# 16 Applications: Infer categorical

In this chapter, we will explore how to conduct statistical inference for categorical variables using the catstats package in RStudio. Review the Preliminaries Chapter for instructions for installing catstats. We encourage you to work through the code in this chapter in your own RStudio session.

Much of the R code we use below is from the tidyverse R package. Load this package and the catstats package prior to running the code in this chapter.

## Making tables from raw data

If you are collecting your own data, then your analysis starts with the raw data frame, with one observational unit per row and one variable per column. In this case, you first need to find counts (frequencies) of observations in each category prior to inference. We can use the count() function in R on the raw data to create a contingency table of the counts for each categorical variable.

In the one-proportion case, suppose we have a data frame called loans with a variable regulate that contains the Yes/No response of the 826 payday loan borrowers from Section 14.3.2 regarding their support for a regulation to require lenders to pull their credit report and evaluate their debt payments. Run the following line of code to create this data set in RStudio.

loans <- data.frame(regulate = sample(c(rep("Yes", 422), rep("No", 404))))

We can obtain counts of borrowers for each response using the count() function in R:

table(loans$regulate) #> #> No Yes #> 404 422 In the R code above, loans is the name of the data set in R, and regulate is the name of the variable. The $ tells R to select the regulate variable from the loans data set. We could have also used the pipe command to generate the table:

loans %>% select(regulate) %>% table()
#> regulate
#>  No Yes
#> 404 422

We can also use table() to compute the proportions in each group:

