N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |
N.d.f: Numerator degree of freedom, D.d.f: Denominator degree
of freedom, es: Exposure Salinity, o: Origin,
lo: Location, p: Population, li: Lineage.
AICc is an inverse indicator of model parsimony, considering fit (logLik
= log-Likelihood) and complexity (df = number of parameters to be
estimated in the candidate model). The ΔAIC < 6 cut-off rule
was used to define the top-model set (Richards, 2005, 2008). The top
model set with a ΔAICc < 6 (AICc difference with the best
candidate model) comprises 2 concurrent models of i, ii, and
iii (in bold) with a weight of evidence
wi ranging from about 8 to 76%. |