title: "Photometric Redshift with CosmoPhotoz"
authors: Rafael S. de Souza, Jonny Elliot, Alberto Krone-Martins, Émille Ishida, Joseph
Hilbe
output: html_document
runtime: shiny

This is a short tutorial explaining how to perform photometric redshift estimation using the CosmoPhotoz R package.

Required libraries

```{r,results='hide',message=FALSE, cache=FALSE} require(CosmoPhotoz) require(ggplot2)


Load the PHAT0 data included in the package. Here we are using 5% of all dataset for training. 


```{r}
data(PHAT0train)

data(PHAT0test)

{r} PC_comb<-computeCombPCA(subset(PHAT0train,select=c(-redshift)), subset(PHAT0test,select=c(-redshift)))

Number of variance explained by each PC {r} PC_comb$PCsum

Add the redshift column to the PCA projections of the Training sample

```{r} Trainpc<-cbind(PC_combx, redshift = PHAT0trainredshift)



Store the PCA projections for the testing sample in the vector Testpc

```{r, echo=FALSE}
Testpc<-PC_comb$y

Train the glm model using Gamma Family. 6 PCs explain 99.5% of data variance. In order to account for small variations in the shape, we include a polynomial term for the 2 first PCs (95% of data variance)

```{r}

Fit<-glmTrainPhotoZ(Trainpc,formula=redshift~poly(Comp.1,2)poly(Comp.2,2)Comp.3Comp.4Comp.5*Comp.6,method="Bayesian",family="gamma")




Once we fit our GLM model, we can predict the redshift for the "photometric" sample
 
```{r, echo=FALSE}

photoz<-predict(Fit$glmfit,newdata = Testpc,type="response")

Store the redshift from the testing sample in the vector specz for comparison

{r, echo=FALSE} specz<-PHAT0test$redshift

Compute basic diagnostic statistics

{r, echo=FALSE} computeDiagPhotoZ(photoz, specz)

Create basic diagnostic plots

Kernel density distribution of the full scatter (specz − photoz) / (1 + specz)

```{r,fig.width=8, fig.height=9} plotDiagPhotoZ(photoz, specz, type = "errordist")


Predicted vs Actuall values
Select 15,000 points to show
```{r}
datashow<-sample(length(photoz),15000)

{r,fig.width=8, fig.height=9} plotDiagPhotoZ(photoz[datashow], specz[datashow], type = "predobs")+coord_cartesian(xlim =c(0,1.5), ylim = c(0,1.5))

Scatter distribution as a function of redshift, violin plot

{r,fig.width=12, fig.height=9} plotDiagPhotoZ(photoz, specz, type = "errorviolins")

Scatter distribution as a function of redshift, box plot

{r,fig.width=12, fig.height=9} plotDiagPhotoZ(photoz, specz, type = "box")

{r, echo=FALSE} shinyAppDir("paste(find.package("CosmoPhotoz"),"/glmPhotoZ-2/",sep=""))