【问题标题】:Using Vector of Character Variables within certain Part of the lm() Function of R在 R 的 lm() 函数的某些部分中使用字符变量向量
【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")     

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


    【解决方案1】:

    您需要将这两个部分都转换为字符串,然后制作公式:

    #the manual bit
    manual <- "log(count_faves+1)~log(views+1)+dateadded+group_url+license+log(precontext.nextphoto.views+1)+log(precontext.prevphoto.views+1)"
    
    #the variables:
    oid_variables <- c("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")     
    
    #paste them together 
    g <- paste("log(", oid_variables, "+1)", collapse="+")
    
    #make the formula
    myformula <- as.formula(paste(manual, '+', g))
    

    然后将公式添加到lm

    lm_carclass_mod <- lm(myformula, data=flickrcar_wo_country         
    

    【讨论】:

    • 完美,非常感谢!这正是我想要的:)
    • 乐于助人:)
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