Bug in example? `cbind()` with `predicted()`

[strengejacke]

I tried to adopt an example, where predict() was used in combination with cbind() and the newdata-data frame. But, e.g. looking at rows 1, 7 and 13, you see that these refer to the same combination hincome = 20, children = absent and response = not.work, but all rows have different predicted probabilities.

That raises the question if I use as.data.frame(predict()), how do I find out which row corresponds to which combination of factor levels and response level?

library(nestedLogit)

data("Womenlf", package = "carData")
m <- nestedLogit(partic ~ hincome + children,
  logits(
    work = dichotomy("not.work", c("parttime", "fulltime")),
    full = dichotomy("parttime", "fulltime")
  ),
  data = Womenlf
)
new <- expand.grid(
  hincome = c(20, 30, 40),
  children = c("absent", "present")
)
cbind(new, predict(m, newdata = new))
#>    hincome children response           p        se.p       logit  se.logit
#> 1       20   absent not.work 0.379973389 0.061556512 -0.48966118 0.2612826
#> 2       30   absent parttime 0.129436733 0.047148453 -1.90594822 0.4184172
#> 3       40   absent fulltime 0.490589878 0.066559091 -0.03764493 0.2663307
#> 4       20  present not.work 0.483361927 0.095465380 -0.06657687 0.3822848
#> 5       30  present parttime 0.224958781 0.095638728 -1.23699902 0.5485371
#> 6       40  present fulltime 0.291679292 0.101605781 -0.88724209 0.4917936
#> 7       20   absent not.work 0.588194721 0.133170256  0.35650744 0.5497867
#> 8       30   absent parttime 0.285272275 0.128164048 -0.91845759 0.6285879
#> 9       40   absent fulltime 0.126533004 0.097928271 -1.93196717 0.8860491
#> 10      20  present not.work 0.747625342 0.037830221  1.08598725 0.2004976
#> 11      30  present parttime 0.199129118 0.034884708 -1.39174629 0.2187448
#> 12      40  present fulltime 0.053245540 0.019740560 -2.87812574 0.3915966
#> 13      20   absent not.work 0.818923571 0.052610678  1.50907156 0.3547877
#> 14      30   absent parttime 0.165901061 0.049355986 -1.61496044 0.3566754
#> 15      40   absent fulltime 0.015175368 0.011491854 -4.17278999 0.7689391
#> 16      20  present not.work 0.873487849 0.059489224  1.93215586 0.5383308
#> 17      30  present parttime 0.122673467 0.057838540 -1.96735317 0.5374096
#> 18      40  present fulltime 0.003838684 0.004592451 -5.55877971 1.2009709

^{Created on 2023-05-31 with reprex v2.0.2}

[friendly]

Hi Daniel
Thank you for testing this for use in the ggeffects package.
It is surprising to me that cbind() works at all in this context, where predict() returns a list of 4 data frames.

Our as.data.frame.predictNestedLogit() function is a little quirky, because to get the factor levels you need to
also supply the newdata argument:

> pred <- predict(m, newdata = new)
> as.data.frame(pred, newdata=new)
   hincome children response           p        se.p       logit  se.logit
1       20   absent not.work 0.379973389 0.061556512 -0.48966118 0.2612826
2       20   absent parttime 0.129436733 0.047148453 -1.90594822 0.4184172
3       20   absent fulltime 0.490589878 0.066559091 -0.03764493 0.2663307
4       30   absent not.work 0.483361927 0.095465380 -0.06657687 0.3822848
5       30   absent parttime 0.224958781 0.095638728 -1.23699902 0.5485371
6       30   absent fulltime 0.291679292 0.101605781 -0.88724209 0.4917936
7       40   absent not.work 0.588194721 0.133170256  0.35650744 0.5497867
8       40   absent parttime 0.285272275 0.128164048 -0.91845759 0.6285879
9       40   absent fulltime 0.126533004 0.097928271 -1.93196717 0.8860491
10      20  present not.work 0.747625342 0.037830221  1.08598725 0.2004976
11      20  present parttime 0.199129118 0.034884708 -1.39174629 0.2187448
12      20  present fulltime 0.053245540 0.019740560 -2.87812574 0.3915966
13      30  present not.work 0.818923571 0.052610678  1.50907156 0.3547877
14      30  present parttime 0.165901061 0.049355986 -1.61496044 0.3566754
15      30  present fulltime 0.015175368 0.011491854 -4.17278999 0.7689391
16      40  present not.work 0.873487849 0.059489224  1.93215586 0.5383308
17      40  present parttime 0.122673467 0.057838540 -1.96735317 0.5374096
18      40  present fulltime 0.003838684 0.004592451 -5.55877971 1.2009709

Perhaps we should issue a warning or think of a better way to avoid this surprise; e.g., I suppose we could attach newdata as an attribute of the predict object.

