问题描述
我想在不完全指定每个变量的情况下将特定变量强制转换为glm回归.我的真实数据集有大约200个变量.到目前为止，我还无法在在线搜索中找到此示例.
I would like to force specific variables into glm regressions without fully specifying each one. My real data set has ~200 variables. I haven't been able to find samples of this in my online searching thus far.
例如(仅有3个变量):
For example (with just 3 variables):
n=200
set.seed(39)
samp = data.frame(W1 = runif(n, min = 0, max = 1), W2=runif(n, min = 0, max = 5))
samp = transform(samp, # add A
A = rbinom(n, 1, 1/(1+exp(-(W1^2-4*W1+1)))))
samp = transform(samp, # add Y
Y = rbinom(n, 1,1/(1+exp(-(A-sin(W1^2)+sin(W2^2)*A+10*log(W1)*A+15*log(W2)-1+rnorm(1,mean=0,sd=.25))))))
如果我想包括所有主要术语，这有一个简单的快捷方式:
If I want to include all main terms, this has an easy shortcut:
glm(Y~., family=binomial, data=samp)
但是说我想包括所有主要术语(W1，W2和A)加上W2 ^ 2:
But say I want to include all main terms (W1, W2, and A) plus W2^2:
glm(Y~A+W1+W2+I(W2^2), family=binomial, data=samp)
这有捷径吗?
[发布前编辑自己:]可行！ glm(formula = Y ~ . + I(W2^2), family = binomial, data = samp)
[editing self before publishing:] This works! glm(formula = Y ~ . + I(W2^2), family = binomial, data = samp)
好的，那这个呢！
我想省略一个主要术语变量，并且仅包括两个主要术语(A，W2)以及W2 ^ 2和W2 ^ 2:A:
I want to omit one main terms variable and include only two main terms (A, W2) and W2^2 and W2^2:A:
glm(Y~A+W2+A*I(W2^2), family=binomial, data=samp)
很明显，只有几个变量确实不需要捷径，但是我处理的是高维数据.当前数据集仅"有200个变量，而另一些则有成千上万.
Obviously with just a few variables no shortcut is really needed, but I work with high dimensional data. The current data set has "only" 200 variables, but some others have thousands and thousands.
推荐答案
创造性地使用.来构建包含所有或几乎所有变量的公式是一种好方法.有时有用的另一个选项是将公式以编程方式构建为字符串，然后使用as.formula:
Your use of . creatively to build the formula containing all or almost all variables is a good and clean approach. Another option that is useful sometimes is to build the formula programatically as a string, and then convert it to formula using as.formula:
vars <- paste("Var",1:10,sep="")
fla <- paste("y ~", paste(vars, collapse="+"))
as.formula(fla)
当然，您可以使fla对象的方式更复杂.
Of course, you can make the fla object way more complicated.
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