in the data frame provided. This is based on the results from glmnet. With

performs OLS if the sum of coef_nonzero is greater than 0. Meaning there

perform OLS since it's coefficient from glmnet was nonzero.

have run, it seems to only occur as this data increases in observations.

sent previously, and the function I am using for glmnet. I'm still not sure

my methods, particularly with foo3.

with the data itself.

I appreciate any help I can get at this point. I've been trying to debug

this function for almost a month.

> Kevin,

>

> I didn't have the data set (you might want to post a link to a

> downloadable file instead), but on a quick look at the code your foo1

> function looks as if it is not guaranteed to return an array of the same

> length each time. It's testing for nonzero coefs in a fitted model and then

> dropping exact zero coefs. That need not (and often will not) return the

> same coefficients every time in a simulation. foo2 does the same general

> kind of thing.

>

> Could that be part of the problem?

>

> S Ellison

>

>

> > -----Original Message-----

> > From: R-help <

[hidden email]> On Behalf Of Kevin Egan

> > Sent: 05 January 2021 12:26

> > To: r-help <

[hidden email]>

> > Subject: [R] Error Running Bootstrap Function within Wrapper Function

> >

> > ===============

> > EXTERNAL EMAIL

> > ===============

> >

> > Hello,

> >

> > I am currently trying to solve a problem with the boot package and

> writing a

> > function within a function in R. I have developed several functions to

> > perform the lasso but continue to receive an error when bootstrapping

> these

> > functions within a wrapper function. When I perform these methods using

> > tsboot outside the wrapper function, I do not get an error. However, when

> > placed within my function I continue to get the error "Error in t[r, ]

> <- res[[r]]

> > : number of items to replace is not a multiple of length."

> >

> > I've attached an example of my functions as well as a data file of the

> data I

> > am using. I'm sorry the file is so large, but I do not get a problem

> with a

> > smaller number of observations.

> >

> >

> > library(boot)

> > library(glmnet)

> > library(np)

> > foo1 <- function(data,index){ #index is the bootstrap sample index

> > x <- data[index, -1] %>%

> > as.matrix() %>%

> > unname()

> > y <- data[index, 1] %>%

> > scale(center = TRUE, scale = FALSE) %>%

> > as.matrix() %>%

> > unname()

> > ols <- lm(y ~ x)

> > # The intercept estimate should be dropped.

> > ols.coef <- as.numeric(coef(ols))[-1]

> > ols.coef[is.na(ols.coef)] <- 0

> > ## The intercept estimate should be dropped.

> > lasso <- cv.glmnet(x, y, alpha = 1,

> > penalty.factor = 1 / abs(ols.coef))

> > # Select nonzero coefficients from bic.out

> > coef <- as.vector(coef(lasso,

> > s = lasso$lambda.min))[-1]

> > return(coef)

> > }

> > foo2 <- function(data, index){ #index is the bootstrap sample index

> > x <- data[index, -1] %>%

> > as.matrix() %>%

> > unname()

> > y <- data[index, 1] %>%

> > scale(center = TRUE, scale = FALSE) %>%

> > as.matrix() %>%

> > unname()

> > # ic.glmnet provides coefficients with lowest BIC

> > ols <- lm(y ~ x)

> > # The intercept estimate should be dropped.

> > ols.coef <- as.numeric(coef(ols))[-1]

> > ols.coef[is.na(ols.coef)] <- 0

> > lasso <- cv.glmnet(x, y, alpha = 1,

> > penalty.factor = 1 / abs(ols.coef))

> > # Select nonzero coefficients from bic.out

> > coef <- as.vector(coef(lasso,

> > s = lasso$lambda.min))[-1]

> > coef_nonzero <- coef != 0

> > if(sum(coef_nonzero) > 0) {

> > ls_obj <- lm(y ~ x[, coef_nonzero, drop = FALSE])

> > ls_coef <- as.vector(coef(ls_obj))[-1]

> > coef[coef_nonzero] <- ls_coef

> > }

> > return(coef)

> > }

> > foo3 <- function(data, num_samples) {

> > bstar <- b.star(data[, 1], round = TRUE)

> > # Select Block Length of circular block result

> > blocklength <- bstar[, 2]

> > init_boot_ts <- tsboot(tseries = data,

> > statistic = foo1,

> > R = num_samples, l = blocklength,

> > sim = "fixed")

> > final_boot_ts <- tsboot(tseries = data,

> > statistic = foo2,

> > R = num_samples,

> > l = blocklength, sim = "fixed")

> > # point estimates

> > final_boot_t0 <- final_boot_ts$t0

> > return(list(point_estimates = final_boot_t0)) } num_samples <- 50

> > test.foo3 <- foo3(data, num_samples = num_samples)

> >

> > Which *sometimes *works, however, sometimes I get the error: “Error in

> t[r,

> > ] <- res[[r]] : number of items to replace is not a multiple of length".

> > I've also gotten the error "Error in x[, coef_nonzero, drop = FALSE]

> > :(subscript) logical subscript too long" at times, when running foo2

> within

> > foo3. Which seems to be unclear as there should never be an index which

> is

> > greater than the number of columns in x.

> > As I stated, this error particularly occurs when I increase the number of

> > bootstrap samples I try to run, and only when I run foo3. When I run foo1

> > and foo2 separately, I don’t get an error at all. I’m wondering if there

> is

> > something I need to add to my function “foo3” to ensure that an error

> like

> > this doesn’t occur since I am calling several other functions within this

> > function, ie., tsboot, foo1, and foo2.

> >

> > Lastly, although I have not provided an example here, I get an error for

> this

> > code when running with lapply for several dataframes similar to the

> > attached.

> >

> > Again, I apologise for attaching such a large data frame but the function

> > seems to work with fewer observations. Perhaps it is a data issue?

> >

> > Thanks

> > ______________________________________________

> >

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http://www.R-project.org/posting-> > guide.html

> > and provide commented, minimal, self-contained, reproducible code.

> >

> >

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