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new and revised vignettes, print bug fix
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| Package: rloadest | ||
| Type: Package | ||
| Title: River Load Estimation | ||
| Version: 0.4.2 | ||
| Date: 2015-07-20 | ||
| Version: 0.4.3 | ||
| Date: 2015-12-03 | ||
| Author: Dave Lorenz, Rob Runkel, Laura De Cicco | ||
| Maintainer: Dave Lorenz <[email protected]> | ||
| Description: Collection of functions to make constituent load estimations | ||
|
|
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| #' Jackknife Statistics | ||
| #' | ||
| #' Compute selected jackknife statistics for a rating-curve load-estimation model. | ||
| #' | ||
| #' @param fit an object of class "loadReg"---output from \code{loadReg}. Can also | ||
| #'be an object of class "censReg." | ||
| #' @param which a character string indicating the "load" or | ||
| #'"concentration" model for an object of class "loadReg" or "censReg" for | ||
| #'an object of class "censReg." | ||
| #' @return An object of class "jackStats" containing these components: | ||
| #'coef, the table of coefficient estimates, the jackknife bias and standard errors\cr | ||
| #'coefficients, the jackknifed coefficients\cr | ||
| #'pctcens, the percentage of left-censored values. \cr | ||
| #'The PRESS statistic and individual jackknife differences are also returned | ||
| #'when the percentage of censoring is 0. | ||
| #' @note The \code{jackStats} function can only be used when the analysis is AMLE. | ||
| #' | ||
| #'Abdi and Williams (2010) describe the jackknife as refering to two related techniques: the first | ||
| #'estimates the parameters, their bias and standard errors and the second evaluates the | ||
| #'predictive performance of the model. The second technique is the PRESS statistic (Helsel | ||
| #'and Hirsch, 2002), but can only be used on uncensored data; it is computed by \code{jackStats} | ||
| #'when no data are censored. The first technique can be used to assess the coefficients of the | ||
| #'regression---the bias should be small and the jackknife standard errors should not be much | ||
| #'different from the standard errors reported for the regression. Efron and Tibshirani (1993) | ||
| #'suggest that the bias is small if the relative bias (biuas divided by the jackknife standard | ||
| #'error) is less than 0.25. | ||
| #' @seealso \code{\link{loadReg}} | ||
| #' @keywords utilities | ||
| #' @references | ||
| #' Abdi, H. and Williams, L.J., 2010, Jackknife, in encyclopedia of research design, | ||
| #'Salkind, N.J., editor: Thousand Oaks, Calif., SAGE Publications, 1719 p. | ||
| #' | ||
| #'Efron, B. and Tibshirani, R.J., 1993, An introduction to the bootstrap: Boca Raton, | ||
| #'Fla., Chapman and Hall/CRC, 436 p. | ||
| #' | ||
| #' Helsel, D.R. and Hirsch, R.M., 2002, Statistical methods in water resources: | ||
| #'U.S. Geological Survey Techniques of Water-Resources Investigations, book 4, | ||
| #'chap. A3, 522 p. | ||
| #'Salkind, | ||
| #' @examples | ||
| #'# From application 1 in the vignettes | ||
| #'data(app1.calib) | ||
| #'app1.lr <- loadReg(Phosphorus ~ model(1), data = app1.calib, | ||
| #' flow = "FLOW", dates = "DATES", conc.units="mg/L", | ||
| #' station="Illinois River at Marseilles, Ill.") | ||
| #'jackStats(app1.lr) | ||
| #' @export | ||
| jackStats <- function(fit, which="load") { | ||
| ## Compute some stats | ||
| which <- match.arg(which, c("load", "concentration", "censReg")) | ||
| if(which == "load") { | ||
| # Verify AMLE | ||
| if(fit$method != "AMLE") { | ||
| stop("The analysis method must be AMLE") | ||
| } | ||
| # initial stuff | ||
| NPAR <- fit$lfit$NPAR | ||
| NOBS <- fit$lfit$NOBSC | ||
| # Get the repsonse, X, and Yeff | ||
| Y <- as.lcens(exp(fit$lfit$YLCAL), exp(fit$lfit$YD), fit$lfit$CENSFLAG) | ||
| X <- fit$lfit$XLCAL | ||
| Yeff <- fit$lfit$YLCAL | ||
| ## The code below computes an effective value for left-censored values | ||
| ## by computing the expected value from the prediction | ||
| ## provided if the method is ever published. No plans for now | ||
