Accepted Solutions
Community Manager
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!
If you use Fit Model's Standard Least Squares for ANCOVA, select 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
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
Community Manager
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!
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.
Community Manager
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.)