【发布时间】:2019-03-21 09:52:00
【问题描述】:
我正在 R 中执行回归分析,如下所示:
lm_carclass_mod <- lm(log(count_faves+1)~log(views+1)+dateadded+group_url+license+log(precontext.nextphoto.views+1)+log(precontext.prevphoto.views+1)+log(oid.Bridge+1)+log(oid.Face+1)+log(oid.Quail+1)+log(oid.Sky+1)+log(oid.Car+1)+log(oid.Auditorium+1)+log(oid.Font+1)+log(oid.Lane+1)+log(oid.Bmw+1)+log(oid.Racing+1)+log(oid.Wheel+1),data=flickrcar_wo_country)
confint(lm_carclass_mod,level=0.95)
summary(lm_carclass_mod)
在我的分析过程中,因变量和一些自变量变化很大,这就是为什么我想继续手动插入它们。
但是,我正在寻找一种方法来用一个函数替换所有“oid....”变量。
到目前为止,我想出了以下:
g <- paste("log(",variables,"+1)", collapse="+")
不幸的是,这在 lm() 函数中不起作用。这样的公式也没有:
g <- as.formula(
paste("log(",variables,"+1)", collapse="+")
)
向量变量包含以下元素:
variables <- ("oid.Bridge", "oid.Face", "oid.Quail", "oid.Off-roading", "oid.Sky", "oid.Car", "oid.Auditorium", "oid.Font", "oid.Lane", "oid.Bmw", "oid.Racing", "oid.Wheel")
在结束我的回归模型应该是这样的:
lm_carclass_mod <- lm(log(count_faves+1)~log(views+1)+dateadded+group_url+license+log(precontext.nextphoto.views+1)+log(precontext.prevphoto.views+1)+g,data=flickrcar_wo_country)
confint(lm_carclass_mod,level=0.95)
summary(lm_carclass_mod)
提前感谢您的帮助!
【问题讨论】:
标签: r function vector linear-regression lm