我已经能够在数据帧中迭代地应用通用添加模型,所以sp_a是响应变量......
sp_a <- rnorm (100, mean = 3, sd = 0.9) var_env_1 <- rnorm (100, mean = 1, sd = 0.3) var_env_2 <- rnorm (100, mean = 5, sd = 1.6) var_env_3 <- rnorm (100, mean = 10, sd = 1.2) data <- data.frame (sp_a, var_env_1, var_env_2,var_env_3) library(mgcv) Gam <- lapply(data[,-1], function(x) summary(gam(data$sp_a ~ s(x))))这会在响应变量和每个解释变量之间迭代地创建GAM。 但是,我将如何从每个模型中提取p值或s.pv。 有人知道怎么做这个吗? 另外,按照他们的AIC分数对它们进行排名会很棒......
Gam1 <- gam(sp_a ~ s(var_env_1)) Gam2 <- gam(sp_a ~ s(var_env_2)) Gam3 <- gam(sp_a ~ s(var_env_3)) AIC(Gam1,Gam2,Gam3)但是从原来的'Gam'输出中选择它。 感谢您提前提供的任何帮助。
I have been able to apply a General Additive Model iteratively across a dataframe, so where sp_a is the response variable...
sp_a <- rnorm (100, mean = 3, sd = 0.9) var_env_1 <- rnorm (100, mean = 1, sd = 0.3) var_env_2 <- rnorm (100, mean = 5, sd = 1.6) var_env_3 <- rnorm (100, mean = 10, sd = 1.2) data <- data.frame (sp_a, var_env_1, var_env_2,var_env_3) library(mgcv) Gam <- lapply(data[,-1], function(x) summary(gam(data$sp_a ~ s(x))))This creates a GAM between the response variable and each explanatory variable iteratively. However, how I would then extract p values or the s.pv from each model. Does anybody know how to do this? Also, it would be great to rank them by their AIC score like this...
Gam1 <- gam(sp_a ~ s(var_env_1)) Gam2 <- gam(sp_a ~ s(var_env_2)) Gam3 <- gam(sp_a ~ s(var_env_3)) AIC(Gam1,Gam2,Gam3)But selecting this from the original 'Gam' output instead. Thank you for any help in advance.
最满意答案
最后,显然我必须删除摘要选项,然后允许我计算所有模型的AIC分数。 其他有趣的格式化方法可以在这里使用lapply在模型列表中找到,因为这些函数适用于不同类型的模型(例如lm,glm)。
Gam <- lapply(data[,-1], function(x) gam(data$sp_a ~ s(x))) sapply(X = Gam, FUN = AIC)In the end, it was evident I had to remove the summary option, that then allowed me to calculate AIC score for all models. Other interesting ways of formatting can be found here Using lapply on a list of models, as these functions work for different kinds of models (e.g. lm, glm).
Gam <- lapply(data[,-1], function(x) gam(data$sp_a ~ s(x))) sapply(X = Gam, FUN = AIC)更多推荐
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