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.
```{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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