--- title: Lecture 8 - Hypothesis testing in R author: Prof. Alexandra Chouldechova date: output: html_document: toc: true toc_depth: 5 fig_width: 5 fig_height: 5 --- ##Agenda - Testing differences in mean between two groups - QQ plots - Tests for 2 x 2 tables, j x k tables - Plotting confidence intervals Let's begin by loading the packages we'll need to get started {r} library(tidyverse)  ## Exploring the birthwt data We'll begin by doing all the same data processing as in previous lectures {r} # Load data from MASS into a tibble birthwt <- as_tibble(MASS::birthwt) # Rename variables birthwt <- birthwt %>% rename(birthwt.below.2500 = low, mother.age = age, mother.weight = lwt, mother.smokes = smoke, previous.prem.labor = ptl, hypertension = ht, uterine.irr = ui, physician.visits = ftv, birthwt.grams = bwt) # Change factor level names birthwt <- birthwt %>% mutate(race = recode_factor(race, 1 = "white", 2 = "black", 3 = "other")) %>% mutate_at(c("mother.smokes", "hypertension", "uterine.irr", "birthwt.below.2500"), ~ recode_factor(.x, 0 = "no", 1 = "yes"))  Over the past two lectures we created various tables and graphics to help us better understand the data. Our focus for today is to run hypothesis tests to assess whether the trends we observed last time are statistically significant. One of the main reasons we want to understand hypothesis testing is that it is important for our tables and figures to convey statistical uncertainty in any cases where it is non-negligible, and where failing to account for it may produce misleading conclusions. ### Testing differences in means One of the most common statistical tasks is to compare an outcome between two groups. The example here looks at comparing birth weight between smoking and non-smoking mothers. To start, it always helps to plot things {r, fig.align='center', fig.width = 5, fig.height = 4} # Create boxplot showing how birthwt.grams varies between # smoking status qplot(x = mother.smokes, y = birthwt.grams, geom = "boxplot", data = birthwt, xlab = "Mother smokes", ylab = "Birthweight (grams)", fill = I("lightblue"))  This plot suggests that smoking is associated with lower birth weight. **How can we assess whether this difference is statistically significant?** Let's compute a summary table {r} # Notice the consistent use of round() to ensure that our summaries # do not have too many decimal values birthwt %>% group_by(mother.smokes) %>% summarize(mean.birthwt = round(mean(birthwt.grams), 0), sd.birthwt = round(sd(birthwt.grams), 0))  The standard deviation is good to have, but to assess statistical significance we really want to have the standard error (which the standard deviation adjusted by the group size). {r} birthwt %>% group_by(mother.smokes) %>% summarize(num.obs = n(), mean.birthwt = round(mean(birthwt.grams), 0), sd.birthwt = round(sd(birthwt.grams), 0), se.birthwt = round(sd(birthwt.grams) / sqrt(num.obs), 0))  #### t-test via t.test() This difference is looking quite significant. To run a two-sample t-test, we can simple use the t.test() function. {r} birthwt.t.test <- t.test(birthwt.grams ~ mother.smokes, data = birthwt) birthwt.t.test  We see from this output that the difference is highly significant. The t.test() function also outputs a confidence interval for us. Notice that the function returns a lot of information, and we can access this information element by element {r} names(birthwt.t.test) birthwt.t.test$p.value # p-value birthwt.t.test$estimate # group means birthwt.t.test$conf.int # confidence interval for difference attr(birthwt.t.test$conf.int, "conf.level") # confidence level  The ability to pull specific information from the output of the hypothesis test allows you to report your results using inline code chunks. That is, you don't have to hardcode estimates, p-values, confidence intervals, etc. {r} # Calculate difference in means between smoking and nonsmoking groups birthwt.t.test$estimate birthwt.smoke.diff <- round(birthwt.t.test$estimate[1] - birthwt.t.test$estimate[2], 1) # Confidence level as a % conf.level <- attr(birthwt.t.test$conf.int, "conf.level") * 100  **Example**: Here's what happens when we knit the following paragraph. {r, eval = FALSE} Our study finds that birth weights are on average r birthwt.smoke.diffg higher in the non-smoking group compared to the smoking group (t-statistic r round(birthwt.t.test$statistic,2), p=r round(birthwt.t.test$p.value, 3), r conf.level% CI [r round(birthwt.t.test$conf.int,1)]g)  **Output**: Our study finds that birth weights are on average r birthwt.smoke.diffg higher in the non-smoking group compared to the smoking group (t-statistic r round(birthwt.t.test$statistic,2), p=r round(birthwt.t.test$p.value, 3), r conf.level% CI [r round(birthwt.t.test$conf.int,1)]g) One other thing to know is that t.test() accepts input in multiple forms. I like using the formula form whenever it's available, as I find it to be more easily interpretable. Here's another way of specifying the same information. {r} with(birthwt, t.test(x=birthwt.grams[mother.smokes=="no"], y=birthwt.grams[mother.smokes=="yes"]))  Specifying x and y arguments to the t.test function runs a t-test to check whether x and y have the same mean. #### What is statistical significance testing doing? Here's a little