Description Usage Arguments Details Value References Examples

View source: R/rlassologitEffect.R

The function estimates (low-dimensional) target coefficients in a high-dimensional logistic model.

1 2 3 4 5 6 7 8 9 10 11 | ```
rlassologitEffects(x, ...)
## Default S3 method:
rlassologitEffects(x, y, index = c(1:ncol(x)), I3 = NULL,
post = TRUE, ...)
## S3 method for class 'formula'
rlassologitEffects(formula, data, I, included = NULL,
post = TRUE, ...)
rlassologitEffect(x, y, d, I3 = NULL, post = TRUE)
``` |

`x` |
matrix of regressor variables serving as controls and potential
treatments. For |

`...` |
additional parameters |

`y` |
outcome variable |

`index` |
vector of integers, logical or names indicating the position (column) or name of variables of x which should be used as treatment variables. |

`I3` |
logical vector with same length as the number of controls; indicates if variables (TRUE) should be included in any case. |

`post` |
logical. If |

`formula` |
An element of class |

`data` |
an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called. |

`I` |
An one-sided formula specifying the variables for which inference is conducted. |

`included` |
One-sided formula of variables which should be included in any case. |

`d` |
variable for which inference is conducted (treatment variable) |

The functions estimates (low-dimensional) target coefficients in a high-dimensional logistic model.
An application is e.g. estimation of a treatment effect *α_0* in a
setting of high-dimensional controls. The function is a wrap function for `rlassologitEffect`

which does inference for only one variable (d).

The function returns an object of class `rlassologitEffects`

with the following entries:

`coefficients` |
estimated value of the coefficients |

`se` |
standard errors |

`t` |
t-statistics |

`pval` |
p-values |

`samplesize` |
sample size of the data set |

`I` |
index of variables of the union of the lasso regressions |

A. Belloni, V. Chernozhukov, Y. Wei (2013). Honest confidence regions for a regression parameter in logistic regression with a loarge number of controls. cemmap working paper CWP67/13.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
## Not run:
library(hdm)
## DGP
set.seed(2)
n <- 250
p <- 100
px <- 10
X <- matrix(rnorm(n*p), ncol=p)
colnames(X) = paste("V", 1:p, sep="")
beta <- c(rep(2,px), rep(0,p-px))
intercept <- 1
P <- exp(intercept + X %*% beta)/(1+exp(intercept + X %*% beta))
y <- rbinom(length(y), size=1, prob=P)
xd <- X[,2:50]
d <- X[,1]
logit.effect <- rlassologitEffect(x=xd, d=d, y=y)
logit.effects <- rlassologitEffects(X,y, index=c(1,2,40))
logit.effects.f <- rlassologitEffects(y ~ X, I = ~ V1 + V2)
## End(Not run)
``` |

hdm documentation built on May 12, 2018, 3:01 a.m.

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