r - 使用公式预测 R 函数内部会产生“找不到对象”错误

问题描述

让我先说在 Stackoverflow 上有与我类似的问题，但我还没有看到他们的回答令我满意，并且给出的答案对我遇到的问题没有帮助。这也是一个很长的问题，但我试图让每个部分都简单易懂。

这是一个概念证明，您可以将公式分配给全局环境中的变量，并将公式变量传递给lm函数并用于predict进行预测。我通过几种方式做到彻底：

fake_data_1 <- data.frame(
  ecks = c(-19:20,-19:20,-19:20), 
  why = c((-19:20)^2, (-19:20)^3/40, abs(-19:20))
)

fake_data_2 <- data.frame(
  ecks =runif(22) 
)

#using basic formula
formula_used <- why ~ ecks 
lm_model <- lm(formula = formula_used, data = fake_data_1)
predict(lm_model, newdata = fake_data_2)


#converting string to formula
formula_used <- as.formula("why ~ ecks")
lm_model <- lm(formula = formula_used, data = fake_data_1)
predict(lm_model, newdata = fake_data_2)


#can use a basic string as well
formula_used <- "why ~ ecks"
lm_model <- lm(formula = formula_used, data = fake_data_1)
predict(lm_model, newdata = fake_data_2)

这是可以在函数内部执行这些过程的概念证明：

#can run this as a function
make_prediction <- function(data_in,y_var,x_var,new_data){
  formula_used <- as.formula(paste(y_var, x_var, sep = " ~ "))
  lm_model <- lm(formula = formula_used,data = data_in)
  predict(lm_model, newdata = data_in)
}
make_prediction(data_in = fake_data_1, y_var = "why", x_var = "ecks", new_data = fake_data_2)


#can explicitly set the environment of the formula: will make sense why I show this later
make_prediction_2 <- function(data_in,y_var,x_var,new_data){
  local_env = environment()
  formula_used <- as.formula(paste(y_var, x_var, sep = " ~ "),env = local_env)
  lm_model <- lm(formula = formula_used,data = data_in)
  predict(lm_model, newdata = new_data)
}

make_prediction_2(data_in = fake_data_1, y_var = "why", x_var = "ecks",new_data = fake_data_2)

正如我在评论中所说，为什么我稍后尝试显式分配环境是有道理的。

现在我正在尝试使用包中的lme函数nlme进行预测。顺便说一句，我不了解这个函数的统计数据，我只是根据我实验室其他人编写的代码来使用它。

这是概念证明，您可以使用此函数通过分配给变量的公式进行预测（暂时不处理称为“随机”的公式：

library(nlme)
#fake data for making model
fake_data_complicated_1 <- data.frame(ecks = c(-19:20,-19:20,-19:20), 
                                    why = c((-19:20)^3, (-19:20)^4/40, abs(-19:20)*100), 
                                    treatment = c(rep("a",times = 40),
                                                  rep("b", times = 40),
                                                  rep("control", times = 40)),
                                    ID = c(rep(c("q","w","e","r"),times = 10),
                                           rep(c("t","y","u","i"),times = 10),
                                           rep(c("h","j","k","l"),times = 10))
)

#fake data for making prediction
fake_data_complicated_2 <- data.frame(ecks = runif(120), 
                                      treatment = c(rep("a",times = 40),
                                                    rep("b", times = 40),
                                                    rep("control", times = 40)),
                                      ID = c(rep(c("q","w","e","r"),times = 10),
                                             rep(c("t","y","u","i"),times = 10),
                                             rep(c("h","j","k","l"),times = 10))
)

可以用一个基本公式来做到这一点：

#can use basic formula as before
fixed_formula <- why ~ ecks * treatment
random_formula <- ~1|ID #not sure what this does in the model but that's not importante


lme_model <- lme(fixed = fixed_formula,
                 random = random_formula,
                 data = fake_data_complicated_1)


predict(lme_model, newdata = fake_data_complicated_2)

可以将字符串转换为公式：

#can use a pasted/converted formula as before
fixed_formula <- as.formula(
  paste("why", paste("ecks", "treatment", sep = " * "), sep = " ~ ")
)

lme_model <- lme(fixed = fixed_formula,
                 random = random_formula,
                 data = fake_data_complicated_1)


predict(lme_model, newdata = fake_data_complicated_2)

另外，该lme函数不会采用原始字符串，但这不是我的主要问题：

#can't use a raw string, this code generates an error
# fixed_formula <-  paste("why", paste("ecks", "treatment", sep = " * "), sep = " ~ ")
# 
# 
# lme_model <- lme(fixed = fixed_formula,
#                  random = random_formula,
#                  data = fake_data_complicated_1)
# 
# 
# predict(lme_model, newdata = fake_data_complicated_2)

这是问题所在：当我尝试将此lme代码放入函数中时，object 'xxxxx' not found出现错误：


#this function does not work!
make_prediction_nlm <- function(data_in,y_var,x_var,treatment_var ,id_var,new_data){
  
  formula_used_nlm <- as.formula(paste(y_var, paste(x_var, treatment_var, sep = " * "), sep = " ~ "))
  random_used <-  as.formula(paste("~1|",id_var,sep = ""))
  
  lme_model <- lme(fixed = formula_used_nlm,
                   random = random_used,
                   data = data_in)
  
  predict(lme_model, newdata = new_data)
}

make_prediction_nlm(data_in = fake_data_complicated_1, 
                y_var = "why", 
                x_var = "ecks", 
                treatment_var = "treatment",
                id_var = "ID",
                new_data = fake_data_complicated_1)

具体的错误是Error in eval(mCall$fixed) : object 'formula_used_nlm' not found

这里的答案：将模型公式传递给另一个函数时找不到对象错误表明，正如我上面所做的那样，我在函数中明确设置了公式的环境。我试过了，它没有用，产生同样的错误：

#neither does this one!
make_prediction_2 <- function(data_in,y_var,x_var,treatment_var ,id_var){
  local_env = environment()
  formula_used_nlm <- as.formula(paste(y_var, paste(x_var, treatment_var, sep = " * "), sep = " ~ "),
   env = local_env)
  
random_used <- as.formula(paste("~1|",id_var,sep = ""), env = local_env)
  
  lme_model <- lme(fixed = formula_used_nlm,
                   random = random_used,
                   data = data_in)
  
  predict(lme_model, newdata = data_in)
}

make_prediction_2(data_in = fake_data_complicated_1,
 y_var = "why", 
x_var = "ecks", 
treatment_var = "treatment",
id_var = "ID")

我也许可以通过使用宏而不是函数来解决这个问题，但如果我能帮助它，如果它甚至可以工作，那不是我想要涉足的事情。现在我将只是复制和粘贴代码而不是编写函数。感谢那些阅读本文的人。

标签： rnlme

r - 使用公式预测 R 函数内部会产生“找不到对象”错误

问题描述

解决方案

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