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Úvod

Výuka

Studium

Výzkum

Odkazy

English

Stránky garanta
programu PMSE

NMSA407 – Central webpage of the exercise classes

Lectures:	Wednesday 10:40 in K1, Thursday 12:20 in K1 (A. Komárek)
Exercise class (MM1):	Monday 15:40 in K4 (M. Maciak)
Exercise class (MM2):	Tuesday 9:00 in K11 (M. Maciak)
Exercise class (MO):	Tuesday 14:00 in K4 (M. Omelka)

R software

Those who are not familiar with the R software are adviced that they already before the first exercise class go through R-related materials available on the webpage of the NMSA230: Software for Mathematics and Stochastics course (in Czech language). In English plenty of introductory materials can be obtained here or by simple web-searching.

mffSM package

Most datasets we will be working on during the exercise classes plus few smaller R functions are available in a form of extension R package mffSM which can be installed ``from a local repository'' upon its download from the appropriate link below. Windows binary file is intended for the MS Windows users (as the title suggests), the source code is intended for those users who are used to compile their software from the source (mostly Linux, Mac etc. users). The mffSM package depends on packages colorspace, lattice, car, which are available in a standard way from CRAN. All those dependency packages should normally be automatically installed if the installation of the mffSM package is performed directly from the R console on an Internet-connected computer using the command (its appropriately modified analogy):

install.packages("C:/WHERE_DOWNLOADED/mffSM_1.0.zip", repos = NULL)

The mffSM package might be updated during the semester. If this happens, the version number (like 1.0) will be increased and new files will be available.

Windows binary:	mffSM_1.0.zip	(published 20140923)
Source code:	mffSM_1.0.tar.gz	(published 20140923)

Announcements

• Requirements to get the course credit are explained in detail in this document (pdf).
• Summary table of points obtained for delivered homeworks and a test. A more detailed table with the points from the test for the exercise classes of M. Maciak are availabe here.
• The secondary test date will be on January, 11 at 10:40 in K1. To prepare for the final test we recommend to be able to answer the questions asked during the exercises classes and in the homework assignments. Some more sample tasks that can appear in the final test can be downloaded here.

R scripts for exercise classes

Class #1 (03/10, 4/10)	Assignment (R script)	(published 20160930)
Class #2 (10/10, 11/10)	Assignment (R script)	(published 20161006)
	Data (csv)
Class #3 (17/10, 18/10)	Assignment (R script)	(published 20161014)
Class #4 (24/10, 25/10)	Assignment (R script)	(published 20161024)
Class #5 (31/10, 1/11)	Assignment (R script)	(published 20160928)
Class #6 (14/11, 15/11)	Assignment (R script)	(published 20161104)
Class #7 (21/11, 22/11)	Assignment (R script)	(published 20161117)
	Data (csv)
Class #8 (28/11, 29/11)	Assignment (R script)	(published 20161124)
Class #9 (5/12, 6/12)	Assignment (R script)	(published 20161202)
Class #10 (12/12, 13/12)	Final test
Class #10 (12/12, 13/12)	Assignment (R script)	(published 20161207)
Class #11 (19/12, 20/12)	Assignment (R script)	(published 20161216)
	Boston2 (csv)
Class #12 (3/1)	Assignment (R script)	(published 20161225)
	Data (RData)	Precalculated results (RData)
Class #13 (9/1, 10/1)	Assignment (R script)	(published 20170105)
	pribram.csv	soilData.RData (dataset)

In the code it is assumed that datasets are stored in the directory called 'Data' which is subdirectory of the working directory.

Homework assignments

• Homework 1 (Deadlines Mo: 17/10, Tu: 18/10) - Assignment (pdf), Hitchhiking (dataset), (published 20160930).
• Homework 2 (Deadlines Mo: 28/11, Tu: 29/11) - Assignment (pdf), Dataset (dataset), (published 20161109).
• Homework 3 (Deadlines 28/12 or 03/01, see the details of the assignment) - Assignment (pdf), dataset, (published 20161212).

Supporting material

• Homework 1 assignment in one of the previous academic years and general comments to quality of delivered reports (in Czech, by Karel Zvára). Dataset potatoes.