Question 7: Survival by fare - Loess
Set the seed to 1. Train a model using Loess with the caret gamLoess method using fare as the only predictor.
What is the accuracy on the test set for the Loess model?
Note: when training models for Titanic Exercises Part 2, please use the S3 method for class formula rather than the default S3 method of caret train() (see ?caret::train for details).
This script is designed to run within the PyQGIS console and provides a complete workflow for processing Night Time Lights (NTL) data. The script performs the following tasks:
The existing color scheme in the image uses a predominantly orange-red palette for most of the map, with some yellows, greens, and blues appearing minimally. While this scheme does show variation, it has several limitations. The heavy use of similar orange and red hues makes it difficult to distinguish between different levels of enablement and population, especially in the low to moderate ranges. Additionally, this color scheme may not be easily interpretable for individuals with color vision deficiencies.
I propose changing to a more effective color scheme based on the ColorBrewer RdYlBu (Red-Yellow-Blue) 5-class diverging color palette.
Baseline color 5-class RdYlBu
We propose refining the NTL classification in GEEST to better reflect local conditions, particularly for small island countries. Instead of using fixed thresholds or coverage percentages, we'll employ local statistics to create a more contextually relevant classification. This approach is supported by Elvidge et al. (2013), who emphasize the importance of considering local context in night-time light analysis.
This rainfall distribution analysis script provides a comprehensive examination of observed and satellite-based daily precipitation data, offering valuable insights for hydrologists, climatologists, and water resource managers. The analysis begins by generating synthetic rainfall data to simulate both ground observations and satellite estimates, including the introduction of extreme events. This approach allows for a controlled comparison between different data sources and methods of analysis.
The script employs a range of advanced statistical techniques to characterize precipitation patterns and extreme events. It utilizes the method of L-moments to fit Gamma distributions to the overall rainfall patterns, providing a robust representation of the general precipitation behavior. For extreme rainfall events, the script applies Generalized Pareto Distributions (GPD) and incorporates cross-validation techniques to ensure reliable modeling of these critical occurrences. This dua
You can paste the below code into an online Python compiler like https://python-fiddle.com/ and grab the result instantly.
This script will generate a plot that illustrates:
The moving window approach is used to balance the advantages of capturing both spatial and temporal variations in precipitation patterns. By considering both spatial and temporal windows, the bias correction method can address local variations and seasonal changes more effectively.
Bias correction of satellite-based precipitation estimates is crucial in climate studies to enhance their accuracy and reliability. This exercise explores a method that combines Linear Scaling (LS) and Empirical Quantile Mapping (EQM) with tail adjustment using the Generalized Pareto Distribution (GPD). This hybrid approach aims to correct both the mean bias and the distributional discrepancies between satellite estimates and ground-based observations.
The following sections will describe the methodology, the role of different functions, their strengths, and weaknesses in achieving accurate bias correction.
You can paste the below code into an online Python compiler like https://python-fiddle.com/ and grab the result instantly.
This script will generate a plot that illustrates:
| # Copy and paste the code below into https://python-fiddle.com/ | |
| # You will get instant result: GEV 25-year threshold: 96.12570045491432 | |
| import numpy as np | |
| from scipy.stats import genextreme | |
| # This data is the maximum daily precipitation in a month (let say May) for the period | |
| # 2001-2023 | |
| data = [ | |
| 35.809994, 55.120003, 97.575005, 17.01, 43.260006, 50.285004, 51.489998, 59.05999, | |
| 58.339996, 82.835, 42.875, 55.574997, 105.149994, 61.155, 83.68001, 73.56, |
The script below try to simulate how Pettitt test detects inhomogeneous or homogeneous on precipitation data. Here's the updated approach:
Daily Precipitation Data:
Generate Monthly Maximum Precipitation Values: