formula error inside function - r

Question

I want use survfit() and basehaz() inside a function, but they do not work. Could you take a look at this problem. Thanks for your help. The following code leads to the error:
library(survival)
n <- 50 # total sample size
nclust <- 5 # number of clusters
clusters <- rep(1:nclust,each=n/nclust)
beta0 <- c(1,2)
set.seed(13)
#generate phmm data set
Z <- cbind(Z1=sample(0:1,n,replace=TRUE),
Z2=sample(0:1,n,replace=TRUE),
Z3=sample(0:1,n,replace=TRUE))
b <- cbind(rep(rnorm(nclust),each=n/nclust),rep(rnorm(nclust),each=n/nclust))
Wb <- matrix(0,n,2)
for( j in 1:2) Wb[,j] <- Z[,j]*b[,j]
Wb <- apply(Wb,1,sum)
T <- -log(runif(n,0,1))*exp(-Z[,c('Z1','Z2')]%*%beta0-Wb)
C <- runif(n,0,1)
time <- ifelse(T<C,T,C)
event <- ifelse(T<=C,1,0)
mean(event)
phmmd <- data.frame(Z)
phmmd$cluster <- clusters
phmmd$time <- time
phmmd$event <- event
fmla <- as.formula("Surv(time, event) ~ Z1 + Z2")
BaseFun <- function(x){
start.coxph <- coxph(x, phmmd)
print(start.coxph)
betahat <- start.coxph$coefficient
print(betahat)
print(333)
print(survfit(start.coxph))
m <- basehaz(start.coxph)
print(m)
}
BaseFun(fmla)
Error in formula.default(object, env = baseenv()) : invalid formula
But the following function works:
fit <- coxph(fmla, phmmd)
basehaz(fit)

Answer 1

It is a problem of scoping.
Notice that the environment of basehaz is:
environment(basehaz)
<environment: namespace:survival>
meanwhile:
environment(BaseFun)
<environment: R_GlobalEnv>
Therefore that is why the function basehaz cannot find the local variable inside the function.
A possible solution is to send x to the top using assign:
BaseFun <- function(x){
assign('x',x,pos=.GlobalEnv)
start.coxph <- coxph(x, phmmd)
print(start.coxph)
betahat <- start.coxph$coefficient
print(betahat)
print(333)
print(survfit(start.coxph))
m <- basehaz(start.coxph)
print(m)
rm(x)
}
BaseFun(fmla)
Other solutions may involved dealing with the environments more directly.

Answer 2

I'm following up on #moli's comment to #aatrujillob's answer. They were helpful so I thought I would explain how it solved things for me and a similar problem with the rpart and partykit packages.
Some toy data:
N <- 200
data <- data.frame(X = rnorm(N),W = rbinom(N,1,0.5))
data <- within( data, expr = {
trtprob <- 0.4 + 0.08*X + 0.2*W -0.05*X*W
Trt <- rbinom(N, 1, trtprob)
outprob <- 0.55 + 0.03*X -0.1*W - 0.3*Trt
Outcome <- rbinom(N,1,outprob)
rm(outprob, trtprob)
})
I want to split the data to training (train_data) and testing sets, and train the classification tree on train_data.
Here's the formula I want to use, and the issue with the following example. When I define this formula, the train_data object does not yet exist.
my_formula <- Trt~W+X
exists("train_data")
# [1] FALSE
exists("train_data", envir = environment(my_formula))
# [1] FALSE
Here's my function, which is similar to the original function. Again,
badFunc <- function(data, my_formula){
train_data <- data[1:100,]
ct_train <- rpart::rpart(
data= train_data,
formula = my_formula,
method = "class")
ct_party <- partykit::as.party(ct_train)
}
Trying to run this function throws an error similar to OP's.
library(rpart)
library(partykit)
bad_out <- badFunc(data=data, my_formula = my_formula)
# Error in is.data.frame(data) : object 'train_data' not found
# 10. is.data.frame(data)
# 9. model.frame.default(formula = Trt ~ W + X, data = train_data,
# na.action = function (x) {Terms <- attr(x, "terms") ...
# 8. stats::model.frame(formula = Trt ~ W + X, data = train_data,
# na.action = function (x) {Terms <- attr(x, "terms") ...
# 7. eval(expr, envir, enclos)
# 6. eval(mf, env)
# 5. model.frame.rpart(obj)
# 4. model.frame(obj)
# 3. as.party.rpart(ct_train)
# 2. partykit::as.party(ct_train)
# 1. badFunc(data = data, my_formula = my_formula)
print(bad_out)
# Error in print(bad_out) : object 'bad_out' not found
Luckily, rpart() is like coxph() in that you can specify the argument model=TRUE to solve these issues. Here it is again, with that extra argument.
goodFunc <- function(data, my_formula){
train_data <- data[1:100,]
ct_train <- rpart::rpart(
data= train_data,
## This solved it for me
model=TRUE,
##
formula = my_formula,
method = "class")
ct_party <- partykit::as.party(ct_train)
}
good_out <- goodFunc(data=data, my_formula = my_formula)
print(good_out)
# Model formula:
# Trt ~ W + X
#
# Fitted party:
# [1] root
# | [2] X >= 1.59791: 0.143 (n = 7, err = 0.9)
##### etc
documentation for model argument in rpart():
model:
if logical: keep a copy of the model frame in the result? If
the input value for model is a model frame (likely from an earlier
call to the rpart function), then this frame is used rather than
constructing new data.
Formulas can be tricky as they use lexical scoping and environments in a way that is not always natural (to me). Thank goodness Terry Therneau has made our lives easier with model=TRUE in these two packages!