dwlm

0th

Percentile

Solves the doubly weighted simple linear model

Fits the simple linear model using weights on both the predictor and the response

Keywords
dwlm
Usage
dwlm(x, y, weights.x = rep(1, length(x)),
weights.y = rep(1, length(y)), subset = rep(TRUE, length(x)),
sigma2.x = rep(0, length(x[subset])),
from = min((y[subset] - mean(y[subset]))/(x[subset] - mean(x[subset]))),
to = max((y[subset] - mean(y[subset]))/(x[subset] - mean(x[subset]))),
n = 1000, max.iter = 100, tol = .Machine$double.eps^0.25, method = c("MLE", "SSE", "R"), trace = FALSE, coef.H0 = c(0,1), alpha = 0.05) Arguments x the predictor values y the response values weights.x the weight attached to the predictor values weights.y the weight attached to the response values subset a logical vector or a numeric vector with the positions to be considered sigma2.x numeric, the variance due to heterogeneity in the predictor value from numeric, the lowest value of the slope to search for a solution to numeric, the highest value of the slope to search for a solution n integer, the number of slices the search interval (from, to) is divided in max.iter integer, the maximum number of allowed iterations tol numeric, the maximum allowed error tolerance method string, the selected method (MSE, SSE, R) as described in the references. trace logical, flag to keep track of the solution coef.H0 numeric vector, the coeffients to test against to for significant difference alpha numeric, the significance level for estimating the Degrees of Equivalence (DoE) Value A list with the following elements: x original pedictor values y original response values weights.x original predictor weigths weights.y original response weights subset original subset parameter coef.H0 original parameter value for hypothesis testing against to coefficients estimated parameters for the linear model solution cov.mle Maximum Likelihood Estimafor for the covariance matrix cov.lse Least Squares Estiimator for the covariance matrix x.hat fitted true predictor value, this is a latent (unobserved) variable y.hat fitted true response value, this is a latent (unobserved) variable df.residuals degrees of freedom MSE mean square error of the solution DoE pointwise degrees of equivalente between the observed and the latent variables u.DoE.mle uncerainty of the pointwise degrees of equivalence using maximum likelihood solution u.DoE.lse uncertainty of the pointwise degrees of equivalence using least squares solution dm.dXj partial gradient of the slope with respect to the jth predictor variable dm.dYj partial gradient of the slope with respect to the jth response variable dc.dXj partial gradient of the intercept with respect to the jth predictor variable dc.dYj partial gradient of the intercept with respect to the jth response variable curr.iter number of iterations curr.tol reached tolerance References Reed, B.C. (1989) "Linear least-squares fits with errors in both coordinates", American Journal of Physics, 57, 642. https://doi.org/10.1119/1.15963 Reed, B.C. (1992) "Linear least-squares fits with errors in both coordinates. II: Comments on parameter variances", American Journal of Physics, 60, 59. https://doi.org/10.1119/1.17044 Ripley and Thompson (1987) "Regression techniques for the detection of analytical bias", Analysts, 4. https://doi.org/10.1039/AN9871200377 See Also lm Aliases • dwlm Examples # NOT RUN { # Example ISO 28037 Section 7 X.i<- c(1.2, 1.9, 2.9, 4.0, 4.7, 5.9) Y.i<- c(3.4, 4.4, 7.2, 8.5, 10.8, 13.5) sd.X.i<- c(0.2, 0.2, 0.2, 0.2, 0.2, 0.2) sd.Y.i<- c(0.2, 0.2, 0.2, 0.4, 0.4, 0.4) # values obtained on sep-26, 2016 dwlm.res <- dwlm(X.i, Y.i, 1/sd.X.i^2, 1/sd.Y.i^2, from = 0, to=3, coef.H0=c(0, 2), tol = 1e-10) dwlm.res$coefficients
dwlm.res$cov.mle[1, 2] dwlm.res$curr.tol
dwlm.res\$curr.iter
# }

Documentation reproduced from package dwlm, version 0.1.0, License: GPL (>= 2)

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