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DTSTART;VALUE=DATE:20201111
DTEND;VALUE=DATE:20201113
DTSTAMP:20201124T013011
CREATED:20201010T135502Z
LAST-MODIFIED:20201111T171900Z
UID:10230-1605052800-1605225599@www.prstatistics.com
SUMMARY:ONLINE COURSE - Introduction to generalised linear models using R and Rstudio (IGLM02) This course will be delivered live
DESCRIPTION:Course Overview:\nIn this two day course\, we provide a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is\, for example\, a binary\, or ordinal\, or count variable\, etc. The specific models we cover include binary\, binomial\, ordinal\, and categorical logistic regression\, Poisson and negative binomial regression for count variables. We will also cover zero-inflated Poisson and negative binomial regression models. On the first day\, we begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next\, we introduce the widely used binary logistic regression model\, which is is a regression model for when the outcome variable is binary. Next\, we cover the ordinal logistic regression model\, specifically the cumulative logit ordinal regression model\, which is used for the ordinal outcome data. We then cover the case of the categorical\, also known as the multinomial\, logistic regression\, which is for modelling outcomes variables that are polychotomous\, i.e.\, have more than two categorically distinct values. On the second day\, we begin by covering Poisson regression\, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover the binomial logistic and negative binomial models\, which are used for similar types of problems as those for which Poisson models are used\, but make different or less restrictive assumptions. Finally\, we will cover zero inflated Poisson and negative binomial models\, which are for count data with excessive numbers of zero observations. \n\nTHIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES \n\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \n\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n\n\nIntended Audience\nThis course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \nVenue – Delivered remotely \nTime zone – GMT \nAvailability – 20 places \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. \n\n\n\nDr. Mark Andrews\n\nWorks at – Senior Lecturer\, Psychology Department\, Nottingham Trent University\, England\nTeaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models\, Path Analysis\, Causal Modelling and Latent Variable Models Using R; Generalised Linear\, Nonlinear and General Additive Models; Python for data science\, machine learning\, and scientific computing \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. \n\n\n\n\nTeaching Format\n\nThis course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (British Summer Time\, UTC+1\, timezone) at:\n• 12pm-2pm\n• 3pm-5pm\n• 6pm-8pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. \nAttendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. For example\, attendees from North America may be able to join the live sessions at 1pm-3pm and 4pm-6pm\, and then catch up with the 10am-12pm recorded session when it is uploaded (which will be soon after 6pm each day). By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better\, as you will be able to see my RStudio and your own RStudio simultaneously. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \nAssumed quantitative knowledge \nWe will assume familiarity with general statistical concepts\, linear models\, statistical inference (p-values\, confidence intervals\, etc). Anyone who has taken undergraduate (Bachelor’s) level introductory courses on (applied) statistics can be assumed to have sufficient familiarity with these concepts. \nAssumed computer background \nMinimal prior experience with R and RStudio is required. Attendees should be familiar with some basic R syntax and commands\, how to write code in the RStudio console and script editor\, how to load up data from files\, etc. \nEquipment and software requirements \nAttendees of the course will need to use RStudio. Most people will want to use their own computer on which they install the RStudio desktop software. This can be done Macs\, Windows\, and Linux\, though not on tablets or other mobile devices. Instructions on how to install and configure all the required software\, which is all free and open source\, will be provided before the start of the course. We will also provide time at the beginning of the workshops to ensure that all software is installed and configured properly. An alternative to using a local installation of RStudio is to use RStudio cloud (https://rstudio.cloud/). This is a free to use and full featured web based RStudio. It is not suitable for computationally intensive work but everything done in this class can be done using RStudio cloud.\nWe will use a number of R packages and installation instructions for these will be posted on GitHub in advance of the course and shared with attendees. