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X-ORIGINAL-URL:https://www.psstatistics.com
X-WR-CALDESC:Events for PS Statistics
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200430
DTEND;VALUE=DATE:20210502
DTSTAMP:20200715T061919
CREATED:20200415T125852Z
LAST-MODIFIED:20200506T140104Z
UID:6164-1588204800-1619913599@www.psstatistics.com
SUMMARY:ONLINE COURSE - Designing Efficient\, Falsifiable\, and Informative Experiments through Sequential Analyses and Equivalence Testing (DRES02)
DESCRIPTION:\nThis course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.\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 \nCourse Overview:\nThis two day course aims to help you to draw better statistical inferences from experimental research. It is common for researchers to design experiments and analyze results once\, when all data has been collected\, using null hypothesis testing. In this workshop we will improve hypothesis testing practices in two ways that will make your experiments more efficient and falsifiable. First\, through practical\, hands on assignments you will learn the basic ideas behind repeatedly analyzing data as it comes in\, while controlling error rates. These sequential analyses procedures are much more efficient than traditional approaches to hypothesis testing\, and are slowly becoming more popular outside biostatistics\, where these approaches are very common. \nFurthermore\, you will learn how to perform equivalence tests\, which are used to show the absence of a meaningful effect. This means you can design studies to yield informative results\, regardless of whether there is or there isn’t an effect. Equivalence tests require specifying a smallest effect size of interest\, and through discussions we will examine which ways you have to think about which effect sizes you find meaningful\, and which effects are too small to matter. The goal of this workshop is to improve the statistical questions you ask when you collect data\, design better and more efficient studies\, and improve the way you draw inferences in your research. You will learn techniques and tools that can be immediately implemented in your own research\, such as thinking about the smallest effect size you are interested in\, justifying your sample size\, and publishing null results. \n\n\nIntended Audience\nThis workshop is ideal for any social scientists\, behavioural scientists including psychologists\, ethologists\, behavioral ecologists\, behavioral neuroscientists\, who are interested in learning how to produce reliable and efficient research. \nVenue – Delivered remotely Time zone – Central European Summer Time\nAvailability – 20 places\nDuration – 2 days\nContact hours – Approx. 14 hours\nECT’s – Equal to 3 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@psstatistics.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\n \nDr. Daniel Lakens\n\n\n \n\n\n\n\n\n— \n\n\nTeaching Format\n\nThis two day course aims to help you to draw better statistical inferences from experimental research. It is common for researchers to design experiments and analyze results once\, when all data has been collected\, using null hypothesis testing. In this workshop we will improve hypothesis testing practices in two ways that will make your experiments more efficient and falsifiable. First\, through practical\, hands on assignments you will learn the basic ideas behind repeatedly analyzing data as it comes in\, while controlling error rates. These sequential analyses procedures are much more efficient than traditional approaches to hypothesis testing\, and are slowly becoming more popular outside biostatistics\, where these approaches are very common. \nAssumed quantitative knowledge\nYou should have some basic knowledge about calculating descriptive statistics\, and how to perform t-tests\, correlations\, and ANOVA’s \nAssumed computer background\nWe will use R in many of the assignments\, but you don’t need any previous knowledge of R – we will mainly use it as a fancy calculator. \nEquipment and software requirements\nA laptop/personal computer with a working version of R or RStudio. R and RStudio are supported by both PC and MAC and can be downloaded for free by following these links. \nhttps://cran.r-project.org/ \nDownload RStudio \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) as internet access may not always be available. \nUNSURE ABOUT SUITABLILITY THEN PLEASE ASK oliverhooker@psstatistics.com \n\n\n\nCourse Programme\n\nDay 1: Sequential Hypothesis Tests \nWe will first discuss traditional hypothesis tests\, explain the meaning of p-values\, and refresh knowledge about Type 1 and Type 2 error rates. Then\, we will learn about the basics of sequential hypothesis testing through simulations. We will see why sequential analyses are more efficient than traditional approaches\, often requiring substantially less observations on average. We will discuss the importance of preregistering analysis plans when using sequential analyses\, and work on a preregistered sequential analysis for your own research. \n09:00-09:15\nWelcome and Introduction \n09:15-10:30\nOverview of the ideas behind