---
title: "Two steps ME estimation"
author: "Jorge Cabral"
output:
rmarkdown::html_vignette:
toc: true
toc_depth: 4
link-citations: yes
bibliography: references.bib
csl: american-medical-association-brackets.csl
description: |
GME estimation followed by GCE estimation.
vignette: >
%\VignetteIndexEntry{Two steps ME estimation}
%\VignetteEncoding{UTF-8}
%\VignetteEngine{knitr::rmarkdown}
editor_options:
markdown:
wrap: 72
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## Introduction
As stated in ["Generalized Cross Entropy framework"](V3_GCE_framework.html#Introduction),
the common situation is the absence of prior information on
$\mathbf{p} = (\mathbf{p_0},\mathbf{p_1},\dots,\mathbf{p_K})$. Yet, it is possible
to include some pre-sample information in the form of
$\mathbf{q} = (\mathbf{q_0},\mathbf{q_1},\dots,\mathbf{q_K})$.
## Two steps
If we assume that generally there is no information on $\mathbf{p}$ we are
defining a uniform distribution for $\mathbf{p}$ and ME estimation is done in
the GME framework (see
["Generalized Maximum Entropy framework"](V2_GME_framework.html#Introduction)).
From that estimation we can also obtain $\mathbf{\hat p}$. If we use
$\mathbf{\hat p}$ as the prior distribution $\mathbf{q}$ we can perform a ME
estimation in the GCE framework (see
["Generalized Cross Entropy framework"](V3_GCE_framework.html#Introduction)).
This procedures can be repeated as many times as required.
```{r,echo=FALSE,eval=TRUE}
library(GCEstim)
load("GCEstim_Two_Steps.RData")
```
Consider `dataGCE` (see ["Generalized Maximum Entropy
framework"](V2_GME_framework.html#Examples) and ["Choosing the supports spaces"](V5_Choosing_Supports.html#Examples)).
```{r,echo=TRUE,eval=TRUE}
coef.dataGCE <- c(1, 0, 0, 3, 6, 9)
```
The two steps GCE estimation can be done by assigning to the argument
`twosteps.n` a value different from $0$. Let us consider $10$ GCE estimations
after a first GME estimation (by default
`support.signal.points = c(1/5, 1/5, 1/5, 1/5, 1/5)`).
```{r,echo=TRUE,eval=TRUE}
res.lmgce.1se.twosteps <-
GCEstim::lmgce(
y ~ .,
data = dataGCE,
twosteps.n = 10
)
```
The trace of the prediction CV-error can be obtained with `plot` and `which = 6`
```{r,echo=TRUE,eval=TRUE, fig.width=6,fig.height=4,fig.align='center'}
plot(res.lmgce.1se.twosteps, which = 6)[[1]]
```
The pre reestimation CV-error is depicted by the red dot, intermediate CV-errors
are represented by orange dots and final/reestimated CV-error corresponds to the
dark red dot. The horizontal dotted line represents the OLS CV-error. Note that
with the increase of reestimation the CV-error decreases.\
Since we are working with simulated data, the true coefficients are known and
the precision error can be determined. The arguments `which = 7` and
`coef = coef.dataGCE` of `plot` allows to obtain the trace
```{r,echo=TRUE,eval=TRUE, fig.width=6,fig.height=4,fig.align='center'}
plot(res.lmgce.1se.twosteps, which = 7, coef = coef.dataGCE)[[1]]
```
We can see that, with the first two reestimations, we get a lower precision error
but from that point forward the model tends to overfit data.
Generally it is recommended to perform only $1$ GCE reestimation. That can be
done by setting `twosteps.n = 1`, the default of `lmgce`
```{r,echo=TRUE,eval=FALSE}
res.lmgce.1se.twosteps.1 <-
GCEstim::lmgce(
y ~ .,
data = dataGCE
)
```
or use `update`
```{r,echo=TRUE,eval=FALSE}
res.lmgce.1se.twosteps.1 <- update(res.lmgce.1se.twosteps, twosteps.n = 1)
```
or, since data is already stored in the `object` we can use the `changestep`
function. This last options is the recommended in this case.
```{r,echo=TRUE,eval=TRUE}
res.lmgce.1se.twosteps.1 <- changestep(res.lmgce.1se.twosteps, 1)
```
`plot` with `which = 2` gives us the "Prediction Error vs supports" plot
```{r,echo=TRUE,eval=TRUE, fig.width=6,fig.height=4,fig.align='center'}
plot(res.lmgce.1se.twosteps.1, which = 2)[[1]]
```
and with `which = 3` we get the "Estimates vs supports" plot.
```{r,echo=TRUE,eval=TRUE, fig.width=6,fig.height=4,fig.align='center'}
plot(res.lmgce.1se.twosteps.1, which = 3)[[1]]
```
In the last two plots are depicted the final solutions. That is to say
that after choosing the support spaces limits based on the defined error,
the number of points of the support spaces and their probability
`support.signal.points = c(1/5, 1/5, 1/5, 1/5, 1/5)`, `twosteps.n = 1` extra
estimation(s) is(are) performed. This estimation uses the GCE framework even if
the previous steps were by default on the GME framework.
The distribution of probabilities used is the one estimated for the chosen
support spaces and it is stored in `object$p0`.
```{r,echo=TRUE,eval=TRUE}
res.lmgce.1se.twosteps.1$p0
```
The final estimated vector of probabilities, `object$p`, is
```{r,echo=TRUE,eval=TRUE, fig.width=6,fig.height=4}
res.lmgce.1se.twosteps.1$p
```
## Conclusion
Doing a comparison between different methods we can conclude that generally we
should use the two steps approach with only $1$ reestimation and choose the
support spaces defined by standardized bounds with the 1se error structure.
```{r, echo=FALSE,eval=TRUE,results = 'asis'}
kableExtra::kable(
cbind(all.data.2,
c(
round(GCEstim::accmeasure(
fitted(res.lmgce.1se.twosteps.1), dataGCE$y, which = "RMSE"
), 3),
round(res.lmgce.1se.twosteps.1$error.measure.cv.mean, 3),
round(GCEstim::accmeasure(
coef(res.lmgce.1se.twosteps.1), coef.dataGCE, which = "RMSE"
), 3)
)),
digits = 3,
align = c(rep('c', times = 5)),
col.names = c("$OLS$",
"$GME_{(RidGME)}$",
"$GME_{(incRidGME_{1se})}$",
"$GME_{(incRidGME_{min})}$",
"$GME_{(std_{1se})}$",
"$GME_{(std_{min})}$",
"$GCE_{(std_{1se})}$"),
row.names = TRUE,
booktabs = FALSE)
```
## References
## Acknowledgements
This work was supported by Fundação para a Ciência e Tecnologia (FCT)
through CIDMA and projects
and .