| Title: | Deming, Theil-Sen, Passing-Bablock and Total Least Squares Regression |
|---|---|
| Description: | Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions. |
| Authors: | Terry Therneau |
| Maintainer: | Terry Therneau <[email protected]> |
| License: | LGPL (>= 2) |
| Version: | 1.4-1 |
| Built: | 2024-10-25 06:19:00 UTC |
| Source: | CRAN |
Arsenate(V) ion in natural river waters, as determined by two assay methods.
data(arsenate)data(arsenate)
A data frame with 30 observations on the following 4 variables.
aasmicrograms/liter, by continuous selective reduction and atomic absorbtion spectectromitry
se.aasestimated standard error of the result
aesmicrograms/liter, by non-selective reduction, cold trapping, and atomic emission spectroscopy
se.aesestimated standard error of the result
The data is found in BD Ripley and M Thompson, Regression techniques for the detection of analytical bias, Analyst 112:377-383, 1987.
Find the MLE line relating x and y when both are measured with error. When the variances of x and y are constant and equal, this is the special case of Deming regression.
deming(formula, data, subset, weights, na.action, cv=FALSE, xstd, ystd, stdpat, conf=.95, jackknife=TRUE, dfbeta=FALSE, id, x=FALSE, y=FALSE, model=TRUE)deming(formula, data, subset, weights, na.action, cv=FALSE, xstd, ystd, stdpat, conf=.95, jackknife=TRUE, dfbeta=FALSE, id, x=FALSE, y=FALSE, model=TRUE)
formula |
a model formula with a single continuous response on the left and a single continuous predictor on the right. |
data |
an optional data frame, list or environment containing the variables in the model. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. |
I
na.action |
a function which indicates what should happen when the data
contain NAs. The default is set by the na.action setting of |
xstd |
optional, the variable name of a vector that contains explicit
error values for each of the predictor values. This data overrides
the |
ystd |
optional, the variable name of a vector that contains explicit
error values for each of the response values. This data overrides
the |
cv |
constant coefficient of variation? The default of
false corresponds to ordinary Deming regression, i.e., an assumption
of constant error. A value of |
stdpat |
pattern for the standard deviation, see comments below.
If this is missing the default is based on the |
conf |
confidence level for the confidence interval |
jackknife |
compute a jackknife estimate of variance. |
dfbeta |
return the dfbeta matrix from the jackknife computation. |
id |
grouping values for the grouped jackknife |
x, y, model
|
logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, or the response) is returned. |
Ordinary least squares regression minimizes the sum of distances between the y values and the regression line, Deming regression minimizes the sum of distances in both the x and y direction. As such it is often appropriate when both x and y are measured with error. A common use is in comparing two assays, each of which is designed to quantify the same compound.
The standard deviation of the x variate variate will often be of the form
for c and d some constants,
where is the overal scale factor; similarly for y with
constants e and f.
Ordinary Deming regression corresponds to c=1 and d=0, i.e., constant
variation over the range of the data.
A more realistic assumption for many laboratory measurments is c=0 and
d=1, i.e., constant coefficient of variation.
Laboratory tests are often assumed to have constant coefficient of
variation rather than constant variance.
There are 3 ways to specify the variation. The first is to directly
set the pattern of (c,d,e,f) for the $x$ and $y$ standard deviations.
If this is omitted, a default of (0,1,0,1) or (1,0,1,0) is chosen, based
on whether the cv option is TRUE or FALSE, respectively.
As a third option, the user can specifiy xstd and ystd
directly as vectors of data values. In this last case any values for
the stdpat or ccs options are ignored.
Note that the two calls deming(y ~ x, cv=TRUE) and
deming(y ~ x, xstd=x, ystd=y) are subtly different. In the second
the standard deviation values are based on the data, and in the first
they will be based on the fitted values. The two outcomes will often be
nearly identical.
Although a cv option of TRUE is often much better
justified than an assumption of constant variance,
assumpting a perfectly constant CV can also be questionable.
Most actual biologic assays will have both a constant and a
proportional component of error, with the former becoming dominant for values
near zero and the latter dominant elsewhere.
If all of the results are far from zero, however, the constant part may
be ignored.
Many times an assay will be done in duplicate, in which case the paired
results can have correlated errors due to sample handling or
manipulation that preceeds splitting it into separate aliquots for
assay, and the ordinary variance will be too small (as it also is when
the duplicate values are averaged together before fitting the regression
line.) A correct grouped jackknife estimate of variance is obtained in
this case by setting id to a vector of sample identifiers.
a object of class 'deming' containing the components:
coefficient |
the coefficient vector, containing the intercept and slope. |
variance |
The jackknife or bootstrap estimate of variance |
ci |
bootstrap confidence intervals, if nboot >0 |
dfbeta |
pptionally, the dfbeta residuals. A 2 column matrix, each row is the change in the coefficient vector if that observation is removed from the data. |
Terry Therneau
BD Ripley and M Thompson, Regression techniques for the detection of analytical bias, Analyst 112:377-383, 1987.
