I have a DataFrame and need to build a model where the predictors follow some naming scheme.

For the example `data`

below, suppose the scheme is “!=y”:

```
using DataFrames
using GLM
data = DataFrame(y=[22.1,20.1,7.1,9.1,1000,200],
x1=[1.1,2.1,3.1,4.1,10,100.2],
x2=[1,2,3,4.0,11.2,100.1])
```

Can anyone suggest a modification to Ex.2 below that would make the models in Ex.1 (`ols1`

) and Ex.2 (`ols2`

) equivalent?

Please note that while `ols2`

does not run, I’m looking for something of comparable terseness, if possible.

**Ex 1:**

```
ols1 = GLM.lm(@formula(y ~ x1 + x2), data)
y ~ 1 + x1 + x2
Coefficients:
────────────────────────────────────────────────────────────────────────────
Estimate Std. Error t value Pr(>|t|) Lower 95% Upper 95%
────────────────────────────────────────────────────────────────────────────
(Intercept) 84.4784 4.76541 17.7274 0.0004 69.3127 99.644
x1 -745.249 8.33275 -89.4361 <1e-5 -771.767 -718.73
x2 747.144 8.34614 89.5196 <1e-5 720.582 773.705
```

**Ex.2**

```
preds = Symbol.(names(data)[findall(names(data) .!= "y")])
2-element Array{String,1}:
"x1"
"x2"
ols2 = GLM.lm(@formula(y ~ preds), data)
ERROR: type NamedTuple has no field preds
```