simulation where we have two groups, a treatment groups and a control group. We're going to simulate observations from both groups. We'll run the simulation two ways. - First simulation (Null case): the treatment has no effect - Second simulation (Non-null case): the treatment on average increases outcome {r} set.seed(12345) # Function to generate data generateSimulationData <- function(n1, n2, mean.shift = 0) { y <- rnorm(n1 + n2) + c(rep(0, n1), rep(mean.shift, n2)) groups <- c(rep("control", n1), rep("treatment", n2)) data.frame(y = y, groups = groups) }  Let's look at a single realization in the null setting. {r, fig.height = 5, fig.width = 7} n1 = 30 n2 = 40 # Observation, null case obs.data <- generateSimulationData(n1 = n1, n2 = n2) obs.data # Box plots qplot(x = groups, y = y, data = obs.data, geom = "boxplot") # Density plots qplot(fill = groups, x = y, data = obs.data, geom = "density", alpha = I(0.5), adjust = 1.5, xlim = c(-4, 6)) # t-test t.test(y ~ groups, data = obs.data)  And here's what happens in a random realization in the non-null setting. {r, fig.height = 5, fig.width = 7} # Non-null case, very strong treatment effect # Observation, null case obs.data <- generateSimulationData(n1 = n1, n2 = n2, mean.shift = 1.5) # Box plots qplot(x = groups, y = y, data = obs.data, geom = "boxplot") # Density plots qplot(fill = groups, x = y, data = obs.data, geom = "density", alpha = I(0.5), adjust = 1.5, xlim = c(-4, 6)) # t-test t.test(y ~ groups, data = obs.data)  More interestingly, let's see what happens if we repeat our simulation 10000 times and look at the p-values. We'll use a moderate effect of 0.5 instead of the really strong effect of 1.5 in this simulation. {r, cache = TRUE} NUM_ITER <- 10000 pvals <- matrix(0, nrow = NUM_ITER, ncol = 2) for(i in 1:NUM_ITER) { # Generate data obs.null <- generateSimulationData(n1 = n1, n2 = n2) obs.alt <- generateSimulationData(n1 = n1, n2 = n2, mean.shift = 0.5) # Record p-values pvals[i, 1] <- t.test(y ~ groups, data = obs.null)$p.value pvals[i, 2] <- t.test(y ~ groups, data = obs.alt)$p.value } pvals <- as.data.frame(pvals) colnames(pvals) <- c("null", "nonnull") # Plotting routine qplot(x = null, data = pvals, xlab = "p-value", xlim = c(0, 1), main = "P-value when treatment has 0 effect") qplot(x = nonnull, data = pvals, xlab = "p-value", xlim = c(0, 1), main = "P-value when treatment has MODERATE effect")  Let's show both histograms on the same plot. {r} # Let's start by reshaping the data # This approach isn't the best one, but it works well for this simple case pvals.df <- data.frame(pvals = c(pvals$null, pvals$nonnull), case = c(rep("null", NUM_ITER), rep("nonnull", NUM_ITER))) # Plot ggplot(data = pvals.df, aes(x = pvals, fill = case)) + geom_histogram(alpha=0.75, position="identity") + xlim(0,1)  ##### Why do the distributions look this way? Let's think back to what you learned about hypothesis testing in your statistics class. You probably learned that if we want to control Type I error (i.e., the likelihood of rejecting then the null hypothesis is true) at some level $\alpha$, we should reject the null when the observed p-value is less than $\alpha$. As a probability statement, this says that: $$\mathbb{P}_{H_0}(\text{p-value} \le \alpha) = \alpha$$ Now, the p-value is a random variable that depends on (i.e., gets its randomness from) the observed data used to compute it. The expression above may look familiar to you. Recall that for a random variable $X$, the CDF (cumulative distribution function) of $X$ is defined as $$F_X(x) = \mathbb{P}(X \le x)$$ So what we know is that, when the null is true, the p-value has the CDF $F(x) = x$ for $0 \le x \le 1$. This is the CDF of the **uniform distribution on the interval $[0,1]$**. Now, looking back at the blue histogram in our simulation, we see that this is exactly what the observed p-value distribution looks like. It looks exactly like a sample of uniform random variables from the interval $[0,1]$. Now how about the distribution of p-values when the null is FALSE? In your statistics class, you would have learned about the **power** of a test, often denoted as $\beta$, which is the likelihood of rejecting when the null is FALSE. i.e., the power is $$\beta = \mathbb{P}_{H_a}(\text{p-value} \le \alpha) ,$$ and we generally want $\beta$ to be much larger than $\alpha$ for all values of $\alpha$. If you look at the p-value distribution for the non-null setting, the power $\beta$ at some threshold $\alpha$ is given by the (normalized) area under the pink curve to the *left* of $\alpha$. This area under the blue curve is equal to $\alpha$. And because p-values tend to be smaller under the non-null, it is greater than $\alpha$ when the null is FALSE. #### What if sample is small and data are non-Gaussian? In your statistics classes you've been taught to approach the t-test with caution. If your data is highly skewed, you would need a very large sample size for the t-statistic to actually be t-distributed. When it doubt, you can run a non-parametric test. Here's how we run a Mann-Whitney U test (aka Wilcoxon rank-sum test) using the wilcox.test() function. {r} # Formula specification birthwt.wilcox.test <- wilcox.test(birthwt.grams ~ mother.smokes, data=birthwt, conf.int=TRUE) birthwt.wilcox.test # x,y