Model predictions based on a fitted biglasso object

Extract predictions (fitted reponse, coefficients, etc.) from a fitted biglasso() object.

Usage

# S3 method for class 'biglasso'
predict(
  object,
  X,
  row.idx = 1:nrow(X),
  type = c("link", "response", "class", "coefficients", "vars", "nvars"),
  lambda,
  which = 1:length(object$lambda),
  ...
)

# S3 method for class 'mbiglasso'
predict(
  object,
  X,
  row.idx = 1:nrow(X),
  type = c("link", "response", "coefficients", "vars", "nvars"),
  lambda,
  which = 1:length(object$lambda),
  k = 1,
  ...
)

# S3 method for class 'biglasso'
coef(object, lambda, which = 1:length(object$lambda), drop = TRUE, ...)

# S3 method for class 'mbiglasso'
coef(object, lambda, which = 1:length(object$lambda), intercept = TRUE, ...)

Arguments

object

A fitted "biglasso" model object.

X

Matrix of values at which predictions are to be made. It must be a bigmemory::big.matrix() object. Not used for type="coefficients".

row.idx

Similar to that in biglasso(), it's a vector of the row indices of X that used for the prediction. 1:nrow(X) by default.

type

Type of prediction:

"link" returns the linear predictors
"response" gives the fitted values
"class" returns the binomial outcome with the highest probability
"coefficients" returns the coefficients
"vars" returns a list containing the indices and names of the nonzero variables at each value of lambda
"nvars" returns the number of nonzero coefficients at each value of lambda

lambda

Values of the regularization parameter lambda at which predictions are requested. Linear interpolation is used for values of lambda not in the sequence of lambda values in the fitted models.

which

Indices of the penalty parameter lambda at which predictions are required. By default, all indices are returned. If lambda is specified, this will override which.

...

Not used.

k

Index of the response to predict in multiple responses regression ( family="mgaussian").

drop

If coefficients for a single value of lambda are to be returned, reduce dimensions to a vector? Setting drop=FALSE returns a 1-column matrix.

intercept

Whether the intercept should be included in the returned coefficients. For family="mgaussian" only.

Value

The object returned depends on type.

Author

Yaohui Zeng and Patrick Breheny

Examples

## Logistic regression
data(colon)
X <- colon$X
y <- colon$y
X.bm <- as.big.matrix(X, backingfile = "")
fit <- biglasso(X.bm, y, penalty = 'lasso', family = "binomial")
coef <- coef(fit, lambda=0.05, drop = TRUE)
coef[which(coef != 0)]
#>  [1]  7.654998e-01 -5.099878e-05 -2.753690e-03 -4.182978e-04  5.594134e-05
#>  [6] -1.183261e-03  4.871641e-04  2.449309e-04  4.574918e-03 -2.180171e-04
#> [11] -1.093833e-03 -1.739847e-03  7.168045e-04  1.007572e-03 -2.752499e-03
#> [16]  4.657996e-03  5.308308e-03
predict(fit, X.bm, type="link", lambda=0.05)[1:10]
#>  [1]  0.8065784 -2.4540880  1.0150133 -0.5253085  2.0519542 -0.7598931
#>  [7]  2.2006058 -2.1774748  3.1595848 -2.7014291
predict(fit, X.bm, type="response", lambda=0.05)[1:10]
#>  [1] 0.69137991 0.07914011 0.73400010 0.37161178 0.88614493 0.31866947
#>  [7] 0.90030390 0.10179158 0.95928473 0.06288908
predict(fit, X.bm, type="class", lambda=0.1)[1:10]
#>  [1] 1 0 1 1 1 0 1 0 1 0
predict(fit, type="vars", lambda=c(0.05, 0.1))
#> $`0.05`
#>  Hsa.8147 Hsa.36689 Hsa.42949 Hsa.22762 Hsa.692.2 Hsa.31801  Hsa.3016  Hsa.5392 
#>       249       377       617       639       765      1024      1325      1346 
#>  Hsa.1832 Hsa.12241 Hsa.44244  Hsa.2928 Hsa.41159 Hsa.33268  Hsa.6814  Hsa.1660 
#>      1423      1482      1504      1582      1641      1644      1772      1870 
#> 
#> $`0.1`
#>  Hsa.8147 Hsa.36689 Hsa.37937  Hsa.3306 Hsa.692.2  Hsa.5392  Hsa.2928 Hsa.33268 
#>       249       377       493       625       765      1346      1582      1644 
#>  Hsa.6814  Hsa.1660 
#>      1772      1870 
#> 
predict(fit, type="nvars", lambda=c(0.05, 0.1))
#> 0.05  0.1 
#>   16   10

Model predictions based on a fitted `biglasso` object

Usage

Arguments

Value

See also

Author

Examples