# README.md In CosmoPhotoz: Photometric redshift estimation using generalized linear models

title: "Photometric Redshift with CosmoPhotoz" authors: Rafael S. de Souza, Jonny Elliot, Alberto Krone-Martins, Ă‰mille 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)
``````
``````PC_comb<-computeCombPCA(subset(PHAT0train,select=c(-redshift)),
subset(PHAT0test,select=c(-redshift)))
``````

Number of variance explained by each PC

``````PC_comb\$PCsum
``````

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

``````Trainpc<-cbind(PC_comb\$x,redshift=PHAT0train\$redshift)

``````

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.3*Comp.4*Comp.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

``````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=""))
``````

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CosmoPhotoz documentation built on May 29, 2017, 3:59 p.m.