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\nCourse Programme\n\nWednesday 11th – Classes from 12:00 to 20:00 \nTopic 1: The general linear model. We begin by providing an overview of the normal\, as in normal distribution\, general linear model\, including using categorical predictor variables. Although this model is not the focus of the course\, it is the foundation on which generalized linear models are based and so must be understood to understand generalized linear models. \nTopic 2: Binary logistic regression. Our first generalized linear model is the binary logistic regression model\, for use when modelling binary outcome data. We will present the assumed theoretical model behind logistic regression\, implement it using R’s glm\, and then show how to interpret its results\, perform predictions\, and (nested) model comparisons. \nTopic 3: Ordinal logistic regression. Here\, we show how the binary logistic regresion can be extended to deal with ordinal data. We will present the mathematical model behind the so-called cumulative logit ordinal model\, and show how it is implemented in the clm command in the ordinal package. \nTopic 4: Categorical logistic regression. Categorical logistic regression\, also known as multinomial logistic regression\, is for modelling polychotomous data\, i.e. data taking more than two categorically distinct values. Like ordinal logistic regression\, categorical logistic regression is also based on an extension of the binary logistic regression case. \nThursday 12th – Classes from 12:00 to 20:00 \nTopic 5: Poisson regression. Poisson regression is a widely used technique for modelling count data\, i.e.\, data where the variable denotes the number of times an event has occurred. \nTopic 6: Binomial logistic regression. When the data are counts but there is a maximum number of times the event could occur\, e.g. the number of items correct on a multichoice test\, the data is better modelled by a binomial logistic regression rather than a Poisson regression. \nTopic 7: Negative binomial regression. The negative binomial model is\, like the Poisson regression model\, used for unbounded count data\, but it is less restrictive than Poisson regression\, specifically by dealing with overdispersed data. \nTopic 8: Zero inflated models. Zero inflated count data is where there are excessive numbers of zero counts that can be modelled using either a Poisson or negative binomial model. Zero inflated Poisson or negative binomial models are types of latent variable models.
URL:https://www.prstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm02/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:PR Statistics,PS Statistics
ATTACH;FMTTYPE=image/jpeg:https://www.prstatistics.com/wp-content/uploads/2020/10/PS-SERIES-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20201125
DTEND;VALUE=DATE:20201127
DTSTAMP:20201124T013011
CREATED:20201008T150900Z
LAST-MODIFIED:20201111T172248Z
UID:10213-1606262400-1606435199@www.prstatistics.com
SUMMARY:ONLINE COURSE - Introduction to mixed models using R and Rstudio (IMMR03) This course will be delivered live
DESCRIPTION:Course Overview:\n\nIn this two day course\, we provide a comprehensive practical and theoretical introduction to multilevel models\, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models\, but also cover multilevel generalized linear models. Likewise\, we will also describe Bayesian approaches to multilevel modelling. On Day 1\, we will begin by focusing on random effects multilevel models. These models make it clear how multilevel models are in fact models of models. In addition\, random effects models serve as a solid basis for understanding mixed effects\, i.e. fixed and random effects\, models. In this coverage of random effects\, we will also cover the important concepts of statistical shrinkage in the estimation of effects\, as well as intraclass correlation. We then proceed to cover linear mixed effects models\, particularly focusing on varying intercept and/or varying slopes regresssion models. On Day 2\, we cover further aspects of linear mixed effects models\, including multilevel models for nested and crossed data data\, and group level predictor variables. On Day 2\, we also cover Bayesian approaches to multilevel levels using the brms R package. \n\nTHIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES \n\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \n\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n\n\nIntended Audience\nThis course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \nVenue – Delivered remotely \nTime zone – GMT \nAvailability – 20 places \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. \n\n\n\nDr. Mark Andrews\n\nWorks at – Senior Lecturer\, Psychology Department\, Nottingham Trent University\, England\nTeaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models\, Path Analysis\, Causal Modelling and Latent Variable Models Using R; Generalised Linear\, Nonlinear and General Additive Models; Python for data science\, machine learning\, and scientific computing \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. \n\n\n\n\nTeaching Format\n\nThis course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (British Summer Time\, UTC+1\, timezone) at:\n10am-12pm\n2pm-4pm\n5pm-7pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. \nAttendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. For example\, attendees from North America may be able to join the live sessions at 1pm-3pm and 4pm-6pm\, and then catch up with the 10am-12pm recorded session when it is uploaded (which will be soon after 6pm each day). By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better\, as you will be able to see my RStudio and your own RStudio simultaneously. