# mte_at: Evaluate Marginal Treatment Effects from a Fitted MTE Model. In localIV: Estimation of Marginal Treatment Effects using Local Instrumental Variables

## Description

`mte_at` evaluates marginal treatment effects at different values of the latent resistance `u` with a given X=x.

## Usage

 `1` ```mte_at(x = NULL, u, model) ```

## Arguments

 `x` Values of the pretreatment covariates at which \textup{MTE}(x, u) is evaluated. It should be a numeric vector whose length is one less than the number of columns of the design matrix X in the outcome model. Default is the sample means. `u` A numeric vector. Values of the latent resistance u at which \textup{MTE}(x, u) is evaluated. Note that the estimation involves extrapolation when the specified u values lie outside of the support of the propensity score. `model` A fitted MTE model returned by `mte`.

## Value

`mte_at` returns a data frame.

 `u` input values of `u`. `x_comp` the x-component of the estimated \textup{MTE}(x, u) `u_comp` the u-component of the estimated \textup{MTE}(x, u) `value` estimated values of \textup{MTE}(x, u)

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10``` ```mod <- mte(selection = d ~ x + z, outcome = y ~ x, data = toydata) mte_vals <- mte_at(u = seq(0.05, 0.95, 0.1), model = mod) if(require("ggplot2")){ ggplot(mte_vals, aes(x = u, y = value)) + geom_line(size = 1) + xlab("Latent Resistance U") + ylab("Estimates of MTE at Mean Values of X") + theme_minimal(base_size = 14) } ```

localIV documentation built on July 2, 2020, 2:35 a.m.