In previous posts (https://statcompute.wordpress.com/2017/01/22/monotonic-binning-with-smbinning-package) and (https://statcompute.wordpress.com/2017/06/15/finer-monotonic-binning-based-on-isotonic-regression), I’ve developed 2 different algorithms for monotonic binning. While the first tends to generate bins with equal densities, the second would define finer bins...continue reading.
A reader, e.g. Mr. Wayne Zhang, of my previous post (https://statcompute.wordpress.com/2018/09/03/playing-map-and-reduce-in-r-by-group-calculation) made a good comment that “Why not use directly either Spark or H2O to derive such computations without involving...continue reading.
On Friday, while working on a project that I needed to union multiple data.frames with different column names, I realized that the base::rbind() function doesn’t take data.frames with different columns...continue reading.
In the post (https://statcompute.wordpress.com/2018/09/15/how-to-avoid-for-loop-in-r), I briefly introduced the idea of vectorization and potential use cases. One might be wondering why we even need the Vectorize() function given the fact that...continue reading.
A FOR loop is the most intuitive way to apply an operation to a series by looping through each item one by one, which makes perfect sense logically but should...continue reading.
When modeling frequency outcomes, we often need to go beyond the standard Poisson regression due to the strict distributional assumption and to consider more flexible alternatives. In general, there are...continue reading.
In the previous post (https://statcompute.wordpress.com/2018/09/03/playing-map-and-reduce-in-r-by-group-calculation), I’ve shown how to employ the MapReduce when calculating by-group statistics. Actually, the same Divide-n-Conquer strategy can be applicable to other use cases, one of...continue reading.
Clojure is such an interesting programming language that it can not only enhance our skill set but also change the way how we should write the program. After learning Clojure,...continue reading.
When it comes to writing wrappers around data steps and procedures in SAS, SAS macros might still be the primary choice for most SASors. In the example below, I am...continue reading.
In the previous post (https://statcompute.wordpress.com/2018/08/26/adjacent-categories-and-continuation-ratio-logit-models-for-ordinal-outcomes), we’ve shown alternative models for ordinal outcomes in addition to commonly used Cumulative Logit models under the proportional odds assumption, which are also known as...continue reading.
In the previous post (https://statcompute.wordpress.com/2018/01/28/modeling-lgd-with-proportional-odds-model), I’ve shown how to estimate a standard Cumulative Logit model with the ordinal::clm function and its use case in credit risk models. To better a...continue reading.
The analysis of high frequency stock transactions has played an important role in the algorithmic trading and the result can be used to monitor stock movements and to develop trading...continue reading.
The co-integration is an important statistical concept behind the statistical arbitrage strategy named “Pairs Trading”. While projecting a stock price with time series models is by all means difficult, it...continue reading.
In the previous post https://statcompute.wordpress.com/2018/03/23/subset-by-values-in-clojure, it’s been demonstrated how to subset by value in Clojure. In the example below, I would show how to subset by index by using the...continue reading.
In the previous post https://statcompute.wordpress.com/2017/09/17/model-non-negative-numeric-outcomes-with-zeros/, I gave a brief introduction about the ZAGA (Zero-Adjusted Gamma) model that provides us a very flexible approach to model non-negative numeric responses. Today, I...continue reading.
Like many useRs, I am also a big fan of the sqldf package developed by Grothendieck, which uses SQL statement for data frame manipulations with SQLite embedded database as the...continue reading.
In my previous post (https://statcompute.wordpress.com/2018/02/25/mle-in-r/), it is shown how to estimate the MLE based on the log likelihood function with the general-purpose optimization algorithm, e.g. optim(), and that the optimizer...continue reading.
Given R data frames stored in the memory, sometimes it is beneficial to sample and examine the data in a large-size csv file before importing into the data frame. To...continue reading.
(require ‘[huri.core :as h] ‘[clojure.core.matrix.dataset :as d] ‘[incanter.core :as i]) (def ds [{:id 1.0 :name “name1”} {:id 2.0 :name “name2”} {:id 3.0 :name “name3”}]) ;; UPDATE THE :NAME COLUMN IN...continue reading.
(require ‘[huri.core :as h] ‘[clojure.core.matrix.dataset :as d] ‘[incanter.core :as i]) (def ds [{:id 1.0 :name “name1”} {:id 2.0 :name “name2”} {:id 3.0 :name “name3”}]) ;; ADD 2 COLUMNS TO THE...continue reading.
GIS / R / R News / Shiny / Visualization
Interactive visualization of geospatial data with R Shiny
April 17, 2021
