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May 23, 2016 12:17 AM
(14864 views)

After running an ANCOVA model with more than two values of the categorical variable, how can I perform pairwise comparisons of slopes?

Hillary.

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Hi hillaryvoet,

If I'm understanding your question correctly, you have a regression with a categorical variable (X1) and continuous variable (X2) and the interaction between them predicting a continuous variable (Y). Your categorical variable (X1) has more than two levels, and you have a statistically significant interaction between X1 and X2 and would like to follow-up on that to see for which groups the regression of Y on to X2 differs. The methods listed by others here don't seem to be testing the pairwise differences in slopes, but rather the pairwise difference in means, assuming a mean level on the continuous regressor (X2). There is a way to get the tests you want, but it'll take a few extra steps. I'll use the Car Physical Data sample dataset for this example.

First I will fit a model like you're working with. I predict Weight on the basis of Country and Turning circle, and their interaction.

In the regression plot we can see directly what you're asking for, three tests of the differences in slopes between each pair, Japan/Other, Japan/USA, Other/USA.

Tests produced under Effect Details for the factor of Country, (for instance LSMeans Differences Student's t), are not tests of slope differences, but are tests of whether the mean weight of cars differ for those countries assuming the mean level of turning circle.

To reveal a test of slopes, we will go to the top-most Red Triangle > Estimates > Indicator Parameterization Estimates

This will show a table of parameter estimates based on an indicator parameterization (as opposed to effects coding, which is what the regular parameter estimates table is based on) which means the LAST level of your categorical factor is used as a reference category, and estimates are differences from that reference category. This will be useful for us.

Notice that we see no estimates with USA. That's because USA is our reference category, so all estimates are differences from what was estimated for the USA. Country[Japan], with the estimate of 256.78, is how much Japan differs from the USA in terms of weight on average. Notice that is the same thing we saw above in the ordered differences report table.

If we look at the interaction parameter estimates we can use the same logic: Country[Japan]*(Turning Circle-38.5862), and the associated estimates, is how the slope for Japan differs from the slope for the USA (by the way, the slope for the USA is listed under "Turning Circle" since it's the reference category, so 155.46 is the USA). Notice that the slope for Japan is nearly the same as the slope for the USA -- different by only a value of 0.06, p = 0.9985. The slope for Other differs from the USA by -42.38, p = 0.2193.

So we're almost there. We're missing one test, Japan vs Other. To get this we need to make JMP treat Japan, or Other, as the reference category, and then rerun this analysis. I'm going to choose to treat Other as the reference category. To change the reference category we'll change the Value Ordering property for the column Country. Right click on the column in the table and go to Column Info. Click on Column Properties, and then select Value Ordering. To change the ordering, select "Other" and click Move Down. The last category listed will be the reference category. Click okay when you're done.

To rerun the model, go to the top-most red triangle of your existing Fit Model output > Script > Redo analysis.

The "Indicator Function Parameterization" now lists the same analyses we saw before, but with "Other" as the reference category.

And now we have our last test, the slope difference between Japan vs Other, which we find in the row "Country[Japan]*(Turning Circle-38.5862)". A difference of 42.45, p = 0.2204.

So we have our three pairwise slope tests.

Japan vs USA, difference = 0.06, p = 0.9985.

Other vs USA, difference = 42.38, p = 0.2193.

Japan vs Other, difference = 42.45, p = 0.2204.

I hope this helps!

32 REPLIES 32

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Re: comparison of slopes

**Estimates** => **Multiple Comparisons** option from the top red triangle for pairwise comparisons. For more info check out the documentation by scrolling down to All Pairwise Comparisons The following screenshot shows Tukey's all pairwise comparisons for age groups

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Re: comparison of slopes

The ANCOVA is performed by running a model with a categorical variable, a

continuous variable, and – in the case I'm interested in – an interaction

term which tests for equality of slopes. Using Estimates – Multiple

Comparisons gives the differences and significance between the Least Square

Means, notbetween the slopes.

**From:** jiancao

**Sent:** Tuesday, May 24, 2016 9:15 PM

**To:** Hillary Voet <hillary.voet@mail.huji.ac.il>

**Subject:** Re: - comparison of slopes

JMP User Community

<https://community.jmp.com/?utm_source=JIVE&utm_medium=email&utm_campaign=System%20Email%20Tracking>

comparison of slopes

reply from jiancao

<https://community.jmp.com/people/jiancao?et=watches.email.thread> in

**Discussions** - View the full discussion

<https://community.jmp.com/message/228740?et=watches.email.thread#228740>

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Re: comparison of slopes

The multiple comparisons test is giving you a comparison of the Least Square Means, but that is the same as the slopes. Consider a case with 2 levels of your categorical variable (let's call it Q) and your continuous variable is called X. Your model is Y = B0 + B1*Q + B2*X + B12*Q*X.

