Using maximum likelihood and bayesian methods to estimate intervals of estimated prevalence
See number of grooms and brides by their age groups from 1988 to 2018 in Slovenia.
In order to ascertain prevalence of a disease, I perform a simulation on various sample sizes and prevalence values.
Ranking of shooters in a national level IDPA competition in Črnotiči (Slovenia).
Free range cats sometimes bring dinner back home.
Estimating population size using spatial-capture-recapture and spatial count models through a simulation.
I try to predict when will StackOverflow community be worth 100.000 questions.
In this iteration, I explore how goodness-of-fit test works in R and when can we expect a "significant" result. Use with caution.
Three ways of calculating confidence intervals for Poisson regression. Calulating CI on link or response yields very little difference, while bootstrapped confidence interval is narrower.
Using package coenocliner simulate community count data and visualize it in lower dimension using NMDS from the vegan package.
As an additional exercise, we simulated two populations with different means and same standard deviation and compared them using a t-test. We repeated this many times and watched how confidence intervals behaves.
On the last day of refresher course Introduction to R, we looked at some basic features of R package ggplot2.
Zanimalo nas je kako lega smodnika v tulcu vpliva na hitrost izstrelka.
Plotting factors in a specific order using ggplot can be tricky to unwary. I demonstrate a little trick of reordering factor levels to get the desired output.
Demonstrating that linear regression works - it can "guess" our simulated parameters. This is meant as skeleton code for an upcoming workshop in R in Ljubljana (organized .
How to calculate different intervals in R. We discuss prediction and confidence intervals using a simple example of linear regression.
I tested how three precision gun powder scales compare to each other given one charge. This is by no means a comprehensive test, but a starting point.
Using package drm, fit a Michaelis-Menten (dose-response) model and visualize the result using package ggplot2.
A short function to draw graphs of CRAN packages.