英文:
predict.lme is unable to interpret a formula defined from a variable
问题
我一直受到一个错误的困扰，这个错误追溯到在一个函数内部运行的predict.lme，它无法解释基于从函数外部传递的变量的公式。我知道这个问题与变量作用域和不同的环境有关，但我一直无法完全理解它或找到一种解决方法。您的帮助将不胜感激。
以下是一个可重现的示例：
# 这将是嵌套函数。train_test_perf <- function(train_data, test_data, model, termLabels) {fixForm <- reformulate(termlabels=termLabels, response="Y")fit <- nlme::lme(fixForm, data=train_data, random=~ 1|ID)train_pred <- predict(fit, newdata=train_data, level=0, na.action=na.exclude)rtrain <- cor.test(train_data$Y, train_pred)test_pred <- predict(fit, newdata=test_data, level=0, na.action=na.exclude)rtest <- cor.test(test_data$Y, test_pred)tmp <- data.frame(Model=model,R_train=rtrain$estimate,R_test=rtest$estimate)return(tmp)}# 这是调用它的函数。myfunc <- function(df, newdf, varList) {for (v in varList) {perf <- train_test_perf(train_data=df, test_data=newdf, model=v, termLabels=v)print(perf)}}# 外部函数调用。myfunc(df=dat, newdf=newdat, varList=list("W", "X"))
运行这个代码会产生以下错误和回溯：
Error in eval(mCall$fixed) : object 'fixForm' not found7.eval(mCall$fixed)6.eval(mCall$fixed)5.eval(eval(mCall$fixed)[-2])4.predict.lme(fit, newdata = train_data, level = 0, na.action = na.exclude)3.predict(fit, newdata = train_data, level = 0, na.action = na.exclude)2.train_test_perf(train_data = df, test_data = newdf, model = v,termLabels = v)1.myfunc(df = dat, newdf = newdat, varList = list("W", "X"))
似乎predict.lme无法访问fixForm变量，但我一直无法找到一种既能定义基于变量的公式又能让predict.lme访问值的方法。我不确定嵌套函数结构是否是这里的问题的一部分 - 如果是的话，我希望找到一种维护这种结构的解决方法，因为我的真实代码包括在myfunc之前和之后发生的一些其他事情。
谢谢，Jeff Phillips
英文:
I have been stymied by an error that traces back to predict.lme, running inside a function, failing to interpret a formula based on a variable that has been passed from outside the function. I know the issue has to do with variable scope and different environments, but I've been unable to fully understand it or find a workaround. Your help would be much appreciated.
Here's a reproducible example:
# This will be the nested function.train_test_perf <- function(train_data, test_data, model, termLabels) {fixForm <- reformulate(termlabels=termLabels, response="Y")fit <- nlme::lme(fixForm, data=train_data, random=~ 1|ID)train_pred <- predict(fit, newdata=train_data, level=0, na.action=na.exclude)rtrain <- cor.test(train_data$Y, train_pred)test_pred <- predict(fit, newdata=test_data, level=0, na.action=na.exclude)rtest <- cor.test(test_data$Y, test_pred)tmp <- data.frame(Model=model,R_train=rtrain$estimate,R_test=rtest$estimate)return(tmp)}# And here is the function that calls it.myfunc <- function(df, newdf, varList) {for (v in varList) {perf <- train_test_perf(train_data=df, test_data=newdf, model=v, termLabels=v)print(perf)}}# The outer function call.myfunc(df=dat, newdf=newdat, varList=list("W", "X"))
Running this gives the following error and traceback:
Error in eval(mCall$fixed) : object 'fixForm' not found7.eval(mCall$fixed)6.eval(mCall$fixed)5.eval(eval(mCall$fixed)[-2])4.predict.lme(fit, newdata = train_data, level = 0, na.action = na.exclude)3.predict(fit, newdata = train_data, level = 0, na.action = na.exclude)2.train_test_perf(train_data = df, test_data = newdf, model = v,termLabels = v)1.myfunc(df = dat, newdf = newdat, varList = list("W", "X"))
It seems clear that predict.lme does not have access to the fixForm variable, but I haven't been able to work out a way to both define a formula based on a variable and have the value accessible to predict.lme. I'm not sure whether the nested function structure is part of the problem here--if it is, I would prefer to find a workaround that would maintain this structure, as my real-life code includes some other things inside myfunc that occur before and after the call to train_test_perf.
Thanks,
Jeff Phillips
答案1
得分: 1
使用变量作为公式不会存储变量，而是存储可能存在的问题的公式。我们可以使用 do.call。
train_test_perf <- function(train_data, test_data, model, termLabels) {fixForm <- reformulate(termlabels=termLabels, response="Y")fit <- do.call(nlme::lme, list(fixForm, data=quote(train_data), random=~ 1|ID))train_pred <- predict(fit, newdata=train_data, level=0, na.action=na.exclude)rtrain <- cor.test(train_data$Y, train_pred)test_pred <- predict(fit, newdata=test_data, level=0, na.action=na.exclude)rtest <- cor.test(test_data$Y, test_pred)tmp <- data.frame(Model=model, R_train=rtrain$estimate,R_test=rtest$estimate)return(tmp)}
最后，将它放在 sapply 中以避免繁琐的 for 循环。
t(sapply(c("W", "X"), \(x) train_test_perf(train_data=dat, test_data=newdat, model=x, termLabels=x)))# Model R_train R_test# [1,] "W" 0.1686495 -0.001738604# [2,] "X" 0.4138526 0.2992374
英文:
Using a variable as formula doesn't stores the variable not the formula which might be the issue. We can use a do.call.
train_test_perf <- function(train_data, test_data, model, termLabels) {fixForm <- reformulate(termlabels=termLabels, response="Y")fit <- do.call(nlme::lme, list(fixForm, data=quote(train_data), random=~ 1|ID))train_pred <- predict(fit, newdata=train_data, level=0, na.action=na.exclude)rtrain <- cor.test(train_data$Y, train_pred)test_pred <- predict(fit, newdata=test_data, level=0, na.action=na.exclude)rtest <- cor.test(test_data$Y, test_pred)tmp <- data.frame(Model=model, R_train=rtrain$estimate,R_test=rtest$estimate)return(tmp)}
Finally put it in an sapply to avoid tedious for loops.
t(sapply(c("W", "X"), \(x) train_test_perf(train_data=dat, test_data=newdat, model=x, termLabels=x)))# Model R_train R_test# [1,] "W" 0.1686495 -0.001738604# [2,] "X" 0.4138526 0.2992374
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