calib.fit: General Standard Curve Fitting

In calibFit: Statistical models and tools for assay calibration

Description

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.

Usage

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",  "log.thpl.pom", "thpl.pom", "log.thpl", 
"thpl", "quad.pom", "quad", "lin.pom","lin"))

Arguments

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, quad.pom, linear regression with a quadratic term fit by POM, quad, standard linear regression fit for quadratic model, lin.pom, linear regression fit by POM, lin standard linear regression fit.

Value

coefficients:	Estimates of the coefficients
se.coefficients:	Estimates of the standard errors for the coefficients
sigma:	Standard deviation of model
cov.unscaled:	Unscaled variance-covariance matrix of the coefficients
pom:	Whether or not POM model was used, TRUE or FALSE.
theta:	The estimated value of POM parameter theta
df.residual:	The residual degree of freedom
fitted.values:	The estimated fitted values
residuals:	The values of the residuals
method:	Which algorithm was used to do the optimization (i.e. ML, RML, EM, etc.)
kused:	The number of iteration to convergence of the GLS fit
status:	Indicated whether the algorithm converged
x:	The x values
y:	The y values
logParm:	Indicated whether a log parameterization of the model used
m:	The number of repeated measures used in the model
cv:	Coefficient of variation used
mdc:	Minimum detectable concentration
rdl:	Reliable detection limit
loq:	Limit of quantitation
gradient:	The gradient matrix based off of the final parameter estimates
lof.test:	An slot returing information from a lack of fit (LOF) test.
var.model:	The type of variance model used
conf.level:	Confidence level used
type:	Whether, fpl, tpl, thpl, lin or quad was used.
rdlwarn:	The general model type used, fpl, thpl or lin

Author(s)

Perry Haaland, Elaine McVey, Daniel Samarov

References

Davidian and Haaland 1990

See Also

calib-class, calib.fit, calib, plot, resid, residuals, show, summary, print, fitted, coefficients, coef

Examples

data(HPLC)
attach(HPLC)
model <- calib.fit(Concentration, Response)

calibFit documentation built on May 2, 2019, 6:15 p.m.