I am trying to use lme function from nlme package inside a lapply loop. This works for lmer function from lme4 package, but produces an error message for lme. How can I loop lme functions similarly to the lmer function in the example below?
library("nlme")
library("lme4")
set.seed(1)
dt <- data.frame(Resp1 = rnorm(100, 50, 23), Resp2 = rnorm(100, 80, 15), Pred = rnorm(100,10,2), group = factor(rep(LETTERS[1:10], each = 10)))
## Syntax:
lmer(Resp1 ~ Pred + (1 |group), data = dt)
lme(Resp1 ~ Pred, random = ~1 | group, data = dt)
## Works for lme4
lapply(c("Resp1", "Resp2"), function(k) {
lmer(substitute(j ~ Pred + (1 | group), list(j = as.name(k))), data = dt)})
## Does not work for nlme
lapply(c("Resp1", "Resp2"), function(k) {
lme(substitute(j ~ Pred, list(j = as.name(k))), random = ~1 | group, data = dt)})
# Error in UseMethod("lme") :
# no applicable method for 'lme' applied to an object of class "call"
PS. I am aware that this solution exists, but I would like to use a method substituting response variable directly in the model function instead of subsetting data using an additional function.
Instead of fiddling around with substitute and eval you also could do the following:
lapply(c("Resp1", "Resp2"), function(r) {
f <- formula(paste(r, "Pred", sep = "~"))
m <- lme(fixed = f, random = ~ 1 | group, data = dt)
m$call$fixed <- f
m})
You could use the same trick if you want to provide different data sets to a modelling function:
makeModel <- function(dat) {
l <- lme(Resp1 ~ Pred, random = ~ 1 | group, data = dat)
l$call$data <- as.symbol(deparse(substitute(dat)))
l
}
I use this snippet quite a bit, when I want to generate a model from within a function and want to update it afterwards.
As #CarlWitthoft suggested, adding eval into the function will solve the issue:
lapply(c("Resp1", "Resp2"), function(k) {
lme(eval(substitute(j ~ Pred, list(j = as.name(k)))), random = ~1 | group, data = dt)})
Also see #thothal's alternative.
Related
I use speedglm to fit a GLM to data. When I call the function directly, the code works as expected, but when I create a function to fit the model, I get an error that an argument is not found.
The variable (w in the example below) clearly exists in the scope of the function but it seems that the variable is evaluated only later within the speedglm function where w is no longer available or so I think. This is where I start questioning my current understanding of R.
Did I make an error while creating the function, does speedglm use some weird trick to scope the variable (source code here) that breaks the normal (?) logic or do I have a wrong understanding of how R functions work?
I am trying to understand this behavior and also fix my train_glm function to make it work with speedglm and weights.
MWE
library(speedglm)
# works as expected
m1 <- speedglm(wt ~ cyl, data = mtcars, weights = mtcars$wt)
# define a small helper function that just forwards its arguments
train_glm <- function(f, d, w) {
speedglm(formula = f, data = d, weights = w)
}
# does not work
m <- train_glm(wt ~ cyl, d = mtcars, w = mtcars$wt)
#> Error in eval(extras, data, env) : object 'w' not found
Even weirder, if I change the code I found the following
# removing the weights as a base case -> WORKS
train_glm3 <- function(f, d) {
speedglm(formula = f, data = d)
}
m3 <- train_glm3(wt ~ cyl, d = mtcars)
# works
# hardcoding the weights inside the function -> BREAKS
train_glm4 <- function(f, d) {
speedglm(formula = f, data = d, weights = d$wt)
}
m4 <- train_glm4(wt ~ cyl, d = mtcars)
# Error in eval(extras, data, env) : object 'd' not found
# creating a new dataset and hardcoding the weights inside the function
# but using the name of the dataset at the highest environment -> WORKS
train_glm5 <- function(f, d) {
speedglm(formula = f, data = d, weights = mtcars2$wt)
}
mtcars2 <- mtcars
m5 <- train_glm5(wt ~ cyl, d = mtcars2)
# works
The solution (thanks to #Mike for the hint) is to evaluate the code either by using the solution given by this answer or by using do.call like so:
library(speedglm)
train_glm_docall <- function(f, d, w) {
do.call(
speedglm,
list(
formula = f,
data = d,
weights = w
)
)
}
m2 <- train_glm_docall(f = wt ~ cyl, d = mtcars, w = mtcars$wt)
class(m2)
#> [1] "speedglm" "speedlm"
I want to run logistic regression for multiple parameters and store the different metrics i.e AUC.
