Description Usage Arguments Examples
Summarizes the number of populations fitted and their estimate distribution parameters. If only 1 population was detected, then it is assumed and is identified to be a negative population. If 2 populations were detected, then the leftmost is identified as the Negative Population and the rightmost is the Positive Population. If 3 or more populations were detected, then the populations between the leftmost and the rightmost will be considered as Rain Populations; which are numbered to make it identifiable in case of multiple Rain Populations (i.e. Rain (1) and Rain (2)).
1 | printSummaryFit(result.popPCR)
|
result.popPCR |
returned value of popPCR() |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | result <- popPCR(x_twoPop, dist = "t")
printSummaryFit(result)
# Output:
# Results of fitting a 2-component t mixture model
#
# Negative Population
# Mix prop. : 0.1522
# Mu : 2136.7435
# Sigma : 4126.8357
# Dof : 12.3562
#
# Positive Population
# Mix prop. : 0.8478
# Mu : 7580.1275
# Sigma : 42621.1894
# Dof : 2.415
result <- popPCR(x_multiPop, dist = "t", maxComponents = 4)
printSummaryFit(result)
# Output:
# Results of fitting a 4-component t mixture model
#
# Negative Population
# Mix prop. : 0.6896
# Mu : 1452.1416
# Sigma : 12526.8931
# Dof : 21.3612
#
# Rain (1) Population
# Mix prop. : 0.142
# Mu : 2142.1118
# Sigma : 10762.5474
# Dof : 186.2947
#
# Rain (2) Population
# Mix prop. : 0.1457
# Mu : 5119.0039
# Sigma : 334959.2499
# Dof : 2.3626
#
# Positive Population
# Mix prop. : 0.0227
# Mu : 8505.9682
# Sigma : 192858.9044
# Dof : 149.8677
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