Function margins not functional with lapply? #94

AnttiSim · May 11, 2018

Please specify whether your issue is about:

a possible bug

Hi,

I have a list of glm model fits. I tried to apply margins for each item in the list with lapply. That however terminates with an error. Choosing one item from the list returns the same error.

I could reproduce that with a simple lm model (see below).

Put your code here:

## load package
library("margins")

## code goes here
    lstData <- split(mtcars, mtcars$gear)
    lstFits <- lapply(lstData, function(x) lm(mpg ~ cyl + wt, data = x))

    ## These both return an error
    lapply(lstFits, margins)
    margins(lstFits[[1]])

    ## The error message is:
    ## Error in eval(model[["call"]][["data"]], env) : object 'x' not found

## traceback
> traceback()
8: eval(model[["call"]][["data"]], env)
7: eval(model[["call"]][["data"]], env)
6: find_data.lm(model, parent.frame())
5: find_data(model, parent.frame())
4: build_datalist(data, at = at)
3: margins.lm(X[[i]], ...)
2: FUN(X[[i]], ...)
1: lapply(lstFits, margins)

## session info for your system
sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

Matrix products: default

locale:
[1] LC_COLLATE=Finnish_Finland.1252 LC_CTYPE=Finnish_Finland.1252
[3] LC_MONETARY=Finnish_Finland.1252 LC_NUMERIC=C
[5] LC_TIME=Finnish_Finland.1252

attached base packages:
[1] stats graphics grDevices utils datasets tcltk methods
[8] base

other attached packages:
[1] bindrcpp_0.2.2 stargazer_5.2.1 lmtest_0.9-36 zoo_1.8-1
[5] sandwich_2.4-0 forcats_0.3.0 stringr_1.3.0 dplyr_0.7.4
[9] purrr_0.2.4 readr_1.1.1 tidyr_0.8.0 tibble_1.4.2
[13] ggplot2_2.2.1 tidyverse_1.2.1

loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 cellranger_1.1.0 pillar_1.2.2 compiler_3.5.0
[5] plyr_1.8.4 bindr_0.1.1 tools_3.5.0 lubridate_1.7.4
[9] jsonlite_1.5 nlme_3.1-137 gtable_0.2.0 lattice_0.20-35
[13] pkgconfig_2.0.1 rlang_0.2.0 psych_1.8.4 cli_1.0.0
[17] rstudioapi_0.7 parallel_3.5.0 haven_1.1.1 xml2_1.2.0
[21] httr_1.3.1 hms_0.4.2 tidyselect_0.2.4 grid_3.5.0
[25] glue_1.2.0 R6_2.2.2 readxl_1.1.0 foreign_0.8-70
[29] modelr_0.1.1 reshape2_1.4.3 magrittr_1.5 scales_0.5.0
[33] rvest_0.3.2 assertthat_0.2.0 mnormt_1.5-5 colorspace_1.3-2
[37] stringi_1.2.2 lazyeval_0.2.1 munsell_0.4.3 broom_0.4.4
[41] crayon_1.3.4

Install the latest version from GitHub and you should be able to mapply(). The issue is that you need to pass a data argument because margins() can't tell what the source data are in this context. Here's an example:

> (lstMargins <- mapply(margins, model = lstFits, data = lstData, SIMPLIFY = FALSE))
$`3`
Average marginal effects
lm(formula = mpg ~ cyl + wt, data = x)

    cyl     wt
 -0.838 -2.473

$`4`
Average marginal effects
lm(formula = mpg ~ cyl + wt, data = x)

   cyl     wt
 -1.64 -5.441

$`5`
Average marginal effects
lm(formula = mpg ~ cyl + wt, data = x)

     cyl    wt
 -0.8501 -6.09

> summary(lstMargins[[1]])
 factor     AME     SE       z      p   lower   upper
    cyl -0.8380 0.5773 -1.4515 0.1466 -1.9695  0.2936
     wt -2.4727 0.8228 -3.0051 0.0027 -4.0855 -0.8600