Suppose I fix Q at the first level, 1. My model simplifies to Y = B0 + B1*(1) + B2*X + B12*(1)*X ==> Y = (B0+B1) + (B2+B12)*X.

Notice that the B12 interaction parameter is the adjustment of the slope for the different levels of the categorical variable. Therefore, testing to see if the least squares means of the interaction terms are different is equivalent to testing if the slopes are different. If B12 for level 1 of the categorical variable is different than B12 for level 2 of the categorical variable, then the slopes for those two categories are different.

Dan Obermiller

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Re: comparison of slopes

Regression parameter estimates from regression : (I assume the estimates on age groups are what you want to test)

The results from running Multiple Comparisons:

Comparing the pair Age 14 to Age 16: the difference in slope: 2.3656086-0.2714001=2.0942085

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Hi hillaryvoet,

If I'm understanding your question correctly, you have a regression with a categorical variable (X1) and continuous variable (X2) and the interaction between them predicting a continuous variable (Y). Your categorical variable (X1) has more than two levels, and you have a statistically significant interaction between X1 and X2 and would like to follow-up on that to see for which groups the regression of Y on to X2 differs. The methods listed by others here don't seem to be testing the pairwise differences in slopes, but rather the pairwise difference in means, assuming a mean level on the continuous regressor (X2). There is a way to get the tests you want, but it'll take a few extra steps. I'll use the Car Physical Data sample dataset for this example.

First I will fit a model like you're working with. I predict Weight on the basis of Country and Turning circle, and their interaction.

In the regression plot we can see directly what you're asking for, three tests of the differences in slopes between each pair, Japan/Other, Japan/USA, Other/USA.

Tests produced under Effect Details for the factor of Country, (for instance LSMeans Differences Student's t), are not tests of slope differences, but are tests of whether the mean weight of cars differ for those countries assuming the mean level of turning circle.

To reveal a test of slopes, we will go to the top-most Red Triangle > Estimates > Indicator Parameterization Estimates

This will show a table of parameter estimates based on an indicator parameterization (as opposed to effects coding, which is what the regular parameter estimates table is based on) which means the LAST level of your categorical factor is used as a reference category, and estimates are differences from that reference category. This will be useful for us.

Notice that we see no estimates with USA. That's because USA is our reference category, so all estimates are differences from what was estimated for the USA. Country[Japan], with the estimate of 256.78, is how much Japan differs from the USA in terms of weight on average. Notice that is the same thing we saw above in the ordered differences report table.

If we look at the interaction parameter estimates we can use the same logic: Country[Japan]*(Turning Circle-38.5862), and the associated estimates, is how the slope for Japan differs from the slope for the USA (by the way, the slope for the USA is listed under "Turning Circle" since it's the reference category, so 155.46 is the USA). Notice that the slope for Japan is nearly the same as the slope for the USA -- different by only a value of 0.06, p = 0.9985. The slope for Other differs from the USA by -42.38, p = 0.2193.

So we're almost there. We're missing one test, Japan vs Other. To get this we need to make JMP treat Japan, or Other, as the reference category, and then rerun this analysis. I'm going to choose to treat Other as the reference category. To change the reference category we'll change the Value Ordering property for the column Country. Right click on the column in the table and go to Column Info. Click on Column Properties, and then select Value Ordering. To change the ordering, select "Other" and click Move Down. The last category listed will be the reference category. Click okay when you're done.

To rerun the model, go to the top-most red triangle of your existing Fit Model output > Script > Redo analysis.

The "Indicator Function Parameterization" now lists the same analyses we saw before, but with "Other" as the reference category.

And now we have our last test, the slope difference between Japan vs Other, which we find in the row "Country[Japan]*(Turning Circle-38.5862)". A difference of 42.45, p = 0.2204.

So we have our three pairwise slope tests.

Japan vs USA, difference = 0.06, p = 0.9985.

Other vs USA, difference = 42.38, p = 0.2193.

Japan vs Other, difference = 42.45, p = 0.2204.

I hope this helps!

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Re: comparison of slopes

Hi Julian,

Thank you for your detailed answer. I really appreciate your going through

the stages of the explanation one by one. One comment and one further

question:

If the categorical variable has more than three levels this means changing

the ordering of the values as many times as necessary to cover all the

pairs of comparisons. This isn't difficult, but it would be nice if JMP

could build this into the menu as a single step.

