我该如何对模型进行微调并运行F检验

问题描述

我有一个小标题，其中我为不同的数据子集拟合了模型。 现在，在进行F检验之前，我想在包含所有点的数据上绘制每个模型，以查看是否通过包含“ Site_class”变量对模型有任何好处。

数据：

sitedata <- structure(list(Site_class = c("1","1","4","All","All"),VarA = c(2.4,6.5,11.3,16.1,20.5,24.3,27.4,30,32,33.6,34.9,36,0.75,2.45,4.75,7.45,10.3,13.05,15.55,17.7,19.4,20.75,21.9,22.8,2.4,1.85,5.15,9.1,13.2,17.1,20.55,23.45,25.9,27.8,29.3,30.55,31.6,1.3,3.8,6.95,10.35,13.7,16.8,19.5,21.8,23.6,25.05,26.25,27.2,1.1,2.6,4.6,6.9,9.3,11.6,13.6,15.2,16.5,17.55,18.4),VarB = c(12,81,220,403,605,806,991,1153,1288,1399,1495,1578,1,11,45,106,189,283,381,473,552,619,675,723,12,4,51,148,286,446,609,760,893,1008,1105,1186,1255,2,27,93,190,307,433,557,670,766,848,917,975,42,94,161,234,304,368,423,469,508)),row.names = c(NA,-83L),class = c("grouped_df","tbl_df","tbl","data.frame"),groups = structure(list(Site_class = c("1",.rows = list(1:12,13:24,25:83)),-3L),class = c("tbl_df",.drop = FALSE))

这是我到目前为止的工作方式。

library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)

#Create model
modfit <- function(df){
  nls(VarB ~ a * (VarA^b),data=df,start=c(a=1,b=1))
}

#Nest the original data.frame
sitedata <- sitedata %>% group_by(Site_class) %>% nest()

#Fit the models
sitedata_model <- sitedata %>% mutate(
  Model= map(.x=data,.f= modfit)
)

#Attempt to plot the models:
ggplot(sitedata_model[[2]][[3]],aes(x=VarA,y=VarB)) + #All the data
  geom_point()+
  geom_function(fun=~sitedata_model[[3]][[1]]) + # I assume I will have to plot them separately?
  geom_function(fun=~sitedata_model[[3]][[2]]) +
  geom_function(fun=~sitedata_model[[3]][[3]]) 

似乎我已经成功创建了模型，但是后来我不理解我对它们进行绘制的要求。我也尝试使用predict，但没有成功。

我如何：

1. 将它们绘制在我的图表上
2. 在所有3个模型之间进行F检验，看是否有差异？

解决方法

geom_function需要一个功能。不是公式，不是模型，而是 函数 。

我对这个几何没有太多经验，我不确定以下内容是否有效，但它确实适用于您的用例：

sitedata_model <- sitedata %>% 
  #Fit the models
  mutate(Model = purrr::map(.x=data,.f= modfit)) %>%
  
  # extract formula from each model,convert to one-sided form,&
  # replace coefficients with fitted values,& store in dataframe
  # as character string 
  rowwise() %>%
  mutate(func = formula(Model) %>% 
           as.character() %>% 
           magrittr::extract(3) %>%
           gsub("VarA",".x",.,fixed = T) %>%
           gsub("a",Model$m$getPars()[1],.) %>%
           gsub("b",Model$m$getPars()[2],.) %>%
           paste("~",collapse = "")) %>%
  ungroup()

# plot
ggplot(data = sitedata_model$data[[3]]) +
  geom_point(aes(x = VarA,y = VarB)) + 
  
  # add formula in each row as a separate geom_function layer
  lapply(seq(1,nrow(sitedata_model)),function(i) geom_function(fun = rlang::as_function(formula(sitedata_model$func[i])),aes(colour = sitedata_model$Site_class[i]))) +
  
  # change legend name (can also change palette / labels / etc.)
  scale_colour_discrete(name = "Site class")

关于进行F检验，您是指方差分析吗？

> anova(sitedata_model$Model[[1]],sitedata_model$Model[[2]],sitedata_model$Model[[3]])
Analysis of Variance Table

Model 1: VarB ~ a * (VarA^b)
Model 2: VarB ~ a * (VarA^b)
Model 3: VarB ~ a * (VarA^b)
  Res.Df Res.Sum Sq  Df  Sum Sq F value Pr(>F)
1     10      60.14                           
2     10      90.36   0    0.00               
3     57     741.38 -47 -651.02  1.5328 0.2386

有关如何解释方差分析输出的说明，请参见here。