It would be nice if this worked better so I'll leave it open for the time being but that's the simplest solution at the time being. If you built the original list using a for loop rather than lapply(), it would probably also work out of the box.

leeper · May 11, 2018

Install the latest version from GitHub and you should be able to mapply(). The issue is that you need to pass a data argument because margins() can't tell what the source data are in this context. Here's an example:

> (lstMargins <- mapply(margins, model = lstFits, data = lstData, SIMPLIFY = FALSE))
$`3`
Average marginal effects
lm(formula = mpg ~ cyl + wt, data = x)

    cyl     wt
 -0.838 -2.473

$`4`
Average marginal effects
lm(formula = mpg ~ cyl + wt, data = x)

   cyl     wt
 -1.64 -5.441

$`5`
Average marginal effects
lm(formula = mpg ~ cyl + wt, data = x)

     cyl    wt
 -0.8501 -6.09

> summary(lstMargins[[1]])
 factor     AME     SE       z      p   lower   upper
    cyl -0.8380 0.5773 -1.4515 0.1466 -1.9695  0.2936
     wt -2.4727 0.8228 -3.0051 0.0027 -4.0855 -0.8600

It would be nice if this worked better so I'll leave it open for the time being but that's the simplest solution at the time being. If you built the original list using a for loop rather than lapply(), it would probably also work out of the box.

Wonderful! The mapply solution works fine for me.

AnttiSim · May 12, 2018

Wonderful! The mapply solution works fine for me.

Hi, I'm running into a similar problem when I try to run this on a list column using a dplyr/tidyr/purrr approach. I'm admittedly not very experienced with the package so maybe I'm doing something fundamentally wrong but here's an example. Sorry for the lengthy code block, but just want to show you different behaviors.

library("magrittr")
library("tidyr")
library("dplyr")
library("purrr")
library("margins")

# attempt to apply margins() across a list column of lm results

# create list column of datasets
# map lm() into each dataset
ests <- mtcars %>%
  gather(key = yvar, value = y, vs, am) %>% 
  group_by(yvar) %>%
  nest(.key = model_data) %>%
  mutate(mod = map(model_data, ~ lm(y ~ mpg, data = .x)))


# first try using lapply()
ests %$%
  lapply(mod, margins)

## Error in eval(model[["call"]][["data"]], env) : object '.x' not found


traceback()
## 20: eval(model[["call"]][["data"]], env)
## 19: eval(model[["call"]][["data"]], env)
## 18: find_data.lm(model, parent.frame())
## 17: find_data(model, parent.frame())
## 16: build_datalist(data, at = at)
## 15: margins.lm(X[[i]], ...)
## 14: FUN(X[[i]], ...)
## 13: lapply(mod, margins)
## 12: eval(substitute(expr), data, enclos = parent.frame())
## 11: eval(substitute(expr), data, enclos = parent.frame())
## 10: with.default(., lapply(mod, margins))
## 9: with(., lapply(mod, margins))
## 8: function_list[[k]](value)
## 7: withVisible(function_list[[k]](value))
## 6: freduce(value, `_function_list`)
## 5: `_fseq`(`_lhs`)
## 4: eval(quote(`_fseq`(`_lhs`)), env, env)
## 3: eval(quote(`_fseq`(`_lhs`)), env, env)
## 2: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
## 1: ests %$% lapply(mod, margins)


# supplying data argument
ests %$%
  lapply(mod, margins, data = model_data)

## output:
## Error in rep(NA_real_, nrow(data)) : invalid 'times' argument
## In addition: Warning message:
## In dydx.default(X[[i]], ...) :
  ## Class of variable, mpg, is unrecognized. Returning NA.