I didn't understand the difference between the regular parameter estimates

and the indicator function parametrization estimates. Nor do I understand

the role of the constant (38.5862) subtracted from the continuous

predictor (turning circle) in obtaining the estimates. When using SAS and

the ESTIMATE statement in PROC GLM one can get an estimate of the

regression slope for each value of the categorical variable. How can I get

these values in JMP?

Thanks again,

Hillary.

**From:** Julian Parris

**Sent:** Wednesday, May 25, 2016 12:44 AM

**To:** hillaryvoet <hillary.voet@mail.huji.ac.il>

**Subject:** You have been mentioned by Julian Parris in Re: comparison of

slopes in JMP User

JMP User Community

<https://community.jmp.com/?utm_source=JIVE&utm_medium=email&utm_campaign=System%20Email%20Tracking>

You have been mentioned

by Julian Parris

<https://community.jmp.com/people/julian?et=notification.mention> *in Re:

comparison of slopes in JMP User* - View Julian Parris's reference to you

<https://community.jmp.com/message/228776?et=notification.mention#228776>

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Re: comparison of slopes

Hi Hillary,

You're welcome! It looks you replied to my post via email so I hope the screenshots came through.

Let me tackle your questions first:

- Difference between regular parameter estimates and the indicator parameter estimates: This has to do with how JMP parameterizes categorical variables. There are two ways to do this: effect coding, and indicator coding. Effect coding, common in designed experiments, models the parameters such that the estimates are with respect the grand mean across all groups. So, the regular parameter estimates list a difference from average response. Indicator coding means that estimates reflect a difference from some baseline or reference group. Both types of parameterization are useful at times, but effect coding, the default in JMP, has better properties for more complicated models and is thus prefered.
- The naming of the interaction terms with subtracting a constant: this is called centering, or using a centered polynomial. That constant is the mean of that variable, and in constructing the interaction term the cross product between variables is found my multiplying not the original values of the variable, but the value of the variable after subtracting the mean. This also has desirable properties for complicated models, and has the useful effect of keeping lower order terms in the model more interpretable. In short, the parameter estimates of lower order terms, say of Japan vs USA in terms of weight, indicate how much those countries differ for cars of average turning circle. Without centering of the turning circle variable, that lower order term would indicate the estimate when cars have 0 turning circle radius (which doesn't really make any sense). For more information about centering a polynomial and its consequences, see my response and video on this post: estimates in multipule regression
- There are a few ways to get the slopes for each group. Using the indicator parameterization table you can just look at the estimate for your continuous variable, and that will be the estimate for the slope of the reference category. Another way is using Analyze > Fit Y by X. Set this up with just your continuous Y and continuous X, click okay, then once you have the output go to the Red Triangle > Group by, and then select your categorical variable. Now, whatever action you perform will be done separately for each X. Go back to the Red Triangle > Fit Line. You will now have as many linear fits as you have levels of your categorical X. You can read off the individual slopes there.

As for your comment, I agree! After I posted my response I started thinking about how utterly manual this is! If you have many levels of your categorical X this would be a bit tedious. So, I worked on an add-in to automate this: Compare Simple Slopes in One-Way ANCOVA (w Interaction) add-in

I hope this helps!

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Re: comparison of slopes

Another option for comparing slopes across groups is the non-linear platform (which has a linear fit option). Using the car data as Julian did if you have weight as the Y, Turning Circle as the X and then Country as the Group and then under the Fit Curve>Polynomials>Fit Linear you will get a linear fit for each group:

Then under the Linear red triangle select "Compare Parameter Estimates" and you will get a comparison of the intercepts and slopes (note they have been estimated for you in the table shown above). This isn't exactly what you were looking for as the comparison of the slopes is done by ANOM so the comparison is to the overall slope: does the slope from any group differ from the overall slope? is the question under consideration with ANOM.

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Re: comparison of slopes

Great suggestion, Karen! That's certainly the most direct way to produce those analyses when a comparison to the overall/mean slope is needed.

Edit: Another option if we're after comparisons to the average slope (even though I prefer ANOM) is requesting Expanded Estimates from the original Fit Model report (Top Red Triangle > Estimates > Expanded Estimates). Expanded estimates show all parameters for all levels of the nominal factors:

Unlike the Indicator Parameterization estimates these are all with respect to the average response. "Turning Circle" here is then the average/overall slope, and the estimates for the interaction parameters are the differences in slope for each region compared to the average.

Interestingly... some of these estimates are ever so slightly different than the ANOM in the non-linear platform. The given estimate in the ANOM table for the slope of Japan is 155.5, which is indeed equal to what we recover from the expanded estimates report in Fit Model: 141.35+14.16 = 155.51. However, in the ANOM report, the mean slope is listed/drawn at 144.71, which isn't correct. (155.52+113.07+155.46)/3 = 141.35.)