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \nAssumed quantitative knowledge \nWe will assume familiarity with general statistical concepts\, linear models\, statistical inference (p-values\, confidence intervals\, etc). Anyone who has taken undergraduate (Bachelor’s) level introductory courses on (applied) statistics can be assumed to have sufficient familiarity with these concepts. \nAssumed computer background \nMinimal prior experience with R and RStudio is required. Attendees should be familiar with some basic R syntax and commands\, how to write code in the RStudio console and script editor\, how to load up data from files\, etc. \nEquipment and software requirements \nAttendees of the course will need to use RStudio. Most people will want to use their own computer on which they install the RStudio desktop software. This can be done Macs\, Windows\, and Linux\, though not on tablets or other mobile devices. Instructions on how to install and configure all the required software\, which is all free and open source\, will be provided before the start of the course. We will also provide time at the beginning of the workshops to ensure that all software is installed and configured properly. An alternative to using a local installation of RStudio is to use RStudio cloud (https://rstudio.cloud/). This is a free to use and full featured web based RStudio. It is not suitable for computationally intensive work but everything done in this class can be done using RStudio cloud.\nWe will use a number of R packages and installation instructions for these will be posted on GitHub in advance of the course and shared with attendees. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\nCourse Programme\n\nWednesday 25th – Classes from 10:00 to 19:00 \nTopic 1: Random effects models. The defining feature of multilevel models is that they are models of models. We begin by using a binomial random effects model to illustrate this. Specifically\, we show how multilevel models are models of the variability in models of different clusters or groups of data. \nTopic 2: Normal random effects models. Normal\, as in normal distribution\, random effects models are the key to understanding the more general and widely used linear mixed effects models. Here\, we also cover the key concepts of statistical shrinkage and intraclass correlation. \nTopic 3: Linear mixed effects models. Next\, we turn to multilevel linear models\, also known as linear mixed effects models. We specifically deal with the cases of varying intercept and/or varying slope linear regression models. \nThursday 26th – Classes from 10:00 to 19:00 \nTopic 4: Multilevel models for nested data. Here\, we will consider multilevel linear models for nested\, as in groups of groups\, data. As an example\, we will look at multilevel linear models applied to data from animals within broods that are themselves within different locations\, and where we model the variability of effects across the broods and across the locations. \nTopic 5: Multilevel models for crossed data. In some multilevel models\, each observation occurs in multiple groups\, but these groups are not nested. For example\, animals may be members of different species and in different locations\, but the species are not subsets of locations\, nor vice versa. These are known as crossed or multiclass data structures. \nTopic 6: Group level predictors. In some multilevel regression models\, predictor variable are sometimes associated with individuals\, and sometimes associated with their groups. In this section\, we consider how to handle these two situations. \nTopic 8: Bayesian multilevel models. All of the models that we have considered can be handled\, often more easily\, using Bayesian models. Here\, we provide an brief introduction to Bayesian models and how to perform examples of the models that we have considered using Bayesian methods and the brms R package.
URL:https://www.prstatistics.com/course/introduction-to-mixed-models-using-r-and-rstudio-immr03/
LOCATION:Delivered remotely (USA east)\, Eastern Daylight Time\, MD\, United States
CATEGORIES:PR Statistics,PS Statistics
ATTACH;FMTTYPE=image/jpeg:https://www.prstatistics.com/wp-content/uploads/2020/10/PS-SERIES-1.jpg
GEO:56.4906712;-4.2026458
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20201127
DTEND;VALUE=DATE:20201212
DTSTAMP:20201124T013011
CREATED:20201008T144755Z
LAST-MODIFIED:20201111T171643Z
UID:10205-1606435200-1607731199@www.prstatistics.com
SUMMARY:ONLINE COURSE - Bayesian hierarchical modelling using R (IBHM05) This course will be delivered live
DESCRIPTION:Course Overview:\nThis course will cover introductory hierarchical modelling for real-world data sets from a Bayesian perspective. These methods lie at the forefront of statistics research and are a vital tool in the scientist’s toolbox. The course focuses on introducing concepts and demonstrating good practice in hierarchical models. All methods are demonstrated with data sets which participants can run themselves. Participants will be taught how to fit hierarchical models using the Bayesian modelling software Jags and Stan through the R software interface. The course covers the full gamut from simple regression models through to full generalised multivariate hierarchical structures. A Bayesian approach is taken throughout\, meaning that participants can include all available information in their models and estimates all unknown quantities with uncertainty. Participants are encouraged to bring their own