使用 R 中的 function monthglm() 拟合广义线性 model (glm) 和月份的分类变量

Question

问题

我有一个数据框（见下文），我想根据季节和年份的协变量使用 function monthglm() 来拟合带有月份分类变量的一般线性 model (glm)。

在我运行以下 function 之后，该文档由Barnett, AG, Dobson, AJ (2010) Analyzing Seasonal Health Data 编写。 施普林格。 （见下文），我不断收到此错误消息。

如果有人可以提供帮助，我将不胜感激。

加载包

library(season)
library(MASS) # for mvrnorm
library(survival) # for coxph
library(ggplot2)

Function：

monthglm<-function(formula,data,family=gaussian(),refmonth=1,
                   monthvar='month',offsetmonth=FALSE,offsetpop=NULL){
  ## checks
  if (refmonth<1|refmonth>12){stop("Reference month must be between 1 and 12")}
  ## original call with defaults (see amer package)
  ans <- as.list(match.call())
  frmls <- formals(deparse(ans[[1]]))
  add <- which(!(names(frmls) %in% names(ans)))
  call<-as.call(c(ans, frmls[add]))
  
  monthvar=with(data,get(monthvar))
  cmonthvar=class(monthvar)
  ## If month is a character, create the numbers
  if(cmonthvar%in%c('factor','character')){
    if(cmonthvar=='character'){
      if(max(nchar(monthvar))==3){mlevels=substr(month.name,1,3)}else{mlevels=month.name}
      monthvar=factor(monthvar,levels=mlevels)
    }
    months=as.numeric(monthvar)
    data$month=months # add to data for flagleap
    months=as.factor(months)
    levels(months)[months]<-month.abb[months]
    months<-relevel(months.u,ref=month.abb[refmonth]) # set reference month
  }
  ## Transform month numbers to names
  if(cmonthvar%in%c('integer','numeric')){
    months.u<-as.factor(monthvar)  
    nums<-as.numeric(nochars(levels(months.u))) # Month numbers
    levels(months.u)[nums]<-month.abb[nums]
    months<-relevel(months.u,ref=month.abb[refmonth]) # set reference month
  }
  ## prepare data/formula
  parts<-paste(formula)
  f<-as.formula(paste(parts[2],parts[1],parts[3:length(formula)],'+months'))
  dep<-parts[2] # dependent variable
  days<-flagleap(data=data,report=FALSE,matchin=T) # get the number of days in each month
  l<-nrow(data)
  if(is.null(offsetpop)==FALSE){poff=with(data,eval(offsetpop))} else{poff=rep(1,l)} # 
  if(offsetmonth==TRUE){moff=days$ndaysmonth/(365.25/12)} else{moff=rep(1,l)} # days per month divided by average month length
  ###  data$off<-log(poff*moff)
  off<-log(poff*moff)  # 
  fit<-glm(formula=f,data=data,family=family,offset=off)
  ## return
  toret<-list()
  toret$call<-call
  toret$glm<-fit
  toret$fitted.values<-fitted(fit)
  toret$residuals<-residuals(fit)
  class(toret)<-'monthglm'
  return(toret)
}

#The levels of a factor must match the observed values. 
#If you want to change how those values print out, you need to change the labels. 

错误信息

model<-monthglm(formula=Frequency_Blue~Year+Monsoon_Season, family=gaussian,
+                       offsetmonth=TRUE, refmonth=1, data=Final_New_Blue)
Error in nochars(levels(months.u)) : could not find function "nochars"

