Model plots showing the relationship between proficiency in the target language and the predicted probability of engaging in between-utterance (left) and within-utterance (middle) code-switching (CS) during Spanish samples (top) and English samples (bottom) for children with DLD (red) versus children with TLD (blue), as well as the predicted rate of within-utterance CS among children who do switch (right). Plots were generated using the ggplot2 package (Version 3.3.6; Wickham, 2016). Left and middle: These model plots are based on predicted values from the logistic regression models estimated using target-language Bilingual English-Spanish Assessment (BESA) scores from the original sample and fixing covariates (age, BESA scores in the nontarget language) at their mean. The gray ribbons show one standard error within the model predictions. The dots show raw participant data indicating children who do (value of 1, along the top) and do not (value of 0, along the bottom) engage in CS as a function of their BESA scores. Right: The model plots in the right-hand column show the predicted rate of utterances containing within-utterance CS (per 50 utterances—a standard target for language samples) among those who do code-switch. The predicted values were generated from the count portion of the hurdle models using target-language BESA scores from the original sample, fixing covariates (age, BESA scores in the nontarget language) at their mean, and using 50 utterances as the total sample length. The predict() function for hurdle models did not generate standard errors along with the predicted values, and thus, there is no ribbon. The dots show raw participant data for the rate of utterances containing within-utterance CS per total utterances produced by the child as a function of BESA scores. S-to-E = Spanish to English; E-to-S = English to Spanish.