specification with(birthwt, wilcox.test(x=birthwt.grams[mother.smokes=="no"], y=birthwt.grams[mother.smokes=="yes"]))  In general, hypothesis tests in R return an object of class htest which has similar attributes to what we saw in the t-test. {r} class(birthwt.wilcox.test)  Here's a summary of the attributes: name | description ------------------------------|--------------------------------- statistic | the value of the test statistic with a name describing it. parameter | the parameter(s) for the exact distribution of the test statistic. p.value | the p-value for the test. null.value | the location parameter mu. alternative | a character string describing the alternative hypothesis method | the type of test applied. data.name | a character string giving the names of the data. conf.int | a confidence interval for the location parameter. (Only present if argument conf.int = TRUE.) estimate | an estimate of the location parameter. (Only present if argument conf.int = TRUE.) ### Is the data normal? I would recommend using a non-parametric test when the data appears highly non-normal and the sample size is small. If you really want to stick to t-testing, it's good to know how to diagnose non-normality. #### qq-plot The simplest thing to look at is a normal qq plot of the data. This is obtained using the stat_qq() function. {r, fig.align='center', fig.width = 5, fig.height = 4} # qq plot p.birthwt <- ggplot(data = birthwt, aes(sample = birthwt.grams)) p.birthwt + stat_qq() + stat_qq_line() # Separate plots for different values of smoking status p.birthwt + stat_qq() + stat_qq_line() + facet_grid(. ~ mother.smokes) # qq plot for 115 observations of truly normal data df <- data.frame(x = rnorm(115)) ggplot(data = df, aes(sample = x)) + stat_qq() + stat_qq_line()  If the data are exactly normal, you expect the points to lie on a straight line. The data we have here are pretty close to lying on a line. Here's what we would see if the data were right-skewed {r, fig.align='center', fig.width = 5, fig.height = 4} set.seed(12345) fake.data <- data.frame(x = rexp(200)) p.fake <- ggplot(fake.data, aes(sample = x)) qplot(x, data = fake.data) p.fake + stat_qq() + stat_qq_line()  If you construct a qqplot and it looks like this, you should be carefully, particularly if your sample size is small. ### Tests for 2x2 tables Here's an example of a 2 x 2 table that we might want to run a test on. This one looks at low birthweight broken down by mother's smoking status. You can think of it as another approach to the t-test problem, this time looking at indicators of low birth weight instead of the actual weights. First, let's build our table using the table() function (we did this back in Lecture 5) {r} weight.smoke.tbl <- with(birthwt, table(birthwt.below.2500, mother.smokes)) weight.smoke.tbl  It looks like there's a positive association between low birthweight and smoking status. To test for significance, we just need to pass our 2 x 2 table into the appropriate function. Here's the result of using fisher's exact test by calling fisher.test {r} birthwt.fisher.test <- fisher.test(weight.smoke.tbl) birthwt.fisher.test attributes(birthwt.fisher.test)  As when using the t-test, we find that there is a significant association between smoking an low birth weight. You can also use the chi-squared test via the chisq.test function. This is the test that you may be more familiar with from your statistics class. {r} chisq.test(weight.smoke.tbl)  You get essentially the same answer by running the chi-squared test, but the output isn't as useful. In particular, you're not getting an estimate or confidence interval for the odds ratio. This is why I prefer fisher.test() for testing 2 x 2 tables. #### Tests for j x k tables Here's a small data set on party affiliation broken down by gender. {r} # Manually enter the data politics <- as.table(rbind(c(762, 327, 468), c(484, 239, 477))) dimnames(politics) <- list(gender = c("F", "M"), party = c("Democrat","Independent", "Republican")) politics # display the data  We may be interested in asking whether men and women have different party affiliations. The answer will be easier to guess at if we convert the rows to show proportions instead of counts. Here's one way of doing this. {r} politics.prop <- prop.table(politics, 1) politics.prop colSums(politics.prop) # Check that columns sum to 1 # Fix dimnames dimnames(politics.prop) <- list(gender = c("F", "M"), party = c("Democrat","Independent", "Republican")) # Output politics.prop  By looking at the table we see that Female are more likely to be Democrats and less likely to be Republicans. We still want to know if this difference is significant. To assess this we can use the chi-squared test (on the counts table, not the proportions table!). {r} chisq.test(politics)  There isn't really a good one-number summary for general $j$ x $k$ tables the way there is for 2 x 2 tables. One thing that we may want to do at this stage is to ignore the Independent category and just look at the 2 x 2 table showing the counts for the Democrat and Republican categories. {r} politics.dem.rep <- politics[,c(1,3)] politics.dem.rep # Run Fisher's exact test fisher.test(politics.dem.rep)  We see that women have significantly higher odds of being Democrat compared to men.