**20**wrote:

Hello, I have run into a new issue (for me) in fitting a limma model.

The model I am fitting includes a number of covariates/additional factors, such as ages and weights of individuals associated with a given "array", several random factors, such as race/ethnicity, region, and the factor defining my primary groups of interest.

E.g. `model.matrix(~ 0 + groups + race + region + scale(age) + scale(weight), data = full_dataset)`

I am making contrasts from pairwise interactions of levels of the `groups`

factor, e.g.
- `high - medium`

- `medium - low`

- `high - low`

```
mat_data <- # defined earlier
design <- # defined earlier
contrasts <- # defined earlier
```

When fitting a limma model, is there any difference between the following, and if so, what exactly:

```
fit <- lmFit(mat_data, design)
fit <- contrasts.fit(fit, contrasts)
fit <- eBayes(fit)
```

and

```
fit <- lmFit(mat_data, design)
fit <- eBayes(fit)
fit <- contrasts.fit(fit, contrasts)
```

Clearly there is some difference, since I see large differences in `topTable`

for each coefficient, particularly in the t-statistic and P-values. However, `logFC`

and `AveExpr`

stay the same, so I believe that the coefficients being estimated are the same.

The only reason I stumbled up this scenario is that I wanted to check the residuals of my fit and found that after calling `contrasts.fit`

I could no longer use `residuals`

or `fitted`

with the `MArrayLM`

object. It seems that after adjusting the model with `contrasts.fit`

, since the coefficients no longer match the design, you can't compute with the original coefficients. So, rather than computing the fit twice, I wanted to see if I could use the fit after the `eBayes`

call to extract the residuals, and then call `contrasts.fit`

to get the contrasts of interest.

Thank you for any help you can provide!

**38k**• written 8 months ago by andrew.birnberg •

**20**