# Using standardization with small noisy values

Let's say we have a dataset of how often people say certain words that looks like


+-------+------+-----+---------+
|       | this | cat | uranium |
+-------+------+-----+---------+
| Alice |   32 |   8 |    0.05 |
| Bob   |   22 |   3 |    0.01 |
| Carol |   25 |   4 |   0.005 |
+-------+------+-----+---------+



If we want to compare the relative usage of the words we can standardize the data's mean and standard deviation to get


+-------+-------+-------+---------+
|       | this  |  cat  | uranium |
+-------+-------+-------+---------+
| Alice |  1.35 |  1.38 |    1.40 |
| Bob   | -1.03 | -0.92 |   -0.57 |
| Carol | -0.31 | -0.46 |   -0.82 |
+-------+-------+-------+---------+



Now we can see that Alice says the word "cat" more often than the word "this" compared to other people, even though the original value of "cat" was much lower than "this". However it also indicates that she says "uranium" significantly more than others, even though the original value for "uranium" was very small. She probably just happened to say it a few times more by chance, and doesn't actually talk about uranium much more than others.

Is there a way to use standardization that takes into account the fact that small values are more susceptible to random noise?

• I would have used a log linear model, but I don't know how to handle the non-integer entries in Uranium. Why isn't "number of times people said a word" an integer? – Paul Hewson Dec 17 '19 at 15:56
• Let's say the numbers represent a percent of total words that person has said. I'll look into a log linear model, thanks – user5920779 Dec 17 '19 at 16:48
• @PaulHewson I'm not sure how a log linear model could be used here, could you give an example? – user5920779 Dec 17 '19 at 22:19

Log linear model involves assuming that the expected counts are a linear function of the person, the word. You would do something like: $$E[Y] = \beta_0 + \beta_1 x_1 + \beta_2 x_2$$. You assume that the $$Y$$ are distributed as a multinomial, but it is possible to fit a Poisson model so that you have.

y <- c(3200, 2200, 2500, 800, 300, 400, 5, 1, 1)
x1 <- gl(3, 3, labels=c("Alice", "Bob", "Carol"))
x2 <- factor(rep(c("this", "cat", "uranium"), 3))
m1 <- glm(y ~ x1 + x2, family = poisson())
summary(m1)


You get output like this:

Coefficients:
Estimate Std. Error z value
(Intercept)  7.64985    0.02050 373.222
x1Bob       -1.66140    0.02816 -58.989
x1Carol     -7.02871    0.37812 -18.589
x2this       0.47085    0.02549  18.475
x2uranium    0.14836    0.02729   5.437


So the intercept is the log of the expected value of the number of times Alice says cat, i.e. $$exp(7.6)$$ about 2900. The other coefficients, because they are on a log scale are telling you how much more likely Bob is to say cat than Alice (exp(-1.66) times as much) or how much more likely Alice is to say this than cat (exp(0.47) times as much). The longest one is that Carol is exp(-7.02871 + 0.14836) times as likely to say uranium than Alice is to say cat.

However, I think for your question you want to fit a model with interactions.

$$E[Y] = \beta_0 + \beta_1 x_1 + \beta_2 x_2$$ + \beta_{12} x_1 * x_2$With those made up numbers I get: (Intercept) 7.69621 0.02132 360.984 x1Bob -1.99243 0.06155 -32.373 x1Carol -7.69621 1.00022 -7.695 x2this 0.37469 0.02770 13.529 x2uranium 0.12783 0.02923 4.373 x1Bob:x2this 0.60614 0.07315 8.287 x1Carol:x2this 1.23474 1.09579 1.127 x1Bob:x2uranium 0.15985 0.08178 1.955 x1Carol:x2uranium -0.12783 1.41451 -0.090  Now this is where life gets interesting. You're not really interested (I think??????) in whether Bob talks more than Alice, or whether people say cat more often than uranium. So these interactions show that given Bob being more/less talkative than Alice and this being more/less popular than cat, Bob says this exp(0.6) 1.8 times more often than you can account for. Carol says uranium exp(-0.12) 0.88 times as often, again conditioned on uranium already being a less popular word, and Carol being more/less talkative. Another way of examining this is through graphical models, but Alan Agresti Analysis of Categorical Data is my goto guide for these kinds of questions. There's a lot more detail, but I hope that at least lets you decide whether log-linear model is the right way to go for your problem. • Thanks for the explanation! If I'm interpreting things correctly the z scores of the interactions seem to have the properties of what I'm looking for. However in the R code it's only showing 4 of the 9 interactions, is there a way to get them all? – user5920779 Dec 19 '19 at 6:04 • This all depends on the contrast matrix you set. I think the default is called treatment contrasts, so you have difference between person 2 and person 1, and person 3 and person 1, as well as between word 2 and word 1 and word 3 and word 1. So you can only have interactions between these. – Paul Hewson Dec 19 '19 at 10:16 • You will also get an expected value and a residual from fitting a log linear model which might make it easier to interpret the results than the coefficients if you are used to thinking of z scores than log linear model coefficients. – Paul Hewson Dec 19 '19 at 10:17 I suppose I should give an update with what I've done. After reading through the textbook Paul suggested, I've settled on using the standardized residuals of the chi squared test. With the dataset  this cat uranium Alice 3200 800 5 Bob 2200 300 1 Carol 2500 400 1  using regular standardization gives  this cat uranium ------- ------- ------- --------- Alice 1.35 1.38 1.41 Bob -1.03 -0.92 -0.70 Carol -0.31 -0.46 -0.70  putting a lot of unwanted emphasis on the smaller values. Using the standardized residuals of the chi squared test gives  this cat uranium Alice -9.289214 9.191701 1.544499 Bob 6.340982 -6.298057 -0.736932 Carol 3.879593 -3.816253 -0.948655  which treats the variation between bigger numbers as more than the variation between smaller numbers. The scores seem pretty high, but I was just comparing the values between categories so for my use case it works well. The R code to do this is y <- c(3200, 2200, 2500, 800, 300, 400, 5, 1, 1) x1 <- gl(3, 3, labels=c("this", "cat", "uranium")) x2 <- factor(rep(c("Alice", "Bob", "Carol"), 3)) chisq.test(xtabs(y ~ x2 + x1))$stdres


and python code is

import pandas as pd
import statsmodels.api as sm

df = pd.DataFrame(
{"this": [3200, 2200, 2500], "cat": [800, 300, 400], "uranium": [5, 1, 1]},
index=["Alice", "Bob", "Carol"],
)
table = sm.stats.Table(df)
print(table.standardized_resids)


Side note: I also tried the standardized residuals of a log linear model, but that gave the exact same results as the chi squared with much more computation time.