perform Deming regression without intercept
There is a bug in the function when you remove the intercept. I'm going to report it.
It is easy to fix, you just have to change 2 lines in the original function. The print does not work correctly, but it is possible to interpret the output.
deming.aux <- function (formula, data, subset, weights, na.action, cv = FALSE, xstd, ystd, stdpat, conf = 0.95, jackknife = TRUE, dfbeta = FALSE, x = FALSE, y = FALSE, model = TRUE) { deming.fit1 <- getAnywhere(deming.fit1)[[2]][[1]] deming.fit2 <- getAnywhere(deming.fit2)[[2]][[1]] Call <- match.call() indx <- match(c("formula", "data", "weights", "subset", "na.action", "xstd", "ystd"), names(Call), nomatch = 0) if (indx[1] == 0) stop("A formula argument is required") temp <- Call[c(1, indx)] temp[[1]] <- as.name("model.frame") mf <- eval(temp, parent.frame()) Terms <- terms(mf) n <- nrow(mf) if (n < 3) stop("less than 3 non-missing observations in the data set") xstd <- model.extract(mf, "xstd") ystd <- model.extract(mf, "ystd") Y <- as.matrix(model.response(mf, type = "numeric")) if (is.null(Y)) stop("a response variable is required") wt <- model.weights(mf) if (length(wt) == 0) wt <- rep(1, n) usepattern <- FALSE if (is.null(xstd)) { if (!is.null(ystd)) stop("both of xstd and ystd must be given, or neither") if (missing(stdpat)) { if (cv) stdpat <- c(0, 1, 0, 1) else stdpat <- c(1, 0, 1, 0) } else { if (any(stdpat < 0) || all(stdpat[1:2] == 0) || all(stdpat[3:4] == 0)) stop("invalid stdpat argument") } if (stdpat[2] > 0 || stdpat[4] > 0) usepattern <- TRUE else { xstd <- rep(stdpat[1], n) ystd <- rep(stdpat[3], n) } } else { if (is.null(ystd)) stop("both of xstd and ystd must be given, or neither") if (!is.numeric(xstd)) stop("xstd must be numeric") if (!is.numeric(ystd)) stop("ystd must be numeric") if (any(xstd <= 0)) stop("xstd must be positive") if (any(ystd <= 0)) stop("ystd must be positive") } if (conf < 0 || conf >= 1) stop("invalid confidence level") if (!is.logical(dfbeta)) stop("dfbeta must be TRUE or FALSE") if (dfbeta & !jackknife) stop("the dfbeta option only applies if jackknife=TRUE") X <- model.matrix(Terms, mf) if (ncol(X) != (1 + attr(Terms, "intercept"))) stop("Deming regression requires a single predictor variable") xx <- X[, ncol(X), drop = FALSE] if (!usepattern) fit <- deming.fit1(xx, Y, wt, xstd, ystd, intercept = attr(Terms, "intercept")) else fit <- deming.fit2(xx, Y, wt, stdpat, intercept = attr(Terms, "intercept")) yhat <- fit$coefficients[1] + fit$coefficients[2] * xx fit$residuals <- Y - yhat if (x) fit$x <- X if (y) fit$y <- Y if (model) fit$model <- mf na.action <- attr(mf, "na.action") if (length(na.action)) fit$na.action <- na.action fit$n <- length(Y) fit$terms <- Terms fit$call <- Call fit}deming.aux(y ~ x + 0, df, xstd=sy, ystd=sy)$`coefficients`[1] 0.000000 4.324481$se[1] 0.2872988 0.7163073$sigma[1] 2.516912$residuals [,1]1 9.194995132 2.130379573 3.000648864 2.167519055 0.001687296 4.758342657 3.441082368 6.400280859 6.6353103910 -1.48624851$model y x (xstd) (ystd)1 2.1459817 -1.6300251 0.48826221 0.488262212 1.3163362 -0.1882407 0.46002166 0.460021663 1.5263967 -0.3409084 0.55771660 0.557716604 -0.9078000 -0.7111417 0.81145673 0.811456735 -1.6768719 -0.3881527 0.01563191 0.015631916 -0.6114545 -1.2417205 0.41675425 0.416754257 -0.7783790 -0.9757150 0.82498713 0.824987138 1.1240046 -1.2200946 0.84072712 0.840727129 -0.3091330 -1.6058442 0.35926078 0.3592607810 0.7215432 0.5105333 0.23674788 0.23674788$n[1] 10$termsy ~ x + 0...To adapt the function I have performed, these steps:
1 .- Load the internal functions of the package.
deming.fit1 <- getAnywhere(deming.fit1)[[2]][[1]]deming.fit2 <- getAnywhere(deming.fit2)[[2]][[1]]2 .- Locate the problem and solve it, executing the function step by step with an example.
Y <- as.matrix(model.response(mf, type = "numeric"))...xx <- X[, ncol(X), drop = FALSE]3 .- Fix other possible error generated by the changes.
In this case, delete the class of the output to avoid an error in the print.
Bug report:
Terry Therneau (the author of deming) uploaded a new version to CRAN, with this problem solved.