Dataframe

   structure(list(Year = c(2015L, 2016L, 2017L, 2015L, 2016L, 2017L, 
2015L, 2016L, 2017L, 2015L, 2016L, 2017L, 2015L, 2016L, 2017L, 
2015L, 2016L, 2017L, 2015L, 2016L, 2017L, 2015L, 2016L, 2017L, 
2015L, 2016L, 2017L, 2015L, 2016L, 2017L, 2015L, 2016L, 2017L, 
2015L, 2016L, 2017L), Month = structure(c(5L, 5L, 5L, 4L, 4L, 
4L, 8L, 8L, 8L, 1L, 1L, 1L, 9L, 9L, 9L, 7L, 7L, 7L, 6L, 6L, 6L, 
2L, 2L, 2L, 12L, 12L, 12L, 11L, 11L, 11L, 10L, 10L, 10L, 3L, 
3L, 3L), .Label = c("April", "August", "December", "Feb", "Jan", 
"July", "June", "Mar", "May", "November", "October", "September"
), class = "factor"), Frequency_Blue_Whales_Year_Month = c(76L, 
78L, 66L, 28L, 54L, 37L, 39L, 31L, 88L, 46L, 23L, 54L, 5L, 8L, 
0L, 0L, 0L, 0L, 0L, 4L, 7L, 22L, 6L, 44L, 10L, 30L, 35L, 88L, 
41L, 35L, 4L, 30L, 43L, 65L, 43L, 90L), Season = structure(c(4L, 
4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
5L, 5L, 5L), .Label = c("Autumn", "Spring", "Summer", "winter", 
"Winter"), class = "factor")), class = "data.frame", row.names = c(NA, 
-36L))

Arguments

Answer 1

通过对代码和您的 function 调用进行以下简单更改，我能够让 model 运行。 我将它命名为monthglm2 ，以将其与package function区分开来。 调用您的数据df ：

library(season)
library(MASS) # for mvrnorm
library(survival) # for coxph
library(ggplot2)


monthglm2<-function(formula,data,family=gaussian(),refmonth=1,
                   monthvar='month',offsetmonth=FALSE,offsetpop=NULL){
  ## checks
  if (refmonth<1|refmonth>12){stop("Reference month must be between 1 and 12")}
  ## original call with defaults (see amer package)
  ans <- as.list(match.call())
  frmls <- formals(deparse(ans[[1]]))
  add <- which(!(names(frmls) %in% names(ans)))
  call<-as.call(c(ans, frmls[add]))

  monthvar=with(data,get(monthvar))
  cmonthvar=class(monthvar)
  ## If month is a character, create the numbers
  if(cmonthvar%in%c('factor','character')){
    if(cmonthvar=='character'){
      if(max(nchar(monthvar))==3){mlevels=substr(month.name,1,3)}else{mlevels=month.name}
      monthvar=factor(monthvar,levels=mlevels)
    }
    months=as.numeric(monthvar)
    data$month=months # add to data for flagleap
    months=as.factor(months)
    levels(months)[months]<-month.abb[months]
    months<-relevel(months,ref=month.abb[refmonth]) # set reference month ### TYPO HERE, changed from months.u
  }
  ## Transform month numbers to names
  if(cmonthvar%in%c('integer','numeric')){
    months.u<-as.factor(monthvar)
    nums<-as.numeric(nochars(levels(months.u))) # Month numbers
    levels(months.u)[nums]<-month.abb[nums]
    months<-relevel(months.u,ref=month.abb[refmonth]) # set reference month
  }
  ## prepare data/formula
  parts<-paste(formula)
  f<-as.formula(paste(parts[2],parts[1],parts[3:length(formula)],'+months'))
  dep<-parts[2] # dependent variable
  days<-flagleap(data=data,report=FALSE,matchin=T) # get the number of days in each month
  l<-nrow(data)
  if(is.null(offsetpop)==FALSE){poff=with(data,eval(offsetpop))} else{poff=rep(1,l)} #
  if(offsetmonth==TRUE){moff=days$ndaysmonth/(365.25/12)} else{moff=rep(1,l)} # days per month divided by average month length
  ###  data$off<-log(poff*moff)
  off<-log(poff*moff)  #
  fit<-glm(formula=f,data=data,family=family,offset=off)
  ## return
  toret<-list()
  toret$call<-call
  toret$glm<-fit
  toret$fitted.values<-fitted(fit)
  toret$residuals<-residuals(fit)
  class(toret)<-'monthglm'
  return(toret)
}


df$year <- df$Year
monthglm2(formula=Frequency_Blue_Whales_Year_Month~Year+Season, family=gaussian(),  offsetmonth=TRUE, monthvar='Month', refmonth=1, data=df)

function 中存在另一个问题，我必须将列重命名为year 。 如果您查看此package的 github，则只有一个贡献者，没有提出任何问题。 使用这样的包有利有弊：它们可能有有用的新方法，但无法快速识别和解决错误。 如果您继续进行季节性分析，我建议您尝试学习如何直接在glm中包含季节性建模