traceback()
## 21: dydx.default(X[[i]], ...)
## 20: FUN(X[[i]], ...)
## 19: lapply(c(varslist$nnames, varslist$lnames), dydx, data = data,
        ## model = model, type = type, eps = eps, as.data.frame = as.data.frame,
        ## ...)
## 18: marginal_effects.lm(model = model, data = data, variables = variables,
        ## type = type, eps = eps, varslist = varslist, ...)
## 17: marginal_effects(model = model, data = data, variables = variables,
        ## type = type, eps = eps, varslist = varslist, ...)
## 16: build_margins(model = model, data = data_list[[i]], variables = variables,
        ## type = type, vcov = vcov, vce = vce, iterations = iterations,
        ## unit_ses = unit_ses, eps = eps, varslist = varslist, ...)
## 15: margins.lm(X[[i]], ...)
## 14: FUN(X[[i]], ...)
## 13: lapply(mod, margins, data = model_data)
## 12: eval(substitute(expr), data, enclos = parent.frame())
## 11: eval(substitute(expr), data, enclos = parent.frame())
## 10: with.default(., lapply(mod, margins, data = model_data))
## 9: with(., lapply(mod, margins, data = model_data))
## 8: function_list[[k]](value)
## 7: withVisible(function_list[[k]](value))
## 6: freduce(value, `_function_list`)
## 5: `_fseq`(`_lhs`)
## 4: eval(quote(`_fseq`(`_lhs`)), env, env)
## 3: eval(quote(`_fseq`(`_lhs`)), env, env)
## 2: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
## 1: ests %$% lapply(mod, margins, data = model_data)




# using mapply() approach
ests %$%
  mapply(margins, mod, data = model_data)

## Output: 
##             [,1]       [,2]
## mpg          Numeric,32 Numeric,32
## cyl          Numeric,32 Numeric,32
## disp         Numeric,32 Numeric,32
## hp           Numeric,32 Numeric,32
## drat         Numeric,32 Numeric,32
## wt           Numeric,32 Numeric,32
## qsec         Numeric,32 Numeric,32
## gear         Numeric,32 Numeric,32
## carb         Numeric,32 Numeric,32
## y            Numeric,32 Numeric,32
## fitted       Numeric,32 Numeric,32
## se.fitted    Numeric,32 Numeric,32
## dydx_mpg     Numeric,32 Numeric,32
## Var_dydx_mpg Numeric,32 Numeric,32
## _weights     Numeric,32 Numeric,32
## _at_number   Integer,32 Integer,32

# not an error just not what I thought I should get
# could be my own ignorance about the behavior of the package



# attempt using map()
ests %>% 
  mutate(marginals = map(mod, margins))

## Output:
## Error in mutate_impl(.data, dots) :
##   Evaluation error: invalid 'times' argument.
## In addition: Warning message:
## In dydx.default(X[[i]], ...) :
##   Class of variable, mpg, is unrecognized. Returning NA.

traceback()

## Output:
## 12: stop(list(message = "Evaluation error: invalid 'times' argument.",
        ## call = mutate_impl(.data, dots), cppstack = NULL))
## 11: mutate_impl(.data, dots)
## 10: mutate.tbl_df(., marginals = map(mod, margins))
## 9: mutate(., marginals = map(mod, margins))
## 8: function_list[[k]](value)
## 7: withVisible(function_list[[k]](value))
## 6: freduce(value, `_function_list`)
## 5: `_fseq`(`_lhs`)
## 4: eval(quote(`_fseq`(`_lhs`)), env, env)
## 3: eval(quote(`_fseq`(`_lhs`)), env, env)
## 2: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
## 1: ests %>% mutate(marginals = map(mod, margins))



# attempt when supplying data using map2()
ests %>% 
  mutate(marginals = map2(mod, model_data, ~ margins(.x, data = .y))) %>%
  select(marginals) %>%
  unnest()

# I won't paste the full output but this returns a tibble
# with original data and appended columns `fitted`, `se.fitted`, and so on 
# not the typical output of margins()