hypothesis testing. Type 1 error control: Why it matters\, and how it works in practice. What is ‘p-hacking’? How can you recognize it\, and prevent it in your own research. What are Type 2 errors? Why is a-priori power analysis important\, but also difficult in practice? How do sequential analyses solve these problems? \n10:30-10:45\nCoffee break \n10:45-12:00\nSequential analyses: How can you design studies by repeatedly collecting data without inflating error rates? What are similarities and differences between Frequentist and Bayesian approaches to sequential analyses? What do you need to think about when planning a study that relies on sequential analyses? \n12:00-13:00\nLunch break \n13:00-15:30\nPractical Assignment on designing Sequential Analyses in R using the rpact package. Thinking about the number and timing of looks\, what to do if in practice you look at a different time\, choice of alpha spending function\, and a discussion of more novel safe testing procedures. \n15:30-15:45\nCoffee break \n15:45-17:00\nWhat does a good pre-registration look like? How do you pre-register your hypothesis when using sequential analyses? Followed by a practical assignment where we guide you through a preregistration for your future study using sequential analyses. What does a good pre-registration look like? How do you pre-register your hypothesis when using sequential analyses? Followed by a practical assignment where we guide you through a preregistration for your future study using sequential analyses. \nDay 2: Prove Yourself Wrong: Statistically Inferring the Absence of an Effect \nIf you can’t be proven wrong\, why should we be impressed if you are right? We will discuss how you can falsify predictions\, and design studies that yield informative results both when there is an effect\, as when there is no effect. We will also discuss how you can publish null results\, and why this is important. In practical assignments we will learn how to specify what a meaningful effect is\, and how you can reject these effects in equivalence tests. \n09:00-09:15\nReflection on Day 1 \n09:15-10:30\nInterpret null-effects using equivalence testing\, the Bayesian ROPE procedure\, and Bayes factors. Practical assignment to analyze existing papers reporting null-results. \n10:30-10:45\nCoffee break \n10:45-12:00\nWhat are the effect sizes you can expect in your own research area? The Question: What would falsify your hypothesis? How can we specify falsifiable predictions? Practical assignment: Through group discussion\, specify the smallest effect size of interest for your own research. \n12:00-13:00\nLunch break \n13:00-15:30\nPractical assignment: Equivalence testing: How do you perform an equivalence test? How do you calculate the power for a test aimed at showing the absence of a meaningful effect? \n15:30-15:45\nCoffee break \n15:45-17:00\nTime for questions and discussion. If time permits\, how do you justify Type 1 and Type 2 errors in planned experiments. What do you do when you observed mixed results? How can you publish null effects? \n\n\n\n
URL:https://www.psstatistics.com/course/designing-reliable-and-efficient-experiments-for-social-sciences-dres02/
LOCATION:Central European Summer Time\, Netherlands
ATTACH;FMTTYPE=image/png:https://www.psstatistics.com/wp-content/uploads/2018/05/DRES01.png
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200723
DTEND;VALUE=DATE:20200725
DTSTAMP:20200715T061919
CREATED:20200521T180934Z
LAST-MODIFIED:20200710T181419Z
UID:6261-1595462400-1595635199@www.psstatistics.com
SUMMARY:ONLINE COURSE - Introduction to generalised linear models using R and Rstudio (IGLM01) This course will be delivered live
DESCRIPTION:\n\nThis course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.\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 – Western European Time +1 – 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@psstatistics.com for full details or to discuss how we can accommodate you). \nCourse 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. \nThis is one module of a five module series – you do not need to attend them all but they are designed to compliment each other. Please see the links below \n“1” June 23rd – 24th Introduction to R for ecologists and evolutionary biologists \nhttps://www.prstatistics.com/course/introduction-to-r-for-ecologists-and-evolutionary-biologists-irfb04/ \n“2”July 23rd – 24th Introduction to generalised linear models using R and Rstudio \nONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM01) This course will be delivered live \n \n“3” August 6th – 7th Introduction to mixed models using R an d Rstudio \nONLINE COURSE – Introduction to mixed models using R and Rstudio (IMMR02) This course will be delivered live \n \n“4” August 20th – 21st Data visualization using GG plot 2 (R and R studio) \nONLINE COURSE – Introduction to data visualization using GG plot 2 (R and Rstudio) (DVGG01) This course will be delivered live \n \n“5” September 3rd – 4th Data wrangling using R and Rstudio \nONLINE COURSE – Introduction data wrangling using R and Rstudio (DWRS01) This course will be delivered live \n \n\n\n\nIntended Audience\nThis course is aimed at anyone who is interested in advanced statistical modelling as it is practiced widely throughout academic scientific research\, as well as widely throughout the public and private sectors. \nVenue – Delivered remotely \nTime zone – Western European Time +1 \nAvailability – 20 places \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 2 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@psstatistics.