data sets for discussion with the course tutors. \n\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \n\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n\n\n\nIntended Audience\nThis course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \nVenue – Delivered remotely \nTime zone – GMT \nAvailability – 20 places \nDuration – 3 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited. \n\n\n\nDr. Andrew Parnell\n\nWorks at – Chartered statistician at University College Dublin\nTeaches – Stable Isotope Mixing Models Using SIAR\, SIBER and MixSIAR; Time Series Models for Ecologists; Bayesian hierarchical modelling; Introduction to Frequentist and Bayesian mixed (Hierarchical) models; Missing Data Analytics. \nAndrew is a chartered statistician and lecturer with over 10 years’ experience. He enjoys working with big\, messy data sets which have hidden relationships that only statistical methods can uncover. Andrews main expertise is in – Feature/variable selection; Multivariate analysis; Stochastic processes and time series analysis; Bayesian inference; Spatial and spatio-temporal modelling; Computational statistics. \nHe has applied such methods in a diverse range of fields including Climate change (estimating past changes in climate from pollen\, estimating changes in extreme for Ireland’s future\, estimating worldwide rates of sea level change); Proteomics (finding biomarkers for prostate cancer; Veterinary Science (estimating the factors contributing to elite racehorse ability); Archaeology (radiocarbon dating and age-depth model building); Ecology (stable isotope mixing models to estimate animal diets\, monitoring fisheries discard rates in the Irish Sea). \n\n\n\n\nTeaching Format\n\nThere will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors\, but participants are welcome to bring their own data. \nAll sessions will be video recorded and made available to all attendees as soon as possible. \nAttendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better\, as you will be able to see my RStudio and your own RStudio simultaneously. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \nAssumed quantitative knowledge \nA basic understanding of regression methods and generalised linear models. \nAssumed computer background \nFamiliarity with R. Ability to import/export data\, manipulate data frames\, fit basic statistical models & generate simple exploratory and diagnostic plots. \nEquipment and software requirements \nA laptop/personal computer with a working version or R\, RStudio\, JAGS and stan installed. All are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/\nhttp://www.rstudio.com/products/rstudio/download/\nhttp://mcmc-jags.sourceforge.net\nhttp://mc-stan.org/ \nIt is essential that you come with all necessary software and packages already installed (you will be sent a list of packages prior to the course) internet access may not always be available. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\nCourse Programme\n\nFriday 27th November – Classes from 09:30 to 17:30 \nModule 3: Simple hierarchical regression models\nModule 4: Hierarchical models for non-Gaussian data\nPractical: Fitting hierarchical models \nFriday 4th December – Classes from 09:30 to 17:30 \nModule 5: Hierarchical models vs mixed effects models\nModule 6: Multivariate and multi-layer hierarchical models\nPractical: Advanced examples of hierarchical models \nFriday 11th December – Classes from 09:30 to 17:30 \nModule 7: Shrinkage and variable selection\nModule 8: Hierarchical models and partial pooling\nPractical: Shrinkage modelling
URL:https://www.prstatistics.com/course/bayesian-hierarchical-modelling-using-r-ibhm05/
LOCATION:Delivered remotely (United Kingdom)\, Western European Time\, United Kingdom
CATEGORIES:PR Statistics,PS Statistics
ATTACH;FMTTYPE=image/jpeg:https://www.prstatistics.com/wp-content/uploads/2020/10/IBHM04.jpg
GEO:55.378051;-3.435973
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20201202
DTEND;VALUE=DATE:20201204
DTSTAMP:20201124T013011
CREATED:20201010T141611Z
LAST-MODIFIED:20201111T173725Z
UID:10240-1606867200-1607039999@www.prstatistics.com
SUMMARY:ONLINE COURSE - Introduction to Scientific\, Numerical\, and Data Analysis Programming in Python (PYSC01) This course will be delivered live
DESCRIPTION:Course Overview:\nPython is one of the most widely used and highly valued programming languages in the world\, and is especially widely used in data science\, machine learning\, and in other scientific computing applications. In order to use Python confidently and competently for these applications\, it is necessary to have a solid foundation in the fundamentals of scientific\, numerical\, and data analysis programming Python. This two day course provides a general introduction to numerical programming in Python\, particularly using numpy\, data processing in Python using Pandas\, data analysis in Python using statsmodels and rpy2. We will also cover the major data visualization and graphics tools in Python\, particularly matplotlib\, seaborn\, and ggplot. Finally\, we will cover some other major scientific Python tools\, such as for symbolic mathematics and parallel programming and code acceleration. Note that in this course\, we will not be teaching Python fundamentals and general purpose programming\, but this knowledge will be assumed\, and is also provided in a preceding two-day course. \n\nTHIS IS ONE COURSE IN OUR