By R. H. Baayen
A simple advent to the statistical research of language facts, designed for college students with a non-mathematical background.
summary: an easy creation to the statistical research of language facts, designed for college students with a non-mathematical history
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Extra resources for Analyzing Linguistic Data : a Practical Introduction to Statistics using R
A curve like this is often referred to as a scatterplot smoother, as it smoothes away all the turbulence around the main trend in the data. The smoothing function that we used here is lowess(), which takes as input the X and Y coordinates of the data points and produces as output the X and Y coordinates of the smooth line. To plot this line, we fed its coordinates into lines(). The basic idea underlying smoothers is to use the observations in a given span (or bin) of values of X to calculate the average increase in Y .
Consequently, we may expect that, in general, words that have a high family size will also have a high value for the complex synsets measure. We also know that higher-frequency words tend to have more family members. The importance of a conditioning plot is that it allows us to inspect the joint correlational structure among three predictors in a single graphical display. 11 consists of six scatterplots, each with its own smoother, which graph log Family Size against log Number of Complex Synsets.
We do not want R to label the five points for the quartiles on the horizontal axis with five tick marks (the small vertical and horizontal lines marking the labeled values on the axes) and the numbers 1 through 5. 2 Visualizing single random variables quartiles. We therefore instruct plot() to forget about tick marks and numbers labeling the horizontal axis, using the option xaxt = "n" : > plot(quantile(lexdec$RT), xaxt = "n", + xlab = "Quartiles", ylab = "log RT") The next step is to add the appropriate labels.