[friendly]

So, would it make things easier if newdata were attached to the result of predict()?

[strengejacke]

Ok, thanks! That seems to work!

See the discrepancies here:

library(nestedLogit)
data(Womenlf, package = "carData")
Womenlf$partic <- with(
  Womenlf,
  factor(partic, levels = c("not.work", "parttime", "fulltime"))
)

m <- nestedLogit::nestedLogit(partic ~ hincome + children,
  dichotomies = nestedLogit::logits(
    work = nestedLogit::dichotomy("not.work", working = c("parttime", "fulltime")),
    full = nestedLogit::dichotomy("parttime", "fulltime")
  ),
  data = Womenlf
)

new <- expand.grid(
  hincome = c(30, 40),
  children = c("absent", "present")
)

cbind(new, predict(m, newdata = new))
#>    hincome children response           p        se.p       logit  se.logit
#> 1       30   absent not.work 0.483361927 0.095465380 -0.06657687 0.3822848
#> 2       40   absent parttime 0.224958781 0.095638728 -1.23699902 0.5485371
#> 3       30  present fulltime 0.291679292 0.101605781 -0.88724209 0.4917936
#> 4       40  present not.work 0.588194721 0.133170256  0.35650744 0.5497867
#> 5       30   absent parttime 0.285272275 0.128164048 -0.91845759 0.6285879
#> 6       40   absent fulltime 0.126533004 0.097928271 -1.93196717 0.8860491
#> 7       30  present not.work 0.818923571 0.052610678  1.50907156 0.3547877
#> 8       40  present parttime 0.165901061 0.049355986 -1.61496044 0.3566754
#> 9       30   absent fulltime 0.015175368 0.011491854 -4.17278999 0.7689391
#> 10      40   absent not.work 0.873487849 0.059489224  1.93215586 0.5383308
#> 11      30  present parttime 0.122673467 0.057838540 -1.96735317 0.5374096
#> 12      40  present fulltime 0.003838684 0.004592451 -5.55877971 1.2009709

as.data.frame(predict(m, newdata = new), newdata = new)
#>    hincome children response           p        se.p       logit  se.logit
#> 1       30   absent not.work 0.483361927 0.095465380 -0.06657687 0.3822848
#> 2       30   absent parttime 0.224958781 0.095638728 -1.23699902 0.5485371
#> 3       30   absent fulltime 0.291679292 0.101605781 -0.88724209 0.4917936
#> 4       40   absent not.work 0.588194721 0.133170256  0.35650744 0.5497867
#> 5       40   absent parttime 0.285272275 0.128164048 -0.91845759 0.6285879
#> 6       40   absent fulltime 0.126533004 0.097928271 -1.93196717 0.8860491
#> 7       30  present not.work 0.818923571 0.052610678  1.50907156 0.3547877
#> 8       30  present parttime 0.165901061 0.049355986 -1.61496044 0.3566754
#> 9       30  present fulltime 0.015175368 0.011491854 -4.17278999 0.7689391
#> 10      40  present not.work 0.873487849 0.059489224  1.93215586 0.5383308
#> 11      40  present parttime 0.122673467 0.057838540 -1.96735317 0.5374096
#> 12      40  present fulltime 0.003838684 0.004592451 -5.55877971 1.2009709

^{Created on 2023-06-01 with reprex v2.0.2}

I don't think newdata needs to be attached to the results of predict(), but you should maybe update the example from the help-page? That's where I had the cbind() approach from.

https://friendly.github.io/nestedLogit/reference/nestedMethods.html

[strengejacke]

Now the plots make much more sense :-)

[friendly]

IThanks; I'll fix the help page example.

[friendly]

Can you please paste the code you used for your ggpredict figures?

[strengejacke]

sure (requires latest GitHub version from https://github.com/strengejacke/ggeffects)

library(ggeffects)
library(nestedLogit)

data("Womenlf", package = "carData")
m <- nestedLogit(partic ~ hincome + children,
  logits(
    work = dichotomy("not.work", c("parttime", "fulltime")),
    full = dichotomy("parttime", "fulltime")
  ),
  data = Womenlf
)

ggpredict(m, c("hincome", "children")) |> plot()
#> Data were 'prettified'. Consider using `terms="hincome [all]"` to get
#>   smooth plots.

