simulation

Polychoric correlation is a measure of association between two ordered categorical variables, each assumed to represent latent continuous variables that have a bivariate standard normal distribution. When we say two variables have a bivariate standard normal distribution, we mean they’re both normally distributed with mean 0 and standard deviation 1, and that they have linear correlation.

In this article we demonstrate how to calculate the sample size needed to achieve a desired power for experiments with an ordered categorical outcome. We assume the data will be analyzed with a proportional odds logistic regression model. We’ll use the R statistical computing environment and functions from the {Hmisc} package to implement the calculations.

Bootstrapping—resampling data with replacement and recomputing quantities of interest—lets analysts approximate sampling distributions for complex estimators and frees them of the reliably unmet assumptions of traditional, parametric inferential statistics. It’s an elegant, intuitive approach in which an analyst exploits the (often) parallel resample-to-sample and sample-to-population relationships to understand uncertainty in an estimate.

In this article we demonstrate how to use simulation in R to estimate power and sample size for proposed logistic regression models that feature two binary predictors and their interaction.

Recall that logistic regression attempts to model the probability of an event conditional on the values of predictor variables. If we have a binary response, y, and two predictors, x and z, that interact, we specify the logistic regression model as follows:

Logistic regression is a flexible tool for modeling binary outcomes. A logistic regression describes the probability, \(P\), of 1/“yes”/“success” (versus 0/“no”/“failure”) as a linear combination of predictors:

\[log(\frac{P}{1-P}) = B_0 + B_1X_1 + B_2X_2 + ... + B_kX_k\]

In R, “growing” an object—extending an atomic vector one element at a time; adding elements one by one to the end of a list; etc.—is an easy way to elicit a mild admonishment from someone reviewing or revising your code. Growing most frequently occurs in the context of for loops: A loop computes a value (or set of values) on each iteration, and it then appends the value(s) to an existing object.

Regression to the mean refers to a phenomena where natural variation within an individual can mistakenly appear as meaningful change over time. To illustrate, imagine a patient who comes in for a regular check-up and is found to have high blood sugar levels. This may be cause for concern, and the doctor recommends several dietary adjustments and schedules a follow-up for the next week. During the follow-up visit, the patient’s blood sugar levels have seemingly returned to a normal range.

The term “multilevel data” refers to data organized in a hierarchical structure, where units of analysis are grouped into clusters. For example, in a cross-sectional study, multilevel data could be made up of individual measurements of students from different schools, where students are nested within schools. In a longitudinal study, multilevel data could be made up of multiple time point measurements of individuals, where time points are nested within individuals.

Least squares is so frequently the method by which linear regressions are estimated that in many write-ups of analyses, explicit mention of the method is omitted. Authors save the ink or pixels otherwise consumed by “least squares” and let it simply be inferred. This is an understandable elision: You could make good money repeatedly betting that when someone says that they fit a linear regression, they did so via least squares. But alternative estimation methods are on offer—and are sometimes preferable.

Analysts and researchers occasionally want to compare the magnitudes of different predictive or causal effects estimated via regression. But comparison is a tricky endeavor when predictor variables are measured on different scales: If y is predicted from x and z, with x measured in kilograms and z measured in years, what does the relative size of the variables’ regression coefficients communicate about which variable is “more strongly” associated with y?

In this article we demonstrate how to simulate data suitable for a multinomial logistic regression model using R. One reason to do this is to gain a better understanding of how multinomial logistic regression models work. Another is to simulate data for the purposes of estimating power and sample size for a planned experiment that will involve a multinomial logistic regression analysis.

When designing an experiment it’s good practice to estimate the number of subjects or observations we’ll need. If we recruit or collect too few, our analysis may be too uncertain or misleading. If we collect too many, we potentially waste time and expense on diminishing returns. The optimal sample size provides enough information to allow us to analyze our research questions with confidence. The traditional approach to sample size estimation is based on hypothesis tests.

R users who have run base R’s prop.test() function to perform a null hypothesis test of a proportion—as when assessing whether a coin is weighted toward heads or whether more than half of the wines a vineyard sold in a given month were reds—may have noticed curious language in the output: The default test is reported as having been performed with a “continuity correction.”

In this article, we plan to get you up and running with gamma regression. But before we dive into that, let’s review the familiar normal distribution. This will provide some scaffolding to help us transition to the gamma distribution.

Say it with me: An X% confidence interval captures the population parameter in X% of repeated samples.

In the course of our statistical educations, many of us had that line (or some variant of it) crammed, wedged, stuffed, and shoved into our skulls until definitional precision was leaking out of noses and pooling on our upper lips like prop blood.

Or, at least, I felt that way.

The power of a test is the probability of correctly rejecting a null hypothesis. For example, let’s say we suspect a coin is not fair and lands heads 65% of the time.

It is well documented that post hoc power calculations are not useful (Althouse, 2020; Goodman & Berlin, 1994; Hoenig & Heisey, 2001). Also known as observed power or retrospective power, post hoc power purports to estimate the power of a test given an observed effect size. The idea is to show that a “non-significant” hypothesis test failed to achieve significance because it wasn’t powerful enough. This allows researchers to entertain the notion that their hypothesized effect may actually exist; they just needed to use a bigger sample size.

Binomial generalized linear mixed models, or binomial GLMMs, are useful for modeling binary outcomes for repeated or clustered measures. For example, let’s say we design a study that tracks what college students eat over the course of 2 weeks, and we’re interested in whether or not they eat vegetables each day. For each student, we’ll have 14 binary events: eat vegetables or not.

When it comes to confidence intervals and hypothesis testing there are two important limitations to keep in mind.

The significance level,¹ \(\alpha\), or the confidence interval coverage, \(1 - \alpha\),

What are robust standard errors? How do we calculate them? Why use them? Why not use them all the time if they’re so robust? Those are the kinds of questions this post intends to address.

A count model is a linear model where the dependent variable is a count. For example, the number of times a car breaks down, the number of rats in a litter, the number of times a young student gets out of his seat, etc. Counts are either 0 or a positive whole number, which means we need to use special distributions to generate the data.

Logistic regression is a method for modeling binary data as a function of other variables. For example we might want to model the occurrence or non-occurrence of a disease given predictors such as age, race, weight, etc. The result is a model that returns a predicted probability of occurrence (or non-occurrence, depending on how we set up our data) given certain values of our predictors. We might also be able to interpret the coefficients in our model to summarize how a change in one predictor affects the odds of occurrence.

On Thursday, October 15, 2015, a disbelieving student posted on Reddit: My stats professor just went on a rant about how R-squared values are essentially useless, is there any truth to this? It attracted a fair amount of attention, at least compared to other posts about statistics on Reddit.

First off, what is endogeneity, and why would we want to simulate it?

Endogeneity occurs when a statistical model has an independent variable that is correlated with the error term. The reason we would want to simulate it is to understand what exactly that definition means.

Let’s first simulate ideal data for simple linear regression using R.

One of the assumptions of the Analysis of Variance (ANOVA) is constant variance. That is, the spread of residuals is roughly equal per treatment level. A common way to assess this assumption is plotting residuals versus fitted values. Recall that residuals are the observed values of your response of interest minus the predicted values of your response. In a one-way ANOVA, this is simply the observed values minus the treatment group mean. For example, below we have a plot of residuals versus fitted values for a one-way ANOVA.