title: error in add_p()' for variable X and test 'fisher.test', p-value omitted
body: <p>I get the error below when I try to use the add_p() function to get a p-value for differences between my by variable (with 10 levels) and a categorical variable with two levels (yes/no). I am not sure how to provide a reproducible example. From the trials data, I imagine my <strong>by</strong> variable would be the "T Stage" variable with 10 levels, and the categorical variables would be: (1) "Chemotherapy Treatment" with 2 levels, and (2) "Chemotherapy Treatment2" with 4 levels. But here is the code I ran.</p>
  
  <pre class="lang-r prettyprint-override"><code>library(gtsummary)
  library(tidyverse)
  miro_def %&gt;% 
    select(mheim, age_dx, time_t1d_yrs, gender, collard, fhist_pandz) %&gt;% 
    tbl_summary(by = mheim, missing = "no",
                type = list(c(gender, collard, fhist_pandz, mheim) ~ "categorical"),
                label = list(gender ~ "Gender", 
                             fhist_pandz ~ "Family history of PD", 
                             age_dx ~ "Age at diagnosis", 
                             time_t1d_yrs ~ "Follow-up(years)")) %&gt;% 
    add_p() %&gt;% 
    # style the output with custom header 
    #modify_header(stat_by = "{level}") %&gt;% 
    # convert to kableExtra as_kable_extra(booktabs = TRUE) %&gt;% 
    # reduce font size to make table fit. # you may also use the `latex_options = "scale_down"` argument here. 
    kable_styling(font_size = 7, latex_options = "scale_down")
  </code></pre>
  
  <p>However, I do get a p-value for this by variable (10 levels) with other variables (which are continous/numeric)</p>
  
  <ol>
  <li>How can I fix this error?</li>
  <li><p>In the case where I have the mentioned multilevel by variable and a multilevel (>2 levels) categorical variable, is there something special I should do to get a p-value?</p>
  
  <p>There was an error in 'add_p()' for variable 'gender' and test 'fisher.test', p-value omitted:
  Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=18540 is too small for this problem,
  (pastp=51.2364, ipn_0:=ipoin[itp=150]=215, stp[ipn_0]=40.6787).
  Increase workspace or consider using 'simulate.p.value=TRUE'
  There was an error in 'add_p()' for variable 'collard' and test 'fisher.test', p-value omitted:
  Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=18570 is too small for this problem,
  (pastp=37.0199, ipn_0:=ipoin[itp=211]=823, stp[ipn_0]=23.0304).
  Increase workspace or consider using 'simulate.p.value=TRUE'
  There was an error in 'add_p()' for variable 'fhist_pandz' and test 'fisher.test', p-value omitted:
  Error in stats::fisher.test(data[[variable]], as.factor(data[[by]])): FEXACT error 7(location). LDSTP=18570 is too small for this problem,
  (pastp=36.4614, ipn_0:=ipoin[itp=58]=1, stp[ipn_0]=31.8106).
  Increase workspace or consider using 'simulate.p.value=TRUE'</p></li>
  </ol>