^{Created on 2023-06-01 with reprex v2.0.2}

[strengejacke]

(r-universe binaries are available, you can now simply install ggeffects running this code: install.packages("ggeffects", repos = "https://strengejacke.r-universe.dev"))

[friendly]

We made a change in our as.data.frame.predictNestedLogit, so the newdata is no longer required, as the predict() method includes the newdata in it's result as a .data component.

Consequently, ggpredict() now gives an error:

data("Womenlf", package = "carData")
m <- nestedLogit(partic ~ hincome + children,
                 logits(
                   work = dichotomy("not.work", c("parttime", "fulltime")),
                   full = dichotomy("parttime", "fulltime")
                 ),
                 data = Womenlf
)

ggpredict(m, c("hincome", "children")) |> plot()

# Data were 'prettified'. Consider using `terms="hincome [all]"` to get smooth plots.
# Error: At least one term specified in `terms` is no valid model term.

The fix for this is to remove the use of newdata in line 13 of get_predictions_nestedLogit.R, the last line shown below

predictions <- as.data.frame(stats::predict(
    model,
    newdata = data_grid,
    ...
  ), newdata = data_grid)

But, don't youj want to call nestedLogit::predict rather than stats::predict here?

[john-d-fox]

Hi,

On 2023-06-01 4:18 p.m., Michael Friendly wrote: Caution: This email may have originated from outside the organization. Please exercise additional caution with any links and attachments. We made a change in our |as.data.frame.predictNestedLogit|, so the |newdata| is no longer required, as the |predict()| method includes the newdata in it's result as a |.data| component. Consequently, |ggpredict()| now gives an error: |data("Womenlf", package = "carData") m <- nestedLogit(partic ~ hincome + children, logits( work = dichotomy("not.work", c("parttime", "fulltime")), full = dichotomy("parttime", "fulltime") ), data = Womenlf ) ggpredict(m, c("hincome", "children")) |> plot() # Data were 'prettified'. Consider using `terms="hincome [all]"` to get smooth plots. # Error: At least one term specified in `terms` is no valid model term. | The fix for this is to remove the use of |newdata| in line 13 of |get_predictions_nestedLogit.R|, the last line shown below |predictions <- as.data.frame(stats::predict( model, newdata = data_grid, ... ), newdata = data_grid) | But, don't youj want to call |nestedLogit::predict| rather than |stats::predict| here?

No -- nestedLogit:::predict.nestedLogit() is a method for the stats::predict() generic. Best, John

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[strengejacke]

Consequently, ggpredict() now gives an error:

Do you have session conflicts? I just tested with the latest nestedLogit package version, and it still works fine. The newdata argument passed to as.data.frame() is silently ignored, which is good, because the code then works for both nestedLogit 0.3.0 and 0.3.1.

library(ggeffects)
library(nestedLogit)
data("Womenlf", package = "carData")
m <- nestedLogit(partic ~ hincome + children,
                 logits(
                   work = dichotomy("not.work", c("parttime", "fulltime")),
                   full = dichotomy("parttime", "fulltime")
                 ),
                 data = Womenlf
)

ggpredict(m, c("hincome", "children")) |> plot()
#> Data were 'prettified'. Consider using `terms="hincome [all]"` to get
#>   smooth plots.

sessioninfo::session_info()
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^{Created on 2023-06-02 with reprex v2.0.2}

[john-d-fox]

Yes, specifying the now-extraneous newdata argument to predict() should be innocuous because it's silently absorbed by ... .