I wrote the function below but I get an error when I call it: Error in eval(predvars, data, env) : object 'X0' not found even if the variable exists in both my training and testing dataset. Any idea?
new.function <- function(a) {
model = glm(extry~a,family=binomial("logit"),data = train_df)
pred.prob <- predict(model,test_df, type='response')
predictFull <- prediction(pred.prob, test_df$extry)
auc_ROCR <- performance(predictFull, measure = "auc")
my_list <- list("AUC" = auc_ROCR)
return(my_list)
}
# Call the function new.function supplying 6 as an argument.
les <- new.function(X0)
The main reason why your function didn't work is that you are trying to call an object into a formula. You can fix it with paste formula function, but that is ultimately quite limiting.
I suggest instead that you consider using update. This allow you more flexibility to change with multiple variable combination, or change a training dataset, without breaking the function.
model = glm(extry~a,family=binomial("logit"),data = train_df)
new.model = update(model, .~X0)
new.function <- function(model){
pred.prob <- predict(model, test_df, type='response')
predictFull <- prediction(pred.prob, test_df$extry)
auc_ROCR <- performance(predictFull, measure = "auc")
my_list <- list("AUC" = auc_ROCR)
return(my_list)
}
les <- new.function(new.model)
The function can be further improved by calling the test_df as a separate argument, so that you can fit it with an alternative testing data.
To run the function in the way you intended, you would need to use non-standard evaluation to capture the symbol and insert it in a formula. This can be done using match.call and as.formula. Here's a fully reproducible example using dummy data:
new.function <- function(a) {
# Convert symbol to character
a <- as.character(match.call()$a)
# Build formula from character strings
form <- as.formula(paste("extry", a, sep = "~"))
model <- glm(form, family = binomial("logit"), data = train_df)
pred.prob <- predict(model, test_df, type = 'response')
predictFull <- ROCR::prediction(pred.prob, test_df$extry)
auc_ROCR <- ROCR::performance(predictFull, "auc")
list("AUC" = auc_ROCR)
}
Now we can call the function in the way you intended:
new.function(X0)
#> $AUC
#> A performance instance
#> 'Area under the ROC curve'
new.function(X1)
#> $AUC
#> A performance instance
#> 'Area under the ROC curve'
If you want to see the actual area under the curve you would need to do:
new.function(X0)$AUC#y.values[[1]]
#> [1] 0.6599759
So you may wish to modify your function so that the list contains auc_ROCR#y.values[[1]] rather than auc_ROCR
Data used
set.seed(1)
train_df <- data.frame(X0 = sample(100), X1 = sample(100))
train_df$extry <- rbinom(100, 1, (train_df$X0 + train_df$X1)/200)
test_df <- data.frame(X0 = sample(100), X1 = sample(100))
test_df$extry <- rbinom(100, 1, (test_df$X0 + test_df$X1)/200)
Created on 2022-06-29 by the reprex package (v2.0.1)
I am trying to use lme function from nlme package inside a lapply loop. This works for lmer function from lme4 package, but produces an error message for lme. How can I loop lme functions similarly to the lmer function in the example below?