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 (and accommodation fees if booked through PS statistics) will be credited. \n\n\n\n \nDr. Mark Andrews\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\n1pm-3pm\n4pm-6pm \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@psstatistics.com \n\n\n\nCourse Programme\n\nThursday 23rd – Classes from 10:00 to 18:00 \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. \nFriday 24th – Classes from 10:00 to 18:00 \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. \n\n\n\n
URL:https://www.psstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm01/
LOCATION:Western European Time\, United Kingdom
ATTACH;FMTTYPE=image/jpeg:https://www.psstatistics.com/wp-content/uploads/2019/04/gnmr01.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200806
DTEND;VALUE=DATE:20200808
DTSTAMP:20200715T061919
CREATED:20200521T181331Z
LAST-MODIFIED:20200615T182434Z
UID:6268-1596672000-1596844799@www.psstatistics.com
SUMMARY:ONLINE COURSE - Introduction to mixed models using R and Rstudio (IMMR02) This course will be delivered live
DESCRIPTION:\n\nThis course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.\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 – Western European Time +1 – 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@psstatistics.com for full details or to discuss how we can accommodate you). \nCourse Overview:\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. \nThis is module three of a five module series – you do not need to attend them all but they are designed to complement each other. Please see the links below \n“1” June 23rd – 24th Introduction to R for ecologists and evolutionary biologists \nhttps://www.prstatistics.com/course/introduction-to-r-for-ecologists-and-evolutionary-biologists-irfb04/ \n“2”July 23rd – 24th Introduction to generalised linear models using R and Rstudio \nONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM01) This course will be delivered live \n \n“3” August 6th – 7th Introduction to mixed models using R an d Rstudio \nONLINE COURSE – Introduction to mixed models using R and Rstudio (IMMR02) This course will be delivered live \n \n“4” August 20th – 21st Data visualization using GG plot 2 (R and R studio) \nONLINE COURSE – Introduction to data visualization using GG plot 2 (R and Rstudio) (DVGG01) This course will be delivered live \n \n“5” September 3rd – 4th Data wrangling using R and Rstudio \nONLINE COURSE – Introduction data wrangling using R and Rstudio (DWRS01) This course will be delivered live \n \n \n\n\n\nIntended Audience\nThis course is aimed at anyone who is interested in advanced statistical modelling generally and multilevel odelling in particular. These methods are practiced widely throughout academic scientific research\, as well as widely throughout the public and private sectors. \nVenue – Delivered remotely \nTime zone – Western European Time +1 \nAvailability – 20 places \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 2 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@psstatistics.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 (and accommodation fees if booked through PS statistics) will be credited. \n\n\n\n \nDr. Mark Andrews\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\n1pm-3pm\n4pm-6pm \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@psstatistics.com \n\n\n\nCourse Programme\n\nThursday 6th – Classes from 10:00 to 18:00 \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. \nFriday 7th – Classes from 10:00 to 18:00 \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. \n\n\n\n
URL:https://www.psstatistics.com/course/introduction-to-mixed-models-using-r-and-rstudio-immr02/
LOCATION:Western European Time\, United Kingdom
ATTACH;FMTTYPE=image/jpeg:https://www.psstatistics.com/wp-content/uploads/2019/04/gnmr01.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20200820
DTEND;VALUE=DATE:20200822
DTSTAMP:20200715T061919
CREATED:20200521T174823Z
LAST-MODIFIED:20200610T125314Z
UID:6250-1597881600-1598054399@www.psstatistics.com
SUMMARY:ONLINE COURSE - Data visualization using GG plot 2 (R and Rstudio) (DVGG01) This course will be delivered live