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On 2023-06-02 2:32 a.m., Daniel wrote: Consequently, ggpredict() now gives an error: Do you have session conflicts? I just tested with the latest /nestedLogit/ package version, and it still works fine. The |newdata| argument passed to |as.data.frame()| is silently ignored, which is good, because the code then works for both /nestedLogit/ 0.3.0 and 0.3.1. library(ggeffects) library(nestedLogit) data("Womenlf",package = "carData") m <- nestedLogit(partic ~ hincome + children, logits( work = dichotomy("not.work", c("parttime","fulltime")), full = dichotomy("parttime","fulltime") ), data = Womenlf ) ggpredict(m, c("hincome","children"))|> plot() #> Data were 'prettified'. Consider using `terms="hincome [all]"` to get #> smooth plots. <https://camo.githubusercontent.com/5422b3f6a0374c31b9c05796a0b23abb822b47adfa57c71aa33dbc90900db302/68747470733a2f2f692e696d6775722e636f6d2f4933514539754c2e706e67> sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.3.0 (2023-04-21 ucrt) #> os Windows 10 x64 (build 19045) #> system x86_64, mingw32 #> ui RTerm #> language (EN) #> collate German_Germany.utf8 #> ctype German_Germany.utf8 #> tz Europe/Berlin #> date 2023-06-02 #> pandoc 3.1.1 @ C:/Users/mail/AppData/Local/Pandoc/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> abind 1.4-5 2016-07-21 [1] CRAN (R 4.3.0) #> backports 1.4.1 2021-12-13 [1] CRAN (R 4.3.0) #> broom 1.0.4 2023-03-11 [1] CRAN (R 4.3.0) #> car 3.1-2 2023-03-30 [1] CRAN (R 4.3.0) #> carData 3.0-5 2022-01-06 [1] CRAN (R 4.3.0) #> cli 3.6.1 2023-03-23 [1] CRAN (R 4.3.0) #> colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.3.0) #> digest 0.6.31 2022-12-11 [1] CRAN (R 4.3.0) #> dplyr 1.1.2 2023-04-20 [1] CRAN (R 4.3.0) #> evaluate 0.21 2023-05-05 [1] CRAN (R 4.3.0) #> fansi 1.0.4 2023-01-22 [1] CRAN (R 4.3.0) #> farver 2.1.1 2022-07-06 [1] CRAN (R 4.3.0) #> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.3.0) #> forcats 1.0.0 2023-01-29 [1] CRAN (R 4.3.0) #> fs 1.6.2 2023-04-25 [1] CRAN (R 4.3.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.3.0) #> ggeffects * 1.2.2.13 2023-06-02 [1] local #> ggplot2 3.4.2 2023-04-03 [1] CRAN (R 4.3.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.3.0) #> gtable 0.3.3 2023-03-21 [1] CRAN (R 4.3.0) #> haven 2.5.2 2023-02-28 [1] CRAN (R 4.3.0) #> hms 1.1.3 2023-03-21 [1] CRAN (R 4.3.0) #> htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.3.0) #> insight 0.19.2 2023-05-23 [1] https://easystats.r-universe.dev (R 4.3.0) #> knitr 1.43 2023-05-25 [1] CRAN (R 4.3.0) #> labeling 0.4.2 2020-10-20 [1] CRAN (R 4.3.0) #> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.3.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.3.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.3.0) #> nestedLogit * 0.3.1 2023-06-02 [1] Github ***@***.***) #> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.3.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.3.0) #> purrr 1.0.1 2023-01-10 [1] CRAN (R 4.3.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.3.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.3.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.3.0) #> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.3.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.3.0) #> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.3.0) #> rlang 1.1.1 2023-04-28 [1] CRAN (R 4.3.0) #> rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.3.0) #> scales 1.2.1 2022-08-20 [1] CRAN (R 4.3.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.3.0) #> sjlabelled 1.2.0 2022-04-10 [1] CRAN (R 4.3.0) #> snakecase 0.11.0 2019-05-25 [1] CRAN (R 4.3.0) #> stringi 1.7.12 2023-01-11 [1] CRAN (R 4.3.0) #> stringr 1.5.0 2022-12-02 [1] CRAN (R 4.3.0) #> styler 1.10.0 2023-05-24 [1] CRAN (R 4.3.0) #> tibble 3.2.1 2023-03-20 [1] CRAN (R 4.3.0) #> tidyr 1.3.0 2023-01-24 [1] CRAN (R 4.3.0) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.3.0) #> utf8 1.2.3 2023-01-31 [1] CRAN (R 4.3.0) #> vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.3.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.3.0) #> xfun 0.39 2023-04-20 [1] CRAN (R 4.3.0) #> yaml 2.3.7 2023-01-23 [1] CRAN (R 4.3.0) #> #> [1] C:/Users/mail/AppData/Local/R/win-library/4.3 #> [2] C:/Program Files/R/R-4.3.0/library #> #> ────────────────────────────────────────────────────────────────────────────── ^Created on 2023-06-02 with reprex v2.0.2 <https://reprex.tidyverse.org> — Reply to this email directly, view it on GitHub <#2 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ADLSAQVNTMDAE3NJUMDEQX3XJGCJRANCNFSM6AAAAAAYVTAJSM>. You are receiving this because you commented.Message ID: ***@***.***>