library("nlme")
library("lme4")
set.seed(1)
dt <- data.frame(Resp1 = rnorm(100, 50, 23), Resp2 = rnorm(100, 80, 15), Pred = rnorm(100,10,2), group = factor(rep(LETTERS[1:10], each = 10)))
## Syntax:
lmer(Resp1 ~ Pred + (1 |group), data = dt)
lme(Resp1 ~ Pred, random = ~1 | group, data = dt)
## Works for lme4
lapply(c("Resp1", "Resp2"), function(k) {
lmer(substitute(j ~ Pred + (1 | group), list(j = as.name(k))), data = dt)})
## Does not work for nlme
lapply(c("Resp1", "Resp2"), function(k) {
lme(substitute(j ~ Pred, list(j = as.name(k))), random = ~1 | group, data = dt)})
# Error in UseMethod("lme") :
# no applicable method for 'lme' applied to an object of class "call"
PS. I am aware that this solution exists, but I would like to use a method substituting response variable directly in the model function instead of subsetting data using an additional function.
Instead of fiddling around with substitute and eval you also could do the following:
lapply(c("Resp1", "Resp2"), function(r) {
f <- formula(paste(r, "Pred", sep = "~"))
m <- lme(fixed = f, random = ~ 1 | group, data = dt)
m$call$fixed <- f
m})
You could use the same trick if you want to provide different data sets to a modelling function:
makeModel <- function(dat) {
l <- lme(Resp1 ~ Pred, random = ~ 1 | group, data = dat)
l$call$data <- as.symbol(deparse(substitute(dat)))
l
}
I use this snippet quite a bit, when I want to generate a model from within a function and want to update it afterwards.
As #CarlWitthoft suggested, adding eval into the function will solve the issue:
lapply(c("Resp1", "Resp2"), function(k) {
lme(eval(substitute(j ~ Pred, list(j = as.name(k)))), random = ~1 | group, data = dt)})
Also see #thothal's alternative.
I am trying to use the lm.cluster function in the package miceadds to get robust clustered standard errors for a multiply imputed dataset.
I am able to get the standard version of it to run but I get the following error when I try to add a subset or weights:
Error in eval(substitute(subset), data, env) :
..1 used in an incorrect context, no ... to look in
Example that works without subset or weights:
require("mice")
require("miceadds")
data(data.ma01)
# imputation of the dataset: use six imputations
dat <- data.ma01[ , - c(1:2) ]
imp <- mice::mice( dat , maxit=3 , m=6 )
datlist <- miceadds::mids2datlist( imp )
# linear regression with cluster robust standard errors
mod <- lapply(datlist, FUN = function(data){miceadds::lm.cluster( data=data ,
formula=read ~ paredu+ female , cluster = data.ma01$idschool )} )
# extract parameters and covariance matrix
betas <- lapply( mod , FUN = function(rr){ coef(rr) } )
vars <- lapply( mod , FUN = function(rr){ vcov(rr) } )
# conduct statistical inference
summary(pool_mi( qhat = betas, u = vars ))
Example that breaks with subset:
mod <- lapply(datlist, FUN = function(data){miceadds::lm.cluster( data=data ,
formula=read ~ paredu+ female , cluster = data.ma01$idschool, subset=
(data.ma01$urban==1))} )
Error during wrapup: ..1 used in an incorrect context, no ... to look in
Example that breaks with weights:
mod <- lapply(datlist, FUN = function(data){miceadds::lm.cluster( data=data ,
formula=read ~ paredu+ female , cluster = data.ma01$idschool,
weights=data.ma01$studwgt)} )
Error during wrapup: ..1 used in an incorrect context, no ... to look in
From searching, I think I am encountering similar issues as others when passing these commands through an lm or glm wrapper (such as: Passing Argument to lm in R within Function or R : Pass argument to glm inside an R function or Passing the weights argument to a regression function inside an R function)
However, I am not sure how to address the issue with the imputed datasets & existing lm.cluster command.
