r - 绘制使用 R 中的 season 包中的函数 monthglm() 生成的一般线性模型 (glm)

Question

问题：

我使用基于Month和Season协变量的函数 monthglm() 拟合了一个带有月份分类变量的一般线性模型 (glm)，该函数由Barnett, AG, Dobson, AJ (2010) Analyzing Seasonal Health Data编写. 施普林格。

协变量“月份”和“季节”似乎混淆了模型。通过查看模型摘要（见下文），有一些警告“系数：（3 由于奇异性未定义）”，因此，恰好有三个月没有正确估计（例如 3 月、9 月和 12 月） )，相反，模型输出显示NA's。所以本质上模型无法区分协变量月和季，因为它们非常相似。

我想知道是否有人可以在处理数据或模型本身方面提供帮助，以便函数monthglm（）能够计算所有月份所有蓝鲸目击事件的平均值和上下置信水平，同时包括协变量'模型中的“月份”和“季节”？因此，绘制的模型（见下文）缺少 3 月、9 月和 12 月的三个置信条。

目标

绘制模型的结果，显示 1 月至 12 月之间的所有月份，使用协变量“月份”和“季节”显示具有上下置信水平的平均蓝鲸目击事件。

如果有人能提供帮助，谢谢！

函数：月glm（）：

     ##Install pacakages

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

    ##R-code for the function monthglm2()
    
    
    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)
}

模型

Sightings$year <- Sightings$Year

model<-monthglm2(formula=Frequency_Blue_Whales_Year_Month~Year+Season, family=poisson(),  
                offsetmonth=TRUE, monthvar='Month', refmonth=1, data=Sightings)

模型输出

Call:  glm(formula = f, family = family, data = data, offset = off)

Coefficients:
 (Intercept)          Year  SeasonSpring  SeasonSummer  Seasonwinter  SeasonWinter     monthsFeb     monthsMar     monthsApr     monthsMay  
  -323.25725       0.16196       0.43926      -0.03365       0.76373       0.91534      -0.06261            NA      -0.23382       0.27876  
   monthsJun     monthsJul     monthsAug     monthsSep     monthsOct     monthsNov     monthsDec  
    -1.97313     -19.55938       0.25231      -1.94416       0.00643       0.77171            NA  

Degrees of Freedom: 35 Total (i.e. Null);  21 Residual
Null Deviance:      940.7 
Residual Deviance: 195.4    AIC: 386.7

摘要（型号）

Number of observations = 36 
Rate ratios 
                  mean      lower     upper      zvalue       pvalue
monthsFeb 9.393137e-01 0.67978181 1.2979315 -0.37944839 7.043549e-01
monthsApr 7.915059e-01 0.54509500 1.1493073 -1.22869325 2.191868e-01
monthsMay 1.321488e+00 0.83554494 2.0900500  1.19180025 2.333396e-01
monthsJun 1.390209e-01 0.03860611 0.5006151 -3.01844013 2.540796e-03
monthsJul 3.202360e-09 0.00000000       Inf -0.01615812 9.871082e-01
monthsAug 1.286991e+00 1.01676543 1.6290337  2.09823277 3.588459e-02
monthsSep 1.431068e-01 0.05831898 0.3511647 -4.24489759 2.186933e-05
monthsOct 1.006450e+00 0.73231254 1.3832102  0.03963081 9.683875e-01
monthsNov 2.163470e+00 1.64625758 2.8431777  5.53616916 3.091590e-08

阴谋

plot(model, ylim=c(0,1.4))

插入 y 标签和 x 标签的错误消息

##I am also unable to plot the x-labels and the y-labels

plot(model, 
+      ylim=c(0,1.4),
+ ylab="Mean Blue Whale Sightings", 
+ xlab="Month")
Error in plot.default(order, toplot$mean, xaxt = "n", xlab = "", ylab = "",  : 
  formal argument "xlab" matched by multiple actual arguments

绘制图

数据框（称为 Sightings）

    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))

标签： rplotlinear-regressionmodelingglm