DESCRIPTION:\n\nThis course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.\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 – Western European Time +1 – 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@psstatistics.com for full details or to discuss how we can accommodate you). \nCourse Overview:\nIn this two day course\, we provide a comprehensive introduction to data visualization in R using ggplot. On the first day\, we begin by providing a brief overview of the general principles data visualization\, and an overview of the general principles behind ggplot. We then proceed to cover the major types of plots for visualizing distributions of univariate data: histograms\, density plots\, barplots\, and Tukey boxplots. In all of these cases\, we will consider how to visualize multiple distributions simultaneously on the same plot using different colours and “facet” plots. We then turn to the visualization of bivariate data using scatterplots. Here\, we will explore how to apply linear and nonlinear smoothing functions to the data\, how to add marginal histograms to the scatterplot\, add labels to points\, and scale each point by the value of a third variable. On Day 2\, we begin by covering some additional plot types that are often related but not identical to those major types covered on Day 1: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. We then consider more fine grained control of the plot by changing axis scales\, axis labels\, axis tick points\, colour palettes\, and ggplot “themes”. Finally\, we consider how to make plots for presentations and publications. Here\, we will introduce how to insert plots into documents using RMarkdown\, and also how to create labelled grids of subplots of the kind seen in many published articles. \nThis is one module of a five module series – you do not need to attend them all but they are designed to complement each other. Please see the links below \n“1” June 23rd – 24th Introduction to R for ecologists and evolutionary biologists \nhttps://www.prstatistics.com/course/introduction-to-r-for-ecologists-and-evolutionary-biologists-irfb04/ \n“2”July 23rd – 24th Introduction to generalised linear models using R and Rstudio \nONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM01) This course will be delivered live \n \n“3” August 6th – 7th Introduction to mixed models using R an d Rstudio \nONLINE COURSE – Introduction to mixed models using R and Rstudio (IMMR02) This course will be delivered live \n \n“4” August 20th – 21st Data visualization using GG plot 2 (R and R studio) \nONLINE COURSE – Introduction to data visualization using GG plot 2 (R and Rstudio) (DVGG01) This course will be delivered live \n \n“5” September 3rd – 4th Data wrangling using R and Rstudio \nONLINE COURSE – Introduction data wrangling using R and Rstudio (DWRS01) This course will be delivered live \n \n\n\n\nIntended Audience\nThis course is aimed at anyone who is interested in doing data visualization using R. Data visualization is a major part of data science and statistical data analysis\, and R is the most widely used program for data science and statistics. Data visualization using R is widely used throughout academic scientific research\, as well as widely throughout the public and private sectors. \nVenue – Delivered remotely \nTime zone – Western European Time +1 \nAvailability – 20 places \nDuration – 5 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 2 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@psstatistics.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 (and accommodation fees if booked through PS statistics) will be credited. \n\n\n\n \nDr. Mark Andrews\n\n\n\n\nTeaching Format\n\nThis course will be practical\, hands-on\, and workshop based. At the beginning on the first day\, there will be a minimal\namount of lecture style presentation\, i.e.\, using slides\, introducing and explaining some key concepts. After that brief\nintroduction\, we will use RStudio and simultaneously write R code to work through all the content of the course. For\nexample\, when covering some plot type\, we will load up the same data set and write the ggplot code to make the plot\nand then modify that code to produce different variants of that plot. Any code that the instructor produces during these\nsessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions\, and\nbetween days\, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take\nplace after each break.\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• 10am-12pm\n• 1pm-3pm\n• 4pm-6pm\nAll sessions will be video recorded and made available to all attendees as soon as possible\, hopefully soon after each\n2hr 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\nconvenient times. For example\, attendees from North America may be able to join the live sessions at 1pm-3pm and\n4pm-6pm\, and then catch up with the 10am-12pm recorded session when it is uploaded (which will be soon after 6pm\neach day). By joining any live sessions that are possible\, this will allow attendees to benefit from asking questions and\nhaving 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\nprocedure for asking questions and raising comments.\nAlthough not strictly required\, using a large monitor or preferably even a second monitor will make the learning\nexperience 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\nmaterials\, such as slides\, data sets\, etc.