#If we know the number of observations:
table(loans$regulate)/826 #> #> No Yes #> 0.489 0.511 #If we don't know the number of observations: table(loans$regulate)/length(loans$regulate) #> #> No Yes #> 0.489 0.511 For comparisons of two proportions, we get a two-way table. Consider the case study of the effect of blood thinners on survival after receiving CPR from Section 15.1.1. Data from this study are stored in a data frame called cpr, with variables survival – giving each patient’s outcome decision – and group – indicating whether they were in the treatment (blood thinner) or control (no blood thinner) group. Run the following lines of code to create this data set in RStudio. survival <- factor(c(rep("Survived", 25), rep("Died", 65))) group <- factor(rep.int(c("control", "treatment", "control", "treatment"), times = c(11, 14, 39, 26))) cpr <- data.frame(survival, group) The glimpse() and summary() functions can help us see what variables are in our data set and the values they take on: glimpse(cpr) #> Rows: 90 #> Columns: 2 #>$ survival <fct> Survived, Survived, Survived, Survived, Survived, Survived, S…
#> $group <fct> control, control, control, control, control, control, control… summary(cpr) #> survival group #> Died :65 control :50 #> Survived:25 treatment:40 To obtain the two-way table of the choices by group, we again use the table() function in R. The key thing to remember here is to put the response variable (outcome) as the first argument and the explanatory variable (grouping) as the second. In our example, survival is the response variable, and group is the explanatory variable. table(cpr$survival, cpr$group) #> #> control treatment #> Died 39 26 #> Survived 11 14 This will set up the table to be useful for making segmented bar plots and using column percentages to compute test statistics. In order to do either of these things, we need to store the table in an R object so we can manipulate it further: data_tbl <- table(cpr$survival, cpr$group) To get column percentages, we use the prop.table() function: prop.table(data_tbl, #Feed in your two-way table margin = 2) #Tell it to compute percentages for columns #> #> control treatment #> Died 0.78 0.65 #> Survived 0.22 0.35 ### Simulation-based inference for one proportion For simulation-based inference, we will use functions included in the catstats package, created for MSU Statistics courses. See the Statistical computing section at the beginning of this textbook for instructions on how to install catstats if you haven’t already. If the package is installed, you can load it into an R session to make the functions available using the library() function: library(catstats) Once you have loaded the package, you will be able to use the functions for simulation-based inference. For one-proportion inference, this is the one_proportion_test() function and one_proportion_bootstrap_CI() function. Returning to the payday loan regulation example, we can obtain a simulation distribution and p-value using the following function call: one_proportion_test( probability_success = 0.5, #null hypothesis probability of success sample_size = 826, #number of observations number_repetitions = 1000, #number of simulations to create as_extreme_as = 0.51, #observed statistic direction = "greater", #alternative hypothesis direction summary_measure = "proportion" #Report number or proportion of successes? ) Note that the observed statistic (as_extreme_as) and the summary_measure input need to match; since we put in the observed statistic as a proportion, we need to tell the function to report the proportion of successes. If they don’t match, you will almost certainly get a p-value of 0 or 1 – this can happen when there is very strong evidence against the null, but it is always good to check your function inputs when you get an extreme outcome to make sure that is what you should be seeing. In the resulting graph, we see the null distribution of simulated proportions, with those greater than the observed value of 0.51 highlighted in blue. In the figure caption, we see that 308 of our 1000 simulations resulted in a proportion of successes at least as large as the observed value, yielding an approximate p-value of 0.308. To find a confidence interval for the true proportion of payday loan borrowers who support the regulation, we use the one_proportion_bootstrap_CI() function: one_proportion_bootstrap_CI( sample_size = 826, #Number of observations number_successes = 422, #Number of observed successes number_repetitions = 1000, #Number of bootstrap draws confidence_level = 0.95 #Confidence level, as a proportion ) This produces a plot of the bootstrapped proportions with the upper and lower bounds of the confidence interval marked, and gives the interval itself in the figure caption: in this case, we are 95% confident that the true proportion of payday loan borrowers who support the proposed regulation is between 0.479 and 0.546. ### Simulation-based inference for difference in two proportions For inference about the difference between two proportions, we use the two_proportion_test() and two_proportion_bootstrap_CI() functions. These functions assume you have a data frame with the group and outcome included as variables. Using the CPR and blood thinner study, a call to two_proportion_test() would look like this: two_proportion_test( formula = survival ~ group, #Always do response ~ explanatory data = cpr, #Name of the data set first_in_subtraction = "treatment", #Value of the explanatory variable that is first in order of subtraction response_value_numerator = "Survived", #Value of response that is a "success" number_repetitions = 1000, as_extreme_as = 0.13, #Observed statistic direction = "two-sided" #Direction of the alternative hypothesis ) The results give a segmented bar plot of the data — you can check that your formula is correct by making sure the explanatory variable is on the $$x$$-axis and the response variable is on the $$y$$-axis. Look to the top right of the bar plot to check that you have the correct order of subtraction. Next to the bar plot, we have the null distribution of simulated differences in proportions, with the observed statistic marked with a vertical line and all values as or more extreme than the observed statistic colored red. The figure caption gives the approximate p-value: in this case 181/1000 = 0.181. There are a couple of things to note when using the two_proportion_test function: • You need to identify which variable is your outcome and which your group using the formula argument. • Specify order of subtraction using first_in_subtraction by putting in EXACTLY the category of the explanatory variable that you want to be first, in quotes — must match capitalization, spaces, etc. for text values! • Specify what is a success using response_value_numerator but putting in EXACTLY the category of the response that you consider a success, in quotes. Again, capitalization, spaces, etc. matter here! To produce a confidence interval for the true difference in the proportion of patients that survive after receiving CPR, we use two_proportion_bootstrap_CI(), which uses most of the same arguments as the two_proportion_test() function: two_proportion_bootstrap_CI( formula = survival ~ group, #Always do response ~ explanatory data = cpr, #Name of the data set first_in_subtraction = "treatment", #Value of the explanatory variable that is first in order of subtraction response_value_numerator = "Survived", #Value of response that is a "success" number_repetitions = 1000, #Number of bootstrap samples confidence_level = 0.99 #Confidence level, as a proportion ) This produces the distribution of bootstrapped statistics with the bounds of the confidence interval marked, and the value included in the caption. Here, we are 99% confident that the true proportion of patients who survive after receiving CPR is between 0.1 lower and 0.35 higher when patients are given blood thinners compared to when they are not. ### Theory-based inference for one proportion For theory-based inference, we can use the built-in R function prop.test().140 For a one-proportion test, we need to tell it the number of successes, the number of trials (sample size), and the null value. Using the payday loan regulation example, a call would look like this: prop.test(x = 422, #Number of successes n = 826, #Number of trials p = .5, #Null hypothesis value alternative = "greater", #Direction of alternative, conf.level = 0.95) #Confidence level as a proportion #> #> 1-sample proportions test with continuity correction #> #> data: 422 out of 826 #> X-squared = 0.3, df = 1, p-value = 0.3 #> alternative hypothesis: true p is greater than 0.5 #> 95 percent confidence interval: #> 0.482 1.000 #> sample estimates: #> p #> 0.511 In this output, we can get our observed statistic $$\hat{p} = 0.51$$ in the last line of the output (under sample estimates: p), and the p-value of 0.2656 from the second line of the output. You should always check that the null value and the alternative hypothesis in the output matches the problem. You might have noticed that the test statistic reported in the output is X-squared rather than our Z-statistic. This test statistic is equal to our Z-statistic squared, and the p-value is found from what is called a $$\chi^2$$ distribution (pronounced “kai squared”). To get the Z-statistic from the X-squared statistic, take the positive square root if $$\hat{p} > \pi_0$$, and the negative square root if $$\hat{p} < \pi_0$$. Use the R output to find the value of the Z-statistic.141 The prop.test() function will also produce the theory-based confidence interval, but to get the correct confidence interval, we need to run the function with a two-sided alternative:142 prop.test(x = 422, #Number of successes n = 826, #Number of trials p = .5, #Null hypothesis value alternative = "two.sided", #Direction of alternative, conf.level = 0.95, #Confidence level as a proportion correct = FALSE) #We will not use a continuity correction #> #> 1-sample proportions test without continuity correction #> #> data: 422 out of 826 #> X-squared = 0.4, df = 1, p-value = 0.5 #> alternative hypothesis: true p is not equal to 0.5 #> 95 percent confidence interval: #> 0.477 0.545 #> sample estimates: #> p #> 0.511 From this output, we obtain a 95% confidence interval for the true proportion of payday loan borrowers who support the new regulation of (0.477, 0.545). ### Theory-based inference for a difference in two proportions When comparing two proportions, we use the same function, prop.test, but the inputs are now “vectors” rather than “scalars”. As an example, we will again use the CPR and blood thinner study from Section 12.3. First, use the table() function to determine the number of successes and the sample size in each category of the explanatory variable: table(cpr$survival, cpr$group) #> #> control treatment #> Died 39 26 #> Survived 11 14 From these results (and the example in Section 12.3), we see that the Treatment group (call this group 1) had 14 survive (successes) out of 40 patients (sample size); the Control group (call this group 2) had 11 survive out of 50 patients. These counts are input into the prop.test function as follows: • x = vector of number of successes = c(num successes in group 1, num successes in group 2) • n = vector of sample sizes = c(sample size for group 1, sample size for group 2) R creates a vector using the c() (or “combine”) function. R will take the order of subtraction to be group 1 $$-$$ group 2. prop.test(x = c(14, 11), #Number successes in group 1 and group 2 n = c(40, 50), #Sample size of group 1 and group 2 alternative = "two.sided", #Match sign of alternative #for order of subtraction #group 1 - group 2 conf.level = 0.99, #Confidence level as a proportion correct = FALSE) #No continuity correction #> #> 2-sample test for equality of proportions without continuity correction #> #> data: c out of c14 out of 4011 out of 50 #> X-squared = 2, df = 1, p-value = 0.2 #> alternative hypothesis: two.sided #> 99 percent confidence interval: #> -0.116 0.376 #> sample estimates: #> prop 1 prop 2 #> 0.35 0.22 The observed proportions in each group are given under sample estimates: prop 1 prop 2. R will always take (prop 1 - prop 2) as the order of subtraction. If the observed proportions don’t match your calculations of the proportion of successes or are in the wrong order of subtraction, go back and check your inputs to the x and n arguments. Here, we obtain a p-value of 0.1712 — little to no evidence against the null hypothesis of no difference between the two groups. Since we used a two-sided alternative in prop.testc, this call also produces the correct confidence interval. Our 99% confidence interval for the true difference in the proportion of patients who survive is $$(-0.116, 0.376)$$. We are 99% confident that the true proportion of patients who survive under the treatment is between 0.116 lower and 0.376 higher than the true proportion of patients who survive under the control condition. The fact that this confidence interval contains zero also shows us that we have little to no evidence of a difference in probability of survival between the two groups. The function prop.test will also take a table created by the table function. First, we need to create our table of counts from the raw data, which then becomes our primary input to prop.test(). There is one important step to take before creating our table: R will always put the categories of a categorical variable in alphabetical order when building tables, unless told otherwise. data_tbl <- table(cpr$survival, cpr$group) data_tbl #> #> control treatment #> Died 39 26 #> Survived 11 14 However, prop.test() will assume that the top row is a “success” and the order of subtraction is (column 1 - column 2). Without re-arranging our table, we would get the wrong order of subtraction and/or the wrong proportion of successes in each group. To fix this, we need to relevel() our inputs to tell R to put them in the order we want: #Switch order of subtraction: cpr$group <- relevel(cpr$group, ref = "treatment") table(cpr$survival, cpr$group) #> #> treatment control #> Died 26 39 #> Survived 14 11 #Switch "success": cpr$survival <- relevel(cpr$survival, ref = "Survived") table(cpr$survival, cpr$group) #> #> treatment control #> Survived 14 11 #> Died 26 39 After re-arranging our table, we can use this data_tbl as the first argument in the prop.test() function: data_tbl <- table(cpr$survival, cpr\$group)