PYTHON SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES \n\n\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \n\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n\nIntended Audience\nThis course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \nVenue – Delivered remotely \nTime zone – GMT \nAvailability – 20 places \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. \n\n\n\nDr. Mark Andrews\n\nWorks at – Senior Lecturer\, Psychology Department\, Nottingham Trent University\, England\nTeaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models\, Path Analysis\, Causal Modelling and Latent Variable Models Using R; Generalised Linear\, Nonlinear and General Additive Models; Python for data science\, machine learning\, and scientific computing \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. \n\n\n\n\nTeaching Format\n\nThis course will be hands-on and workshop based. Throughout each day\, there will be some brief introductory remarks for each new topic\, introducing and explaining key concepts. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (British\nSummer Time\, UTC+1\, timezone) at: \n• 12pm-2pm\n• 3pm-5pm\n• 6pm-8pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. \nAttendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better\, as you will be able to see my RStudio and your own RStudio simultaneously. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \nAssumed quantitative knowledge \nWe will assume only a minimal amount of familiarity with some general statistical and mathematical concepts. These\nconcepts will arise when we discuss numerical computing\, symbolic maths\, and statistics and machine learning.\nHowever\, expertise and proficiency with these concepts are not necessary. Anyone who has taken any undergraduate\n(Bachelor’s) level course on (applied) statistics or mathematics can be assumed to have sufficient familiarity with these concepts. \nAssumed computer background \nWe assume familiarity with using Python and knowledge of general purpose programming in Python. This topics are\ncovered comprehensively in a preceding two-day course\, which will provide all the prerequisites for this course. \nEquipment and software requirements \nAttendees of the course must use a computer with Python (version 3) installed. All the required software\, including Python itself\, the development and programming environment tools\, and the Python packages\, are free and open source and are available on Windows\, MacOs\, and Linux. Instructions on how to install and configure all the software\nwill be provided before the start of the course. We will also provide time during the workshops to ensure that all\nsoftware is installed and configured properly. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\nCourse Programme\n\nWednesday 2nd – Classes from 12:00 to 20:00 \nDay 1\n• Topic 1: Numerical programming with numpy. Although not part of Python’s official standard library\, the numpy\npackage is the part of the de facto standard library for any scientific and numerical programming. Here we will\nintroduce numpy\, especially numpy arrays and their built in functions (i.e. “methods”). Here\, we will also consider\nhow to speed up numpy code using the Numba just-in-time compiler. \n• Topic 2: Data processing with pandas. The pandas library provides means to represent and manipulate data frames.\nLike numpy\, pandas can be see as part of the de facto standard library for data oriented uses of Python. Here\, we\nwill focus on data wrangling including selecting rows and columns by name and other criteria\, applying functions\nto the selected data\, aggregating the data. For this\, we will use Pandas directly\, and also helper packages like siuba. \nThursday 3rd – Classes from 12:00 to 20:00 \nDay 2\n• Topic 3: Data Visualization. Python provides many options for data visualization. The matplotlib library is a low level plotting library that allows for considerable control of the plot\, albeit at the price of a considerable amount ofm low level code. Based on matplotlib\, and providing a much higher level interface to the plot\, is the seaborn library. This allows us to produce complex data visualizations with a minimal amount of code. Similar to seaborn is ggplot\, which is a direct port of the widely used R based visualization library. \n• Topic 4: Statistical data analysis. In this section\, we will describe how to perform widely used statistical analysis in Python. Here we will start with the statsmodels\, which provides linear and generalized linear models as well as many other widely used statistical models. We will also cover rpy2\, which is and interface from Python to R. This allows us to access all of the the power of R from within Python. \n• Topic 5: Symbolic mathematics. Symbolic mathematics systems\, also known as computer algebra systems\, allow us\nto algebraically manipulate and solve symbolic mathematical expression. In Python\, the principal symbolic\nmathematics library is sympy. This allows us simplify mathematical expressions\, compute derivatives\, integrals\,\nand limits\, solve equations\, algebraically manipulate matrices\, and more. \n• Topic 6: Parallel processing. In this section\, we will cover how to parallelize code to take advantage of multiple processors. While there are many ways to accomplish this in Python\, here we will focus on the multiprocessing
