Predictions of a DiscSurv object

Return the model predictions of a DiscSurv object

Usage

# S3 method for class 'DiscSurv'
predict(
  fit,
  data,
  Event.char,
  Z.char,
  Time.char,
  lambda,
  which = 1:length(fit$lambda),
  type = c("response", "vars", "nvars"),
  return.Array = TRUE,
  which.lambda = "all",
  ...
)

Arguments

fit

a DiscSurv object.

data

an dataframe or list object that contains the variables for prediction.

Event.char

name of the event indicator in data as a character string.

Z.char

names of covariates in data as vector of character strings.

Time.char

name of the observation time in data as a character string.

lambda

values of the regularization parameter lambda at which predictions are requested. For values of lambda not in the sequence of fitted models, linear interpolation is used.

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.

type

type of prediction:

• response: fitted values (i.e., exp(eta)/(1 + exp(eta)))

• vars: the indices for the non-zero coefficients

• nvars: the number of non-zero coefficients

which.lambda

determine which lambda values are included in the output of the prediction. By default, its value is set to "all," resulting in a matrix of predicted values for each lambda presented as a list. However, if specific numeric values are provided, only the predicted matrix corresponding to those specified values will be included in the output.

...

Examples

data(DiscTime)
data <- DiscTime$data
Event.char <- DiscTime$Event.char
Z.char <- DiscTime$Z.char
Time.char <- DiscTime$Time.char
fit <- DiscSurv(data, Event.char, Z.char, Time.char)
predict(fit, data, Event.char, Z.char, Time.char, lambda = fit$lambda, type = "response", which.lambda = fit$lambda[1])[1:5,]
#>   Individual [Time: 0.53] [Time: 1.03] [Time: 3.92] [Time: 6.74] [Time: 12.5]
#> 1          1     0.121372    0.1081081           NA           NA           NA
#> 2          2     0.121372    0.1081081   0.07692308   0.08661417   0.04932735
#> 3          3     0.121372           NA           NA           NA           NA
#> 4          4     0.121372    0.1081081   0.07692308   0.08661417           NA
#> 5          5     0.121372    0.1081081   0.07692308   0.08661417   0.04932735
predict(fit, data, Event.char, Z.char, Time.char, lambda = 0.01, type = "vars")
#> Z1 Z2 Z3 Z4 Z5 
#>  1  2  3  4  5