K Linnet, Estimation of the linear relationship between the measurements of two methods with proportional errors. Statistics in Medicine 9:1463-1473, 1990.
# Data from Ripley and Thompson fit <- deming(aes ~ aas, data=arsenate, xstd=se.aas, ystd=se.aes) print(fit) ## Not run: Coef se(coef) lower 0.95 upper 0.95 Intercept 0.1064 0.2477 -0.3790 0.5919 Slope 0.9730 0.1430 0.6928 1.2532 Scale= 1.358 ## End(Not run) plot(1:30, fit$dfbeta[,2]) #subject 22 has a large effect on the slope # Constant proportional error fit (constant CV) fit2 <- deming(new.lot ~ old.lot, ferritin, cv=TRUE, subset=(period==3))# Data from Ripley and Thompson fit <- deming(aes ~ aas, data=arsenate, xstd=se.aas, ystd=se.aes) print(fit) ## Not run: Coef se(coef) lower 0.95 upper 0.95 Intercept 0.1064 0.2477 -0.3790 0.5919 Slope 0.9730 0.1430 0.6928 1.2532 Scale= 1.358 ## End(Not run) plot(1:30, fit$dfbeta[,2]) #subject 22 has a large effect on the slope # Constant proportional error fit (constant CV) fit2 <- deming(new.lot ~ old.lot, ferritin, cv=TRUE, subset=(period==3))
For each of seven periods in which there was a new batch of reagent, a small set of patient samples was assayed for ferritin content using both the old and new batches.
data(ferritin)data(ferritin)
A data frame with 162 observations on the following 4 variables.
samplesample identifier
periodthe transition number, 1 to 7
old.lotassay result using the old lot of the reagent
new.lotassay result using the new lot
The samples from each period are distinct.
In the second data set ferritin2 outliers have been
added to the data for period 2, excess noise added to one lot in
period 4, and deterministic laboratory error to period 6.
Blinded data from a clinical laboratory.
data(ferritin) temp <- ferritin[ferritin$period <4,] plot(temp$old.lot, temp$new.lot, type='n', log='xy', xlab="Old lot", ylab="New Lot") text(temp$old.lot, temp$new.lot, temp$period, col=temp$period)data(ferritin) temp <- ferritin[ferritin$period <4,] plot(temp$old.lot, temp$new.lot, type='n', log='xy', xlab="Old lot", ylab="New Lot") text(temp$old.lot, temp$new.lot, temp$period, col=temp$period)
Passing-Bablock regression is a robust regression method for two variables that is symmetric in x and y.
pbreg(formula, data, subset, weights, na.action, conf=.95, nboot = 0, method=1, eps=sqrt(.Machine$double.eps), x = FALSE, y = FALSE, model = TRUE)pbreg(formula, data, subset, weights, na.action, conf=.95, nboot = 0, method=1, eps=sqrt(.Machine$double.eps), x = FALSE, y = FALSE, model = TRUE)
formula |
a model formula with a single continuous response on the left and a single continuous predictor on the right. |
data |
an optional data frame, list or environment containing the variables in the model. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. |
I
na.action |
a function which indicates what should happen when the data
contain NAs. The default is set by the na.action setting of |
conf |
the width of the computed confidence limit |
nboot |
number of bootstrap samples used to compute standard errors and/or confidence limits. |
method |
which of 3 related methods to use for the computation |
eps |
the tolerance used to detect tied values in x and y |
x, y, model
|
logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, or the response) is returned. |
There are 3 related estimators under this heading. Method 1 is the original Passing-Bablock (1983) method, which is equal to a Theil-Sen estimate symmetric about the y=x line. Method 2 is the first extended method of the 1988 paper, designed to be scale invariant. Method 3 is the second extended method from the 1985 paper, the "scissors" estimate which is symmetric about both the x and y axes, and is also scale invariant.
The default confidence interval estimate is based on that derived by Sen, which is in turn based on the relationship to Kendall's tau. A theoretical justification of this approach for methods 2 and 3 is lacking, and we recommend a bootstrap based confidence interval based on 500-1000 replications.
pbreg returns an object of class "pbreg".
The generic accessor functions
coef, fitted and residuals
extract the relevant components.
