contrast[myidx] <- 1
        contrast <- diag(contrast,nrow=length(contrast))[which(contrast!=0),,drop=FALSE]
        if (!missing(null) && length(null)>1) null <- null[attributes(myidx)$ord]
    }
    ### Wald test
    if (!missing(contrast)) {
        B <- contrast
        p <- coef(object)
        pname <- names(p)
        B <- rbind(B);
        colnames(B) <- if (is.vector(contrast)) names(contrast) else colnames(contrast)
        if (missing(Sigma)) {
            Sigma <- vcov(object)
        }
        if (ncol(B)<length(p)) {
            nn <- colnames(B)
            myidx <- parpos(Model(object),p=nn)
            B0 <- matrix(0,nrow=nrow(B),ncol=length(coef(object)))
            B0[,myidx] <- B[,attributes(myidx)$ord]
            B <- B0
        }
        if (missing(null)) null <- rep(0,nrow(B))
        if (length(null)==1) null <- rep(null,nrow(B))
        Bp <- B%*%p
        V <- B%*%Sigma%*%t(B)
        ct <- cbind(Bp,diag(V)^.5)
        p <- 1-(1-level)/2
        qp <- if(!is.null(df)) qt(p,df=df) else qnorm(p)
        ct <- cbind(ct,ct[,1] + qp*cbind(-1,1)%x%ct[,2])
        colnames(ct) <- c("Estimate","Std.Err",paste0(c(1-p,p)*100,"%"))
        rownames(ct) <- rep("",nrow(ct))
        Q <- t(Bp-null)%*%Inverse(V)%*%(Bp-null)
        if (any(is.nan(V)) ||any(is.na(V))) {
          df <- 0
        } else {
          df <- tryCatch(qr(V)$rank, error=function(...) 0)
        }
        names(df) <- "df"
        attributes(Q) <- NULL; names(Q) <- "chisq";
        pQ <- ifelse(df==0,NA,pchisq(Q,df,lower.tail=FALSE))

        method = "- Wald test -";
        cnames <- c()
        if (!is.null(pname)) {
            msg <- c()
            for (i in seq_len(nrow(B))) {
                Bidx <- which(B[i,]!=0)
                Bval <- abs(B[i,Bidx]); Bval[Bval==1] <- ""
                sgn  <- rep(" + ",length(Bval)); sgn[sign(B[i,Bidx])==-1] <- " - ";
                if (sgn[1] == " + ") {
                  sgn[1] <- ""
                } else {
                    sgn[1] <- "-"
                }
                cnames <- c(cnames,
                            paste0(sgn,Bval,
                                   paste0("[",pname[Bidx],"]"),collapse=""))
                msg <- c(msg,paste0(cnames[i]," = ",null[i]))
            }
            method <- c(method,"","Null Hypothesis:",msg)
            ##method <- c(method,"","Observed:",paste(formatC(as.vector(Bp)),collapse=" "))
        }
        rownames(ct) <- cnames
        res <- list(##data.name=hypothesis,
            statistic = Q, parameter = df,
            p.value=pQ, method = method, estimate=ct, vcov=V, coef=ct[,1],
            null=null, cnames=cnames
        )
        class(res) <- "htest"
        attributes(res)$B <- B
        return(res)
    }

    ### Score test
    if (!missing(scoretest)) {
        altmodel <- Model(object)
        if (inherits(scoretest,"formula")) scoretest <- list(scoretest)
        for (i in scoretest) {
            regression(altmodel) <- i
        }
        p0 <- numeric(length(coef(altmodel)))
        idx <-  match(coef(Model(object)),coef(altmodel))
        p0[idx] <- coef(object)
        Sc2 <- score(altmodel,p=p0,data=model.frame(object),weigth=Weights(altmodel),
                     estimator=object$estimator,...)
        I <- information(altmodel,p=p0,n=object$data$n,
                         data=model.frame(object),weigth=Weights(object),
                         estimator=object$estimator,...
                         )
        iI <- try(solve(I), silent=TRUE)
        Q <- ifelse (inherits(iI, "try-error"), NA, ## Score test
                     ## rbind(Sc)%*%iI%*%cbind(Sc)
        (Sc2)%*%iI%*%t(Sc2)
        )
        attributes(Q) <- NULL; names(Q) <- "chisq"
        df <- length(p0)-length(coef(object)); names(df) <- "df"
        pQ <- ifelse(df==0,NA,pchisq(Q,df,lower.tail=FALSE))
        res <- list(data.name=as.character(scoretest),
                    statistic = Q, parameter = df,
                    p.value=pQ, method = "- Score test -")
        class(res) <- "htest"
        return(res)
    }

    ### Likelihood ratio test
    objects <- list(object,...)
    if (length(objects)<2) {
        if (!(inherits(object,"lvmfit"))) {
            cc <- rbind(logLik(object),AIC(object))
            rownames(cc) <- c("logLik","AIC")
            colnames(cc) <-  ""
            return(cc)
        }
        L0 <- logLik(object)
        L1 <- satmodel(object,logLik=TRUE)
        df <- attributes(L1)$df-attributes(L0)$df; names(df) <- "df"
        Q <- abs(2*(L0-L1));
        attributes(Q) <- NULL; names(Q) <- "chisq";
        pQ <- ifelse(df==0,NA,pchisq(Q,df,lower.tail=FALSE))

        values <- c(L0,L1); names(values) <- c("log likelihood (model)", "log likelihood (saturated model)")
        res <- list(statistic = Q, parameter = df,
                    p.value=pQ, method = "- Likelihood ratio test -",
                    estimate = values)
        class(res) <- "htest"
        return(res)
    }
    if (length(objects)==2)
        return(comparepair(objects[[1]],objects[[2]]))
    res <- list()
    for (i in seq_len(length(objects)-1)) {
        res <- c(res, list(comparepair(objects[[i]],objects[[i+1]])))
    }
    return(res)
}


comparepair <- function(x1,x2) {
    l1 <- do.call("logLik",list(x1),envir=parent.frame(2))
    l2 <- do.call("logLik",list(x2),envir=parent.frame(2))
    df1 <- attributes(l1)$df;  df2 <- attributes(l2)$df;
    if (is.null(df1)) {
        df1 <- length(do.call("coef",list(x1),envir=parent.frame(2)))
        df2 <- length(do.call("coef",list(x2),envir=parent.frame(2)))
    }
    Q <- abs(2*(l1-l2))
    names(Q) <- "chisq"
    df <- abs(df1-df2); names(df) <- "df"
    p <- pchisq(Q,df=df,lower.tail=FALSE)
    values <- c(l1,l2); names(values) <- c("log likelihood (model 1)", "log likelihood (model 2)")

    res <- list(statistic = Q, parameter = df,
                p.value= p, method = "- Likelihood ratio test -",
                estimate = values)
    class(res) <- "htest"
    return(res)
}
