tt.test <- function(x, ...) UseMethod("tt.test")

tt.test.default <-function(nx,mx,vx,ny=NULL,my=NULL,vy=NULL, alternative = c("two.sided", "less", "greater"),
         mu = 0, paired = FALSE, var.equal = FALSE, conf.level = 0.95,
         ...)
{
    alternative <- match.arg(alternative)


    if(missing(nx)||!missing(nx)&&(length(nx) != 1 || is.na(nx)))
        stop("'nx' must be a single number")
    if(!missing(nx)&&(nx<= 0))
        stop("'nx' must be a positive number")

    if(missing(mx)||!missing(mx)&&(length(mx) != 1 || is.na(mx)))
        stop("'mx' must be a single number")

    if(missing(vx)||!missing(vx)&&(length(vx) != 1 || is.na(vx)))
        stop("'vx' must be a single number")

    if(!missing(mu) && (length(mu) != 1 || is.na(mu)))
        stop("'mu' must be a single number")

    if(!missing(conf.level) &&
       (length(conf.level) != 1 || !is.finite(conf.level) ||
        conf.level < 0 || conf.level > 1))
        stop("'conf.level' must be a single number between 0 and 1")

    if( !is.null(ny) ){


        if(is.null(my)||is.null(vy)  )
        stop("some data is incorrect")
        else{

                if(length(ny) != 1 || is.na(ny))
                stop("'ny' must be a single number")

                if(ny<= 0)
                stop("'ny' must be a positive number")


                if(length(my) != 1 || is.na(my))
                 stop("'my' must be a single number")

                 if(length(vx) != 1 || is.na(vy))
                 stop("'vy' must be a single number")





        }
     }





#comprobaciones del vector y

  #dname <- paste(deparse(substitute(x)),"and",
  #deparse(substitute(y)))


  #   if(paired)
  #        xok <- yok <- complete.cases(x,y)
  #   else {
  #         yok <- !is.na(y)
  #        xok <- !is.na(x)
  #   }
  #    y <- y[yok]
  #   }


    #else {
    #dname <- deparse(substitute(x))
    #IF( paired ) stop("'y' is missing for paired test")
    #xok <- !is.na(x)
    #yok <- NULL
    #}



    #x <- x[xok]
    #if( paired ) {
    #x <- x-y
    #y <- NULL
    #}
    #nx <- length(x)
    #mx <- mean(x)
    #vx <- var(x)
    estimate <- mx


# condicional si tenemos una variable x o tenemos dos variables x,y.

    if(is.null(ny)) {
        if(nx < 2) stop("not enough 'x' observations")

    df <- nx-1
    stderr <- sqrt(vx/nx)

        if(stderr < 10 *.Machine$double.eps * abs(mx))
            stop("data are essentially constant")
    tstat <- (mx-mu)/stderr
    method <- ifelse(paired,"Pai
    red t-test","One Sample t-test")
    names(estimate) <- ifelse(paired,"mean of the differences","mean of x")
    } else {
   # ny <- length(y)
        if(nx < 1 || (!var.equal && nx < 2))
            stop("not enough 'x' observations")
    if(ny < 1 || (!var.equal && ny < 2))
            stop("not enough 'y' observations")
        if(var.equal && nx+ny < 3) stop("not enough observations")
    #my <- mean(y)
    #vy <- var(y)
    method <- paste(if(!var.equal)"Welch", "Two Sample t-test")
    estimate <- c(mx,my)
    names(estimate) <- c("mean of x","mean of y")
    if(var.equal) {
        df <- nx+ny-2
            v <- 0
            if(nx > 1) v <- v + (nx-1)*vx
            if(ny > 1) v <- v + (ny-1)*vy
        v <- v/df
        stderr <- sqrt(v*(1/nx+1/ny))
    } else {
        stderrx <- sqrt(vx/nx)
        stderry <- sqrt(vy/ny)
        stderr <- sqrt(stderrx^2 + stderry^2)
        df <- stderr^4/(stderrx^4/(nx-1) + stderry^4/(ny-1))
    }
        if(stderr < 10 *.Machine$double.eps * max(abs(mx), abs(my)))
            stop("data are essentially constant")
        tstat <- (mx - my - mu)/stderr
    }
    if (alternative == "less") {
    pval <- pt(tstat, df)
    cint <- c(-Inf, tstat + qt(conf.level, df) )
    }
    else if (alternative == "greater") {
    pval <- pt(tstat, df, lower = FALSE)
    cint <- c(tstat - qt(conf.level, df), Inf)
    }
    else {
    pval <- 2 * pt(-abs(tstat), df)
    alpha <- 1 - conf.level
        cint <- qt(1 - alpha/2, df)
    cint <- tstat + c(-cint, cint)
    }
    cint <- mu + cint * stderr
    names(tstat) <- "t"
    names(df) <- "df"
    names(mu) <- if(paired || !is.null(ny)) "difference in means" else "mean" #hemos cambiado y por ny
    attr(cint,"conf.level") <- conf.level
    rval <- list(statistic = tstat, parameter = df, p.value = pval,
           conf.int=cint, estimate=estimate, null.value = mu,
           alternative=alternative,
           method=method) # data.name=dname detrás de method
    class(rval) <- "htest"
    return(rval)
}

t.test.formula <-
function(formula, data, subset, na.action, ...)
{
    if(missing(formula)
       || (length(formula) != 3)
       || (length(attr(terms(formula[-2]), "term.labels")) != 1))
        stop("'formula' missing or incorrect")
    m <- match.call(expand.dots = FALSE)
    if(is.matrix(eval(m$data, parent.frame())))
        m$data <- as.data.frame(data)
    m[[1]] <- as.name("model.frame")
    m$... <- NULL
    mf <- eval(m, parent.frame())
    DNAME <- paste(names(mf), collapse = " by ")
    names(mf) <- NULL
    response <- attr(attr(mf, "terms"), "response")
    g <- factor(mf[[-response]])
    if(nlevels(g) != 2)
        stop("grouping factor must have exactly 2 levels")
    DATA <- split(mf[[response]], g)
    names(DATA) <- c("x", "y")
    y <- do.call("tt.test", c(DATA, list(...)))   #cambiar tt.test por t.test
    y$data.name <- DNAME
    if(length(y$estimate) == 2)
        names(y$estimate) <- paste("mean in group", levels(g))
    y
}