stats::prop.test(x = data_tbl,
alternative = "two.sided",
conf.level = 0.99, #Confidence level as a proportion
correct = FALSE)  #No continuity correction
#>
#>  2-sample test for equality of proportions without continuity correction
#>
#> data:  data_tbl
#> X-squared = 2, df = 1, p-value = 0.2
#> alternative hypothesis: two.sided
#> 99 percent confidence interval:
#>  -0.14  0.46
#> sample estimates:
#> prop 1 prop 2
#>   0.56   0.40

## 16.1catstats function summary

In the previous section, you were introduced to four new R functions in the catstats library. Here we provide a summary of these functions. You can also access the help files for these functions using the ? command. For example, type ?one_proportion_test into your R console to bring up the help file for the one_proportion_test function.

1. one_proportion_test: Simulation-based hypothesis test for a single proportion.

• probability_success = null value
• sample_size = sample size ($$n$$)
• summary_measure = one of "number" or "proportion" (quotations are important here!) to simulate either sample counts or sample proportions (needs to match the form of the observed statistic)
• direction = one of "greater", "less", or "two-sided" (quotations are important here!) to match the sign in $$H_A$$
• as_extreme_as = value of observed statistic
• number_repetitions = number of simulated samples to generate (should be at least 1000!)
2. one_proportion_bootstrap_CI: Bootstrap confidence interval for one proportion.

• sample_size = sample size ($$n$$)
• number_successes = number of successes (note that $$\hat{p}$$ = number_successes/sample_size)
• confidence_level = confidence level as a decimal (e.g., 0.90, 0.95, etc)
• number_repetitions = number of simulated samples to generate (should be at least 1000!)
3. two_proportion_test: Simulation-based hypothesis test for a single proportion.

• formula = y ~ x where y is the name of the response variable in the data set and x is the name of the explanatory variable
• data = name of data set
• first_in_subtraction = category of the explanatory variable which should be first in subtraction, written in quotations
• response_value_numerator = category of the response variable which we are counting as a “success”, written in quotations
• direction = one of "greater", "less", or "two-sided" (quotations are important here!) to match the sign in $$H_A$$
• as_extreme_as = value of observed difference in proportions
• number_repetitions = number of simulated samples to generate (should be at least 1000!)
4. two_proportion_bootstrap_CI: Bootstrap confidence interval for one proportion.

• formula = y ~ x where y is the name of the response variable in the data set and x is the name of the explanatory variable
• data = name of data set
• first_in_subtraction = category of the explanatory variable which should be first in subtraction, written in quotations
• response_value_numerator = category of the response variable which we are counting as a “success”, written in quotations
• confidence_level = confidence level as a decimal (e.g., 0.90, 0.95, etc)
• number_repetitions = number of simulated samples to generate (should be at least 1000!)