VFP (version 1.4.3)

predict_mean: Finding X-Value for Given Y-Value Using a Bisection-Approach.

Description

For given variability-values (Y-axis) on one of three scales (see 'type'), those values on the X-axis are determined which give fitted values equal to the specification.

Usage

predict_mean(
  obj,
  type = c("vc", "sd", "cv"),
  model.no = NULL,
  alpha = 0.05,
  newdata = NULL,
  tol = 1e-04,
  ci = TRUE,
  ...
)

Value

(data.frame) with variables "Mean" (X-value), "VC", "SD" or "CV" depending on 'type', "Diff" the difference to the specified Y-value, "LCL" and "UCL" as limits of the 100x(1-alpha)% CI.

Arguments

obj

(object) of class 'VFP'

type

(character) "vc" = variance, "sd" = standard deviation = sqrt(variance), "cv" = coefficient of variation

model.no

(integer) specifying which model to use in case 'obj' represents multiple fitted models

alpha

(numeric) value specifying the 100x(1-alpha)% confidence interval for the predicted value(s)

newdata

(numeric) values representing variability-values on a specific scale ('type')

tol

(numeric) tolerance value relative to 'newdata' specifying the stopping criterion for the bisection algorithm, also used to evaluate equality of lower and upper bounds in a bisection step for checking whether a boundary can be determined or not

ci

(logical) indicates whether confidence intervals for predicted concentrations are required (TRUE) or not (FALSE), if 'newdata' contains many values the overall computation time can be minimized to 1/3 leaving out runs of the bisection-algorithm for LCL and UCL

...

additional parameter passed forward or used internally

Author

Andre Schuetzenmeister

Details

This is achieved using a bisection algorithm which converges according to the specified tolerance 'tol'. In case of 'type="cv"', i.e. if specified Y-values are coefficients of variation, these are interpreted as percentages (15 = 15%).

See Also

fit_vfp, predict.VFP, plot.VFP

Examples

# \donttest{

# perform variance component analyses first
library(VCA)
data(CA19_9)
fits.CA19_9 <- anovaVCA(result~site/day, CA19_9, by="sample")

# extract repeatability
mat.CA19_9 <- get_mat(fits.CA19_9, "error")
res.CA19_9 <- fit_vfp(mat.CA19_9, 1:9)
summary(res.CA19_9)
print(res.CA19_9)

# predict CA19_9-concentration with 5\% CV
predict_mean(res.CA19_9, newdata=5) 

# this is used in function plot.VFP as well
plot(res.CA19_9, Prediction=list(y=5), type="cv")
plot(res.CA19_9, Prediction=list(y=5), type="cv", 
		xlim=c(0, 80), ylim=c(0, 10))
# }