Suppose you have a list of variable names in a vector and you want to construct some standard linear model from the vector, how can we do it in R. Actually it is pretty easy in R. By using two R functions, that is, *formula* and *paste*, linear model can be constructed automatically

1.additive model

`PredictorVariables<- c("x1","x2")`

Apply approach: We can then construct a formula as follows:

PredictorVariables <- paste(“x”, 1:100, sep=””)

Apply approach: We can then construct a formula as follows:

```
Formula <- formula(paste("y ~ ",
paste(PredictorVariables, collapse=" + ")))
lm(Formula, Data)
```

2.multiplicative model

Apply approach: We can then construct a formula as follows:

PredictorVariables <- paste(“x”, 1:100, sep=””)

Apply approach: We can then construct a formula as follows:

```
Formula <- formula(paste("y ~ ",
paste(PredictorVariables, collapse=" * ")))
lm(Formula, Data)
```

If you have more than two variable names in the vector, the mode can be quite complex, including higher order of interactions.

By using the similar idea, you can also include random terms in the constructed model.

Appendix: **Get all column headers that fit to certain patterns**.

Suppose I have data frame called my.df and there are some column headers include string “trt_”, we can use the following methods to get all columns headers with “trt_” and store them into a vector.

```
my.colnames <- colnames(my.df)
my.headers <- my.colnames[grepl("trt_", my.colnames)]
```