get_data should return data in wide format

[cjvanlissa]

Or create function that merges LPA results with original data file

[jrosen48]

I agree this is a good idea

[jrosen48]

related to #118

Is there a straightforward way to do something like this using base (especially for spread():

library(tidyLPA)
#> tidyLPA has received a major update, with a much easier workflow and improved functionality. However, you might have to update old syntax to account for the new workflow. See vignette('introduction-to-major-changes') for details!
#> 
#> Mplus is not installed. Use only package = 'mclust' when calling estimate_profiles().
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidyr)

m <- pisaUSA15[1:100, ] %>%
    select(broad_interest, enjoyment, self_efficacy) %>%
    single_imputation() %>%
    estimate_profiles(3)

get_data(m) %>% 
    mutate(Class_prob = paste0("Class_", Class_prob)) %>% 
    spread(Class_prob, Probability)
#> # A tibble: 100 x 10
#>    model_number classes_number broad_interest enjoyment self_efficacy Class
#>           <dbl>          <dbl>          <dbl>     <dbl>         <dbl> <dbl>
#>  1            1              3          0.853       2.4          1.57     3
#>  2            1              3          1           1            4        2
#>  3            1              3          1           1.2          3.5      2
#>  4            1              3          1           2            2        3
#>  5            1              3          1           2            3        3
#>  6            1              3          1           2.2          2.29     3
#>  7            1              3          1           2.6          1.86     3
#>  8            1              3          1           2.6          2.38     3
#>  9            1              3          1           2.8          2.62     3
#> 10            1              3          1           4            1.5      1
#> # … with 90 more rows, and 4 more variables: id <int>, Class_1 <dbl>,
#> #   Class_2 <dbl>, Class_3 <dbl>

^{Created on 2019-07-17 by the reprex package (v0.2.1)}

[cjvanlissa]

See my pull request: get_data returns data in wide format when applied to an object of class tidyProfile (one element of a tidyLPA object), or when applied to a tidyLPA object of length one. It returns long format when applied to a tidyLPA object containing multiple tidyProfile analyses (because then the wide format does not make sense).

[jrosen48]

Thanks sir!

---

From: C. J. van Lissa notifications@github.com Sent: Wednesday, July 17, 2019 3:21 PM To: data-edu/tidyLPA Cc: Joshua Rosenberg; Comment Subject: Re: [data-edu/tidyLPA] get_data should return data in wide format (#117)

See my pull request: get_data returns data in wide format when applied to an object of class tidyProfile (one element of a tidyLPA object), or when applied to a tidyLPA object of length one. It returns long format when applied to a tidyLPA object containing multiple tidyProfile analyses (because then the wide format does not make sense).

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[jrosen48]

This was addressed in #122

[benjaminwnelson]

Can tidyLPA be used with repeated measures and if so, then should data be in long or wide format? Thanks!

[cjvanlissa]

@benjaminwnelson yes it can, but you can't fit a longitudinal model to the data. The data should be in wide format, multilevel mixture models are not available.

[benjaminwnelson]

Thank you! Would you advise that after pulling out LPA groups, you could then join that factor variable with a long dataset in order to do MLM modeling with the LPA group membership?

[cjvanlissa]

These are substantive questions; I would recommend finding a statistical collaborator who can help out with this. But briefly: The function ?get_data does what you're asking, but this approach ignores classification uncertainty. If entropy is > .90, the resulting bias may be negligible. In other cases, you will need to conduct 3-step analysis, which uses the classification probability matrix (see the literature).

[benjaminwnelson]

Yes, will definitely consult on this. Thank you!