URL:https://www.prstatistics.com/course/introduction-to-scientific-numerical-and-data-analysis-programming-in-python-pysc01/
LOCATION:Delivered remotely (USA east)\, Eastern Daylight Time\, MD\, United States
CATEGORIES:PR Informatics,PR Statistics,PS Statistics
ATTACH;FMTTYPE=image/jpeg:https://www.prstatistics.com/wp-content/uploads/2020/10/pythondsap-1.jpg
GEO:56.4906712;-4.2026458
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20201209
DTEND;VALUE=DATE:20201211
DTSTAMP:20201124T013011
CREATED:20201010T141822Z
LAST-MODIFIED:20201111T173827Z
UID:10242-1607472000-1607644799@www.prstatistics.com
SUMMARY:ONLINE COURSE - Machine Learning and Deep Learning using Python (PYML01) This course will be delivered live
DESCRIPTION:Course Overview:\n\nPython is one of the most widely used and highly valued programming languages in the world\, and is especially widely used in machine learning and for deep learning. In this two day course\, we provide an introduction to machine learning and deep learning using Python. We begin by providing an overview of the machine learning and deep learning landscape\, and discuss the prominent role that Python has come to play in this area. We then turn to machine learning in practice\, and for this\, we will primarily using the widely used and acclaimed scikit-learn toolbox. We begin with binary and multiclass classification problems\, then look at decision trees and random forests\, then look at unsupervised learning methods\, all of which are major topics in machine learning. We then cover artificial neural networks and deep learning. For this\, we will using the PyTorch deep learning toolbox. Here\, we will cover the relatively easy to understand multilayer perceptron and then turn to convolutional neural networks. \n\nTHIS IS ONE COURSE IN OUR PYTHON SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES \n\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \n\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n\n\nIntended Audience\nThis course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \nVenue – Delivered remotely \nTime zone – GMT \nAvailability – 20 places \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. \n\n\n\nDr. Mark Andrews\n\nWorks at – Senior Lecturer\, Psychology Department\, Nottingham Trent University\, England\nTeaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models\, Path Analysis\, Causal Modelling and Latent Variable Models Using R; Generalised Linear\, Nonlinear and General Additive Models; Python for data science\, machine learning\, and scientific computing \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. \n\n\n\n\nTeaching Format\n\nThis course will be hands-on and workshop based. Throughout each day\, there will be some brief introductory remarks\nfor each new topic\, introducing and explaining key concepts.\nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (UK local time)\nat:\n• 12pm-2pm\n• 3pm-5pm\n• 6pm-8pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. Attendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. Although not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better. All the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \nAssumed quantitative knowledge \nWe will assume familiarity with some general statistical and mathematical concepts such as matrix algebra\, calculus\,probability distributions. However\, expertise with these concepts are not necessary. Anyone who has taken anyundergraduate (Bachelor’s) level course in mathematics\, or even advanced high school level\, can be assumed to havesufficient familiarity with these concepts. \nAssumed computer background \nWe assume familiarity with using Python\, general purpose programming in Python\, and numerical programming in\nPython. Note that both of these topics covered comprehensively in two preceding two-day courses\, which together will provide all the computing prerequisites for this course. \nEquipment and software requirements \nAttendees of the course must use a computer with Python (version 3) installed. All the required software\, including\nPython itself\, the development and programming environment tools\, and the Python packages\, are free and open\nsource and are available on Windows\, MacOs\, and Linux. Instructions on how to install and configure all the software will be provided before the start of the course. We will also provide time during the workshops to ensure that all software is installed and configured properly. For deep learning\, we will also make use of Google’s Colaboratory https://colab.research.google.com/\, which will give us access to graphical processing units. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\nCourse Programme\n\nWednesday 9th – Classes from 10:00 to 20:00 \nDay 1\n• Topic 1: Machine learning and Deep Learning Landscape. Concepts like machine learning\, deep learning\, big data\,\ndata science have become widely used and celebrated in the last 10 years. However\, their definitions are relatively\nnebulous\, and how they related to one another and to major fields like artificial intelligence and general statistics are not simple matters. In this introduction\, we briefly overview the field of machine learning and deep learning\, discussing its major characteristics and sub-topics\, and also discuss the prominence of Python in the area. \n• Topic 2: Classification problems. Classification problems is one of the bread and butter topics in machine learning\, and is usually the first topic covered in introductions to machine learning. Here\, we will cover image classification (itself a “hello world” type problem in machine learning)\, and particularly focus on logistic regression and support vector machines (SVMs). \n• Topic 3: Decision trees and random forests. Decision trees are a widely used machine learning method\, particularly for classification. Random forests are an ensemble learning extension of decision trees whereby large number of decision tree classifiers are aggregated\, which often leads to much improved performance. \nThursday 10th – Classes from 10:00 to 20:00 \nDay 2\n• Topic 4: Unsupervised machine learning. Unsupervised learning is essentially a method of finding patterns in\nunclassified data. Here\, we will look at two of the most widely used unsupervised techniques: k-means clustering\nand probabilistic mixture models. \n• Topic 5: Introducing artificial neural networks and deep learning with PyTorch. Python provides many popular\nlibraries for artificial neural networks and deep learning. These include Keras and TensorFlow. Here\, we will use\nPyTorch\, which is a relatively new but increasingly high-level neural network model building and training\nlanguage. These models often use graphical processing units (GPUs) for computing. Given that most personal\ncomputers don’t have adequate GPUs\, we will use Google’s Colaboratory https://colab.research.google.com/\,\nwhich is a free Python Jupyter notebook service from Google. \n• Topic 6: Multilayer perceptons. Multilayer perceptrons are very powerful\, yet relatively easy to understand\,\nartificial neural networks. They are also the simplest type of deep learning model. Here\, we will build and train a multilayer perceptron for a classification problem. \n• Topic 7: Convolutional neural networks. Convolutional neural networks (CNNs) have proved high successful at\nimage classification\, primarily due to their translation invariance\, which is highly suitable for computational vision. Here\, we use PyTorch to build and train a CNN for image classification.
URL:https://www.prstatistics.com/course/machine-learning-and-deep-learning-using-python-pyml01/
LOCATION:Delivered remotely (USA east)\, Eastern Daylight Time\, MD\, United States
CATEGORIES:PR Informatics,PR Statistics,PS Statistics
ATTACH;FMTTYPE=image/jpeg:https://www.prstatistics.com/wp-content/uploads/2020/10/pythondsap-1.jpg
GEO:56.4906712;-4.2026458
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20201216
DTEND;VALUE=DATE:20201218
DTSTAMP:20201124T013011
CREATED:20201111T180821Z
LAST-MODIFIED:20201111T180821Z
UID:10656-1608076800-1608249599@www.prstatistics.com
SUMMARY:ONLINE COURSE - Nonlinear Regression using Generalized Additive Models (GAMR01) This course will be delivered live
DESCRIPTION:Course Overview:\nThis course provides a general introduction to nonlinear regression analysis using generalized additive models. As anM introduction\, we begin by covering practically and conceptually simple extensions to the general and generalized linear models framework using polynomial regression. We will then cover more powerful and flexible extensions of this modelling framework by way of the general concept of basis functions\, which includes spline and radial basis functions. \nWe then move on to the major topic of generalized additive models (GAMs) and generalized additive mixed models (GAMMs)\, which can be viewed as the generalization of all the basis function regression topics\, but cover a wider range of topic including nonlinear spatial and temporal models and interaction. \n\nTHIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES \n\nThis is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link\, a good internet connection is essential. \n\nTIME ZONE – GMT – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you). \n\n\nIntended Audience\nThis course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research\, and also widely throughout the public\, and private sector. \nVenue – Delivered remotely \nTime zone – GMT \nAvailability – NA \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 1 ECT’s \nLanguage – English \nPLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered\, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. \n\n\n\nDr. Mark Andrews\n\nWorks at – Senior Lecturer\, Psychology Department\, Nottingham Trent University\, England\nTeaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models\, Path Analysis\, Causal Modelling and Latent Variable Models Using R; Generalised Linear\, Nonlinear and General Additive Models; Python for data science\, machine learning\, and scientific computing \nMark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham\, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition\, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis\, particularly as applied to data from the social and behavioural sciences. Since 2015\, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences. \n\n\n\n\nTeaching Format\n\nThis course will be largely practical\, hands-on\, and workshop based. For each topic\, there will first be some lecture style presentation\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories. Then\, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and between days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break. \nThe course will take place online using Zoom. On each