\, will be shared via GitHub. \nAssumed quantitative knowledge \nWe will assume only a very minimal amount of familiarity with some general statistical concepts. Anyone who has\ntaken any undergraduate (Bachelor’s) level introductory course on (applied) statistics can be assumed to have\nsufficient 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\ncommands\, 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. We 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@psstatistics.com \n\n\n\nCourse Programme\n\nThursday 20th – Classes from 10:00 to 18:00 \nOn each day\, we will cover a set of topics. Some of these topics will be very brief\, maybe requiring as little as 15 minutes\, while others will require a number of hours. Also\, at the beginning of this first day\, we will deal with some general “housekeeping” before we start.\n• Topic 1: What is data visualization. Data visualization is a means to explore and understand our data and should be a major part of any data analysis. Here\, we briefly discuss why data visualization is so important and what the\nmajor principles behind it are.\n• Topic 2: Introducing ggplot. Though there are many options for visualization in R\, ggplot is simply the best. Here\, we briefly introduce the major principles behind how ggplot works\, namely how it is a layered grammar of\ngraphics.\n• Topic 3: Visualizing univariate data. Here\, we cover a set of major tools for visualizing distributions over single variables: histograms\, density plots\, barplots\, Tukey boxplots. In each case\, we will explore how to plot multiple groups of data simultaneously using different colours and also using facet plots.\n• Topic 4: Scatterplots. Scatterplots and their variants are used to visualize bivariate data. Here\, in addition to\ncovering how to visualize multiple groups using colours and facets\, we will also cover how to provide marginal\nplots on the scatterplots\, labels to points\, and how to obtain linear and nonlinear smoothing of the plots. \nFriday 21st – Classes from 10:00 to 18:00 \nTopic 5: More plot types. Having already covered the most widely used general purpose plots on Day 1\, we now\nturn to cover a range of other major plot types: frequency polygons\, area plots\, line plots\, uncertainty plots\, violin plots\, and geospatial mapping. Each of these are important and widely used types of plots\, and knowing them will expand your repertoire.\n• Topic 6: Fine control of plots. Thus far\, we will have mostly used the default for the plot styles and layouts. Here\, we will introduce how to modify things like the limits and scales on the axes\, the positions and nature of the axis ticks\, the colour palettes that are used\, and the different types of ggplot themes that are available.\n• Topic 7: Plots for publications and presentations: Thus far\, we have primarily focused on data visualization as a\nmeans of interactively exploring data. Often\, however\, we also want to present our plots in\, for example\, published\narticles or in slide presentations. It is simple to save a plot in different file formats\, and then insert them into a document. However\, a much more efficient way of doing this is to use RMarkdown to run the R code and\nautomatically insert the resulting figure into a\, for example\, Word document\, pdf document\, html page\, etc. In\naddition\, here we will also cover how to make labelled grids of subplots like those found in many scientific articles. \n\n\n\n
URL:https://www.psstatistics.com/course/introduction-to-data-visualization-using-gg-plot-2-r-and-rstudio-dvgg01/
LOCATION:Western European Time\, United Kingdom
ATTACH;FMTTYPE=image/jpeg:https://www.psstatistics.com/wp-content/uploads/2019/04/gnmr01.jpg
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20200903
DTEND;VALUE=DATE:20200905
DTSTAMP:20200715T061919
CREATED:20200521T175431Z
LAST-MODIFIED:20200617T015804Z
UID:6254-1599091200-1599263999@www.psstatistics.com
SUMMARY:ONLINE COURSE - Data wrangling using R and Rstudio (DWRS01) This course will be delivered live
DESCRIPTION:\n\nThis course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.\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 – Western European Time +1 – 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@psstatistics.com for full details or to discuss how we can accommodate you). \nCourse Overview:\nIn this two day course\, we provide a comprehensive practical introduction to data wrangling using R. In particular\, we focus on tools provided by R’s tidyverse\, including dplyr\, tidyr\, purrr\, etc. Data wrangling is the art of taking raw and messy data and formating and cleaning it so that data analysis and visualization etc may be performed on it. Done poorly\, it can be a time consuming\, labourious\, and error-prone. Fortunately\, the tools provided by R’s tidyverse allow us to do data wrangling in a fast\, efficient\, and high-level manner\, which can have dramatic consequence for ease and speed with which we analyse data. On Day 1 of this course\, having covered how to read data of different types into R\, we cover in detail all the dplyr tools such as select\, filter\, mutate\, etc. Here\, we will also cover the pipe operator (%>%) to create data wrangling pipelines that take raw messy data on the one end and return cleaned tidy data on the other. On Day 2\, we cover how to perform descriptive or summary statistics on our data using dplyr’s summarize and group_by functions. We