Posts Tagged ‘ GLM ’

Statistical computing with Scala free on-line course

May 31, 2017
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Statistical computing with Scala free on-line course

I’ve recently delivered a three-day intensive short-course on Scala for statistical computing and data science. The course seemed to go well, and the experience has convinced me that Scala should be used a lot more by statisticians and data scientists for a range of problems in statistical computing. In particular, the simplicity of writing fast … Continue reading Statistical computing with Scala free on-line course

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Simple Distributions for Mixtures?

February 3, 2016
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Simple Distributions for Mixtures?

The idea of GLMs is that given some covariates,  has a distribution in the exponential family (Gaussian, Poisson, Gamma, etc). But that does not mean that  has a similar distribution… so there is no reason to test for a Gamma model for  before running a Gamma regression, for instance. But are there cases where it might work? That the non-conditional distribution is the same (same family at least) than the conditional ones? For…

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ChainLadder 0.2.2 is out with improved glmReserve function

September 29, 2015
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ChainLadder 0.2.2 is out with improved glmReserve function

We released version 0.2.2 of ChainLadder a few weeks ago. This version adds back the functionality to estimate the index parameter for the compound Poisson model in glmReserve using the cplm package by Wayne Zhang. Ok, what does this all mean? I will r...

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Computational Time of Predictive Models

September 25, 2015
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Computational Time of Predictive Models

Tuesday, at the end of my 5-hour crash course on machine learning for actuaries, Pierre asked me an interesting question about computational time of different techniques. I’ve been presenting the philosophy of various algorithm, but I forgot to mention computational time. I wanted to try several classification algorithms on the dataset used to illustrate the techniques > rm(list=ls()) > myocarde=read.table( "http://freakonometrics.free.fr/myocarde.csv", head=TRUE,sep=";") > levels(myocarde$PRONO)=c("Death","Survival") But the dataset is rather small,…

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Bayesian regression models using Stan in R

September 1, 2015
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Bayesian regression models using Stan in R

It seems the summer is coming to end in London, so I shall take a final look at my ice cream data that I have been playing around with to predict sales statistics based on temperature for the last couple of weeks [1], [2], [3].Here I will use the new b...

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Visualising theoretical distributions of GLMs

August 18, 2015
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Visualising theoretical distributions of GLMs

Two weeks ago I discussed various linear and generalised linear models in R using ice cream sales statistics. The data showed not surprisingly that more ice cream was sold at higher temperatures.icecream

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Calling R from Scala sbt projects using rscala

August 15, 2015
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Calling R from Scala sbt projects using rscala

Overview In the previous post I showed how the rscala package (which has replaced the jvmr package) can be used to call Scala code from within R. In this post I will show how to call R from Scala code. I have previously described how to do this using jvmr. This post is really just … Continue reading Calling R from Scala sbt projects using rscala

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Calling R from Scala sbt projects using rscala

August 15, 2015
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Calling R from Scala sbt projects using rscala

Overview In the previous post I showed how the rscala package (which has replaced the jvmr package) can be used to call Scala code from within R. In this post I will show how to call R from Scala code. I have previously described how to do this using jvmr. This post is really just … Continue reading Calling R from Scala sbt projects using rscala

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Generalised Linear Models in R

August 4, 2015
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Generalised Linear Models in R

Linear models are the bread and butter of statistics, but there is a lot more to it than taking a ruler and drawing a line through a couple of points.Some time ago Rasmus Bååth published an insightful blog article about how such models could be descr...

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Choosing a Classifier

July 21, 2015
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Choosing a Classifier

In order to illustrate the problem of chosing a classification model consider some simulated data, > n = 500 > set.seed(1) > X = rnorm(n) > ma = 10-(X+1.5)^2*2 > mb = -10+(X-1.5)^2*2 > M = cbind(ma,mb) > set.seed(1) > Z = sample(1:2,size=n,replace=TRUE) > Y = ma*(Z==1)+mb*(Z==2)+rnorm(n)*5 > df = data.frame(Z=as.factor(Z),X,Y) A first strategy is to split the dataset in two parts, a training dataset, and a testing dataset. >…

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