Terry Therneau
Passing, H. and Bablock, W. (1983). A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in Clinical Chemistry, Part I. J. Clin. Chem. Clin. Biochem. 21:709-720.
Passing, H. and Bablock, W. (1984). Comparison of several regression procedures for method comparison studies and determination of sample size. Application of linear regression procedures for method comparison studies in Clinical Chemistry, Part II. J. Clin. Chem. Clin. Biochem. 22:431-435.
Bablock, W., Passing, H., Bender, R. and Schneider, B. (1988). A general regression procedure for method transformations. Application of linear regression procedures for method comparison studies in Clinical Chemistry, Part III. J. Clin. Chem. Clin. Biochem. 26:783-790.
afit1 <- pbreg(aes ~ aas, data= arsenate) afit2 <- pbreg(aas ~ aes, data= arsenate) rbind(coef(afit1), coef(afit2)) # symmetric results 1/coef(afit1)[2]afit1 <- pbreg(aes ~ aas, data= arsenate) afit2 <- pbreg(aas ~ aes, data= arsenate) rbind(coef(afit1), coef(afit2)) # symmetric results 1/coef(afit1)[2]
Thiel-Sen regression is a robust regression method for two variables. The symmetric option gives a variant that is symmentric in x and y.
theilsen(formula, data, subset, weights, na.action, conf=.95, nboot = 0, symmetric=FALSE, eps=sqrt(.Machine$double.eps), x = FALSE, y = FALSE, model = TRUE)theilsen(formula, data, subset, weights, na.action, conf=.95, nboot = 0, symmetric=FALSE, eps=sqrt(.Machine$double.eps), x = FALSE, y = FALSE, model = TRUE)
formula |
a model formula with a single continuous response on the left and a single continuous predictor on the right. |
data |
an optional data frame, list or environment containing the variables in the model. |
subset |
an optional vector specifying a subset of observations to be used in the fitting process. |
weights |
an optional vector of weights to be used in the fitting process. |
na.action |
a function which indicates what should happen when the data
contain NAs. The default is set by the na.action setting of |
conf |
the width of the computed confidence limit. |
nboot |
number of bootstrap samples used to compute standard errors and/or confidence limits. If this is 0 or missing then an asypmtotic formula is used. |
symmetric |
compute an estimate whose slope is symmetric in x and y. |
eps |
the tolerance used to detect tied values in x and y |
x, y, model
|
logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, or the response) is returned. |
One way to characterize the slope of an ordinary least squares line
is that =0, where where is the
correlation coefficient and r is the vector of residuals from the
fitted line.
Thiel-Sen regression replaces with Kendall's
, a non-parametric alternative.
It it resistant to outliers while retaining good statistical
efficiency.
The symmetric form of the estimate is based on solving the inverse
equation: find that rotation of the original data such that
for the rotated data.
(In a similar fashion,the rotation such the least squares slope is zero yields
Deming regression.)
In this case it is possible to have multiple solutions, i.e., slopes
that yeild a 0 correlation, although this is rare unless the deviations
from the fitted line are large.
The default confidence interval estimate is based on the result of Sen, which is in turn based on the relationship to Kendall's tau and is essentially an inversion of the confidence interval for tau. The argument does not extend to the symmetric case, for which we recommend using a bootstrap confidence interval based on 500-1000 replications.
theilsen returns an object of class "theilsen" with components
coefficients |
the intercept and slope |
residuals |
residuals from the fitted line |
angle |
if the symmetric option is chosen, this contains all of the solutions for the angle of the regression line |
n |
number of data points |
model, x, y
|
optional componets as specified by the x, y, and model arguments |
terms |
the terms object corresponding to the formula |
na.action |
na.action information, if applicable |
call |
a copy of the call to the function |
The generic accessor functions
coef, residuals, and terms
extract the relevant components.
Terry Therneau
Thiel, H. (1950), A rank-invariant method of linear and polynomial regression analysis. I, II, III, Nederl. Akad. Wetensch., Proc. 53: 386-392, 521-525, 1397-1412.
Sen, P.B. (1968), Estimates of the regression coefficient based on Kendall's tau, Journal of the American Statistical Association 63: 1379-1389.
afit1 <- theilsen(aes ~ aas, symmetric=TRUE, data= arsenate) afit2 <- theilsen(aas ~ aes, symmetric=TRUE, data= arsenate) rbind(coef(afit1), coef(afit2)) # symmetric results 1/coef(afit1)[2]afit1 <- theilsen(aes ~ aas, symmetric=TRUE, data= arsenate) afit2 <- theilsen(aas ~ aes, symmetric=TRUE, data= arsenate) rbind(coef(afit1), coef(afit2)) # symmetric results 1/coef(afit1)[2]