calib.fit {calib} | R Documentation |
calib.fit utilizes two main model types, linear and logistic, for the purpose of standard curve fitting. It also incorporates several alternatives within each model type to allow for more flexible and reliable fitting.
calib.fit(x, y, b1start, b2start, b3start, b4start, calcDiagnostics = TRUE, m, cv = 0.2, conf = 0.95, mx = 50, lof.calc = T, lowLim = 0.001, type = c("log.fpl.pom", "fpl.pom", "log.fpl", "fpl", "log.tpl.pom", "tpl.pom", "log.tpl", "tpl", "quad.pom", "log.thpl.pom", "thpl.pom", "log.thpl", "thpl", "lin.pom"))
x |
Independent variable (for example dose) |
y |
Dependent variable (for example response) |
b1start |
Starting value for b1 in either the fpl , tpl or thpl models. |
b2start |
Starting value for b2 in either the fpl , tpl or thpl models. |
b3start |
Starting value for b3 in either the fpl , tpl or thpl models. |
b4start |
Starting value for b4 in either the fpl or tpl models. |
calcDiagnostics |
Should diagnostics (i.e. mdc, rdl and loq) be calculated. Default to TRUE . |
m |
Number of repeated measurements |
cv |
The acceptable coefficient of variation. The limits of quantitation are calculated with this constraint |
conf |
The confidence level used for the determining the prediction interval |
mx |
The maximum number of iterations used in the non-linear least-squares fit |
lof.calc |
Should the lack of fit statistics be calculated. The default is TRUE. |
lowLim |
If there are x values equal to zero what (small) positive value be used to
approximate it. Defaults is 0.001. |
type |
Can take the values log.fpl.pom , the log parameterized four
parameter logistic regression (fpl ) fit with power of the mean (POM),
fpl.pom , fpl fit with POM, log.fpl , log parameterized fpl , fpl ,
standard fpl , thpl.pom , three parameter logistic (thpl ) regression fit
with POM, thpl without POM thpl , log parameterized POM thpl ,
log.thpl.pom , log parameterized thpl , log.thpl , lin.quad.pom ,
linear regression with
a quadrattic term fit by POM, lin.pom , linear regression fit by POM. |
|
Estimates of the coefficients |
|
Estimates of the standard errors for the coefficients |
|
Standard deviation of model |
|
Unscaled variance-covariance matrix of the coefficients |
|
Whether or not POM model was used, TRUE or FALSE. |
|
The estimated value of POM parameter theta |
|
The residual degree of freedom |
|
The estimated fitted values |
|
The values of the residuals |
|
Which algorithm was used to do the optimization (i.e. ML, RML, EM, etc.) |
|
The number of iteration to convergence of the GLS fit |
|
Indicated whether the algorithm converged |
|
The x values |
|
The y values |
|
Indicated whether a log parameterization of the model used |
|
The number of repeated measures used in the model |
|
Coefficient of variation used |
|
Minimum detectable concentration |
|
Reliable detection limit |
|
Limit of quantitation |
|
The gradient matrix based off of the final parameter estimates |
|
An slot returing information from a lack of fit (LOF) test. |
|
The type of variance model used |
|
Confidence level used |
|
Whether, fpl, tpl, thpl, lin or quad was used. |
|
The general model type used, fpl, thpl or lin |
Perry Haaland, Elaine McVey, Daniel Samarov
Davidian and Haaland 1990
calib-class, calib.fit, calib, plot, resid, residuals,
show, summary, print, fitted, coefficients,
coef
data(HPLC) attach(HPLC) model <- calib.fit(Concentration, Response)