Description Usage Arguments Details Value Note References See Also Examples
View source: R/ThresholdROC3states.R
This function calculates the threshold estimates and their corresponding confidence intervals in a threestate setting.
1 2 3 4 5 
k1 
vector containing the first sample values. 
k2 
vector containing the second sample values. 
k3 
vector containing the third sample values. 
rho 
3dimensional vector of prevalences. 
costs 
cost matrix. Costs should be entered as a 3x3 matrix, where the first row corresponds to the costs associated with the classification of subjects in state 1 (C11, C12 and C13), second row corresponds to the costs associated with the classification of subjects in state 2 (C21, C22 and C23) and the third row corresponds to the costs associated with classification of subjects in state 3 (C31, C32, C33), where Cij is the cost of classifying an individual of class i as class j. Default cost values are a combination of costs that leads to the same thresholds as the Youden index method (see References for details). 
dist1 
distribution to be assumed for the first population. Default, 
dist2 
distribution to be assumed for the second population. Default, 
dist3 
distribution to be assumed for the third population. Default, 
start 
when the three distributions 
ci 
should a confidence interval be calculated? Default, 
ci.method 
method to be used for the confidence intervals calculation. The user can specify just the initial letters. Default, 
B 
number of bootstrap resamples when 
alpha 
significance level for the confidence interval. Default, 0.05. 
na.rm 
a logical value indicating whether 
Parameters dist1
, dist2
and dist3
can be chosen between the following 2parameter distributions: "beta"
, "cauchy"
, "chisq"
(chisquared), "gamma"
, "lnorm"
(lognormal), "logis"
(logistic), "norm"
(normal) and "weibull"
.
For parameter ci.method
the user can choose between "param"
(parametric confidence intervals are calculated when assuming a trinormal underlying model) and "boot"
(the confidence intervals are calculated by bootstrap).
When at least one of the distributions is not "norm"
, the function internally uses the thresTH3()
function, which requires two intervals in which the two thresholds are expected to be found. These intervals are determined by the default values of thresTH3()
. When all the distributions are "norm"
, the function uses the nlm()
function, which requires two starting values (passed through the argument start
).
An object of class thres3
, which is a list with two components:
T 
a list of at least ten components:
When not all distributions are normal,

CI 
When
When
When 
It is assumed that k1
is the sample with lower values and k3
is the one taking higher values. If that is not the case, k1
, k2
and k3
(and the corresponding parameters) are reordered as needed.
Efron B, Tibshirani RJ. (1993). An introduction to the bootstrap, Chapman & Hall.
Skaltsa K, Jover L, Fuster D, Carrasco JL. (2012). Optimum threshold estimation based on cost function in a multistate diagnostic setting. Statistics in Medicine, 31:10981109.
thresTH3
, plot.thres3
, lines.thres3
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  set.seed(1234)
n < 100
k1 < rlnorm(n)
k2 < rnorm(n, 3, 1)
k3 < rnorm(n, 5, 1)
rho < c(1/3, 1/3, 1/3)
# assuming trinormality
start < c(mean(k1), mean(k3))
thres3(k1, k2, k3, rho, dist1="norm", dist2="norm", dist3="norm", start=start, ci.method="param")
# not assuming trinormality
thres3(k1, k2, k3, rho, B=1000, ci.method="boot", dist1="lnorm", dist2="norm", dist3="norm")
# supress confidence intervals calculation
thres3(k1, k2, k3, rho, ci=FALSE, dist1="lnorm", dist2="norm", dist3="norm")

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