ChisqrGoodnessOfFit: Compute the chisquare goodness of fit statistic for ROC...

In dpc10ster/rjafroc: Artificial Intelligence Systems and Observer Performance

Compute the chisquare goodness of fit statistic for ROC fitting model

Description

Compute the chisquare goodness of fit statistic for specified ROC data fitting model

Usage

ChisqrGoodnessOfFit(fpCounts, tpCounts, parameters, model, lesDistr)

Arguments

fpCounts	The FP counts table
tpCounts	The TP counts table
parameters	The parameters of the model including cutoffs, see details
model	The fitting model: "BINORMAL", "CBM" or "RSM
lesDistr	The lesion distribution matrix; not needed for "BINORMAL" or "CBM" models. Array [1:maxLL,1:2]. The probability mass function of the lesion distribution for diseased cases. The first column contains the actual numbers of lesions per case. The second column contains the fraction of diseased cases with the number of lesions specified in the first column. The second column must sum to unity.

Details

For model = "BINORMAL" the parameters are c(a,b,zetas). For model = "CBM" the parameters are c(mu,alpha,zetas). For model = "RSM" the parameters are c(mu,lambda,nu,zetas). Due to the sparsity of the data, in most cases the goodness of fit statistic cannot be calculated as the criterion of at least 5 counts in each cell (TP and FP) is usually not met. An exception dataset is shown below.

Value

A list with the following elements:

chisq	The chi-square statistic
pVal	The p-value of the fit
df	The degrees of freedom

Examples

## Test with TONY data for which chisqr can be calculated
ds <- DfFroc2Roc(dataset01)
fit <- FitBinormalRoc(ds, 2, 3) # trt 2 and rdr 3
## fitted a,b and zeta parameters from preceding line were used to call the
## function as shown below:
fpCounts = c(119,  30,   9,  19,   7,   1)
tpCounts = c(10, 11,  7, 16, 29, 16)
gfit = ChisqrGoodnessOfFit(fpCounts, tpCounts, 
parameters = c(fit$a, fit$b, fit$zetas), model="BINORMAL") 

dpc10ster/rjafroc documentation built on Jan. 18, 2024, 4:37 a.m.