如果代码花费的时间太长，则补救措施永远不会给出结果

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这是我数据的原始结构：除人和activity外，所有变量都是解释变量，因变量是完成。

   data.frame': 581755 obs. of  68 variables:
     **$ activity          : int  37033 37033 37033 37033 37033 37033 37033 37033 37033 37033 ...
     $ people         : int  5272 5272 5272 5272 5272 5272 5272 5272 5272 5272 ...
     $ completion: num 0 0 0 0 0 0 0 0 0 0 ...
     *$ active            : int  0 2 2 2 2 2 2 2 2 2 ...
     $ overdue           : int  0 0 0 0 0 0 0 0 0 0 ...
     $ wdsp              : num  5.7 5.7 7.7 6.4 3.9 5.8 3.5 6.3 4.8 9.4 ...
     $ rain              : num  0 0 0 0 0 0 0 0 0 0 ...
     $ UserCompletionRate: num [1:581755, 1] NaN -1.55 -1.55 -1.55 -1.55 ...
      ..- attr(*, "scaled:center")= num 0.462
      ..- attr(*, "scaled:scale")= num 0.298
     $ DayofWeekSu       : num  0 0 0 0 0 1 0 0 0 0 ...
     $ DayofWeekMo       : num  0 0 0 0 0 0 1 0 0 0 ...
     $ DayofWeekTu       : num  1 0 0 0 0 0 0 1 0 0 ...
     $ DayofWeekWe       : num  0 1 0 0 0 0 0 0 1 0 ...
     $ DayofWeekTh       : num  0 0 1 0 0 0 0 0 0 1 ...
     $ DayofWeekFr       : num  0 0 0 1 0 0 0 0 0 0 ...

     $ MonthofYearJan    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearFeb    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearMar    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearApr    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearMay    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearJun    : num  1 1 1 1 1 1 1 1 1 1 ...
     $ MonthofYearJul    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearAug    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearSep    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearOct    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ MonthofYearNov    : num  0 0 0 0 0 0 0 0 0 0 ...
     $ cold              : num  0 0 0 0 0 0 0 0 0 0 ...
     $ hot               : num  0 0 0 0 0 0 0 0 0 0 ...
     $ overduetask       : num  0 0 0 0 0 0 0 0 0 0 ...***

My data relates if activities from people are completed or not and affected by sunshine and all other variables posted above - completion is the outcome variable and sunshine would be the explanatory variable; this is a simplified version.

 df <-  people = c(1,1,1,2,2,3,3,4,4,5,5),
        activity = c(1,1,1,2,2,3,4,5,5,6,6),
        completion = c(0,0,1,0,1,1,1,0,1,0,1),
        sunshine = c(1,2,3,4,5,4,6,2,4,8,4)

到目前为止，我已将此代码用于木屐：

model<- as.formula("completion ~  sunshine")
clog_full = glm(model,data=df,family = binomial(link = cloglog))
summary(clog_full)


> using package glmmML
 model_re<- as.formula("completion ~  sunshine")
    > clog_re = glmmML(model_re,cluster = people, data= df,family =
    > binomial(link = cloglog)) summary(clog_re)
> 
> using package lme4
> 
> model_re1<- as.formula("completion ~  (1|people) + sunshine") clog_re1
> = glmer(model_re1, data=df,family = binomial(link = cloglog)) summary(clog_re1) summ(clog_re1, exp = TRUE)

Answer 1

您显然没有发布挂起的代码，因为我们看到了这个：

df <-  people = c(1,1,1,2,2,3,3,4,4,5,5),
Error: unexpected ',' in "df <-  people = c(1,1,1,2,2,3,3,4,4,5,5),"

如果您需要一个数据帧，您首先需要输入缺少的 data.frame 调用：

df <-  data.frame( people = c(1,1,1,2,2,3,3,4,4,5,5),
activity = c(1,1,1,2,2,3,4,5,5,6,6),
completion = c(0,0,1,0,1,1,1,0,1,0,1),
sunshine = c(1,2,3,4,5,4,6,2,4,8,4) )

如果您输入缺少的换行符，其余代码将毫无困难地运行：

library(lme4)
  model_re1<- as.formula("completion ~  (1|people) + sunshine")
  clog_re1   = glmer(model_re1, data=df,family = binomial(link = cloglog)) 
  summary(clog_re1)
  summ(clog_re1, exp = TRUE)  # there is no function named `summ` so that throws an error but does not hang

library( glmmML)
  model_re<- as.formula("completion ~  sunshine")
  clog_re = glmmML(model_re,cluster = people, data= df,family =
                        binomial(link = cloglog)) 
  summary(clog_re)

如果代码花费的时间太长，则补救措施永远不会给出结果

Remedies if code takes too long, never gives resuluts

performance

r

lme4