使用公式预测 R 函数内部会产生“找不到对象”错误
Using formula to predict inside of R function generates `object not found` error
首先让我说一下，在 Whosebug 上也有与我类似的问题，但我没有看到他们给我满意的答案，而且给出的答案并不能帮助我解决我遇到的问题.这也是一个很长的问题，但我尽量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函数并使用predict进行预测。为了彻底，我用几种方法来做：
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，我稍后尝试显式分配环境的原因是有道理的。
现在我正在尝试使用 nlme 包中的 lme 函数进行预测。顺便说一句，我不了解此功能的统计信息，我只是根据我实验室其他人编写的代码使用它。
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：
#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，lme 函数不会接受原始字符串，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)
问题是：当我尝试将此 lme 代码放入函数中时，出现 object 'xxxxx' not found 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)
具体错误是Error in eval(mCall$fixed) : object 'formula_used_nlm' not found
这里的一个答案：Object not found error when passing model formula to another function表明，正如我上面所做的那样，我在函数中明确设置了公式的环境。我试过了，但没有用，产生了同样的错误：
#neither does this one!
make_prediction_2 <- function(data_in,y_var,x_var,treatment_var ,id_var){
local_env = environment()
formula_used_nlm <- as.formula(paste(y_var, paste(x_var, treatment_var, sep = " * "), sep = " ~ "),
env = local_env)
random_used <- as.formula(paste("~1|",id_var,sep = ""), env = local_env)
lme_model <- lme(fixed = formula_used_nlm,
random = random_used,
data = data_in)
predict(lme_model, newdata = data_in)
}
make_prediction_2(data_in = fake_data_complicated_1,
y_var = "why",
x_var = "ecks",
treatment_var = "treatment",
id_var = "ID")
我也许可以通过使用宏而不是函数来解决这个问题，但如果我能帮助它，如果它能工作的话，我不想涉足这个问题。现在我将只复制和粘贴代码而不是编写函数。感谢阅读本文的人。
出于某种原因，lme 函数需要在调用中使用文字公式。它不希望在那里看到变量。它使用非标准评估来尝试将响应与固定效应项分开。这样的话，确实和公式的环境没有关系。
解决此问题的最简单方法是使用 do.call 将公式注入到调用中。这应该有效
make_prediction_nlm <- function(data_in,y_var,x_var,treatment_var ,id_var,new_data){
formula_used_nlm <- as.formula(paste(y_var, paste(x_var, treatment_var, sep = " * "), sep = " ~ "))
random_used <- as.formula(paste("~1|",id_var,sep = ""))
lme_model <- do.call("lme", list(fixed = formula_used_nlm,
random = random_used,
data = quote(data_in)))
predict(lme_model, newdata = new_data)
}
这只会在您传递 newdata= 时真正影响 predict 函数，因为它会返回查看原始调用是什么。
如果您查看 nlme:::predict.lme（nlme 包命名空间中的隐藏函数），您会注意到这一行：
fixed <- eval(eval(mCall$fixed)[-2])
函数尝试提取固定分量，移除 left-hand 侧（[-2] 所做的），然后 re-evaluate。
@MrFlick 的解决方案有效，并且可能比我找到的那个更具原则性，即插入行
lme_model$call$fixed <- formula_used_nlm
就在函数中 predict() 调用之前。这明确地将符号替换为评估值 ...
如果 fixed <- eval(...) 行被替换为在父框架或公式环境中工作的 eval() 的适当变体或 ...
也是可能的
首先让我说一下，在 Whosebug 上也有与我类似的问题，但我没有看到他们给我满意的答案，而且给出的答案并不能帮助我解决我遇到的问题.这也是一个很长的问题，但我尽量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函数并使用predict进行预测。为了彻底，我用几种方法来做：
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，我稍后尝试显式分配环境的原因是有道理的。
现在我正在尝试使用 nlme 包中的 lme 函数进行预测。顺便说一句，我不了解此功能的统计信息，我只是根据我实验室其他人编写的代码使用它。
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：
#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，lme 函数不会接受原始字符串，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)
问题是：当我尝试将此 lme 代码放入函数中时，出现 object 'xxxxx' not found 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)
具体错误是Error in eval(mCall$fixed) : object 'formula_used_nlm' not found
这里的一个答案：Object not found error when passing model formula to another function表明，正如我上面所做的那样，我在函数中明确设置了公式的环境。我试过了，但没有用，产生了同样的错误：
#neither does this one!
make_prediction_2 <- function(data_in,y_var,x_var,treatment_var ,id_var){
local_env = environment()
formula_used_nlm <- as.formula(paste(y_var, paste(x_var, treatment_var, sep = " * "), sep = " ~ "),
env = local_env)
random_used <- as.formula(paste("~1|",id_var,sep = ""), env = local_env)
lme_model <- lme(fixed = formula_used_nlm,
random = random_used,
data = data_in)
predict(lme_model, newdata = data_in)
}
make_prediction_2(data_in = fake_data_complicated_1,
y_var = "why",
x_var = "ecks",
treatment_var = "treatment",
id_var = "ID")
我也许可以通过使用宏而不是函数来解决这个问题，但如果我能帮助它，如果它能工作的话，我不想涉足这个问题。现在我将只复制和粘贴代码而不是编写函数。感谢阅读本文的人。
出于某种原因，lme 函数需要在调用中使用文字公式。它不希望在那里看到变量。它使用非标准评估来尝试将响应与固定效应项分开。这样的话，确实和公式的环境没有关系。
解决此问题的最简单方法是使用 do.call 将公式注入到调用中。这应该有效
make_prediction_nlm <- function(data_in,y_var,x_var,treatment_var ,id_var,new_data){
formula_used_nlm <- as.formula(paste(y_var, paste(x_var, treatment_var, sep = " * "), sep = " ~ "))
random_used <- as.formula(paste("~1|",id_var,sep = ""))
lme_model <- do.call("lme", list(fixed = formula_used_nlm,
random = random_used,
data = quote(data_in)))
predict(lme_model, newdata = new_data)
}
这只会在您传递 newdata= 时真正影响 predict 函数，因为它会返回查看原始调用是什么。
如果您查看 nlme:::predict.lme（nlme 包命名空间中的隐藏函数），您会注意到这一行：
fixed <- eval(eval(mCall$fixed)[-2])
函数尝试提取固定分量，移除 left-hand 侧（[-2] 所做的），然后 re-evaluate。
@MrFlick 的解决方案有效，并且可能比我找到的那个更具原则性，即插入行
lme_model$call$fixed <- formula_used_nlm
就在函数中 predict() 调用之前。这明确地将符号替换为评估值 ...
如果 fixed <- eval(...) 行被替换为在父框架或公式环境中工作的 eval() 的适当变体或 ...