slm {care} | R Documentation |

## Shrinkage Estimation of Regression Coefficients

### Description

`slm`

fits a linear model and computes
(standardized) regression coefficients by plugin of shrinkage estimates of correlations and variances.
Using the argument `predlist`

several models can be fitted on the same data set.

`make.predlist`

constructs a `predlist`

argument for use with `slm`

.

### Usage

```
slm(Xtrain, Ytrain, predlist, lambda, lambda.var, diagonal=FALSE, verbose=TRUE)
## S3 method for class 'slm'
predict(object, Xtest, verbose=TRUE, ...)
make.predlist(ordering, numpred, name="SIZE")
```

### Arguments

`Xtrain` |
Matrix of predictors (columns correspond to variables). |

`Ytrain` |
Univariate continous response variable. |

`predlist` |
A list specifying the predictors to be included when fitting the linear regression. Each entry in the list is a vector containing the indices of variables used per model. If left unspecified single full-sized model using all variables in Xtrain is assumed. For a given ordering of covariables a suitable |

`lambda` |
The correlation shrinkage intensity (range 0-1).
If not specified (the default) it is estimated using an
analytic formula from Sch\"afer and Strimmer (2005). For |

`lambda.var` |
The variance shrinkage intensity (range 0-1). If
not specified (the default) it is estimated
using an analytic formula from Opgen-Rhein and Strimmer
(2007). For |

`diagonal` |
If |

`verbose` |
If |

`object` |
An |

`Xtest` |
A matrix containing the test data set. Note that the rows correspond to observations and the columns to variables. |

`...` |
Additional arguments for generic predict. |

`ordering` |
The ordering of the predictors (most important predictors are first). |

`numpred` |
The number of included predictors (may be a scalar or a vector). The predictors
are included in the order specified by |

`name` |
The name assigned to each model is |

### Details

The regression coefficients are obtained by estimating the joint joint covariance matrix of the response and the predictors, and subsequently computing the the regression coefficients by inversion of this matrix - see Opgen-Rhein and Strimmer (2007). As estimators for the covariance matrix either the standard empirical estimator or a Stein-type shrinkage estimator is employed. The use of the empirical covariance leads to the OLS estimates of the regression coefficients, whereas otherwise shrinkage estimates are obtained.

### Value

`slm`

returns a list with the following components:

`regularization`

: The shrinkage intensities used for estimating correlations and variances.

`std.coefficients`

: The standardized regression coefficients, i.e. the regression coefficients
computed from centered and standardized input data. Thus, by construction the intercept is zero.
Furthermore, for `diagonal=TRUE`

the standardized regression coefficient for each predictor is
identical to the respective marginal correlation.

`coefficients`

: Regression coefficients.

`numpred`

: The number of predictors used in each investigated model.

`R2`

: For `diagonal=TRUE`

this is the multiple correlation coefficient
between the response and the predictor, or the proportion of explained variance, with range
from 0 to 1.
For `diagonal=TRUE`

this equals the sum of squared marginal
correlations. Note that this sum may be larger than 1!

`sd.resid`

: The residual unexplained error.

`predict.slm`

returns the means predicted for each sample and model as well as the corresponding
predictive standard deviations (attached as attribute "sd").

### Author(s)

Korbinian Strimmer (https://strimmerlab.github.io).

### References

Opgen-Rhein, R., and K. Strimmer. 2007. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst. Biol. 1: 37. <DOI:10.1186/1752-0509-1-37>

Sch\"afer, J., and K. Strimmer. 2005. A shrinkage approach to large-scale covariance estimation and implications for functional genomics. Statist. Appl. Genet. Mol. Biol. 4: 32. <DOI:10.2202/1544-6115.1175>

### See Also

### Examples

```
# load care library
library("care")
## example with large number of samples and small dimension
## (using empirical estimates of regression coefficients)
# diabetes data
data(efron2004)
x = efron2004$x
y = efron2004$y
n = dim(x)[1]
d = dim(x)[2]
xnames = colnames(x)
# empirical regression coefficients
fit = slm(x, y, lambda=0, lambda.var=0)
fit
# note that in this example the regression coefficients
# and the standardized regression coefficients are identical
# as the input data have been standardized to mean zero and variance one
# compute corresponding t scores / partial correlations
df = n-d-1
pcor = pcor.shrink(cbind(y,x), lambda=0)[-1,1]
t = pcor * sqrt(df/(1-pcor^2))
t.pval = 2 - 2 * pt(abs(t), df)
b = fit$coefficients[1,-1]
cbind(b, pcor, t, t.pval)
# compare results with those from lm function
lm.out = lm(y ~ x)
summary(lm.out)
# prediction of fitted values at the position of the training data
lm.out$fitted.values
mu.hat = predict(fit, x) # precticted means
mu.hat
attr(mu.hat, "sd") # predictive error
sd(y-mu.hat)
# ordering of the variables using squared empirical CAR score
car = carscore(x, y, lambda=0)
ocar = order(car^2, decreasing=TRUE)
xnames[ocar]
# CAR regression models with 5, 7, 9 included predictors
car.predlist = make.predlist(ocar, numpred = c(5,7,9), name="CAR")
car.predlist
slm(x, y, car.predlist, lambda=0, lambda.var=0)
# plot regression coefficients for all possible CAR models
p=ncol(x)
car.predlist = make.predlist(ocar, numpred = 1:p, name="CAR")
cm = slm(x, y, car.predlist, lambda=0, lambda.var=0)
bmat = cm$coefficients[,-1]
bmat
par(mfrow=c(2,1))
plot(1:p, bmat[,1], type="l",
ylab="estimated regression coefficients",
xlab="number of included predictors",
main="CAR Regression Models for Diabetes Data",
xlim=c(1,p+1), ylim=c(min(bmat), max(bmat)))
for (i in 2:p) lines(1:p, bmat[,i], col=i, lty=i)
for (i in 1:p) points(1:p, bmat[,i], col=i)
for (i in 1:p) text(p+0.5, bmat[p,i], xnames[i])
plot(1:p, cm$R2, type="l",
ylab="estimated R2",
xlab="number of included predictors",
main="Proportion of Explained Variance",
ylim=c(0,0.6))
R2max = max(cm$R2)
lines(c(1,p), c(R2max, R2max), col=2)
par(mfrow=c(1,1))
## example with small number of samples and large dimension
## (using shrinkage estimates of regression coefficients)
data(lu2004)
dim(lu2004$x) # 30 403
fit = slm(lu2004$x, lu2004$y)
fit
```

*care*version 1.1.11 Index]