# goal: hoping to get output as a list something like this
list(margins(lm(vs ~ mpg, data = mtcars)),
     margins(lm(am ~ mpg, data = mtcars)))

sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/ libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/ libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] bindrcpp_0.2.2 margins_0.3.23 purrr_0.2.4 dplyr_0.7.4 tidyr_0.8.0
[6] magrittr_1.5

loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 utf8_1.1.3 crayon_1.3.4 assertthat_0.2.0
[5] MASS_7.3-49 R6_2.2.2 pillar_1.2.2 cli_1.0.0
[9] rlang_0.2.0 data.table_1.11.4 prediction_0.3.6 glue_1.2.0
[13] compiler_3.5.0 pkgconfig_2.0.1 tidyselect_0.2.4 bindr_0.1.1
[17] tibble_1.4.2

mikedecr · Jun 18, 2018

Hi, I'm running into a similar problem when I try to run this on a list column using a dplyr/tidyr/purrr approach. I'm admittedly not very experienced with the package so maybe I'm doing something fundamentally wrong but here's an example. Sorry for the lengthy code block, but just want to show you different behaviors.

library("magrittr")
library("tidyr")
library("dplyr")
library("purrr")
library("margins")

# attempt to apply margins() across a list column of lm results

# create list column of datasets
# map lm() into each dataset
ests <- mtcars %>%
  gather(key = yvar, value = y, vs, am) %>% 
  group_by(yvar) %>%
  nest(.key = model_data) %>%
  mutate(mod = map(model_data, ~ lm(y ~ mpg, data = .x)))


# first try using lapply()
ests %$%
  lapply(mod, margins)

## Error in eval(model[["call"]][["data"]], env) : object '.x' not found


traceback()
## 20: eval(model[["call"]][["data"]], env)
## 19: eval(model[["call"]][["data"]], env)
## 18: find_data.lm(model, parent.frame())
## 17: find_data(model, parent.frame())
## 16: build_datalist(data, at = at)
## 15: margins.lm(X[[i]], ...)
## 14: FUN(X[[i]], ...)
## 13: lapply(mod, margins)
## 12: eval(substitute(expr), data, enclos = parent.frame())
## 11: eval(substitute(expr), data, enclos = parent.frame())
## 10: with.default(., lapply(mod, margins))
## 9: with(., lapply(mod, margins))
## 8: function_list[[k]](value)
## 7: withVisible(function_list[[k]](value))
## 6: freduce(value, `_function_list`)
## 5: `_fseq`(`_lhs`)
## 4: eval(quote(`_fseq`(`_lhs`)), env, env)
## 3: eval(quote(`_fseq`(`_lhs`)), env, env)
## 2: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
## 1: ests %$% lapply(mod, margins)


# supplying data argument
ests %$%
  lapply(mod, margins, data = model_data)

## output:
## Error in rep(NA_real_, nrow(data)) : invalid 'times' argument
## In addition: Warning message:
## In dydx.default(X[[i]], ...) :
  ## Class of variable, mpg, is unrecognized. Returning NA.

traceback()
## 21: dydx.default(X[[i]], ...)
## 20: FUN(X[[i]], ...)
## 19: lapply(c(varslist$nnames, varslist$lnames), dydx, data = data,
        ## model = model, type = type, eps = eps, as.data.frame = as.data.frame,
        ## ...)
## 18: marginal_effects.lm(model = model, data = data, variables = variables,
        ## type = type, eps = eps, varslist = varslist, ...)
## 17: marginal_effects(model = model, data = data, variables = variables,
        ## type = type, eps = eps, varslist = varslist, ...)
## 16: build_margins(model = model, data = data_list[[i]], variables = variables,
        ## type = type, vcov = vcov, vce = vce, iterations = iterations,
        ## unit_ses = unit_ses, eps = eps, varslist = varslist, ...)
## 15: margins.lm(X[[i]], ...)
## 14: FUN(X[[i]], ...)
## 13: lapply(mod, margins, data = model_data)
## 12: eval(substitute(expr), data, enclos = parent.frame())
## 11: eval(substitute(expr), data, enclos = parent.frame())
## 10: with.default(., lapply(mod, margins, data = model_data))
## 9: with(., lapply(mod, margins, data = model_data))
## 8: function_list[[k]](value)
## 7: withVisible(function_list[[k]](value))
## 6: freduce(value, `_function_list`)
## 5: `_fseq`(`_lhs`)
## 4: eval(quote(`_fseq`(`_lhs`)), env, env)
## 3: eval(quote(`_fseq`(`_lhs`)), env, env)
## 2: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
## 1: ests %$% lapply(mod, margins, data = model_data)




