BIC derivation in the case of individual fits The total BIC should penalize -2LL by (nb of parameters) x ln(nb of data points). In this case, we should distinguish between the data for each id#occ, which is used to optimize Nparamparameters, and the data for all individuals, which ...
Information CriterionFormula AICaic = -2*logL + 2*numParam BICbic = -2*logL + log(numObs)*numParam AICcaicc = aic + [2*numParam*(numParam + 1)]/(numObs – numParam – 1) CAICcaic = -2*logL + (log(numObs) + 1)*numParam ...
Information CriterionFormula AICaic = -2*logL + 2*numParam BICbic = -2*logL + log(numObs)*numParam AICcaicc = aic + [2*numParam*(numParam + 1)]/(numObs – numParam – 1) CAICcaic = -2*logL + (log(numObs) + 1)*numParam ...
如果scope缺失,则初始模型将用作upper模型。 scope指定的模型可以作为更新object的模板,如update.formula使用的那样。因此,在scope公式中使用.意味着“已经存在的内容”,而.^2表示现有项的所有交互。 使用glm与变量scale配合存在潜在问题,因为在这种情况下,偏差不仅仅与最大化对数似然相关。函数extractAIC的"glm"方法对...
This factor must be greater than a; equal to 2 in the AIC case and equal to lnN in the BIC case. Notice that the latter depends on the number of data points. It can be shown that there is the simple relation between the BIC and the Bayes factor,...
For example, if you have more data samples, you will have smaller standard error, narrower confidence interval and smaller standard deviation.But according to this BIC's formula, the statistical model with more sample data would get penalized, which means having less chance to ge...
Sow equation and adjusted R2 formula <- y ~ poly(x, 3, raw = TRUE) p <- ggplot(my.data, aes(x, y2, color = group)) + geom_point() + geom_smooth(aes(fill = group), method = "lm", formula = formula) + stat_poly_eq( aes(label = paste(..eq.label.., ..adj.rr.label...
scope指定的模型可以作为更新object的模板,如update.formula使用的那样。 使用glm与变量scale配合存在潜在问题,因为在这种情况下,偏差不仅仅与最大化对数似然相关。extractAIC的glm方法对gaussian系列进行了适当的调整,但可能需要针对其他情况进行修改。 (binomial和poisson系列默认修复了scale,并且不对应于变量scale的特定 max...
Fit polynomial regression line and add labels:# Polynomialregression. Sow equation and adjusted R2formula <- y ~ poly(x, 3, raw = TRUE)p <- ggplot(my.data, aes(x, y2, color = group)) + geom_point() + geom_smooth(aes(fill = group), method = "lm", formula = formula) + stat...
AIC, BIC, Bayesian evidence and a notion on simplicity of cosmological model Recent astronomical observations indicate that the Universe is in the phase of accelerated expansion. There are many cosmological models which explain this phenomenon, but should we prefer those models over the simplest one ...