'Estimate std' diaporamas de présentation

Generalized Linear Models Classification

Generalized Linear Models Classification. SPH 247 Statistical Analysis of Laboratory Data. Generalized Linear Models . The type of predictive model one uses depends on a number of issues; one is the type of response.

1.03k views • 69 slides

More on the linear model

More on the linear model. Categorical predictors. men. RT ~ Noise + Gender. women. resp ~ Condition. Demo. set.seed (666) pred = c(rep(0,20),rep(1,20)) resp = c( rnorm (20,mean=2,sd=1), rnorm (20,mean=2,sd=1)) for( i in 1:10){ resp = c( resp [1:20], resp [21:40]+1)

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Weighting

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Linear Modelling III

Linear Modelling III. Richard Mott Wellcome Trust Centre for Human Genetics. Synopsis. Logistic Regression Mixed Models. Logistic Regression and Statistical Genetics. Applicable to Association Studies Data: Binary outcomes ( eg disease status)

550 views • 41 slides

Nested and Paired recap

Nested and Paired recap. Nested and Paired sampling. When you have sampled one thing many times. Why? To handle pseudoreplication. or To reduce noise. Nested vs Paired tests. Nested A fixed factor above a random factor Paired A fixed factor within a random factor

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Data Analysis Using R: 5. Analysis of Variance

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Multilevel modeling in R

Multilevel modeling in R. Tom Dunn and Thom Baguley, Psychology, Nottingham Trent University Thomas.Baguley@ntu.ac.uk. 1. Models for repeated measures or clustered data. Repeated measures ANOVA. Usual practice is psychology is to analyze repeated measures data using ANOVA:

595 views • 28 slides

Logistic Regression

Logistic Regression. Example: Survival of Titanic passengers. We want to know if the probability of survival is higher among children Outcome (y) = survived/not Explanatory (x) = age at death > t itan<- read.table ("D:/ RShortcourse /titanic.txt ", sep =",",header=T, na =".")

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Multiple Regression Analysis

Multiple Regression Analysis. Example: Housing Prices in Boston. Read data in, look at distribution of DV. > boston <-read.csv("C:/Users/Elisabeth Root/Desktop/Quant/ R/ boston.csv",header =T) > names( boston ) [1] "OBS." "TOWN" "TOWN." "TRACT" "LON" "LAT" "MEDV"

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Logistic regression

Logistic regression. Analysis of proportion data. We know how many times an event occurred, and how many times did not occur. We want to know if these proportions are affected by a treatment or a factor Examples: Proportion dying Proportion responding to a treatment

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Logistic Regression

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????????? GLM ? generalized linear Models

????????? GLM ? generalized linear Models. ?????????????????? ???????????????. Generalized Linear Models. Linear Model response variable ?? intercept + slope * explanatory variable lm(y~ x + f ??? ) ? lm(y~x + f -1) (no intercept). require(graphics)

295 views • 12 slides