day\, the live video broadcasts will occur between (British Summer Time\, UTC+1\, timezone) at:\n10am-12pm\n2pm-4pm\n5pm-7pm \nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each 2hr session. \nAttendees in different time zones will be able to join in to some of these live broadcasts\, even if all of them are not convenient times. For example\, attendees from North America may be able to join the live sessions at 1pm-3pm and 4pm-6pm\, and then catch up with the 10am-12pm recorded session when it is uploaded (which will be soon after 6pm each day). By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and having discussions\, rather than just watching prerecorded sessions. \nAt the start of the first day\, we will ensure that everyone is comfortable with how Zoom works\, and we’ll discuss the procedure for asking questions and raising comments. \nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning experience better\, as you will be able to see my RStudio and your own RStudio simultaneously. \nAll the sessions will be video recorded\, and made available immediately on a private video hosting website. Any materials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \nAssumed quantitative knowledge \nWe assume familiarity with linear regression analysis\, and the major concepts of classical inferential statistics (p-values\, hypothesis testing\, confidence intervals\, model comparison\, etc). Some familiarity with common generalized linear models such as logistic or Poisson regression will also be assumed. \nAssumed computer background \nR experience is desirable but not essential. Although we will be using R extensively\, all the code that we use will be made available\, and so attendees will just to add minor modifications to this code. Attendees should install R and RStudio on their own computers before the workshops\, and have some minimal familiarity with the R environment. \nEquipment and software requirements \nA computer with a working version of R or RStudio is required. R and RStudio are both available as free and open\nsource software for PCs\, Macs\, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/. All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed\, and a full list of required packages will be made available to all attendees prior to the course. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@prstatistics.com \n\n\n\nCourse Programme\n\nWednesday 16th – Classes from 10:00 to 19:00 \nTopic 1: Regression modelling overview. We begin with a brief overview and summary of regression modelling in general. The purpose of this is to provide a brief recap of general and generalized linear models\, and to show how nonlinear regression fits into this very widely practiced framework. \nTopic 2: Polynomial regression. Polynomial regression is both a conceptually and practically simple extension of linear modelling and so provides a straightforward and simple means to perform nonlinear regression. Polynomial regression also leads naturally to the concept of basis function function regression and thus is bridge between the general or generalized linear models and nonlinear regression modelling using generalized additive models. \nTopic 3: Spline and basis function regression: Nonlinear regression using splines is a powerful and flexible non-parametric or semi-parametric nonlinear regression method. It is also an example of a basis function regression method. Here\, we will cover spline regression using the splines::bs and splines::ns functions that can be used with lm\, glm\, etc. We also look at regression using radial basis functions\, which is closely related to spline regression. Understanding basis functions is vital for understanding Generalized Additive Models. \nThursday 17th – Classes from 10:00 to 19:00 \nTopic 4: Generalized additive models. We now turn to the major topic of generalized additive models (GAMs). GAMs generalize many of concepts and topics covered so far and represent a powerful and flexible framework for nonlinear modelling. In R\, the mgcv package provides a extensive set of tools for working with GAMs. Here\, we will provide an in-depth coverage of mgcv including choosing smooth terms\, controlling overfitting and complexity\, prediction\, model evaluation\, and so on. \nTopic 5: Interaction nonlinear regression: A powerful feature of GAMs is the ability to model nonlinear interactions\, whether between two continuous variables\, or between one continuous and one categorical variable. Amongst other things\, interactions between continuous variables allow us to do spatial and spatio-temporal modelling. Interactions between categorical and continuous variables allow us to model how nonlinear relationships between a predictor and outcome change as a function of the value of different categorical variables. \nTopic 6: Generalized additive mixed models. GAMs can also be used in linear mixed effects\, aka multilevel\, models where they are known as generalized additive mixed models (GAMMs). GAMMs can also be used with the mgcv package.
URL:https://www.prstatistics.com/course/nonlinear-regression-using-generalized-additive-models-gamr01-2/
LOCATION:Delivered remotely (USA east)\, Eastern Daylight Time\, MD\, United States
CATEGORIES:PR Statistics,PS Statistics
ATTACH;FMTTYPE=image/jpeg:https://www.prstatistics.com/wp-content/uploads/2020/10/PS-SERIES-1.jpg
GEO:56.4906712;-4.2026458
END:VEVENT
END:VCALENDAR