# using mapply() approach
ests %$%
  mapply(margins, mod, data = model_data)

## Output: 
##             [,1]       [,2]
## mpg          Numeric,32 Numeric,32
## cyl          Numeric,32 Numeric,32
## disp         Numeric,32 Numeric,32
## hp           Numeric,32 Numeric,32
## drat         Numeric,32 Numeric,32
## wt           Numeric,32 Numeric,32
## qsec         Numeric,32 Numeric,32
## gear         Numeric,32 Numeric,32
## carb         Numeric,32 Numeric,32
## y            Numeric,32 Numeric,32
## fitted       Numeric,32 Numeric,32
## se.fitted    Numeric,32 Numeric,32
## dydx_mpg     Numeric,32 Numeric,32
## Var_dydx_mpg Numeric,32 Numeric,32
## _weights     Numeric,32 Numeric,32
## _at_number   Integer,32 Integer,32

# not an error just not what I thought I should get
# could be my own ignorance about the behavior of the package



# attempt using map()
ests %>% 
  mutate(marginals = map(mod, margins))

## Output:
## Error in mutate_impl(.data, dots) :
##   Evaluation error: invalid 'times' argument.
## In addition: Warning message:
## In dydx.default(X[[i]], ...) :
##   Class of variable, mpg, is unrecognized. Returning NA.

traceback()

## Output:
## 12: stop(list(message = "Evaluation error: invalid 'times' argument.",
        ## call = mutate_impl(.data, dots), cppstack = NULL))
## 11: mutate_impl(.data, dots)
## 10: mutate.tbl_df(., marginals = map(mod, margins))
## 9: mutate(., marginals = map(mod, margins))
## 8: function_list[[k]](value)
## 7: withVisible(function_list[[k]](value))
## 6: freduce(value, `_function_list`)
## 5: `_fseq`(`_lhs`)
## 4: eval(quote(`_fseq`(`_lhs`)), env, env)
## 3: eval(quote(`_fseq`(`_lhs`)), env, env)
## 2: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
## 1: ests %>% mutate(marginals = map(mod, margins))



# attempt when supplying data using map2()
ests %>% 
  mutate(marginals = map2(mod, model_data, ~ margins(.x, data = .y))) %>%
  select(marginals) %>%
  unnest()

# I won't paste the full output but this returns a tibble
# with original data and appended columns `fitted`, `se.fitted`, and so on 
# not the typical output of margins()


# goal: hoping to get output as a list something like this
list(margins(lm(vs ~ mpg, data = mtcars)),
     margins(lm(am ~ mpg, data = mtcars)))

sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.5

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/ libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/ libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] bindrcpp_0.2.2 margins_0.3.23 purrr_0.2.4 dplyr_0.7.4 tidyr_0.8.0
[6] magrittr_1.5

loaded via a namespace (and not attached):
[1] Rcpp_0.12.16 utf8_1.1.3 crayon_1.3.4 assertthat_0.2.0
[5] MASS_7.3-49 R6_2.2.2 pillar_1.2.2 cli_1.0.0
[9] rlang_0.2.0 data.table_1.11.4 prediction_0.3.6 glue_1.2.0
[13] compiler_3.5.0 pkgconfig_2.0.1 tidyselect_0.2.4 bindr_0.1.1
[17] tibble_1.4.2

@mikedecr The mapply() strategy should work in your case, but you need to pass the SIMPLIFY = FALSE argument - that's why you're getting the confusing "matrix of lists" result.

Though I'm not a purrr expert, I think the map2() function might also work for you in this case.

leeper · Jun 18, 2018

@mikedecr The mapply() strategy should work in your case, but you need to pass the SIMPLIFY = FALSE argument - that's why you're getting the confusing "matrix of lists" result.

Though I'm not a purrr expert, I think the map2() function might also work for you in this case.

@mikedecr And I see you already tried that. The result is probably what you want - margins() returns a data frame and summary(margins()) returns the data frame of AMEs and standard errors that I imagine you'd like to obtain.

I might also be able to suggest something else if I have a better sense of the big picture. You say you have a list column - are you bootstrapping or something similar?

leeper · Jun 18, 2018

@mikedecr And I see you already tried that. The result is probably what you want - margins() returns a data frame and summary(margins()) returns the data frame of AMEs and standard errors that I imagine you'd like to obtain.

I might also be able to suggest something else if I have a better sense of the big picture. You say you have a list column - are you bootstrapping or something similar?

@leeper Thanks, I think I was a little confused because sometimes I would run margins() on a dummy example and get the summary-style output, but I was getting the data frame in my application. Using map2() and including the additional summary() appears to give me what I'm looking for. I'll test it with another simple example and let you know if it doesn't work out, but in case you don't hear back from me, you can consider my problem addressed! Thanks for the clarification, this is gonna be a huge help for what I'm currently hacking through.

mikedecr · Jun 18, 2018

@leeper Thanks, I think I was a little confused because sometimes I would run margins() on a dummy example and get the summary-style output, but I was getting the data frame in my application. Using map2() and including the additional summary() appears to give me what I'm looking for. I'll test it with another simple example and let you know if it doesn't work out, but in case you don't hear back from me, you can consider my problem addressed! Thanks for the clarification, this is gonna be a huge help for what I'm currently hacking through.

I've just added a new function to the package - margins_summary(...) - that provides simple sugar for the nested expression summary(margins(...)) so that you obtain a probably more-expected result in situations such as this. I've also added some examples based upon this issue to ? margins showcasing functionality.

leeper · Jul 22, 2018

I've just added a new function to the package - margins_summary(...) - that provides simple sugar for the nested expression summary(margins(...)) so that you obtain a probably more-expected result in situations such as this. I've also added some examples based upon this issue to ? margins showcasing functionality.

I got a similar error message when using the cplot() function:

error in eval(model[["call"]][["data"]], env) :
promise already under evaluation: recursive default argument reference or earlier problems?

I figured out that if your own data frame is named "data" and used in the regression glm() function, then cplot() gives the error above...

(this post is not related to the issues above....)

NEN11 · Aug 1, 2018

I got a similar error message when using the cplot() function:

error in eval(model[["call"]][["data"]], env) :
promise already under evaluation: recursive default argument reference or earlier problems?

I figured out that if your own data frame is named "data" and used in the regression glm() function, then cplot() gives the error above...

(this post is not related to the issues above....)

@NEN11 can you please open a new issue?

leeper · Aug 1, 2018

@NEN11 can you please open a new issue?

leeper added bug question and removed bug labels May 11, 2018

leeper closed this in 3f32640 Jul 22, 2018

leeper/margins

Function margins not functional with lapply? #94

AnttiSim commented May 11, 2018 •

edited

Edited 1 time

AnttiSim edited May 11, 2018 (most recent)

leeper added bug question and removed bug labels May 11, 2018

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leeper commented Aug 1, 2018

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Function margins not functional with lapply? #94

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AnttiSim commented May 11, 2018 • edited Edited 1 time AnttiSim edited May 11, 2018 (most recent)

leeper added bug question and removed bug labels May 11, 2018

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AnttiSim commented May 11, 2018 •

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