| #Yeff[fit$lfit$CENSFLAG] <- fit$lfit$XLCAL[fit$lfit$CENSFLAG, ,drop=FALSE] %*% | ||
| # fit$lfit$PARAML[seq(NPAR)] + fit$lfit$RESID[fit$lfit$CENSFLAG] | ||
| # | ||
| # Other Info | ||
| dist <- "lognormal" | ||
| parms <- fit$lfit$PARAML[seq(fit$lfit$NPAR)] | ||
| parnames <- colnames(fit$lfit$XLCAL) | ||
| } else if(which == "concentration") { | ||
| # Verify AMLE | ||
| if(fit$method != "AMLE") { | ||
| stop("The analysis method must be AMLE") | ||
| } | ||
| # initial stuff | ||
| NPAR <- fit$cfit$NPAR | ||
| NOBS <- fit$cfit$NOBSC | ||
| # Get the repsonse, X, and Yeff | ||
| Y <- as.lcens(exp(fit$cfit$YLCAL), exp(fit$cfit$YD), fit$cfit$CENSFLAG) | ||
| X <- fit$cfit$XLCAL | ||
| Yeff <- fit$cfit$YLCAL | ||
| ## See comment above | ||
| #Yeff[fit$cfit$CENSFLAG] <- fit$cfit$XLCAL[fit$cfit$CENSFLAG, ,drop=FALSE] %*% | ||
| # fit$cfit$PARAML[seq(NPAR)] + fit$cfit$RESID[fit$cfit$CENSFLAG] | ||
| # | ||
| # Other Info | ||
| dist <- "lognormal" | ||
| parms <- fit$cfit$PARAML[seq(fit$cfit$NPAR)] | ||
| parnames <- colnames(fit$cfit$XLCAL) | ||
| } else { # must be censReg | ||
| # Verify AMLE | ||
| if(fit$method != "AMLE") { | ||
| stop("The analysis method must be AMLE") | ||
| } | ||
| # initial stuff | ||
| NPAR <- fit$NPAR | ||
| NOBS <- fit$NOBSC | ||
| dist <- fit$dist | ||
| # Get the repsonse, X, and Yeff | ||
| if(dist == "lognormal") { | ||
| Y <- as.lcens(exp(fit$YLCAL), exp(fit$YD), fit$CENSFLAG) | ||
| } else { | ||
| Y <- as.lcens(fit$YLCAL, fit$YD, fit$CENSFLAG) | ||
| } | ||
| X <- fit$XLCAL | ||
| Yeff <- fit$YLCAL | ||
| ## See comment above | ||
| #Yeff[fit$CENSFLAG] <- fit$XLCAL[fit$CENSFLAG, ,drop=FALSE] %*% | ||
| # fit$PARAML[seq(NPAR)] + fit$RESID[fit$CENSFLAG] | ||
| # | ||
| # Other Info | ||
| parms <- fit$PARAML[seq(fit$NPAR)] | ||
| parnames <- colnames(fit$XLCAL) | ||
| } | ||
| # do it | ||
| # set up res and coeff storage | ||
| pre <- numeric(NOBS) | ||
| coeff <- matrix(0, nrow=NOBS, ncol=NPAR) | ||
| for(i in seq(NOBS)) { | ||
| tmp <- censReg_AMLE.fit(Y[-i], X[-i,], dist) | ||
| coeff[i,] <- tmp$PARAML[-(NPAR + 1L)] | ||
| pre[i] <- Yeff[i] - coeff[i,, drop=FALSE] %*% t(X[i,,drop=FALSE]) | ||
| } | ||
| # Compute the jackknife bias and variance of coeffs | ||
| out <- (NOBS - 1)*(rep(parms, each=NOBS) - coeff) | ||
| bias <- -1/NOBS*colSums(out) | ||
| var <- 1/(NOBS*(NOBS-1)) * (colSums(out^2) - NOBS * bias^2) | ||
| coef <- cbind(est=parms, bias=bias, stderr=sqrt(var)) | ||
| rownames(coef) <- parnames | ||
| retval <- list(coef=coef, coefficients=coeff, | ||
| pctcens=pctCens(Y)) | ||
| # Include press if no censoring | ||
| if(retval$pctcens == 0) { | ||
| retval$press <- sum(pre^2) | ||
| retval$pre <- pre | ||
| } | ||
| class(retval) <- "jackStats" | ||
| return(retval) | ||
| } |
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| #' Print Results | ||
| #' | ||
| #' Print the results of a jackknife analysis of a censored regression. | ||
| #' | ||
| #' @param x an object of class "jackStats"---output from \code{jackStats}. | ||
| #' @param digits the number of significant digits to print. | ||
| #' @param \dots further arguments passed to or from other methods. | ||
| #' @return The object \code{x} is returned invisibly. | ||
| #' @note The printed output includes the original original estimate, | ||
| #'the jackknife bias and standard error and the relative bias for | ||
| #'each parameter in the regression model. | ||
| #' | ||
| #' @seealso \code{\link{loadReg}} | ||
| #' @keywords utilities | ||
| #' @export | ||
| #' @method print jackStats | ||
| print.jackStats <- function(x, digits=4, ...) { | ||
| ## | ||
| if(x$pctcens == 0) { | ||
| cat("jackknife estimates:\n\n", | ||
| "PRESS: ", signif(x$press, digits), "\nCoefficients:\n", sep="") | ||
| } else { | ||
| cat("jackknife estimates:\n\nCoefficients:\n", sep="") | ||
| } | ||
| coef <- x$coef | ||
| # compute the relative bias | ||
| coef <- cbind(coef, abs(coef[,2L]/coef[,3L])) | ||
| colnames(coef) <- c("Estimate", "Bias", "Std. Error", "Rel. Bias") | ||
| print(coef, digits=digits) | ||
| invisible(x) | ||
| } |
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