Thanks
This works fine with the estimatr package which is on CRAN and the estimatr::lm_robust() function. Two notes: (1) you can change the type of standard errors using se_type = and (2) I keep idschool in the data because we like the clusters to be in the same data.frame as we fit the model on.
library(mice)
library(miceadds)
library(estimatr)
# imputation of the dataset: use six imputations
data(data.ma01)
dat <- data.ma01[, -c(1)] # note I keep idschool in data
imp <- mice::mice( dat , maxit = 3, m = 6)
datlist <- miceadds::mids2datlist(imp)
# linear regression with cluster robust standard errors
mod <- lapply(
datlist,
function (dat) {
estimatr::lm_robust(read ~ paredu + female, dat, clusters = idschool)
}
)
# subset
mod <- lapply(
datlist,
function (dat) {
estimatr::lm_robust(read ~ paredu + female, dat, clusters = idschool, subset = urban == 1)
}
)
# weights
mod <- lapply(
datlist,
function (dat) {
estimatr::lm_robust(read ~ paredu + female, dat, clusters = idschool, weights = studwgt)
}
)
# note that you can use the `se_type` argument of lm_robust()
# to change the vcov estimation
# extract parameters and covariance matrix
betas <- lapply(mod, coef)
vars <- lapply(mod, vcov)
# conduct statistical inference
summary(pool_mi( qhat = betas, u = vars ))
I'm no expert, but there is an issue with the passing of the weights to lm(). I know this is not an ideal situation, but I managed to get it to work by modifying the lm.cluster() function to hard code the weights pass and then just used my own.
lm.cluster <- function (data, formula, cluster, wgts=NULL, ...)
{
TAM::require_namespace_msg("multiwayvcov")
if(is.null(wgts)) {
mod <- stats::lm(data = data, formula = formula)
} else {
data$.weights <- wgts
mod <- stats::lm(data = data, formula = formula, weights=data$.weights)
}
if (length(cluster) > 1) {
v1 <- cluster
}
else {
v1 <- data[, cluster]
}
dfr <- data.frame(cluster = v1)
vcov2 <- multiwayvcov::cluster.vcov(model = mod, cluster = dfr)
res <- list(lm_res = mod, vcov = vcov2)
class(res) <- "lm.cluster"
return(res)
}
i am trying to familiarize myself with the caret package. I would previously use rpart directly - e.g. with the following syntax
fit_rpart=rpart(y~.,data=dt1,method="anova").
i have specified anova as i am aiming for regression (rather than classification)
with caret - i would the following syntax:
rpart_fit <- train(y ~ ., data = dt1, method = "rpart",trControl=fitControl)
my question is, as the method slot is already used, where/how can i still specify method="anova"?
Many thanks in advance!
You can make a custom method using the current rpart code. First, get the current code:
library(caret)
rpart_code <- getModelInfo("rpart", regex = FALSE)[[1]]
You then just add the extra option to the code. This method is somewhat convoluted since it handles a bunch of different cases, but here is the edit:
rpart_code$fit <- function(x, y, wts, param, lev, last, classProbs, ...) {
cpValue <- if(!last) param$cp else 0
theDots <- list(...)
if(any(names(theDots) == "control")) {
theDots$control$cp <- cpValue
theDots$control$xval <- 0
ctl <- theDots$control
theDots$control <- NULL
} else ctl <- rpart.control(cp = cpValue, xval = 0)
## check to see if weights were passed in (and availible)
if(!is.null(wts)) theDots$weights <- wts
modelArgs <- c(list(formula = as.formula(".outcome ~ ."),
data = if(is.data.frame(x)) x else as.data.frame(x),
control = ctl,
method = "anova"),
theDots)
modelArgs$data$.outcome <- y
out <- do.call("rpart", modelArgs)
if(last) out <- prune.rpart(out, cp = param$cp)
out
}
then test:
library(rpart)
set.seed(445)
mod <- train(pgstat ~ age + eet + g2 + grade + gleason + ploidy,
data = stagec,
method = rpart_code,
tuneLength = 8)
Max
In caret 'method' refers to the type of model you would like to use, so for example rpart or lm (linear regression) or rf (random forest).
What you're referring to is defined as 'metric' in caret.
If your y-variable is a continuous variable, the metric will be default set to maximizing RMSE. So you don't have to do anything.
You could also explicitly specify this by:
rpart_fit <- train(y ~ ., data = dt1, method = "rpart",trControl=fitControl, metric="RMSE")