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Table 2 Estimates of model fit for linear, logistic, and cusp competing models by goal condition

From: The regulatory properties of anger under different goal orientations: the effects of normative and outcome goals

Model tested N. of Par AIC AICc BIC R2 (%)
Normative performance goal condition
Linear model 5 116.251 118.126 124.439 7.8
Logistic model 6 114.735 117.445 124.561 15.9
Cusp model 7 112.738 116.472 124.201 33.6
Outcome performance goal condition
Linear model 5 231.282 232.282 243.192 3.4
Logistic model 6 225.562 226.712 239.854 12.3
Cusp model 7 233.696 235.252 250.370 − 0.02
  1. N = number of estimated parameters; AIC = Akaike information criterion; AICc = corrected Akaike criterion; BIC = Bayesian information criterion
  2. As noted in the text R-square values using cusp can take on negative values and that was evident when modeling achievement in the outcome performance goal condition