# simulate_extra

##### Simulate extrapolated data

Simulated data scenarios described in the paper from Ryan and Culp (2015).

##### Usage

```
simulate_extra(n_source = 100, n_target = 100, p = 1000, shift = 10,
scenario = "same", response = "linear", sigma2 = 2.5)
```

##### Arguments

- n_source
Number of source samples (labeled)

- n_target
Number of target samples (unlabeled)

- p
Number of variables (

`p > 10`

)- shift
The shift applied to the first 10 columns of xU.

- scenario
Simulation scenario. One of

`"same"`

(same distribution),`"lucky"`

(extrapolation with lucky \(\beta\)),`"unlucky"`

(extrapolation with unlucky \(\beta\))- response
Type of response:

`"linear"`

or`"logit"`

- sigma2
The variance of the error term, linear response case.

##### Value

A list, with

- xL
data frame with the labeled (source) data

- yL
labels associated with

`xL`

- xU
data frame with the unlabeled (target) data

- yU
labels associated with

`xU`

(for validation/testing)

##### References

Ryan, K. J., & Culp, M. V. (2015). On semi-supervised linear regression in covariate shift problems. *The Journal of Machine Learning Research, 16*(1), 3183-3217.

##### See Also

##### Examples

```
# NOT RUN {
set.seed(0)
data = simulate_extra()
train = s2Data(data$xL, data$yL, data$xU)
valid = s2Data(data$xU, data$yU, preprocess = train)
model = s2netR(train, s2Params(0.1))
ypred = predict(model, valid$xL)
plot(ypred, valid$yL)
# }
```

*Documentation reproduced from package s2net, version 1.0, License: GPL (>= 2)*