Currently only is compatible with question of type "yesno"
.
build_counts( formula, data, keep_vars = NULL, name_ones_as = "yes", name_trls_as = "n_response", multiple_qIDs = FALSE, verbose = TRUE )
formula | the model formula used to fit the multilevel regression model.
Should be of the form |
---|---|
data | A cleaned CCES dataset, e.g. from ccc_std_demographics which is
then combined with outcome and contextual data in cces_join_slim.
By default it expects the LHS outcome to be named |
keep_vars | Variables that will be kept as a cell variable, regardless of whether it is specified in a formula. Input as character vector. |
name_ones_as | What to name the variable that represents the number of successes in the binomial |
name_trls_as | What to name the variable that represents the number of trials in the binomial. |
multiple_qIDs | Does the data contain multiple outcomes in long form and
therefore require the counts to be built for each outcome? Defaults to |
verbose | Show warning messages? Defaults to TRUE |
A dataframe of cells. The following variables have fixed names and
will be assumed by ccesMRPrun::fit_brms_binomial
:
yes
: the number of successes observed in the cell
n_response
the number of non-missing responses, representing the number
of trials.
#> #>#>#> #>#>#> #>ccc_samp_std <- ccc_samp %>% mutate(y = sample(c("For", "Against"), size = n(), replace = TRUE)) %>% ccc_std_demographics()#> age variable modified to bins. Original age variable is now in age_orig.ccc_samp_out <- build_counts(y ~ age + gender + educ + (1|cd), ccc_samp_std) ccc_samp_out#> # A tibble: 945 x 6 #> age gender educ cd n_response yes #> <fct> <fct> <fct> <chr> <int> <dbl> #> 1 18 to 24 years Male HS or Less CA-39 1 1 #> 2 18 to 24 years Male HS or Less FL-25 1 0 #> 3 18 to 24 years Male HS or Less GA-10 1 1 #> 4 18 to 24 years Male HS or Less IL-07 1 0 #> 5 18 to 24 years Male HS or Less LA-01 1 0 #> 6 18 to 24 years Male HS or Less MA-05 1 1 #> 7 18 to 24 years Male HS or Less NC-09 1 1 #> 8 18 to 24 years Male HS or Less NC-11 1 1 #> 9 18 to 24 years Male HS or Less PA-11 1 1 #> 10 18 to 24 years Male Some College IA-02 1 1 #> # … with 935 more rows# alternative options build_counts(y ~ educ + (1|cd), ccc_samp_std, name_ones_as = "success", name_trls_as = "trials")#> # A tibble: 716 x 4 #> educ cd trials success #> <fct> <chr> <int> <dbl> #> 1 HS or Less AK-01 1 0 #> 2 HS or Less AL-02 3 2 #> 3 HS or Less AL-03 2 0 #> 4 HS or Less AL-05 1 0 #> 5 HS or Less AR-02 2 1 #> 6 HS or Less AR-03 2 2 #> 7 HS or Less AZ-04 1 1 #> 8 HS or Less AZ-06 1 0 #> 9 HS or Less AZ-08 1 0 #> 10 HS or Less CA-06 2 1 #> # … with 706 more rowsbuild_counts(y ~ educ + (1|cd), ccc_samp_std, keep_vars = "state")#> # A tibble: 716 x 5 #> educ cd state n_response yes #> <fct> <chr> <chr> <int> <dbl> #> 1 HS or Less AK-01 Alaska 1 0 #> 2 HS or Less AL-02 Alabama 3 2 #> 3 HS or Less AL-03 Alabama 2 0 #> 4 HS or Less AL-05 Alabama 1 0 #> 5 HS or Less AR-02 Arkansas 2 1 #> 6 HS or Less AR-03 Arkansas 2 2 #> 7 HS or Less AZ-04 Arizona 1 1 #> 8 HS or Less AZ-06 Arizona 1 0 #> 9 HS or Less AZ-08 Arizona 1 0 #> 10 HS or Less CA-06 California 2 1 #> # … with 706 more rows