The PM 2.5 checker written by R has been working nicely for me. I put a shortcut icon of this small script on my desktop PC, to check the air … Continue reading →

Following the successful 3rd R in Insurance conference in Amsterdam last year, we return to London this year.The registration for the 4th conference on R in Insurance on Monday 11 July 2016 at Cass Business School has opened. This one-day conference will focus again on applications in insurance and actuarial science that...

Are you ready to upgrade your R skills? Register soon to secure your seat. On January 28 and 29, 2016, Hadley Wickham will teach his popular Master R Developer Workshop at the Westin San Francisco Airport. The workshop is offered only 3 times a year and the San Francisco class is already nearly 50% full. This

Introduction I was watching a video of David Suzuki being interviewed on Australian TV, and there were some questions about the “pause” in temperature in the GISS dataset. I thought I’d like to check for myself, and reasoned that I may as well update the giss dataset in the ocedata R package. As always seems to be the case, the data format is changed...

This is probably my shortest post ever.All my QGIS processing scripts (R and Python) and models that I already blogged about, plus some extra are now available at GitHub.

In my previous blog I have explained about linear regression. In today’s post I will explain about logistic regression. Consider a scenario where we need to predict a medical condition of a patient (HBP) ,HAVE HIGH BP or NO HIGH BP, based on some observed symptoms – Age, weight, Issmoking, Systolic...

Now that we fitted the classifier and run some preliminary tests, in order to get a grasp at how our model is doing when predicting creditability we need to run some cross validation methods.Cross validation is a model evaluation method that does not use conventional fitting measures (such as R^2 of linear regression) when trying to evaluate the model....

Logistic regression is a method for fitting a regression curve, y = f(x), when y is a categorical variable. The typical use of this model is predicting y given a set of predictors x. The predictors can be continuous, categorical or a mix of both. The categorical variable y, in general, can assume different values.

My previous post covered the basics of logistic regression. We must now examine the model to understand how well it fits the data and generalizes to other observations. The evaluation process involves the assessment of three distinct areas – goodness of fit, tests of individual predictors, and validation of predicted values – in order to

e-mails with the latest R posts.

(You will not see this message again.)