then turn to combining and merging data. Here\, we will consider how to concatenate data frames\, including concatenating all data files in a folder\, as well as cover the powerful SQL like join operations that allow us to merge information in different data frames. The final topic we will consider is how to “pivot” data from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider. \nThis is one module of a five module series – you do not need to attend them all but they are designed to compliment each other. Please see the links below \n“1” June 23rd – 24th Introduction to R for ecologists and evolutionary biologists \nhttps://www.prstatistics.com/course/introduction-to-r-for-ecologists-and-evolutionary-biologists-irfb04/ \n“2”July 23rd – 24th Introduction to generalised linear models using R and Rstudio \nONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM01) This course will be delivered live \n \n“3” August 6th – 7th Introduction to mixed models using R an d Rstudio \nONLINE COURSE – Introduction to mixed models using R and Rstudio (IMMR02) This course will be delivered live \n \n“4” August 20th – 21st Data visualization using GG plot 2 (R and R studio) \nONLINE COURSE – Introduction to data visualization using GG plot 2 (R and Rstudio) (DVGG01) This course will be delivered live \n \n“5” September 3rd – 4th Data wrangling using R and Rstudio \nONLINE COURSE – Introduction data wrangling using R and Rstudio (DWRS01) This course will be delivered live \n \n \n\n\n\nIntended Audience\nThis course is aimed at anyone who is involved in real world data analysis\, where the raw data is messy and complex. Data analysis of this kind is practiced widely throughout academic scientific research\, as well as widely throughout the public and private sectors. \nVenue – Delivered remotely \nTime zone – Western European Time +1 \nAvailability – 20 places \nDuration – 2 days \nContact hours – Approx. 15 hours \nECT’s – Equal to 2 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@psstatistics.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 (and accommodation fees if booked through PS statistics) will be credited. \n\n\n\n \nDr. Mark Andrews\n\n\n\n\nTeaching Format\n\nThis course will be practical\, hands-on\, and workshop based. For some topics\, there will a very minimal amount of lecture style presentations\, i.e.\, using slides or blackboard\, to introduce and explain key concepts and theories\, but almost all of our time will be spent doing data wrangling 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\n1pm-3pm\n4pm-6pm \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 only the most basic of statistical concepts\, such as descriptive statistics. We will not even assume that participants will have taken university level courses on statistics. \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@psstatistics.com \n\n\n\nCourse Programme\n\nThursday 3rd – Classes from 10:00 to 18:00 \nTopic 1: Reading in data. We will begin by reading in data into R using tools such as readr and readxl. Almost all types of data can be read into R\, and here we will consider many of the main types\, such as csv\, xlsx\, sav\, etc. Here\, we will also consider how to contol how data are parsed\, e.g.\, so that they are read as dates\, numbers\, strings\, etc.\nTopic 2: Wrangling with dplyr. For the remainder of Day 1\, we will next cover the very powerful dplyr R package. This package supplies a number of so-called “verbs” — select\, rename\, slice\, filter\, mutate\, arrange\, etc. — each of which focuses on a key data manipulation tools\, such as selecting or changing variables. These verbs also have _if\, _at\, _all variants that we will consider. All of these verbs can be chained together using “pipes” (represented by %>%). Together\, these create powerful data wrangling pipelines that take raw data as input and return cleaned data as output. Here\, we will also learn about the key concept of “tidy data”\, which is roughly where each row of a data frame is an observation and each column is a variable. \nFriday 4th – Classes from 10:00 – 18:00 \nTopic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with great effect to summarize data using descriptive statistics.\nTopic 4: Merging and joining data frames. There are multiple ways to combine data frames\, with the simplest being “bind” operations\, which are effectively horizontal or vertical concatenations. Much more powerful are the SQL like “join” operations. Here\, we will consider the inner_join\, left_join\, right_join\, full_join operations. In this section\, we will also consider how to use purrr to read in and automatically merge large sets of files.\nTopic 5: Pivoting data. Sometimes we need to change data frames from “long” to “wide” formats. The R package tidyr provides the tools pivot_longer and pivot_wider for doing this. \n\n\n\n
URL:https://www.psstatistics.com/course/introduction-data-wrangling-using-r-and-rstudio-dwrs01/
LOCATION:Western European Time\, United Kingdom
ATTACH;FMTTYPE=image/jpeg:https://www.psstatistics.com/wp-content/uploads/2019/04/gnmr01.jpg
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