Description Usage Arguments Details Value
View source: R/validation_MRMC_Create_dataList_MRMC_Hit_from_rate_etc.R
In order to describe what this function calculates explicitly, let us denote a specified true model parameter by θ_0, from which fake datasets are replicated and denoted by:
D_1,D_2,...,D_k,... D_K.
We obtains estimates
θ(D_1),...,θ(D_K)
for each replicated dataset. Using these estimates, we calculate the mean of the absolute errors (= an absolute difference between estimates and a true parameter θ_0 ), namely,
\frac{1}{K}∑_{k=1}^K  θ(D_k)  θ_0 ,
or the variance of estimates:
\frac{1}{K}∑_{k=1}^K ( θ(D_k)  \frac{1}{K}∑_{k=1}^K θ(D_k) )^2.
Revised 2019 Nov 1
Revised 2020 Jan
Revised 2020 March
1 2 3 4 5 6 7 8 9 10 11 12  error_MRMC(
replication.number = 2,
initial.seed = 123,
mu.truth = BayesianFROC::mu_truth,
v.truth = BayesianFROC::v_truth,
z.truth = BayesianFROC::z_truth,
NI = 200,
NL = 1142,
ModifiedPoisson = FALSE,
summary = FALSE,
ite = 1111
)

replication.number 
For fixed number of lesions, images, the dataset of hits and false alarms are replicated, and the number of replicated datasets are specified by this variable. 
initial.seed 
The variable

mu.truth 
array of dimension (M,Q). Mean of the signal distribution of binormal assumption. 
v.truth 
array of dimension (M,Q). Standard Deviation of represents the signal distribution of binormal assumption. 
z.truth 
This is a parameter of the latent Gaussian assumption for the noise distribution. 
NI 
Number of Images. 
NL 
Number of Lesions. 
ModifiedPoisson 
Logical, that is If Similarly, If For more details, see the author's paper in which I explained per image and per lesion. (for details of models, see vignettes , now, it is omiited from this package, because the size of vignettes are large.) If \frac{F_1+F_2+F_3+F_4+F_5}{N_L}, \frac{F_2+F_3+F_4+F_5}{N_L}, \frac{F_3+F_4+F_5}{N_L}, \frac{F_4+F_5}{N_L}, \frac{F_5}{N_L}, where N_L is a number of lesions (signal). To emphasize its denominator N_L, we also call it the False Positive Fraction (FPF) per lesion. On the other hand, if \frac{F_1+F_2+F_3+F_4+F_5}{N_I}, \frac{F_2+F_3+F_4+F_5}{N_I}, \frac{F_3+F_4+F_5}{N_I}, \frac{F_4+F_5}{N_I}, \frac{F_5}{N_I}, where N_I is the number of images (trial). To emphasize its denominator N_I, we also call it the False Positive Fraction (FPF) per image. The model is fitted so that
the estimated FROC curve can be ragraded
as the expected pairs of FPF per image and TPF per lesion ( or as the expected pairs of FPF per image and TPF per lesion ( If On the other hand, if So,data of FPF and TPF are changed thus, a fitted model is also changed whether Revised 2019 Dec 8 Revised 2019 Nov 25 Revised 2019 August 28 
summary 
Logical: 
ite 
A variable to be passed to the function 
2019 Sept 6 I found this program, I made this in several month ago? I forgot when this function is made. It well works, so it helps me now.
list of errors, or vaiance of estimates over all replicated datasets.
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