[英]Using lapply on a list of models
我已經生成了一個模型列表,並希望創建一個匯總表。
例如,這里有兩個模型:
x <- seq(1:10)
y <- sin(x)^2
model1 <- lm(y ~ x)
model2 <- lm(y ~ x + I(x^2) + I(x^3))
和兩個公式,第一個從公式的組成部分生成方程
get.model.equation <- function(x) {
x <- as.character((x$call)$formula)
x <- paste(x[2],x[1],x[3])
}
第二個生成模型名稱作為字符串
get.model.name <- function(x) {
x <- deparse(substitute(x))
}
有了這些,我創建了一個匯總表
model.list <- list(model1, model2)
AIC.data <- lapply(X = model.list, FUN = AIC)
AIC.data <- as.numeric(AIC.data)
model.models <- lapply(X = model.list, FUN = get.model)
model.summary <- cbind(model.models, AIC.data)
model.summary <- as.data.frame(model.summary)
names(model.summary) <- c("Model", "AIC")
model.summary$AIC <- unlist(model.summary$AIC)
rm(AIC.data)
model.summary[order(model.summary$AIC),]
一切正常。 我想使用get.model.name將模型名稱添加到表中
x <- get.model.name(model1)
這給了我想要的“model1”。
所以現在我將該函數應用於模型列表
model.names <- lapply(X = model.list, FUN = get.model.name)
但現在取代了model1,我得到了X [[1L]]
如何獲得model1而不是X [[1L]] ?
我正在尋找一個看起來像這樣的表:
Model Formula AIC
model1 y ~ x 11.89136
model2 y ~ x + I(x^2) + I(x^3) 15.03888
你想要這樣的東西嗎?
model.list <- list(model1 = lm(y ~ x),
model2 = lm(y ~ x + I(x^2) + I(x^3)))
sapply(X = model.list, FUN = AIC)
我會做這樣的事情:
model.list <- list(model1 = lm(y ~ x),
model2 = lm(y ~ x + I(x^2) + I(x^3)))
# changed Reduce('rbind', ...) to do.call(rbind, ...) (Hadley's comment)
do.call(rbind,
lapply(names(model.list), function(x)
data.frame(model = x,
formula = get.model.equation(model.list[[x]]),
AIC = AIC(model.list[[x]])
)
)
)
# model formula AIC
# 1 model1 y ~ x 11.89136
# 2 model2 y ~ x + I(x^2) + I(x^3) 15.03888
使用ldply
另一種選擇, 但請參閱下面的hadley評論,以便更有效地使用ldply
:
# prepare data
x <- seq(1:10)
y <- sin(x)^2
dat <- data.frame(x,y)
# create list of named models obviously these are not suited to the data here, just to make the workflow work...
models <- list(model1=lm(y~x, data = dat),
model2=lm(y~I(1/x), data=dat),
model3=lm(y ~ log(x), data = dat),
model4=nls(y ~ I(1/x*a) + b*x, data = dat, start = list(a = 1, b = 1)),
model5=nls(y ~ (a + b*log(x)), data=dat, start = setNames(coef(lm(y ~ log(x), data=dat)), c("a", "b"))),
model6=nls(y ~ I(exp(1)^(a + b * x)), data=dat, start = list(a=0,b=0)),
model7=nls(y ~ I(1/x*a)+b, data=dat, start = list(a=1,b=1))
)
library(plyr)
library(AICcmodavg) # for small sample sizes
# build table with model names, function, AIC and AICc
data.frame(cbind(ldply(models, function(x) cbind(AICc = AICc(x), AIC = AIC(x))),
model = sapply(1:length(models), function(x) deparse(formula(models[[x]])))
))
.id AICc AIC model
1 model1 15.89136 11.89136 y ~ x
2 model2 15.78480 11.78480 y ~ I(1/x)
3 model3 15.80406 11.80406 y ~ log(x)
4 model4 16.62157 12.62157 y ~ I(1/x * a) + b * x
5 model5 15.80406 11.80406 y ~ (a + b * log(x))
6 model6 15.88937 11.88937 y ~ I(exp(1)^(a + b * x))
7 model7 15.78480 11.78480 y ~ I(1/x * a) + b
對於我來說,如何用ldply
函數中的列名替換.id
,任何提示都不是很明顯的?
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